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3,300 | Toyota Smarthome Untrimmed: Real-World Untrimmed Videos for Activity Detection | Designing activity detection systems that can be successfully deployed in daily-living environments requires datasets that pose the challenges typical of real-world scenarios. In this paper, we introduce a new untrimmed daily-living dataset that features several real-world challenges: Toyota Smarthome Untrimmed (TSU). TSU contains a wide variety of activities performed in a spontaneous manner. The dataset contains dense annotations including elementary, composite activities and activities involving interactions with objects. We provide an analysis of the real-world challenges featured by our dataset, highlighting the open issues for detection algorithms. We show that current state-of-the-art methods fail to achieve satisfactory performance on the TSU dataset. Therefore, we propose a new baseline method for activity detection to tackle the novel challenges provided by our dataset. This method leverages one modality (i.e. optic flow) to generate the attention weights to guide another modality (i.e RGB) to better detect the activity boundaries. This is particularly beneficial to detect activities characterized by high temporal variance. We show that the method we propose outperforms state-of-the-art methods on TSU and on another popular challenging dataset, Charades. |
3,301 | Real-Time Monocular Joint Perception Network for Autonomous Driving | Comprehensive and accurate perception of the real 3D world is the basis of autonomous driving. However, many perceptual methods focus on a single task or object type, and the accuracy of existing multi-task or multi-object methods is difficult to balance against their real-time performance. This paper presents a unified framework for concurrent dynamic multi-object joint perception, which introduces a real-time monocular joint perception network termed MJPNet. In MJPNet relative weightings are automatically learned by a series of developed network branches. By training an end-to-end deep convolutional neural network on a shared feature encoder and many proposed decoding sub-branches, the information of the 2D category and 3D position/pose/size of an object are reconstructed both simultaneously and accurately. Moreover, the effective information among subtasks is transferred by multi-stream learning, guaranteeing the accuracy of each task. Compared to various state-of-the-arts, comprehensive evaluations on the benchmark of challenging image sequences demonstrate the superior performance of our 2D detection and 3D reconstruction of depth, lateral distance, orientation, and heading angle. Moreover, on the KITTI test set, the real-time runtime (up to 15 fps) of MJPNet significantly outran the public state-of-the-art visual detection methods. Accompanying video: https://youtu.be/Z-goToOlI94. |
3,302 | HBsAg sT123N mutation induces stronger antibody responses to HBsAg and HBcAg and accelerates in vivo HBsAg clearance | Immune escape mutants with mutations in the hepatitis B surface antigen (HBsAg) major hydrophilic region (MHR) often emerge in association with diagnostic failure or breakthrough of HBV infection in patients with anti-HBs antibodies. Some mutants harboring substitutions to Asn in HBsAg MHR may have an additional potential N-glycosylation site. We have previously showed that sT123N substitution could generate additional N-glycosylated forms of HBsAg. In the present study, 1.3-fold-overlength HBV genomes containing the sT123N substitution were digested from the pHBV1.3-sT123N construct and subcloned into the pAAV vector to generate pAAV1.3-sT123N for hydrodynamic injection (HI) in mice. Viral expression and replication were phenotypically characterized by transient transfection. The results demonstrated that sT123N substitution impaired virion secretion, resulting in intracellular retention of HBcAg. Using the HBV HI mouse model, we found that mice mounted significantly stronger antibody responses to HBsAg and HBcAg, which accelerated HBsAg clearance. Thus, additional N-glycosylation generated by amino acid substitutions in HBsAg MHR may significantly modulate specific host immune responses and influence HBV infection in vivo. Our results help further the understanding of the role of immune escape mutants with N-linked glycosylation in the biology of HBV infection. |
3,303 | BUSIFusion: Blind Unsupervised Single Image Fusion of Hyperspectral and RGB Images | Hyperspectral images (HSIs) provide rich spectral information that has been widely used in numerous computer vision tasks. However, their low spatial resolution often prevents their use in applications such as image segmentation and recognition. Fusing low-resolution HSIs with high-resolution RGB images to reconstruct high-resolution HSIs has attracted great research attention recently. In this paper, we propose an unsupervised blind fusion network that operates on a single HSI and RGB image pair and requires neither known degradation models nor any training data. Our method takes full advantage of an unrolling network and coordinate encoding to provide a state-of-the-art HSI reconstruction. It can also estimate the degradation parameters relatively accurately through the neural representation and implicit regularization of the degradation model. The experimental results demonstrate the effectiveness of our method both in simulations and in our real experiments. The proposed method outperforms other state-of-the-art nonblind and blind fusion methods on two popular HSI datasets. Our related code and data is available at https://github.com/CPREgroup/Real-Spec-RGB-Fusion. |
3,304 | U-LanD: Uncertainty-Driven Video Landmark Detection | This paper presents U-LanD, a framework for automatic detection of landmarks on key frames of the video by leveraging the uncertainty of landmark prediction. We tackle a specifically challenging problem, where training labels are noisy and highly sparse. U-LanD builds upon a pivotal observation: a deep Bayesian landmark detector solely trained on key video frames, has significantly lower predictive uncertainty on those frames vs. other frames in videos. We use this observation as an unsupervised signal to automatically recognize key frames on which we detect landmarks. As a test-bed for our framework, we use ultrasound imaging videos of the heart, where sparse and noisy clinical labels are only available for a single frame in each video. Using data from 4,493 patients, we demonstrate that U-LanD can exceedingly outperform the state-of-the-art non-Bayesian counterpart by a noticeable absolute margin of 42% in R-2 score, with almost no overhead imposed on the model size. |
3,305 | Performance analysis of 1300 nm SLEDs - impact of temperature and length scaling | The impact of self-heating and cavity length on the spectral emission properties of SLEDs is investigated using a state-of-the-art simulation tool. Simulated data are compared to measurements for two InP-based benchmark devices operating around 1300 nm, and excellent agreement is achieved in either case. |
3,306 | Cannabinoids in Neurodegenerative Disorders and Stroke/Brain Trauma: From Preclinical Models to Clinical Applications | Cannabinoids form a singular family of plant-derived compounds (phytocannabinoids), endogenous signaling lipids (endocannabinoids), and synthetic derivatives with multiple biological effects and therapeutic applications in the central and peripheral nervous systems. One of these properties is the regulation of neuronal homeostasis and survival, which is the result of the combination of a myriad of effects addressed to preserve, rescue, repair, and/or replace neurons, and also glial cells against multiple insults that may potentially damage these cells. These effects are facilitated by the location of specific targets for the action of these compounds (e.g., cannabinoid type 1 and 2 receptors, endocannabinoid inactivating enzymes, and nonendocannabinoid targets) in key cellular substrates (e.g., neurons, glial cells, and neural progenitor cells). This potential is promising for acute and chronic neurodegenerative pathological conditions. In this review, we will collect all experimental evidence, mainly obtained at the preclinical level, supporting that different cannabinoid compounds may be neuroprotective in adult and neonatal ischemia, brain trauma, Alzheimer's disease, Parkinson's disease, Huntington's chorea, and amyotrophic lateral sclerosis. This increasing experimental evidence demands a prompt clinical validation of cannabinoid-based medicines for the treatment of all these disorders, which, at present, lack efficacious treatments for delaying/arresting disease progression, despite the fact that the few clinical trials conducted so far with these medicines have failed to demonstrate beneficial effects. |
3,307 | Ethnoracial Disparities in Posttraumatic Stress Disorder Symptoms during the COVID-19 Pandemic: A Brief Report | Despite the well-identified vulnerability of older adults during the COVID-19 pandemic, it is unclear about their experiences with COVID-related posttraumatic stress disorder symptomology (COVID-PTSD). This study examined ethnoracial disparities in the level of, and factors associated with, COVID-PTSD using a national data set, including 1926 Whites and 488 ethnoracial minorities. Results showed that ethnoracial minorities reported a greater COVID-PTSD than Whites. COVID-related distress was the common risk factor of COVID-PTSD for the both groups. Being a female and greater social support were associated with COVID-PTSD only for Whites, whereas higher education, greater IADL and fewer ADL limitations were associated with COVID-PTSD for ethnoracial minorities. The findings provided preliminary, but generalizable understanding of ethnoracial disparities in COVID-PTSD, among the Medicare beneficiaries aged ≥65. |
3,308 | CED-Net: Crops and Weeds Segmentation for Smart Farming Using a Small Cascaded Encoder-Decoder Architecture | Convolutional neural networks (CNNs) have achieved state-of-the-art performance in numerous aspects of human life and the agricultural sector is no exception. One of the main objectives of deep learning for smart farming is to identify the precise location of weeds and crops on farmland. In this paper, we propose a semantic segmentation method based on a cascaded encoder-decoder network, namely CED-Net, to differentiate weeds from crops. The existing architectures for weeds and crops segmentation are quite deep, with millions of parameters that require longer training time. To overcome such limitations, we propose an idea of training small networks in cascade to obtain coarse-to-fine predictions, which are then combined to produce the final results. Evaluation of the proposed network and comparison with other state-of-the-art networks are conducted using four publicly available datasets: rice seeding and weed dataset, BoniRob dataset, carrot crop vs. weed dataset, and a paddy-millet dataset. The experimental results and their comparisons proclaim that the proposed network outperforms state-of-the-art architectures, such as U-Net, SegNet, FCN-8s, and DeepLabv3, over intersection over union (IoU), F1-score, sensitivity, true detection rate, and average precision comparison metrics by utilizing only (1/5.74 x U-Net), (1/5.77 x SegNet), (1/3.04 x FCN-8s), and (1/3.24 x DeepLabv3) fractions of total parameters. |
3,309 | Worsening Heart Failure and Atrial Flutter in a Patient Secondary to Cardiac Resynchronization Therapy Dyssynchrony: A Case Report | Cardiac resynchronization therapy-defibrillator (CRT-D) and/or cardiac resynchronization therapy-pacemaker (CRT-P) play an important role in improving cardiac synchronization and reducing the risk of ventricular fibrillation arrest (VFA) in patients with severe left ventricular systolic dysfunction (LVSD). Patients with LVSD may notice worsening symptoms when CRT-D or CRT-P is in dyssynchrony. We present a case of 59-year-old patient who presented with worsening shortness of breath (SOB) and progressive exertional dyspnea for the past few weeks accompanied by pink, frothy sputum, occasional urinary incontinence and urge. He was known to have severe LVSD with an ejection fraction of 10% and had CRT-D in situ. Clinical examination revealed bilateral crepitation and normal heart sounds. A chest radiograph showed pulmonary oedema. An electrocardiogram (ECG) showed atrial fibrillation (AF)/flutter with wide QRS complexes. The patient was treated for acute pulmonary oedema and had CRT-D reprogrammed to achieve biventricular synchrony. He was treated with intravenous furosemide and alternate day metolazone initially. He showed significant subjective and objective improvement and was planned for outpatient synchronized intra-device cardioversion. This case is important because patients with severe LVSD with malfunctioning cardiac resynchronization therapy can result in worsening heart failure (HF) leading to higher morbidity and mortality. |
3,310 | Video Captioning Using Global-Local Representation | Video captioning is a challenging task as it needs to accurately transform visual understanding into natural language description. To date, state-of-the-art methods inadequately model global-local vision representation for sentence generation, leaving plenty of room for improvement. In this work, we approach the video captioning task from a new perspective and propose a GLR framework, namely a global-local representation granularity. Our GLR demonstrates three advantages over the prior efforts. First, we propose a simple solution, which exploits extensive vision representations from different video ranges to improve linguistic expression. Second, we devise a novel global-local encoder, which encodes different video representations including long-range, short-range and local-keyframe, to produce rich semantic vocabulary for obtaining a descriptive granularity of video contents across frames. Finally, we introduce the progressive training strategy which can effectively organize feature learning to incur optimal captioning behavior. Evaluated on the MSR-VTT and MSVD dataset, we outperform recent state-of-the-art methods including a well-tuned SA-LSTM baseline by a significant margin, with shorter training schedules. Because of its simplicity and efficacy, we hope that our GLR could serve as a strong baseline for many video understanding tasks besides video captioning. Code will be available. |
3,311 | Emission estimates and air quality simulation on Lombardy during lockdown | This paper illustrates the study carried out by ARPA Lombardia to quantify the variation in daily emissions of the main pollutants and their impacts on air quality in Lombardy during the anti-COVID-19 lockdown between the end of February and the end of May 2020. A methodology for emission estimates was developed over Lombardy for this purpose and later was extended to larger areas: the Po-basin, (LIFE PREPAIR 2020) and the entire Italy (PULVIRUS 2021). In this study, the daily emissions estimates were derived by combining data from air emission inventory of Lombardy and a set of indicators that allowed to update the estimates and describe the temporal and spatial variations of the emission sources. The calculation of emission variation was conducted for all the main pollutants (PM10, NH3, NOx, SO2, NMVOC) and the greenhouse gases; then, the impact on air quality concentrations was simulated by the chemical and transport model FARM, that also allows to track secondary particulate and its variability in time and space on the basis of nonlinear processes and weather conditions. The estimated emission reduction, compared to the expected average value in the absence of anti-COVID-19 measures, daily varies depending on pollutants and is mainly affected by reductions in road traffic emissions and an estimated increase in domestic heating emissions. Simulations confirm strong reductions of NO2 atmospheric average concentrations, slightly variations of PM10 averages and a potential growth of tropospheric ozone. |
3,312 | Clinical Predictors of Subacute Myocardial Dysfunction in Multisystem Inflammatory Syndrome in Children (MIS-C) Associated with COVID-19 | Multisystem Inflammatory Syndrome in Children (MIS-C) often involves a post-viral myocarditis and associated left ventricular dysfunction. We aimed to assess myocardial function by strain echocardiography after hospital discharge and to identify risk factors for subacute myocardial dysfunction. We conducted a retrospective single-center study of MIS-C patients admitted between 03/2020 and 03/2021. Global longitudinal strain (GLS), 4-chamber longitudinal strain (4C-LS), mid-ventricular circumferential strain (CS), and left atrial strain (LAS) were measured on echocardiograms performed 3-10 weeks after discharge and compared with controls. Among 60 MIS-C patients, hypotension (65%), ICU admission (57%), and vasopressor support (45%) were common, with no mortality. LVEF was abnormal (< 55%) in 29% during hospitalization but only 4% at follow-up. Follow-up strain abnormalities were prevalent (GLS abnormal in 13%, 4C-LS in 18%, CS in 16%, LAS in 5%). Hypotension, ICU admission, ICU and hospital length of stay, and any LVEF < 55% during hospitalization were factors associated with lower strain at follow-up. Higher peak C-reactive protein (CRP) was associated with hypotension, ICU admission, total ICU days, and with lower follow-up GLS (r = - 0.55; p = 0.01) and CS (r = 0.41; p = 0.02). Peak CRP < 18 mg/dL had negative predictive values of 100% and 88% for normal follow-up GLS and CS, respectively. A subset of MIS-C patients demonstrate subclinical systolic and diastolic function abnormalities at subacute follow-up. Peak CRP during hospitalization may be a useful marker for outpatient cardiac risk stratification. MIS-C patients with hypotension, ICU admission, any LVEF < 55% during hospitalization, or a peak CRP > 18 mg/dL may warrant closer monitoring than those without these risk factors. |
3,313 | Obese older adults suffer foot pain and foot-related functional limitation | There is evidence to suggest being overweight or obese places adults at greater risk of developing foot complications such as osteoarthritis, tendonitis and plantar fasciitis. However, no research has comprehensively examined the effects of overweight or obesity on the feet of individuals older than 60 years of age. Therefore we investigated whether foot pain, foot structure, and/or foot function is affected by obesity in older adults. Three hundred and twelve Australian men and women, aged over 60 years, completed validated questionnaires to establish the presence of foot pain and health related quality of life. Foot structure (anthropometrics and soft tissue thickness) and foot function (ankle dorsiflexion strength and flexibility, toe flexor strength, plantar pressures and spatiotemporal gait parameters) were also measured. Obese participants (BMI >30) were compared to those who were overweight (BMI=25-30) and not overweight (BMI <25). Obese participants were found to have a significantly higher prevalence of foot pain and scored significantly lower on the SF-36. Obesity was also associated with foot-related functional limitation whereby ankle dorsiflexion strength, hallux and lesser toe strength, stride/step length and walking speed were significantly reduced in obese participants compared to their leaner counterparts. Therefore, disabling foot pain and altered foot structure and foot function are consequences of obesity for older adults, and impact upon their quality of life. Interventions designed to reduce excess fat mass may relieve loading of the foot structures and, in turn, improve foot pain and quality of life for older obese individuals. |
3,314 | Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art | Low-cost sensors (LCS) are becoming popular for air quality monitoring (AQM). They promise high spatial and temporal resolutions at low-cost. In addition, citizen science applications such as personal exposure monitoring can be implemented effortlessly. However, the reliability of the data is questionable due to various error sources involved in the LCS measurement. Furthermore, sensor performance drift over time is another issue. Hence, the adoption of LCS by regulatory agencies is still evolving. Several studies have been conducted to improve the performance of low-cost sensors. This article summarizes the existing studies on the state-of-the-art of LCS for AQM. We conceptualize a step by step procedure to establish a sustainable AQM setup with LCS that can produce reliable data. The selection of sensors, calibration and evaluation, hardware setup, evaluation metrics and inferences, and end user-specific applications are various stages in the LCS-based AQM setup we propose. We present a critical analysis at every step of the AQM setup to obtain reliable data from the low-cost measurement. Finally, we conclude this study with future scope to improve the availability of air quality data. |
3,315 | FISTA-Net: Learning a Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging | Inverse problems are essential to imaging applications. In this letter, we propose a model-based deep learning network, named FISTA-Net, by combining the merits of interpretability and generality of the model-based Fast Iterative Shrinkage/Thresholding Algorithm (FISTA) and strong regularization and tuning-free advantages of the data-driven neural network. By unfolding the FISTA into a deep network, the architecture of FISTA-Net consists of multiple gradient descent, proximal mapping, and momentum modules in cascade. Different from FISTA, the gradient matrix in FISTA-Net can be updated during iteration and a proximal operator network is developed for nonlinear thresholding which can be learned through end-to-end training. Key parameters of FISTA-Net including the gradient step size, thresholding value and momentum scalar are tuning-free and learned from training data rather than hand-crafted. We further impose positive and monotonous constraints on these parameters to ensure they converge properly. The experimental results, evaluated both visually and quantitatively, show that the FISTA-Net can optimize parameters for different imaging tasks, i.e. Electromagnetic Tomography (EMT) and X-ray Computational Tomography (X-ray CT). It outperforms the state-of-the-art model-based and deep learning methods and exhibits good generalization ability over other competitive learning-based approaches under different noise levels. |
3,316 | COMPUTER MODELLING AND RECOMMENDATIONS FOR RESTORATION OF THE HISTORICAL TRANSLUCENT STRUCTURES OF THE PUSHKIN STATE MUSEUM OF FINE ARTS | On the basis of the previous examinations of the historical windows of the main building of the Pushkin State Museum of Fine Arts by the authors [1] using a certified software package, the multi-variant analysis of the methods of increasing efficiency of the existing translucent structures was conducted. The recommendations for restoration of the historical translucent structures which are the parts of this state-protected cultural heritage object were developed. |
3,317 | Methodological approach and tools for systems thinking in health systems research: technical assistants' support of health administration reform in the Democratic Republic of Congo as an application | In the field of development cooperation, interest in systems thinking and complex systems theories as a methodological approach is increasingly recognised. And so it is in health systems research, which informs health development aid interventions. However, practical applications remain scarce to date. The objective of this article is to contribute to the body of knowledge by presenting the tools inspired by systems thinking and complexity theories and methodological lessons learned from their application. These tools were used in a case study. Detailed results of this study are in process for publication in additional articles. Applying a complexity 'lens', the subject of the case study is the role of long-term international technical assistance in supporting health administration reform at the provincial level in the Democratic Republic of Congo. The Methods section presents the guiding principles of systems thinking and complex systems, their relevance and implication for the subject under study, and the existing tools associated with those theories which inspired us in the design of the data collection and analysis process. The tools and their application processes are presented in the results section, and followed in the discussion section by the critical analysis of their innovative potential and emergent challenges. The overall methodology provides a coherent whole, each tool bringing a different and complementary perspective on the system. |
3,318 | Climbing experience in glass eels: A cognitive task or a matter of physical capacities? | The European eel is a panmictic species, whose decline has been recorded since the last 30 years. Among human-induced environmental factors of decline, the impact of water dams during species migration is questioned. Indeed, water impoundments can be a severe obstacle for young eels trying to reach the upstream freshwater zones, even if they are equipped with fish-friendly passes. The passage by such devices could be an important event shaping the outcome of the future life and life history traits of eels. We studied what phenotypic traits were associated with the event of experience of passage by water obstacles. We analyzed specific enzyme activities and/or gene transcription levels in the muscle and brain to test whether the obstacle passage is rather a physical or cognitive task. We found that after a long period of maintenance under homogenous conditions, transcription levels of several genes linked to synaptic plasticity, neurogenesis and thyroid activity differed among the field-experience groups. In contrast, muscle gene transcription levels or enzymatic activities did not show any differences among fish groups. We suggest that cognitive processes such as learning and memory acquisition rather than swimming-related metabolic capacities are involved in passage of water obstacles by young eels. |
3,319 | Trying to Interpret Modern Art as a Finite-Element Practitioner | In this essay, our main aim is to identify the similarities between the techniques used by the 19th and 20th century painters, and scientists dealing with the Finite-Difference/Finite-Element methods. First, we discuss the concept of representation in art, together with its mathematical formulation. We next formulize the perception, and conclude with a discussion about the analogies between artistic movements and some concepts of the Finite-Element Method. |
3,320 | Social Play Behavior Is Critical for the Development of Prefrontal Inhibitory Synapses and Cognitive Flexibility in Rats | Sensory driven activity during early life is critical for setting up the proper connectivity of the sensory cortices. We ask here whether social play behavior, a particular form of social interaction that is highly abundant during postweaning development, is equally important for setting up connections in the developing prefrontal cortex (PFC). Young male rats were deprived from social play with peers during the period in life when social play behavior normally peaks [postnatal day 21-42] (SPD rats), followed by resocialization until adulthood. We recorded synaptic currents in layer 5 cells in slices from medial PFC of adult SPD and control rats and observed that inhibitory synaptic currents were reduced in SPD slices, while excitatory synaptic currents were unaffected. This was associated with a decrease in perisomatic inhibitory synapses from parvalbumin-positive GABAergic cells. In parallel experiments, adult SPD rats achieved more reversals in a probabilistic reversal learning (PRL) task, which depends on the integrity of the PFC, by using a more simplified cognitive strategy than controls. Interestingly, we observed that one daily hour of play during SPD partially rescued the behavioral performance in the PRL, but did not prevent the decrease in PFC inhibitory synaptic inputs. Our data demonstrate the importance of unrestricted social play for the development of inhibitory synapses in the PFC and cognitive skills in adulthood and show that specific synaptic alterations in the PFC can result in a complex behavioral outcome.SIGNIFICANCE STATEMENT This study addressed the question whether social play behavior in juvenile rats contributes to functional development of the prefrontal cortex (PFC). We found that rats that had been deprived from juvenile social play (social play deprivation - SPD) showed a reduction in inhibitory synapses in the PFC and a simplified strategy to solve a complex behavioral task in adulthood. Providing one daily hour of play during SPD partially rescued the cognitive skills in these rats, but did not prevent the reduction in PFC inhibitory synapses. Our results demonstrate a key role for unrestricted juvenile social play in PFC development and emphasize the complex relation between PFC circuit connectivity and cognitive function. |
3,321 | BAS: A BTI-based aging aware synthesis in FPGAs | Aging mechanisms in FPGA devices cause performance degradation and lead to lifetime reduction. Among multiple aging mechanisms, Biased-Temperature-Instability (BTI) aging mechanism is the dominant one. BTI decreases the Static-Noise-Margin (SNM) of SRAM cells leading to more Soft-Error-Rate (SER) and lower SRAMs' stability in FPGAs. This paper proposes the BAS (BTI Aware Synthesis), a three-step post-synthesis and pre-place-and-route tool to reduce the impact of BTI in FPGA's LUTs significantly using the Bit-Flipping and Boolean Matching techniques. BAS receives a standard EDIF format synthesis output file from a synthesis tool (e.g., C.edf) and finally creates two output EDIF files with the same functionality as the original input file (e.g., C1.edf and C2.edf), while all LUT SRAMs contents are mutually flipped. The two BTI-Aware-created EDIF synthesis files are periodically used for FPGA's reconfiguration. Our experimental results demonstrates that BAS improves SNM and SER on average by 16.1% and 15.7% compared to previous state-of-the-art techniques, respectively. BAS has two main advantages over previous state-of-the-art techniques: (1) it is more flexible and applicable as an embedded tool within worldwide commercial synthesis tools, and (2) unlike the previous state-of-the-art techniques, it does not need the map and details of the FPGAs. |
3,322 | Catalase-peroxidase StKatG is a bacterial manganese oxidase from endophytic Salinicola tamaricis | Manganese (Mn) oxides in iron/manganese plaques are widely distributed in the rhizosphere of wetland plants and contribute significantly to elemental cycling and pollutant removal. Mn oxides are primarily produced by bacterial processes using Mn oxidases. However, the molecular mechanism underlying the formation of rhizosphere Mn oxides is still largely unknown. This study identified a manganese-oxidizing enzyme, the catalase-peroxidase StKatG, from an endophytic bacterium Salinicola tamaricis from the wetland plant. The gene encoding StKatG was cloned and overexpressed in Escherichia coli. The recombinant StKatG displayed different structure and enzymatic properties from the previously reported Mn oxidases. The enzyme activity of StKatG yielded Mn oxides with the mixed-valent state: Mn(II), Mn(III), and Mn(IV). The optimum pH and temperature for StKatG are 7.5 and 50 °C, respectively. Structurally, StKatG is organized into two domains, whereas the reported Mn oxidases are mainly single-domain proteins. Based on the site-directed mutagenesis studies, the presence of aspartic acid (Asp) residues in the loop of StKatG are critical to Mn-oxidizing activity. These findings identified a novel bacterial Mn oxidase and provided insights into the molecular mechanism of Mn oxidation in the plant rhizosphere. |
3,323 | A benchtop brain injury model using resected donor tissue from patients with Chiari malformation | The use of live animal models for testing new therapies for brain and spinal cord repair is a controversial area. Live animal models have associated ethical issues and scientific concerns regarding the predictability of human responses. Alternative models that replicate the 3D architecture of the central nervous system have prompted the development of organotypic neural injury models. However, the lack of reliable means to access normal human neural tissue has driven reliance on pathological or post-mortem tissue which limits their biological utility. We have established a protocol to use donor cerebellar tonsillar tissue surgically resected from patients with Chiari malformation (cerebellar herniation towards the foramen magnum, with ectopic rather than diseased tissue) to develop an in vitro organotypic model of traumatic brain injury. Viable tissue was maintained for approximately 2 weeks with all the major neural cell types detected. Traumatic injuries could be introduced into the slices with some cardinal features of post-injury pathology evident. Biomaterial placement was also feasible within the in vitro lesions. Accordingly, this 'proof-of-concept' study demonstrates that the model offers potential as an alternative to the use of animal tissue for preclinical testing in neural tissue engineering. To our knowledge, this is the first demonstration that donor tissue from patients with Chiari malformation can be used to develop a benchtop model of traumatic brain injury. However, significant challenges in relation to the clinical availability of tissue were encountered, and we discuss logistical issues that must be considered for model scale-up. |
3,324 | The State of the Art in Empirical User Evaluation of Graph Visualizations | While graph drawing focuses more on the aesthetic representation of node-link diagrams, graph visualization takes into account other visual metaphors making them useful for graph exploration tasks in information visualization and visual analytics. Although there are aesthetic graph drawing criteria that describe how a graph should be presented to make it faster and more reliably explorable, many controlled and uncontrolled empirical user studies flourished over the past years. The goal of them is to uncover how well the human user performs graph-specific tasks, in many cases compared to previously designed graph visualizations. Due to the fact that many parameters in a graph dataset as well as the visual representation of them might be varied and many user studies have been conducted in this space, a state-of-the-art survey is needed to understand evaluation results and findings to inform the future design, research, and application of graph visualizations. In this article, we classify the present literature on the topmost level into graph interpretation, graph memorability, and graph creation where the users with their tasks stand in focus of the evaluation, not the computational aspects. As another outcome of this work, we identify the white spots in this field and sketch ideas for future research directions. |
3,325 | Solar assisted air conditioning of buildings - an overview | Goal of this contribution is to draw a picture about some general issues for using solar thermal energy for air conditioning of buildings. The following topics are covered: A basic analysis of the thermodynamic limits for the use of heat cooling in combination with solar thermal energy is drawn; thereby fundamental insights about control needs for solar thermal driven cooling are obtained. A short overview about the state-of-the-art of available technologies, such as closed thermal driven cooling cycles (e.g., absorption, adsorption) and open cooling cycles (e.g., desiccant employing either solid or liquid sorbents) is given and needs and perspectives for future developments are described. The state-of-the-art of application of solar assisted air-conditioning in Europe is given and some example installations are presented. An overview about new developments of open and closed heat driven cooling cycles for application in combination with solar thermal collectors is given and some of these new systems are outlined more in detail. (c) 2006 Elsevier Ltd. All rights reserved. |
3,326 | Integration of ART-Kohonen neural network and case-based reasoning for intelligent fault diagnosis | This paper presents a new approach for integrating case-based reasoning (CBR) with an ART-Kohonen neural network (ART-KNN) to enhance fault diagnosis. When solving a new problem, the neural network is used to make hypotheses and to guide the CBR module in the search for a similar previous case that supports one of the hypotheses. The knowledge acquired by the network is interpreted and mapped into symbolic diagnosis descriptors, which are kept and used by the system to determine whether a final answer is credible, and to build explanations for the reasoning carried out. ART-KNN, synthesizing the theory of adaptive resonance theory and the learning strategy of Kohonen neural network, can solve the plasticity-stability dilemma of conventional neural networks. It can carry out 'on-line' training without forgetting previously trained patterns (stable training), and recode previously trained categories adaptive to changes in the environment and is self-organizing, which differs from most of networks that only can be carried out off-line. The proposed system has been used in the faults diagnosis of electric motor to verify the system performance. The result shows the proposed system performs better than self-organizing feature map (SOFM) based system with respect to classification rate. (C) 2003 Elsevier Ltd. All rights reserved. |
3,327 | Geodesics-Based Image Registration: Applications To Biological And Medical Images Depicting Concentric Ring Patterns | In many biological or medical applications, images that contain sequences of shapes are common. The existence of high inter-individual variability makes their interpretation complex. In this paper, we address the computer-assisted interpretation of such images and we investigate how we can remove or reduce these image variabilities. The proposed approach relies on the development of an efficient image registration technique. We first show the inadequacy of state-of-the-art intensity-based and feature-based registration techniques for the considered image datasets. Then, we propose a robust variational method which benefits from the geometrical information present in this type of images. In the proposed non-rigid geodesics-based registration, the successive shapes are represented by a level-set representation, which we rely on to carry out the registration. The successive level sets are regarded as elements in a shape space and the corresponding matching is that of the optimal geodesic path. The proposed registration scheme is tested on synthetic and real images. The comparison against results of state-of-the-art methods proves the relevance of the proposed method for this type of images. |
3,328 | Analysis of HDACi-Coupled Nanoparticles: Opportunities and Challenges | Systemic administration of histone deacetylase inhibitors (HDACi), like valproic acid (VPA), is often associated with rapid drug metabolization and untargeted tissue distribution. This requires high-dose application that can lead to unintended side effects. Hence, drug carrier systems such as nanoparticles (NPs) are developed to circumvent these disadvantages by enhancing serum half-life as well as organ specificity.This chapter gives a summary of the biological characterization of HDACi-coupled NPs in vitro, including investigation of cellular uptake, biocompatibility, as well as intracellular drug release and activity. Suitable methods, opportunities, and challenges will be discussed to provide general guidelines for the analysis of HDACi drug carrier systems with a special focus on recently developed cellulose-based VPA-coupled NPs. |
3,329 | Inducible deletion of raptor and mTOR from adult skeletal muscle impairs muscle contractility and relaxation | Skeletal muscle weakness has been associated with different pathological conditions, including sarcopenia and muscular dystrophy, and is accompanied by altered mammalian target of rapamycin (mTOR) signalling. We wanted to elucidate the functional role of mTOR in muscle contractility. Most loss-of-function studies for mTOR signalling have used the drug rapamycin to inhibit some of the signalling downstream of mTOR. However, given that rapamycin does not inhibit all mTOR signalling completely, we generated a double knockout for mTOR and for the scaffold protein of mTORC1, raptor, in skeletal muscle. We found that double knockout in mice results in a more severe phenotype compared with deletion of raptor or mTOR alone. Indeed, these animals display muscle weakness, increased fibre denervation and a slower muscle relaxation following tetanic stimulation. This is accompanied by a shift towards slow-twitch fibres and changes in the expression levels of calcium-related genes, such as Serca1 and Casq1. Double knockout mice show a decrease in calcium decay kinetics after tetanus in vivo, suggestive of a reduced calcium reuptake. In addition, RNA sequencing analysis revealed that many downregulated genes, such as Tcap and Fhod3, are linked to sarcomere organization. These results suggest a key role for mTOR signalling in maintaining proper fibre relaxation in skeletal muscle. KEY POINTS: Skeletal muscle wasting and weakness have been associated with different pathological conditions, including sarcopenia and muscular dystrophy, and are accompanied by altered mammalian target of rapamycin (mTOR) signalling. Mammalian target of rapamycin plays a crucial role in the maintenance of muscle mass and functionality. We found that the loss of both mTOR and raptor results in contractile abnormalities, with severe muscle weakness and delayed relaxation following tetanic stimulation. These results are associated with alterations in the expression of genes involved in sarcomere organization and calcium handling and with an impairment in calcium reuptake after contraction. Taken together, these results provide a mechanistic insight into the role of mTOR in muscle contractility. |
3,330 | Evaluation of Semantic Segmentation Methods for Land Use with Spectral Imaging Using Sentinel-2 and PNOA Imagery | Land use classification using aerial imagery can be complex. Characteristics such as ground sampling distance, resolution, number of bands and the information these bands convey are the keys to its accuracy. Random Forest is the most widely used approach but better and more modern alternatives do exist. In this paper, state-of-the-art methods are evaluated, consisting of semantic segmentation networks such as UNet and DeepLabV3+. In addition, two datasets based on aircraft and satellite imagery are generated as a new state of the art to test land use classification. These datasets, called UOPNOA and UOS2, are publicly available. In this work, the performance of these networks and the two datasets generated are evaluated. This paper demonstrates that ground sampling distance is the most important factor in obtaining good semantic segmentation results, but a suitable number of bands can be as important. This proves that both aircraft and satellite imagery can produce good results, although for different reasons. Finally, cost performance for an inference prototype is evaluated, comparing various Microsoft Azure architectures. The evaluation concludes that using a GPU is unnecessarily costly for deployment. A GPU need only be used for training. |
3,331 | Unmasking a two-faced protein | Single-molecule fluorescence spectroscopy and molecular dynamics simulations illuminate the structure and dynamics of PSD-95, a protein involved in neural plasticity. |
3,332 | Improved split-ubiquitin screening technique to identify surface membrane protein-protein interactions | Yeast-based methods are still the workhorse for the detection of protein-protein interactions (PPIs) in vivo. Yeast two-hybrid (Y2H) systems, however, are limited to screening for a specific group of molecules that interact in a particular cell compartment. For this reason, the split-ubiquitin system (SUS) was developed to allow screening of cDNA libraries of full-length membrane proteins for protein-protein interactions in Saccharomyces cerevisiae. Here we demonstrate that a modification of the widely used membrane SUS involving the transmembrane (TM) domain of the yeast receptor Wsc1 increases the stringency of screening and improves the selectivity for proteins localized in the plasma membrane (PM). |
3,333 | The economic costs of a multisectoral nutrition programme implemented through a credit platform in Bangladesh | Bangladesh struggles with undernutrition in women and young children. Nutrition-sensitive agriculture programmes can help address rural undernutrition. However, questions remain on the costs of multisectoral programmes. This study estimates the economic costs of the Targeting and Re-aligning Agriculture to Improve Nutrition (TRAIN) programme, which integrated nutrition behaviour change and agricultural extension with a credit platform to support women's income generation. We used the Strengthening Economic Evaluation for Multisectoral Strategies for Nutrition (SEEMS-Nutrition) approach. The approach aligns costs with a multisectoral nutrition typology, identifying inputs and costs along programme impact pathways. We measure and allocate costs for activities and inputs, combining expenditures and micro-costing. Quantitative and qualitative data were collected retrospectively from implementers and beneficiaries. Expenditure data and economic costs were combined to calculate incremental economic costs. The intervention was designed around a randomised control trial. Incremental costs are presented by treatment arm. The total incremental cost was $795,040.34 for a 3.5-year period. The annual incremental costs per household were US$65.37 (Arm 2), USD$114.15 (Arm 3) and $157.11 (Arm 4). Total costs were led by nutrition counselling (37%), agriculture extension (12%), supervision (12%), training (12%), monitoring and evaluation (9%) and community events (5%). Total input costs were led by personnel (68%), travel (12%) and supplies (7%). This study presents the total incremental costs of an agriculture-nutrition intervention implemented through a microcredit platform. Costs per household compare favourably with similar interventions. Our results illustrate the value of a standardised costing approach for comparison with other multisectoral nutrition interventions. |
3,334 | DESIGN OF A HYBRID COMPUTATIONAL FLUID DYNAMICS-MONTE CARLO RADIATION TRANSPORT METHODOLOGY FOR RADIOACTIVE PARTICULATE RESUSPENSION STUDIES | There are numerous scenarios where radioactive particulates can be displaced by external forces. For example, the detonation of a radiological dispersal device in an urban environment will result in the release of radioactive particulates that in turn can be resuspended into the breathing space by external forces such as wind flow in the vicinity of the detonation. A need exists to quantify the internal (due to inhalation) and external radiation doses that are delivered to bystanders; however, current state-of-the-art codes are unable to calculate accurately radiation doses that arise from the resuspension of radioactive particulates in complex topographies. To address this gap, a coupled computational fluid dynamics and Monte Carlo radiation transport approach has been developed. With the aid of particulate injections, the computational fluid dynamics simulation models characterize the resuspension of particulates in a complex urban geometry due to air-flow. The spatial and temporal distributions of these particulates are then used by the Monte Carlo radiation transport simulation to calculate the radiation doses delivered to various points within the simulated domain. A particular resuspension scenario has been modeled using this coupled framework, and the calculated internal (due to inhalation) and external radiation doses have been deemed reasonable. GAMBIT and FLUENT comprise the software suite used to perform the Computational Fluid Dynamics simulations, and Monte Carlo N-Particle eXtended is used to perform the Monte Carlo Radiation Transport simulations. |
3,335 | High-Performance Shielded Coplanar Waveguides for the Design of CMOS 60-GHz Bandpass Filters | This paper presents optimized very high performance CMOS slow-wave shielded CPW transmission lines (S-CPW TLines). They are used to realize a 60-GHz bandpass filter, with T-junctions and open stubs. Owing to a strong slow-wave effect, the longitudinal length of the S-CPW is reduced by a factor up to 2.6 compared to a classical microstrip topology in the same technology. Moreover, the quality factor of the realized S-CPWs reaches 43 at 60 GHz, which is about two times higher than the microstrip one and corresponds to the state of the art concerning S-CPW TLines with moderate width. For a proof of concept of complex passive device realization, two millimeter-wave filters working at 60 GHz based on dual-behavior-resonator filters have been designed with these S-CPWs and measured up to 110 GHz. The measured insertion loss for the first-order (respectively, second-order) filter is -2.6 dB (respectively, -4.1 dB). The comparison with a classical microstrip topology and the state-of-the-art CMOS filter results highlights the very good performance of the realized filters in terms of unloaded quality factor. It also shows the potential of S-CPW TLines for the design of high-performance complex CMOS passive devices. |
3,336 | Ways of preventing surgeon burnout | In surgical practice, numerous sources of stress (stressors) are unpredictable, two examples being daily workload and postoperative complications. They may help to explain surgeon burnout, of which the prevalence (34 to 53%) has been the subject of many studies. That said, even though assessments are legion, recommended solutions have been few and far between, especially insofar as by nature and training, surgeons are disinclined to interest themselves in burnout, which they are prone to consider as something experienced by "others". The objective of this attempt at clarification is to identify in the literature the strategies put forward in view of avoiding surgeon burnout, and to assess the impact of this phenomenon not only on the surgeon's professional and personal entourage, but also on patient safety. Prevention-based strategies, many of them focused on modifiable stressors, will be detailed. |
3,337 | Multichannel Speech Enhancement With Own Voice-Based Interfering Speech Suppression for Hearing Assistive Devices | Enhancementof a desired speech signal in the presence of competing or interfering speech remains an unsolved problem, as it can be hard to determine which of the speech signals is the one of interest. In this paper, we propose a multichannel noise reduction algorithm which uses the presence of the user's own voice signal, e.g. during conversations with the target speaker, as an asset to efficiently identify interfering speech and noise. Specifically, following the typical speech pattern in natural conversations, the presence of an own voice may indicate the absence of the target speech, hence undesired speech and noise can be identified and estimated during own voice presence. In contrast to conventional noise reduction systems, the proposed noise reduction systems use the user's own voice to identify interfering speech that otherwise could be confused with the target speech. We demonstrate the performance of the proposed noise reduction systems in a comparison against state-of-the-art noise reduction systems in terms of beamforming performance for hearing assistive devices. The results show that the proposed beamforming scheme in particular outperforms state-of-the-art methods in terms of ESTOI and PESQ in situations with a target speaker and a strong interfering speaker. |
3,338 | Chinese flower-bird character generation based on pencil drawings or brush drawings | Chinese flower-bird characters are gems of traditional Chinese art. It is an artistic font and the strokes of the characters are designed as beautiful patterns of flowers, birds, etc. The generation of such characters requires painters' great efforts. Imagine that if we only need to sketch the outline of the ideal flower-bird characters using a pencil, and then we can quickly obtain these artistic characters, which will be of great significance in promoting their development, allowing more people to appreciate and even create this art by computer. Recently, with the development of deep learning and the invention of generative adversarial networks (GANs), some studies on font generation have made new progress. However, there is no research on the generation of flower-bird characters. We provide a solution by designing a GAN-based architecture to generate flower-bird characters. More specifically, a generator inspired by U-Net translates pencil drawings or brush drawings to flower-bird characters, and a patch-level discriminator distinguishes whether the received image is real. In addition to adversarial loss, a valid loss term called structural similarity loss is designed to further drive the network to generate satisfactory images. The quantitative analysis and user perceptual validation show the effectiveness of our method. (C) 2019 SPIE and IS&T |
3,339 | Stress-Induced Diabetes: A Review | It has long been established that stress has a significant impact on metabolic function. Type 2 diabetes may be initiated by psychological and physical stress. The central and peripheral nervous systems are both involved in the neuroendocrine framework that underlies the underlying processes. The release of catecholamines and a rise in serum glucocorticoid concentrations caused by psychological stress enhance the requirement for insulin and insulin resistance. Experiencing persistent hyperglycemia in people with diabetes may be influenced by stress. Blood sugar levels may rise due to hormones being released in response to stress. Although this has adaptive significance in a healthy patient, in the long run, it can cause insulin resistance and lead to diabetes. Additionally, diabetes may cause abnormalities in the regulation of these stress hormones. |
3,340 | Building reflective capacity and improving well-being of early childhood professionals through an embedded cross-system model of infant early childhood mental health consultation | Infant and Early Childhood Mental Health Consultation (IECMHC) aims to improve early childhood professionals' abilities to promote children's mental health through relationship building and collaboration. Using a longitudinal, matched-comparison group design, a 3-year pilot study of a cross-system, embedded model of IECMHC assessed teachers and home visitors in intervention and comparison programs in reflective capacity, burnout, and perceptions of children's behavior. A sample of 136 staff (n = 72 intervention group; n = 64 comparison group; 21% Black; 51% White; 28% Latina/Hispanic) participated in surveys over a 21-month implementation period. A subsample of staff (n = 26) participated in interviews that included a narrative measure of reflective capacity; and a smaller subsample of teachers only (n = 21) completed assessments of children. Staff in the intervention group significantly increased reflective capacity after 21 months. For the staff interview subsample, receiving the intervention predicted lower levels of burnout at 12-15 months post-baseline. Among teachers completing child assessments, those with higher reflective capacity rated children's behaviors more positively than teachers with lower reflective capacity. We conclude that this IECMHC model successfully improved reflective capacity in staff. Future research should investigate reflective capacity as a potential mechanism of change for IECMHC. |
3,341 | Ultrasonographic pathological grading of prostate cancer using automatic region-based Gleason grading network | The Gleason scoring system is a reliable method for quantifying the aggressiveness of prostate cancer, which provides an important reference value for clinical assessment on therapeutic strategies. However, to the best of our knowledge, no study has been done on the pathological grading of prostate cancer from single ultrasound images. In this work, a novel Automatic Region-based Gleason Grading (ARGG) network for prostate cancer based on deep learning is proposed. ARGG consists of two stages: (1) a region labeling object detection (RLOD) network is designed to label the prostate cancer lesion region; (2) a Gleason grading network (GNet) is proposed for pathological grading of prostate ultrasound images. In RLOD, a new feature fusion structure Skip-connected Feature Pyramid Network (CFPN) is proposed as an auxiliary branch for extracting features and enhancing the fusion of high-level features and low-level features, which helps to detect the small lesion and extract the image detail information. In GNet, we designed a synchronized pulse enhancement module (SPEM) based on pulse-coupled neural networks for enhancing the results of RLOD detection and used as training samples, and then fed the enhanced results and the original ones into the channel attention classification network (CACN), which introduces an attention mechanism to benefit the prediction of cancer grading. Experimental performance on the dataset of prostate ultrasound images collected from hospitals shows that the proposed Gleason grading model outperforms the manual diagnosis by physicians with a precision of 0.830. In addition, we have evaluated the lesions detection performance of RLOD, which achieves a mean Dice metric of 0.815. |
3,342 | A high-speed RSD-based flexible ECC processor for arbitrary curves over general prime field | This workpresents a novel high-speed redundant-signed-digit (RSD)-based elliptic curve cryptographic (ECC) processor for arbitrary curves over a general prime field. The proposed ECC processor works for any value of the prime number and curve parameters. It is based on a new high speed Montgomery multiplier architecture which uses different parallel computation techniques at both circuit level and architectural level. At the circuit level, RSD and carry save techniques are adopted while pre-computation logic is incorporated at the architectural level. As a result of these optimization strategies, the proposed Montgomery multiplier offers a significant reduction in computation time over the state-of-the-art. At the system level, to further enhance the overall performance of the proposed ECC processor, Montgomery ladder algorithm with (X,Y)-only common Z coordinate (co-Z) arithmetic is adopted. The proposed ECC processor is synthesized and implemented on different Xilinx Virtex (V) FPGA families for field sizes of 256 to 521 bits. On V-6 platform, it computes a single 256 to 521 bits scalar point multiplication operation in 0.65 to 2.6ms which is up to 9 times speed-up over the state-of-the-art. |
3,343 | U-series and radiocarbon cross dating of speleothems from Nerja Cave (Spain): Evidence of open system behavior. Implication for the Spanish rock art chronology | Two stalagmites from Nerja cave (Andalusia, Spain) were studied. The cave is well known because of its long human occupation from the Upper Palaeolithic to the Chalcolithic and its abundant parietal prehistoric Art. The aims of this study were twofold: i) to compare uranium/thorium (Th-230/U-234) and Carbon-14 (C-14) ages obtained all along the growth axis of the stalagmites in order to understand the consequences of diagenetic processes on the validity of radiometric ages; ii) as one of the stalagmites contains black layers, attributed to combustion soot, to establish when these intense hearths were used and by which culture. Th-230/U-234 and C-14 ages were coupled with mineralogical studies using FTIR (Fourier-transform infrared spectroscopy) and thin section observations. The first stalagmite (GN16-9b) displays Th-230/U-234 ages in stratigraphic order, and compatible with C-14 ages corrected for a few percent of dead carbon. Homogeneous composition of aragonitic crystals characterized by their needle-like texture is observed throughout this speleothem. For the second stalagmite (GN16-7), in contrast,Th- 230/U-234 ages display large significant inversions and discordant results on the upper part and at the base of the stalagmite, suggesting a possible open system behavior for this chronometer. Interestingly, 14C ages are in stratigraphic order all along the stalagmite and are compatible with Th-230/U-234 ages only in its central part. Mineralogical studies display evidence of aragonite to calcite transformation at the top and a complex mineralogical assemblage with interlayered silicates (possibly clays) and calcitic mineralogy for the base of GN16-7. In these parts, discordant Th-230/U-234 ages were measured. In the middle part of the stalagmite, however, where the fibrous aragonite is well preserved, the 14C and Th-230/U-234 ages agree. Our data suggest that in the case of aragonite to calcite transformation as shown here, Th-230/U-234 ages are biased, but C-14 ages seem to remain accurate, as already observed in aragonitic marine bio minerals. C-14 ages obtained are used for the chronology of the soot layer, determined here between 7900 and 5500 years Cal BP, coherent with previous analysis of charcoals in the same sector of the cave. This study highlights the importance of working with at least two chronometers when stratigraphic age verification is not possible, as is the case of some parietal CaCO3 thin layers used for rock art dating. Recent Th-230/U-234 ages published for carbonate deposits on Spanish parietal Art are discussed in light of this demonstration. (C) 2022 Elsevier Ltd. All rights reserved. |
3,344 | Recurrent Poisson Factorization for Temporal Recommendation | Poisson Factorization (PF) is the gold standard framework for recommendation systems with implicit feedback whose variants show state-of-the-art performance on real-world recommendation tasks. However, they do not explicitly take into account the temporal behavior of users which is essential to recommend the right item to the right user at the right time. In this paper, we introduce Recurrent Poisson Factorization (RPF) framework that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit feedback. RPF treats time as a natural constituent of the model, and takes important factors for recommendation into consideration to provide a rich family of time-sensitive factorization models. They include Hierarchical RPF that captures the consumption heterogeneity among users and items, Dynamic RPF that handles dynamic user preferences and item specifications, Social RPF that models the social-aspect of product adoption, Item-Item RPF that considers the inter-item correlations, and eXtended Item-Item RPF that utilizes items' metadata to better infer the correlation among engagement patterns of users with items. We also develop an efficient variational algorithm for approximate inference that scales up to massive datasets. We demonstrate RPFs superior performance over many state-of-the-art methods on synthetic dataset, and wide variety of large scale real-world datasets. |
3,345 | Harmonizing Pathological and Normal Pixels for Pseudo-Healthy Synthesis | Synthesizing a subject-specific pathology-free image from a pathological image is valuable for algorithm development and clinical practice. In recent years, several approaches based on the Generative Adversarial Network (GAN) have achieved promising results in pseudo-healthy synthesis. However, the discriminator (i.e., a classifier) in the GAN cannot accurately identify lesions and further hampers from generating admirable pseudo-healthy images. To address this problem, we present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. Then, we apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem existing in medical image segmentation. Furthermore, a reliable metric is proposed by utilizing two attributes of label noise to measure the health of synthetic images. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods. The method achieves better performance than the existing methods with only 30% of the training data. The effectiveness of the proposed method is also demonstrated on the LiTS and the T1 modality of BraTS. The code and the pre-trained model of this study are publicly available at https://github.com/Au3C2/Generator-Versus-Segmentor. |
3,346 | Fluorescence Molecular Tomography Reconstruction of Small Targets Using Stacked Auto-Encoder Neural Networks | As a noninvasive and quantitative method, fluorescence molecular tomography (FMT) has many potential applications in biomedical field. It has the power to resolve in three-dimension (3D), the molecular processes in small animal in-vivo in both theory and practice. This paper proposes to solve the problem of reconstruction error and speed by using stacked auto-encoders (SAE). A finite element method (FEM) solution to the Laplace transformed time-domain coupled diffusion equations is employed as the forward model. The reconstruction model is formulated under the framework of SAE. Numerical simulation experiments were conducted to compare the reconstruction results of SAE and algebraic reconstruction technique (ART). We demonstrated that the proposed reconstruction algorithm can retrieve the positions and shapes of the targets more accurately than ART. This advantage of SAE is especially reflected in the reconstruction for small targets with a radius of 2 mm and 3 mm. |
3,347 | A Single-Blind, Randomized, Placebo Controlled Study to Evaluate the Benefits and Safety of Endourage Targeted Wellness Formula C Sublingual +Drops in People with Post-Acute Coronavirus Disease 2019 Syndrome | Introduction: Coronavirus Disease 2019 (COVID-19) causes a wide range of symptoms, including death. As persons recover, some continue to experience symptoms described as Post-Acute COVID-19 Syndrome (PACS). The objectives of this study were to measure the efficacy of Formula C™, a cannabidiol (CBD)-rich, whole-flower terpene-rich preparation in managing PACS symptoms. Materials and Methods: This randomized, placebo-controlled, single-blind, open-label crossover study was conducted in 2021. Informed consent was obtained from participants, and they were randomized to two treatment groups. Group 1 (n=15) received blinded active product for 28 days, and Group 2 (n=16) received blinded placebo for 28 days (Treatment Period 1). Both groups crossed over to open-label active product for 28 days (Treatment Period 2) with a safety assessment at day 70. Patient-Reported Outcomes Measurement Information System (PROMIS®) scores and the Patient Global Impression of Change (PGIC) score were used to assess primary and secondary objectives. Safety assessments were also done at each visit. Results: Twenty-four participants completed study, with 8 withdrawals, none related to study product. PGIC and PROMIS scores improved across both groups at day 28. This raised questions about the placebo. A reanalysis of the placebo confirmed absence of CBD and unexpected medical concentration of terpenes. The study continued despite no longer having a true placebo. The improved scores on outcome measures were maintained across the open label treatment period. There were no safety events reported throughout the study. Discussion: For persons with PACS who are nonresponsive to conventional therapies, this study demonstrated symptom improvement for participants utilizing Formula C. In addition, the benefits seen in Group 2 suggest the possibility that non-CBD formulations rich in antioxidants, omega-3, and omega-6 fatty acids, gamma-linoleic acid, and terpenes may also have contributed to the overall improvement of the partial active group through the study. Conclusion: Given that both groups demonstrated improvement, both formulations may be contributing to these findings. Limitations include the small number of participants, the lack of a true placebo, and limited time on study products. Additional studies are warranted to explore both CBD-rich hemp products and hempseed oil as treatment options for PACS. Trial Registration ClinicalTrials.gov Identifier: NCT04828668. |
3,348 | HEVC coding-unit decision algorithm using tree-block classification and statistical data analysis | We propose a fast coding unit (CU) depth decision algorithm in the High Efficiency Video Coding (HEVC) procedure based on statistical analysis. First, we derive a set of optimized weights of surrounding CU decisions to predict the current CU decision for 3 different Largest Coding Unit (LCU) classes. Second, for a given predicted current CU decision, we analyze the possible true current CU decisions, aiming to find the correspondence. A corresponding table is found and can be used to achieve target prediction accuracy. Third, for early termination of the encoding processes, the 3 early termination methods in a state-of-the-art work, as well as their different combinations, are evaluated. We show that using one of them is sufficient for saving time while encoding to keep the implementation complexity low. Compared with full CU search in HEVC standards, the proposed method reduces the encoding time by 57 and 49 % on average with Low Delay and Random Access profiles, respectively, with acceptable bitrate and PSNR performances. Compared with two state-of-the-art methods, the encoding time reduction is up to 23 and 13 % with Low Delay profile, 7 and 3 % with Random Access profile, on average, whereas the performances of bitrate and PSNR are similar. |
3,349 | Leveraging event-based semantics for automated text simplification | Automated Text Simplification (ATS) aims to transform complex texts into their simpler variants which are easier to understand to wider audiences and easier to process with natural language processing (NLP) tools. While simplification can be applied on lexical, syntactic, and discourse level, all previously proposed ATS systems only operated on the first two levels, thus failing at simplifying texts on the discourse level. We present a semantically-motivated ATS system which is the first system that is applied on the discourse level. By exploiting the state-of-the-art event extraction system, it is the first ATS system able to eliminate large portions of irrelevant information from texts, by maintaining only those parts of the original text that belong to factual event mentions. A few handcrafted rules ensure that the output of the system is syntactically simple, by placing each factual event mention in a separate short sentence, while the state-of-the-art unsupervised lexical simplification module, based on using word embeddings, replaces complex and infrequent words with their simpler variants. We perform a thorough evaluation, both automatic and manual, showing that our system produces more readable and simpler texts than the state-of-the-art ATS systems. Our newly proposed post-editing evaluation further reveals that our system requires less human effort for correcting grammaticality and meaning preservation on news articles than the state-of-the-art ATS system. (C) 2017 Elsevier Ltd. All rights reserved. |
3,350 | A Simple Model of Speech Communication and its Application to Intelligibility Enhancement | We introduce a model of communication that includes noise inherent in the message production process as well as noise inherent in the message interpretation process. The production and interpretation noise processes have a fixed signal-to-noise ratio. The resulting system is a simple but effective model of human communication. The model naturally leads to a method to enhance the intelligibility of speech rendered in a noisy environment. State-of-the-art experimental results confirm the practical value of the model. |
3,351 | Multi-Objective Memetic Search Algorithm for Multi-Objective Permutation Flow Shop Scheduling Problem | The multiobjective permutation flowshop scheduling problem (MOPFSSP) is one of the most popular machine scheduling problems with extensive engineering relevance of manufacturing systems. There have been many attempts at solving MOPFSSP using heuristic and meta-heuristic methods, such as evolutionary algorithm. In this paper, a novel multiobjective memetic search algorithm (MMSA), is proposed to solve the MOPFSSP with makespan and total flowtime. First, a problem-specific Nawaz-Enscore-Hoam heuristic is used to initialize the population to enhance the quality of the initial solution. Second, a global search embedded with a perturbation operation is used to improve the solution of the entire population. Then, a single insert-based local search is used to improve each individual and then a further local search strategy is used to find the better solution for the non-improved individual in the single insert-based local search. The performance of our proposed algorithm is validated and compared with the four state-of-the-art algorithms on a number of benchmark problem. The experimental results show that the proposed MMSA provides better solutions than several state-of-the-art algorithms. |
3,352 | Fine-tuning Convolutional Neural Networks for fine art classification | The increasing availability of large digitized fine art collections opens new research perspectives in the intersection of artificial intelligence and art history. Motivated by the successful performance of Convolutional Neural Networks (CNN) for a wide variety of computer vision tasks, in this paper we explore their applicability for art-related image classification tasks. We perform extensive CNN fine-tuning experiments and consolidate in one place the results for five different art-related classification tasks on three large fine art datasets. Along with addressing the previously explored tasks of artist, genre, style and time period classification, we introduce a novel task of classifying artworks based on their association with a specific national artistic context. We present state-of-the-art classification results of the addressed tasks, signifying the impact of our method on computational analysis of art, as well as other image classification related research areas. Furthermore, in order to question transferability of deep representations across various source and target domains, we systematically compare the effects of domain-specific weight initialization by evaluating networks pre-trained for different tasks, varying from object and scene recognition to sentiment and memorability labelling. We show that fine-tuning networks pre-trained for scene recognition and sentiment prediction yields better results than fine-tuning networks pre-trained for object recognition. This novel outcome of our work suggests that the semantic correlation between different domains could be inherent in the CNN weights. Additionally, we address the practical applicability of our results by analysing different aspects of image similarity. We show that features derived from fine-tuned networks can be employed to retrieve images similar in either style or content, which can be used to enhance capabilities of search systems in different online art collections. (C) 2018 Elsevier Ltd. All rights reserved. |
3,353 | Remote dielectric sensing and lung ultrasound to assess pulmonary congestion | We investigated the agreement between remote dielectric sensing (ReDS) system, which is a recently introduced non-invasive technology to quantify the degree of pulmonary congestion, and lung ultrasound (LUS), which is a gold standard to assess the existence of severe pulmonary congestion. Consecutive patients who were hospitalized to examine the cause of heart failure and treat their heart failure in our institute were prospectively included. They received LUS and simultaneous ReDS measurements. Three or more B-lines at each LUS zone was assigned to B-profile positive, indicating the existence of significant pulmonary congestion. ReDS values ≥ 35% were defined as significant pulmonary congestion. A total of 19 heart failure patients were included (77 years, 13 men). Plasma B-type natriuretic peptide level was 131 (36, 416) pg/ml. Three patients had B-profile, indicating significant pulmonary congestion, and two of them had ≥ 35% of ReDS (sensitivity 66.7%, specificity 87.5%, and negative predictive value 93.3%). Most of the patients (79%) had lower B-lines below 3 and did not satisfy the criteria of B-profile, irrespective of wide ranges of ReDS values. ReDS system had as acceptable predictability as LUS in assessing the existence of significant pulmonary congestion. ReDS would be recommended to rule out significant pulmonary congestion or quantify the degree of less significant pulmonary congestion. |
3,354 | Meta-Parameter Free Unsupervised Sparse Feature Learning | We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on CIFAR-10, STL-10 and UCMerced show that the method achieves the state-of-the-art performance, providing discriminative features that generalize well. |
3,355 | Regression-Based Cardiac Motion Prediction From Single-Phase CTA | State of the art cardiac computed tomography (CT) enables the acquisition of imaging data of the heart over the entire cardiac cycle at concurrent high spatial and temporal resolution. However, in clinical practice, acquisition is increasingly limited to 3-D images. Estimating the shape of the cardiac structures throughout the entire cardiac cycle from a 3-D image is therefore useful in applications such as the alignment of preoperative computed tomography angiography (CTA) to intra-operative X-ray images for improved guidance in coronary interventions. We hypothesize that the motion of the heart is partially explained by its shape and therefore investigate the use of three regression methods for motion estimation from single-phase shape information. Quantitative evaluation on 150 4-D CTA images showed a small, but statistically significant, increase in the accuracy of the predicted shape sequences when using any of the regression methods, compared to shape-independent motion prediction by application of the mean motion. The best results were achieved using principal component regression resulting in point-to-point errors of 2.3 +/- 0.5 mm, compared to values of 2.7 +/- 0.6 mm for shape-independent motion estimation. Finally, we showed that this significant difference withstands small variations in important parameter settings of the landmarking procedure. |
3,356 | Attention-Based Dropout Layer for Weakly Supervised Single Object Localization and Semantic Segmentation | Both weakly supervised single object localization and semantic segmentation techniques learn an object's location using only image-level labels. However, these techniques are limited to cover only the most discriminative part of the object and not the entire object. To address this problem, we propose an attention-based dropout layer, which utilizes the attention mechanism to locate the entire object efficiently. To achieve this, we devise two key components, 1) hiding the most discriminative part from the model to capture the entire object, and 2) highlighting the informative region to improve the classification power of the model. These allow the classifier to be maintained with a reasonable accuracy while the entire object is covered. Through extensive experiments, we demonstrate that the proposed method effectively improves the weakly supervised single object localization accuracy, thereby achieving a new state-of-the-art localization accuracy on the CUB-200-2011 and a comparable accuracy existing state-of-the-arts on the ImageNet-1k. The proposed method is also effective in improving the weakly supervised semantic segmentation performance on the Pascal VOC and MS COCO. Furthermore, the proposed method is more efficient than existing techniques in terms of parameter and computation overheads. Additionally, the proposed method can be easily applied in various backbone networks. |
3,357 | Providing Re-Essure-ance to the Nickel-Allergic Patient Considering Hysteroscopic Sterilization | Essure is a popular method of permanent sterilization that offers a minimally invasive approach that avoids the risks of traditional sterilization procedures in the operating room. Despite the rarity of complications, there has been concern in the popular media over the safety of Essure. We describe the third reported case of systemic contact dermatitis due to the nickel component of the device, with a resolution of symptoms following surgical removal of the inserts. Despite these cases, we believe that extremely rare complications such as this should not dissuade patients from choosing this safe, effective method of sterilization. |
3,358 | State of the Art Lower Limb Robotic Exoskeletons for Elderly Assistance | The number of elderly populations is rapidly increasing. Majority of elderly people face difficulties while walking because the muscular activity or other gait-related parameters start to deteriorate with aging. Therefore, the quality of life among them can be suffered. To make their life more comfortable, service providing robotic solutions in terms of wearable powered exoskeletons should be realized. Assistive powered exoskeletons are capable of providing additional torque to support various activities, such as walking, sit to stand, and stand to sit motions to subjects with mobility impairments. Specifically, the powered exoskeletons try to maintain and keep subjects' limbs on the specified motion trajectory. The state of the art of currently available lower limb assistive exoskeletons for weak and elderly people is presented in this paper. The technology employed in the assistive devices, such as actuation and power supply types, control strategies, their functional abilities, and the mechanism design, is thoroughly described. The outcome of studied literature reveals that there is still much work to be done in the improvement of assistive exoskeletons in terms of their technological aspects, such as choosing proper and effective control methods, developing user friendly interfaces, and decreasing the costs of device to make it more affordable, meanwhile ensuring safe interaction for the end-users. |
3,359 | Coverage path planning with targetted viewpoint sampling for robotic free-form surface inspection | Surface metrology systems are increasingly used for inspecting dimensional quality in manufacturing. The gauge of these measurement systems is often mounted as an end-effector on robotic systems to exploit the robots' high degrees of freedom to reposition the gauge to different viewpoints. With this repositioning flexibility, a planning methodology becomes necessary in order to carefully plan the viewpoints, as well as the optimal sequence and quickest path to move the gauge to each viewpoint. This paper investigates coverage path planning for robotic single-sided dimensional inspection of free-form surfaces. Reviewing existing feasible state-of-the-art methodologies to solve this problem led to identifying an unexplored opportunity to improve the coverage path planning, specifically by replacing random viewpoint sampling strategy. This study reveals that a non-random targetted viewpoint sampling strategy significantly contributes to solution quality of the resulting planned coverage path. By deploying optimisation during the viewpoint sampling, an optimal set of admissible viewpoints can be obtained, which consequently significantly shortens the cycle-time for the inspection task. Results that evaluate the proposed viewpoint sampling strategy for two industrial sheet metal parts, as well as a comparison with the state-of-the-art are presented. The results show up to 23.8% reduction in cycle-time for the inspection task when using targetted viewpoints sampling. |
3,360 | Complex features extraction with deep learning model for the detection of COVID19 from CT scan images using ensemble based machine learning approach | Recently the most infectious disease is the novel Coronavirus disease (COVID 19) creates a devastating effect on public health in more than 200 countries in the world. Since the detection of COVID19 using reverse transcription-polymerase chain reaction (RT-PCR) is time-consuming and error-prone, the alternative solution of detection is Computed Tomography (CT) images. In this paper, Contrast Limited Histogram Equalization (CLAHE) was applied to CT images as a preprocessing step for enhancing the quality of the images. After that, we developed a novel Convolutional Neural Network (CNN) model that extracted 100 prominent features from a total of 2482 CT scan images. These extracted features were then deployed to various machine learning algorithms - Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF). Finally, we proposed an ensemble model for the COVID19 CT image classification. We also showed various performance comparisons with the state-of-art methods. Our proposed model outperforms the state-of-art models and achieved an accuracy, precision, and recall score of 99.73%, 99.46%, and 100%, respectively. |
3,361 | An Architecture to Accelerate Convolution in Deep Neural Networks | In the past few years, the demand for real-time hardware implementations of deep neural networks (DNNs), especially convolutional neural networks (CNNs), has dramatically increased, thanks to their excellent performance on a wide range of recognition and classification tasks. When considering real-time action recognition and video/image classification systems, latency is of paramount importance. Therefore, applications strive to maximize the accuracy while keeping the latency under a given application-specific maximum: in most cases, this threshold cannot exceed a few hundred milliseconds. Until now, the research on DNNs has mainly focused on achieving a better classification or recognition accuracy, whereas very few works in literature take in account the computational complexity of the model. In this paper, we propose an efficient computational method, which is inspired by a computational core of fully connected neural networks, to process convolutional layers of state-of-the-art deep CNNs within strict latency requirements. To this end, we implemented our method customized for VGG and VGG-based networks which have shown state-of-the-art performance on different classification/recognition data sets. The implementation results in 65-nm CMOS technology show that the proposed accelerator can process convolutional layers of VGGNet up to 9.5 times faster than state-of-the-art accelerators reported to-date while occupying 3.5 mm(2). |
3,362 | A Deep Learning Architecture for Meningioma Brain Tumor Detection and Segmentation | The meningioma brain tumor detection and segmentation method is a complex process due to its low intensity pixel profile. In this article, the meningioma brain tumor images were detected and tumor regions were segmented using a convolutional neural network (CNN) classification approach. The source brain MRI images were decomposed using the discrete wavelet transform and these decomposed sub bands were fused using an arithmetic fusion technique. The fused image was data augmented in order to increase the sample size. The data augmented images were classified into either healthy or malignant using a CNN classifier. Then, the tumor region in the classified meningioma brain image was segmented using an connection component analysis algorithm. The tumor region segmented meningioma brain image was compressed using a lossless compression technique. The proposed method stated in this article was experimentally tested with the sets of meningioma brain images from an open access dataset. The experimental results were compared with existing methods in terms of sensitivity, specificity and tumor segmentation accuracy. |
3,363 | Site-specific art and geodiversity: socio-environmental effects of a special spatial investment in four Geoparks of France and Spain | This paper highlights the intentionalities and effects of artistic production of space in several peripheral, natural and rural areas characterized by high value geodiversity. The art productions discussed in this article are located in four UNESCO global geoparks (Monts d'Ardeche and Haute-Provence in France, Sobrarbe in the Aragonese Pyrenees and Lanzarote in Canary Islands, Spain). Site-specific art installed in natural settings will oftentimes call upon this geodiversity, or its heritage component (sometimes exceptional in geomorphology, aesthetics or age), for inspiration in both forms and materials used. This article is primarily concerned with the artistic and geographical specificities of these artworks, and then sketches a panorama of artistic production of space of the four geoparks selected in France and Spain. Finally, the << services >> provided by these artistic programs to the protected areas and to all the actors who animate them will be discussed. It appears that these various artistic productions can serve as tools of territorial control that allow certain artists, institutions and even administrations or businesses to claim or reclaim spaces they can/did not directly manage. This phenomenon could be a positive form of territorial control when it leads to the improved protection of geodiversity. However, it may also be criticized when it involves forms of political instrumentalization and uses strategies of territorial marketing, thus opening the door to tourist overcrowding. |
3,364 | Ultrafast Ultrasound Imaging as an Inverse Problem: Matrix-Free Sparse Image Reconstruction | Conventional ultrasound (US) image reconstruction methods rely on delay-and-sum (DAS) beamforming, which is a relatively poor solution to the image reconstruction problem. An alternative to DAS consists in using iterative techniques, which require both an accurate measurement model and a strong prior on the image under scrutiny. Toward this goal, much effort has been deployed in formulating models for US imaging, which usually require a large amount of memory to store the matrix coefficients. We present two different techniques, which take advantage of fast and matrix-free formulations derived for the measurement model and its adjoint, and rely on sparsity of US images in well-chosen models. Sparse regularization is used for enhanced image reconstruction. Compressed beamforming exploits the compressed sensing framework to restore high-quality images from fewer raw data than state-of-the-art approaches. Using simulated data and in vivo experimental acquisitions, we show that the proposed approach is three orders of magnitude faster than non-DAS state-of-the-art methods, with comparable or better image quality. |
3,365 | Light field reconstruction using hierarchical features fusion | Light field imagery has attracted increasing attention for its capacity of simultaneously capturing intensity values of light rays from multiple directions. Such imagery technique has become widely accessible with the emergence of consumer-grade devices, e.g. Lytro, and the Virtual Reality (VR) / Augmented Reality (AR) areas. Light field reconstruction is a critical topic to mitigate the trade-off problem between the spatial and angular resolutions. Learning-based methods have attained outstanding performance among the recently proposed methods, however, the state-of-the-art methods still suffer from heavy artifacts in the case of occlusion. This is likely to be a consequence of failure in capturing the semantic information from the limited spatial receptive field during training. It is crucial for light field reconstruction to learn semantic features and understand a wider context in both the angular and spatial dimensions. To address this issue, we introduce a novel end-to-end U-Net with SAS network (U-SAS-Net) to extract and fuse hierarchical features, both local and semantic, from a relatively large receptive field while establishing the relation of the correlated sub-aperture images. Experimental results on extensive light field datasets demonstrate that our method produces a state-of-the-art performance that exceeds the previous works by more than 0.6 dB PSNR with the fused hierarchical features, especially the semantic features for handling scenes with occlusion and the local features for recovering the rich details. Meanwhile, our method is at a substantially lower cost which takes 48% parameters and less than 10% computation of the previous state-of-the-art method. (C) 2020 Elsevier Ltd. All rights reserved. |
3,366 | Ni-based catalysts used in heterogeneous catalytic ozonation for organic pollutant degradation: a minireview | Among various advanced oxidation processes for wastewater treatment, heterogeneous catalytic ozonation (HCO) has a growing interest in pollutant degradation, e.g., pesticides, pharmaceuticals, cresols, detergents, polymers, dyes, and others. Direct oxidation with ozone can occur by this route or indirectly, generating reactive oxygen species through the catalytic activation of the ozone molecule. Then, many catalytic materials were evaluated, such as unsupported and supported oxides, activated carbon, nanocarbons, carbon nitride, and mesoporous materials. This review focuses on the properties and performance of Ni-based catalysts (NiO, supported NiO, Ni ferrites, and M-Ni bimetallic), emphasizing the reaction mechanisms and the importance of the reactive oxygen species in removing toxic organic compounds. |
3,367 | Saliency-based deep convolutional neural network for no-reference image quality assessment | In this paper, we proposed a novel method for No-Reference Image Quality Assessment (NR-IQA) by combining deep Convolutional Neural Network (CNN) with saliency map. We first investigate the effect of depth of CNNs for NR-IQA by comparing our proposed ten-layer Deep CNN (DCNN) for NR-IQA with the state-of-the-art CNN architecture proposed by Kang et al. (2014). Our results show that the DCNN architecture can deliver a higher accuracy on the LIVE dataset. To mimic human vision, we introduce saliency maps combining with CNN to propose a Saliency-based DCNN (SDCNN) framework for NR-IQA. We compute a saliency map for each image and both the map and the image are split into small patches. Each image patch is assigned with a patch importance value based on its saliency patch. A set of Salient Image Patches (SIPs) are selected according to their saliency and we only apply the model on those SIPs to predict the quality score for the whole image. Our experimental results show that the SDCNN framework is superior to other state-of-the-art approaches on the widely used LIVE dataset. The TID2008 and the CISQ image quality datasets are utilised to report cross-dataset results. The results indicate that our proposed SDCNN can generalise well on other datasets. |
3,368 | Innovation and Development: An Analysis of Landscape Construction Factors in Quanzhou Maritime Silkroad Art Park | From the perspective of tourists, this paper takes Quanzhou Maritime Silkroad Art Park as the research object to study the botanical landscape factors concerned with tourists in the theme park. Through a questionnaire survey, and combined with interviews, the collected results were scientifically analysed using the data. According to the statistical results, the factors of plant landscape construction in the theme park concerned with tourists were summarised, extracted, and named, which were "plant landscape healing", "plant landscape culture", "plant landscape continuity", "plant landscape spatial sense", and "plant landscape aesthetic sense". Through an in-depth analysis of the five common factors of the construction of modern theme park plant landscapes, this study creatively centred on the construction of theme park landscapes and established a scientific evaluation system, combined with the development and construction of the park, and put forward innovative and constructive suggestions based on the summary and analysis results. It provides a scientific reference for plant landscape construction in other theme parks. |
3,369 | Embryonic and extraembryonic tissues during mammalian development: shifting boundaries in time and space | The first few days of embryonic development in eutherian mammals are dedicated to the specification and elaboration of the extraembryonic tissues. However, where the fetus ends and its adnexa begins is not always as self-evident during the early stages of development, when the definitive body axes are still being laid down, the germ layers being specified and a discrete form or bodyplan is yet to emerge. Function, anatomy, histomorphology and molecular identities have been used through the history of embryology, to make this distinction. In this review, we explore them individually by using specific examples from the early embryo. While highlighting the challenges of drawing discrete boundaries between embryonic and extraembryonic tissues and the limitations of a binary categorization, we discuss how basing such identity on fate is the most universal and conceptually consistent. This article is part of the theme issue 'Extraembryonic tissues: exploring concepts, definitions and functions across the animal kingdom'. |
3,370 | Low-Dose Lung CT Image Restoration Using Adaptive Prior Features From Full-Dose Training Database | The valuable structure features in full-dose computed tomography (FdCT) scans can be exploited as prior knowledge for low-dose CT (LdCT) imaging. However, lacking the capability to represent local characteristics of interested structures of the LdCT image adaptively may result in poor preservation of details/textures in LdCT image. This paper aims to explore a novel prior knowledge retrieval and representation paradigm, called adaptive prior features assisted restoration algorithm, for the purpose of better restoration of the low-dose lung CT images by capturing local features from FdCT scans adaptively. The innovation lies in the construction of an offline training database and the online patch-search scheme integrated with the principal components analysis (PCA). Specifically, the offline training database is composed of 3-D patch samples extracted from existing full-dose lung scans. For online patch-search, 3-D patches with structure similar to the noisy target patch are first selected from the database as the training samples. Then, PCA is applied on the training samples to retrieve their local prior principal features adaptively. By employing the principal features to decompose the noisy target patch and using an adaptive coefficient shrinkage technique for inverse transformation, the noise of the target patch can be efficiently removed and the detailed texture can be well preserved. The effectiveness of the proposed algorithm was validated by CT scans of patients with lung cancer. The results show that it can achieve a noticeable gain over some state-of-the-art methods in terms of noise suppression and details/textures preservation. |
3,371 | Convexity constrained efficient superpixel and supervoxel extraction | This paper presents an efficient superpixel (SP) and supervoxel (SV) extraction method that aims improvements over the state-of-the-art in terms of both accuracy and computational complexity. Segmentation performance is improved through convexity constrained distance utilization, whereas computational efficiency is achieved by replacing complete region processing by a boundary adaptation technique. Starting from the uniformly distributed, rectangular (cubical) equal size (volume) superpixels (supervoxels), region boundaries are iteratively adapted towards object edges. Adaptation is performed by assigning the boundary pixels to the most similar neighboring SPs (SVs). At each iteration, SP (SV) regions are updated; hence, progressively converging to compact pixel groups. Detailed experimental comparisons against the state-of-the-art competing methods validate the performance of the proposed technique considering both accuracy and speed. (C) 2015 Elsevier B.V. All rights reserved. |
3,372 | A fluorescent and colorimetric sensor based on ionic liquids for the on-site monitoring trace gaseous SO2 | SO2 could cause severe environmental pollution and health threat, so real-time and on-site monitoring of SO2 has attracted considerable attention. This work proposed a novel ionic liquid-based sensor, called trihexyl (tetradecyl) phosphonium fluorescein ionic liquid, which can accurately detect SO2 with its fluorescent and colorimetric dual-readout assay without seventeen gases interference (eg: NO, N2, CO2, O2, COS, HCl, CHCl3). GC-MS was also used to verify the validation of the detection method. First, this fluorescein-based IL sensor exhibited fluorescence green and colorimetric yellow signals. When the sensor was exposed to gaseous SO2, the green fluorescence quenched, and the colorimetric yellow color faded due to chemical bond interaction. Also, the proposed IL sensor exhibited good linearity in the SO2 concentration range of 5.0-95.0 ppm with a detection limit of 0.9 ppm (fluorescence) and 1.9 ppm (colorimetry), and recoveries of 97%∼103% with RSD less than 1.21%. Besides, the IL sensor could be easily assembled into a paper device by simple immersion, and the paper strip was exploited to realize a semiquantitative visual detection of SO2. These results indicated that the proposed fluorescence-colorimetric dual-signal chemosensor could be used as intelligent paper labels for real-time and on-site monitoring of SO2 in ambient air. |
3,373 | Facile Synthesis of Uranium Complexes with a Pendant Borane Lewis Acid and 1,2-Insertion of CO into a U-N Bond | In this contribution, we illustrate uranium complexes bearing a pendant borate (i.e. 1 and 2) or a pendant borane (i.e. 3 and 4) moiety via reaction of the highly strained uranacycle I with various 3-coordinate boranes. Complexes 3 and 4 represent the first examples of uranium complexes with a pendant borane Lewis acid. Moreover, complex 3 was capable of activation of CO, delivering a new CO activation mode, and an abnormal CO 1,2-insertion pathway into a U-N bond. The importance of the pendant borane moiety was confirmed by the controlled experiments. |
3,374 | Looking for Abnormalities in Mammograms With Self- and Weakly Supervised Reconstruction | Early breast cancer screening through mammography produces every year millions of images world-wide. Despite the volume of the data generated, these images are not systematically associated with standardized labels. Current protocols encourage giving a malignancy probability to each studied breast but do not require the explicit and burdensome annotation of the affected regions. In this work, we address the problem of abnormality detection in the context of such weakly annotated datasets. We combine domain knowledge about the pathology and clinically available image-wise labels to propose a mixed self- and weakly supervised learning framework for abnormalities reconstruction. We also introduce an auxiliary classification task based on the reconstructed regions to improve explainability. We work with high-resolution imaging that enables our network to capture different findings, including masses, micro-calcifications, distortions, and asymmetries, unlike most state-of-the-art works that mainly focus on masses. We use the popular INBreast dataset as well as our private multi-manufacturer dataset for validation and we challenge our method in segmentation, detection, and classification versus multiple state-of-the-art methods. Our results include image-wise AUC up to 0.86, overall region detection true positives rate of 0.93, and the pixel-wise F-1 score of 64% on malignant masses. |
3,375 | Modeling strategies to analyse longitudinal biomarker data: An illustration on predicting immunotherapy non-response in non-small cell lung cancer | Serum tumor markers acquired through a blood draw are known to reflect tumor activity. Their non-invasive nature allows for more frequent testing compared to traditional imaging methods used for response evaluations. Our study aims to compare nine prediction methods to accurately, and with a low false positive rate, predict progressive disease despite treatment (i.e. non-response) using longitudinal tumor biomarker data. Bi-weekly measurements of CYFRA, CA-125, CEA, NSE, and SCC were available from a cohort of 412 advanced stage non-small cell lung cancer (NSCLC) patients treated up to two years with immune checkpoint inhibitors. Serum tumor marker measurements from the first six weeks after treatment initiation were used to predict treatment response at 6 months. Nine models with varying complexity were evaluated in this study, showing how longitudinal biomarker data can be used to predict non-response to immunotherapy in NSCLC patients. |
3,376 | Energy harvesting: State-of-the-art | This paper presents a brief history of energy harvesting for low-power systems followed by a review of the state-of-the-art of energy harvesting techniques, power conversion, power management, and battery charging. The advances in energy harvesting from vibration, thermal, and RF sources are reviewed as well as power management techniques. Examples of discrete form implementation and integrated form implementation using microelectromechanical systems (MEMS) and CMOS microelectronic processes are also given. The comparison between the reviewed works concludes this paper. (C) 2010 Elsevier Ltd. All rights reserved. |
3,377 | Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks | Coronary heart disease is a common cause of death despite being preventable. To treat the underlying plaque deposits in the arterial walls, intravascular optical coherence tomography can be used by experts to detect and characterize the lesions. In clinical routine, hundreds of images are acquired for each patient, which require automatic plaque detection for fast and accurate decision support. So far, automatic approaches rely on classic machine learning methods and deep learning solutions have rarely been studied. Given the success of deep learning methods with other imaging modalities, a thorough understanding of deep learning-based plaque detection for future clinical decision support systems is required. We address this issue with a new data set consisting of in vivo patient images labeled by three trained experts. Using this data set, we employ the state-of-the-art deep learning models that directly learn plaque classification from the images. For improved performance, we study different transfer learning approaches. Furthermore, we investigate the use of Cartesian and polar image representations and employ data augmentation techniques tailored to each representation. We fuse both representations in a multi-path architecture for more effective feature exploitation. Last, we address the challenge of plaque differentiation in addition to detection. Overall, we find that our combined model performs best with an accuracy of 91.7%, a sensitivity of 90.9%, and a specificity of 92.4%. Our results indicate that building a deep learning-based clinical decision support system for plaque detection is feasible. |
3,378 | Chronic lithium treatment ameliorates ketamine-induced mania-like behavior via the PI3K-AKT signaling pathway | Ketamine, a rapid-acting antidepressant drug, has been used to treat major depressive disorder and bipolar disorder (BD). Recent studies have shown that ketamine may increase the potential risk of treatment-induced mania in patients. Ketamine has also been applied to establish animal models of mania. At present, however, the underlying mechanism is still unclear. In the current study, we found that chronic lithium exposure attenuated ketamine-induced mania-like behavior and c-Fos expression in the medial prefrontal cortex (mPFC) of adult male mice. Transcriptome sequencing was performed to determine the effect of lithium administration on the transcriptome of the PFC in ketamine-treated mice, showing inactivation of the phosphoinositide 3-kinase (PI3K)-protein kinase B (AKT) signaling pathway. Pharmacological inhibition of AKT signaling by MK2206 (40 mg/kg), a selective AKT inhibitor, reversed ketamine-induced mania. Furthermore, selective knockdown of AKT via AAV-AKT-shRNA-EGFP in the mPFC also reversed ketamine-induced mania-like behavior. Importantly, pharmacological activation of AKT signaling by SC79 (40 mg/kg), an AKT activator, contributed to mania in low-dose ketamine-treated mice. Inhibition of PI3K signaling by LY294002 (25 mg/kg), a specific PI3K inhibitor, reversed the mania-like behavior in ketamine-treated mice. However, pharmacological inhibition of mammalian target of rapamycin (mTOR) signaling with rapamycin (10 mg/kg), a specific mTOR inhibitor, had no effect on ketamine-induced mania-like behavior. These results suggest that chronic lithium treatment ameliorates ketamine-induced mania-like behavior via the PI3K-AKT signaling pathway, which may be a novel target for the development of BD treatment. |
3,379 | MC-LSTM: Real-Time 3D Human Action Detection System for Intelligent Healthcare Applications | Due to the movement expressiveness and privacy assurance of human skeleton data, 3D skeleton-based action inference is becoming popular in healthcare applications. These scenarios call for more advanced performance in application-specific algorithms and efficient hardware support. Warnings on health emergencies sensitive to response speed require low latency output and action early detection capabilities. Medical monitoring that works in an always-on edge platform needs the system processor to have extreme energy efficiency. Therefore, in this paper, we propose the MC-LSTM, a functional and versatile 3D skeleton-based action detection system, for the above demands. Our system achieves state-of-the-art accuracy on trimmed and untrimmed cases of general-purpose and medical-specific datasets with early-detection features. Further, the MC-LSTM accelerator supports parallel inference on up to 64 input channels. The implementation on Xilinx ZCU104 reaches a throughput of 18 658 Frames-Per-Second (FPS) and an inference latency of 3.5 ms with the batch size of 64. Accordingly, the power consumption is 3.6 W for the whole FPGA+ARM system, which is 37.8x and 10.4x more energy-efficient than the high-end Titan X GPU and i7-9700 CPU, respectively. Meanwhile, our accelerator also keeps a 4 similar to 5x energy efficiency advantage against the low-power high-performance Firefly-RK3399 board carrying an ARM Cortex-A72+A53 CPU. We further synthesize an 8-bit quantized version on the same hardware, providing a 48.8% increase in energy efficiency under the same throughput. |
3,380 | Explicit Modeling of Human-Object Interactions in Realistic Videos | We introduce an approach for learning human actions as interactions between persons and objects in realistic videos. Previous work typically represents actions with low-level features such as image gradients or optical flow. In contrast, we explicitly localize in space and track over time both the object and the person, and represent an action as the trajectory of the object w.r.t. to the person position. Our approach relies on state-of-the-art techniques for human detection [32], object detection [10], and tracking [39]. We show that this results in human and object tracks of sufficient quality to model and localize human-object interactions in realistic videos. Our human-object interaction features capture the relative trajectory of the object w.r.t. the human. Experimental results on the Coffee and Cigarettes dataset [25], the video dataset of [19], and the Rochester Daily Activities dataset [29] show that 1) our explicit human-object model is an informative cue for action recognition; 2) it is complementary to traditional low-level descriptors such as 3D-HOG [23] extracted over human tracks. We show that combining our human-object interaction features with 3D-HOG improves compared to their individual performance as well as over the state of the art [23], [29]. |
3,381 | Accurate and Efficient Optic Disc Detection and Segmentation by a Circular Transformation | Under the framework of computer-aided diagnosis, this paper presents an accurate and efficient optic disc (OD) detection and segmentation technique. A circular transformation is designed to capture both the circular shape of the OD and the image variation across the OD boundary simultaneously. For each retinal image pixel, it evaluates the image variation along multiple evenly-oriented radial line segments of specific length. The pixels with the maximum variation along all radial line segments are determined, which can be further exploited to locate both the OD center and the OD boundary accurately. Experiments show that OD detection accuracies of 99.75%, 97.5%, and 98.77% are obtained for the STARE dataset, the ARIA dataset, and the MES-SIDOR dataset, respectively, and the OD center error lies around six pixels for the STARE dataset and the ARIA dataset which is much smaller than that of state-of-the-art methods ranging 14-29 pixels. In addition, the OD segmentation accuracies of 93.4% and 91.7% are obtained for STARE dataset and ARIA dataset, respectively, that consists of many severely degraded images of pathological retinas that state-of-the-art methods cannot segment properly. Furthermore, the algorithm runs in 5 s, which is substantially faster than many of the state-of-the-art methods. |
3,382 | K-means Cluster Algorithm Applied for Geometric Shaping Based on Iterative Polar Modulation in Inter-Data Centers Optical Interconnection | The demand of delivering various services is driving inter-data centers optical interconnection towards 400 G/800 G, which calls for increasing capacity and spectrum efficiency. The aim of this study is to effectively increase capacity while also improving nonlinear noise anti-interference. Hence, this paper presents a state-of-the-art scheme that applies the K-means cluster algorithm in geometric shaping based on iterative polar modulation (IPM). A coherent optical communication simulation system was established to demonstrate the performance of our proposal. The investigation reveals that the gap between IPM and Shannon limit has significantly narrowed in terms of mutual information. Moreover, when compared with IPM and QAM using the blind phase searching under the same order at HD-FEC threshold, the IPM-16 using the K-means algorithm achieves 0.9 dB and 1.7 dB gain; the IPM-64 achieves 0.3 dB and 1.1 dB gain, and the IPM-256 achieves 0.4 dB and 0.8 dB gain. The robustness of nonlinear noise and high capacity enable this state-of-the-art scheme to be used as an optional modulation format not only for inter-data centers optical interconnection but also for any high speed, long distance optical fiber communication system. |
3,383 | Video Error Correction Using Soft-Output and Hard-Output Maximum Likelihood Decoding Applied to an H.264 Baseline Profile | Error concealment has long been identified as the last line of defense against transmission errors. Since error handling is outside the scope of video coding standards, decoders may choose to simply ignore corrupted packets or attempt to decode their content. In this paper, we present a novel joint source-channel decoding approach that can be applied to received video packets containing transmission errors. Soft-output information is combined with our novel syntax-element-level maximum likelihood decoding framework to effectively extract valid macroblocks from corrupted H.264 slices. Simulation results show that our video error correction strategy provides an average peak signal-to-noise ratio (PSNR) improvement near 2 dB compared to the error concealment approach used by the H.264 reference software, as well as an average PSNR improvement of 0.8 dB compared to state-of-the-art error concealment. The proposed method is also applicable when only hard-information is available, in which case it performs better than state-of-the-art error concealment especially in high error conditions. Finally, in our simulations, the proposed method increased the decoder computational complexity by only 5% to 20%, making it applicable for real-time applications. |
3,384 | Constrained Statistical Modelling of Knee Flexion From Multi-Pose Magnetic Resonance Imaging | Reconstruction of the anterior cruciate ligament (ACL) through arthroscopy is one of the most common procedures in orthopaedics. It requires accurate alignment and drilling of the tibial and femoral tunnels through which the ligament graft is attached. Although commercial computer-assisted navigation systems exist to guide the placement of these tunnels, most of them are limited to a fixed pose without due consideration of dynamic factors involved in different knee flexion angles. This paper presents a new model for intraoperative guidance of arthroscopic ACL reconstruction with reduced error particularly in the ligament attachment area. The method uses 3D preoperative data at different flexion angles to build a subject-specific statistical model of knee pose. To circumvent the problem of limited training samples and ensure physically meaningful pose instantiation, homogeneous transformations between different poses and local-deformation finite element modelling are used to enlarge the training set. Subsequently, an anatomical geodesic flexion analysis is performed to extract the subject-specific flexion characteristics. The advantages of the method were also tested by detailed comparison to standard Principal Component Analysis (PCA), nonlinear PCA without training set enlargement, and other state-of-the-art articulated joint modelling methods. The method yielded sub-millimetre accuracy, demonstrating its potential clinical value. |
3,385 | Utilizing CyTOF to Examine Hematopoietic Stem and Progenitor Phenotype | Regulation of hematopoiesis is dependent upon interactions between hematopoietic stem/progenitor cells and niche components, requiring a highly diverse array of different cell-cell interactions and cell signaling events. The overwhelming diversity of the components that can regulate hematopoiesis, especially when factoring in how the cell surface and intracellular protein expression profiles of hematopoietic stem/progenitor cells and niche components differ between homeostatic conditions and stressed conditions such as aging and irradiation, can make utilizing techniques like flow cytometry daunting, particularly while examining small cell populations such as hematopoietic stem cells (HSCs). Due to the complexity of the hematopoietic system, high-dimensional single-cell genomics and proteomics are constantly performed to understand the heterogeneity and expression profiles within this system. This chapter describes one such single-cell assay, which utilizes mass cytometry Time of Flight (CyTOF) technology to determine differences in expression profile within HSC, using changes in HSC populations due to gender and aging. |
3,386 | Denoising of Diffusion MRI Data via Graph Framelet Matching in x-q Space | Diffusion magnetic resonance imaging (DMRI) suffers from lower signal-to-noise-ratio (SNR) due to MR signal attenuation associated with the motion of water molecules. To improve SNR, the non-local means (NLM) algorithm has demonstrated state-of-the-art performance in noise reduction. However, existing NLM algorithms do not take into account explicitly the fact that DMRI signal can vary significantly with local fiber orientations. Applying NLM naively can hence blur subtle structures and aggravate partial volume effects. To overcome this limitation, we improve NLM by performing neighborhood matching in non-flat domains and removing noise with information from both x-space (spatial domain) and q-space (wavevector domain). Specifically, we first encode the q-space sampling domain using a graph. We then perform graph framelet transforms to extract robust rotation-invariant features for each sampling point in x-q space. The resulting features are employed for robust neighborhood matching to locate recurrent information. Finally, we remove noise via an NLM framework. To adapt to the various types of noise in multi-coil MR imaging, we transform the signal before denoising so that it is Gaussian-distributed, allowing noise removal to be carried out in an unbiased manner. Our method is able to more effectively locate recurrent information in white matter structures with different orientations, avoiding the blurring effects caused by naively applying NLM. Experiments on synthetic, repetitively-acquired, and infant DMRI data demonstrate that our method is able to preserve subtle structures while effectively removing noise. |
3,387 | Pedestrian Detection: An Evaluation of the State of the Art | Pedestrian detection is a key problem in computer vision, with several applications that have the potential to positively impact quality of life. In recent years, the number of approaches to detecting pedestrians in monocular images has grown steadily. However, multiple data sets and widely varying evaluation protocols are used, making direct comparisons difficult. To address these shortcomings, we perform an extensive evaluation of the state of the art in a unified framework. We make three primary contributions: 1) We put together a large, well-annotated, and realistic monocular pedestrian detection data set and study the statistics of the size, position, and occlusion patterns of pedestrians in urban scenes, 2) we propose a refined per-frame evaluation methodology that allows us to carry out probing and informative comparisons, including measuring performance in relation to scale and occlusion, and 3) we evaluate the performance of sixteen pretrained state-of-the-art detectors across six data sets. Our study allows us to assess the state of the art and provides a framework for gauging future efforts. Our experiments show that despite significant progress, performance still has much room for improvement. In particular, detection is disappointing at low resolutions and for partially occluded pedestrians. |
3,388 | Correlation between Electrolyte Chemistry and Solid Electrolyte Interphase for Reversible Ca Metal Anodes | The development of rechargeable Ca metal batteries (RCMBs) is hindered by the Ca2+ passivating solid electrolyte interphases (SEIs). The cation solvation structure dictated by electrolyte chemistry plays a critical role in the SEIs properties. While a relatively weak cation-solvent binding is preferred in Li metal anodes to promote anion-derived SEIs, we demonstrate an enhanced Ca deposition/stripping reversibility under a strong cation-solvent interaction, which is materialized in strongly-solvating solvent and highly-dissociated salt combinations. Such electrolyte formulations benefit the formation of solvent-occupied solvation structure and minimize the anion reduction, resulting in organic-rich/CaF2 -poor SEIs for reversible Ca metal anodes. Furthermore, RCMBs paired with an organic cathode using the optimized electrolytes are demonstrated as a proof-of-concept. Our work reveals the paradigm shift in SEIs design for Ca metal anodes, opening up new opportunities for emerging RCMBs. |
3,389 | Uncertainty quantification and management in additive manufacturing: current status, needs, and opportunities | One of the major barriers that hinder the realization of significant potential of metal-based additive manufacturing (AM) techniques is the variation in the quality of the manufactured parts. Uncertainty quantification (UQ) and uncertainty management (UM) can resolve this challenge based on the modeling and simulation of the AM process. This paper reviews the research state of the art and discusses needs and opportunities in the UQ/UM of the AM processes, with a focus on laser powder bed fusion AM. The major methods and models of laser powder bed fusion AM process are summarized first. The current research work in UQ of AM processes is then reviewed. Based on the review of AM process models and current UQ approaches for the AM process, this paper presents insights into how the current state of the art UQ and UM techniques can be applied to AM to improve the product quality. Future research needs in UQ and UM of AM are also discussed. Laser sintering of metal nanoparticles, which is part of the micro-AM process, is used as an example to illustrate the application of UQ and UM in the AM. |
3,390 | Spontaneous colonic perforation with collagenous colitis in an elderly patient | Collagenous colitis (CC) is a variant of microscopic colitis that causes chronic, non-bloody, and watery diarrhea. The natural history of CC is generally benign and serious complications are rare. Perforation, especially spontaneous perforation, is a particularly rare complication. A 90-year-old woman presented with acute abdominal pain. She was diagnosed with peritonitis due to colonic perforation, and partial colectomy was performed. Macroscopic findings showed well-circumscribed longitudinal ulcer, and a pathological examination revealed descending colon perforation with CC. She had no history of examination and the case was considered to be spontaneous. The postoperative course was uneventful and she had no recurrence of CC after changing from the suspected drug (lansoprazole) to an H2-blocker. The characteristics of perforation by CC are characteristic longitudinal ulcer and micro-perforation. If it can be diagnosed accurately, conservative treatment may be an option. In spontaneous cases, the history of medication and the site of perforation may assist in this decision. |
3,391 | Softwarized IoT Network Immunity Against Eavesdropping With Programmable Data Planes | State-of-the-art mechanisms against eavesdropping first encrypt all packet payloads in the application layer and then split the packets into multiple network paths. However, versatile eavesdroppers could simultaneously intercept several paths to intercept all the packets, classify the packets into streams using transport fields, and analyze the streams by brute-force. In this article, we propose a programming protocol-independent packet processors (P4)-based network immune scheme (P4NIS) against the intractable eavesdropping. Specifically, P4NIS is equipped with three lines of defenses to provide a softwarized network immunity. Packets are successively processed by the third, second, and first line of defenses. The third line basically encrypts all packet payloads in the application layer using cryptographic mechanisms. Additionally, the second line re-encrypts all packet headers in the transport layer to distribute the packets from one stream into different streams, and disturbs eavesdroppers to classify the packets correctly. Besides, the second line adopts a programmable design for dynamically changing encryption algorithms. Complementally, the first line uses programmable forwarding policies which could split all the double-encrypted packets into different network paths disorderly. Using a paradigm of programmable data planes-P4, we implement P4NIS and evaluate its performances. Experimental results show that P4NIS can increase difficulties of eavesdropping and transmission throughput effectively compared with state-of-the-art mechanisms. Moreover, if P4NIS and state-of-the-art mechanisms have the same level of defending eavesdropping, P4NIS can decrease the encryption cost by 69.85%-81.24%. |
3,392 | A simple and versatile strategy for sensitive SIDA-UHPLC-MS/MS analysis of Alternaria toxins in olive oil | Alternaria toxins are naturally occurring contaminants found in natural products. Given the prevalence of Alternaria toxins and the complexity of oil-rich matrices, achieving ultra-trace analysis has become a daunting task. A new sample pretreatment technique, i.e., cold-induced liquid-liquid microextraction combined with serially-coupled-columns for SIDA-UHPLC-MS/MS, was developed and reported for the first time. Theoretical and experimental investigations on the mechanism and key parameters revealed that the proposed method achieved simultaneous purification and enrichment in one-step sample extraction with a superior limit of quantitation (0.15-1.5 μg kg-1), without further sample manipulation, such as fat removal or solvent exchange procedures prior to LC-MS. The method was validated taking into consideration EU guidelines and showed acceptable linearity (r ≥ 0.9991), accuracy with recoveries between 75 and 114% and precision with RSD≤9.7% for all of the analytes studied. It was successfully applied to the analysis of twenty samples sourced from the Mediterranean region in order to gain first insights into Alternaria toxins contaminations in olive oils. This technical approach is well suited for large-scale studies in a high-throughput and cost-effective quality assurance laboratory environments, and it has the potential to detect ultra-trace levels of toxins in complex samples, which may lead to the development of new and sustainable sample preparation procedures. |
3,393 | Evaluating Postoperative Immobilization Following Hip Reconstruction in Children With Cerebral Palsy | Objectives Currently, there is no standardized protocol for postoperative immobilization techniques in patients with cerebral palsy undergoing hip reconstructive procedures. The purpose of this study was to evaluate the effects of several methods of postoperative immobilization and to determine which postoperative immobilization technique has the fewest complications. Materials and methods A retrospective cohort study of pediatric patients with cerebral palsy who underwent hip reconstructive procedures, in which a hip spica cast, Petrie cast, or abduction pillow was placed for postoperative hip immobilization, was conducted. Patients who underwent revision surgery and those without cerebral palsy were excluded from the analysis. The final cohort consisted of 70 cases. Demographics, laterality of surgery, procedure type, hip immobilization technique, and 30-day postoperative complications were recorded. Complications were defined as those related to casting immobilization, such as re-dislocation or loss of surgical fixation, and soft tissue complications, such as pressure ulcers or any superficial or deep wound infection. Results Of the 70 patients, 27 received spica casting, 28 received Petrie casting, and 15 received an abduction pillow. The complication rates, as defined in the methods section, were 14.8% for the spica cast group, 17.9% for Petrie cast, and 26.7% for abduction pillow. There was no significant difference in complication rates among spica cast, Petrie cast, or abduction pillow groups (P=0.76). Conclusions There was no significant difference in length of stay, pain control duration, or complication rates among the three methods of immobilization. Clinicians should be advised of the comparable outcomes among the postoperative immobilization techniques. |
3,394 | Organochlorine pesticides in follicular fluid of women undergoing assisted reproductive technologies from central China | Female infertility rates have increased by approximately 4% since the 1980s. There is evidence of adverse effects on female fertility in relation to exposure of chemical pollution in recent years. Follicular fluid samples were collected from 127 woman patients (aged 20-35) who underwent assisted reproductive technologies (ART) and had no records indicating occupational exposure to OCPs. Seventeen OCPs were analyzed in this study. The results showed that methoxychlor was dominant accounted for 13.4% of total OCPs with a mean concentration of 167.9 +/- 33.9 ng/g lipid weight (lw), followed by heptachlor-epoxide, hexachlorocyclohexanes, endrin and DDT. The concentrations of OCPs in the follicular fluid samples in the present study were moderate in comparison with those reported from developed or industrialized countries. All these pollutants can accumulate in different tissues of human body through diet, drinking water and respiration. No correlation between patient age and OCP concentrations was observed in this study. (C) 2015 Elsevier Ltd. All rights reserved. |
3,395 | Ocular manifestations of central insulin resistance | Central insulin resistance, the diminished cellular sensitivity to insulin in the brain, has been implicated in diabetes mellitus, Alzheimer's disease and other neurological disorders. However, whether and how central insulin resistance plays a role in the eye remains unclear. Here, we performed intracerebroventricular injection of S961, a potent and specific blocker of insulin receptor in adult Wistar rats to test if central insulin resistance leads to pathological changes in ocular structures. 80 mg of S961 was stereotaxically injected into the lateral ventricle of the experimental group twice at 7 days apart, whereas buffer solution was injected to the sham control group. Blood samples, intraocular pressure, trabecular meshwork morphology, ciliary body markers, retinal and optic nerve integrity, and whole genome expression patterns were then evaluated. While neither blood glucose nor serum insulin level was significantly altered in the experimental or control group, we found that injection of S961 but not buffer solution significantly increased intraocular pressure at 14 and 24 days after first injection, along with reduced porosity and aquaporin 4 expression in the trabecular meshwork, and increased tumor necrosis factor α and aquaporin 4 expression in the ciliary body. In the retina, cell density and insulin receptor expression decreased in the retinal ganglion cell layer upon S961 injection. Fundus photography revealed peripapillary atrophy with vascular dysregulation in the experimental group. These retinal changes were accompanied by upregulation of pro-inflammatory and pro-apoptotic genes, downregulation of anti-inflammatory, anti-apoptotic, and neurotrophic genes, as well as dysregulation of genes involved in insulin signaling. Optic nerve histology indicated microglial activation and changes in the expression of glial fibrillary acidic protein, tumor necrosis factor α, and aquaporin 4. Molecular pathway architecture of the retina revealed the three most significant pathways involved being inflammation/cell stress, insulin signaling, and extracellular matrix regulation relevant to neurodegeneration. There was also a multimodal crosstalk between insulin signaling derangement and inflammation-related genes. Taken together, our results indicate that blocking insulin receptor signaling in the central nervous system can lead to trabecular meshwork and ciliary body dysfunction, intraocular pressure elevation, as well as inflammation, glial activation, and apoptosis in the retina and optic nerve. Given that central insulin resistance may lead to neurodegenerative phenotype in the visual system, targeting insulin signaling may hold promise for vision disorders involving the retina and optic nerve. |
3,396 | Melanoma arising in a nevus of Ito: novel genetic mutations and a review of the literature on cutaneous malignant transformation of dermal melanocytosis | Dermal melanocytosis refers to a spectrum of benign melanocytic proliferations that includes Mongolian spot, nevus of Ota and nevus of Ito. These lesions most commonly occur in persons of Asian or African descent and are often present at birth or develop during childhood. Very rarely, dermal melanocytoses undergo malignant transformation. There have been only 13 reports in the literature of primary cutaneous melanoma arising in dermal melanocytoses. We report a case of a Chinese woman with melanoma arising in a congenital nevus of Ito. We performed targeted next-generation sequencing of the tumor which revealed mutations of GNAQ and BAP1, suggesting that alterations in these two genes led to malignant transformation of the nevus of Ito. We also provide a summary of reports in the literature regarding primary cutaneous melanoma arising in the context of dermal melanocytosis. |
3,397 | Learning Joint-Space Codes for Calibration-Free Parallel MR Imaging | The integration of compressed sensing and parallel imaging (CS-PI) has shown an increased popularity in recent years to accelerate magnetic resonance (MR) imaging. Among them, calibration-free techniques have presented encouraging performances due to its capability in robustly handling the sensitivity information. Unfortunately, existing calibration-free methods have only explored joint-sparsity with direct analysis transform projections. To further exploit joint-sparsity and improve reconstruction accuracy, this paper proposes to Learn joINt-sparse coDes for caliBration-free parallEl mR imaGing (LINDBERG) by modeling the parallel MR imaging problem as an l(2)-l(F)-l(2,1) minimization objective with an l(2) norm constraining data fidelity, Frobenius norm enforcing sparse representation error and the l(2,1) mixed norm triggering joint sparsity across multichannels. A corresponding algorithm has been developed to alternatively update the sparse representation, sensitivity encoded images and K-space data. Then, the final image is produced as the square root of sum of squares of all channel images. Experimental results on both physical phantom and in vivo data sets show that the proposed method is comparable and even superior to state-of-the-art CS-PI reconstruction approaches. Specifically, LINDBERG has presented strong capability in suppressing noise and artifacts while reconstructing MR images from highly undersampled multichannel measurements. |
3,398 | Antiretroviral Therapy (ART) Adherence and Prenatal Alcohol Use among Women Who Are Pregnant with HIV in South Africa | This brief report emphasizes the need to focus on women with HIV who are pregnant who use alcohol or other drugs. A recently completed implementation science study tested a gender-focused behavioral intervention, the Women's Health CoOp (WHC), to improve antiretroviral therapy (ART) adherence and reduce alcohol use among women with HIV. The study identified 33 participants who had a positive pregnancy test result at the baseline assessment, of whom five participants remained pregnant during the 6-month duration of the study. Of the 33 pregnant participants at the baseline assessment, 55% reported past-month alcohol use, with 27% reporting a history of physical abuse and 12% reporting a history of sexual abuse. The five women who remained pregnant at 6 months showed improved ART adherence and reduced prenatal alcohol use. The gender-focused WHC intervention shows promise as a cost-effective, sustainable, behavioral intervention to address these intersecting syndemic issues. Future research should focus on identifying the needs of women with HIV who are pregnant who use alcohol or other drugs and developing tailored evidence-based behavioral interventions such as the WHC for preventing FASD in addition to improving ART adherence in this key population of women and reducing the economic burden on society. |
3,399 | STUDY ON APPLICATION OF IEOH MING PEI'S DESIGN CONCEPT IN LANDSCAPE ARCHITECTURE DESIGN | Ieoh Ming Pei is a modern world-renowned architect with a large number of excellent architectural works worldwide. His architectural design inherits the traditional architectural concept and integrates the Chinese and Western architectural ideas, which does not stick to stereotype and conventions, and dare to innovate, producing a significant and profound influence on China and even the world's architectural industry. This study introduced the background of Ieoh Ming Pei (hereinafter referred to as Pei or I.M. Pei )'s architectural design concept, analyzed Pei's concept from the perspectives of "tradition combined with modernity" and "art integrated with technology", discussed the influence of Pei's design concept on modem landscape architecture design in terms of "art law" and "connotation emotion", and expounded the function of Pei's concept on landscape architecture design, which is of great importance for landscape architecture design and has a place in the powerful world. |
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