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3,800 | Innate and Peripheral Immune Alterations after Traumatic Brain Injury Are Regulated in a Gut Microbiota-Dependent Manner in Mice | Traumatic brain injury (TBI) patients are at high risk for disruption of the gut microbiome. Previously, we have demonstrated that broad-spectrum antibiotic exposure after TBI drastically alters the gut microbiota and modulates neuroinflammation, neurogenesis, and long-term fear memory. However, these data did not determine if the impact of antibiotic exposure on the brain's response to injury was mediated directly by antibiotics or indirectly via modulation of the gut microbiota. We designed two different approaches to address this knowledge gap. One was utilizing fecal microbiota transplantation (FMT) from control and antibiotic-treated mice (treated with vancomycin, neomycin, ampicillin, and metronidazole [VNAM]) into germ-free (GF) mice prior to injury, and the other was exposing specific pathogen-free (SPF) mice to a 2-week period of antibiotics prior to injury but discontinuing antibiotics 72 h prior to injury. GF mice receiving FMT from VNAM-treated mice (GF-VNAM) demonstrated reduced gut bacterial alpha diversity and richness compared with GF mice receiving control FMT. At 7 days post-injury, GF-VNAM had increased microglial activation, reduced infiltration of T cells, and decreased neurogenesis. Similarly, SPF mice exposed to antibiotics prior to but not after injury demonstrated similar alterations in neuroinflammation and neurogenesis compared with control mice. These data support our hypothesis implicating the gut microbiota as an important modulator of the neuroinflammatory process and neurogenesis after TBI and provide an exciting new approach for neuroprotective therapeutics for TBI. |
3,801 | Using multiple spatio-temporal features to estimate video quality | In this paper, we propose a new video quality metric based on a set of multiple features that incorporate texture, saliency, spatial activity, and temporal attributes. A random forest regression algorithm is used to combine these features and obtain a video quality score. Experimental results show that the proposed metric has a good performance when tested on several benchmark video quality databases, outperforming current state-of-the-art full-reference video quality metrics. |
3,802 | Assessing engagement of scheduled tribe communities in the functioning of village health sanitation & nutrition committees in India | India is home to the largest population of indigenous tribes in the world. Despite initiative of the National Rural Health Mission, now National Health Mission (NHM) and various tribal development programmes since India's Independence, disparity in healthcare for Scheduled Tribes (STs) prevails. The constitution of Village Health Sanitation and Nutrition Committees (VHSNCs) in 2007 by the NHM is a step towards decentralized planning and community engagement to improve health, nutrition and sanitation services. VHSNCs are now present in almost all States of the country. However, several reports including the 12th Common Review Mission report have highlighted that these committees are not uniformly following guidelines and lack clarity about their mandates, with no clear visibility of their functioning in tribal areas. We therefore conducted a review of studies to assess the participation of the VHSNCs in tribal dominated States in order to know in detail about their functioning and gaps if any that require intervention. Several deviations from the existing guidelines of NHM were identified and we concluded that in order to sustain and perform well, VHSNCs not only require, mobilization and strict monitoring but also motivation and willingness of its members to bring in a radical change at the grassroot level. With continuous supervision and support from both the Government and various non-governmental organizations, handholding, strategic deployment of workforce, community participation and sustained financial support, VHSNCs would be able to facilitate delivery of better healthcare to the indigenous population. |
3,803 | A measurement based feasibility study of space-frequency MIMO detection and decoding techniques for next generation wireless LANs | This article presents a performance evaluation of various multi-antenna concepts based on OFDM for Wireless LANs. The studies are based on state-of-the-art measured channel data in the 5GHz band. The investigated techniques include: spatial multiplexing (BLAST), space frequency trellis coded modulation, their concatenation, turbo bit interleaved coded modulation and turbo space frequency trellis coded modulation. The studies aim to assess the MIMO concepts for future high speed WLANs. |
3,804 | Novel Fisher discriminant classifiers | At the present, several applications need to classify high dimensional points belonging to highly unbalanced classes. Unfortunately, when the training set cardinality is small compared to the data dimensionality ("small sample size" problem) the classification performance of several well-known classifiers strongly decreases. Similarly, the classification accuracy of several discriminative methods decreases when non-linearly separable, and unbalanced, classes are treated. In this paper we firstly survey state of the art methods that employ improved versions of Linear Discriminant Analysis (LDA) to deal with the above mentioned problems; secondly, we propose a family of classifiers based on the Fisher subspace estimation, which efficiently deal with the small sample size problem, non-linearly separable classes, and unbalanced classes. The promising results obtained by the proposed techniques on benchmark datasets and the comparison with state of the art predictors show the efficacy of the proposed techniques. (C) 2012 Elsevier Ltd. All rights reserved. |
3,805 | [The place of users and their families in tomorrow's healthcare system] | Partnership in health is an innovative concept that brings about change. It is part of an attempt to synthesize support practices and actions of political and citizen scope. Its application poses challenges and issues for the actors of tomorrow's health system. |
3,806 | Overcoming model bias for robust offline deep reinforcement learning | State-of-the-art reinforcement learning algorithms mostly rely on being allowed to directly interact with their environment to collect millions of observations. This makes it hard to transfer their success to industrial control problems, where simulations are often very costly or do not exist, and exploring in the real environment can potentially lead to catastrophic events. Recently developed, model-free, offline RL algorithms, can learn from a single dataset (containing limited exploration) by mitigating extrapolation error in value functions. However, the robustness of the training process is still comparatively low, a problem known from methods using value functions. To improve robustness and stability of the learning process, we use dynamics models to assess policy performance instead of value functions, resulting in MOOSE (MOdel-based Offline policy Search with Ensembles), an algorithm which ensures low model bias by keeping the policy within the support of the data. We compare MOOSE with state-of-the-art model-free, offline RL algorithms BRAC, BEAR and BCQ on the Industrial Benchmark and MuJoCo continuous control tasks in terms of robust performance, and find that MOOSE outperforms its model-free counterparts in almost all considered cases, often even by far. |
3,807 | Bi-modal biometric authentication on mobile phones in challenging conditions | This paper examines the issue of face, speaker and bi-modal authentication in mobile environments when there is significant condition mismatch. We introduce this mismatch by enrolling client models on high quality biometric samples obtained on a laptop computer and authenticating them on lower quality biometric samples acquired with a mobile phone. To perform these experiments we develop three novel authentication protocols for the large publicly available MOBIO database. We evaluate state-of-the-art face, speaker and bi-modal authentication techniques and show that inter-session variability modelling using Gaussian mixture models provides a consistently robust system for face, speaker and bi-modal authentication. It is also shown that multi-algorithm fusion provides a consistent performance improvement for face, speaker and bi-modal authentication. Using this bi-modal multi-algorithm system we derive a state-of-the-art authentication system that obtains a half total error rate of 63% and 1.9% for Female and Male trials, respectively. (C) 2013 Elsevier B.V. All rights reserved. |
3,808 | Switching linear dynamical systems for noise robust speech recognition | Real world applications such as hands-free dialling in cars may have to deal with potentially very noisy environments. Existing state-of-the-art solutions to this problem use feature-based HMMs, with a preprocessing stage to clean the noisy signal. However, the effect that raw signal noise has on the induced HMM features is poorly understood, and limits the performance of the HMM system. An alternative to feature-based HMMs is to model the raw signal, which has the potential advantage that including an explicit noise model is straightforward. Here we jointly model the dynamics of both the raw speech signal and the noise, using a Switching Linear Dynamical System (SLDS). The new model was tested on isolated digit utterances corrupted by Gaussian noise. Contrary to the Autoregressive HMM and its derivatives, which provides a model of uncorrupted raw speech, the SLDS is comparatively noise robust and also significantly outperforms a state-of-the-art feature-based HMM. The computational complexity of the SLDS scales exponentially with the length of the time series. To counter this we use Expectation Correction which provides a stable and accurate linear-time approximation for this important class of models, aiding their further application in acoustic modeling. |
3,809 | Plant Growth Hormones and Nanomaterial Interface: Exploring the connection from development to defense | The global increase in nanotechnology applications has been unprecedented and has now moved into the area of agriculture and food production. Applications with promising potential in sustainable agriculture include nanobiosensors, nanofertilizers, nanopesticides, nano-mediated remediation strategies for contaminated soils and nanoscale strategies to increase crop production and protection. Given this, the impact of nanomaterials/nanoparticles (NPs) on plant species needs to be thoroughly evaluated as this represents a critical interface between the biosphere and the environment. Importantly, phytohormones represent a critical class of biomolecules to plant health and productivity; however, the impact of NPs on these molecules is poorly understood. In addition, phytohormones, and associated pathways, are widely explored in agriculture to influence several biological processes for the improvement of plant growth and productivity under natural as well as stressed conditions. However, the impact of exogenous applications of phytohormones on NP-treated plants has not been explored. The importance of hormone signaling and cross-talk with other metabolic systems makes these biomolecules ideal candidates for a thorough assessment of NP impacts on plant species. This article presents a critical evaluation of the existing yet limited literature available on NP-phytohormone interactions in plants. In addition, the developing strategy of nano-enabled precision delivery of phytohormones via nanocarriers will be explored. Finally, directions for future research and critical knowledge gaps will be identified for this important aspect of nano-enabled agriculture. |
3,810 | Vitamin D and Colorectal Cancer: Current Perspectives and Future Directions | Vitamin D is considered to be the main mediator of the beneficial effects of sun exposure. In humans, highest expression of Vitamin D receptors is found in the intestinal tract. In addition, 1α,25-dihydroxyvitamin D3 (or calcitriol), the most active Vitamin D metabolite, plays important homeostatic roles in the intestine, particularly calcium absorption. Vitamin D deficiency is defined as a serum 25-hydroxyvitamin D [25(OH)D] level of < 20 ng/mL. Previous studies show that higher circulating 25(OH)D levels are associated with reduced risk of colorectal cancer (CRC) and improved survival. Most research to date has been conducted in animals, specifically mice. Although human studies have a limited number of participants, one study recruiting a large cohort of patients with advanced or metastatic CRC revealed that higher plasma 25(OH)D levels are associated with improved overall and progression-free survival. However, the effects of Vitamin D supplementation on incidence and mortality of CRC remain inconclusive. Although Vitamin D may help to prevent cancer, there is a paucity of research demonstrating conclusively that Vitamin D alters prognosis after chemotherapy. Here, we review the mechanisms by which Vitamin D affects CRC, as well as the results of clinical, epidemiological, and human intervention studies. We also discuss current perspectives and future directions regarding Vitamin D and CRC. |
3,811 | Deep Learning on Multiphysical Features and Hemodynamic Modeling for Abdominal Aortic Aneurysm Growth Prediction | Prediction of abdominal aortic aneurysm (AAA) growth is of essential importance for the early treatment and surgical intervention of AAA. Capturing key features of vascular growth, such as blood flow and intraluminal thrombus (ILT) accumulation play a crucial role in uncovering the intricated mechanism of vascular adaptation, which can ultimately enhance AAA growth prediction capabilities. However, local correlations between hemodynamic metrics, biological and morphological characteristics, and AAA growth rates present high inter-patient variability that results in that the temporal-spatial biochemical and mechanical processes are still not fully understood. Hence, this study aims to integrate the physics-based knowledge with deep learning with a patch-based convolutional neural network (CNN) approach by incorporating important multiphysical features relating to its pathogenesis for validating its impact on AAA growth prediction. For this task, we observe that the unstructured multiphysical features cannot be directly employed in the kernel-based CNN. To tackle this issue, we propose a parameterization of features to leverage the spatio-temporal relations between multiphysical features. The proposed architecture was tested on different combinations of four features including radius, intraluminal thrombus thickness, time-average wall shear stress, and growth rate from 54 patients with 5-fold cross-validation with two metrics, a root mean squared error (RMSE) and relative error (RE). We conduct extensive experiments on AAA patients, the results show the effect of leveraging multiphysical features and demonstrate the superiority of the presented architecture to previous state-of-the-art methods in AAA growth prediction. |
3,812 | The opportunistic nature of gut commensal microbiota | The abundance of gut commensals has historically been associated with health-promoting effects despite the fact that the definition of good or bad microbiota remains condition-specific. The beneficial or pathogenic nature of microbiota is generally dictated by the dimensions of host-microbiota and microbe-microbe interactions. With the increasing popularity of gut microbiota in human health and disease, emerging evidence suggests opportunistic infections promoted by those gut bacteria that are generally considered beneficial. Therefore, the current review deals with the opportunistic nature of the gut commensals and aims to summarise the concepts behind the occasional commensal-to-pathogenic transformation of the gut microbes. Specifically, relevant clinical and experimental studies have been discussed on the overgrowth and bacteraemia caused by commensals. Three key processes and their underlying mechanisms have been summarised to be responsible for the opportunistic nature of commensals, viz. improved colonisation fitness that is dictated by commensal-pathogen interactions and availability of preferred nutrients; pathoadaptive mutations that can trigger the commensal-to-pathogen transformation; and evasion of host immune response as a survival and proliferation strategy of the microbes. Collectively, this review provides an updated concept summary on the underlying mechanisms of disease causative events driven by gut commensal bacteria. |
3,813 | ConsInstancy: learning instance representations for semi-supervised panoptic segmentation of concrete aggregate particles | We present a semi-supervised method for panoptic segmentation based on ConsInstancy regularisation, a novel strategy for semi-supervised learning. It leverages completely unlabelled data by enforcing consistency between predicted instance representations and semantic segmentations during training in order to improve the segmentation performance. To this end, we also propose new types of instance representations that can be predicted by one simple forward path through a fully convolutional network (FCN), delivering a convenient and simple-to-train framework for panoptic segmentation. More specifically, we propose the prediction of a three-dimensional instance orientation map as intermediate representation and two complementary distance transform maps as final representation, providing unique instance representations for a panoptic segmentation. We test our method on two challenging data sets of both, hardened and fresh concrete, the latter being proposed by the authors in this paper demonstrating the effectiveness of our approach, outperforming the results achieved by state-of-the-art methods for semi-supervised segmentation. In particular, we are able to show that by leveraging completely unlabelled data in our semi-supervised approach the achieved overall accuracy (OA) is increased by up to 5% compared to an entirely supervised training using only labelled data. Furthermore, we exceed the OA achieved by state-of-the-art semi-supervised methods by up to 1.5%. |
3,814 | [Measuring the emergency psychiatric units activity: An experimental application of the Delphi Method] | Psychiatric emergency units (UUP) are nowadays important gateways to healthcare. Whether integrated into general emergency departments or not, these units have very heterogeneous resources and organisations which are not always in line with a populations' needs. The increasing activity of emergency departments in recent years and the recurrent psychiatric bed shortages have shed light upon the weaknesses of this key link in the mental healthcare process. The Seine-Saint-Denis is a department of France located in the Grand Paris metropolis in the Île-de-France region. Ranked third in terms of population size in France, it is marked by social precariousness. With regard to mental health, it has one of the lowest rates of psychiatric beds per capita in France. A great deal of thought has been ongoing for five years on how best to upgrade the offer of unscheduled psychiatric care, particularly the management of emergencies. The growing imbalance between demand and supply depending on living areas urges a rapid equalization of resources. This operation requires an accurate activity characterization, allowing more effective organizations and adequate resource allocation. We sought to characterize the activity of psychiatric emergencies by selecting quantitative and qualitative indicators by means of a consensus method, the Delphi Method, which consists of iterative questioning of an expert group. We first submitted 36 potential criteria to twenty-five experts. Twenty obtained a weak to a strong consensus. Seventeen were then selected as potentially useful for activity characterization. In a second time, we tested the consensus on selected indicators by interviewing a panel of 19 experts. A strong consensus was found on four criteria: "Number of visits for psychiatric advice>2000/year", "Number of emergency room visits>40,000/year", "Density of adult hospital beds<150 per 100,000 inhabitants", "Passage rate for homeless patients and/or outside the sector>10%". Using these criteria in the classification of UUPs would test their validity and provide a potentially helpful tool for improving organizations and resource allocation. |
3,815 | Protective effect of biogenic selenium nanoparticles against diquat-induced acute toxicity via regulation of gut microbiota and its metabolites | Selenium nanoparticles (SeNPs) with unique biological properties have been suggested as a safer and more effective platform for delivering of Selenium for biological needs. In this study, we investigated the association between gut microbiota altered by SeNPs supplementation and its metabolites under oxidative stress conditions through 16S rDNA gene sequencing analysis and untargeted metabolomics. The results showed that dietary supplementation with SeNPs attenuated diquat-induced acute toxicity in mice, as demonstrated by lower levels of inflammatory effector cells, and biochemical markers in serum such as aspartate aminotransferase (AST), alanine aminotransferase (ALT), blood urea nitrogen (BUN) and lactate dehydrogenase (LDH). SeNPs also reversed the perturbed gut microbiota composition induced by diquat, decreased the Firmicutes/Bacteroidetes ratio, and increased the abundance of beneficial bacteria such as Akkermansia, Muribaculaceae, Bacteroides and Parabacteroides. Untargeted fecal metabolomics showed that SeNPs can regulate the production of steroids and steroid derivatives, organonitrogen compounds, pyridines and derivatives and other metabolites. Microbiome-metabolome correlation analysis suggested that Parabacteroides was the key bacteria for the SeNPs intervention, which might upregulate the levels of metabolites such as trimethaphan, emedastine, berberine, desoxycortone, tetrahydrocortisone. This study demonstrated that dietary SeNPs supplementation can extensively modulate the gut microbiota and its metabolism, thereby alleviating diquat-induced acute toxicity. |
3,816 | Investigation of the Role of Feature Selection and Weighted Voting in Random Forests for 3-D Volumetric Segmentation | This paper describes a novel 3-D segmentation technique posed within the Random Forests (RF) classification framework. Two improvements over the traditional RF framework are considered. Motivated by the high redundancy of feature selection in the traditional RF framework, the first contribution develops methods to improve voxel classification by selecting relatively "strong" features and neglecting "weak" ones. The second contribution involves weighting each tree in the forest during the testing stage, to provide an unbiased and more accurate decision than provided by the traditional RF. To demonstrate the improvement achieved by these enhancements, experimental validation is performed on adult brain MRI and 3-D fetal femoral ultrasound datasets. In a comparison of the new method with a traditional Random Forest, the new method showed a notable improvement in segmentation accuracy. We also compared the new method with other state-of-the-art techniques to place it in context of the current 3-D medical image segmentation literature. |
3,817 | Robust Approach for Disparity Map Estimation Based on Multilevel Decomposition | A novel approach for dense disparity map (DM) estimation is designed. The proposed frameworks include together processing of stereo pairs using the following blocks: CIEL*a*b*color space conversion, stereo matching via multilevel scheme, employing Normalized Cross-Correlation or Structural Similarity Index Measure, color segmentation by k-means on a*b*color plane, and adaptive post-filtering. The Bad Matching Pixels and Structural Similarity Index Measure criteria are applied in order to compare the performance of the proposed approach against state-of-the-art techniques. Two designed frameworks appear outperform existing state-of-the-art techniques in terms of objective criteria as well as in subjective visual perception. |
3,818 | Reconfigurable Network Systems and Software-Defined Networking | Modern high-speed networks have evolved from relatively static networks to highly adaptive networks facilitating dynamic reconfiguration. This evolution has influenced all levels of network design and management, introducing increased programmability and configuration flexibility. This influence has extended from the lowest level of physical hardware interfaces to the highest level of network management by software. A key representative of this evolution is the emergence of software-defined networking (SDN). In this paper, we review the current state of the art in reconfigurable network systems, covering hardware reconfiguration, SDN, and the interplay between them. We take a top-down approach, starting with a tutorial on software-defined networks. We then continue to discuss programming languages as the linking element between different levels of software and hardware in the network. We review electronic switching systems, highlighting programmability and reconfiguration aspects, and describe the trends in reconfigurable network elements. Finally, we describe the state of the art in the integration of photonic transceiver and switching elements with electronic technologies, and consider the implications for SDN and reconfigurable network systems. |
3,819 | Nasopharyngeal microbiome of COVID-19 patients revealed a distinct bacterial profile in deceased and recovered individuals | The bacterial co-infections in SARS-CoV-2 patients remained the least explored subject of clinical manifestations that may also determine the disease severity. Nasopharyngeal microbial community structure within SARS-CoV-2 infected patients could reveal interesting microbiome dynamics that may influence the disease outcomes. Here, in this research study, we analyzed distinct nasopharyngeal microbiome profile in the deceased (n = 48) and recovered (n = 29) COVID-19 patients and compared it with control SARS-CoV-2 negative individuals (control) (n = 33). The nasal microbiome composition of the three groups varies significantly (PERMANOVA, p-value <0.001), where deceased patients showed higher species richness compared to the recovered and control groups. Pathogenic genera, including Corynebacterium (LDA score 5.51), Staphylococcus, Serratia, Klebsiella and their corresponding species were determined as biomarkers (p-value <0.05, LDA cutoff 4.0) in the deceased COVID-19 patients. Ochrobactrum (LDA score 5.79), and Burkholderia (LDA 5.29), were found in the recovered group which harbors ordinal bacteria (p-value <0.05, LDA-4.0) as biomarkers. Similarly, Pseudomonas (LDA score 6.19), and several healthy nasal cavity commensals including Veillonella, and Porphyromonas, were biomarkers for the control individuals. Healthy commensal bacteria may trigger the immune response and alter the viral infection susceptibility and thus, may play important role and possible recovery that needs to be further explored. This research finding provide vital information and have significant implications for understanding the microbial diversity of COVID-19 patients. However, additional studies are needed to address the microbiome-based therapeutics and diagnostics interventions. |
3,820 | A Global Spatial Similarity Optimization Scheme to Track Large Numbers of Dendritic Spines in Time-Lapse Confocal Microscopy | Dendritic spines form postsynaptic contact sites in the central nervous system. The rapid and spontaneous morphology changes of spines have been widely observed by neurobiologists. Determining the relationship between dendritic spine morphology change and its functional properties such as memory learning is a fundamental yet challenging problem in neurobiology research. In this paper, we propose a novel algorithm to track the morphology change of multiple spines simultaneously in time-lapse neuronal images based on nonrigid registration and integer programming. We also propose a robust scheme to link disappearing-and-reappearing spines. Performance comparisons with other state-of-the-art cell and spine tracking algorithms, and the ground truth show that our approach is more accurate and robust, and it is capable of tracking a large number of neuronal spines in time-lapse confocal microscopy images. |
3,821 | Ultra-Widefield OCT Angiography | Optical Coherence Tomography Angiography (OCTA), a functional extension of OCT, has the potential to replace most invasive fluorescein angiography (FA) exams in ophthalmology. So far, OCTA's field of view is however still lacking behind fluorescence fundus photography techniques. This is problematic, because many retinal diseases manifest at an early stage by changes of the peripheral retinal capillary network. It is therefore desirable to expand OCTA's field of view to match that of ultra-widefield fundus cameras. We present a custom developed clinical high-speed swept-source OCT (SS-OCT) system operating at an acquisition rate 8-16 times faster than today's state-of-the-art commercially available OCTA devices. Its speed allows us to capture ultra-wide fields of view of up to 90 degrees with an unprecedented sampling density and hence extraordinary resolution by merging two single shot scans with 60 degrees in diameter. To further enhance the visual appearance of the angiograms, we developed for the first time a three-dimensional deep learning based algorithm for denoising volumetric OCTA data sets. We showcase its imaging performance and clinical usability by presenting images of patients suffering from diabetic retinopathy. |
3,822 | Modelling unsaturated soils with the Material Point Method. A discussion of the state-of-the-art | Many natural hazards involve large deformations of unsaturated soils, e.g. rainfall-induced landslides, embankment collapses due to wetting, seepage-induced instabilities of dams and levees, etc. The study of these phenomena requires accounting for the complex hydro-mechanical interactions between solid skeleton and pore fluids and modelling large deformations to predict the post-failure behaviour, which poses significant computational challenges. In recent years, several hydro-mechanical coupled MPM formulations were developed to model saturated and unsaturated soils. These approaches are slightly different in terms of governing equations, integration schemes and have been implemented in different MPM software; thus, they benefit from various computational features. The purpose of this paper is to present an overview of the available MPM approaches to model unsaturated soils discussing differences and similarities of the formulations and their impact on the results under different conditions in a range of geotechnical applications. In addition, the effect of partially saturated conditions on the critical time step in explicit numerical integration schemes is studied for the first time. Different analytical expressions are derived and compared with the numerical results.Published by Elsevier Ltd. |
3,823 | The animated assessment of theory of mind for people with schizophrenia (AToMS): development and psychometric evaluation | Theory of mind (ToM) deficits in people with schizophrenia have been reported and associated with impaired social interactions. Thus, ToM deficits may negatively impact social functioning and warrant consideration in treatment development. However, extant ToM measures may place excessive cognitive demands on people with schizophrenia. Therefore, the study aimed to develop a comprehensible Assessment of ToM for people with Schizophrenia (AToMS) and evaluate its psychometric properties. The AToMs was developed in 5 stages, including item formation, expert review, content validity evaluation, animation production, and cognitive interviews of 25 people with schizophrenia. The psychometric properties of the 16-item AToMS (including reliability and validity) were then tested on 59 people with schizophrenia. The newly developed animated AToMS assesses 8 ToM concepts in the cognitive and affective dimensions while placing minimal neurocognitive demands on people with schizophrenia. The AToMS presented satisfactory psychometric properties, with adequate content validity (content validity index = 0.91); mostly moderate item difficulty (item difficulty index = 0.339-0.966); good discrimination (coefficients = 0.379-0.786), internal consistency (Cronbach's α = 0.850), and reliability (intraclass correlation coefficient = 0.901 for test-retest, 0.997 for inter-rater); and satisfactory convergent and divergent validity. The AToMS is reliable and valid for evaluating ToM characteristics in people with schizophrenia. Future studies are warranted to examine the AToMS in other populations (e.g., people with affective disorders) to cross-validate and extend its utility and psychometric evidence. |
3,824 | Convergence studies on iterative algorithms for image reconstruction | We introduce a general iterative scheme for image reconstruction based on Landweber's method. In our configuration, a sequential block-iterative (SeqBI) version can be readily formulated from a simultaneous block-iterative (SimBI) version, and vice versa. This provides a mechanism to derive new algorithms from known ones. It is shown that some widely used iterative algorithms, such as the algebraic reconstruction technique (ART), simultaneous ART (SART), Cimmino's, and the recently designed diagonal weighting and component averaging algorithms, are special examples of the general scheme. We prove convergence of the general scheme under conditions more general than assumed in earlier studies, for its SeqBI and SimBI versions in the consistent and inconsistent cases, respectively. Our results suggest automatic relaxation strategies for the SeqBI and SimBI versions and characterize the dependence of the limit image on the initial guess. It is found that in all cases the limit is the sum of the minimum norm solution of a weighted least-squares problem and an oblique projection of the initial image onto the null space of the system matrix. |
3,825 | Apply MIKE 11 model to study impacts of climate change on water resources and develop adaptation plan in the Mekong Delta, Vietnam: a case of Can Tho city | Can Tho city in the Mekong Delta is in the top ten areas affected by climate change. Therefore, assessing climate change impacts, social and economic activities require proposed solutions to respond to climate change. This study aims to (i) apply the MIKE 11 model (Hydrodynamic module and Advection-Dispersion module) to simulate the impacts of climate change scenarios on water resources in Can Tho city; (ii) calculate water balance in Can Tho city; and (iii) suggest climate change adaptation plan for sustainable social-economic activities of the city. The results show that when the rainfall changes due to climate change, the flow rate tends to decrease at high tide and increase at low tide. When the sea level rises due to climate change, the flow rate tends to increase at high tide and decrease at low tide. For 2030, the flow will decrease up to 15.6% and 14.3% at the low tide period for RCP 2.6 and RCP 8.5 compared to the present, respectively. The flow will increase up to 63.5% and 58.9% at the high tide period for RCP 2.6 and RCP 8.5 compared to the present, respectively. The water demand evaluation shows that the water resource reserve in Can Tho city meets water demands in current and future scenarios under climate change. While rainwater and groundwater can provide enough water in the rainy season, the city has to use surface water during the dry season due to a lack of rainwater. Of these, agriculture contributes the most water demands (85%). Eight adaptation measures to climate change for Can Tho city are developed from 2021 to 2050. |
3,826 | Texture Regulation of Metal-Organic Frameworks, Microwave Absorption Mechanism-Oriented Structural Optimization and Design Perspectives | Texture regulation of metal-organic frameworks (MOFs) is essential for controlling their electromagnetic wave (EMW) absorption properties. This review systematically summarizes the recent advancements in texture regulation strategies for MOFs, including etching and exchange of central ions, etching and exchange of ligands, chemically induced self-assembly, and MOF-on-MOF heterostructure design. Additionally, the EMW absorption mechanisms in approaches based on structure-function dependencies, including nano-micro topological engineering, defect engineering, interface engineering, and hybrid engineering, are comprehensively explored. Finally, current challenges and future research orientation are proposed. This review aims to provide new perspectives for designing MOF-derived EMW-absorption materials to achieve essential breakthroughs in mechanistic investigations in this promising field. |
3,827 | Building urban community resilience through university extension: community engagement and the politics of knowledge | Many land-grant universities are examining approaches to community engagement to better align with the US land-grant mission of knowledge democratization. With a growing majority of the United States' population living in urbanized spaces, it is a societal imperative for university engagement initiatives to devise strategies for engaging people on the complexity of urban issues central to individual and community wellbeing. Effective urban engagement demands collaboration and strong relationships with urban organizations and residents to co-create approaches to urban concerns. Through narrative-based inquiry, we explore urban engagements within Penn State Extension (PSE) across the Commonwealth of Pennsylvania (USA). PSE, located administratively in the College of Agricultural Sciences, is charged with carrying out Penn State's land-grant commitment to serve Pennsylvania's citizens through community engagement and nonformal education in the agricultural and food, human, and social sciences. We examine extension educator and faculty practices, program development, community engagements, and experiences, and those of community stakeholders. This work draws upon democratic methods to uncover the undergirding philosophies of engagement within PSE and how communities experience those engagements. This project offers an entry-point to longer-term applied research to develop a broadly applicable theory and praxis of translational research, engagement, and change privileging urban community resilience. |
3,828 | Eosinophilic granulomatosis with polyangiitis | This review aims to describe the epidemiology, pathogenesis, clinical manifestations, diagnosis, treatment, and prognosis of eosinophilic granulomatosis with polyangiitis (EGPA). Eosinophilic granulomatosis with polyangiitis is a small to medium vessel necrotizing vasculitis, typically classified with granulomatosis with polyangiitis (GPA) and microscopic polyangitis (MPA) as antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). However, less than 50% of patients with EGPA have a positive ANCA test. Among all the vasculitides, asthma and eosinophilia are unique features of EGPA. Eosinophilic granulomatosis with polyangiitis is very rare and the diagnosis may be missed as the disease evolves over time. Polyneuropathies are common and may be severe, requiring aggressive immunosuppressive therapy. Heart involvement is the most common cause of death in EGPA. Biopsy of involved tissue supports a clinically suspected diagnosis but is not always feasible. Treatment of EGPA is primarily dictated by the severity of disease and prognostic factors. More severe disease frequently requires the use of aggressive therapy such as cyclophosphamide. Once treatment is initiated, patients can achieve good control of symptoms; unfortunately, disease relapses are common and prolonged treatment with corticosteroids is often necessary for asthma management. A better understanding of the disease heterogeneity is needed for the development of better therapies. |
3,829 | Emotional Creativity in Art Education: An Exploratory Analysis and Research Trends | The emotions that human beings experience have a key role in the environments in which they operate. In art education, creative processes are influenced by the emotions and experiences lived by the individual, enabling a more emotional and creative design to make life more pleasant. The aim was to examine the research during the period 1917-2020 on the development of emotional creativity in art education. Mathematical and statistical techniques were applied to 984 articles carried from Elsevier's Scopus database. The findings yielded data on the scientific productivity of the journal, authors, research institutions, and countries/territories that promoted this field. The data showed an exponential trend, mostly in the last decade. Five lines of research stand out: emotion, higher education, education, art, and leadership. Moreover, five future research directions related to visual art education, affective paradigm, metacompetency, expressive arts therapy group, and cognitive empathy were detected. This study establishes the link between psychology, neuroscience, and artistic education to constitute the decision-making of the promoters of this topic of research. The analysis of international research allowed us to focus the future publications of academics and researchers, in addition to guaranteeing an adequate approach to the objectives of the institutions and funding centers. |
3,830 | Boundary Objects: Engaging and Bridging Needs of People in Participatory Research by Arts-Based Methods | Background: Participatory health research (PHR) is a research approach in which people, including hidden populations, share lived experiences about health inequities to improve their situation through collective action. Boundary objects are produced, using arts-based methods, to be heard by stakeholders. These can bring about dialogue, connection, and involvement in a mission for social justice. This study aims to gain insight into the value and ethical issues of boundary objects that address health inequalities. A qualitative evaluation is conducted on three different boundary objects, created in different participatory studies with marginalized populations (mothers in poverty, psychiatric patients, and unemployed people). A successful boundary object evokes emotions among those who created the objects and those encountering these objects. Such objects move people and create an impulse for change. The more provocative the object, the more people feel triggered to foster change. Boundary objects may cross personal boundaries and could provoke feelings of discomfort and ignorance. Therefore, it is necessary to pay attention to ethics work. Boundary objects that are made by people from hidden populations may spur actions and create influence by improving the understanding of the needs of hidden populations. A dialogue about these needs is an essential step towards social justice. |
3,831 | Enhanced Acidic Water Oxidation by Dynamic Migration of Oxygen Species at the Ir/Nb2 O5-x Catalyst/Support Interfaces | Catalyst/support interaction plays a vital role in catalysis towards acidic oxygen evolution (OER), and the performance reinforcement is currently interpreted by either strain or electron donation effect. We herein report that these views are insufficient, where the dynamic evolution of the interface under potential bias must be considered. Taking Nb2 O5-x supported iridium (Ir/Nb2 O5-x ) as a model catalyst, we uncovered the dynamic migration of oxygen species between IrOx and Nb2 O5-x during OER. Direct spectroscopic evidence combined with theoretical computation suggests these migrations not only regulate the in situ Ir structure towards boosted activity, but also suppress its over-oxidation via spontaneously delivering excessive oxygen from IrOx to Nb2 O5-x . The optimized Ir/Nb2 O5-x thus demonstrated exceptional performance in scalable water electrolyzers, i.e., only need 1.839 V to attain 3 A cm-2 (surpassing the DOE 2025 target), and no activity decay during a 2000 h test at 2 A cm-2 . |
3,832 | Periodontal Management of Gummy Smile Due to Altered Passive Eruption: A Case Report | Today's population is expanding quickly, and there is a growing desire for aesthetics. Smiles and other friendly facial expressions communicate joy and assurance. They are the essential elements of nonverbal communication and play a significant part in establishing a person's first impression. The altered passive eruption, which results in the excessive gingival display (EGD) when the gingival edge is situated incisal to the cervical convexity of the crown, is one of the factors affecting aesthetics. It has an impact on the patient's appearance and grin. The management of EGD becomes crucial. The following case study covers the control of EGD with a crown lengthening operation. |
3,833 | MobileXNet: An Efficient Convolutional Neural Network for Monocular Depth Estimation | Depth estimation from a single RGB image has attracted great interest in autonomous driving and robotics. State-of-the-art methods are usually designed on top of complex and extremely deep network architectures, which require more computational resources. Moreover, the inherent characteristic of the backbone used by the existing approaches results in severe spatial information loss in the produced feature maps, which impairs the accuracy of depth estimation on small sized images. In this study, we aimed to design a novel and efficient Convolutional Neural Network (CNN) to address these problems. Specifically, we stacked two shallow encoder-decoder style subnetworks successively in a unified network. Extensive experiments have been conducted on the NYU depth v2, KITTI, Make3D and Unreal data sets. Experimental results show that the proposed network achieves comparable accuracy to state-of-the-art methods that have extremely deep architectures but runs at a much faster speed on a single, less powerful GPU. |
3,834 | Automated Multi-Sensor 3D Reconstruction for the Web | The Internet has become a major dissemination and sharing platform for 3D content. The utilization of 3D measurement methods can drastically increase the production efficiency of 3D content in an increasing number of use cases where 3D documentation of real-life objects or environments is required. We demonstrated a developed, highly automated and integrated content creation process of providing reality-based photorealistic 3D models for the web. Close-range photogrammetry, terrestrial laser scanning (TLS) and their combination are compared using available state-of-the-art tools in a real-life project setting with real-life limitations. Integrating photogrammetry and TLS is a good compromise for both geometric and texture quality. Compared to approaches using only photogrammetry or TLS, it is slower and more resource-heavy but combines complementary advantages of each method, such as direct scale determination from TLS or superior image quality typically used in photogrammetry. The integration is not only beneficial, but clearly productionally possible using available state-of-the-art tools that have become increasingly available also for non-expert users. Despite the high degree of automation, some manual editing steps are still required in practice to achieve satisfactory results in terms of adequate visual quality. This is mainly due to the current limitations of WebGL technology. |
3,835 | A Hybrid CMOS-Memristor Neuromorphic Synapse | Although data processing technology continues to advance at an astonishing rate, computers with brain-like processing capabilities still elude us. It is envisioned that such computers may be achieved by the fusion of neuroscience and nano-electronics to realize a brain-inspired platform. This paper proposes a high-performance nano-scale Complementary Metal Oxide Semiconductor (CMOS)-memristive circuit, which mimics a number of essential learning properties of biological synapses. The proposed synaptic circuit that is composed of memristors and CMOS transistors, alters its memristance in response to timing differences among its pre- and post-synaptic action potentials, giving rise to a family of Spike Timing Dependent Plasticity (STDP). The presented design advances preceding memristive synapse designs with regards to the ability to replicate essential behaviours characterised in a number of electrophysiological experiments performed in the animal brain, which involve higher order spike interactions. Furthermore, the proposed hybrid device CMOS area is estimated as 600 mu m(2) in a 0.35 mu m process-this represents a factor of ten reduction in area with respect to prior CMOS art. The new design is integrated with silicon neurons in a crossbar array structure amenable to large-scale neuromorphic architectures and may pave the way for future neuromorphic systems with spike timing-dependent learning features. These systems are emerging for deployment in various applications ranging from basic neuroscience research, to pattern recognition, to Brain-Machine-Interfaces. |
3,836 | Robust Learning-Based Parsing and Annotation of Medical Radiographs | In this paper, we propose a learning-based algorithm for automatic medical image annotation based on robust aggregation of learned local appearance cues, achieving high accuracy and robustness against severe diseases, imaging artifacts, occlusion, or missing data. The algorithm starts with a number of landmark detectors to collect local appearance cues throughout the image, which are subsequently verified by a group of learned sparse spatial configuration models. In most cases, a decision could already be made at this stage by simply aggregating the verified detections. For the remaining cases, an additional global appearance filtering step is employed to provide complementary information to make the final decision. This approach is evaluated on a large-scale chest radiograph view identification task, demonstrating a very high accuracy (> 99.9%) for a posteroanterior/anteroposterior (PA-AP) and lateral view position identification task, compared with the recently reported large-scale result of only 98.2% (Luo, et al., 2006). Our approach also achieved the best accuracies for a three-class and a multiclass radiograph annotation task, when compared with other state of the art algorithms. Our algorithm was used to enhance advanced image visualization workflows by enabling content-sensitive hanging-protocols and auto-invocation of a computer aided detection algorithm for identified PA-AP chest images. Finally, we show that the same methodology could be utilized for several image parsing applications including anatomy/organ region of interest prediction and optimized image visualization. |
3,837 | An Exploration of Dance Learning Stress Sources of Elementary School Dance Class Students with Artistic Abilities: The Influences of Psychological Capital and Self-Concept | The purpose of this study is to explore the factors which may cause the increase of students' stress in dance class in elementary school. In this study, students' demographic variables, psychological capital (which includes four sub-constructs), and self-concept (which includes five sub-constructs) were used as predicting variables to estimate their influences on dance class students' stress level. A structured questionnaire was distributed to 450 elementary art talent class students with 412 valid responses. Structural equation modeling was used to test the relationships proposed by the study. As for demographic variables, the results show that the grade, gender, and the dance class hours per week had no significant influences on stress, while the seniority level had a negative influence, which indicated that junior dance students had more stress than senior students. As for psychological capital, self-efficacy and optimism had negative influences on stress, while the other two sub-constructs, hope and resilience, did not have a significant influence on stress. As for physical self-concept, the worry of overweight had positive influences on their stress, while appearance, physical ability performance, health status, and satisfaction of body parts had no significant influence on stress. Based on the research findings, suggestions were made to reduce students' pressure in learning dance. |
3,838 | An Accurate Process-Induced Variability-Aware Compact Model-Based Circuit Performance Estimation for Design-Technology Co-Optimization | In sub-10-nm fin field-effect transistors (FinFETs), line-edge roughness (LER) and metal-gate granularity (MGG) are the two most dominant sources of variability and are mostly modeled semi-empirically. In this work, compact models of LER and MGG are used. We show an accurate process-induced variability (PIV)-aware compact model-based circuit performance estimation for design-technology co-optimization (DTCO). This work is carried out using an experimentally validated Berkeley Short-channel IGFET Model-Common MultiGate (BSIM-CMG) model on a 7-nm FinFET node. First, we have shown performance benchmarking of LER and MGG models with the state of the art and shown similar to 4x (similar to 2.3x) accuracy improvement for nMOS (pMOS) in the estimation of device figure of merits (DFoMs). Second, ring oscillator (RO) and static random-access memory (SRAM) circuit's performance estimation is carried out for LER and MGG variability. Furthermore, similar to 22% more optimistic estimate of (sigma/mu)(SHM) (static hold margin) compared to the state-of-the-art model with V-DD variation is shown. Finally, we demonstrate our improved DFoM accuracy translated to more accurate circuit figure of merits (CFoMs) performance estimation. For worst-case SHM (3(sigma/mu)(SHM)@V-DD = 0.75 V) compared to state of the art, dynamic (standby) power reduction by similar to 73% (similar to 61%) is shown. Thus, our enhanced variability model accuracy enables more credible DTCO with significantly better performance estimates. |
3,839 | M/G/1-type Markov processes: A tutorial | M/G/1-type processes are commonly encountered when modeling modern complex computer and communication systems. In this tutorial, we present a detailed survey of existing solution methods for M/G/1-type processes, focusing on the matrix-analytic methodology. From first principles and using simple examples, we derive the fundamental matrix-analytic results and lay out recent advances. Finally, we give an overview of an existing, state-of-the-art software tool for the analysis of M/G/1-type processes. |
3,840 | Acoustic Source Localization With Distributed Asynchronous Microphone Networks | We propose a method for localizing an acoustic source with distributed microphone networks. Time Differences of Arrival (TDOAs) of signals pertaining the same sensor are estimated through Generalized Cross-Correlation. After a TDOA filtering stage that discards measurements that are potentially unreliable, source localization is performed by minimizing a fourth-order polynomial that combines hyperbolic constraints from multiple sensors. The algorithm turns to exhibit a significantly lower computational cost compared with state-of-the-art techniques, while retaining an excellent localization accuracy in fairly reverberant conditions. |
3,841 | A novel approach to define the local region of dynamic selection techniques in imbalanced credit scoring problems | Lenders, such as banks and credit card companies, use credit scoring models to evaluate the potential risk posed by lending money to customers, and therefore to mitigate losses due to bad credit. The profitability of the banks thus highly depends on the models used to decide on the customer's loans. State-of-the-art credit scoring models are based on machine learning and statistical methods. One of the major problems of this field is that lenders often deal with imbalanced datasets that usually contain many paid loans but very few not paid ones (called defaults). Recently, dynamic selection methods combined with ensemble methods and preprocessing techniques have been evaluated to improve classification models in imbalanced datasets presenting advantages over the static machine learning methods. In a dynamic selection technique, samples in the neighborhood of each query sample are used to compute the local competence of each base classifier. Then, the technique selects only competent classifiers to predict the query sample. In this paper, we evaluate the suitability of dynamic selection techniques for credit scoring problem, and we present Reduced Minority k-Nearest Neighbors (RMkNN), an approach that enhances state of the art in defining the local region of dynamic selection techniques for imbalanced credit scoring datasets. This proposed technique has a superior prediction performance in imbalanced credit scoring datasets compared to state of the art. Furthermore, RMkNN does not need any preprocessing or sampling method to generate the dynamic selection dataset (called DSEL). Additionally, we observe an equivalence between dynamic selection and static selection classification. We conduct a comprehensive evaluation of the proposed technique against state-of-the-art competitors on six real-world public datasets and one private one. Experiments show that RMkNN improves the classification performance of the evaluated datasets regarding AUC, balanced accuracy, H-measure, G-mean, F-measure, and Recall. (C) 2020 Elsevier Ltd. All rights reserved. |
3,842 | A comprehensive approach to winery wastewater treatment: a review of the state-of the-art | Winery industries generate large volumes of high-strength wastewater whose characteristics greatly vary depending on either seasons, production technologies or scale of the wineries. Winery wastewater (WW) is persistent to degrade by means of the conventional activated sludge process because of the high organic loading and polyphenolic content especially during vintage. To face this situation, a number of processes have recently been attempted as alternatives or integrative to biological treatments. However, there is still no agreement on the best practice to treat WW. Despite even more stringent standards, untreated or partially treated effluents continue to be improperly discharged into aquatic or soil matrixes, influencing microbial communities and physicochemical soil properties. This work presents a review on the state-of-the-art of management of wastewater originated from winery industries. Advantages and drawbacks of the treatment technologies at bench-, pilot-, and full-scale applications in the scientific literature have been considered to draw out a sustainable management scheme. |
3,843 | Pareto Optimal Design for Synthetic Biology | Recent advances in synthetic biology call for robust, flexible and efficient in silico optimization methodologies. We present a Pareto design approach for the bi-level optimization problem associated to the overproduction of specific metabolites in Escherichia coli. Our method efficiently explores the high dimensional genetic manipulation space, finding a number of trade-offs between synthetic and biological objectives, hence furnishing a deeper biological insight to the addressed problem and important results for industrial purposes. We demonstrate the computational capabilities of our Pareto-oriented approach comparing it with state-of-the-art heuristics in the overproduction problems of i) 1,4-butanediol, ii) myristoyl-CoA, iii) malonyl-CoA, iv) acetate and v) succinate. We show that our algorithms are able to gracefully adapt and scale to more complex models and more biologically-relevant simulations of the genetic manipulations allowed. The Results obtained for 1,4-butanediol overproduction significantly outperform results previously obtained, in terms of 1,4-butanediol to biomass formation ratio and knock-out costs. In particular overproduction percentage is of +662.7%, from 1.425 mmolh(-1) gDW(-1) (wild type) to 10.869 mmolh(-1) gDW(-1), with a knockout cost of 6. Whereas, Pareto-optimal designs we have found in fatty acid optimizations strictly dominate the ones obtained by the other methodologies, e.g., biomass and myristoyl-CoA exportation improvement of +21.43% (0.17 h(-1)) and +5.19% (1.62 mmol h(-1)gDW(-1)),respectively. Furthermore CPU time required by our heuristic approach is more than halved. Finally we implement pathway oriented sensitivity analysis, epsilon-dominance analysis and robustness analysis to enhance our biological understanding of the problem and to improve the optimization algorithm capabilities. |
3,844 | Robotic Process Automation: A Scientific and Industrial Systematic Mapping Study | The automation of robotic processes has been experiencing an increasing trend of interest in recent times. However, most of literature describes only theoretical foundations on RPA or industrial results after implementing RPA in specific scenarios, especially in finance and outsourcing. This paper presents a systematic mapping study with the aim of analyzing the current state-of-the-art of RPA and identifying existing gaps in both, scientific and industrial literature. Firstly, this study presents an in-depth analysis of the 54 primary studies which formally describe the current state of the art of RPA. These primary studies were selected as a result of the conducting phase of the systematic review. Secondly, considering the RPA study performed by Forrester, this paper reviews 14 of the main commercial tools of RPA, based on a classification framework defined by 48 functionalities and evaluating the coverage of each of them. The result of the study concludes that there are certain phases of the RPA lifecycle that are already solved in the market. However, the Analysis phase is not covered in most tools. The lack of automation in such a phase is mainly reflected by the absence of technological solutions to look for the best candidate processes of an organization to be automated. Finally, some future directions and challenges are presented. |
3,845 | What Drives the Assembly of Plant-associated Protist Microbiomes? Investigating the Effects of Crop Species, Soil Type and Bacterial Microbiomes | In a field experiment we investigated the influence of the environmental filters soil type (i.e. three contrasting soils) and plant species (i.e. lettuce and potato) identity on rhizosphere community assembly of Cercozoa, a dominant group of mostly bacterivorous soil protists. Plant species (14%) and rhizosphere origin (vs bulk soil) with 13%, together explained four times more variation in cercozoan beta diversity than the three soil types (7% explained variation). Our results clearly confirm the existence of plant species-specific protist communities. Network analyses of bacteria-Cercozoa rhizosphere communities identified scale-free small world topologies, indicating mechanisms of self-organization. While the assembly of rhizosphere bacterial communities is bottom-up controlled through the resource supply from root (secondary) metabolites, our results support the hypothesis that the net effect may depend on the strength of top-down control by protist grazers. Since grazing of protists has a strong impact on the composition and functioning of bacteria communities, protists expand the repertoire of plant genes by functional traits, and should be considered as 'protist microbiomes' in analogy to 'bacterial microbiomes'. |
3,846 | An Empirical Comparison of Combinatorial Testing, Random Testing and Adaptive Random Testing | We present an empirical comparison of three test generation techniques, namely, Combinatorial Testing (CT), Random Testing (RT) and Adaptive Random Testing (ART), under different test scenarios. This is the first study in the literature to account for the (more realistic) testing setting in which the tester may not have complete information about the parameters and constraints that pertain to the system, and to account for the challenge posed by faults (in terms of failure rate). Our study was conducted on nine real-world programs under a total of 1683 test scenarios (combinations of available parameter and constraint information and failure rate). The results show significant differences in the techniques' fault detection ability when faults are hard to detect (failure rates are relatively low). CT performs best overall; no worse than any other in 98 percent of scenarios studied. ART enhances RT, and is comparable to CT in 96 percent of scenarios, but its computational cost can be up to 3.5 times higher than CT when the program is highly constrained. Additionally, when constraint information is unavailable for a highly-constrained program, a large random test suite is as effective as CT or ART, yet its computational cost of test generation is significantly lower than that of other techniques. |
3,847 | Horseshoe Kidney With a Documented Giant Calculi: A Case Report | The horseshoe kidney is the most frequent genitourinary fusion abnormality. The horseshoe kidney is a combination of the anatomical abnormalities of ectopia and malrotation. Along with other anomalies, it is linked to malrotations, fluctuating blood flow, high ureter insertion, a tendency to establish a ureteropelvic junction, and blockage in up to one-third of patients, and these are all symptoms of this condition. Kidney calculus and pelvic ureteric junction (PUJ) obstruction are one of horseshoe kidneys' most prevalent side effects and are seen in approximately one-third of the patients. In our case report, we discuss the treatment of a 61-year-old male patient who had been complaining of abdominal pain for the past few years, was found to have a horseshoe kidney, a history of recurrent renal calculi with a non-functioning right side portion, and recurrent urinary tract infections (UTI) treated with open surgery. The patient symptomatically alleviated his symptoms at the three-month follow-up after open surgery; there were no complaints of discomfort or abdominal fullness, and the patient resumed daily routines. |
3,848 | Extracellular Vesicles in Cancer Drug Resistance: Roles, Mechanisms, and Implications | Extracellular vesicles (EVs) are cell-derived nanosized vesicles that mediate cell-to-cell communication via transporting bioactive molecules and thus are critically involved in various physiological and pathological conditions. EVs contribute to different aspects of cancer progression, such as cancer growth, angiogenesis, metastasis, immune evasion, and drug resistance. EVs induce the resistance of cancer cells to chemotherapy, radiotherapy, targeted therapy, antiangiogenesis therapy, and immunotherapy by transferring specific cargos that affect drug efflux and regulate signaling pathways associated with epithelial-mesenchymal transition, autophagy, metabolism, and cancer stemness. In addition, EVs modulate the reciprocal interaction between cancer cells and noncancer cells in the tumor microenvironment (TME) to develop therapy resistance. EVs are detectable in many biofluids of cancer patients, and thus are regarded as novel biomarkers for monitoring therapy response and predicting prognosis. Moreover, EVs are suggested as promising targets and engineered as nanovehicles to deliver drugs for overcoming drug resistance in cancer therapy. In this review, the biological roles of EVs and their mechanisms of action in cancer drug resistance are summarized. The preclinical studies on using EVs in monitoring and overcoming cancer drug resistance are also discussed. |
3,849 | Development trends and review of free-machining steels | This work presents the technologies more widely used to improve the machinability of metal materials, mainly focusing on the free-machining steels. A wide literature and patent review are included with the purpose of presenting the state of the art on free-machining steels. |
3,850 | Degradation Mechanisms of High-Power LEDs for Lighting Applications: An Overview | This paper reports on the degradation mechanisms that limit the reliability of high-power light-emitting diodes (LEDs) for lighting applications. The study is based on the experimental characterization of state-of-the-art LEDs fabricated by leading manufacturers. We demonstrate that, despite high potential reliability, high-power LEDs may suffer from a number of degradation mechanisms that affect the stability of the blue semiconductor LED chip and of the phosphor layer used for the generation of white light. More specifically, we describe the following relevant mechanisms: 1) the optical degradation of LEDs, due to an increase in the nonradiative recombination rate, which can be correlated to modifications in the forward-bias current-voltage characteristics; 2) the variation in forward voltage, due to the increase in series resistance; 3) the optical degradation of phosphor layers used for blue-to-white light conversion; and 4) the failure of LEDs submitted to "hot plugging," which is the direct connection of an LED chain to an energized power supply, due to the generation of high current spikes. Results provide an overview on the failure mechanisms that limit the reliability of state-of-the-art LEDs and on the role of current and temperature in determining the failure of the devices. |
3,851 | Single Image Super-Resolution Based on Wiener Filter in Similarity Domain | Single image super-resolution (SISR) is an ill-posed problem aiming at estimating a plausible high-resolution (HR) image from a single low-resolution image. Current state-of-the-art SISR methods are patch-based. They use either external data or internal self-similarity to learn a prior for an HR image. External data-based methods utilize a large number of patches from the training data, while self-similarity-based approaches leverage one or more similar patches from the input image. In this paper, we propose a self-similarity-based approach that is able to use large groups of similar patches extracted from the input image to solve the SISR problem. We introduce a novel prior leading to the collaborative filtering of patch groups in a 1D similarity domain and couple it with an iterative back-projection framework. The performance of the proposed algorithm is evaluated on a number of SISR benchmark data sets. Without using any external data, the proposed approach outperforms the current non-convolutional neural network-based methods on the tested data sets for various scaling factors. On certain data sets, the gain is over 1 dB, when compared with the recent method A+. For high sampling rate (x4), the proposed method performs similarly to very recent state-of-the-art deep convolutional network-based approaches. |
3,852 | The Cost of Resistance to Diamide Insecticide Varies With the Host Plant in Spodoptera frugiperda (Lepidoptera: Noctuidae) | Fitness costs associated with insect resistance to insecticides can be exploited to implement resistance management programs. However, most of these studies are restricted to evaluating biological traits on artificial diets. Here, we investigated the fitness cost associated with chlorantraniliprole in Spodoptera frugiperda (J.E. Smith) feeding on corn, soybean, and cotton plants. We used a near-isogenic strain of S. frugiperda resistant to chlorantraniliprole (Iso-RR), a susceptible strain (SS), and heterozygotes strains (H1 and H2) to evaluate several biological and population growth parameters. Larval survival of the Iso-RR strain was on average 90% on corn, 65% on soybean, and 57% on cotton plants. Development time of the larval stage also differed among host plants, Iso-RR strain took on average 14, 17, and 26 days to reach the pupal stage on corn, soybean, and cotton plants respectively. Net reproductive rate, intrinsic rate of population increase, and finite rate of population increase were higher for Iso-RR strain feeding on corn plants than other host plants. The relative fitness, based on the intrinsic rate of population increase, of S. frugiperda resistant strain on corn, soybean, and cotton plants were 1.04, 0.85, and 0.88, respectively. Therefore, no fitness cost was observed for S. frugiperda feeding on corn plants, but a significant fitness cost was observed when this pest fed on soybean and cotton plants. We showed that the food source influences the fitness cost of S. frugiperda resistant to diamide. Such information may help to implement resistance management strategies based on each crop. |
3,853 | Genotoxic effect of two inks used in the art of tattoo | The present study provides answers about the genotoxic effect of two inks used in the art of tattooing. Currently, there is no information on the genotoxic effects that some of the chemical compounds in this inks can produce on human health. The objective of this study was to know the genotoxic effects of black and white ink, mostly used in tattoo art. In this investigation, the micronucleus test was applied in peripheral blood cells of CD-1 mice and the structural chromosomal aberration test in human lymphocyte cultures. The results obtained indicated that the average values of both the dividing cells (% IM), the frequency of micronuclei (% MCN) in the peripheral blood cells of the mice and the average number of structural chromosomal aberrations in human lymphocyte cells showed significant differences, therefore they were subjected to Dunnett's multiple comparison test analysis (p < 0.05). It was concluded that the chemical components of the white and black inks have cytotoxic effects in peripheral blood cells of the CD-1 mouse and human lymphocyte cells, while the chemical compounds of the two inks produce genotoxic effects in peripheral blood cells of CD-1 mice and only the white ink had genotoxic effects on human lymphocyte cells at a 10 mg/ml concentration. |
3,854 | Large-scale online learning of implied volatilities | Online-learning-based approaches do not suffer from generalization errors because the data are used once anddiscarded rather than reused. This characteristic enables incredible accuracy in estimating implied volatilitiesover a wide input area, outperforming existing state-of-the-art studies. In addition, an iterative method furtherimproves the estimates. The iterative method takes the estimate as an initial guess and corrects it, quicklygiving a virtually true value. Additionally, optimizing the network structure with TensorRT significantlyreduces the time spent on these processes |
3,855 | Management of patients with lymphoma and COVID-19: Narrative review and evidence-based practical recommendations | Patients with hematologic malignancies can be immunocompromized because of their disease, anti-cancer therapy, and concomitant immunosuppressive treatment. Furthermore, these patients are usually older than 60 years and have comorbidities. For all these reasons they are highly vulnerable to infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and have an increased risk of developing severe/critical Coronavirus disease 2019 (COVID-19) compared to the general population. Although COVID-19 vaccination has proven effective in reducing the incidence of severe/critical disease, vaccinated patients with lymphoma may not be protected as they often fail to develop a sufficient antiviral immune response. There is therefore an urgent need to address the management of patients with lymphoma and COVID-19 in the setting of the ongoing pandemic. Passive immunization with monoclonal antibodies against SARS-CoV-2 is a currently available complementary drug strategy to active vaccination for lymphoma patients, while monoclonal antibodies and antiviral drugs (remdesivir, ritonavir-boosted nirmatrelvir, and molnupiravir) have proven effective in preventing the progression to severe/critical COVID-19. In this narrative review we present the most recent data documenting the characteristics and outcomes of patients with concomitant lymphoma and COVID-19. Our ultimate goal is to provide practice-oriented guidance in the management of these vulnerable patients from diagnosis to treatment and follow-up of lymphoma. To this purpose, we will first provide an overview of the main data concerning prognostic factors and fatality rate of lymphoma patients who develop COVID-19; the outcomes of COVID-19 vaccination will also be addressed. We will then discuss current COVID-19 prophylaxis and treatment options for lymphoma patients. Finally, based on the literature and our multidisciplinary experience, we will summarize a set of indications on how to manage patients with lymphoma according to COVID-19 exposure, level of disease severity and former history of infection, as typically encountered in clinical practice. |
3,856 | Deep Relational Reasoning for the Prediction of Language Impairment and Postoperative Seizure Outcome Using Preoperative DWI Connectome Data of Children With Focal Epilepsy | Prolonged seizures in children with focal epilepsy (FE) may impair language functions and often reoccur after surgical intervention. This study is aimed at developing a novel deep relational reasoning network to investigate whether conventional diffusion-weighted imaging connectome analysis can be improved when predicting expressive and receptive scores of preoperative language impairments and classifying postoperative seizure outcomes (seizure freedom or recurrence) in individual FE children. To deeply reason the dependencies of axonal connections that are sparsely distributed in the whole brain, this study proposes the "dilated CNN + RN", a dilated convolutional neural network (CNN) combined with a relation network (RN). The performance of the dilated CNN + RN was evaluated using whole brain connectome data from 51 FE children. It was found that when compared with other state-of-the-art algorithms, the dilated CNN + RN led to an average improvement of 90.2% and 97.3% in predicting expressive and receptive language scores, and 2.2% and 4% improvement in classifying seizure freedom and seizure recurrence, respectively. These improvements were independent of the prefixed connectome densities. Also, the dilated CNN + RN could provide an explainable artificial intelligence (AI) model by computing gradient-based regression/classification activation maps. This mapping analysis revealed left superior-medial frontal cortex, bilateral hippocampi, and cerebellum as crucial hubs, facilitating important connections that were most predictive of language function and seizure refractoriness after surgery. |
3,857 | Current trends in the epidemiology of malaria | Malaria is one of the worlds most important infectious diseases, occurring in many tropical and subtropical countries. The causative agent is a parasitic protozoan of the genus Plasmodium, transmitted to humans by infected mosquitoes. More than 200 million people get malaria every year worldwide, and hundreds of thousands of them, mostly children under 5 years of age, die of it. Thanks to prevention programmes implemented by various organisations headed by the World Health Organisation (WHO) with the aim of eliminating malaria, cases have been declining in recent years. However, particularly in African countries, malaria continues to be a health and economic issue. |
3,858 | Better Regulation of End-Of-Life Care: A Call For A Holistic Approach | Existing regulation of end-of-life care is flawed. Problems include poorly-designed laws, policies, ethical codes, training, and funding programs, which often are neither effective nor helpful in guiding decision-making. This leads to adverse outcomes for patients, families, health professionals, and the health system as a whole. A key factor contributing to the harms of current regulation is a siloed approach to regulating end-of-life care. Existing approaches to regulation, and research into how that regulation could be improved, have tended to focus on a single regulatory instrument (e.g., just law or just ethical codes). As a result, there has been a failure to capture holistically the various forces that guide end-of-life care. This article proposes a response to address this, identifying "regulatory space" theory as a candidate to provide the much-needed holistic insight into improving regulation of end-of-life care. The article concludes with practical implications of this approach for regulators and researchers. |
3,859 | Characterization and dating of San rock art in the Metolong catchment, Lesotho: A preliminary investigation of technological and stylistic changes | Recent research on Later Stone Age (LSA) San rock art in southern Africa has unveiled some of the paint recipes the artists employed. However, these discoveries still need to be linked to human activities in or near the rock shelters where the paintings were made. In this paper, we report characterization and dating results from the catchment of the Metolong Dam, Phuthiatsana Valley, Lesotho. A total of 92 rock painting samples, six grindstones with traces of colouring materials from an excavated context, and 17 potential raw colouring materials were studied. We identified three previously unreported ingredients used by the artists: manganese oxides, calcined bones, and soot. Grindstones are stained with the same raw materials that the painters used. We propose that one of them may have served to prepare the red pigment used to make a human figure and a bichrome eland at Ha Makotoko, but direct links remain difficult to establish with certainty. The potential colouring materials in the valley are red clays, white clays (kaolinite and illite-or-montmorillonite), and gypsum, three compounds used as paints by the artists. Tests conducted to verify their suitability for paintings show these materials may have been ground, but settling (after pre-grinding) offers a quicker and easier way to obtain a fine powder as observed in the paints. Finally, 12 AMS dates provide an initial framework for studying the changing use of paint recipes in the Phuthiatsana Valley over time. Charcoal appears to have been employed over a period of at least 3000 years and carbon black for at least 2000 years, with soot seemingly used only before 2000 cal. BP. This study is currently the largest characterization and dating study of LSA rock art in southern Africa and shows the potential that such combined investigations offer for linking excavated and parietal components of the region's huntergatherer archaeological record. |
3,860 | Contextual deconvolution network for semantic segmentation | In this paper, we propose a Contextual Deconvolution Network (CDN) and focus on context association in decoder network. Specifically, in upsampling path, we introduce two types of contextual modules to model the interdependencies of features in channel and spatial dimensions respectively. The channel contextual module captures image-level semantic information by aggregating the feature maps across spatial dimensions, and clarifies global ambiguity of features. Meanwhile, the spatial contextual module obtains patch-level semantic context by learning a spatial weight map, and enhance the feature discrimination. We embed the two contextual modules into individual components of the decoder network, thus improving the representation power and gaining more precise segment results. Thorough evaluations are performed on four challenging datasets, i.e., PASCAL VOC 2012, ADE20K, PASCAL-Context and Cityscapes dataset. Our approach achieves competitive performance with state-of-the-art models on PASCAL VOC 2012, ADE20K and Cityscapes dataset, and new state-of-the-art performance on PASCAL-Context dataset. (C) 2019 Published by Elsevier Ltd. |
3,861 | Benchmarking state-of-the-art imbalanced data learning approaches for credit scoring | The goal of credit scoring is to identify abnormalities, aiding decision making and maintaining the order of financial transactions. Due to the small number of default records, one inevitably faces a class imbalance problem when handling financial data. The class imbalance problem has received a lot of attention because of the eco-nomic loss that can occur when one fails to accurately identify default samples. To solve this problem, there are various classic and mature approaches to learning imbalanced data, including resampling approaches, cost -sensitive strategies, and so on. Especially in recent years, generative adversarial networks (GANs) have attrac-ted the attention of researchers to explore these networks' effects as imbalanced data learning tools. However, no attention has been paid to the systematic scoring and comparison of these traditional and state-of-the-art imbalanced data learning approaches in relation to credit scoring. Therefore, choosing several related data -sets, we compare the performance of the traditional approaches and GANs in solving the class imbalance problem of credit scoring; at the same time, with the help of benchmark analysis, we provide some suggestions for relevant research. |
3,862 | Fast and Fully Automatic Left Ventricular Segmentation and Tracking in Echocardiography Using Shape-Based B-Spline Explicit Active Surfaces | Cardiac volume/function assessment remains a critical step in daily cardiology, and 3-D ultrasound plays an increasingly important role. Fully automatic left ventricular segmentation is, however, a challenging task due to the artifacts and low contrast-to-noise ratio of ultrasound imaging. In this paper, a fast and fully automatic framework for the full-cycle endocardial left ventricle segmentation is proposed. This approach couples the advantages of the B-spline explicit active surfaces framework, a purely image information approach, to those of statistical shape models to give prior information about the expected shape for an accurate segmentation. The segmentation is propagated throughout the heart cycle using a localized anatomical affine optical flow. It is shown that this approach not only outperforms other state-of-the-art methods in terms of distance metrics with a mean average distances of 1.81 +/- 0.59 and 1.98 +/- 0.66 mm at end-diastole and end-systole, respectively, but is computationally efficient (in average 11 s per 4-D image) and fully automatic. |
3,863 | A Novel Nonparametric Maximum Likelihood Estimator for Probability Density Functions | Parametric maximum likelihood (ML) estimators of probability density functions (pdfs) are widely used today because they are efficient to compute and have several nice properties such as consistency, fast convergence rates, and asymptotic normality. However, data is often complex making parametrization of the pdf difficult, and nonparametric estimation is required. Popular nonparametric methods, such as kernel density estimation (KDE), produce consistent estimators but are not ML and have slower convergence rates than parametric ML estimators. Further, these nonparametric methods do not share the other desirable properties of parametric ML estimators. This paper introduces a nonparametric ML estimator that assumes that the square-root of the underlying pdf is band-limited (BL) and hence "smooth". The BLML estimator is computed and shown to be consistent. Although convergence rates are not theoretically derived, the BLML estimator exhibits faster convergence rates than state-of-the-art nonparametric methods in simulations. Further, algorithms to compute the BLML estimator with lesser computational complexity than that of KDE methods are presented. The efficacy of the BLML estimator is shown by applying it to (i) density tail estimation and (ii) density estimation of complex neuronal receptive fields where it outperforms state-of-the-art methods used in neuroscience. |
3,864 | Dating ancient paintings of Mogao Grottoes using deeply learnt visual codes | Cultural heritage is the asset of all the peoples of the world. The preservation and inheritance of cultural heritage is conducive to the progress of human civilization. In northwestern China, there is a world heritage site - Mogao Grottoes - that has a plenty of mural paintings showing the historical cultures of ancient China. To study these historical cultures, one critical procedure is to date the mural paintings, i.e., determining the era when they were created. Until now, most mural paintings at Mogao Grottoes have been dated by directly referring to the mural texts or historical documents. However, some are still left with creation-era undetermined due to the lack of reference materials. Considering that the drawing style of mural paintings was changing along the history and the drawing style can be learned and quantified through painting data, we formulate the problem of mural-painting dating into a problem of drawing-style classification. In fact, drawing styles can be expressed not only in color or curvature, but also in some unknown forms - the forms that have not been observed. To this end, besides sophisticated color and shape descriptors, a deep convolution neural network is designed to encode the implicit drawing styles. 3860 mural paintings collected from 194 different grottoes with determined creation-era labels are used to train the classification model and build the dating method. In experiments, the proposed dating method is applied to seven mural paintings which were previously dated with controversies, and the exciting new dating results are approved by the Dunhuang experts. |
3,865 | Characterization of the use-wear and residues resulting from limestone working. Experimental approach to the parietal art of La Vina rock shelter (La Manzaneda, Asturias, Spain) | The Palaeolithic rock engravings that are located along the Nal ' on river basin in Northern Spain (central area of Asturias) have been studied from various perspectives (morphology, depth, style, manual range), but no use-wear studies on the stone tools used to produce such engravings have ever been undertaken. This paper aims to explore a new approach to this type of incisions based on use-wear analysis of experimental lithic tools used to engrave limestone blocks and slabs. Our results show that the use-wear traces generated by engraving limestone are welldeveloped and can be defined with specific criteria. The principal objective of this study was to provide the first experimental reference collection of use-wear resulting from engraving limestone using flint and quartzite experimental tools to compare with the traces that appear on tools in the archaeological record in contexts with parietal and portable art and, more specifically, to add a new approach to the multidisciplinary study of the La Vina rock shelter. |
3,866 | A Data-Driven Framework for Inter-Frequency Handover Failure Prediction and Mitigation | With 5G already deployed, challenges related to handover exacerbate due to the dense base station deployment operating on a motley of frequencies. In this paper, we present and evaluate a novel data-driven solution, to reduce inter-frequency handover failures (HOFs), hereafter referred to as TORIS (Transmit Power Tuning-based Handover Success Rate Improvement Scheme). TORIS is designed by developing and integrating two sub-solutions. First sub-solution consists of an Artificial Intelligence (AI)-based model to predict inter-frequency HOFs. In this model, we achieve higher than the state-of-the-art accuracy by leveraging two approaches. First, we devise a novel feature set by exploiting domain knowledge gathered from extensive drive test data analysis. Second, we exploit an extensive set of data augmentation techniques to address the class imbalance in training the HOF prediction model. The data augmentation techniques include Chow-Liu Bayesian Network and Generative Adversarial Network further improved by focusing the sampling only on the borderline. We also compare the performance of state-of-the-art AI models for predicting HOFs with and without augmented data. Results show that AdaBoost yields best performance for predicting HOFs. The second sub-solution is a heuristic scheme to tune the transmit (Tx) power of serving and target cells. Unlike the state-of-the-art approaches for HOF reduction that tune cell individual offset, TORIS targets the main cause of HOFs i.e., poor signal quality and propagation condition, by proactively varying the Tx power of the cells whenever a HOF is anticipated. Results show that TORIS outperforms the state-of-the-art HOF reduction solution and yields 40%-75% reduction in HOFs. |
3,867 | Imaging of Bone Marrow Involvement in Lymphoma: State of the Art and Future Directions | Accurate detection of bone marrow involvement in patients with lymphoma is of crucial importance because of the prognostic and therapeutic consequences. Bone marrow trephine biopsy (BMB) is currently regarded as the method of choice for the evaluation of the bone marrow in lymphoma, but it is invasive, has a risk of complications, and lacks sufficient sensitivity due to the possibility of sampling errors. Bone marrow imaging, if accurate, may (partially) replace BMBand/or may improve the sensitivity of BMB by guiding the biopsy to the location that appears to be involved by lymphoma at imaging. In this scientific communication, general concepts of bone marrow imaging, state-of-the-art imaging modalities, and future imaging strategies for the assessment of the bone marrow in lymphoma will be reviewed and discussed. |
3,868 | A meta-analysis on morphological, physiological and biochemical responses of plants with PGPR inoculation under drought stress | Plant growth-promoting rhizobacteria (PGPR) can help plants to resist drought stress. However, the mechanisms of how PGPR inoculation affect plant status under drought remain incompletely understood. We performed a meta-analysis of plant response to PGPR inoculation by compiling data from 57 PGPR-inoculation studies, including 2, 387 paired observations on morphological, physiological and biochemical parameters under drought and well-watered conditions. We compare the PGPR effect on plants performances among different groups of controls and treatments. Our results reveal that PGPR enables plants to restore themselves from drought-stressed to near a well-watered state, and that C4 plants recover better from drought stress than C3 plants. Furthermore, PGPR is more effective underdrought than well-watered conditions in increasing plant biomass, enhancing photosynthesis and inhibiting oxidant damage, and the responses of C4 plants to the PGPR effect was stronger than that of C3 plants under drought conditions. Additionally, PGPR belonging to different taxa and PGPR with different functional traits have varying degrees of drought-resistance effects on plants. These results are important to improve our understanding of the PGPR beneficial effects on enhanced drought-resistance of plants. |
3,869 | Extracting semantics from audiovisual content: The final frontier in multimedia retrieval | Multimedia understanding is a fast emerging interdisciplinary research area. There is tremendous potential for effective use of multimedia content through intelligent analysis. Diverse application areas are increasingly relying on multimedia understanding systems. Advances in multimedia understanding are related directly to advances in signal processing, computer vision, pattern recognition, multimedia databases, and smart sensors. We review the state-of-the-art techniques in multimedia retrieval. In particular we discuss how multimedia retrieval can be viewed as a pattern recognition problem. We discuss, how reliance on powerful pattern recognition and machine learning techniques is increasing in the field of multimedia retrieval. We review state-of-the-art multimedia understanding systems with particular emphasis on a system for semantic video indexing centered around multijects and multinets. We discuss how semantic retrieval is centered around concepts and context and also discuss various mechanisms for modeling concepts and context. |
3,870 | ICTs, growth, and environmental quality nexus: dynamic panel threshold regression | ICTs (information and communication technologies) have emerged as a potent new force. Digitalization, modernization, and automation of the manufacturing process are expected to facilitate ICT adoption, resulting in increased genuine environmental concerns. This research aims to examine the impact of ICTs on environmental quality and the relationship between ICTs, environmental quality, and economic growth. Dynamic panel threshold regression was employed, and the sample countries comprised 69 developing countries from 2010 to 2019. The threshold technique will identify the precise threshold value of ICTs and highlights the impacts of ICTs on the environmental quality nexus when above and below the threshold value in developing countries. Empirical evidence suggests that ICTs positively impact environmental quality (CO2) when above the ICTs threshold value. However, ICTs provide a positive but insignificant impact on environmental quality when below the ICTs threshold value of 4.699. Additionally, ICTs affect the economic growth and environmental quality nexus, with increasing economic growth resulting in a decrease in CO2 emissions in developing countries when ICTs are below the threshold value. Thus, the ICTs threshold value should be used to ensure that ICTs adoption promotes sustainable economic growth and resolves environmental degradation issues in developing nations. |
3,871 | An Investigation into Art Therapy Aided Health and Well-Being Research: A 75-Year Bibliometric Analysis | Considering the physical, and psychological impacts and challenges brought about the coronavirus disease 2019 (COVID-19), art therapy (AT) provides opportunities to promote human health and well-being. There are few systematic analysis studies in the fields of AT, which can provide content and direction for the potential value and impact of AT. Therefore, this paper aims to critically analyze the published work in the field of AT from the perspective of promoting health and well-being, and provides insights into current research status, hotspots, limitations, and future development trends of AT. This paper adopts a mixed method of quantitative and qualitative analysis including bibliometric analysis and keyword co-occurrence analysis. The results indicate that: (1) the current studies on AT are mostly related to research and therapeutic methods, types of AT, research populations and diseases, and evaluation of therapeutic effect of AT. The research method of AT mainly adopts qualitative research, among which creative arts therapy and group AT are common types of AT, and its main research populations are children, veterans, and adolescents. AT-aided diseases are trauma, depression, psychosis, dementia, and cancer. In addition, the therapeutic methods are mainly related to psychotherapy, drama, music, and dance/movement. Further, computer systems are an important evaluation tool in the research of AT; (2) the future development trend of AT-aided health and well-being based on research hotspots, could be focused on children, schizophrenia, well-being, mental health, palliative care, veterans, and the elderly within the context of addressing COVID-19 challenges; and (3) future AT-aided health and well-being could pay more attention to innovate and integrate the therapeutic methods of behavior, movement, and technology, such as virtual reality and remote supervision. |
3,872 | The Importance and Quantification of Plutonium Binding in Human Lungs | Epidemiological studies have shown that the main risk arising from exposure to plutonium aerosols is lung cancer, with other detrimental effects in the bone and liver. A realistic assessment of these risks, in turn, depends on the accuracy of the dosimetric models used to calculate doses in such studies. A state-of-the-art biokinetic model for plutonium, based on the current International Commission on Radiological Protection biokinetic model, has been developed for this purpose in an epidemiological study involving the plutonium exposure of Mayak workers in Ozersk, Russia. One important consequence of this model is that the lung dose is extremely sensitive to the fraction (f(b)) of plutonium, which becomes bound to lung tissue after it dissolves. It has been shown that if just 1% of the material becomes bound in the bronchial region, this will double the lung dose. Furthermore, f(b) is very difficult to quantify from experimental measurements. This paper summarizes the work carried out thus far to quantify f(b). Bayesian techniques have been used to analyze data from different sources, including both humans and dogs, and the results suggest a small, but nonzero, fraction of < 1%. A Bayesian analysis of 20 Mayak workers exposed to plutonium nitrate suggests an f(b) between 0 and 0.3%. Based on this work, the International Commission on Radiological Protection is currently considering the adoption of a value of 0.2% for the default bound fraction for all actinides in its forthcoming recommendations on internal dosimetry. In an attempt to corroborate these findings, further experimental work has been carried out by the US Transuranium and Uranium Registries. This work has involved direct measurements of plutonium in the respiratory tract tissues of workers who have been exposed to soluble plutonium nitrate. Without binding, one would not expect to see any activity remaining in the lungs at long times after exposure since it would have been cleared by the natural process of mucociliary clearance. Further supportive study of workers exposed to plutonium oxide is planned. This paper ascertains the extent to which these results corroborate previous inferences concerning the bound fraction. |
3,873 | State of the art in the application of QSAR techniques for predicting mixture toxicity in environmental risk assessment | The focus of regulatory chemical risk assessment has been mainly placed on single chemicals rather than mixtures. However, living organisms and the environment might be exposed to mixtures of chemicals. Many scientific studies have revealed that mixture toxicity can arise from the combined effects of components present at levels below their individual no-effect concentrations. Predictive approaches will be essential for estimating mixture toxicity, as the number of possible mixtures is extremely large. Although predictive models are virtually indispensable for estimating mixture toxicity for both scientific and regulatory purposes, risk assessors encounter substantial difficulties in using conventional models, mainly due to the lack of information on the modes of toxic action of the mixture constituents. Alternative models that use different information instead of the modes of action thus need to be developed. The objective of this study is to investigate the state of the art in predictive models based on quantitative structure-activity relationship techniques for estimating the toxicity of mixture components, and to identify future challenges hindering more reliable mixture risk assessment for environmental risk assessment. Alternative models need to be developed not only to overcome the limitations of conventional models, but also to improve their performance. |
3,874 | Advanced Simulation of Quantum Computations | Quantum computation is a promising emerging technology which, compared to conventional computation, allows for substantial speed-ups, e.g., for integer factorization or database search. However, since physical realizations of quantum computers are in their infancy, a significant amount of research in this domain still relies on simulations of quantum computations on conventional machines. This causes a significant complexity which current state-of-the-art simulators try to tackle with a rather straight forward array-based representation and by applying massive hardware power. There also exist solutions based on decision diagrams (i.e., graph-based approaches) that try to tackle the exponential complexity by exploiting redundancies in quantum states and operations. However, these existing approaches do not fully exploit redundancies that are actually present. In this paper, we revisit the basics of quantum computation, investigate how corresponding quantum states and quantum operations can be represented even more compactly, and, eventually, simulated in a more efficient fashion. This leads to a new graph-based simulation approach which outperforms state-of-the-art simulators (array-based as well as graph-based). Experimental evaluations show that the proposed solution is capable of simulating quantum computations for more qubits than before, and in significantly less run-time (several magnitudes faster compared to previously proposed simulators). An implementation of the proposed simulator is publicly available online at http://www.jku.at/iic/eda/quantum_simulation. |
3,875 | [Engage health and medico-social institutions in a sustainable development or CSR approach] | Health and medico-social establishments have every interest in embarking on corporate social responsibility for establishments: decarbonisation, compliance with regulations, attractiveness strategy, links with territorial partners, economic interest, etc. To support them, the Fédération hospitalière de France has put forward proposals to the presidential candidates, which are intended to be supported throughout Emmanuel Macron's new five-year term. |
3,876 | Rhodotorula sp.-based biorefinery: a source of valuable biomolecules | The development of an effective, realistic, and sustainable microbial biorefinery depends on several factors, including as one of the key aspects an adequate selection of microbial strain. The oleaginous red yeast Rhodotorula sp. has been studied as one powerful source for a plethora of high added-value biomolecules, such as carotenoids, lipids, and enzymes. Although known for over a century, the use of Rhodotorula sp. as resource for valuable products has not yet commercialized. Current interests for Rhodotorula sp. yeast have sparked from its high nutritional versatility and ability to convert agro-food residues into added-value biomolecules, two attractive characteristics for designing new biorefineries. In addition, as for other yeast-based bioprocesses, the overall process sustainability can be maximized by a proper integration with subsequent downstream processing stages, for example, by using eco-friendly solvents for the recovery of intracellular products from yeast biomass. This review intends to reflect on the current state of the art of microbial bioprocesses using Rhodotorula species. Therefore, we will provide an analysis of bioproduction performance with some insights regarding downstream separation steps for the extraction of high added-value biomolecules (specifically using efficient and sustainable platforms), providing information regarding the potential applications of biomolecules produced by Rhodotorula sp, as well as detailing the strengths and limitations of yeast-based biorefinery approaches. Novel genetic engineering technologies are further discussed, indicating some directions on their possible use for maximizing the potential of Rhodotorula sp. as cell factories. KEY POINTS: • Rhodotorula sp. are valuable source of high value-added compounds. • Potential of employing Rhodotorula sp. in a multiple product biorefinery. • Future perspectives in the biorefining of Rhodotorula sp. were discussed. |
3,877 | Fifty Years of Acoustic Feedback Control: State of the Art and Future Challenges | The acoustic feedback problem has intrigued researchers over the past five decades, and a multitude of solutions has been proposed. In this survey paper, we aim to provide an overview of the state of the art in acoustic feedback control, to report results of a comparative evaluation with a selection of existing methods, and to cast a glance at the challenges for future research. |
3,878 | Are morphological changes necessary to mediate the therapeutic effects of electroconvulsive therapy? | The neurotrophic hypothesis has become the favorite model to explain the antidepressant properties of electroconvulsive therapy (ECT). It is based on the assumption that a restoration of previously defective neural networks drives therapeutic effects. Recent data in rather young patients suggest that neurotrophic effects of ECT might be detectable by diffusion tensor imaging. We here aimed to investigate whether the therapeutic response to ECT necessarily goes along with mesoscopic effects in gray matter (GM) or white matter (WM) in our patients in advanced age. Patients (n = 21, 15 males and 7 females) suffering from major depressive disorder were treated with ECT. Before the start of treatment and after the completion of the index series, they underwent magnetic resonance imaging, including a diffusion-weighed sequence. We used voxel-based morphometry to assess GM changes and tract-based spatial statistics and an SPM-based whole-brain analysis to detect WM changes in the course of treatment. Patients significantly improved clinically during the course of ECT. This was, however, not accompanied by GM or WM changes. This result challenges the notion that mesoscopic brain structure changes are an obligatory prerequisite for the antidepressant effects of ECT. |
3,879 | Semantic-Oriented Labeled-to-Unlabeled Distribution Translation for Image Segmentation | Automatic medical image segmentation plays a crucial role in many medical applications, such as disease diagnosis and treatment planning. Existing deep learning based models usually regarded the segmentation task as pixel-wise classification and neglected the semantic correlations of pixels across different images, leading to vague feature distribution. Moreover, pixel-wise annotated data is rare in medical domain, and the scarce annotated data usually exhibits the biased distribution against the desired one, hindering the performance improvement under the supervised learning setting. In this paper, we propose a novel Labeled-to-unlabeled Distribution Translation (L2uDT) framework with Semantic-oriented Contrastive Learning (SoCL), mainly for addressing the aforementioned issues in medical image segmentation. In SoCL, a semantic grouping module is designed to cluster pixels into a set of semantically coherent groups, and a semantic-oriented contrastive loss is advanced to constrain group-wise prototypes, so as to explicitly learn a feature space with intra-class compactness and inter-class separability. We then establish a L2uDT strategy to approximate the desired data distribution for unbiased optimization, where we translate the labeled data distribution with the guidance of extensive unlabeled data. In particular, a bias estimator is devised to measure the distribution bias, then a gradual-paced shift is derived to progressively translate the labeled data distribution to unlabeled one. Both labeled and translated data are leveraged to optimize the segmentation model simultaneously. We illustrate the effectiveness of the proposed method on two benchmark datasets, EndoScene and PROSTATEx, and our method achieves state-of-the-art performance, which clearly demonstrates its effectiveness for medical image segmentation. The source code is available at https://github.com/CityU-AIM-Group/L2uDT. |
3,880 | Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models | Agri-food sector performance strongly impacts global economy, which means that developing optimisation models to support the decision-making process in agri-food supply chains (AFSC) is necessary. These models should contemplate AFSC's inherent characteristics and sources of uncertainty to provide applicable and accurate solutions. To the best of our knowledge, there are no conceptual frameworks available to design AFSC through mathematical programming modelling while considering their inherent characteristics and sources of uncertainty, nor any there literature reviews that address such characteristics and uncertainty sources in existing AFSC design models. This paper aims to fill these gaps in the literature by proposing such a conceptual framework and state of the art. The framework can be used as a guide tool for both developing and analysing models based on mathematical programming to design AFSC. The implementation of the framework into the state of the art validates its. Finally, some literature gaps and future research lines were identified. |
3,881 | Road Centerline Extraction via Semisupervised Segmentation and Multidirection Nonmaximum Suppression | Accurate road centerline extraction from remotely sensed images plays a significant role in road map generation and updating. In the road extraction problem, the acquisition of labeled data is time consuming and costly; thus, there are only a small amount of labeled samples in reality. In the existing centerline extraction algorithms, the thinning-based algorithms always produce small spurs that reduce the smoothness and accuracy of the road centerline; the regression-based algorithms can extract a smooth road network, but they are time consuming. To solve the aforementioned problems, we propose a novel road centerline extraction method, which is constructed based on semi-supervised segmentation and multiscale filtering (MF) and multidirection nonmaximum suppression (M-NMS) (MF&M-NMS). Specifically, a semisupervised method, which explores the intrinsic structures between the labeled samples and the unlabeled ones, is introduced to obtain the segmentation result. Then, a novel MF&M-NMS-based algorithm is proposed to gain a smooth and complete road centerline network. Experimental results on a public data set demonstrate that the proposed method achieves comparable or better performances by comparing with the stateof-the-art methods. In addition, our method is nearly ten times faster than the state-of-the-art methods. |
3,882 | Protective effects of probiotics against tannin-induced immunosuppression in broiler chickens | Tannins (TAs) are an anti-nutritional substance commonly used as a natural feed additive for livestock. However, our previous study described the dose-dependent adverse effects of TA on immune responses and growth in chickens. In this study, we evaluated the protective effects of a probiotic preparation (BT) consisting of three different bacteria (Bacillus mesenteric, Clostridium butyricum, and Streptococcus faecalis) against TA-induced immunosuppression in chickens. Forty chicks were divided into 4 groups as follows: the CON group (basal diet), BT group supplemented with 3 g BT/kg diet, tannic acid (TA) group supplemented with 30 g TA/kg diet, and BT+TA group supplemented with 3 g BT/kg diet + 30 g TA/kg diet. The feeding trial lasted for 35 days. Lymphocyte subset, macrophage phagocytosis, cytokine mRNA expression, and primary and secondary IgY immune responses were evaluated. BT supplementation significantly improved TA-induced reductions in final body weight, body weight gain, feed intake, and relative weights of lymphoid organs compared with the TA group. Furthermore, in the spleen and cecal tonsil (CT), the relative populations of CD4+, CD8+, and CD4+CD8+ cells in the BT+TA group were significantly ameliorated compared with the TA group. Additionally, comparison with the TA group showed that the chickens in the BT+TA group had an improved relative population of B cells in the CT and that macrophage phagocytosis in the spleen was significantly increased. Chickens in the BT+TA group showed significant increases in IFN-γ and IL-4 mRNA expression in the spleen compared with the TA group. The primary and secondary IgY responses were significantly improved. These results revealed that supplementation with BT protects against TA-induced immunosuppression in chickens. |
3,883 | Evaluation of peripheral nerve regeneration in Murphy Roths Large mouse strain following transection injury | Aim: Murphy Roths Large (MRL/MpJ) mice have demonstrated the ability to heal with minimal or no scar formation in several tissue types. In order to identify a novel animal model, this study sought to evaluate whether this attribute applies to peripheral nerve regeneration. Materials & methods: This was a two-phase study. 6-week-old male mice were divided into two interventional groups: nerve repair and nerve graft. The MRL/MpJ was compared with the C57BL/6J strain for evaluation of both functional and histological outcomes. Results: MRL/MpJ strain demonstrated superior axon myelination and less scar formation, however functional outcomes did not show significant difference between strains. Conclusion: Superior histological outcomes did not translate into superior peripheral nerve regeneration in MRL/MpJ strain. |
3,884 | Audio Inpainting | We propose the audio inpainting framework that recovers portions of audio data distorted due to impairments such as impulsive noise, clipping, and packet loss. In this framework, the distorted data are treated as missing and their location is assumed to be known. The signal is decomposed into overlapping time-domain frames and the restoration problem is then formulated as an inverse problem per audio frame. Sparse representation modeling is employed per frame, and each inverse problem is solved using the Orthogonal Matching Pursuit algorithm together with a discrete cosine or a Gabor dictionary. The Signal-to-Noise Ratio performance of this algorithm is shown to be comparable or better than state-of-the-art methods when blocks of samples of variable durations are missing. We also demonstrate that the size of the block of missing samples, rather than the overall number of missing samples, is a crucial parameter for high quality signal restoration. We further introduce a constrained Matching Pursuit approach for the special case of audio declipping that exploits the sign pattern of clipped audio samples and their maximal absolute value, as well as allowing the user to specify the maximum amplitude of the signal. This approach is shown to outperform state-of-the-art and commercially available methods for audio declipping in terms of Signal-to-Noise Ratio. |
3,885 | An Effective Adversarial Attack on Person Re-Identification in Video Surveillance via Dispersion Reduction | Person re-identification across a network of cameras, with disjoint views, has been studied extensively due to its importance in wide-area video surveillance. This is a challenging task due to several reasons including changes in illumination and target appearance, and variations in camera viewpoint and camera intrinsic parameters. The approaches developed to re-identify a person across different camera views need to address these challenges. More recently, neural network-based methods have been proposed to solve the person re-identification problem across different camera views, achieving state-of-the-art performance. In this paper, we present an effective and generalizable attack model that generates adversarial images of people, and results in very significant drop in the performance of the existing state-of-the-art person re-identification models. The results demonstrate the extreme vulnerability of the existing models to adversarial examples, and draw attention to the potential security risks that might arise due to this in video surveillance. Our proposed attack is developed by decreasing the dispersion of the internal feature map of a neural network to degrade the performance of several different state-of-the-art person re-identification models. We also compare our proposed attack with other state-of-the-art attack models on different person re-identification approaches, and by using four different commonly used benchmark datasets. The experimental results show that our proposed attack outperforms the state-of-art attack models on the best performing person re-identification approaches by a large margin, and results in the most drop in the mean average precision values. |
3,886 | Synergistic polymorphic interactions of phase II metabolizing genes and their association toward lung cancer susceptibility in North Indians | Lung cancer is a multifactorial carcinoma with diverse heterogeneity. Genetic variations in drug-metabolizing enzymes may lead to defective detoxification and clearance of carcinogenic compounds. The high-order gene-gene interaction has been carried out between different genotypes of Phase II detoxification genes (NQO1, SULT1A1, NAT2, and EPHX1). Our results depict the genetic combination of SULT1A1 R213H with NAT2 × 5B L161L, SULT1A1 R213H with NAT2 × 5C K268R, EPHX1 H139R and NAT2 × 5B L161L exhibit a protective effect towards lung cancer risk. Further, the triple combinations of NQO1 P187S, EPHX1 Y113H, and EPHX1 H139R; NQO1 P187S, EPHX1 Y113H, and NAT2 × 6 R197Q; NQO1 P187S, EPHX1 Y113H, and NAT2 × 7 G286E; SULT1A1 R213H, EPHX1 H139R, and NAT2 × 7 G286E suggested a two-fold increased risk of lung cancer for subjects. Genetic polymorphisms of phase II detoxifying genes (NAT2, NQO1, EPHX1, SULT1A1) are prognostic markers for lung cancer. |
3,887 | Social interaction can select for reduced ability | Animals, including humans, differ in a wide range of physical and cognitive abilities ranging from measures of running speed and physical strength to learning ability and intelligence. We consider the evolution of ability when individuals interact pairwise over their contribution to a common good. In this interaction, the contribution of each is assumed to be the best given their own ability and the contribution of their partner. Since there is a tendency for individuals to partially compensate for a low contribution by their partner, low-ability individuals can do well. As a consequence, for benefit and cost structures for which individuals have a strong response to partner's contribution, there can be selection for reduced ability. Furthermore, there can be disruptive selection on ability, leading to a bimodal distribution of ability under some modes of inheritance. |
3,888 | A prospective analysis of red blood cell membrane polyunsaturated fatty acid levels and risk of non-Hodgkin lymphoma | Published studies report inconsistent associations of polyunsaturated fatty acid (PUFA) intake with non-Hodgkin lymphoma (NHL) risk. We conducted a nested case-control study in Nurses' Health Study and Health Professionals Follow-Up Study participants to evaluate a hypothesis of inverse association of pre-diagnosis red blood cell (RBC) membrane PUFA levels with risk of NHL endpoints. We confirmed 583 NHL cases and matched 583 controls by cohort/sex, age, race and blood draw date/time. We estimated odds ratios (OR) and 95% confidence intervals (CI) for risk of NHL endpoints using logistic regression. RBC PUFA levels were not associated with all NHL risk; cis 20:2n-6 was associated with follicular lymphoma risk (OR [95% CI] per one standard deviation increase: 1.35 [1.03-1.77]), and the omega-6/omega-3 PUFA ratio was associated with diffuse large B-cell lymphoma risk (2.33 [1.23-4.43]). Overall, PUFA did not demonstrate a role in NHL etiology; the two unexpected positive associations lack clear biologic explanations. |
3,889 | Vestibular paroxysmia entails vestibular nerve function, microstructure and endolymphatic space changes linked to root-entry zone neurovascular compression | Combining magnetic resonance imaging (MRI) sequences that permit the determination of vestibular nerve angulation (NA = change of nerve caliber or direction), structural nerve integrity via diffusion tensor imaging (DTI), and exclusion of endolymphatic hydrops (ELH) via delayed gadolinium-enhanced MRI of the inner ear (iMRI) could increase the diagnostic accuracy in patients with vestibular paroxysmia (VP). Thirty-six participants were examined, 18 with VP (52.6 ± 18.1 years) and 18 age-matched with normal vestibulocochlear testing (NP 50.3 ± 16.5 years). This study investigated whether (i) NA, (ii) DTI changes, or (iii) ELH occur in VP, and (iv) to what extent said parameters relate. Methods included vestibulocochlear testing and MRI data analyses for neurovascular compression (NVC) and NA verification, DTI and ELS quantification. As a result, (i) NA increased NVC specificity. (ii) DTI structural integrity was reduced on the side affected by VP (p < 0.05). (iii) 61.1% VP showed mild ELH and higher asymmetry indices than NP (p > 0.05). (iv) "Disease duration" and "total number of attacks" correlated with the decreased structural integrity of the affected nerve in DTI (p < 0.001). NVC distance within the nerve's root-entry zone correlated with nerve function (Roh = 0.72, p < 0.001), nerve integrity loss (Roh = - 0.638, p < 0.001), and ELS volume (Roh = - 0.604, p < 0.001) in VP. In conclusion, this study is the first to link eighth cranial nerve function, microstructure, and ELS changes in VP to clinical features and increased vulnerability of NVC in the root-entry zone. Combined MRI with NVC or NA verification, DTI and ELS quantification increased the diagnostic accuracy at group-level but did not suffice to diagnose VP on a single-subject level due to individual variability and lack of diagnostic specificity. |
3,890 | Andean caravan ceremonialism in the lowlands of the Atacama Desert: The Cruces de Molinos archaeological site, northern Chile | Camelid caravans have played a key role in the complex systems of interregional social interaction that characterizes Andean history. In the northernmost region of Chile, the most frequent archaeological indicators of these caravan systems are trails and rock art images. Cruces de Molinos (LL-43), a rock art site in the Lluta valley, 1100 masl, 40 km from the Pacific littoral, expands the ceremonial role of rock art sites, materialized, not only as regards the iconography portrayed and alluding to these practices, but also in terms of articulated carcass remains and detached anatomical units of camelids, intentionally deposited in a cache beneath one of the engraved blocks. This paper analyzes the site considering the visual imagery, spatial location, archaeological deposits and features associated with rock art. Based on the predominance of camelid and caravan motifs in rock art images, the extraordinary setting and location of the site on the valleys upper slopes, which is far removed from local settlements, but closely connected with a llama caravan trade network linking the chaupiyunga ecozone with the highlands (sierra and Altiplano ecozones), we suggest that Cruces de Molinos was not a rest stop for caravanners, but a ceremonial place, and not for local farmers, but for highland herders. According to seven accelerator mass spectrometry (AMS) dates that place the occupation between cal. 1060-1190 CE in the Late Intermediate period. |
3,891 | A Novel Threshold Detection Technique for the Automatic Construction of Attribute Profiles in Hyperspectral Images | Attribute profiles are well-acknowledged as one of the most significant techniques to characterize spectral-spatial properties of a hyperspectral image. The spectral-spatial content of an attribute profile is influenced by the threshold values considered during its construction. In this article, we propose a robust method to detect the threshold values automatically by overcoming the limitations of the existing techniques. The proposed method employs a tree structure representing the connected components of the image and evaluates attribute values at each node. Then, a total characteristic function (TCF) is defined that represents these attribute values in a nondecreasing order. The defined TCF is analyzed using a novel technique to detect a few informative thresholds for the construction of a low-dimensional attribute profile representing substantial spectral-spatial information. The proposed threshold detection method is computationally efficient. To assess the effectiveness of the proposed technique experiments are conducted on three real hyperspectral datasets using six different attributes and the results are compared to the recent state-of-the-art method. The results demonstrate that the proposed method has several advantages over the existing state-of-the-art method. |
3,892 | MouseGAN plus plus : Unsupervised Disentanglement and Contrastive Representation for Multiple MRI Modalities Synthesis and Structural Segmentation of Mouse Brain | Segmenting the fine structure of the mouse brain on magnetic resonance (MR) images is critical for delineating morphological regions, analyzing brain function, and understanding their relationships. Compared to a single MRI modality, multimodal MRI data provide complementary tissue features that can be exploited by deep learning models, resulting in better segmentation results. However, multimodal mouse brain MRI data is often lacking, making automatic segmentation of mouse brain fine structure a very challenging task. To address this issue, it is necessary to fuse multimodal MRI data to produce distinguished contrasts in different brain structures. Hence, we propose a novel disentangled and contrastive GAN-based framework, named MouseGAN++, to synthesize multiple MR modalities from single ones in a structure-preserving manner, thus improving the segmentation performance by imputing missing modalities and multi-modality fusion. Our results demonstrate that the translation performance of our method outperforms the state-of-the-art methods. Using the subsequently learned modality-invariant information as well as the modality-translated images, MouseGAN++ can segment fine brain structures with averaged dice coefficients of 90.0% (T2w) and 87.9% (T1w), respectively, achieving around +10% performance improvement compared to the state-of-the-art algorithms. Our results demonstrate that MouseGAN++, as a simultaneous image synthesis and segmentation method, can be used to fuse cross-modality information in an unpaired manner and yield more robust performance in the absence of multimodal data. We release our method as a mouse brain structural segmentation tool for free academic usage at https://github.com/yu02019. |
3,893 | Concomitant Ruxolitinib and Ibrutinib for Graft-Versus-Host Disease (GVHD): The First Reported Use in Pediatric Patients | Allogeneic hematopoietic stem cell transplant (alloHSCT) can be a life-saving treatment for patients with hematological disorders but far too often carries the feared complication of graft-versus-host disease (GVHD). The first-line treatment of GVHD is typically corticosteroids, but steroid-refractory chronic GVHD (cGVHD) has led to the Food and Drug Administration (FDA) approval of ruxolitinib (Jakafi), ibrutinib (Imbruvica), and belumosudil (Rezurock). Patient 1 was a four-year-old female diagnosed with natural killer (NK) cell dysfunction who underwent alloHSCT with cells from a 9/10 National Marrow Donor Program (NMDP) donor and subsequently developed chronic GVHD (cGVHD) of the skin and gut. This cGVHD was refractory to steroids and ibrutinib but improved with the administration of concomitant ibrutinib and ruxolitinib. Patient 2 was a one-year-old male with sickle cell anemia. The patient was transplanted under a haploidentical protocol from the mother but developed bronchiolitis obliterans organizing pneumonia (BOOP) and pathology-confirmed GVHD. This cGVHD was steroid-refractory and resolved with the administration of concomitant ibrutinib and ruxolitinib. To our knowledge, this is the first reported use of concomitant ruxolitinib and ibrutinib in pediatric patients. The combination was well tolerated with no significant adverse events. Neither patient had to discontinue these drugs. We propose a further investigation into this dual therapy in cGVHD either compared to steroids or as a second-line option. |
3,894 | MS-RMAC: Multiscale Regional Maximum Activation of Convolutions for Image Retrieval | Recent works have demonstrated that image descriptors produced by convolutional feature maps provide state-of-the-art performance for image retrieval and classification problems. However, features from a single convolutional layer are not robust enough for shape deformation, scale variation, and heavy occlusion. In this letter, we present a simple and straightforward approach for extracting multiscale (MS) regional maximum activation of convolutions features from different layers of the convolutional neural network. And we also propose aggregating MS features into a single vector by a parameter-free hedge method for image retrieval. Extensive experimental results on three challenging benchmark datasets indicate that the proposed method achieved outstanding performance against state-of-the-art methods. |
3,895 | Appropriate points choosing for subspace learning over image classification | Dimension reduction techniques are very important, as high-dimensional data are ubiquitous in many real-world applications, especially in this era of big data. In this paper, we propose a novel supervised dimensionality reduction method, called appropriate points choosing based DAG-DNE (Apps-DAG-DNE). In Apps-DAG-DNE, we choose appropriate points to construct adjacency graphs, for example, it chooses nearest neighbors to construct inter-class graph, which can build a margin between samples if they belong to the different classes, and chooses farthest points to construct intra-class graph, which can establish relationships between remote samples if and only if they belong to the same class. Thus, Apps-DAG-DNE could find a good representation for original data. To investigate the performance of Apps-DAG-DNE, we compare it with the state-of-the-art dimensionality reduction methods on Caltech-Leaves and Yale datasets. Extensive experimental demonstrates that the proposed Apps-DAG-DNE outperforms other dimensionality reduction methods and achieves state-of-the-art performance for image classification. |
3,896 | PD-L1 expression on immune cells, but not on tumor cells, is a favorable prognostic factor for patients with intrahepatic cholangiocarcinoma | Cholangiocarcinoma, the second most common liver malignancy, after hepatocarcinoma is highly aggressive and usually diagnosed in advanced cases. In the era of personalized medicine, targeted therapy protocols are limited for cholangiocarcinoma and the only potential curative treatment, surgical resection, is seldom applicable.This retrospective study included all cases with pathology-confirmed intrahepatic cholangiocarcinoma admitted in a tertiary healthcare facility during a 10-year timeframe. Clinical information, laboratory values, imaging studies, and survival data were retrieved, and PD-L1 immunostaining was performed on representative pathology slides, for each case. From the total of 136 included cases (49 surgical resections and 87 liver biopsies), 38.97% showed PD-L1 positivity on tumoral cells, 34.8% on tumor infiltrating immune cells, 10.11% on epithelial cells within the peritumoral area and 15.95% on immune cells from the peritumoral area. Overall survival was significantly higher in the first two scenarios. However, after adjusting for age, tumor number, tumor size, and tumor differentiation in a multivariate analysis, only PD-L1 positivity on tumor infiltrating immune cells remained a favorable prognostic for survival. High immune cell counts also correlated with increased overall survival.Our study demonstrated that PD-1/PD-L1 checkpoint pathway in the microenvironment of intrahepatic cholangiocarcinoma bears prognostic significance. PD-L1 expression on immune cells, in both resection and biopsy specimens, might be a strong independent predictor for a favorable outcome. |
3,897 | Task-Specific Image Partitioning | Image partitioning is an important preprocessing step for many of the state-of-the-art algorithms used for performing high-level computer vision tasks. Typically, partitioning is conducted without regard to the task in hand. We propose a task-specific image partitioning framework to produce a region-based image representation that will lead to a higher task performance than that reached using any task-oblivious partitioning framework and existing supervised partitioning framework, albeit few in number. The proposed method partitions the image by means of correlation clustering, maximizing a linear discriminant function defined over a superpixel graph. The parameters of the discriminant function that define task-specific similarity/dissimilarity among superpixels are estimated based on structured support vector machine (S-SVM) using task-specific training data. The S-SVM learning leads to a better generalization ability while the construction of the superpixel graph used to define the discriminant function allows a rich set of features to be incorporated to improve discriminability and robustness. We evaluate the learned task-aware partitioning algorithms on three benchmark datasets. Results show that task-aware partitioning leads to better labeling performance than the partitioning computed by the state-of-the-art general-purpose and supervised partitioning algorithms. We believe that the task-specific image partitioning paradigm is widely applicable to improving performance in high-level image understanding tasks. |
3,898 | RAP-CLA: A Reconfigurable Approximate Carry Look-Ahead Adder | In this brief, we propose a fast yet energy-efficient reconfigurable approximate carry look-ahead adder (RAP-CLA). This adder has the ability of switching between the approximate and exact operating modes making it suitable for both error-resilient and exact applications. The structure, which is more area and power efficient than state-of-the-art reconfigurable approximate adders, is achieved by some modifications to the conventional carry look ahead adder (CLA). The efficacy of the proposed RAP-CLA adder is evaluated by comparing its characteristics to those of two state-of-the-art reconfigurable approximate adders as well as the conventional (exact) CLA in a 15 nm FinFET technology. The results reveal that, in the approximate operating mode, the proposed 32-bit adder provides up to 55% and 28% delay and power reductions compared to those of the exact CLA, respectively, at the cost of up to 35.16% error rate. It also provides up to 49% and 19% lower delay and power consumption, respectively, compared to other approximate adders considered in this brief. Finally, the effectiveness of the proposed adder on two image processing applications of smoothing and sharpening is demonstrated. |
3,899 | Ultra-wideband elliptical slot antenna fed by tapered microstrip line with U-shaped tuning stub | A printed elliptical slot antenna fed by tapered microstrip line with U-shaped tuning stub is proposed for ultra-wideband (UWB) applications in this paper. The design parameters for achieving optimal performance art investigated. The operation and characteristics of the proposed antenna are also analyzed. Good agreement is obtained between the simulation and the experiment. (c) 2005 Wiley Periodicals, Inc. |
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