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- Research article
- Open access
- Published:
Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation
BMC Medical Informatics and Decision Making volume 21, Article number: 152 (2021)
Mechanical Ventilation (MV) is a complex and central treatment process in the care of critically ill patients. It influences acid–base balance and can also cause prognostically relevant biotrauma by generating forces and liberating reactive oxygen species, negatively affecting outcomes. In this work we evaluate the use of a Recurrent Neural Network (RNN) modelling to predict outcomes of mechanically ventilated patients, using standard mechanical ventilation parameters.
We performed our analysis on VENTILA dataset, an observational, prospective, international, multi-centre study, performed to investigate the effect of baseline characteristics and management changes over time on the all-cause mortality rate in mechanically ventilated patients in ICU. Our cohort includes 12,596 adult patients older than 18, associated with 12,755 distinct admissions in ICUs across 37 countries and receiving invasive and non-invasive mechanical ventilation. We carry out four different analysis. Initially we select typical mechanical ventilation parameters and evaluate the machine learning model on both, the overall cohort and a subgroup of patients admitted with respiratory disorders. Furthermore, we carry out sensitivity analysis to evaluate whether inclusion of variables related to the function of other organs, improve the predictive performance of the model for both the overall cohort as well as the subgroup of patients with respiratory disorders.
Predictive performance of RNN-based model was higher with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.72 (± 0.01) and Average Precision (AP) of 0.57 (± 0.01) in comparison to RF and LR for the overall patient dataset. Higher predictive performance was recorded in the subgroup of patients admitted with respiratory disorders with AUC of 0.75 (± 0.02) and AP of 0.65 (± 0.03). Inclusion of function of other organs further improved the performance to AUC of 0.79 (± 0.01) and AP 0.68 (± 0.02) for the overall patient dataset and AUC of 0.79 (± 0.01) and AP 0.72 (± 0.02) for the subgroup with respiratory disorders.
The RNN-based model demonstrated better performance than RF and LR in patients in mechanical ventilation and its subgroup admitted with respiratory disorders. Clinical studies are needed to evaluate whether it impacts decision-making and patient outcomes.
Trial registration: NCT02731898 (https://clinicaltrials.gov/ct2/show/NCT02731898), prospectively registered on April 8, 2016.
In the field of medicine, the use of computer-based algorithms for aiding diagnostic as well as therapeutic decisions has become a highly popular matter of often controversial discussions, whereas the question whether Artificial Intelligence (AI) might replace physicians someday arises time and again [1]. Even though it seems unlikely that AI will ever fully replace professional health care workers, it is advantageous to use computing power to analyse "big data" for the benefit of the patients. To solve complex mathematical problems, Deep Learning (DL) methods based on recurrent neural networks are used nowadays, especially in problems with temporal dependencies. In RNN weighted input values get summated and repeatedly updated to generate an output which best reflects the outcome of interest [2]. Furthermore, a memory function is generated by recurrent feedback mechanisms. The Long Short-Term Memory model (LSTM) by Hochreiter and Schmidhuber solves complex tasks by a constant error flow ("constant error carousels") within memory cells with an opening and closing gate function, thereby enabling a quasi-sustained short-term memory [3]. Since their introduction, RNNs and especially LSTMs have been used for various tasks like handwriting recognition or speech recognition and in diverse healthcare applications [4]. Machine Learning (ML) already influences daily life more than we might be aware and it is indispensable for the technology industry.
In critical care medicine, the concept of ML for analysing complex and often highly heterogeneous patient collectives seems reasonable under various circumstances [5]. Different studies have evaluated the use of ML for the treatment of sepsis, assessing patient prognosis and/or risk for prolonged clinical courses and several other applications [6]. Regarding assessment of patients on mechanical ventilation and/ or prognostication of ICU-patients by AI, various studies were conducted that demonstrated that ML can be used as a prognostication tool for ICU-mortality [7, 8]. Parreco et al. were able to reliably identify patients at risk for tracheostomy and prolonged MV in their study on 20,262 ICU stays out of the MIMIC-III database [9]. Chen et al. were able to detect ventilator-associated pneumonia in patients on MV by using ML for the analysis of sensor arrays on exhaled breath samples [10]. Different other studies with promising results have been conducted in this field, which makes a future use of ML in clinical daily routine on the ICU likely.
We aim to investigate performance of these methods in a multi-centre cohort of patients in mechanical ventilation. In this investigation we rely on the VENTILA study group, a prospective, observational, international multi-centre cohort study that enrols patients on mechanical ventilation during a 28-day follow-up period. It comprises a large patient collective, generating a large amount of data and consequently rendering it suitable for the use of machine-learning methods. Mechanical ventilation is a complex and central treatment process in the care of critically ill patients. Not only represents a key element for treating respiratory insufficiency, but also significantly influences acid–base balance and can also cause prognostically relevant biotrauma by generating forces and liberating reactive oxygen species [11, 12]. It therefore represents a general outcome-relevant process for ICU patients. We aimed to evaluate the use of a LSTM-based model on a subgroup of mechanically ventilated critically ill patients out of the VENTILA study group to predict the outcome by using six standard mechanical ventilation parameters in our model. We follow STROBE guidelines [13] for reporting observational studies and provide the checklist of prediction model development and validation as a supplementary material.
Setting and data sources
VENTILA cohort dataset is a combination of four observational, prospective, international multi-center studies [14,15,16], performed to investigate the effect of baseline characteristics and management changes over time on the all-cause mortality rate in mechanically ventilated patients in ICU. VENTILA cohort includes adult patients older than 18, admitted to ICU receiving invasive (endotracheal tube or tracheostomy) and non-invasive (bilevel positive airway pressure (BIPAP) or continuous positive airway pressure (CPAP) with nasal or facial mask) mechanical ventilation for at least 12 and 1 h, respectively. Data recorded for all the patients included basic demographics, cause of requiring mechanical ventilation, the occurrence of complications, ICU and hospital discharge outcome and length of stay. Furthermore, daily collected data, resulting in a single average value, are recorded for variables such as arterial gases, mechanical ventilation parameters and variables related to the function of other organs. All the patients in this cohort study were followed for mortality and length-of-stay outcomes during the period of receiving mechanical ventilation, ICU stay, up to 28 days over one month period in 1998, 2004, 2010, and 2016. Only the investigative group members and research coordinators at each site were aware of the purpose and the precise timing of the study.
We evaluate our method on a sub-sample of VENTILA dataset containing data associated with 12,755 distinct hospital admissions for 12,596 adult patients (aged 18 years or above) admitted during one-month sample periods (in 2004, 2010 and 2016) from participating ICUs across 37 countries.
Study subjects
We retrospectively evaluated the model on the overall VENTILA dataset (n = 12,755) as well as a sub-group of VENTILA patients that were admitted with respiratory disorders, specifically COPD, Asthma, interstitial lung disorders, ARDS or Pneumonia (n = 2674). Mortality rate in this subgroup was 36% (n = 960), while the overall morality was 31% (n = 3935).
Statistical analysis and variable selection
We use mean and standard deviation to express continuous variables, while categorical variables are expressed as a percentage. No strong linear correlations were found between input variables and the target outcome, as shown in Fig. 1.
We carry out four different analysis. Initially we selected six mechanical ventilation parameters as input to our model, namely PaO2_FiO2, peak Pressure, plateau Pressure, applied PEEP, driving pressure and tidal Volume/PBW, as well as age and BMI. We derive the model based on these variables for both: (1) patients in the overall dataset and (2) a subgroup of patients admitted with respiratory disorders.
Furthermore, we carry out a sensitivity analyses to evaluate whether inclusion of variables related to the function of other organs, such as such as kidneys (creatinine) and liver (bilirubin) improves the predictive performance of the model. This analysis is carried out for both: the overall dataset as well as on the subgroup of patients with respiratory disorders.
Dataset pre-processing and missing values handling
Datasets were prepared for the analysis in several steps. Initially, outliers and noisy measurements were removed from the data by defining clinically valid intervals for each variable and considering out of interval values as missing values. Secondly, the Fill-Forward imputation methods were applied on each ICU-stay by forward propagating available values to use the nearest valid measurements. Since one of the most common reasons for missingness in ICU data is different frequency of measurements, using the nearest measured value becomes a suitable imputation strategy. Furthermore, imputation of variables completely missing during each ICU-stay was done using median of the variables in the training set. Finally, the data was normalized and scaled to have zero mean and unit variance such that variables with different scales can contribute equally to the analysis. The variable with the least missing values was ph (5.71%), while the highest was driving pressure (51.54%). The rest of the variables were as follows, PaCO2 (6.03%), appliedPEEP (7.97%), Creatinine (8.15%), PaO2_FiO2 (10.6%), tidalVolume (12.35%), tidalvolume/PBW (12.35%), peakPressure (17.4%), Bilirubin (25.94%), plateauPressure (51.33%).
Predictive performance metrics
We assess the performance of our models using a range of performance metrics, including area under the ROC curve (AUC), area under the precision-recall curve (AUPRC) (also known as average precision) as well as positive predictive value (PPV), negative predictive value (NPV). We use Mathews correlation coefficient (MCC) [17] to compare quality of binary classifications between the different algorithms [18]. Lastly, we also investigate how well our model is calibrated, by plotting observed survival probability versus the survival probability predicted by our model using the calibration curve.
Machine learning model
Our model uses ventilation parameters as an input to predict the likelihood of the patient dying. We restricted the analysis to include ventilation parameters only in order to evaluate their predictive power, while the rest of the variables from this dataset were excluded from the analysis. Patients were included if they had at the variables documented at least once, while the remaining patients were excluded.
In terms of machine learning algorithms we chose to evaluate Logistic Regression (LR) as the baseline model, Random Forest (RF), an ensemble of decision trees that has shown great performance in predicting clinical outcomes [19, 20] and the Long Short-Term Memory (LSTM) neural network [3], a type of Recurrent Neural Network (RNN). As RF and LR are unable to process sequences directly, we expanded each sequence into a single vector that was then fed to RF and LR. While there is some information loss in terms of timing of measurements, this approach attempts to minimise the loss, rendering the comparison as fair as possible between the algorithms.
The proposed LSTM network consist of one layer with 512 units, tanh activation function and Xavier normal weight initializer. Each patient record is classified in two possible outcomes using a SoftMax function in the output layer. We have also evaluated the Sigmoid function, but without discernible difference in the performance. Model derivation (training) is carried out for 150 epochs with batch size of 64 using binary cross entropy as loss function and Adam optimizer with learning rate 0.001. To ensure robustness and generalizability of the model we use a dropout layer with 0.5 and a custom L1 regularization layer with parameter of 0.0005. We use Dropout [21] to force the neural network to learn a more robust internal representation such that our model can generalise outcome predictions on data of future patients, while we use L1 regularisation method to reduce model complexity and susceptibility to overfitting, increasing generalisability.
The performance of each model was evaluated using stratified five-fold cross-validation with 10 times repetition. First, we split the data randomly into model derivation set (80%) and model validation set (20%). Then we built the model and tuned the hyper-parameters based on the validation set. We repeat five-fold cross-validation 10 times on the tuned model to reduce possible bias and evaluate generalisability, where for each run we calculate mean and standard deviation. The LSTM model is implemented using PyTorch [22] open source machine learning framework and we also used the scikit-learn software library for the non-RNN models implementation.
The overall dataset contained 12,755 ICU stays with complete data on ventilation parameters included in this study, where 3935 ICU stays were recorded as dead (30.85%). The respiratory disorders subgroup contained 2674 ICU stays with 960 ICU stays recorded as dead. The most common diagnosis for this subgroup was pneumonia (n = 1368) followed by COPD (n = 527), ARDS (n = 501), CPD_nonCOPD (n = 180) and asthma (n = 98). Survivors are compared to non-survivors for the overall patient dataset (Table 1(a)) as well as the subgroup of patients with respiratory disorders (Table 1(b)).
We compared our LSTM-based model with both random forrest and logistic regression methods. Predictive performance of LSTM-based model was higher with AUC of 0.72 and Average Precision (AP) of 0.57 in comparison to RF and LR for the overall patient dataset. Higher predictive performance of AUC of 0.75 and AP of 0.65 was recorded in the subgroup of patients admitted with respiratory disorders, as shown in Fig. 2a, b.
Other performance measures, such as PPV, NPV and MCC are detailed in Table 2(a) and (b), where LSTM-based model outperforms both RF and LR in the majority of performance metrics.
Sensitivity analysis
We also performed a sensitivity analysis to evaluate whether inclusion of function of other organs, such as kidney and liver could improve predictive performance of the model. This analysis was carried out for the overall dataset as well as for the subgroup of patients admitted with respiratory disorders, where we included creatinine and bilirubin variables as well as pH and PaCO2, in addition to the variables used for the main analysis. These variables were chosen based on the review of literature as well as suggestion from the clinicians.
As it can be seen from Fig. 3a, b, the inclusion of variables related to kidney and liver function increased predictive performance significantly with AUC of 0.79 and AP 0.68 for the overall patient dataset and AUC of 0.79 and AP 0.72, for the subgroup of patients with respiratory disorders. As shown in Table 3(a) and (b) LSTM based model outperforms RF and LR in the majority of performance metrics.
Variable importance and model interpretability
A common criticism of LSTM-based models in particular and neural-network models in general, is that they are regarded as black-box models [23, 24]. We sought to address this issue by conducting a model interpretability analysis, to understand how the model ranked the importance of variables when predicting mortality outcomes. We used the Integrated Gradients (IG) method whose objective is to illustrate the relationship between a model's prediction outcome and its' input variables [25]. IG method explains outcomes of the LSTM models based on the gradients of the prediction outcomes with respect to input variables. By computing attribution of each variables, it ranks all the variables based on their importance. The attribution values measure the effect of each feature relative to the prediction for a baseline, which in our case was set to zero. As a result, the top three ranked variables of the LSTM model were creatinine, PaO2_FiO2, and pH (for the overall patient dataset) and pH, appliedPEEP, and Bilirubin (for the subgroup of patients with respiratory disorders). A graphical representation of the variables for each model and their ranking is provided in Fig. 4.
Predictive model calibration
While the ability of the model to discriminate between patients at higher risk of having an event from those at lower risk is an important aspect, alone it is not sufficient. As such, we also consider model calibration, which measures how similar is the predicted absolute risk to the true observed risk in groups of patients. Poorly calibrated models will underestimate or overestimate the outcome of interest. As such we assess our model calibration by comparing predicted and observed risk of mortality at the whole patient population (mean calibration) as well as a subgroup of patients with respiratory disorders. As it can be seen in Fig. 5, all models achieved a very good calibration in predicting mortality risk for both the overall patient dataset and the subgroup of patients with respiratory disorders, even though models based on neural networks are typically poorly calibrated, as reported in the literature [26].
In this study a recurrent neural network-based model outperformed random forest and logistic regression models regarding outcome prognostication in a large cohort of mechanically ventilated, critically ill patients of the VENTILA study group when using six common ventilation parameters, extended by age and body mass index (BMI). Predictive performance was even increased when serum bilirubin (as a marker of intact liver function), serum creatinine (as a marker of unscathed kidney function), as well as pH and PaCO2 (as indicators of general metabolic and respiratory performance) were included.
pH, as the expression and common endpoint of both the metabolic and respiratory situation, was a relevant and potent predictor of outcome in both subgroups. In both subgroups, biomarkers for organ failure (bilirubin and creatinine, respectively) were relevant for the outcome of the patients evaluated. Also, in the subgroup of primarily pulmonary patients the classical ventilation parameters were not the singularly decisive parameters for the outcome, although the respiratory parameters were present in a relatively high granularity. Ultimately, however, this is not surprising, as even in critical patients with initial respiratory problems, a systemic cascade of inflammation and stress is set in motion that transcends the underlying pulmonological starting point.
Machine learning is a promising approach for multiple applications in modern medicine. Especially in critical care, studies with a vast number of patients are commonly not available for certain disease entities. Computer-based approaches expand our possibilities by facilitating the use of highly complex models with lots of different parameters and can therefore aid in complex clinical decision-making [1, 6, 27,28,29]. Of course, the cat bites its tail here, as ML benefits especially from large databases with significant patient numbers to exclude noisy data. Albeit the motto "the more, the better" certainly applies here, Shillan et al. were able to show that even for study sizes with 1000–10,000 individuals, satisfactory forecasts with AUCs around 0.83 can be achieved [30]. As demonstrated in our study, the use of additional variables can improve test performance and enable high prognostic accuracy in comparatively medium-sized patient collectives.
An association between the invasiveness of mechanical ventilation and/or oxygenation indices (especially the P/F value or Horowitz index) and mortality has been shown repeatedly in the past [14]. Different ventilatory parameters were found to be associated with mortality in previous studies. High driving pressures and tidal volumes, as well as low oxygenation indices were shown to be associated with higher mortality in mechanically ventilated patients, especially in individuals with acute respiratory distress syndrome (ARDS) in multiple previous studies [31,32,33,34,35,36]. It therefore seemed reasonable to combine various key ventilation parameters as mortality predictors in our ML model. Albeit mortality has decreased over time, higher age is a known predictor of worse outcome in mechanically ventilated patients, whereas low BMI-values were associated with decreased survival in the past [37, 38]. Hence it seemed rational to additionally incorporate such common, but outcome-relevant general patient characteristics into the model. As pulmonary performance is already indirectly covered by ventilation settings, inclusion of further non-ventilator associated, but vital organ function parameters seemed reasonable (namely serum creatinine and bilirubin). Although affected by ventilation strategies and certainly also kidney function, blood pH as a marker of metabolic integrity and partial pressure of carbon dioxide (paCO2) as an indicator of satisfactory ventilation were also included in our model and yielded an even higher predictive performance. It should be mentioned that several challenges could have affected the performance of the model, including the heterogeneity of data, given the multi-centre nature of the dataset spanning across diverse ICUs and countries; consequently, the heterogeneity of data collection protocols; and the averaging of ventilation parameters into a single daily value.
As already stated in the past, ML algorithms often lack transparency compared with conventional statistical analyses as they are not as reproducible for most external readers [30]. Nevertheless, considering the possibilities of our time, it seems reasonable trying to integrate them into our clinical practice in order to reflect our decisions in a sober light, based on different algorithms irrespective of gut feeling or other personal bias. Replacing medical specialists with artificial intelligence is certainly not the right way to go. Nevertheless, it seems rational, to reflect and analyse complex situations independently based on measurable (hard) criteria and therefore be able to make even better decisions for our patients in future.
Theoretically, and in this context, we consider our study to be theses-generating, an algorithm could not only serve to predict the outcome based on ventilation parameters. Rather, an attempt could be made to explore an optimal ventilation strategy on the basis of large data sets. An algorithm would also be conceivable, which as a (nearly) closed loop suggests ventilation parameters adapted to the individual situation, based on ventilation parameters, but also biomarkers and possibly other clinical and radiological variables, to the clinician. However, this is in any case beyond our data and analysis. Ultimately, our analysis can also be seen as a "word of caution" in this context: the high value of biomarkers (bilirubin and creatinine) underlines the relevance of a holistic approach. Therefore, despite all the enthusiasm for digital revolutions, it is important never to forget clinical relevance and practicability. In addition to these rather pragmatic considerations, ethical considerations regarding the use of AI in everyday clinical practice are also highly relevant. Algorithms, especially those that have a relatively direct influence on therapy, must be subjected to a critical, evidence-based evaluation—i.e. randomized clinical studies—in analogy to medical production.
Firstly, this is a retrospective study lacking a randomisation process, prospective screening, and inclusion of patients and a control group, therefore this study can only be thesis-generating. Secondly no specific protocol for the collection of predictive variables (e.g., specific timespan and/or clinical situations when to document MV parameters) was applied, which could further dispose of the study to selection bias as well as imputation strategy for the missing data. Lastly, it should be noted that LR and RF algorithms have not been designed to process the sequences directly, in contrast to LSTM, which may explain the difference in performance between these algorithms.
The result of our analysis has shown that the RNN-based model demonstrated better performance than RF and LR in patients in mechanical ventilation and its subgroup admitted with respiratory disorders. However, it is necessary to validate our results in further studies. We speculate that a dataset with higher granularity—for example, more closely timed records—could lead to an even higher predictive power of AI. The next step would then be to develop algorithms that not only seek to predict outcome, but also suggest alternative ventilation parameters based on prior data, and, for example, seek to ensure even better use and application of evidence-based treatment strategies such as low driving pressure ventilation. If, in a next step, these suggestions—for example, in randomized trials—are associated with a survival benefit for our patients, then a further step would be the development of "closed loop" ventilation systems that seek to optimize the ventilation of critically ill patients on the basis of collected parameters and within evidence-based limits. However, this is currently to be classified as a theoretical possibility and we recall that the strict standards of evidence-based medicine must also be applied to AI—any algorithms must also prove their efficacy and safety in randomized trials. Medical ethics and legal issues must also be evaluated and discussed with all stakeholders at an early stage—how much control are physicians willing and able to relinquish, how much automated treatment are patients prepared to receive? These are interesting issues that currently remain unresolved and as a consequence we consider our study to be thesis generating.
Availability of data and materials
The data that support the findings of this study are available from the VENTILA Study Group, but restrictions apply to the availability of these data and so are not publicly available. Data are however available from the authors upon reasonable request.
Average Precision
Acute Respiratory Distress Syndrome
Area under the curve
Area under the precision recall curve
Body mass index
Chronic obstructive pulmonary disease
Deep Learning
Graphical processing unit
Integrated gradient
- LoS:
Length of stay
Logistic Regression
Long short-term memory
Matthews correlation coefficient
Medical Information Mart for Intensive Care
Mechanical ventilation
Negative Predictive Value
Predicted body weight
Positive end-expiratory pressure
Positive Predictive Value
Random Forest
Recurrent Neural Network
Receiver Operating Characteristics
Simplified Acute Physiology Score
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Ranieri VM, Rubenfeld GD, Thompson BT, et al. Acute respiratory distress syndrome: The Berlin definition. JAMA. 2012;307(23):2526–33. https://doi.org/10.1001/jama.2012.5669.
Davies K, Bourdeaux C, Peiris T, Gould T. Oxygenation index outperforms the P/F ratio for mortality prediction. Crit Care. 2014;18(Suppl 1):266. https://doi.org/10.1186/cc13456.
Neto AS, et al. Lung-protective ventilation with low tidal volumes and the occurrence of pulmonary complications in patients without acute respiratory distress syndrome. Crit Care Med. 2015;43(10):2155–63. https://doi.org/10.1097/CCM.0000000000001189.
Amato MBP, et al. Driving pressure and survival in the acute respiratory distress syndrome. N Engl J Med. 2015;372(8):747–55. https://doi.org/10.1056/NEJMsa1410639.
Aoyama H, Pettenuzzo T, Aoyama K, Pinto R, Englesakis M, Fan E. Association of driving pressure with mortality among ventilated patients with acute respiratory distress syndrome. Crit Care Med. 2018;46(2):300–6. https://doi.org/10.1097/CCM.0000000000002838.
Lerolle N, et al. Increased intensity of treatment and decreased mortality in elderly patients in an intensive care unit over a decade*. Crit Care Med. 2010;38(1):59–64. https://doi.org/10.1097/CCM.0b013e3181b088ec.
Zhao Y, Li Z, Yang T, Wang M, Xi X. Is body mass index associated with outcomes of mechanically ventilated adult patients in intensive critical units? A systematic review and meta-analysis. PLoS ONE. 2018;13(6):e0198669. https://doi.org/10.1371/journal.pone.0198669.
We acknowledge the support of the VENTILA study group.
Open Access funding enabled and organized by Projekt DEAL.
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Authors and Affiliations
CJ, BW, BM and VO conceived the presented idea. BM and VO developed the theory and performed the computations. VO, BW, CJ and BM led the drafting of the manuscript. CJ, BW and VO supervised the findings of this work. FF, OP, RR, KR, AM, BD, AW, FR, MG, LD, MD, BV, MA, NN, SM, AB, MK, RR, PA, NC, GY, FA, MJ, DM, AA, YS, AA, BW, AE and CJ contributed to data collection. All authors read and approved the final manuscript.
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A study protocol was provided to participating centres. Ethics Committees of each participating institution approved the protocol, a waiver of informed consent was obtained according to local regulations. Participating centres in this trial are available from the trial registration website https://clinicaltrials.gov/ct2/show/NCT02731898.
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The authors declare that they have no competing interests.
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Cite this article
Mamandipoor, B., Frutos-Vivar, F., Peñuelas, O. et al. Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation. BMC Med Inform Decis Mak 21, 152 (2021). https://doi.org/10.1186/s12911-021-01506-w
DOI: https://doi.org/10.1186/s12911-021-01506-w

subset: commoncrawl


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Craig Gillespie directed this true story about "the most daring rescue mission in the history of the U.S. Coast Guard."
Watch USC Annenberg Media's live State of the Union recap and analysis here.
Matthew Kenney specializes in raw foods, making nut-based cheese the star of many appetizer platters (@MKlifestyle/Twitter).
Make even the pickiest eater happy with these nine restaurants that accommodate every allergy.
L.A.'s guide to the city's best cookies.


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I've been in the process of mirroring a page for over a day. Granted it has a rather large forum, but this seems a bit ridiculous to me as I have 10 connections available and its only using one to test the links instead of testing and grabbing at the same time. Is there any way to speed this up?
Luge Harlow
Xavier Roche
Created with FORUM 2.0.11


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Windows 11 is a new version of Microsoft Windows that no one saw coming. Windows 10 was supposed to be the last of every Windows with lifetime support from Microsoft and no expiration date. But to everyone's surprise, Windows 11 was unveiled and it did not receive much praise due to a new learning curve added to the UI interface, its obvious bugginess, and the feeling of an incomplete feature set.
For gamers though, Windows 11 is a huge upgrade in many aspects when compared to Windows 10. You gain noticeable upgrades in performance and a boost in battery life for laptops. To justify this, We have compiled a list of reasons as to why you should upgrade your pc to Windows 11 right now:
Windows 11 is a step up for gaming
One of the main features that Windows 11 provides is the removal of historical clutters unnecessary code bits of previous Windows versions. It also brings new gaming technologies to PC only available to Xbox consoles. The new features give small to big improvements even though no optimization has happened for present games to fully utilize the raw potential of Windows 11.
Gaming consoles had a giant leap in their performance due to a technology called DirectStorage. How it works is simple, the graphics card directly loads assets from the SSD drastically reducing loading time and increasing in-game performance.
Up until now, PC games had to add transition phases to the games to load the next parts of the game to provide an illusion of seamless transition since it is unable to take advantage of the speeds of the SSD. Windows 11 brings the technology necessary called DirectStorage. It's a feature of the operating system that lets the GPU accelerate transfer speeds of assets between itself and the SSD relieving the CPU of that burden, which means more CPU power for gaming hence more FPS.
Auto-HDR for Existing Games
This feature adds HDR to games that only support SDR. Using some fancy computer magic it converts SDR content into HDR-like content. It might not be like the actual HDR content but will make the content close to HDR giving a burst of clarity to SDR. Games will pop out more than normal and provide more immersion than what was possible before due to this feature\
Improved Performance and Efficiency
Windows 10 includes a "Game mode" which promises to boost the performance of PC games. But the prioritization and task management are unpolished and the performance and efficiency gained is lackluster.
Windows 11 learns for it and improves the "Game mode" significantly. Now, Windows 11's deprioritizes non-game processes to a greater degree to get the most out of PC hardware. While not perfect like consoles, it will provide necessary improvements to Improve the Performance and Efficiency of your system.
Next-generation CPU Support
The newest Intel chips contain a Big-Little style chip design called hybrid architecture. This means intel chips have performance cores and efficiency cores. The performance cores deal with heavy tasks and the efficiency cores deal with background and light tasks. Windows 11 have a special schedular that is more productive and reliable at distributing tasks for better performance and efficiency. For gaming applications, every performance core will be prioritized to gaming while background tasks will be handled by efficiency cores.
This feature is only applicable to 12th gen intel CPUs.
So, what are you waiting for? If you are a hardcore gamer then feel free to upgrade to Windows 11 to enjoy all these exciting features.


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Last updated: 20 Mar 2013 Update History
Report Status: Archived
Report Pages: 140
Analyst: Paul Kwon
Publication Overview
The countries covered in this report include: Bahrain, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Syria, Turkey, United Arab Emirates, Yemen.
Researcher:- Paul Kwon
Current publication date:- March 2013 (11th Edition)
Executive Summary
The Middle East is no different from the rest of the world when it comes to digital media and broadband demand as the young and tech savvy are big consumers of content. Driving this usage pattern is demographics as an estimated 50% of the population is under the age of 30.
Until the focus on broadband by mobile operators strong competition was lacking in many of the region's broadband markets due to non existent or immature network access regulatory or unfavourable economics to support development of a Greenfield operator to engage in extensive infrastructure-based competition. However a number of countries, particularly in the Gulf, did commence building sophisticated broadband access infrastructure. Examples include Qatar's national broadband network and the FttH network deployment undertaken by UAE's Etisalat.
Underpinning mobile broadband growth is the need by mobile operators to develop new revenue growth opportunities as mobile voice markets reach maturity. In many instances the downlink speeds advertised by the mobile operators which have deployed HSPA or LTE networks is higher that offered by competing fixed broadband offerings.
The Middle East's growing Internet user base is driving demand for Arabic content, with entertainment content a key area of focus for end users. The lack of Arabic language support by some content providers and the need to translate existing content into Arabic has led to a number of initiatives to create the necessary content. In some instances entrepreneurs have instead targeted content offerings towards Middle Eastern Diaspora communities in Europe or the United States, where affordable broadband allows consumption of high bandwidth content such as streaming Video-on-Demand.
ICANN's move to allow non-Latin script to be used in web addresses has allowed further localisation of content, increasing accessibility.
Typical subscription and advertising-based revenue models in the digital media market face a number of challenges in the Middle East; including the lack of reliable measuring system for media as well as underdeveloped e-commerce and online payment infrastructure.
In the broadcasting space, telecom operators are emulating their global counterparts by launching IPTV offerings, which in many instances face tough competition from the region's flourishing Direct-to-Home (DTH) satellite TV sector and a number of Middle East-based VoD platforms.
Data in this report is the latest available at the time of preparation and may not be for the current year.
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Our Blog
The Inside Scoop on Speech Therapy & Language Development
Does My Child Need Speech Therapy? A Guide For Parents from a Mom and SLP
As parents, we live with a never-ending litany of worries about our kids: Are they eating enough vegetables? Getting too much screen time? On track for their developmental milestones? As both a mom and an SLP, I have those same concerns, and while I can't help a child learn to love brussel sprouts or to dislike cartoons, I can help you recognize if your child's speech and language development is on track.
The Overlap of Occupational Therapy and Speech-Language Pathology
Twin sisters, in the related fields of OT and SLP share how they collaborate when working with children to facilitate development across many domains.
The SLP Holiday Gift Guide
Have you ever wondered what your SLP would buy as a holiday gift for a child? Check out this round-up of holiday gift guides for lots of wonderful suggestions.
Thanksgiving Books
This season marks change as we head toward the new year. Books often allow us to expose kids to ideas and environments they may not encounter on a daily basis. That is why I love using Thanksgiving books to support my clients during this time.
Social Skills Goals
There are many different areas of speech and language development we work on as SLPs. In my work, I see many children with needs in building their expressive language skills and speech sound production. But SLPs work on receptive language, social language (aka...
How to Implement Core Vocabulary in Your Therapy
Core vocabulary is defined as a small set of simple words, in any language,
that are used frequently and across contexts (Cross, Baker, Klotz & Badman, 1997). Core vocabulary makes up about 80% of the words we use throughout the day.
Joint Attention
We need to establish social skills like joint attention, eye contact, and use of gestures before we can anticipate a child to use language.
Where is the Bear? Tools for Teaching Prepositions
Teaching prepositions is no small task. They do not fit some of the typical rules for grammar which can make it more complicated for children to understand. That's why we need to get our kids up and moving to teach this complicated concept!
Playing With Purpose: a Dollhouse
A dollhouse is a perfect choice of toy–ripe with opportunities for creativity to blossom and speech and language skills to grow.
Six Steps to Bilingual Success!
As a speech-language pathologist currently working in the USA, I am often asked by parents if
they should only speak English, as to not confuse their child. I reassure them that research
show whichever level a child can speak and understand in one language, they can speak and
understand a second (or even third!) language to the same level.
Start Playing With Purpose
Learn how to purposefully and intentionally interact with your child during play and help them increase opportunities for speech and language development with our Playing with Purpose book!


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Glide comes packed full of features and more get added with every update. It features an extensive list of editable options via the Appearance panel, flickr stream, recent posts, fully responsive videos, slider and images.
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The Welsh Government has agreed to delay a new target for GP practices to answer 90% of calls within two minutes after concerns were raised about the impact during the pandemic.
The target was due to be introduced from April as part of the GP contract, but has been pushed back for a year following negotiations with BMA Wales' GP committee, which labelled it 'unachievable'.
BMA Wales said it told the Welsh Government that increased telephone traffic during Covid-19, the Covid vaccination programme and the reintroduction of shielding all meant GP practices would not be able to meet the target this year.
The contract previously set a goal for GP practices to answer 90% of calls within two minutes of the recorded message ending, and said the wider aim was to reduce patient waiting times, and that 'no patient should need to ring multiple times in order to make contact with a practice'.
A Welsh Government spokesperson told Pulse: 'After consultation, we have agreed not to monitor the access standard, which requires telephone calls to be answered within two minutes.
'Practices will not be penalised for not meeting this standard while they handle high volumes of calls from people with queries about the vaccination process.'
The target will instead come into effect in April 2021.
GPC Wales chair Dr Phil White said: 'Given the unprecedented pressures on general practice and the wider NHS – including delivering the largest vaccination campaign in history – now is not the time to introduce major contractual changes. What's needed is stability, reassurance and support, allowing GPs and their teams to continue to support their local communities.
'We will continue discussions with the Welsh Government in the months to come, ensuring any changes later in the year deliver the best for general practice and their patients – as we try to rebuild and restore services after the pandemic.'
A study in 2019 commissioned by the Welsh Government found that patients were finding it difficult to get on-the-day GP appointments because of 'lengthy delays' when calling their GP practices.


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Why reps are not using their digital sales aids … and how to inspire them to change
By Angela Tenuta and Wendy Blackburn, executive VPs at Intouch Solutions
At Intouch we hold a number of truths to be self-evident, but perhaps the most self-evident of the lot is this: that digital sales aids only work if sales reps actually use them.
Seems pretty obvious, right?
The trouble is, not everyone does use them.
How do we know? Companies were coming to us and telling us. Over a 12-month period we tracked utilization data from 23 sales force apps across 11 brands. We found that app usage by sales reps is about 71 percent per month – i.e., 71 percent of reps will use a given digital sales aid in the course of a given month. Or, put the opposite way, 29 percent won't. And digital sales aid use declines by nearly half – 46 percent – on average from launch to six months out.
Given the amount of time and effort that goes into building each digital sales aid app, and the fact that new prescriptions correlate fairly closely with app use (another finding in our study), those numbers ought to make any good brand manager grind her teeth.
Why the decline? In parallel with the digital sales aid tracking, we also surveyed the health care practitioners who were on the other end of all these sales presentations, and found that about half of them were seeing the same content being presented more than once by the same sales rep. And two-thirds of HCPs reported that the content in the digital sales aids they saw was not customized to their practice or their patients.
We also found some startling gaps between the percentages of HCPs who were exposed to particular kinds of content and the percentages who want to be exposed to said content. For example, while the percentage of digital sales presentations that include drug comparisons rose by 23 percent in the past four years, the percentage of HCPs who want to see drug comparisons exceeded the percentage who actually got to see them by 35 percent. These gaps were similar for MOA videos – 37 percent – and patient education print materials and videos – 34 percent.
Given all this, the rapid decline in usage of digital sales aids isn't all that surprising. Why would a sales rep continue to use a digital sales aid when it's just repeating content from the last call and doesn't include the information the HCP wants to see?
The good news is that fixing these problems isn't going to require a Congressional investigation, or even a focus group. We asked the reps and the HCPs themselves how to improve the situation, and they told us.
1. Give HCPs what they want.
And what do they want? As mentioned above, they want more drug comparisons, more MOA videos, and more patient education materials. Also, according to our survey, they want less clutter and more graphics and animations. They want clinical relevance and brevity, not "talking points." They want materials that can be digested later at leisure, when no sales reps are hovering. So DSA developers need to thin out all the verbiage and thicken the rich content, and they need to bake in "download later" and instant email capabilities.
2. Give reps what they want.
And what do they want? They want a broad range of flexible tools that are easy to select and customize for each visit. Like the HCPs, they want rich content and less clutter. And they want an app that a normal human without a degree in computer science can use effectively while under time pressure. Usability is just as important as content, but far too many reps in our survey complained of inconsistent design, trouble finding the right place to tap, missing links, required gestures that are too complex, response latency, and other usability headaches.
Strong app designers who inherently understand usability will deliver apps that won't stress out your reps.
3. Give it to them more frequently.
If you remember nothing else from this discussion of digital sales aids, remember this: nothing enhances digital sales aid app usage rates more than updates. According to our research, apps that are updated only once during the course of a year have a usage rate of just 39 percent, but two updates per year push that usage rate up to 55 percent, and three or more drives it to 74 percent. This should not come as a surprise to anyone – after all, what physician wants to see the same content over and over again? But plenty of brands out there in the wild are still updating annually or biannually – likely a habit carried over from the print-it-and-forget-it days.
So in order for a digital sales aid to continue to be effective beyond the first two months or so – the point at which reps usually run out of content from the core app and HCPs start asking, "Can you show me something new?" – the brand team has to start adding what we call "boosts."
What is a boost? It's a new app component that helps augment the brand's core clinical story – what the HCPs saw in the first two months – with something a little more interactive and responsive, often tied to milestones like medical conferences or health awareness months, or to regional or national sales meetings. Boost applications can be compartmentalized to areas like MOA, interactive visualizations of patient responses to therapy, convention events, timely product updates, new drug comparison data, patient education materials – basically anything out there that can serve to augment the clinical tale that appeared on day one, and that takes an angle that wouldn't be expected on the first visit but might serve to keep a HCP's attention on the third or seventh or tenth.
For example, a brand came out with a new formulation and new treatment regimen. Neurologists weren't used to working out the proper dosage or explaining the new regimen to patients. So we created a simple interactive tool that reps could share with HCPs to help them calculate the right dosing based on inputs such as patient weight. It also provided talking points for counseling patients on the new regimen.
It is important to note that this boost was planned as part of the sales calendar, not just ad hoc based on some fortuitous occurrence. The brand team knew well in advance that the new formulation would be coming, so the boost was built into the app deployment calendar from the beginning. While unexpected but useful surprises may always pop up, the timing, content, and deployment of boosts should be a part of the app development conversation from the very start. Or, put another way, on January 1st when your digital sales aid launches you should already know what new features will be appearing in it on March 1st, May 1st, and July 1st and should be hard at work building those features, if you haven't built them already.
Bottom line – building a strong digital sales aid isn't a one-time activity, or even a once-a-year activity. If you set it and forget it for more than a month or two, your reps will do the same. Doing DSAs right means having long-term strategy like a chess grandmaster, knowing what the next ten moves will be and having them ready when the time comes. And those aids need to do a better job of fulfilling the needs and expectations of actual sales reps and physicians; they need to stop looking like print materials moved to a digital medium and start looking like every other component of modern digital life, with rich media and interactive tools and frequent updates and consumer-grade usability. Do all that, and your DSA might do more than just win awards; it might actually boost sales.


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151 High Street, Prahran
Currently occupied by a heritage listed, two-storey brick building built in 1891, and altered in 1937 to accommodate retail frontage to High street, this former Sunday School has served as an important landmark to the community of Prahran.
The proposed development will create two distinct forms, maintaining the identity and history of the existing building and creating a new identity for the addition that will house 45 apartments over five floors. The unique architectural language and striking geometry of the gothic design has strongly informed our design of the residences which have a contemporary feel and boast all the luxuries and amenities today's discerning professional aspires to.
The south and east heritage façades of the Gothic Revival building will be conserved and utilised as the primary entrance point to the development, providing ground and first floor commercial offerings and community amenity. the existing building is translated into the new addition, drawing on form, geometry and composition to sympathetically stitch the old to the new. The new building is deliberately conceived as a singular addition to minimise visual clutter and celebrate the purity of both components. Sympathetic material choices celebrate the craftsmanship and design of both buildings.


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Trehalase (EC 220.127.116.11) hydrolyzes the main haemolymph sugar of insects, trehalose, into the essential cellular substrate glucose. Trehalase in locust flight muscle is bound to membranes that appear in the microsomal fraction upon tissue fractionation, but the exact location in vivo has remained elusive. Trehalase has been proposed to be regulated by a novel type of activity control that is based on the reversible transformation of a latent (inactive) form into an overt (active) form. Most trehalase activity from saline-injected controls was membrane-bound (95%) and comprised an overt form (∼25%) and a latent form (75%). Latent trehalase could be assayed only after the integrity of membranes had been destroyed. Trehazolin, a potent tight-binding inhibitor of trehalase, is confined to the extracellular space and has been used as a tool to gather information on the relationship between latent and overt trehalase. Trehazolin was injected into the haemolymph of locusts, and the trehalase activity of the flight muscle was determined at different times over a 30-day period. Total trehalase activity in locust flight muscle was markedly inhibited during the first half of the interval, but reappeared during the second half. Inhibition of the overt form preceded inhibition of the latent form, and the time course suggested a reversible precursor–product relation (cycling) between the two forms. The results support the working hypothesis that trehalase functions as an ectoenzyme, the activity of which is regulated by reversible transformation of latent into overt trehalase.
Glucose is an essential substrate for energy metabolism in animals. In vertebrates, it is the principal sugar in the blood, which serves to distribute it to the various organs of the body. In most insects, however, glucose is a minor compound in the haemolymph, whereas the disaccharide trehalose is the main blood sugar (for review, see Wyatt, 1967; Steele, 1985; Becker et al., 1996; Candy et al., 1997; Elbein et al., 2003; Thompson, 2003). The rationale for this is as follows.
Insects have an open circulatory system, i.e. their organs are bathed in the haemolymph, from which substrates diffuse into the tissues. The efficacy of diffusion can be improved by increasing the concentration of substrates, and this seems necessary to meet metabolic demands, which can be extremely high in insects, especially when they engage in flight (for reviews, see Kammer and Heinrich, 1978; Wegener, 1996). High concentrations of haemolymph glucose would be critical because glucose is rather toxic, owing to its tendency to react non-enzymatically with proteins. Protein glycation is a major factor in the pathology of diabetes (Cohen, 1986). In insects, this problem does not arise because the two glucose units in trehalose are combined in an α-1,1-α-link, thus eliminating their reactive groups. Trehalose is produced from nutritional carbohydrate or stored glycogen by the fat body and released into the haemolymph. This metabolic detour is costly, as trehalose synthesis from glucose requires ATP and UTP (see Candy et al., 1997). Given the parsimony principle of nature, this again indicates the physiological significance of trehalose.
In locusts, haemolymph trehalose is a main store of carbohydrate and an important substrate for flight. Catabolism of trehalose is low in the flight muscle of resting locusts yet swiftly increased upon flight, and is again reduced when, during prolonged flight, the insects switch to oxidizing fat as the main substrate (for reviews, see Jutsum and Goldsworthy, 1976; van der Horst et al., 1978; Wegener, 1996; Mentel et al., 2003). To be used in muscle metabolism, trehalose must be hydrolyzed into glucose by trehalase (EC 220.127.116.11). The potential activity of trehalase in the flight muscle of locusts is high and the reaction is strongly exergonic, so that the activity of trehalase in flight muscle must be strictly controlled in a reversible manner, lest all available trehalose be split. How trehalase activity is regulated in locust flight muscle is still unknown, as none of the established mechanisms for the control of enzyme activities seems to operate (Worm, 1981; Vaandrager et al., 1989; Becker et al., 1996; Wegener et al., 2003; Liebl et al., 2010).
Trehalase in the flight muscle of locusts and other insects with synchronous flight muscles is bound to membranes that appear in the microsomal fraction upon tissue fractionation (Gussin and Wyatt, 1965; Worm, 1981; Vaandrager, 1989), but the exact cellular localization has not yet been established. However, it has been observed that trehalase activity in homogenates of flight muscles from cockroaches, moths and locusts (Zebe and McShan, 1959; Gussin and Wyatt, 1965; Gilby et al., 1967; Candy, 1974) is increased after the disruption of membrane integrity, e.g. by detergents. According to this observation, various forms of trehalase activity can be differentiated. Trehalase activity that can be measured directly in tissue homogenates by merely adding the substrate trehalose is designated as overt trehalase activity. Overt trehalase activity comprises soluble trehalase (i.e. trehalase activity in the supernatant after centrifugation at 100,000 g for 60 min) and bound (particulate) overt trehalase activity that is in the sediment after 100,000 g centrifugation. Detergents have no effect on soluble trehalase activity, but bring about an increase in trehalase activity of the sediment. This additional activity is called latent trehalase activity. Bound overt trehalase plus latent trehalase make up total bound trehalase activity. The latter activity plus soluble activity accounts for total trehalase activity (for details see Materials and methods and Discussion). The sources and physiological functions of the different forms of trehalase activity remain unknown.
Interest in trehalase was boosted when very potent and highly specific competitive inhibitors of trehalase were discovered and applied to insects. These inhibitors, such as trehazolin (Ando et al., 1991) or validoxylamine A (Asano et al., 1990; Asano, 2003), are analogues of trehalose of bacterial origin that bind tightly to trehalase. When injected into insects, these inhibitors affect motor activity, feeding, metabolism, growth, development, reproduction and flight (Kono et al., 1993; Kono et al., 1994a; Kono et al., 1994b; Ando et al., 1995a; Tanaka et al., 1998), thus indicating that trehalose metabolism is involved in many aspects of insect physiology. The inhibitors interfere specifically with the hydrolysis of trehalose (Ando et al., 1995b) but apparently do not block its production and release into the haemolymph by the fat body (Kono et al., 1995; Wegener et al., 2003; Liebl et al., 2010). The prominent inhibitor-induced increase in haemolymph trehalose was noticed early and has been reported repeatedly (Kono et al., 1993; Kono et al., 1999). The precipitous decrease in haemolymph glucose was later discovered as a spectacular metabolic effect that appeared to be a crucial factor in trehazolin toxicity in locusts (Wegener et al., 2003).
Trehalase inhibitors seem to have long-lasting effects on metabolism, suggesting that trehalase activity might also be reduced for considerable time spans. This has recently been confirmed for total trehalase activity in locust flight muscle (Liebl et al., 2010). Because trehalase in locust flight muscle homogenates appears in different forms – soluble, overt and latent – with yet unknown locations and functions in vivo, a detailed study on the long-term effects of a trehalase inhibitor on the various forms of the enzyme seemed desirable. It was expected that the inhibitory effects on the trehalase system of flight muscle would indicate possible functions of the different forms of trehalase in intact locusts and also shed light on possible relationships between them. For these reasons, locusts were injected with a sublethal dose of trehazolin and the following questions were addressed: (1) How are the overt and latent forms of trehalase affected by trehazolin? (2) What are the time courses of inhibition and reappearance of total as well as overt and latent trehalase activities?
The results of the present study indicate that trehazolin irreversibly inhibits trehalase activity in locust flight muscle. They further suggest a reversible precursor–product relationship between latent and overt trehalase activity in locust flight muscle that could be pivotal in the regulation of trehalase activity in vivo. The mechanism of control appears to be based on reversibly sequestering the enzyme from its substrate. In essence, trehalase in locust flight muscle could represent a novel type of reversible regulation of enzyme activity.
Treatment of locusts
Adult male locusts (Locusta migratoria, L.; Orthoptera), approximately 2 weeks after their final moult, were purchased from commercial suppliers and kept under crowded conditions as previously described (Wegener et al., 2003; Liebl et al., 2010). The haemolymph of the locusts was injected with 20 μg of trehazolin (experimental) in 5 μl of Hoyle's saline (130 mmol l–1 NaCl, 10 mmol l–1 KCl, 2 mmol l–1 MgCl2, 2 mmol l–1 CaCl2, 4 mmol l–1 NaHCO3, 6 mmol l–1 Na2HPO4) (Hoyle, 1953) or with 5 μl saline (control). To this end, the needle of a 10 μl Hamilton syringe was inserted into the thoracic cavity through the soft membrane at the base of the right hind leg. The locusts were then kept under crowded conditions as before and provided with food and water ad libitum. Three trehazolin-injected experimental locusts were sacrificed and analyzed per day during the first 10 days of a 30-day period. Thereafter, analyses were performed at 13, 22, 24 and 30 days post-injection. Some of the injected locusts died during the course of the experiments, but mortality did not increase noticeably (compared with untreated locusts of the same batch).
Fractionation of locust flight muscle
In order to characterize the effects of trehazolin on the different forms of trehalase activity, locust flight muscle was fractionated by homogenization, centrifugation and treatment with detergent. Locusts were sacrificed by cutting off the tip of the abdomen and swiftly removing the head together with the adhering digestive tract. The thorax was then isolated by cutting off the abdomen, the legs and most of the wings. The thorax was cut into halves by median ventral and dorsal cuts. After removing large tracheae and air sacs as well as adhering parts of the fat body, the right half-thorax was tethered with a cotton thread to the stump of a wing and carefully washed in Hoyle's saline solution in a beaker on a magnetic stirrer at 4°C for 1 h (with one change of medium after 30 min) to remove adhering haemolymph and non-specifically bound trehazolin from the tissue. The half-thorax was carefully blotted with soft tissue to remove adhering solution, and muscle was removed and collected in Eppendorf tubes on ice. The flight muscles were fractionated as follows (see Fig. 1). The tissue was thoroughly homogenized in 9 parts (v/w) of homogenization buffer (50 mmol l–1 sodium maleate, pH 6.5) in a small hand-homogenizer (2 ml, glass in glass; Neolab, Heidelberg, Germany). After removing an aliquot from the homogenate [fraction 1 (F1)] to measure total overt trehalase activity, the homogenate was centrifuged in a Sorvall RC5C (40,000 g, 4°C, 60 min; Thermo Fisher Scientific, Langenselbold, Germany) and the supernatant [fraction 2 (F2), containing soluble trehalase activity, a subfraction of the total overt trehalase] was stored on ice. The sediment was resuspended with 9 parts (v/w, minus the volume of the aliquot taken from fraction 1) of homogenization buffer with a small microhomogenizer and by repeated vigorous pipetting in and out of a 1000 μl pipette tip. This yielded fraction 3 (F3), from which an aliquot was removed to measure bound overt trehalase activity. Triton X-100 (final concentration 1%, v/v) was added to the remaining suspension, which was then sonicated (Sonifier, micro tip; Branson, Schwäbisch Gmünd, Germany), thus solubilizing bound trehalase [fraction 4 (F4), total bound trehalase activity].
Assay of trehalase activities
A discontinuous two-step assay was used that combined hydrolysis of trehalose at pH 6.5 and 30°C (step 1) and spectrophotometric measurement of the produced glucose at pH 7.6 and 25°C (step 2).
In step 1, two aliquots from each tissue fraction were pre-incubated for 20 min in Eppendorf reaction vessels in sodium acetate (120 mmol l–1, pH 6.5). The trehalase reaction was started by adding trehalose at a final concentration of 20 mmol l–1 to one of the vessels and distilled water to the other (total volume 500 μl). After 20 min incubation, reactions were stopped with 50 μl of 15% perchloric acid. The assays were vortexed, neutralized with 10 μl of potassium carbonate (5 mol l–1 K2CO3) and centrifuged at 10,000 g for 5 min to precipitate the potassium perchlorate. The supernatants were kept at –20°C for measuring glucose in step 2 of the assay.
Step 2 of the assay used 96-well microtiter plates and the Multireader Ascent (Pharmacia, Freiburg, Germany) to enzymatically measure the glucose produced by trehalase in step 1. The assay was performed in a volume of 200 μl at 25°C and pH 7.6 and comprised 150 mmol l–1 triethanolamine, 10 mmol l–1 MgCl2, 0.6 mmol l–1 NADP+, 0.5 mmol l–1 ATP and either 10 or 20 μl sample from step 1. After 5 min, the assays were started by adding a mixture of hexokinase and glucose-6-phosphate dehydrogenase (0.21 and 0.33 U well–1, respectively). The extinctions at 340 nm were read after 10 and 15 min to make sure all glucose had been transformed. The readings were corrected by subtracting the glucose present in the blank assays (without addition of trehalose, see above) to ensure only glucose produced by trehalase activity was taken into account. This glucose production (=trehalase activity) in step 1 has a linear relationship with incubation time and amount of tissue fraction.
Presentation of data and statistics
Trehalase activities are given as international units (U) per g fresh muscle and are means ± s.e.m. based on the number of locusts (N). One U is the activity hydrolyzing 1 μmol trehalose min–1 (at 30°C). The Mann–Whitney U-test was used to analyze data for statistical significance.
Effects of trehazolin on locust behaviour
Locusts injected with Hoyle's saline (control) behaved as usual whereas those injected with trehazolin in saline (experimental) showed reduced spontaneous motor activity and hardly jumped to escape when they were approached or picked up. Consumption of food was also reduced during the first third of the experiment. After ∼10 days, the behaviour of trehazolin-injected locusts had returned to normal.
Demonstration of various forms of trehalase activity by flight muscle fractionation
Fractionation of flight muscle from control locusts (injected with 5 μl of Hoyle's saline) resulted in different forms of trehalase activity (see Fig. 1, Table 1). On average, ∼25% of the total trehalase activity was measured in the homogenate (F1) by adding trehalose (at a final concentration of 20 mmol l–1) to start the reaction. This trehalase activity, the total overt trehalase activity, was separated into two distinct forms of overt trehalase by centrifugation (see Table 1). Close to 20% of total overt trehalase remained in the supernatant after centrifugation at 40,000 g whereas more than 80% was in the sediment. Control experiments using ultra-centrifugation (100,000 g for 1 h in a Beckman centrifuge) gave very similar results. Therefore, trehalase in the supernatant (F2) was designated soluble trehalase activity. Soluble trehalase could not be activated by Triton X-100 or other detergents.
In order to analyze the trehalase in the sediment, the centrifugation pellets were resuspended and their trehalase activity was measured and characterized as bound overt trehalase activity (F3). Under ideal conditions, the sum of the soluble and bound overt trehalase activities should equal the total overt trehalase activity. In the present experiments, bound overt trehalase activity was usually higher than expected and, in some cases, even exceeded total bound trehalase activity. These observations indicate that resuspension of the pellets transformed some latent trehalase activity into overt trehalase activity (see below and Discussion).
Addition of Triton X-100 (1% final concentration) to the resuspended pellets plus sonication (yielding F4) caused a marked increase in trehalase activity, and all trehalase in F4 was solubilized (i.e. could not be sedimented by ultra-centrifugation). This result was interpreted as evidence for the existence of an occluded form of trehalase (latent trehalase) in F3 that was bound to cellular membranes and inactive because it was sequestered from its substrate (see below and Discussion). The trehalase activity in F4 was hence regarded as representing total bound activity (total particulate trehalase activity), and its latent fraction was calculated as the trehalase activity in F4 minus that in F3 (see Fig. 1). Total trehalase activity was calculated as the trehalase activity in F4 plus that in F2.
Effects of trehazolin on total trehalase activity of locust flight muscle
Control locusts, injected with Hoyle's saline and analyzed after 1, 10, 20 and 30 days, had very similar trehalase activities in all fractions of flight muscle. In particular, the data do not indicate an effect of ageing on any form of trehalase over the 30-day period of the experiment (see Table 1).
Injection of trehazolin into locust haemolymph had dramatic effects on trehalase activity in fractions of flight muscle. The total activity of trehalase was reduced to 1.75 U g–1 one day after the injection (compared with 4.64 U g–1 in controls). It was decreased further on day 2 and reached very low values from day 4 to day 8, with activities between 0.50 (on day 6) and 0.70 U g–1 (on day 8). The activities were hardly elevated until day 13, but had significantly increased to 2.8 and 3.0 U g–1 on days 22 and 24, respectively (Fig. 2). When the experiment was finished at day 30, total trehalase activity had returned to control levels.
Effects of trehazolin on trehalase forms in locust flight muscle fractions
The changes in trehalase activity with time become more meaningful if the overt and latent trehalase that together make up the total activity are presented separately (see Fig. 3). The activity of total overt trehalase decreased rapidly and reached a minimum of 0.15 U g–1 two days after the injection of trehazolin. Overt activity remained low until day 4, when it transiently increased to a plateau between days 5 and 9 (with trehalase activities ranging from 0.33 to 0.49 U g–1). During the last 10 days of the experiment, overt activity returned to control levels (Fig. 3).
The activity of latent trehalase (i.e. the activity that appeared after destruction of the structural integrity of membranes by detergents) from the flight muscle of trehazolin-injected locusts initially followed a time course similar to that of overt trehalase, but lagged behind by 4 to 5 days. Overt trehalase had minimal activity from day 2 to day 4, whereas latent trehalase was minimal between days 6 and 8, when it fell to levels between 2.4% (at day 6) and 1.6% (at day 8) of the control values. Interestingly, during this minimum phase of latent activity, the activity of overt trehalase went through a transient maximum. The mirror-symmetry of the resulting curves suggests a relationship between the two forms of trehalase that is consistent with the view that latent trehalase was a precursor or a storage form of overt trehalase (see Fig. 3 and Discussion).
Analysis of data using a Mann–Whitney U-test revealed that the time courses of overt and latent trehalase activity were significantly different (for details see legend of Fig. 3).
Effects of trehazolin on locust behaviour
Injection of 20 μg of trehazolin markedly reduced motor activity and food consumption in experimental locusts (see also Wegener et al., 2003; Liebl et al., 2010). This is regarded as a consequence of the fact that the poisoned locusts were unable to utilize carbohydrates properly. The inhibitor trehazolin suppresses the production of glucose from trehalose, and glucose seems to be vital for physiological functions and survival of locusts (see Wegener et al., 2003).
Forms of trehalase activity in locust flight muscle
Fractionation of flight muscle from locust revealed the existence of different forms of trehalase activity in vitro. Soluble trehalase activity comprised a minor subfraction of overt trehalase activity, ∼5% of total trehalase activity, and part of the soluble trehalase could have been artificially detached from membranes during homogenization. Soluble trehalase does not seem to play a significant role in flight muscle because there is no conclusive proof for its existence in flight muscle in vivo and its activity in flight muscle fractions is minor and appears not to be regulated.
Latent trehalase, which is always associated with membranes, is the main and most interesting form of trehalase activity in homogenized flight muscle from resting locusts. The existence of bound (particulate) trehalase that could be activated by membrane destruction has been reported (Gussin and Wyatt, 1965; Candy, 1974; Worm, 1981; Vaandrager et al., 1989; Wegener et al., 2003), but its source and function have remained elusive. In the present study, latent trehalase activity was underestimated rather than overestimated because the resuspension of the membrane pellet produced more overt trehalase activity than expected (see Results and Table 1). This could be due to damage to membranes that (artificially) transformed latent into overt trehalase activity (see below).
Latent trehalase in vitro was inactive, even at high substrate concentrations, unless it was activated (i.e. became solubilized and, hence, overt) by the destruction of membranes with detergent. This indicates that inhibition of activity in latent trehalase does not act on the enzyme directly, but is due to a barrier that separates trehalase from its substrate. The barrier is obviously based on structurally intact membranes. This view is supported by the observation that all forms of trehalase that were active in vitro had high affinities for trehalose. Soluble trehalase (F2), the bound overt trehalase (F3) and solubilized (previously bound overt plus latent) trehalase (F4) showed Michaelis–Menten kinetics and Km-values of ∼1 mmol l–1 (P.S. and G.W., unpublished results). This strongly indicates that the substrate trehalose has easy access to the active site of the enzyme in active forms of trehalase but not in latent trehalase. Results of the present study support the view that there is a dynamic precursor–product relationship between the latent and overt forms of trehalase activity in vivo.
Trehazolin and total trehalase activity in locust flight muscle
Trehazolin is a potent tight-binding inhibitor of locust flight muscle trehalase in vitro, and also caused a marked long-term inhibition of total trehalase activity of flight muscle in vivo. Contrary to our expectation (see below), the inhibition was not complete, which prompted speculations about the compartmentation of trehazolin and trehalase in locust flight muscle. The remaining trehalase activity in trehazolin-injected locusts could not have been due to dissociation of the trehalase-trehazolin complex during washing of the flight muscles (60 min at 4°C) because trehalase inhibited by trehazolin could not be reactivated by extended dialysis under these conditions (C.M. and G.W., unpublished results). Ando et al. have reported the same result on trehalase from the Bombyx midgut (Ando et al., 1995b).
The concentration of trehazolin that causes 50% inhibition in purified flight muscle trehalase (IC50) was ∼10–7 mol l–1 (at 20 mmol l–1 trehalose without pre-incubation of enzyme and inhibitor) (see Wegener et al., 2003). Even if the 20 μg trehazolin (=54.6 nmol) injected into locust haemolymph had been evenly distributed in the total water space of a locust (∼1 ml), the concentration of trehazolin would still be ≥50 μmol l–1, i.e. more than 2 orders of magnitude higher than the IC50in vitro. However, trehazolin obviously does not permeate intact cellular membranes, but seems confined to the haemolymph (extracellular space). Liebl et al. determined the haemolymph concentration of trehazolin as 142.3 μmol l–1 two hours after injection of 20 μg (Liebl et al., 2010), and this concentration accounted for almost all of the injected trehazolin. The concentration of trehazolin in the haemolymph at this time was hence about 1000-times the IC50. Like trehazolin, trehalose is apparently not transported from the haemolymph into the muscle cells, as no transporter for trehalose has yet been reported in locusts. The observation that trehalase was only partially inhibited by trehazolin after 24 h incubation in vivo (when the haemolymph concentration was >10–4 mol l–1) (see Liebl et al., 2010) is an indication that trehazolin had access to only a fraction of trehalase molecules of flight muscle in vivo. This indicates that another fraction of trehalase in locust flight muscle can (temporarily) be confined to a metabolic compartment not reached by trehazolin.
The effects of trehazolin on the different forms of trehalase activity led to a working hypothesis about the relationship between overt and latent trehalase and the mechanism by which trehalase might be controlled in locust flight muscle.
Trehazolin as a tool to study overt and latent trehalase activities and their relationship
The activity of all forms of trehalase in flight muscle from locusts injected with Hoyle's saline (controls) showed no age-related changes over the 30 days of the experiment (Table 1). The changes in trehalase activities in flight muscle from locusts injected with trehazolin must therefore be attributed to the trehalase inhibitor. These changes were dramatic, long lasting and indicated a dynamic relationship between the overt and latent forms of trehalase activity.
The time course of total trehalase activity after injection of trehazolin was represented by a simple and typical curve characterized by a precipitous initial decrease, followed by a prolonged phase of minimal activity and a slow recovery thereafter (Fig. 2). However, total trehalase activity is not a simple parameter, but the sum of overt and latent trehalase activities. When the components were displayed separately it became apparent that they followed significantly different time courses (for P-values, see legend of Fig.3) and that the seemingly simple curve of total activity concealed a dynamic relationship between latent and overt trehalase. Overt trehalase dropped precipitously to an early minimum but then increased to a transient plateau (between days 5 and 10) when latent trehalase reached a corresponding minimum. These counterbalancing changes suggest that the transiently increased overt activity is brought about at the expense of latent activity. Latent activity could therefore be interpreted as a precursor or a storage form of trehalase activity that was not initially exposed to the inhibitor, but was later transformed into overt trehalase and then inhibited by trehazolin.
As a working hypothesis we interpret overt trehalase (in vitro) as originating from trehalase (in vivo) that was exposed to the inhibitor in the haemolymph, whereas the in vitro latent form of trehalase is derived from trehalase shielded from the inhibitor by membranes. Trehalase inhibitors such as trehazolin are structurally similar to trehalose and they act as competitive inhibitors with a high affinity for trehalase, probably by mimicking the transition state of trehalose in the trehalase reaction (Ando et al., 1995b). Two observations are pertinent to the current discussion and support the working hypothesis: (1) as mentioned before, trehazolin injected into locust haemolymph remains in this compartment, from which it is excreted within ∼10 days (Liebl et al., 2010) and (2) trehalase from locust flight muscle is a mannose-rich, N-linked glycoprotein (P.S. and G.W., unpublished results). If bound to membranes, a glycoprotein of this type is orientated in such a way that the sugar moieties will be directed either towards the extracellular space (if the protein is localized in plasma membranes) or towards the lumen of intracellular membrane structures (e.g. endoplasmic reticulum, Golgi complex, vesicles). This fact is significant because no transporter for trehalose has been reported in locusts. If trehalose cannot permeate cell membranes, trehalase must function as an ectoenzyme as has been suggested (Wegener et al., 2003). Trehalose would hence be hydrolyzed extracellularly and the product glucose transported across the plasma membrane. The mechanism(s) that control the reversible transformation of the overt and latent forms of trehalase could be the basis of a novel type of reversible regulation of enzyme activity. This makes trehalase an intriguing enzyme beyond the field of insect physiology.
We would like to thank Professor Jürgen Markl, Head of the Department of Zoology, University of Mainz, and the Deans of the Faculty of Biology, Professors Harald Paulsen and Erwin Schmidt, for support. We are also very grateful to Dr Eva Maria Griebeler for advice on statistics and to Dr Ulrich Knollmann for preparing the figures.
Our work has been supported by Deutsche Forschungsgemeinschaft (DFG, D-53175 Bonn; grants WE 494/12).


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Currently, the president of the United States is (thankfully) Democrat Joe Biden. President Biden replaces a twice impeached Donald Trump in a fierce election in which both candidates received more votes than any previous candidate in history.
His victory, coupled with a stunning sweep of the Georgia Senate races by Democrats Jon Ossoff and Rev. Raphael Warnock give Democrats control over both houses of Congress and the Executive Branch. The last time that happened, President Obama passed the Affordable Care Act. The time before, President Clinton passed a tax overhaul that resulted in 8 years of deficit reduction and 4 years of budget surpluses. Both times, the Democrats lost full control of Congress in the next midterm election.
Vice President Kamala Harris was serving her first term in the U.S. Senate when she was elected to her current position.
A lifelong public safety and civil rights leader, Kamala D. Harris was the first Black and first woman to serve as Attorney General of California and the second Black woman to be elected to the United States Senate in history. She is now the first woman, the first Black, and the first Asian American to serve as Vice President of the United States.
Currently, the president of the United States is Republican Donald Trump. Mr. Trump was elected in a stunning upset after failing to garner an endorsement from virtually any major newspaper in the country.
His victory, coupled with a Republican controlled House and Senate, has fired up a Democratic base that has much work to do in order to recapture the House and to defend the vast majority of Senate seats that are up for election in 2018.
White House Links


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Recruiting trends that influenced by Healthcare
Although last year many employees were affected by the layoff due to the Covid-19 situation. But the healthcare industry has increased the demand for this profession. As can be seen in India, Healthcare and Technology remain an important recruitment sector.
The pandemic has accelerated demand for skilled talent as India leverages technology and expands healthcare facilities to support the recovery of affected populations.
Currently, Healthcare has made a big change in the direction of employment.
Let's take a look at recruiting trends.
1. 'Quality' is more important than 'Quantity'
The post-covid-19 era has started but there's still a level of uncertainty. Though companies might have decided to roll out openings and hire , the number of openings might be surprisingly low. Recruiters might be asked to hire one single person with a maximum skill set.
2. Diversity in society
The world is fighting not only the global pandemic but also a global issue that is racism. To combat the issue of racism, corporates have been playing a significant role by incorporating diversity and an inclusivity hiring strategy. Many companies have taken this hiring strategy with utmost seriousness and molded their hiring strategies accordingly. Apart from companies, candidates want to be a part of organizations that practice diversity.
3. Change in Employee Benefits
Gone are the days when employees only looked at the paycheck before joining a company or organization. After the aftermath created by the pandemic, more and more candidates are looking at medical benefits from their prospective companies. Apart from remuneration, there are several benefits like tuition fees, time off, health insurance, mental health resources, and health club membership which candidates look for.
4. Work from home
The pandemic has taught us that almost everything on this planet can be used on the internet. Employees are preferring the option to work remotely from their convenient location. The need to rent homes in expensive cities is no longer a reality as an increasing number of companies are changing their mode of work to complete work-from-home setup.
5. Omni Channel Recruitment is the key
Communication with customers through a variety of channels and linking different channels into one is important. There are innumerable numbers of platforms from where recruiters can source, interview, and recruit talent. The key is to identify which platform will be best for a particular position. This is where upskilling and reskilling of recruiters come into play. This is where Recruiters need to focus on and increase their skills.