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Symbiotic bacteria affect competence for pathogen transmission in insect vectors, including mosquitoes. However, knowledge on mosquito-microbiome-pathogen interactions remains limited, largely due to methodological reasons. The current, cost-effective practice of sample pooling used in mosquito surveillance and epidemiology prevents correlation of individual traits (i.e., microbiome profile) and infection status. Moreover, many mosquito studies employ laboratory-reared colonies that do not necessarily reflect the natural microbiome composition and variation in wild populations. As a consequence, epidemiological and microbiome studies in mosquitoes are to some extent uncoupled, and the interactions among pathogens, microbiomes, and natural mosquito populations remain poorly understood. This study focuses on the effect the pooling practice poses on mosquito microbiome profiles, and tests different approaches to find an optimized low-cost methodology for extensive sampling while allowing for accurate, individual-level microbiome studies. We tested the effect of pooling by comparing wild-caught, individually processed mosquitoes with pooled samples. With individual mosquitoes, we also tested two methodological aspects that directly affect the cost and feasibility of broad-scale molecular studies: sample preservation and tissue dissection. Pooling affected both alpha- and beta-diversity measures of the microbiome, highlighting the importance of using individual samples when possible. Both RNA and DNA yields were higher when using inexpensive reagents such as NAP (nucleic acid preservation) buffer or absolute ethanol, without freezing for short-term storage. Microbiome alpha- and beta-diversity did not show overall significant differences between the tested treatments compared to the controls (freshly extracted samples or dissected guts). However, the use of standardized protocols is highly recommended to avoid methodological bias in the data.

The Minimum-Volume Covering Ellipsoid (MVCE) problem is an important optimization problem that comes up in various areas of engineering and statistics. In this paper, we improve the state-of-the-art Wolfe-Atwood algorithm for solving the MVCE problem with pooling and batching procedures. This implementation yields significant improvements on the runtime of the algorithm for large-scale instances of the MVCE problem, which is demonstrated on quite extensive computational experiments.

Abstract Background Deep Neural Networks (DNN), in particular, Convolutional Neural Networks (CNN), has recently achieved state-of-art results for the task of Drug-Drug Interaction (DDI) extraction. Most CNN architectures incorporate a pooling layer to reduce the dimensionality of the convolution layer output, preserving relevant features and removing irrelevant details. All the previous CNN based systems for DDI extraction used max-pooling layers. Results In this paper, we evaluate the performance of various pooling methods (in particular max-pooling, average-pooling and attentive pooling), as well as their combination, for the task of DDI extraction. Our experiments show that max-pooling exhibits a higher performance in F1-score (64.56%) than attentive pooling (59.92%) and than average-pooling (58.35%). Conclusions Max-pooling outperforms the others alternatives because is the only one which is invariant to the special pad tokens that are appending to the shorter sentences known as padding. Actually, the combination of max-pooling and attentive pooling does not improve the performance as compared with the single max-pooling technique.

Multinational Trials - Recommendations on the Translations Required, Approaches to Using the Same Language in Different Countries, and the Approaches to Support Pooling the Data: The ISPOR Patient-Reported Outcomes Translation and Linguistic Validation Good Research Practices Task Force Report Abstract. Objectives: With the internationalization of clinical trial programs, there is an increased need to translate and culturally adapt patient-reported outcome (PRO) measures. Although guidelines for good practices in translation and linguistic validation are available, the ISPOR Patient-Reported Outcomes Translation and Linguistic Validation Task Force identified a number of areas where they felt that further discussion around methods and best practices would be beneficial. The areas identified by the team were as follows: 1) the selection of the languages required for multinational trials; 2) the approaches suggested when the same language is required across two or more countries; and 3) the assessment of measurement equivalence to support the aggregation of data from different countries. Methods: The task force addressed these three areas, reviewed the available literature, and had multiple discussions to develop this report. Results: Decision aid tools have also been developed and presented for the selection of languages and the approaches suggested for the use of the same language in different countries. Conclusion: It is hoped that this report and the decision tools proposed will assist those involved with multinational trials to 1) decide on the translations required for each country; 2) choose the approach to use when the same language is spoken in more than one country; and 3) choose methods to gather evidence to support the pooling of data collected using different language versions of the same tool.

Abstract Background Porcine circovirus type 2 (PCV2) diagnostics in live pigs often involves pooled serum and/or oral fluid samples for group-level determination of viral load by quantitative real-time polymerase chain reaction (qPCR). The purpose of the study was to compare the PCV2 viral load determined by qPCR of paired samples at the pen level of pools of sera (SP) from 4 to 5 pigs and the collective oral fluid (OF) from around 30 pigs corresponding to one rope put in the same pen. Pigs in pens of 2 finishing herds were sampled by cross-sectional (Herd 1) and cross-sectional with follow-up (Herd 2) study designs. In Herd 1, 50 sample pairs consisting of SP from 4 to 5 pigs and OF from around 23 pigs were collected. In Herd 2, 65 sample pairs consisting of 4 (SP) and around 30 (OF) pigs were collected 4 times at 3-week intervals. Results A higher proportion of PCV2-positive pens (86% vs. 80% and 100% vs. 91%) and higher viral loads (mean difference: 2.10 and 1.83 log(10) PCV2 copies per ml) were found in OF versus SP in both herds. The OF cut-off value corresponding to a positive SP (>3 log(10) PCV2 copies per ml) was estimated to 6.5 and 7.36 log(10) PCV2 copies per ml for Herds 1 and 2, respectively. Significant correlations between SP and OF results were found in Herd 1 (rho = 0.69) and the first sampling in Herd 2 (rho = 0.39), but not for the subsequent consecutive 3 samplings in Herd 2. Conclusions The proportion and viral loads of PCV2 positive pens were higher in collective OF (including up to 30 pigs) compared to SP (including 4–5 pigs) of the same pens. Also, OF seemed to detect the PCV2 infection earlier with OF values just below 6.5 (Herd 1) and 7.36 (Herd 2) log(10) being associated with a negative SP for the same pen. Nevertheless, a statistically significant correlation between SP and OF could not be found for all sampling time points, probably due to a high within-pen variation in individual pig viral load becoming very evident in SP of only four or five pigs. Consequently, the results imply that OF is well suited for detecting presence of PCV2 but less so for determining the specific viral load of pigs in a pen.

Influenza A virus (IAV) active surveillance in pigs prior to weaning is commonly conducted by collecting individual samples, mostly nasal swabs. Recently, the use of udder skin wipes collected from lactating sows was identified as an effective sampling method to indicate IAV status of suckling piglets prior to weaning. However, there is limited information on the effect of pooling multiple udder wipes on the ability to detect IAV. We evaluated the effect of pooling 3, 5, or 10 udder wipes on the sensitivity of detecting IAV and compared the results with testing the wipes individually. The likelihood of detecting positive udder wipes decreased with pooling when the initial positive cycle threshold value was ≥31.5; pooling of up to 3 samples could be performed without affecting sensitivity significantly. Our results support pooling of udder skin wipes to conduct surveillance of IAV in pigs prior to weaning. [ABSTRACT FROM AUTHOR]

Content-based image retrieval (CBIR) aims to retrieve images from a given image collection according to similarities of image contents. In this paper, we focus on retrieval of scene images. We propose a siamese spatial attentive model which bases on siamese architecture and incorporates attention mechanism to generate compatible image embeddings. It extracts local features with a convolutional neural network (CNN), which starts with pre-trained parameters and is well fine-tuned for retrieval. Spatial attention pooling is proposed to take feature maps as input and generate weights for local features, which are then used to refine local features via weighted sum-pooling. Such pooling alleviates impacts from disturbance and concentrates on meaningful parts of images. Therefore, the model is able to output robust representations for noisy images. We also propose a multi-stage training scheme for the model, which leads to better performance than normal one-pass training scheme. Extensive experimental results on benchmark image retrieval datasets show that our model is competitive in retrieval performance. [ABSTRACT FROM AUTHOR]

• We propose Ordered Weighted Operators as the pooling operation in image classification methods. • We provide the methodology to learn the weights of the OWA-pooling operator. • We analyse the performance of the OWA-pooling in Bag-of-Words features. • The proposed pooling method outperforms average pooling and maximum pooling. Spatial pooling is an important step in computer vision systems like Convolutional Neural Networks or the Bag-of-Words method. The spatial pooling purpose is to combine neighbouring descriptors to obtain a single descriptor for a given region (local or global). The resultant combined vector must be as discriminant as possible, in other words, must contain relevant information, while removing irrelevant and confusing details. Maximum and average are the most common aggregation functions used in the pooling step. To improve the aggregation of relevant information without degrading their discriminative power for image classification, we introduce a simple but effective scheme based on Ordered Weighted Average (OWA) aggregation operators. We present a method to learn the weights of the OWA aggregation operator in a Bag-of-Words framework and in Convolutional Neural Networks, and provide an extensive evaluation showing that OWA based pooling outperforms classical aggregation operators. [ABSTRACT FROM AUTHOR]

• SRP makes descriptors more diversity by encouraging channel to have more responses. • SRP improves channel-wise attention networks without additional cost in test phase. • Several classification tasks verify the effectiveness of SRP. Global Average Pooling (GAP) is used by default on the channel-wise attention mechanism to extract channel descriptors. However, the simple global aggregation method of GAP is easy to make the channel descriptors have homogeneity, which weakens the detail distinction between feature maps, thus affecting the performance of the attention mechanism. In this work, we propose a novel method for channel-wise attention network, called Stochastic Region Pooling (SRP), which makes the channel descriptors more representative and diversity by encouraging the feature map to have more or wider important feature responses. Also, SRP is the general method for the attention mechanisms without any additional parameters or computation. It can be widely applied to attention networks without modifying the network structure. Experimental results on image recognition datasets including CIAFR-10/100, ImageNet and three Fine-grained datasets (CUB-200-2011, Stanford Cars and Stanford Dogs) show that SRP brings the significant improvements of the performance over efficient CNNs and achieves the state-of-the-art results. [ABSTRACT FROM AUTHOR]

The mobile cloud computing has become an emerging technology where the mobile computing is integrated with cloud computing to process the mobile data. Besides the advantages of mobile cloud computing, there are some issues which include power consumption, resource scarcity, quality of service, security and computational cost. In this paper, in order to minimize total power consumption with better performance, the neural network based optimization methods using artificial neural network and convolutional neural network models were implemented by varying variance and loudness. From the experimental results it is observed that, by using optimization in the neural network, the power consumption has been reduced by 53.68% and obtained improvement using convolutional neural network which further reduced the power consumption by 30.3% with minimum root mean square error compared with other algorithms. [ABSTRACT FROM AUTHOR]

Background: Pooled testing, or pooling, has been used for decades to efficiently diagnose relatively rare conditions, such as infection in blood donors. Programmes for the prevention of mother-to-child transmission of HIV and for antiretroviral therapy (ART) are being rolled out in much of Africa and are largely successful. This increases the need for early infant diagnosis (EID) of HIV using qualitative nucleic acid testing and for virological monitoring of patients on ART using viral load testing. While numbers of patients needing testing are increasing, infant HIV infections and ART failures are becoming rarer, opening an opportunity for pooled testing approaches. Aim: This review highlights the need for universal EID and viral load coverage as well as the challenges faced. We introduce the concept of pooled testing and highlight some important considerations before giving an overview of studies exploring pooled testing for EID and virological monitoring. Results: For ART monitoring, pooling has been shown to be accurate and efficient; for EID it has not been tried although modelling shows it to be promising. The final part attempts to place pooling into the context of current mother-to-child transmission of HIV and ART programmes and their expected trajectories over the next years. Conclusion: Several points warrant consideration: pre-selection to exclude samples with an elevated pre-test probability of positivity from pooled testing, the use of dried blood or plasma spots, and choosing a pooling strategy that is both practically feasible and economical. Finally, novel ideas are suggested to make pooling even more attractive. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 pandemic has hugely impacted the economy of many countries, and there is an acute shortage of diagnostic resources. With the exponential increase in the number of cases and necessity to screen large number of people, there is a steep increase in the demand for diagnostic kits. Pooled-sample testing is a promising strategy to screen a large population rapidly with limited resources. The aim of this work was to compile a cohesive literature review of the effectiveness and accuracy of pooled-sample testing in the detection of SARS-CoV-2 and critically analyze its limitations. Medline, Google Scholar, Embase, and preprint servers (e.g., bioRxiv) were searched for literature on pooled testing for diagnosis of COVID-19, and out of initial 60 articles/reports, nine original articles were retained. Optimal pool size (number of samples in a pool) seemed to be dependent on factors like prevalence or rate of positivity in community. In low-prevalence localities pool size of around 30 seemed to be effective as observed by some authors. All the researchers had found significant reduction in number of tests (depending on pool size, stages, and pooling design), leading to conservation of resources. Pooling can be done with extracted RNA eluate or directly with patient's sample before extraction. This leads to further reduction in consumables, time and manpower. Risk of false negativity in samples with high-threshold cycle (i.e., low-viral load) value was a concern. Some researchers suggest adding few additional cycles to lower the chances of missing positive cases with low-Ct value. Lower limit of detection (LoD) of RT-PCR kits, that is, sensitivity of kits was another factor to consider. Thus, in a country like India, given the economic benefit and scarcity of resources, pooling strategy can be very effective, especially in low-prevalence areas and in low-risk contacts. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Environmental testing for Salmonella Enteritidis is required for U.S. shell egg producers with -3,000 hens on a farm. The egg producer assumes all costs for the mandatory testing. According to the U.S. Food and Drug Administration (FDA) Egg Rule, either manure scraper or drag swabs can be collected according to published guidelines and requirements. The present study was undertaken to determine the efficacy of Salmonella detection with one-, two-, and four-swab pools of either manure scraper or drag swabs. Resistant isolates of Salmonella serovars Enteritidis (1,000 ppm of streptomycin), Heidelberg (200 ppm of nalidixic acid [NA]), Typhimurium (200 ppm of NA), and Kentucky (200 ppm of NA) were utilized. Low (approximately 8.4 CFU) and high (approximately 84 CFU) levels of inocula were introduced onto a single swab within a pool. Single flocks from each conventional cage (manure scraper swabs) and cage-free barn (drag swabs) were monitored throughout the study at the ages required under the FDA Egg Rule. The highest and most consistent recovery of inoculum was found in single swab samples. For low dose inocula, recovery of isolates was low from single manure scraper swabs (57.9 to 29.2%) and decreased as more swabs were added to the pool. Recovery of isolates from manure scraper swabs was higher for high dose inocula, although Salmonella Heidelberg was outcompeted by the naturally occurring flora and had the lowest rate of recovery among the isolates tested. One- and two-swab pools of drag swabs had similar rates of recovery at both low and high doses for Salmonella Enteritidis, Salmonella Heidelberg, and Salmonella Typhimurium. When Salmonella Enteritidis and Salmonella Kentucky were combined in an inoculum, Salmonella Enteritidis was recovered at a much higher rate than was Salmonella Kentucky for all types of swabs and doses of inocula. Pooling of two drag swabs allowed for similar detection of low and high dose Salmonella, but the pooling of manure scraper swabs decreased detection of low dose Salmonella. [ABSTRACT FROM AUTHOR]

• First approach in designing Multiple Instance Pooling function through metaheuristics i.e. Genetic algorithm. • A trainable pooling method is proposed - Genetic Pooling. • Genetic Pooling outperforms other MIL pooling methods in terms of AUC and average precision score. Multiple Instance Learning is a weakly supervised learning technique which is particularly well suited for medical data analysis as the class labels are often not available at desired granularity. Multiple Instance Learning through Deep Neural Networks is relatively a new paradigm in machine learning. The most important part of Multiple Instance Learning through Deep Neural Networks is designing a trainable pooling function which determines the instance-to bag relationship. In this paper, we propose a Multiple Instance pooling technique based on Genetic Algorithm called Genetic Pooling. In this technique, instance labels inside a bag are optimized by minimizing bag-level losses. The main contribution of the paper is that the bag level pooling layer for generating attention weights for bag instances are replaced by random initialization of attention weights and finding the optimized attention weights through Genetic Algorithm. [ABSTRACT FROM AUTHOR]

Recent works have shown that convolutional neural networks (CNNs) are now the most effective machine learning method for solving various computer vision problems. A key advantage of CNNs is that they extract features automatically; users do not need to know what features should be extracted for a certain task. It is typically believed that the deeper the CNNs are, the higher the features that can be extracted and the more powerfully the resulting representations networks will be. Therefore, present-day CNNs are becoming substantially deeper. Previous works have proven that not all features extracted by deep CNNs are useful. In this paper, we tentatively consider a question: how do we simply remove the useless features? We propose a simple pooling method called feature pooling to compress features extracted in deep CNNs. In contrast to traditional CNNs, which input feature maps from the previous layer directly to the next layer, feature pooling compresses features from the channel below, reconstructs feature maps and then sends them to the next layer. We evaluate feature pooling based on two tasks: image classification and image denoising. Each task has a distinct network architecture and uses several benchmarks. Promising results are achieved in both tasks, especially image denoising, in which we obtain state-of-the-art results. This finding verifies the previous proposition that feature pooling is a straightforward method to perform further feature compression in CNNs. We have also observed that feature pooling has several competitive advantages: it reduces the number of parameters, increases the compactness of the networks, and strengthens the representation power with both high effectiveness and wide applicability. [ABSTRACT FROM AUTHOR]

Symbiotic bacteria affect competence for pathogen transmission in insect vectors, including mosquitoes. However, knowledge on mosquito-microbiome-pathogen interactions remains limited, largely due to methodological reasons. The current, cost-effective practice of sample pooling used in mosquito surveillance and epidemiology prevents correlation of individual traits (i.e., microbiome profile) and infection status. Moreover, many mosquito studies employ laboratory-reared colonies that do not necessarily reflect the natural microbiome composition and variation in wild populations. As a consequence, epidemiological and microbiome studies in mosquitoes are to some extent uncoupled, and the interactions among pathogens, microbiomes, and natural mosquito populations remain poorly understood. This study focuses on the effect the pooling practice poses on mosquito microbiome profiles, and tests different approaches to find an optimized low-cost methodology for extensive sampling while allowing for accurate, individual-level microbiome studies. We tested the effect of pooling by comparing wild-caught, individually processed mosquitoes with pooled samples. With individual mosquitoes, we also tested two methodological aspects that directly affect the cost and feasibility of broad-scale molecular studies: sample preservation and tissue dissection. Pooling affected both alpha- and beta-diversity measures of the microbiome, highlighting the importance of using individual samples when possible. Both RNA and DNA yields were higher when using inexpensive reagents such as NAP (nucleic acid preservation) buffer or absolute ethanol, without freezing for short-term storage. Microbiome alpha- and beta-diversity did not show overall significant differences between the tested treatments compared to the controls (freshly extracted samples or dissected guts). However, the use of standardized protocols is highly recommended to avoid methodological bias in the data. [ABSTRACT FROM AUTHOR]

Background: Recommendations from the American Society of Veterinary Clinical Pathology (ASVCP) are to calculate the between‐run coefficient of variation (CV) based on measuring one replicate per day on quality control materials (QCMs) or pooled patient samples over a minimum of 20 days. However, this recommendation is not always followed by researchers. Objectives: We aimed to determine if a reduction in the number of replicates using QCM or individual or pooled samples would provide CV results similar to those obtained based on ASVCP recommendations. Methods: CVs were calculated for three measurands, namely urea, creatinine, and C‐reactive protein based on the analytic results of the following groups: (a) QCM measured once daily for 20 days (considered as the reference for comparison), b) QCM measured once daily for 5 days, (c) five different canine serum samples measured once daily for 5 days, and (d) a pooled canine serum measured once daily for 5 days. CVs were calculated for two different measurand concentrations. Results: Compared with the reference method, significantly different CVs were obtained with all methods except for when the QCM was measured once daily for 5 days. The use of the five different individual samples also provided significantly different CVs compared with the use of a pooled sample. Conclusions: The results indicate that different protocols for determining between‐run imprecision calculations can give different results compared with the reference procedure and that this should be taken into consideration when evaluating the total error associated with a test. [ABSTRACT FROM AUTHOR]

Pooling colostrum is commonly practiced on Irish dairy farms. Pooling can result in dilution when colostrums with high and low IgG concentrations are mixed, thereby predisposing calves to failure of passive immunity. The objectives of this study were to compare IgG concentrations in colostrum from individual cows with colostrum pooled from several cows, and assess serum IgG concentrations, morbidity, and mortality among calves fed colostrum from their own dam, from a different cow, or pooled from several cows. We hypothesized that pooling colostrum reduces IgG concentration due to dilution compared with colostrum from individual cows, and that calves fed pooled colostrum achieve lower serum IgG concentrations than calves fed colostrum from individual cows. Calves were randomly assigned to 1 of 3 groups: (1) fed colostrum from their own dam (n = 20); (2) fed colostrum from a different dam (n = 20); or (3) fed pooled colostrum (n = 18). A sample of colostrum fed to each calf was collected. Serum samples were collected from calves at birth (0 h) and at 24 h after colostrum feeding. Colostrum and serum IgG concentrations were measured by radial immunodiffusion. Calves were weighed at birth and at weaning, and the health status of each calf was assessed twice daily. Health assessment was based on general demeanor, rectal temperature, fecal consistency, respiratory rate, and the presence of cough, nasal, or ocular discharge. Colostrum and serum IgG concentrations, and weaning weights were compared using ANOVA. Associations between group and morbidity or mortality rates were compared using Χ2 or Fisher's exact tests. Median and 95% confidence intervals (95% CI) of IgG concentrations of colostrum were 99.4 (81.8-111.5), 95.2 (84.1-107.2), and 100.7 (90.5-104.4) g/L for own dam, different dam, and pooled groups, respectively. We did not find any differences in colostrum IgG concentrations among the colostrum sources. Median (95% CI) serum IgG concentrations at 24 h were 52.0 (45.6-65.9), 55.7 (51.2-65.9), and 53.1 (46.2-63.7) g/L for calves that received colostrum from own dam, different dam, and pooled, respectively. All calves achieved adequate passive immunity. Serum IgG concentrations at 24 h, weaning weights, and proportions of morbidity and mortality were not different among the 3 groups. Our results suggest that on dairy farms where median colostrum IgG concentrations are high and colostrum management is optimal, pooling has a minimal effect on passive immunity and subsequent calf health. [ABSTRACT FROM AUTHOR]

State-of-the-art speech recognition is witnessing its golden era as convolutional neural network (CNN) becomes the leader in this domain. CNN based acoustic models have been shown significant improvement in speech recognition tasks. This improvement is achieved due to the special components of CNN, i.e., local filters, weight sharing, and pooling. However, lack of core understanding renders this powerful model as a black-box machine. Although, CNN is performing well in speech recognition still further investigation will help in achieving better recognition rate. Pooling is a very important component of CNN that reduces the dimensionality of the feature-map and offers compact feature representation. Various pooling methods like max pooling, average pooling, stochastic pooling, mixed pooling, L p pooling, multi-scale orderless pooling, and spectral pooling have their own advantages and disadvantages. In this paper, we deeply explore the state-of-the-art pooling for speech recognition tasks. This paper also helps to investigate that which pooling method performs well in which condition. This work explores different pooling methods for different architectures on Hindi speech dataset. The experimental results show that max pooling performs well when tested for clean speech and stochastic pooling works well in the noisy environment. [ABSTRACT FROM AUTHOR]

Background: Molecular detection of low-density Plasmodium falciparum infections is essential for surveillance studies conducted to inform malaria control strategies in close-to-elimination settings. Molecular monitoring of residual malaria infections usually requires a large study size, therefore sampling and diagnostic processes need to be economical and optimized for high-throughput. A method comparison was undertaken to identify the most efficient diagnostic procedure for processing large collections of community samples with optimal test sensitivity, simplicity, and minimal costs. Methods: In a reactive case detection study conducted on Zanzibar, parasitaemia of 4590 individuals of all ages was investigated by a highly sensitive quantitative (q) PCR that targets multiple var gene copies per parasite genome. To reduce cost, a first round of positivity screening was performed on pools of dried blood spots from five individuals. Ten cycles of a pre-PCR were performed directly on the filter paper punches, followed by qPCR. In a second round, samples of positive pools were individually analysed by pre-PCR and qPCR. Results: Prevalence in household members and neighbors of index cases was 1.7% (78/4590) with a geometric mean parasite density of 58 parasites/µl blood. Using qPCR as gold standard, diagnostic sensitivity of rapid diagnostic tests (RDTs) was 37% (29/78). Infections positive by qPCR but negative by RDT had mean densities of 15 parasites/µl blood. Conclusion: The approach of pre-screening reactive case detection samples in pools of five was ideal for a low prevalence setting such as in Zanzibar. Performing direct PCR on filter paper punches saves substantial time and justifies the higher cost for a polymerase suitable for amplifying DNA directly from whole blood. Molecular monitoring in community samples provided a more accurate picture of infection prevalence, as it identified a potential reservoir of infection that was largely missed by RDT. The developed qPCR-based methodology for screening large sample sets represents primarily a research tool that should inform the design of malaria elimination strategies. It may also prove beneficial for diagnostic tasks in surveillance-response activities. [ABSTRACT FROM AUTHOR]

Accurate glioma detection using magnetic resonance imaging (MRI) is a complicated job. In this research, deep learning model is presented for glioma and stroke lesion detection. The proposed architecture consists of 14 layers. The first input layer is followed by three convolutional layers while 5th, 6th and 7th layers correspond to batch normalization, followed by next three layers of rectified linear unit (ReLU). Eleventh layer is average pooling 2D which is followed by fully connected (FC), softmax and classification layers respectively. The presented method is verified on six MICCAI databases namely multimodal brain tumor segmentation (BRATS) 2013, 2014, 2015, 2016, 2017 and sub-acute ischemic stroke lesion segmentation (SISS-ISLES) 2015. The computational time is also measured across each benchmark dataset such as 53 s on BRATS 2013, 26 s on BRATS 2014, 41 s on BRATS 2015, 36 s on BRATS 2016, and 38 s on BRATS 2017 and 4.13 s on ISLES 2015 proving that the proposed technique has less processing time. The proposed method achieved 0.9943 ACC, 1.00 SP, 0.9839 SE on BRATS 2013, 0.9538 ACC, 0.9991 SP, 0.7196 SE on BRATS 2014, 0.9978 ACC, 1.00 SP, 0.9919 SE on BRATS 2015, 0.9569 ACC, 0.9491 SP, 0.9755 SE on BRAST 2016, 0.9778 ACC, 0.9770 SP, 0.9789 SE on BRATS 2017 and 0.9227 ACC, 1.00 SP, 0.8814 SP on ISLES 2015 datasets respectively. [ABSTRACT FROM AUTHOR]

Background: When research focuses on biomarker assessment in settings where per-assay costs are high relative to per-subject costs, a biospecimen pooling study design can be extremely cost-effective. However, designing a study to maximize cost savings is complicated by the fact that pooled measurements are typically subject to processing error, inducing additional variability caused by combining biospecimens, and may also be affected by assay-related measurement error.Methods: We provide formulas and an interactive web application (hereafter called app) for designing a pooling study to compare group means. Power and sample size formulas are justified by Central Limit Theorem arguments that make no distributional assumptions on the biomarker. Errors can be assumed mean-0 additive or mean-1 multiplicative, the latter being well-suited for skewed biomarkers.Results: User inputs for the app include usual power parameters as well as per-assay and per-subject costs and information about the errors: which are present, whether they are additive or multiplicative, and their variances. The app generates plots revealing the optimal pool size, required number of assays, cost savings, and sensitivity to the hard-to-predict processing error variance.Conclusions: These tools should aid in the design and deployment of pooling studies powered to detect group mean differences while minimizing total study costs. [ABSTRACT FROM AUTHOR]

This paper poses a problem for Lewis' Principal Principle in a subjective Bayesian framework: we show that, where chances inform degrees of belief, subjective Bayesianism fails to validate normal informal standards of what is reasonable. This problem points to a tension between the Principal Principle and the claim that conditional degrees of belief are conditional probabilities. However, one version of objective Bayesianism has a straightforward resolution to this problem, because it avoids this latter claim. The problem, then, offers some support to this version of objective Bayesianism. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: The function of pooling and the ways that countries organize this is critical for countries' progress towards universal health coverage, but its potential as a policy instrument has not received much attention. We provide a simple classification of country pooling arrangements and discuss the specific ways that fragmentation manifests in each and the typical challenges with respect to universal health coverage objectives associated. This can help countries assess their pooling setup and contribute to identifying policy options to address fragmentation or mitigate its consequences. Methods: The paper is based on a review of published and grey literature in PubMed, Google and Google Scholar and our information gathered from our professional work in countries on health financing policies. We examined the nature and structure of pooling in more than 100 countries across all income groups to develop the classification. Findings: We propose eight broad types of pooling arrangements: (1.) a single pool; (2.) territorially distinct pools; (3.) territorially overlapping pools in terms of service and population coverage; (4.) different pools for different socio-economic groups with population segmentation; (5.) different pools for different population groups, with explicit coverage for all; (6.) multiple competing pools with risk adjustment across the pools; and in combination with types (1.)-(6.), (7.) fragmented systems with voluntary health insurance, duplicating publicly financed coverage; and (8.) complementary or supplementary voluntary health insurance. However, we recognize that any classification is a simplification of reality and does not substitute for a country-specific analysis of pooling arrangements. Conclusion: Pooling arrangements set the potential for redistributive health spending. The extent to which the potential redistributive and efficiency gains established by a particular pooling arrangement are realized in practice depends on its interaction and alignment with the other health financing functions of revenue raising and purchasing, including the links between pools and the service benefits and populations they cover. [ABSTRACT FROM AUTHOR]

In this paper, we explore the effect of cross-training workers in Dual Resource Constrained (DRC) systems with machines having different mean processing times. By means of queuing and simulation analysis, we show that the detrimental effects of pooling (cross-training) previously found in single resource constrained (SRC) heterogeneous systems are also apparent in DRC heterogeneous systems. Fully cross-training workers in DRC heterogeneous systems is only beneficial if the differences between mean processing times are not too large, otherwise cross-training should be pursued within homogeneous subgroups of machines. Due to the limited machine availability, DRC systems are unable to use some of the potential assignment flexibility from cross-trained workers (pooled queues) that can be used in SRC systems. However, it appears that this restriction in the DRC system may even improve the system mean flow (waiting) time performance compared to the SRC system for relatively large differences in processing time. Finally, in fully flexible multiple server queuing systems, restricting the assignment flexibility by applying a decentral when-rule (i.e. a commonly used labour assignment rule in practice and research) instead of a central when-rule also seems to improve the mean flow time performance under processing time differences. [ABSTRACT FROM AUTHOR]

Research has shown that automation, carpooling, and electrification combined in a mobility on demand framework have the potential for theoretically large emissions reductions. However, less research has been done regarding consumer preferences and behavioral responses to this vision of transportation of the future. In this paper we use choice experiment data collected from an online ride-hailing survey to quantify consumer preferences for these technologies. Our results suggest there are major barriers to consumers' willing to ride in such vehicles. We find that respondents require large discounts to ride in driverless vehicles or to carpool with other passengers, even holding ride time constant. Though they are open to riding in EVs and HEVs, they are not willing to pay more to do so. Safety concerns and an unwillingness to ride with strangers appear to be the primary drivers of these preferences. [ABSTRACT FROM AUTHOR]

The progress in technology has led to an increase in the amount of data that can be generated and processed. Downsampling methods are employed to eliminate unnecessary data but can also lead to the loss of valuable information. Herein, we designed a pooling algorithm, Conditional-Pooling , that provides a transitive structure composed of average (avg.) and max-pooling methods, and it hosts the advantageous behaviors from both. We examined our approach on several image recognition and detection tasks using deep neural networks. The results reveal that our method excels over major rivals like max-pooling and avg. pooling when tested on MNIST, Fashion-MNIST, and CIFAR for classification, Pascal VOC for detection, COCO minitrains, and ADORESet for both classification and detection. The output of Conditional-Pooling preserves important image features such as edges and corners and contains more salient features that lead to more accurate results. The code for Conditional-Pooling will be available at https://github.com/bayraktare/conditional_pooling. • Adaptive pooling for novel precision and accuracy especially for complex scenes. • Exceeding existing methods on popular visual tasks regarding various metrics. • Game-changing results for noise elimination and image downsampling. [ABSTRACT FROM AUTHOR]