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This paper offers a systematic treatment of risk-sharing rules for insurance losses, based on a list of relevant properties. A number of candidate risk-sharing rules are considered, including the conditional mean risk-sharing rule proposed in Denuit and Dhaene and the newly introduced quantile risk-sharing rule. Their compliance with the proposed properties is established. Then, methods for building new risk-sharing rules are discussed. The results derived in this paper are helpful in the development of peer‐to‐peer insurance (or crowdsurance), as well as to manage contingent risk funds where a given budget is distributed among claimants.

The sustainable development of marine fisheries depends on the accurate measurement of data on fish stocks. Semantic segmentation methods based on deep learning can be applied to automatically obtain segmentation masks of fish in images to obtain measurement data. However, general semantic segmentation methods cannot accurately segment fish objects in underwater images. In this study, a Dual Pooling‐aggregated Attention Network (DPANet) to adaptively capture long‐range dependencies through an efficient and computing‐friendly manner to enhance feature representation and improve segmentation performance is proposed. Specifically, a novel pooling‐aggregate position attention module and a pooling‐aggregate channel attention module are designed to aggregate contexts in the spatial dimension and channel dimension, respectively. These two modules adopt pooling operations along the channel dimension and along the spatial dimension to aggregate information, respectively, thus reducing computational costs. In these modules, attention maps are generated by four different paths and are aggregated into one. The authors conduct extensive experiments to validate the effectiveness of the DPANet and achieve new state‐of‐the‐art segmentation performance on the well‐known fish image dataset DeepFish as well as on the underwater image dataset SUIM, achieving a Mean IoU score of 91.08% and 85.39% respectively, while significantly reducing FLOPs of attention modules by about 93%.

The rapid spread of the novel coronavirus disease 2019 (COVID‐19) causes a significant impact on public health. It is critical to diagnose COVID‐19 patients so that they can receive reasonable treatments quickly. The doctors can obtain a precise estimate of the infection's progression and decide more effective treatment options by segmenting the CT images of COVID‐19 patients. However, it is challenging to segment infected regions in CT slices because the infected regions are multi‐scale, and the boundary is not clear due to the low contrast between the infected area and the normal area. In this paper, a coarse‐refine segmentation network is proposed to address these challenges. The coarse‐refine architecture and hybrid loss is used to guide the model to predict the delicate structures with clear boundaries to address the problem of unclear boundaries. The atrous spatial pyramid pooling module in the network is added to improve the performance in detecting infected regions with different scales. Experimental results show that the model in the segmentation of COVID‐19 CT images outperforms other familiar medical segmentation models, enabling the doctor to get a more accurate estimate on the progression of the infection and thus can provide more reasonable treatment options.

Because of issues such as viewpoint changes, posture variations, and background cluttering, the task of person reidentification (Re‐ID) remains challenging. The model of combining global features and part features has been widely used in person Re‐ID technology in recent years, but these efforts ignored feature interaction between the convolutional layers and thus lost detailed information conducive to identifying pedestrians under different cameras. To achieve interaction between hierarchical features, a multibranch network with hierarchical bilinear pooling (MBN‐HBP) is proposed. The network consists of a global branch, a part‐level branch, and a hierarchical bilinear pooling (HBP) branch. The person features extracted by the network include not only global and part‐level features but also detailed HBP features. The final feature representation will be more robust to deal with the complex surveillance environment. By conducting comprehensive experiments, competitive performance on the Market‐1501, DukeMTMC‐Re‐ID, and CUHK03 datasets is obtained.

ObjectiveWe sought to apply natural language processing to the task of automatic risk of bias assessment in preclinical literature, which could speed the process of systematic review, provide information to guide research improvement activity, and support translation from preclinical to clinical research.Materials and MethodsWe use 7,840 full-text publications describing animal experiments with yes/no annotations for five risk of bias items. We implement a series of models including baselines (support vector machine, logistic regression, random forest), neural models (convolutional neural network, recurrent neural network with attention, hierarchical neural network) and models using BERT with two strategies (document chunk pooling and sentence extraction). We tune hyperparameters to obtain the highest F1 scores for each risk of bias item on the validation set and compare evaluation results on the test set to our previous regular expression approach.ResultsThe F1 scores of best models on test set are 82.0% for random allocation, 81.6% for blinded assessment of outcome, 82.6% for conflict of interests, 91.4% for compliance with animal welfare regulations and 46.6% for reporting animals excluded from analysis. Our models significantly outperform regular expressions for four risk of bias items.ConclusionFor random allocation, blinded assessment of outcome, conflict of interests and animal exclusions, neural models achieve good performance, and for animal welfare regulations, BERT model with sentence extraction strategy works better.

The road extraction from high resolution remote sensing image is of great importance in a variety of applications. Recently, the abundant deep convolutional neural networks are proposed for road extraction task. However, the existing approaches lack suitable strategy to utilize multiple views road features for road extraction, which fails to extract road with smooth appearance and accurate boundary under complex scenes. To address this problem, the authors propose a novel deep residual and pyramid pooling network (DRPPNet) for extracting road regions from high resolution remote sensing image. The DRPPNet consists of three parts: deep residual network (DResNet), pyramid pooling module (PPM) and deep decoder (DD). Specially, the DResNet uses several residual blocks to extract deep road features from input images, which can enhance learning ability of DRPPNet and avoid gradient vanish. Then, PPM is proposed to fuse road features from multiple views and it aims to address disadvantage of single view feature. Finally, the DD is used to recover size of feature maps to input size. Extensive experiments on two challenging road datasets demonstrate that proposed method outperforms the state‐of‐the‐art methods greatly on performance of road extraction task.

Funder: Queensland Government Accelerate Postdoctoral Research Fellowship, Outbreaks of infectious viruses resulting from spillover events from bats have brought much attention to bat‐borne zoonoses, which has motivated increased ecological and epidemiological studies on bat populations. Field sampling methods often collect pooled samples of bat excreta from plastic sheets placed under‐roosts. However, positive bias is introduced because multiple individuals may contribute to pooled samples, making studies of viral dynamics difficult. Here, we explore the general issue of bias in spatial sample pooling using Hendra virus in Australian bats as a case study. We assessed the accuracy of different under‐roost sampling designs using generalized additive models and field data from individually captured bats and pooled urine samples. We then used theoretical simulation models of bat density and under‐roost sampling to understand the mechanistic drivers of bias. The most commonly used sampling design estimated viral prevalence 3.2 times higher than individual‐level data, with positive bias 5–7 times higher than other designs due to spatial autocorrelation among sampling sheets and clustering of bats in roosts. Simulation results indicate using a stratified random design to collect 30–40 pooled urine samples from 80 to 100 sheets, each with an area of 0.75–1 m2, and would allow estimation of true prevalence with minimum sampling bias and false negatives. These results show that widely used under‐roost sampling techniques are highly sensitive to viral presence, but lack specificity, providing limited information regarding viral dynamics. Improved estimation of true prevalence can be attained with minor changes to existing designs such as reducing sheet size, increasing sheet number, and spreading sheets out within the roost area. Our findings provide insight into how spatial sample pooling is vulnerable to bias for a wide range of systems in disease ecology, where optimal sampling design is influenced by pathogen prevalence, host population density, and patterns of aggregation.

Time-varying covariates are often available in survival studies and estimation of the hazard function needs to be updated as new information becomes available. In this paper, we investigate several different easy-to-implement ways that random forests can be used for dynamic estimation of the survival or hazard function from discrete-time survival data. The results from a simulation study indicate that all methods can perform well, and that none dominates the others. In general, situations that are more difficult from an estimation point of view (such as weaker signals and less data) favour a global fit, pooling over all time points, while situations that are easier from an estimation point of view (such as stronger signals and more data) favor local fits.

As the world faced the devastation of the COVID‐19 pandemic in late 2019 and early 2020, numerous clinical trials were initiated in many locations in an effort to establish the efficacy (or lack thereof) of potential treatments. As the pandemic has been shifting locations rapidly, individual studies have been at risk of failing to meet recruitment targets because of declining numbers of eligible patients with COVID‐19 encountered at participating sites. It has become clear that it might take several more COVID‐19 surges at the same location to achieve full enrollment and to find answers about what treatments are effective for this disease. This paper proposes an innovative approach for pooling patient‐level data from multiple ongoing randomized clinical trials (RCTs) that have not been configured as a network of sites. We present the statistical analysis plan of a prospective individual patient data (IPD) meta‐analysis (MA) from ongoing RCTs of convalescent plasma (CP). We employ an adaptive Bayesian approach for continuously monitoring the accumulating pooled data via posterior probabilities for safety, efficacy, and harm. Although we focus on RCTs for CP and address specific challenges related to CP treatment for COVID‐19, the proposed framework is generally applicable to pooling data from RCTs for other therapies and disease settings in order to find answers in weeks or months, rather than years.

In conventional reallocation, farmers' preferences are used to determine the location of their new parcels in predetermined blocks. The most common conflict that can arise during this process is that demand may be high for some blocks. The manner of resolving such disputes, which deal directly with landowners' rights, can affect the success or failure of land consolidation projects. In this study, a novel model for land reallocation is proposed which is based on the principle that the initial situation of the landowner's parcels before land consolidation will be comparable with the new situation that includes all of his/her rights. For this, a spatial similarity-based approach was proposed, taking into account geometric, ownership, physical, and locational criteria. Then, the agricultural land reallocation model (LR-MOPSO) was developed using the multi-objective particle swarm algorithm. Three objectives were defined: (1) simultaneous consideration of farmers' priority and spatial similarity criteria; (2) pooling of farmers' fragmented parcels; and (3) optimal placement of parcels within the block. The LR-MOPSO model was applied to an Iranian case study and results were compared with a conventional approach. With this model decision-makers in land consolidation projects will be able to redistribute parcels in a more transparent way, while dealing with landowners' rights.

Female user (driver and rider) safety is a serious concern for ride‐hailing platforms. One way to address this concern is to migrate from the traditional “pooling” system that matches riders with drivers without considering gender to a “hybrid” system with a “female‐only” option. Will such a hybrid system result in a win‐win‐win outcome for all involved parties (riders, drivers, and the platform)? To answer this question, we investigate the performance of the two operational systems: a pooling system and a hybrid system. For each system, we analyze a two‐stage queueing game to determine the equilibrium “joining” and “participating” behavior of riders and drivers, and then derive the platform's optimal pricing and wage decisions. We posit a mismatch cost incurred by a safety‐concerned female user when she is matched with a male counterpart in a ride. By comparing the equilibrium outcomes associated with the pooling and the hybrid systems, we draw the following conclusions: when safety‐concerned female users’ mismatch cost is above a certain level, switching from a pooling system to a hybrid system can result in a win‐win outcome for safety‐concerned female users and the platform. However, male and safety‐unconcerned female users might be worse off due to this change in the system configuration. Our results also help us to rectify some of our intuitions about these two systems. One, in the pooling system, reducing the mismatch cost associated with safety‐concerned female drivers may not lead to more female riders joining the pooling system, even though it boosts the platform's profit in general. Two, in the hybrid system, it is not necessary for female riders to pay a higher price when they opt for female drivers instead of male drivers. We also relate our results to certain system configurations adopted by various ride‐hailing platforms to address female safety concerns in different countries.

Chronic medical conditions often necessitate regular testing for proper treatment. Regular testing of all afflicted individuals may not be feasible due to limited resources, as is true with HIV monitoring in resource-limited settings. Pooled testing methods have been developed in order to allow regular testing for all while reducing resource burden. However, the most commonly used methods do not make use of covariate information predictive of treatment failure, which could improve performance. We propose and evaluate four prediction-driven pooled testing methods that incorporate covariate information to improve pooled testing performance. We then compare these methods in the HIV treatment management setting to current methods with respect to testing efficiency, sensitivity, and number of testing rounds using simulated data and data collected in Rakai, Uganda. Results show that the prediction-driven methods increase efficiency by up to 20% compared with current methods while maintaining equivalent sensitivity and reducing number of testing rounds by up to 70%. When predictions were incorrect, the performance of prediction-based matrix methods remained robust. The best performing method using our motivating data from Rakai was a prediction-driven hybrid method, maintaining sensitivity over 96% and efficiency over 75% in likely scenarios. If these methods perform similarly in the field, they may contribute to improving mortality and reducing transmission in resource-limited settings. Although we evaluate our proposed pooling methods in the HIV treatment setting, they can be applied to any setting that necessitates testing of a quantitative biomarker for a threshold-based decision.

The pathophysiology of major depressive disorder (MDD) has been explored to be highly associated with the dysfunctional integration of brain networks. It is therefore imperative to explore neuroimaging biomarkers to aid diagnosis and treatment. In this study, we developed a spatiotemporal graph convolutional network (STGCN) framework to learn discriminative features from functional connectivity for automatic diagnosis and treatment response prediction of MDD. Briefly, dynamic functional networks were first obtained from the resting‐state fMRI with the sliding temporal window method. Secondly, a novel STGCN approach was proposed by introducing the modules of spatial graph attention convolution (SGAC) and temporal fusion. A novel SGAC was proposed to improve the feature learning ability and special anatomy prior guided pooling was developed to enable the feature dimension reduction. A temporal fusion module was proposed to capture the dynamic features of functional connectivity between adjacent sliding windows. Finally, the STGCN proposed approach was utilized to the tasks of diagnosis and antidepressant treatment response prediction for MDD. Performances of the framework were comprehensively examined with large cohorts of clinical data, which demonstrated its effectiveness in classifying MDD patients and predicting the treatment response. The sound performance suggests the potential of the STGCN for the clinical use in diagnosis and treatment prediction., In this study, we developed a spatiotemporal graph convolutional network framework to learn discriminative features from functional connectivity for automatic diagnosis and treatment response prediction of major depressive disorder. Performances of the framework were comprehensively examined with large cohorts of clinical data, which demonstrated its effectiveness in classifying major depressive disorder patients and predicting the treatment response.

This study develops a dynamic model to examine how a firm selling new non‐durable experience goods can signal its high quality with dynamic spot‐pricing or price commitment. Since consumers who buy and use the product will learn its quality, the firm's early period price will endogenously determine the number of informed consumers in the later period. Without price commitment, the high‐quality firm prefers the pooling outcome in the first period, generating enough informed consumers to induce a separating equilibrium outcome in the second period where both types of firms serve only their respective first‐period buyers. By contrast, if firms can commit to future prices, the high‐quality firm can signal its quality by committing to an increasing price‐path, either a lower‐than‐first‐best price for the early period with a first‐best price for the later period or the first‐best early period price with a higher‐than‐first‐best future price. Price commitment will benefit the high‐quality firm by lowering its signaling cost and hurt the low‐quality firm by increasing its cost of mimicking the high‐quality firm. Interestingly, the firm's price commitment can either increase or decrease consumer surplus and social welfare. We show that a longer time horizon can allow the high‐quality firm to costlessly signal its quality by maintaining its high first‐best price for all periods. Our results are robust even when social learning (e.g., through consumer reviews) is considered.

Tissue analysis using histopathological images is the most prevailing as well as a challenging task in the treatment of cancer. The clinical assessment of tissues becomes very tough as high variability in the magnification levels makes the situation worst for any pathologist to deal with the benign and malignant stages of cancer. One of the possible ways to address such a pathetic situation could be an advanced machine learning approach. Hence, a convolutional neural network (CNN) architecture is proposed to create an automated system for magnification independent multi‐classification of breast cancer histopathological images. This automated system offers high productivity and consistency in diagnosing the eight different classes of breast cancer from a balanced BreakHis dataset. The system utilises an efficient training methodology to learn the discerning features from images of different magnification levels. Data augmentation techniques are also employed to overcome the problem of overfitting. Additionally, the performance of CNN architecture has been improved in a significant manner by adopting an appropriate pooling strategy and optimisation technique. Based on that, we have achieved an accuracy of 80.76%, 76.58%, 79.90%, and 74.21% at the magnification 40X, 100X, 200X, and 400X, respectively. The proposed model outperforms the handcrafted approaches with an average accuracy of 80.47% at 40X magnification level.

Background/Objective Digital anthropometric (DA) assessments are increasingly being administered with three‐dimensional (3D) optical devices in clinical settings that manage patients with obesity and related metabolic disorders. However, anatomic measurement sites are not standardized across manufacturers, precluding use of published reference values and pooling of data across research centers. Subjects/Methods This study aimed to develop universal 3D analysis software by applying novel programming strategies capable of producing device‐independent DA estimates that agree with conventional anthropometric (CA) measurements made at well‐defined anatomic sites. A series of technical issues related to proprietary methods of 3D geometrical reconstruction and image analysis were addressed in developing major software components. To evaluate software accuracy, comparisons were made to CA circumference measurements made with a flexible tape at eleven standard anatomic sites in up to 35 adults scanned with three different commercial 3D optical devices. Results Overall, group mean CA and DA values across the three systems were in good agreement, with ∼2 cm systematic differences; CA and DA estimates were highly correlated (all p‐values

Purpose Breast mass segmentation is a prerequisite step in the use of computer-aided tools designed for breast cancer diagnosis and treatment planning. However, mass segmentation remains challenging due to the low contrast, irregular shapes, and fuzzy boundaries of masses. In this work, we propose a mammography mass segmentation model for improving segmentation performance. Methods We propose a mammography mass segmentation model called SAP-cGAN, which is based on an improved conditional generative adversarial network (cGAN). We introduce a superpixel average pooling layer into the cGAN decoder, which utilizes superpixels as a pooling layout to improve boundary segmentation. In addition, we adopt a multiscale input strategy to enable the network to learn scale-invariant features with increased robustness. The performance of the model is evaluated with two public datasets: CBIS-DDSM and INbreast. Moreover, ablation analysis is conducted to evaluate further the individual contribution of each block to the performance of the network. Results Dice and Jaccard scores of 93.37% and 87.57%, respectively, are obtained for the CBIS-DDSM dataset. The Dice and Jaccard scores for the INbreast dataset are 91.54% and 84.40%, respectively. These results indicate that our proposed model outperforms current state-of-the-art breast mass segmentation methods. The superpixel average pooling layer and multiscale input strategy has improved the Dice and Jaccard scores of the original cGAN by 7.8% and 12.79%, respectively. Conclusions Adversarial learning with the addition of a superpixel average pooling layer and multiscale input strategy can encourage the Generator network to generate masks with increased realism and improve breast mass segmentation performance through the minimax game between the Generator network and Discriminator network.

Egypt's rate of unemployment averaged 11% over the period 1993–2018. Although this has motivated abundant research, it has focused on nation‐wide unemployment, with little attention to unemployment at the regional (i.e., governorate) level. Indeed, there are wide disparities in unemployment among governorates. Moreover, high unemployment governorates may be clustered together in a way that yields spatial autocorrelation. The present paper therefore fills this gap in empirical literature by analysing unemployment by region using a spatial panel data model (for 27 governorates over the 2006–2016 period). It establishes the existence of (positive) spatial dependence in regional unemployment. Regional unemployment is further found to be affected by low levels of education among the governorates' populations (illiterates, and holders of intermediate and above intermediates) pointing to a mismatch between the skills demanded and the skills supplied at the governorate level. Though services have the highest share in average employment across governorates (0.64), followed by agriculture (0.26), neither of the two indicators (proxies) of activity mix is found to be statistically significant. Other variables expected to affect regional unemployment were not statistically significant (female and youth unemployment, despite high prevalence of both female and youth unemployment across the governorates of Egypt). Policy implications include: the pooling of efforts between neighbouring governorates to address unemployment; matching leading activities in the governorates with the ranks of the unemployed; using apprenticeship schemes; increasing female labour force participation; and promoting private sector job creation.

Meta-analyzing count data can be challenging when follow-up time varies across studies. Simply pooling aggregate data over time-periods would result in biased estimates, which may erroneously inform clinical decision-making. In this study, we exploit the convolution property of the Poisson distribution to develop a likelihood for observed cumulative counts over varying follow-up periods, where different Poisson distributions are used to represent the data generating processes for the latent counts in pre-defined successive intervals of follow-up. We illustrate this approach using an example of poststroke seizures, a case in which risk may change over time, and mimic its survival duration with time-varying hazard. Data were extracted from observational studies (1997-2016) reporting poststroke seizures over a maximum of 10 years of follow-up. Three clinically meaningful follow-up time intervals were considered: 0 to 7 days, 8 to 365 days, and 1 to 10 years poststroke. External validation was performed using claims data. Results suggest the incidence rate of seizures was 0.0452 (95% confidence interval: 0.0429, 0.0475), 0.0001 (0, 0.016), and 0.0647 (0.0441, 0.0941) for the three time intervals, respectively, indicating that the risk of seizures changes over time poststroke. We found that the model performed well against the incidence rate of seizures among actual retrospective cohort from claims data. The piecewise Poisson model presents a flexible way to meta-analyze count data over time and mimic survival curves. The results of the piecewise Poisson model are readily interpretable and may spur meaningful clinical action. The method may also be applied to other diseases. HIGHLIGHTS: It is challenging to perform a meta-analysis when follow-up time varies across studies. Ideally, outcomes over different time-periods should be pooled with individual patient-level data (IPD). A new model was developed to meta-analyze count data over time using aggregate-level data from previous published studies. The piecewise Poisson model could be a useful tool to estimate time-vary hazards given available data, and mimic survival curves over time which would be readily interpretable.

Designing effective settings for performance measures (e.g., fill rate) of a service-level agreement (SLA) is challenging. This challenge is intensified when a firm adopts the pooling inventory model to allocate inventory/capacity to multiple buyers. Each buyer has its own service-level contract outlining the required service level, the penalty structures, and the performance review period (PRP) length, which might not be the same as other buyers. This means the supplier requires an effective resource allocation policy whereby the different requirements of multiple buyers are integrated into a pooling model and capacities/inventories are allocated in the most effective way. Given a base-stock replenishment policy and finite time horizon PRP, in this study we propose two new (anticipative) allocation policies—foresight linear programming (FLP) and two-stage stochastic (TS)—and compare them with existing allocation policies. These allocation policies are developed for different penalty structures of linear, lump-sum, hybrid, and no-penalty settings. Results show that suppliers benefit from longer PRPs if linear or hybrid penalty structures are employed. We also find that when the length of PRP of buyers is not identical, TS is the recommending policy. Further, results provide a guideline for selecting the best resource allocation policy under various SLA terms, in particular, where buyers' PRP lengths are not identical.

BACKGROUND AND OBJECTIVES: Although about 80% of coronavirus disease-2019 (COVID-19) cases are reported to be mild, the remaining 20% of cases often result in severe disease with the potential of crushing already overstrained health care services. There has been sustainable growth of COVID-19 cases worldwide since mid-May 2020. To keep tabs on community transmission of COVID-19 infection screening of the samples from a large population is needed which includes asymptomatic/symptomatic individuals along with the migrant population. This requires extra resources, man power, and time for detection of severe acute respiratory syndrome coronavirus 2 by real-time polymerase chain reaction (RT-PCR). In the current scenario, the pooled sample testing strategy advocated by the Indian Council of Medical Research, New Delhi is a new approach that is very promising in resource-limited settings. In this study, we have evaluated the pooled strategy in terms of accurate testing results, utilization of consumables, and identification of borderline positive cases. MATERIALS AND METHODS: Between April and June 2020, we performed COVID-19 testing by RT-PCR from areas with varying prevalence of population referred to COVID laboratory, Dr Ram Manohar Lohia Institute of Medical Sciences, Lucknow. In the first step, the samples are collated into pools of 5 or 10. These pools are tested by RT-PCR. Negative pools were reported as negative whereas positive pools of 5 and 10 are then deconvoluted and each sample is tested individually. RESULTS: In the present study, we tested 4620 samples in 462 pools of 10 and 14 940 samples in 2990 pools of 5. Among 10 samples pool, 61 (13%) pools flagged positive in the first step. In the second step, among 61 pools (610 samples) deconvoluted strategy was followed in which 72 individual samples came positive. The pooled-sample testing strategy helps saves substantial resources and time during surge testing and enhanced pandemic surveillance. This approach requires around 76% to 93% fewer tests done in low to moderate prevalence settings and group sizes up to 5-10 in a population, compared to individual testing. CONCLUSIONS: Pooled-sample PCR analysis strategies can save substantial resources and time for COVID-19 mass testing in comparison with individual testing without compromising the resulting outcome of the test. In particular, the pooled-sample approach can facilitate mass screening in the early coming stages of COVID-19 outbreaks, especially in low- and middle-income settings, and control the spread by meticulous testing of all risk groups.

As the battle against coronavirus disease 2019 pandemic continues, an increase in workload and medical expenses have been a concern to the health care system worldwide. Developing a measure that helps to conserve the health care resource is, therefore, highly desirable, and the pooling of the specimens for testing is one of the attractive strategies. Recently we showed that saliva could be a potential alternative specimen for the detection of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) by real‐time polymerase chain reaction (RT‐PCR). In the present study, we performed the pooling of saliva specimens for testing by SARS‐CoV‐2 RT‐PCR. We showed that the saliva pool of either five or ten samples, by allowing the detection of either gene in the pool at an increased cycle threshold cut‐off value, further performing individual sample testing in the positive pools did not compromise the detection of SARS‐CoV‐2. This article is protected by copyright. All rights reserved.

Can a school or district improve student achievement simply by switching to a higher‐quality textbook or curriculum? We conducted the first multi‐textbook, multi‐state effort to estimate textbook efficacy following widespread adoption of the Common Core State Standards (CCSS) and associated changes in the textbook market. Pooling textbook adoption and student test score data across six geographically and demographically diverse U.S. states, we found little evidence of differences in average achievement gains for schools using different math textbooks. We found some evidence of greater variation in achievement gains among schools using pre‐CCSS editions, which may have been more varied in their content than post‐CCSS editions because they were written for a broader set of standards. We also found greater variation among schools that had more exposure to a given text. However, these differences were small. Despite considerable interest and attention to textbooks as a low‐cost, “silver bullet” intervention for improving student outcomes, we conclude that the adoption of a new textbook or set of curriculum materials, on its own, is unlikely to achieve this goal.

This article studies the effect of utilizing past credit information about borrowers on interest rates, collateral requirements, and credit rationing. Specifically, I develop a two‐period lending model with asymmetric information and endogenous contract terms, assuming a pooling equilibrium. I show that when default information is utilized privately, contract terms decrease under reasonable assumptions. When default information is also shared with other lenders, non‐defaulters enjoy better contract terms in period 2, while defaulters are offered worse terms, resulting in reduced adverse selection and more equitable terms on average for risk‐neutral borrowers. Finally, when borrowers are aware that information is utilized, information sharing also reduces moral hazard, collateral requirements, and credit rationing for all borrowers.

In the age of a pandemic, such as the ongoing one caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the world faces a limited supply of tests, personal protective equipment, and factories and supply chains are struggling to meet the growing demands. This study aimed to evaluate the efficacy of specimen pooling for testing of SARS-CoV-2 virus, to determine whether costs and resource savings could be achieved without impacting the sensitivity of the testing. Ten previously tested nasopharyngeal and throat swab specimens by real-time polymerase chain reaction (PCR), were pooled for testing, containing either one or two known positive specimens of varying viral concentrations. Specimen pooling did not affect the sensitivity of detecting SARS-CoV-2 when the PCR cycle threshold (Ct) of original specimen was lower than 35. In specimens with low viral load (Ct > 35), 2 of 15 pools (13.3%) were false negative. Pooling specimens to test for Coronavirus Disease 2019 infection in low prevalence (≤1%) areas or in low risk populations can dramatically decrease the resource burden on laboratory operations by up to 80%. This paves the way for large-scale population screening, allowing for assured policy decisions by governmental bodies to ease lockdown restrictions in areas with a low incidence of infection, or with lower-risk populations.

We develop and demonstrate methods to perform sensitivity analyses to assess sensitivity to plausible departures from missing at random in incomplete repeated binary outcome data. We use multiple imputation in the not at random fully conditional specification framework, which includes one or more sensitivity parameters (SPs) for each incomplete variable. The use of an online elicitation questionnaire is demonstrated to obtain expert opinion on the SPs, and highest prior density regions are used alongside opinion pooling methods to display credible regions for SPs. We demonstrate that substantive conclusions can be far more sensitive to departures from the missing at random assumption (MAR) when control and intervention nonresponders depart from MAR differently, and show that the correlation of arm specific SPs in expert opinion is particularly important. We illustrate these methods on the iQuit in Practice smoking cessation trial, which compared the impact of a tailored text messaging system versus standard care on smoking cessation. We show that conclusions about the effect of intervention on smoking cessation outcomes at 8 week and 6 months are broadly insensitive to departures from MAR, with conclusions significantly affected only when the differences in behavior between the nonresponders in the two trial arms is larger than expert opinion judges to be realistic.

Land access is central to natural resource development projects. Business strategy for securing continuing tenure to support operational activities is therefore a key factor in determining the overall success of resource projects. A major shortcoming in resettlement planning has been the failure to address basic inequities stemming from the transfer of land assets. Compensation for land in conventional models neglects to account for the potential or intended use of the developer, meaning that affected people (APs) assets are grossly undervalued in the environment in which they acquired. Not only do resource projects under‐compensate APs for their losses, but programming for livelihood restoration and the enjoyment of project benefits more generally has proven to be almost universally poor. Performance outcomes reflect short‐term project milestones but lack a strategic dimension regarding how the company will maintain land access and the continuing support of its nearest stakeholders. Innovations are needed on at least two fronts: (i) the recognition of AP land values in mining project developments and (ii) enterprise financing to avoid the issue of companies driving incurred compensation costs downward. This article outlines the conceptual basis of a land pooling model as the means for securing financial equity for people displaced by mining projects. The authors conclude that the land pooling model of resettlement (LPMR) can improve the short‐ to medium‐term economic conditions of APs and advances suggestions to further promote a balance of interests in mining‐induced displacement and resettlement cases.

This paper examines how positive or negative perceptions about innovation affect firms' strategic behavior when certifying their products. In particular, we consider two types of firm (innovative and noninnovative) which choose between three signals: (a) certified claim, (b) uncertified claim, and (c) no claim. The consumer, either exhibiting positive or negative perceptions, is uninformed about the firm's type and only observes the firm's claim. We find that a separating equilibrium arises in which information about the innovation is revealed to consumers. We also identify a pooling equilibrium in which both types of firm choose the same claim, concealing information from consumers. We show that regulation requiring mandatory certification can hinder information transmission. Our results also indicate that changes in product perceptions do not necessarily facilitate information transmission.

Automated vehicles (AVs) have emerged rapidly in recent years, becoming a focus of high expectations and heated debates. Advocates argue that the arrival of AVs will make driving safer, greener, cheaper, and faster, bringing ubiquitous access to transportation while significantly reducing traffic congestion and environmental impacts. Skeptics, in contrast, suggest that the appeal of AVs will induce additional driving, offsetting or even overwhelming the positive effects of increased automation. Many analysts now believe that the solution lies in ensuring that most vehicle trips are shared to serve the same number of passenger miles with fewer vehicle miles, reducing traffic congestion. However, these analyses fail to recognize that reducing congestion will induce yet more demand for driving and attract riders from other transportation modes including public transit, which is already experiencing falling ridership in many cities. In this study, we explore the impact of AVs and pooling on consumer mode choice and the effect on the performance of both road and public transit systems. We show that the well‐intentioned move to promote pooling may have the unintended consequence of triggering a public transit death spiral, leading to both worse public transit quality and more rather than less traffic congestion. We argue that the deployment of AVs and pooling can be effective at accelerating the transition to sustainable urban mobility, but only when accompanied by policies that make driving less attractive, not more.

A wide range of analytic and data sharing options are available in non-experimental multi-database studies designed to assess the real-world benefits and risks of medical products. Researchers often consider six scientific domains when choosing among these options – study design, exposure type, outcome type, covariate summarization technique, covariate adjustment method, and data sharing approach. This article reviews available analytic and data sharing options and discuss key scientific and practical considerations when choosing among these options in multi-database studies of comparative effectiveness and safety of medical products. The scientific considerations must be balanced against what the data-contributing sites are able or willing to share. While pooling of person-level datasets remains the most familiar and analytically flexible approach, newer analytic and data sharing approaches that share less granular summary-level information may be equally valid and preferred in some multi-database studies, especially when sharing of person-level data is challenging or infeasible.

JAH received funding from The Oceans of Change Project. DMP received funding from the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland) and their support is gratefully acknowledged. MASTS is funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions". 1. Sediment dwelling, microscopic primary producers, that occupy sediments in the photic zone, are commonly referred to as microphytobenthos (MPB). The MPB are essential components of soft‐sediment systems, but are often overlooked when assessing coastal ecosystem functionality and service delivery. 2. The MPB are involved in several complex interactions and feedback that underpin the delivery of vital ecosystem services. MPB profoundly influence the flow and cycling of carbon and nutrients, such as nitrogen, directly and indirectly underpinning highly productive shallow water marine food webs. The MPB can also stabilize sediments through the formation of biofilms, and significantly improve water quality by mediating the benthic–pelagic coupling of nutrients, sediment and pollutants. 3.The functional role of the MPB is compromised by increasing anthropogenic pressures such as nutrient enrichment, sedimentation, herbicides and emerging contaminants such as microplastic pollution. However, MPB are extremely good at buffering the effects of these land‐sourced stressors at the interface between land and sea. 4. Synthesis. Society often appreciates the final provisioning of goods and services from our coastal marine environments. However, provisioning services are only possible due to the multitude of supporting and regulating services that underpin them. Microphytobenthos (MPB) are central to benthic ecological networks, and contribute to ecosystem service delivery through various pathways. Understanding the critical role of MPB in complex networks is therefore essential to appreciate their importance in ecosystem function and service delivery into the future. Postprint

High rates of mortality on long term carcinogenicity studies can often result in challenges when it comes to the statistical analysis of tumor incidence. The current regulatory advice often results in treated groups being terminated earlier than the control group. However, this advice rarely considers the impact of this action on the statistical analyses. The nature of these analyses means that groups terminated at different times may not be directly comparable due to age differences of the animals. Here we discuss the issues related to this and investigate several approaches of how to incorporate these groups within the statistical analyses. Although no single method appears to resolve these issues consistently, inclusion of the early terminated group is still informative. Depending on the timing of the early termination, either pooling of the groups into a single terminal kill (TK) interval or reassignment of intervals based purely on time of death (ie, no separate TK interval) appear preferable. However, to draw meaningful conclusions the time of onset of a given tumor must also be considered alongside incident rates and any statistical findings.

Standard methods for meta-analysis are limited to pooling tasks in which a single effect size is estimated from a set of independent studies. However, this setting can be too restrictive for modern meta-analytical applications. In this contribution, we illustrate a general framework for meta-analysis based on linear mixed-effects models, where potentially complex patterns of effect sizes are modeled through an extended and flexible structure of fixed and random terms. This definition includes, as special cases, a variety of meta-analytical models that have been separately proposed in the literature, such as multivariate, network, multilevel, dose-response, and longitudinal meta-analysis and meta-regression. The availability of a unified framework for meta-analysis, complemented with the implementation in a freely available and fully documented software, will provide researchers with a flexible tool for addressing nonstandard pooling problems.

Samples from multiple animals may be pooled and tested to reduce costs of surveillance for infectious agents in aquatic animal populations. The primary advantage of pooling is increased population-level coverage when prevalence is low (

textabstractOur study contributes to the scholarly debate whether organizational units should have a narrow focus and admit a homogeneous patient cluster or whether they should admit a pool of patient clusters. We investigate whether the benefits of increased volume through pooling patients outweigh the disadvantages of increased heterogeneity and pursue our analysis in the context of neonatal care. Our empirical studies relies on 4020 patient episodes collected in 18 German neonatal intensive care units and we distinguish between two patient clusters that differ with respect to the inherent medical risk and operational heterogeneity. Cluster 1 consists of very-low birth weight (VLBW) infants with increased risk of complications but similar service trajectories and lower operational heterogeneity. Cluster 2 contains non-VLBW infants with lower risk of complications but more diversity in disease patterns and higher operational heterogeneity. Our analysis shows that cluster volume, that is, the unit's absolute patient volume in a cluster, is positively related to process outcomes as indicated by decreasing length of stay. This relationship is found for both clusters. Regarding focus, we do not find any evidence of positive effects. In fact, we even find that cluster focus, that is, the unit's relative volume of the cluster, is detrimentally related to process outcomes for non-VLBW patients with lower risk of complications and more operational heterogeneity. This indicates that organizational units providing services for complex patients should not have a narrow focus, but should rather provide services for related patient clusters in order to achieve higher volume levels within the unit.

Funding: Marine Scotland Science and the Marine Alliance for Science and Technology for Scotland (MASTS) pooling initiative, and their support is gratefully acknowledged. MASTS is funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions. 1. The population of bottlenose dolphins in eastern Scotland has undergone significant range expansion since the 1990s, when a Special Area of Conservation was established for the population. 2. Distribution of this population is well described within areas of its range where intensive work has been carried out, such as the inner Moray Firth, St Andrews Bay and the Tay estuary area. However, elsewhere in their range, habitat use is less well understood. 3. In this study, a large‐scale and long‐term passive acoustic array was used to gain a better understanding of bottlenose dolphin habitat use in eastern Scottish waters, complementing and augmenting existing visual surveys. 4. Data from the array were analysed using a three‐stage approach. First, acoustic occupancy results were reported; second, temporal trends were modelled; and third, a spatial–temporal‐habitat model of acoustic occupancy was created. 5. Results from the acoustic occupancy are in agreement with visual studies that found that areas near known foraging locations were consistently occupied. Results from the temporal trend analysis were inconclusive. Habitat modelling showed that, throughout their range, bottlenose dolphins are most likely to be detected closer to shore, and at a constant distance from shore, in deeper water. Postprint

This paper aims to verify the presence of the learning-by-exporting effect on total factor productivity growth. The study starts, as is typical in this context, by addressing the pre-entry selection bias at firm level but differs from the literature by focusing on the distribution of the outcome and considering the presence of the different influences of macroeconomic factors on exporters and non-exporters. Additionally, the paper addresses the panel attrition, a current source of estimation bias in longitudinal studies. The analysis is based on a panel of Italian manufacturing firms in the 1998–2007 period. We design an experiment by aligning and pooling cohorts of firms that allows us to obtain a sufficiently large group of firms entering the international market. Our results show that internationalisation affects firms’ productivity and that the effect is heterogeneous over total factor productivity distribution and larger for the firms at the bottom section of the distribution itself. Furthermore, we observe that the learning-by-exporting effect may be confounded without i) considering that domestic and exporter firms may afford heterogeneous demand cycles and ii) managing the dropout of some firms from the panel.