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Mesenchymal stromal stem cells (MSCs) possess a remarkable potential for numerous clinical applications due to their unique properties including selfrenewal, immunomodulation, paracrine actions and multilineage differentiation. However, the translation of MSC-based Advanced Therapy Medicinal Products (ATMPs) into the clinic has frequently met with inconsistent outcomes. One of the suspected reasons for this issue is the inherent and extensive variability that exists among such ATMPs, which makes the interpretation of their clinical efficacy difficult to assess, as well as to compare the results of various studies. This variability stems from numerous reasons including differences in tissue sources, donor attributes, variances in manufacturing protocols, as well as modes of administration. MSCs can be isolated from various tissues including bone marrow, umbilical cord, adipose tissue and others, each with its unique phenotypic and functional characteristics. While MSCs from different sources do share common features, they also exhibit distinct gene expression profiles and functional properites. Donor-specific factors such as age, sex, body mass index, and underlying health conditions can influence MSC phenotype, morphology, differentiation potential and function. Moreover, variations in preparation of MSC products introduces additional heterogeneity as a result of cell culture media composition, presence or absence of added growth factors, use of different serum supplements and culturing techniques. Once MSC products are formulated, storage protocols play a pivotal role in its efficacy. Factors that affect cell viability include cell concentration, delivery solution and importantly, post-thawing protocols where applicable. Ensuing, differences in administration protocols can critically affect the distribution and functionallity of administered cells. As MSC-based therapies continue to advance through numerous clinical trials, implication of strategies to reduce product heterogeneity is imperative. Central to addressing these challenges is the need for precise prediction of clinical responses, which require well-defined MSC populations and harmonized assessment of their specific functions. By addressing these issues by meaningful approaches, such as, e.g., MSC pooling, the field can overcome barriers to advance towards more consistent and effective MSC-based therapies. [ABSTRACT FROM AUTHOR]

Crowding, a fundamental limit in object recognition, is believed to result from excessive integration of nearby items in peripheral vision. To understand its pooling mechanisms, we measured subjects' internal response distributions in an orientation crowding task. Contrary to the prediction of an averaging model, we observed a pattern suggesting that the perceptual judgement is made based on choosing the largest response across the noise-perturbed items. A model featuring first-stage averaging and second-stage signed-max operation predicts the diverse errors made by human observers under various signal strength levels. These findings suggest that different rules operate to resolve the bottleneck at early and high-level stages of visual processing, implementing a combination of linear and nonlinear pooling strategies. [ABSTRACT FROM AUTHOR]

Background: In 2022, fewer than half of persons with tuberculosis (TB) had access to molecular diagnostic tests for TB due to their high costs. Studies have found that the use of artificial intelligence (AI) software for chest X-ray (CXR) interpretation and sputum specimen pooling can each reduce the cost of testing. We modeled the combination of both strategies to estimate potential savings in consumables that could be used to expand access to molecular diagnostics. Methods: We obtained Xpert testing and positivity data segmented into deciles by AI probability scores for TB from the community- and healthcare facility-based active case finding conducted in Bangladesh, Nigeria, Viet Nam, and Zambia. AI scores in the model were based on CAD4TB version 7 (Zambia) and qXR (all other countries). We modeled four ordinal screening and testing approaches involving AI-aided CXR interpretation to indicate individual and pooled testing. Setting a false negative rate of 5%, for each approach we calculated additional and cumulative savings over the baseline of universal Xpert testing, as well as the theoretical expansion in diagnostic coverage. Results: In each country, the optimal screening and testing approach was to use AI to rule out testing in deciles with low AI scores and to guide pooled vs individual testing in persons with moderate and high AI scores, respectively. This approach yielded cumulative savings in Xpert tests over baseline ranging from 50.8% in Zambia to 57.5% in Nigeria and 61.5% in Bangladesh and Viet Nam. Using these savings, diagnostic coverage theoretically could be expanded by 34% to 160% across the different approaches and countries. Conclusions: Using AI software data generated during CXR interpretation to inform a differentiated pooled testing strategy may optimize TB diagnostic test use, and could extend molecular tests to more people who need them. The optimal AI thresholds and pooled testing strategy varied across countries, which suggests that bespoke screening and testing approaches may be needed for differing populations and settings. [ABSTRACT FROM AUTHOR]

Lung diseases represent a significant global health threat, impacting both well-being and mortality rates. Diagnostic procedures such as Computed Tomography (CT) scans and X-ray imaging play a pivotal role in identifying these conditions. X-rays, due to their easy accessibility and affordability, serve as a convenient and cost-effective option for diagnosing lung diseases. Our proposed method utilized the Contrast-Limited Adaptive Histogram Equalization (CLAHE) enhancement technique on X-ray images to highlight the key feature maps related to lung diseases using DenseNet201. We have augmented the existing Densenet201 model with a hybrid pooling and channel attention mechanism. The experimental results demonstrate the superiority of our model over well-known pre-trained models, such as VGG16, VGG19, InceptionV3, Xception, ResNet50, ResNet152, ResNet50V2, ResNet152V2, MobileNetV2, DenseNet121, DenseNet169, and DenseNet201. Our model achieves impressive accuracy, precision, recall, and F1-scores of 95.34%, 97%, 96%, and 96%, respectively. We also provide visual insights into our model's decision-making process using Gradient-weighted Class Activation Mapping (Grad-CAM) to identify normal, pneumothorax, and atelectasis cases. The experimental results of our model in terms of heatmap may help radiologists improve their diagnostic abilities and labelling processes. [ABSTRACT FROM AUTHOR]

Predicting the timing of phenological events is important in agriculture, especially high-revenue products. A project sponsored by USDA-ARS had the objective of adapting a previously developed model for estimating proportions of insects in different development stages as a function of temperature (degree) and time (days) for predicting bloom in almond orchards. Data for the model normally form a two-way table of counts, with rows corresponding to sample percentages of different development stages and columns to sampling times. In this study, we report a technique developed to estimate sample sizes of multinomial and product multinomial models using a method of moments and determine the empirical coverage of sample size. This study aims to determine an appropriate sample size for data collection. This involves establishing a sampling distribution for the Pearson statistic, defined as the product of the sample size and the deviance of empirical proportions from population proportions. The intended outcome is to predict the optimal timing for harvesting crops at desired development stages when coupled with the phenology model, for which variability of the maximum likelihood estimates of the phenology model depends on sample size. [ABSTRACT FROM AUTHOR]

Abstract Background In 2022, fewer than half of persons with tuberculosis (TB) had access to molecular diagnostic tests for TB due to their high costs. Studies have found that the use of artificial intelligence (AI) software for chest X-ray (CXR) interpretation and sputum specimen pooling can each reduce the cost of testing. We modeled the combination of both strategies to estimate potential savings in consumables that could be used to expand access to molecular diagnostics. Methods We obtained Xpert testing and positivity data segmented into deciles by AI probability scores for TB from the community- and healthcare facility-based active case finding conducted in Bangladesh, Nigeria, Viet Nam, and Zambia. AI scores in the model were based on CAD4TB version 7 (Zambia) and qXR (all other countries). We modeled four ordinal screening and testing approaches involving AI-aided CXR interpretation to indicate individual and pooled testing. Setting a false negative rate of 5%, for each approach we calculated additional and cumulative savings over the baseline of universal Xpert testing, as well as the theoretical expansion in diagnostic coverage. Results In each country, the optimal screening and testing approach was to use AI to rule out testing in deciles with low AI scores and to guide pooled vs individual testing in persons with moderate and high AI scores, respectively. This approach yielded cumulative savings in Xpert tests over baseline ranging from 50.8% in Zambia to 57.5% in Nigeria and 61.5% in Bangladesh and Viet Nam. Using these savings, diagnostic coverage theoretically could be expanded by 34% to 160% across the different approaches and countries. Conclusions Using AI software data generated during CXR interpretation to inform a differentiated pooled testing strategy may optimize TB diagnostic test use, and could extend molecular tests to more people who need them. The optimal AI thresholds and pooled testing strategy varied across countries, which suggests that bespoke screening and testing approaches may be needed for differing populations and settings.

The goal of this study was to test if CFD-based virtual angiograms could be used to automatically discriminate between intracranial aneurysms (IAs) with and without flow stagnation. Time density curves (TDC) were extracted from patient digital subtraction angiography (DSA) image sequences by computing the average gray level intensity inside the aneurysm region and used to define injection profiles for each subject. Subject-specific 3D models were reconstructed from 3D rotational angiography (3DRA) and computational fluid dynamics (CFD) simulations were performed to simulate the blood flow inside IAs. Transport equations were solved numerically to simulate the dynamics of contrast injection into the parent arteries and IAs and then the contrast retention time (RET) was calculated. The importance of gravitational pooling of contrast agent within the aneurysm was evaluated by modeling contrast agent and blood as a mixture of two fluids with different densities and viscosities. Virtual angiograms can reproduce DSA sequences if the correct injection profile is used. RET can identify aneurysms with significant flow stagnation even when the injection profile is not known. Using a small sample of 14 IAs of which seven were previously classified as having flow stagnation, it was found that a threshold RET value of 0.46 s can successfully identify flow stagnation. CFD-based prediction of stagnation was in more than 90% agreement with independent visual DSA assessment of stagnation in a second sample of 34 IAs. While gravitational pooling prolonged contrast retention time it did not affect the predictive capabilities of RET. CFD-based virtual angiograms can detect flow stagnation in IAs and can be used to automatically identify aneurysms with flow stagnation even without including gravitational effects on contrast agents.

In convolutional neural networks, pooling methods are used to reduce both the size of the data and the number of parameters after the convolution of the models. These methods reduce the computational amount of convolutional neural networks, making the neural network more efficient. Maximum pooling, average pooling, and minimum pooling methods are generally used in convolutional neural networks. However, these pooling methods are not suitable for all datasets used in neural network applications. In this study, a new pooling approach to the literature is proposed to increase the efficiency and success rates of convolutional neural networks. This method, which we call MAM (Maximum Average Minimum) pooling, is more interactive than other traditional maximum pooling, average pooling, and minimum pooling methods and reduces data loss by calculating the more appropriate pixel value. The proposed MAM pooling method increases the performance of the neural network by calculating the optimal value during the training of convolutional neural networks. To determine the success accuracy of the proposed MAM pooling method and compare it with other traditional pooling methods, training was carried out on the LeNet-5 model using CIFAR-10, CIFAR-100, and MNIST datasets. According to the results obtained, the proposed MAM pooling method performed better than the maximum pooling, average pooling, and minimum pooling methods in all pool sizes on three different datasets. [ABSTRACT FROM AUTHOR]

Abstract Mesenchymal stromal cells (MSC) from adult bone marrow are the most commonly used cells in clinical trials. MSCs from single donors are the preferred starting material but suffer from a major setback of being heterogeneous that results in unpredictable and inconsistent clinical outcomes. To overcome this, we developed a method of pooling MSCs from different donors and created cell banks to cater clinical needs. Initially, the master cell banks (MCBs) were created at passage 1 (P1) from the bone marrow MSCs isolated from of nine different donors. At this stage, MCBs from three different donors were mixed in equal proportion and expanded till P3 to create working cell banks. Further, the pooled cells and individual donor MSCs were expanded till P5 and cryopreserved and extensively characterised. There was a large heterogeneity among the individual donor MSCs in terms of growth kinetics (90% Coefficient of variation (CV) for cell yield and 44% CV for population doubling time at P5), immunosuppressive ability (30% CV at 1:1 and 300% CV at 1:10 ratio), and the angiogenic factor secretion potential (20% CV for VEGF and71% CV for SDF-1). Comparatively, the pooled cells have more stable profiles (60% CV for cell yield and 7% CV for population doubling time at P5) and exhibit better immunosuppressive ability (15% CV at 1:1 and 32% CV at 1:10 ratio ) and consistent secretion of angiogenic factors (16% CV for VEGF and 51% CV for SDF-1). Further pooling does not compromise the trilineage differentiation capacity or phenotypic marker expression of the MSCs. The senescence and in vitro tumourigenicity characteristics of the pooled cells are also similar to those of individual donor MSCs. We conclude that pooling of MSCs from three different donors reduces heterogeneity among individual donors and produces MSCs with a consistent secretion and higher immunosuppressive profile.

Abstract Observational data provide invaluable real-world information in medicine, but certain methodological considerations are required to derive causal estimates. In this systematic review, we evaluated the methodology and reporting quality of individual-level patient data meta-analyses (IPD-MAs) conducted with non-randomized exposures, published in 2009, 2014, and 2019 that sought to estimate a causal relationship in medicine. We screened over 16,000 titles and abstracts, reviewed 45 full-text articles out of the 167 deemed potentially eligible, and included 29 into the analysis. Unfortunately, we found that causal methodologies were rarely implemented, and reporting was generally poor across studies. Specifically, only three of the 29 articles used quasi-experimental methods, and no study used G-methods to adjust for time-varying confounding. To address these issues, we propose stronger collaborations between physicians and methodologists to ensure that causal methodologies are properly implemented in IPD-MAs. In addition, we put forward a suggested checklist of reporting guidelines for IPD-MAs that utilize causal methods. This checklist could improve reporting thereby potentially enhancing the quality and trustworthiness of IPD-MAs, which can be considered one of the most valuable sources of evidence for health policy.

Mesenchymal stromal cells (MSC) from adult bone marrow are the most commonly used cells in clinical trials. MSCs from single donors are the preferred starting material but suffer from a major setback of being heterogeneous that results in unpredictable and inconsistent clinical outcomes. To overcome this, we developed a method of pooling MSCs from different donors and created cell banks to cater clinical needs. Initially, the master cell banks (MCBs) were created at passage 1 (P1) from the bone marrow MSCs isolated from of nine different donors. At this stage, MCBs from three different donors were mixed in equal proportion and expanded till P3 to create working cell banks. Further, the pooled cells and individual donor MSCs were expanded till P5 and cryopreserved and extensively characterised. There was a large heterogeneity among the individual donor MSCs in terms of growth kinetics (90% Coefficient of variation (CV) for cell yield and 44% CV for population doubling time at P5), immunosuppressive ability (30% CV at 1:1 and 300% CV at 1:10 ratio), and the angiogenic factor secretion potential (20% CV for VEGF and71% CV for SDF-1). Comparatively, the pooled cells have more stable profiles (60% CV for cell yield and 7% CV for population doubling time at P5) and exhibit better immunosuppressive ability (15% CV at 1:1 and 32% CV at 1:10 ratio) and consistent secretion of angiogenic factors (16% CV for VEGF and 51% CV for SDF-1). Further pooling does not compromise the trilineage differentiation capacity or phenotypic marker expression of the MSCs. The senescence and in vitro tumourigenicity characteristics of the pooled cells are also similar to those of individual donor MSCs. We conclude that pooling of MSCs from three different donors reduces heterogeneity among individual donors and produces MSCs with a consistent secretion and higher immunosuppressive profile. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNNs) are a kind of artificial neural network designed to extract features and find out patterns for tasks such as segmentation, recognizing objects, and drawing up classification. Within a CNNs architecture, pooling operations are used until the number of parameters and the computational complexity are reduced. Numerous papers have focused on investigating the impact of pooling on the performance of Convolutional Neural Networks (CNNs), leading to the development of various pooling models. Recently, a fuzzy pooling operation based on type-1 fuzzy sets was introduced to cope with the local imprecision of the feature maps. However, in fuzzy set theory, it is not always accurate to assume that the degree of non-membership of an element in a fuzzy set is simply the complement of the degree of membership. This is due to the potential existence of a hesitation degree, which implies a certain level of uncertainty. To overcome this limitation, intuitionistic fuzzy sets (IFS) were introduced to incorporate the concept of a degree of hesitation. In this paper, we introduce a novel pooling operation based on intuitionistic fuzzy sets to incorporate the degree of hesitation heretofore neglected by a fuzzy pooling operation based on classical fuzzy sets, and we investigate its performance in the context of image classification. Intuitionistic pooling is performed in four steps: bifuzzification (by the transformation of data through the use of membership and non-membership maps), first aggregation (through the transformation of the IFS into a standard fuzzy set, second aggregation (through the transformation and use of a sum operator), and the defuzzification of feature map neighborhoods by using a max operator. IFS pooling is used for the construction of an intuitionistic pooling layer that can be applied as a drop-in replacement for the current, fuzzy (type-1) and crisp, pooling layers of CNN architectures. Various experiments involving multiple datasets demonstrate that an IFS-based pooling can enhance the classification performance of a CNN. A benchmarking study reveals that this significantly outperforms even the most recent pooling models, especially in stochastic environments. [ABSTRACT FROM AUTHOR]

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Volkswagen Technical Development (TE) is responsible for all prototype development and prototype production for the Volkswagen brand and has its own logistics department (TE-Logistics). In the logistics of prototype parts in the automotive industry, new versions of prototype parts (henceforth referred to as updating parts) are repeatedly assembled in finished prototype vehicles. These updating parts are stored in warehouses and provided to an assembly site to ensure a timely assembly of the associated prototype vehicles. As the internal warehouse on the company site is not large enough for the high variety of parts, an additional external warehouse in the logistics network is needed. However, since prototype parts are unique, the allocation of the parts in suitable warehouses is particularly important. Currently, the various warehouses and the short-term demands repeatedly lead to reactive transshipments between the warehouses. To this end, we developed an approach for proactive transshipments based on a machine learning forecast and a mixed-integer linear programming model for planning proactive transshipments of parts between the warehouses to minimize transport costs. The model is based on a probability estimation of future demands to anticipate the expected optimal warehouse. After the model had revealed high improvement potential through a case study with real-world data in terms of costs and availability time compared to the current reactive process, we derived decision rules and developed a rule-based heuristic algorithm that leads to the optimal solution for the industrial use case. We implemented the heuristic with a spreadsheet-based decision support system (DSS) for daily transshipment planning. After successful test implementation, TE-Logistics estimated the annual cost savings for transport to be approximately 10%. [ABSTRACT FROM AUTHOR]

A necessary component of understanding vector‐borne disease risk is accurate characterization of the distributions of their vectors. Species distribution models have been successfully applied to data‐rich species but may produce inaccurate results for sparsely documented vectors. In light of global change, vectors that are currently not well‐documented could become increasingly important, requiring tools to predict their distributions. One way to achieve this could be to leverage data on related species to inform the distribution of a sparsely documented vector based on the assumption that the environmental niches of related species are not independent. Relatedly, there is a natural dependence of the spatial distribution of a disease on the spatial dependence of its vector. Here, we propose to exploit these correlations by fitting a hierarchical model jointly to data on multiple vector species and their associated human diseases to improve distribution models of sparsely documented species. To demonstrate this approach, we evaluated the ability of twelve models – which differed in their pooling of data from multiple vector species and inclusion of disease data – to improve distribution estimates of sparsely documented vectors. We assessed our models on two simulated datasets, which allowed us to generalize our results and examine their mechanisms. We found that when the focal species is sparsely documented, incorporating data on related vector species reduces uncertainty and improves accuracy by reducing overfitting. When data on vector species are already incorporated, disease data only marginally improve model performance. However, when data on other vectors are not available, disease data can improve model accuracy and reduce overfitting and uncertainty. We then assessed the approach on empirical data on ticks and tick‐borne diseases in Florida and found that incorporating data on other vector species improved model performance. This study illustrates the value in exploiting correlated data via joint modeling to improve distribution models of data‐limited species. [ABSTRACT FROM AUTHOR]

Category level 6D object pose estimation aims to predict the rotation, translation and size of object instances in any scene. In current research methods, global average pooling (first-order) is usually used to explore geometric features, which can only capture the first-order statistical information of the features and do not fully utilize the potential of the network. In this work, we propose a new high-order pose estimation network (HoPENet), which enhances feature representation by collecting high-order statistics to model high-order geometric features at each stage of the network. HoPENet introduces a global high-order enhancement module and utilizes global high-order pooling operations to capture the correlation between features and fuse global information. In addition, this module can capture long-term statistical correlations and make full use of contextual information. The entire network finally obtains a more discriminative feature representation. Experiments on two benchmarks, the virtual dataset CAMERA25 and the real dataset REAL275, demonstrate the effectiveness of HoPENet, achieving state-of-the-art (SOTA) pose estimation performance. [ABSTRACT FROM AUTHOR]

Observational data provide invaluable real-world information in medicine, but certain methodological considerations are required to derive causal estimates. In this systematic review, we evaluated the methodology and reporting quality of individual-level patient data meta-analyses (IPD-MAs) conducted with non-randomized exposures, published in 2009, 2014, and 2019 that sought to estimate a causal relationship in medicine. We screened over 16,000 titles and abstracts, reviewed 45 full-text articles out of the 167 deemed potentially eligible, and included 29 into the analysis. Unfortunately, we found that causal methodologies were rarely implemented, and reporting was generally poor across studies. Specifically, only three of the 29 articles used quasi-experimental methods, and no study used G-methods to adjust for time-varying confounding. To address these issues, we propose stronger collaborations between physicians and methodologists to ensure that causal methodologies are properly implemented in IPD-MAs. In addition, we put forward a suggested checklist of reporting guidelines for IPD-MAs that utilize causal methods. This checklist could improve reporting thereby potentially enhancing the quality and trustworthiness of IPD-MAs, which can be considered one of the most valuable sources of evidence for health policy. [ABSTRACT FROM AUTHOR]

Human age and gender prediction from facial images has garnered significant attention due to its importance in various applications. Traditional models struggle with large-scale variations in unfiltered images. Convolutional Neural Networks (CNNs) have emerged as effective tools for facial analysis due to their robust performance. This paper presents a novel CNN approach for robust age and gender classification using unconstrained real-world images. The CNN architecture includes convolution, pooling, and fully connected layers for feature extraction, dimension reduction, and mapping to output classes. Adience and UTKFace datasets were utilized, with the best training and testing accuracies achieved using an 80% training and 20% testing data split. Robust image pre-processing and data augmentation techniques were applied to handle dataset variations. The proposed approach outperformed existing methods, achieving age prediction accuracies of 86.42% and 81.96%, and gender prediction accuracies of 97.65% and 96.32% on the Adience and UTKFace datasets, respectively. [ABSTRACT FROM AUTHOR]

Mesenchymal stromal stem cells (MSCs) possess a remarkable potential for numerous clinical applications due to their unique properties including self-renewal, immunomodulation, paracrine actions and multilineage differentiation. However, the translation of MSC-based Advanced Therapy Medicinal Products (ATMPs) into the clinic has frequently met with inconsistent outcomes. One of the suspected reasons for this issue is the inherent and extensive variability that exists among such ATMPs, which makes the interpretation of their clinical efficacy difficult to assess, as well as to compare the results of various studies. This variability stems from numerous reasons including differences in tissue sources, donor attributes, variances in manufacturing protocols, as well as modes of administration. MSCs can be isolated from various tissues including bone marrow, umbilical cord, adipose tissue and others, each with its unique phenotypic and functional characteristics. While MSCs from different sources do share common features, they also exhibit distinct gene expression profiles and functional properites. Donor-specific factors such as age, sex, body mass index, and underlying health conditions can influence MSC phenotype, morphology, differentiation potential and function. Moreover, variations in preparation of MSC products introduces additional heterogeneity as a result of cell culture media composition, presence or absence of added growth factors, use of different serum supplements and culturing techniques. Once MSC products are formulated, storage protocols play a pivotal role in its efficacy. Factors that affect cell viability include cell concentration, delivery solution and importantly, post-thawing protocols where applicable. Ensuing, differences in administration protocols can critically affect the distribution and functionallity of administered cells. As MSC-based therapies continue to advance through numerous clinical trials, implication of strategies to reduce product heterogeneity is imperative. Central to addressing these challenges is the need for precise prediction of clinical responses, which require well-defined MSC populations and harmonized assessment of their specific functions. By addressing these issues by meaningful approaches, such as, e.g., MSC pooling, the field can overcome barriers to advance towards more consistent and effective MSC-based therapies.

Predicting the timing of phenological events is important in agriculture, especially high-revenue products. A project sponsored by USDA-ARS had the objective of adapting a previously developed model for estimating proportions of insects in different development stages as a function of temperature (degree) and time (days) for predicting bloom in almond orchards. Data for the model normally form a two-way table of counts, with rows corresponding to sample percentages of different development stages and columns to sampling times. In this study, we report a technique developed to estimate sample sizes of multinomial and product multinomial models using a method of moments and determine the empirical coverage of sample size. This study aims to determine an appropriate sample size for data collection. This involves establishing a sampling distribution for the Pearson statistic, defined as the product of the sample size and the deviance of empirical proportions from population proportions. The intended outcome is to predict the optimal timing for harvesting crops at desired development stages when coupled with the phenology model, for which variability of the maximum likelihood estimates of the phenology model depends on sample size.

Acquisition of adequate transfer of passive immunity (ATPI) by calves depends on the absorption of sufficient mass of colostral immunoglobulin G (IgG). Several studies report conflicting evidence regarding the ability of feeding pooled colostrum to achieve ATPI. Pooling colostrum is practical and efficient for some dairies, and recommendations are required to prevent failure of transfer of passive immunity (FTPI) in calves following pooling. This study aimed to determine the effect of pooling colostrum on serum IgG concentrations, FTPI, and preweaning mortality in calves. A prospective study was performed on two conventional Jersey dairy farms where heat treatment of colostrum occurred in the same colostrum processing kitchen. Four to 10 cows contributed to colostrum pools. A sample of the colostrum pool fed to the calves and serum from calves at 24-72 h was collected for IgG concentration determination by single radial immunodiffusion assay. Multivariable and logistic regression analyses were performed to evaluate factors that predicated serum IgG concentrations and the probability of FTPI, respectively. A Cox proportional hazard model analysis was performed to determine risk factors for mortality over the preweaning period. A total of 164 calves fed 28 colostrum pools were enrolled. Birth weight, number of colostrum feedings and pool IgG concentrations were significant predictors of calf serum IgG concentrations at 24-72 h, whereas the number of colostrum feedings and age at bleeding to determine passive transfer status were not significant predictors of calf serum IgG concentrations at 24-72 h. The prevalence of FTPI was 4.9%. Birth weight, pool IgG concentrations, number of colostrum feedings, and age at bleeding to check for passive transfer status were not significant predictors of the probability of FTPI at 24-72 h. The incidence of mortality was 4.3%. Passive transfer status was not a predictor of mortality. Our study demonstrates the effect of pooling colostrum on serum IgG concentrations even in herds where colostrum with higher median colostrum IgG concentrations is fed to calves. The results emphasize the recommendations to assess pooled colostrum IgG concentrations before feeding calves.

Graph Pooling technology plays an important role in graph node classification tasks. Sorting pooling technologies maintain large-value units for pooling graphs of varying sizes. However, by analyzing the statistical characteristic of activated units after pooling, we found that a large number of units dropped by sorting pooling are negative-value units that contain representative information and can contribute considerably to the final decision. To maintain more representative information, we proposed a novel pooling technology, called Geometric Pooling (GP), containing the unique node features with negative values by measuring the similarity of all node features. We reveal the effectiveness of GP from the entropy reduction view. The experiments were conducted on TUdatasets to show the effectiveness of GP. The results showed that the proposed GP outperforms the SOTA graph pooling technologies by $1\%~\sim ~5\%$ with fewer parameters.

With the progressive and availability of various search tools, interest in the evaluation of information retrieval based on user perspective has grown tremendously among researchers. The Information Retrieval System Evaluation is done through Cranfield-paradigm in which the test collections provide the foundation of the evaluation process. The test collections consist of a document corpus, topics, and a set of relevant judgments. The relevant judgments are the documents which retrieved from the test collections based on the topics. The accuracy of the evaluation process is based on the number of relevant documents in the relevance judgment set, called qrels. This paper presents a comprehensive study, which discusses the various ways to improve the number of relevant documents in the qrels to improve the quality of qrels and through that increase the accuracy of the evaluation process. Different ways in which each methodology was performed to retrieve more relevant documents were categorized, described, and analyzed, resulting in an inclusive flow of these methodologies.

Utilizing individual annotation of panoramic radiographs, a comprehensive deep learning multi-scale spatial pooling (ms-SP)-based panoptic segmentation technique is tested for its effectiveness in segmenting teeth autonomously. On a panoramic radiograph, each tooth was meticulously tagged by an oral radiologist to accurately depict its real structure. From the initial data points, we used the augmentation strategy to create training samples to reduce over-fitting. With the proposed multi-scale spatial pooling (ms-SP), a completely deep learning approach was used to locate and identify the dental traits. Performance was evaluated using the F1 score, and visual analysis. The suggested method resulted in a mean IoU of 87% and an F1 score of 98.9%, accuracy of 98.5%, recall of 93%, precision of 94.5%, dice score of 94.5% and PFOM is 80.5%. The segmentation technique was evaluated visually, and the results were very similar to the actual data. The technique produced effective results for automating the segmentation of teeth on panoramic dental photos. The suggested technique may be advantageous for the first stages of forensic identification and diagnostic automation, which both involve similar segmentation tasks. [ABSTRACT FROM AUTHOR]

In structural equation modeling, when multiple imputation is used for handling missing data, model fit evaluation involves pooling likelihood-ratio test statistics across imputations. Under the normality assumption, the two most popular pooling approaches were proposed by Li et al. (Statistica Sinica, 1(1), 65–92, 1991) and Meng and Rubin (Biometrika, 79(1), 103–111, 1992). When the assumption of normality is violated, it is not clear how well these pooling approaches work with the test statistics generated from various robust estimators and multiple imputation methods. Jorgensen and colleagues (2021) implemented these pooling approaches in their R package semTools; however, no systematical evaluation has been conducted. In this simulation study, we examine the performance of these approaches in working with different imputation methods and robust estimators under nonnormality. We found that the naïve pooling approach based on Meng and Rubin (Biometrika, 79(1), 103–111, 1992; D3SN) worked the best when combining with the normal-theory-based imputation and either MLM or MLMV estimator. [ABSTRACT FROM AUTHOR]

Point cloud completion plays a crucial role in point cloud processing tasks, as it enhances data quality, assists in generating accurate 3D models, and provides reliable data support for various applications. However, existing point cloud completion algorithms based on deep neural networks use a simple single-level global feature extraction method, which does not fully exploit latent semantic information and leads to the loss of some detailed information during the encoding process. To address these issues, this paper proposes a novel point cloud completion network that employs a multi-scale feature fusion approach and introduces a new pooling method by combining an attention mechanism. Experimental results demonstrate that the proposed network achieves the State-of-the-Art (SOTA) performance on three datasets, namely PCN, ShapeNet34, and ShapeNet55, indicating its superior feature representation capability and completion effectiveness. [ABSTRACT FROM AUTHOR]

This paper proposes SE-DenseNet-HP, a novel finger vein recognition model that integrates DenseNet with a squeeze-and-excitation (SE)-based channel attention mechanism and a hybrid pooling (HP) mechanism. To distinctively separate the finger vein patterns from their background, original finger vein images are preprocessed using region-of-interest (ROI) extraction, contrast enhancement, median filtering, adaptive thresholding, and morphological operations. The preprocessed images are then fed to SE-DenseNet-HP for robust feature extraction and recognition. The DenseNet-based backbone improves information flow by enhancing feature propagation and encouraging feature reuse through feature map concatenation. The SE module utilizes a channel attention mechanism to emphasize the important features related to finger vein patterns while suppressing less important ones. HP architecture used in the transitional blocks of SE-DenseNet-HP concatenates the average pooling method with a max pooling strategy to preserve both the most discriminative and contextual information. SE-DenseNet-HP achieved recognition accuracy of 99.35% and 93.28% on the good-quality FVUSM and HKPU datasets, respectively, surpassing the performance of existing methodologies. Additionally, it demonstrated better generalization performance on the FVUSM, HKPU, UTFVP, and MMCBNU_6000 datasets, achieving remarkably low equal error rates (EERs) of 0.03%, 1.81%, 0.43%, and 1.80%, respectively. [ABSTRACT FROM AUTHOR]

Object detection in surveillance videos is challenging task whenever the objects are similar to other objects and overlapped with each other. Sometimes objects may be masked or cover with any transparent or non transparent material. Due to coverage of objects with opaque materials, object detection becomes difficult. Once object have been identified, it is require to recognize such object leads to any anomaly in that particular environment or not. There is huge demand for intelligent systems with the capability of automatically identifying anomalous situations or objects in an environment present in videos. Due to this, a wide range of approaches have been proposed to build an effective system that would ensure public security. Intension of this article is to specify various Object detection and Anomaly detection techniques. [ABSTRACT FROM AUTHOR]

Hypertensive retinopathy (HR) results from the microvascular retinal changes triggered by hypertension, which is the most common leading cause of preventable blindness worldwide. Therefore, it is necessary to develop an automated system for HR detection and evaluation using retinal images. We aimed to propose an automated approach to identify and categorize the various degrees of HR severity. A new network called the spatial convolution module (SCM) combines cross-channel and spatial information, and the convolution operations extract helpful features. The present model is evaluated using publicly accessible datasets ODIR, INSPIREVR, and VICAVR. We applied the augmentation to artificially increase the dataset of 1200 fundus images. The different HR severity levels of normal, mild, moderate, severe, and malignant are finally classified with the reduced time when compared to the existing models because in the proposed model, convolutional layers run only once on the input fundus images, which leads to a speedup and reduces the processing time in detecting the abnormalities in the vascular structure. According to the findings, the improved SVM had the highest detection and classification accuracy rate in the vessel classification with an accuracy of 98.99% and completed the task in 160.4 s. The ten-fold classification achieved the highest accuracy of 98.99%, i.e., 0.27 higher than the five-fold classification accuracy and the improved KNN classifier achieved an accuracy of 98.72%. When computation efficiency is a priority, the proposed model's ability to quickly recognize different HR severity levels is significant. [ABSTRACT FROM AUTHOR]

The article focuses on the impact of document ordering strategies such as prioritization and randomization and assessor types such as Gold and Bronze on the relevance assessments of web pages in information retrieval. Topics include the efficiency and agreement of assessors, the similarity and consistency of system rankings, and the reasons for differences between Bronze and Gold assessors' judgments.

In this study, we propose a new evaluation scheme to assess the strengths and limitations of collocation extraction measures and explore type-sensitive methods for extracting collocations. We introduced the pooling strategy widely used in Information Retrieval and automated the evaluation process using online dictionaries. Sixteen well-known metrics are evaluated based on their effectiveness and then distributional and linguistic compared. The results show that Group A methods (e.g. z-score, Dice, PMI) are more effective in extracting low-frequency collocations with relatively small extraction scales. In contrast, Group B methods (e.g. t-test, LMI, LLR) perform better at finding high-frequency collocations, most of which outperform Group A methods as the extraction scale increases. Moreover, Group A prefers NN collocations, while Group B identifies collocations with a wide range of syntactic structures. This study provides suggestions for studies to identify hybrid extraction methods as well as for language educators and dictionary compilers. [ABSTRACT FROM AUTHOR]

Specific T cell populations in the skin have been demonstrated as important disease drivers in several dermatoses. Due to the unique skin architecture, these cells are not grouped together in structures but dispersedly spread out throughout the epidermis. Following tissue disruption and isolation, only about 10% of skin T cells are recovered and any in vitro expansion may alter their bona fide phenotype. The Nanostring GeoMx system was developed to address cellular phenotype and protein expression in a tissue spatial context. To do so, regions of interest (ROI) must exceed a certain area threshold (usually 100 μm in diameter) to generate a sufficient signal‐to‐noise ratio. Here, we present an approach that allows for the pooling of numerous smaller ROIs within the skin, enabling T cell and melanocyte phenotyping. Skin samples from healthy individuals and vitiligo patients were analysed using the GeoMx system and several immune profiling panels. A sufficient signal‐to‐noise ratio was achieved by pooling smaller ROIs and analysing them as a single group. While this prevents spatial analysis, this method allows for detailed analysis of cells as a population in the context of their physiological environment, making it possible to investigate in situ phenotype of rare cells in different tissue compartments. [ABSTRACT FROM AUTHOR]

Recently, object detection has become a research hotspot in computer vision, which often detects regular images with small viewing angles. In order to obtain a field of view without blind spots, fisheye cameras, which have distortions and discontinuities, have come into use. The fisheye camera, which has a wide viewing angle, and an unmanned aerial vehicle equipped with a fisheye camera are used to obtain a field of view without blind spots. However, distorted and discontinuous objects appear in the captured fisheye images due to the unique viewing angle of fisheye cameras. It poses a significant challenge to some existing object detectors. To solve this problem, this paper proposes a PGDS-YOLOv8s model to solve the issue of detecting distorted and discontinuous objects in fisheye images. First, two novel downsampling modules are proposed. Among them, the Max Pooling and Ghost's Downsampling (MPGD) module effectively extracts the essential feature information of distorted and discontinuous objects. The Average Pooling and Ghost's Downsampling (APGD) module acquires rich global features and reduces the feature loss of distorted and discontinuous objects. In addition, the proposed C2fs module uses Squeeze-and-Excitation (SE) blocks to model the interdependence of the channels to acquire richer gradient flow information about the features. The C2fs module provides a better understanding of the contextual information in fisheye images. Subsequently, an SE block is added after the Spatial Pyramid Pooling Fast (SPPF), thus improving the model's ability to capture features of distorted, discontinuous objects. Moreover, the UAV-360 dataset is created for object detection in fisheye images. Finally, experiments show that the proposed PGDS-YOLOv8s model on the VOC-360 dataset improves mAP@0.5 by 19.8% and mAP@0.5:0.95 by 27.5% compared to the original YOLOv8s model. In addition, the improved model on the UAV-360 dataset achieves 89.0% for mAP@0.5 and 60.5% for mAP@0.5:0.95. Furthermore, on the MS-COCO 2017 dataset, the PGDS-YOLOv8s model improved AP by 1.4%, AP 50 by 1.7%, and AP 75 by 1.2% compared with the original YOLOv8s model. [ABSTRACT FROM AUTHOR]

Group (pooled) testing has long been used for the monitoring and detection of infectious diseases. In group testing, pools comprised of individual biospecimens are amalgamated and tested initially, and then the individuals belonging to positive pools are retested to complete case identification. This procedure and its variants can offer substantial savings with regard to time and testing cost. Unfortunately, these savings come at the expense of a complex data structure; e.g., ambiguities due to imperfect testing and complex dependence caused by individuals potentially being tested in multiple pools. To account for these complex features, several advanced statistical methods have been proposed. Regretfully, these methods are non-trivial to implement, especially for non-statistician. Recognizing this as an important issue in surveillance programs, we have developed a userfriendly R package called groupTesting that can be used to analyze group testing data. In particular, our package consists of R functions which possess a great deal of versatility and generality, and can be used in estimating both proportions and binary regression functions. The computing efficiency of our package is greatly enhanced through the strategic implementation of compiled Fortran subroutines. The primary features of the R package are illustrated using HIV and chlamydia data. [ABSTRACT FROM AUTHOR]

Power priors are used for incorporating historical data in Bayesian analyses by taking the likelihood of the historical data raised to the power α as the prior distribution for the model parameters. The power parameter α is typically unknown and assigned a prior distribution, most commonly a beta distribution. Here, we give a novel theoretical result on the resulting marginal posterior distribution of α in case of the normal and binomial model. Counterintuitively, when the current data perfectly mirror the historical data and the sample sizes from both data sets become arbitrarily large, the marginal posterior of α does not converge to a point mass at α=1 but approaches a distribution that hardly differs from the prior. The result implies that a complete pooling of historical and current data is impossible if a power prior with beta prior for α is used. [ABSTRACT FROM AUTHOR]

Forensic audio analysis is a foundation stone of many crime investigations. In forensic evidence; the audio file of the human voice is analyzed to extract much information in addition to the content of the speech, such as the speaker's identity, emotions, gender, origin, etc. The accurate determination of individuals into groups based on their age development stage and their gender are often used as early investigations to differentiate them and determine the legal rights and responsibilities associated with them. This work introduces a light CNN model with a new architecture to detect the human age-evolution being's stage (kids or adults) at the same time the gender of the adult one (male or female) based on the individual's voice characteristics, which offers a balance between computational efficiency and model accuracy. The temporal information in the audio file is prepared by scaled and normalized. Then this information is exploited to extract and track the unique and salient audio features that make up the pattern of the feature map for each target class through some convolutional layers followed by maxpooling layers. Finally, The decision is made based on these feature maps by some fully connected layers. Successful and promising results are accomplished in terms of accuracy and loss functions which realize 0.99 and 0.017 respectively over the riched Voxceleb2 dataset. The proposed model underscores the importance of leveraging Light DNNs for gender and age-evolution detection, offering a robust and ethically sound solution for real-world applications in the field of audio forensics such as span speaker identification, victim profiling, deception detection, and more, contributing to the advancement of audio forensic analysis. [ABSTRACT FROM AUTHOR]

Researchers have been concentrating on categorizing text into several categories during the past few years based on tweets, SMS messages, etc. This study focuses on categorizing the justified calls based on call content from India's emergency response support systems. The method becomes substantially more challenging because of the close relationship between certain emergence classes and the scarcity of resources on call transcripts. This work uses five labels for classification: Unintentional_Injury_Event, Event_Related, Medical_Emergency, Aid_Related, and Crime_Related. This research performs a variety of tests to classify emergency-related calls. We employed established machine learning techniques and transformer-based NLP models, like AlBERT, RoBERTa, and MobileBERT. The proposed PLE-MobileBERT approach outperforms other models in terms of speed and effectiveness, adding the model's ability to understand phrase-level information. The proposed PLE-MobileBERT provides an accuracy: 95.31% percent and a macroaverage F1 score: 94.8%, which is superior to the others. [ABSTRACT FROM AUTHOR]

Point cloud is a simple but accurate form of data in the 3D domain, and its disorder brings the challenge of feature representation. The transformer structure which has been successfully used in natural language processing helps to establish connections between discrete points in the point cloud data. In this work, by focusing on adapting the self-attention mechanism to point cloud data, we propose a point cloud transformer pooling (PCTP) method combined with the typical set abstraction (SA) structure. In the proposed PCTP, we use the transformer structure to fuse non-local features while pooling local features. The SA structure is widely used in various point cloud networks for various tasks, so we apply the PCTP module to multiple baselines containing SA-like structures. The preliminary experimental results show that the proposed PCTP can significantly improve multiple tasks with a small additional computational cost. [ABSTRACT FROM AUTHOR]

Problem definition: Energy storage has become an indispensable part of power distribution systems, necessitating prudent investment decisions. We analyze an energy storage facility location problem and compare the benefits of centralized storage (adjacent to a central energy generation site) versus distributed storage (localized at demand sites). This problem encompasses optimizing storage capacities across all locations, with the objective of minimizing the total storage investment and energy generation costs. Methodology/results: We employ a stylized model that captures essential features of an energy distribution system, including convex costs, stochastic demand, storage efficiency, and line losses. Using dynamic programming, we optimize storage operations and derive value function properties that are key to analyzing the storage investment decisions. We discern fundamental differences between centralization/localization decisions at the capacity investment stage and the centralization/localization decisions at the storage operations level. Operationally, centrally stored energy offers more flexibility, which is consistent with the conventional understanding of inventory pooling. However, we find that localized storage often emerges as the preferred option at the investment stage under various circumstances. Managerial implications: Storage investment should first be made at the demand locations with positive minimum demand regardless of the level of demand variability. Subsequent storage investment should consider the tradeoffs between centralized versus localized investment. Operationally, the relative magnitudes of storage and line losses drive different optimal storage policies. Despite the differences, these policies are guided by common principles such as pooling inventory and balancing local storage levels. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2020.0652. [ABSTRACT FROM AUTHOR]

Recently, Graph Convolutional neural Networks (GCNs) have attracted much attention by generalizing convolutional neural networks to graph data, which includes redefining convolution and pooling operations on graphs. Due to the limitation that graph data can only focus on dyadic relations, it cannot perform well in real practice. In contrast, a hypergraph can capture high-order data interaction and is easy to deal with complex data representation using its flexible hyperedges. However, the existing methods for hypergraph convolutional networks are still not mature, and there is no effective operation for hypergraph pooling currently. Therefore, a hypergraph pooling network with a self-attention mechanism is proposed. Using a hypergraph structure for data modeling, this model can learn node hidden features with high-order data information through hypergraph convolution operation which introduces a selfattention mechanism, select important nodes both on structure and content through hypergraph pooling operation, and then obtain more accurate hypergraph representation. Experiments on text classification, dish classification, and protein classification tasks show that the proposed method outperforms recent state-of-the-art methods. [ABSTRACT FROM AUTHOR]