The combination operation of grouping and ensemble coding for structured biological motion crowds in working memory.

Massive studies have explored biological motion (BM) crowds processing for their remarkable social significance, primarily focused on uniformly distributed ones. However, real-world BM crowds often exhibit hierarchical structures rather than uniform arrangements. How such structured BM crowds are processed remains a subject of inquiry. This study investigates the representation of structured BM crowds in working memory (WM), recognizing the pivotal role WM plays in our social interactions involving BM. We propose the group-based ensemble hypothesis and test it through a member identification task. Participants were required to discern whether a presented BM belonged to a prior memory display of eight BM, each with distinct walking directions. Drawing on prominent Gestalt principles as organizational cues, we constructed structured groups within BM crowds by applying proximity and similarity cues in Experiments 1 and 2, respectively. In Experiment 3, we deliberately weakened the visibility of stimuli structures by increasing the similarity between subsets, probing the robustness of results. Consistently, our findings indicate that BM aligned with the mean direction of the subsets was more likely to be recognized as part of the memory stimuli. This suggests that WM inherently organizes structured BM crowds into separate ensembles based on organizational cues. In essence, our results illuminate the simultaneous operation of grouping and ensemble encoding mechanisms for BM crowds within WM.

Selective Classes and Early Health Inequalities in Comprehensive Schools in Finland.

BACKGROUND: The origin of inequalities in health outcomes has been explained by health selection and social causation models. Health selection processes operate particularly at school age. We study, if student allocation to teaching groups with aptitude tests (selective vs general class) differentiates adolescents by health behaviors and mental health. METHODS: Finnish schoolchildren 12-13 years from 12 selective classes, n = 248; 41 general classes, n = 703 answered a questionnaire on addictive products (tobacco, snus, alcohol, and energy drinks), digital media use, and mental health (health complaints, anxiety, and depression). Structural equation modeling was conducted to identify structures between outcomes, SEP (socioeconomic position), class type, and academic performance. RESULTS: Students in the selective classes reported less addictive digital media and addictive products use than students in the general classes. Differences in academic performance or SEP between the class types did not solely explain these differences. Mental health was not related to the class type. SEP was indirectly associated with health behaviors via the class type and academic performance. CONCLUSIONS: Selecting students to permanent teaching groups with aptitude tests differentiates students according to risky health behaviors. The impact of education policies using student grouping should also be evaluated in terms of students' health.

Bioinformatics and machine learning to support nanomaterial grouping.

Nanomaterials (NMs) offer plenty of novel functionalities. Moreover, their physicochemical properties can be fine-tuned to meet the needs of specific applications, leading to virtually unlimited numbers of NM variants. Hence, efficient hazard and risk assessment strategies building on New Approach Methodologies (NAMs) become indispensable. Indeed, the design, the development and implementation of NAMs has been a major topic in a substantial number of research projects. One of the promising strategies that can help to deal with the high number of NMs variants is grouping and read-across. Based on demonstrated structural and physicochemical similarity, NMs can be grouped and assessed together. Within an established NM group, read-across may be performed to fill in data gaps for data-poor variants using existing data for NMs within the group. Establishing a group requires a sound justification, usually based on a grouping hypothesis that links specific physicochemical properties to well-defined hazard endpoints. However, for NMs these interrelationships are only beginning to be understood. The aim of this review is to demonstrate the power of bioinformatics with a specific focus on Machine Learning (ML) approaches to unravel the NM Modes-of-Action (MoA) and identify the properties that are relevant to specific hazards, in support of grouping strategies. This review emphasizes the following messages: 1) ML supports identification of the most relevant properties contributing to specific hazards; 2) ML supports analysis of large omics datasets and identification of MoA patterns in support of hypothesis formulation in grouping approaches; 3) omics approaches are useful for shifting away from consideration of single endpoints towards a more mechanistic understanding across multiple endpoints gained from one experiment; and 4) approaches from other fields of Artificial Intelligence (AI) like Natural Language Processing or image analysis may support automated extraction and interlinkage of information related to NM toxicity. Here, existing ML models for predicting NM toxicity and for analyzing omics data in support of NM grouping are reviewed. Various challenges related to building robust models in the field of nanotoxicology exist and are also discussed.

Genetic analysis of popping quality traits and development of superior quality popcorn hybrids for a sustainable popcorn breeding program in India.

BACKGROUND: Popcorn is the most popular specialty maize and it makes a significant contribution to the Indian and global economies. Despite perfect exploration of heterosis in field corn, progress in popcorn breeding remains constrained due to its narrow genetic base, leading to a significant dependence on imports. In this study, 15 landrace- and population-derived inbreds from temperate and tropical germplasm were crossed with five testers, which are the parents of superior popcorn hybrids, in a line × tester mating design. RESULTS: Significant variation was observed in popping quality and agronomic traits among crosses evaluated across three locations representing diverse maize-based agro-climatic zones in India. Additive genetic variance governed the traits related to popping quality, whereas dominance variance was responsible for the agronomic traits. In addition to significant heterosis specific to certain traits, we identified promising crosses that exhibited superior performance in both popping quality and grain yield (GY). The genotype + genotype × environment (GGE) biplot methodology identified PMI-PC-104 and PMI-PC-101 as the best discriminating testers for popping quality traits and Dpcl-15-90 for GY. Lines PMI-PC-205, PMI-PC-207, and PMI-PC-209 were the best general combiners for popping quality traits and GY. The heterotic groups identified based on GGE-biplots and the magnitude, direction and stability of combining ability effects would serve in the development of competitive popcorn hybrids for a sustainable popcorn market. CONCLUSION: Using the additive nature of popping quality traits and the dominant nature of GY, recurrent intrapopulation selection can be employed to derive desirable popping quality inbreds with high GY for genetic enhancement. Desirable popping quality alleles from novel germplasm can thus be combined with high-yielding domestic elite inbreds to establish a sustainable popcorn breeding program. © 2024 Society of Chemical Industry.

Trivial State Fuzzy Processing for Error Reduction in Healthcare Big Data Analysis towards Precision Diagnosis.

There is a significant public health concern regarding medical diagnosis errors, which are a major cause of mortality. Identifying the root cause of these errors is challenging, and even if one is identified, implementing an effective treatment to prevent their recurrence is difficult. Optimization-based analysis in healthcare data management is a reliable method for improving diagnostic precision. Analyzing healthcare data requires pre-classification and the identification of precise information for precision-oriented outcomes. This article introduces a Cooperative-Trivial State Fuzzy Processing method for significant data analysis with possible derivatives. Trivial State Fuzzy Processing operates on the principle of fuzzy logic-based processing applied to structured healthcare data, focusing on mitigating errors and uncertainties inherent in the data. The derivatives are aided by identifying and grouping diagnosis-related and irrelevant data. The proposed method mitigates invertible derivative analysis issues in similar data grouping and irrelevance estimation. In the grouping and detection process, recent knowledge of the diagnosis progression is exploited to identify the functional data for analysis. Such analysis improves the impact of trivial diagnosis data compared to a voluminous diagnosis history. The cooperative derivative states under different data irrelevance factors reduce trivial state errors in healthcare big data analysis.

Grouping of short alkyl-chain branched carboxylic acids for developmental toxicity.

Read-across (RAx) and grouping of chemicals into categories are well-known concepts in toxicology. Recently, ECHA proposed a grouping approach for branched-chain carboxylic acids (BCAs) including more than 60 branched-chain saturated carboxylic acids for hazard identification. Grouping was based only on structural considerations. Due to developmental effects of two members, ECHA postulated that "all short carbon chain acids … are likely reproductive and developmental toxicants". This work analyzes available data for BCAs. The number of compounds in the group can be significantly reduced by eliminating metal and organic salts of BCAs, compounds of unknown or variable composition, and complex reaction products or biological materials (UVCB compounds). For the resulting reduced number of compounds, grouping is supported by similar physicochemical data and expected similar biotransformation. However, analysis of adverse effects for compounds in the group and mechanistic information show that BCAs, as a class, do not cause developmental effects in rats. Rather, developmental toxicity is limited to selected BCAs with specific structures that share a common mode of action (histone deacetylase inhibition). Thus, the proposed grouping is unreasonably wide and the more detailed analyses show that structural similarity alone is not sufficient for grouping branched-chain carboxylic acids for developmental toxicity.

[Clinical Evaluation of Dural (Spinal) Membrane Repair Materials].

Objective: To select high-quality and cost-effective dural (spinal) membrane repair materials, in order to reduce the cost of consumables procurement, save medical insurance funds, and optimize hospital operation and management. Methods: Taking the BS06B disease group (spinal cord and spinal canal surgery without extremely severe or severe complications and comorbidities, mainly diagnosed as congenital tethered cord syndrome) as an example, a retrospective analysis was conducted on the relevant data of surgical treatment for congenital tethered cord syndrome conducted in our hospital from January 2021 to June 2023. Safety and efficacy indicators in clinical application (incidence of postoperative epidural hemorrhage, incidence of postoperative purulent cerebrospinal meningitis, incidence of cerebrospinal fluid leakage, surgical duration, and postoperative hospital stay) were compared. Results: There was no difference in safety and effectiveness between different brands of dura mater repair materials. Conclusion: For the repair of small incisions in dura mater surgery, high-quality and cost-effective dura mater repair materials can be selected to reduce hospital costs and control expenses for the disease group.

A multivariate process quality correlation diagnosis method based on grouping technique.

Correlation diagnosis in multivariate process quality management is an important and challenging issue. In this paper, a new diagnostic method based on quality component grouping is proposed. Firstly, three theorems describing the properties of the covariance matrix of multivariate process quality are established based on the statistical viewpoint of product quality, to prove the correlation decomposition theorem, which decomposes the correlation of all the quality components into a series of correlations of components pairs, and then by using the factor analysis method, all quality components are grouped in order to maximize the correlations in the same groups and minimize the ones between different groups. Finally, on the basis of correlations between different groups are ignored, T2 control charts of component pairs in the same groups are established to form the diagnostic model. Theoretical analysis and practice prove that for the multivariate process quality whose the correlations between different components vary considerably, the grouping technique enables the size of the correlation diagnostic model to be drastically reduced, thus allowing the proposed method can be used as a generalized theoretical model for the correlation diagnosis.

Normalised similarity assessment to inform grouping of advanced multi-component nanomaterials by means of an Asymmetric Sigmoid function.

This manuscript presents a procedure for similarity assessment as a basis for grouping of multi component nanomaterials (MCNMs). This methodology is an adaptation of the approach by Zabeo et al. (2022), which includes an impactful change: the calculated similarities are normalised in the [0,1] domain by means of asymmetric Logistic scaling to simplify comparisons among properties' distances. This novel approach allows for grouping of nanomaterials that is not affected by the dataset, so that group membership will not change when new candidates are included in the set of assessed materials. It can be applied to assess groups of MCNMs as well as mixed groups of multi and single component nanomaterials as well as chemicals. To facilitate the application of the proposed methodology, a software script was developed by using the Python programming language, which is currently undergoing migration to a user-friendly web-based tool. The presented approach was tested against a real industrial case study provided by the Andalusian Innovation Centre for Sustainable Solution (CIAC): SiO2-ZnO hybrid nanocomposite used in building coatings, which is designed to facilitate photocatalytic removal of NOx gases from the atmosphere. The results of applying the methodology in the case study demonstrated that ZnO is dissimilar from the other candidates mainly due to its different dissolution profiles.

Effects of a Diagnosis-Related Group Payment Reform on Length and Costs of Hospitalization in Sichuan, China: A Synthetic Control Study.

Background: Diagnosis-related group (DRG) payment policies are increasingly recognized as crucial instruments for addressing health care overprovision and escalating health care costs. The synthetic control method (SCM) has emerged as a robust tool for evaluating the efficacy of health policies worldwide. Methods: This study focused on Panzhihua city in Sichuan Province, a pilot city for DRG payment reform implementation, serving as the treatment group. In contrast, 20 nonpilot cities within the province were utilized as potential control units. A counterfactual control group was constructed to evaluate the changes in average inpatient stay duration and health care organization costs following the DRG payment reform initiated in 2018. Results: Focusing on Panzhihua, Sichuan Province, the analysis reveals that following the reform in March 2018, the average length of hospital stay in Panzhihua decreased by 1.35 days during 2019-2021. Additionally, the average cost per hospitalization dropped by 855.48 RMB, the average cost of medication per hospitalization decreased by 68.51 RMB, and the average cost of diagnostic and therapeutic procedures per hospitalization declined by 136.37 RMB. While global evidence backs DRGs for efficiency and cost reduction, challenges persist in addressing emerging issues like new conditions. Conclusion: Since its introduction in 2018, the DRG payment reform in Sichuan Province has effectively reduced both the duration of hospital stays and the operational costs of health care facilities. However, potential drawbacks include compromised service quality and an elevated risk of patient readmission, indicating a need for further refinement in the implementation of DRG payment reforms in China.

Evidence for a role of synchrony but not common fate in the perception of biological group movements.

Extensive research has shown that observers are able to efficiently extract summary information from groups of people. However, little is known about the cues that determine whether multiple people are represented as a social group or as independent individuals. Initial research on this topic has primarily focused on the role of static cues. Here, we instead investigate the role of dynamic cues. In two experiments with male and female human participants, we use EEG frequency tagging to investigate the influence of two fundamental Gestalt principles - synchrony and common fate - on the grouping of biological movements. In Experiment 1, we find that brain responses coupled to four point-light figures walking together are enhanced when they move in sync vs. out of sync, but only when they are presented upright. In contrast, we found no effect of movement direction (i.e., common fate). In Experiment 2, we rule out that synchrony takes precedence over common fate by replicating the null effect of movement direction while keeping synchrony constant. These results suggest that synchrony plays an important role in the processing of biological group movements. In contrast, the role of common fate is less clear and will require further research.

Structured learning in time-dependent Cox models.

Cox models with time-dependent coefficients and covariates are widely used in survival analysis. In high-dimensional settings, sparse regularization techniques are employed for variable selection, but existing methods for time-dependent Cox models lack flexibility in enforcing specific sparsity patterns (ie, covariate structures). We propose a flexible framework for variable selection in time-dependent Cox models, accommodating complex selection rules. Our method can adapt to arbitrary grouping structures, including interaction selection, temporal, spatial, tree, and directed acyclic graph structures. It achieves accurate estimation with low false alarm rates. We develop the sox package, implementing a network flow algorithm for efficiently solving models with complex covariate structures. sox offers a user-friendly interface for specifying grouping structures and delivers fast computation. Through examples, including a case study on identifying predictors of time to all-cause death in atrial fibrillation patients, we demonstrate the practical application of our method with specific selection rules.

Grouping strategies for assessing and managing persistent and mobile substances.

Background: Persistent, mobile and toxic (PMT), or very persistent and very mobile (vPvM) substances are a wide class of chemicals that are recalcitrant to degradation, easily transported, and potentially harmful to humans and the environment. Due to their persistence and mobility, these substances are often widespread in the environment once emitted, particularly in water resources, causing increased challenges during water treatment processes. Some PMT/vPvM substances such as GenX and perfluorobutane sulfonic acid have been identified as substances of very high concern (SVHCs) under the European Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulation. With hundreds to thousands of potential PMT/vPvM substances yet to be assessed and managed, effective and efficient approaches that avoid a case-by-case assessment and prevent regrettable substitution are necessary to achieve the European Union's zero-pollution goal for a non-toxic environment by 2050. Main: Substance grouping has helped global regulation of some highly hazardous chemicals, e.g., through the Montreal Protocol and the Stockholm Convention. This article explores the potential of grouping strategies for identifying, assessing and managing PMT/vPvM substances. The aim is to facilitate early identification of lesser-known or new substances that potentially meet PMT/vPvM criteria, prompt additional testing, avoid regrettable use or substitution, and integrate into existing risk management strategies. Thus, this article provides an overview of PMT/vPvM substances and reviews the definition of PMT/vPvM criteria and various lists of PMT/vPvM substances available. It covers the current definition of groups, compares the use of substance grouping for hazard assessment and regulation, and discusses the advantages and disadvantages of grouping substances for regulation. The article then explores strategies for grouping PMT/vPvM substances, including read-across, structural similarity and commonly retained moieties, as well as the potential application of these strategies using cheminformatics to predict P, M and T properties for selected examples. Conclusions: Effective substance grouping can accelerate the assessment and management of PMT/vPvM substances, especially for substances that lack information. Advances to read-across methods and cheminformatics tools are needed to support efficient and effective chemical management, preventing broad entry of hazardous chemicals into the global market and favouring safer and more sustainable alternatives.

Retracing the rabbit's path: Effects of altering the second flash position in the visual saltation illusion.

Two dots shown in quick succession at one point and a third at a distance on the same linear path creates an illusion of intervening flashes on a visual field, a phenomenon known as the reduced visual rabbit illusion or visual saltation illusion. This study presents this illusion in a novel way by altering the position of the second flash, which has been typically presented only in the same position as the first flash. A series of experiments were conducted to observe whether saltation would occur if the second flash was presented in the same position as the third flash, out of sequential order relative to the first and last flash, or out of linear alignment at the midpoint between the first and the last flash. When all three flashes were presented in quick succession, participants misperceived the second flash to occur close to the midpoint between the first and last flash. Saltation was achieved in all three novel conditions, hinting a particular neurological process may be responsible for shared outcomes.

A decision tree-based study of pulmonary tuberculosis diagnosis-related groups.

BACKGROUND: Globally, pulmonary tuberculosis is a significant public health and social problem. OBJECTIVE: We investigated the factors influencing the hospitalization cost of patients with pulmonary tuberculosis and grouped cases based on a decision tree model to provide a reference for enhancing the management of diagnosis-related groups (DRGs) of this disease. METHODS: The data on the first page of the medical records of patients with the primary diagnosis of pulmonary tuberculosis were extracted from the designated tuberculosis hospital. The influencing factors of hospitalization cost were determined using the Wilcoxon rank sum test and multiple linear stepwise regression analysis, and the included cases were grouped using the chi-squared automated interaction test decision tree model, with these influential factors used as classification nodes. In addition, the included cases were grouped according to the ZJ-DRG grouping scheme piloted in Zhejiang Province, and the differences between the two grouping methods were compared. RESULTS: The length of hospital stay, respiratory failure, sex, and age were the determining factors of the hospitalization cost of patients with pulmonary tuberculosis, and these factors were incorporated into the decision tree model to form eight case combinations. The reduction in variance (RIV) using this grouping method was 60.60%, the heterogeneity between groups was high, the coefficients of variance ranged from 0.29 to 0.47, and the intra-group difference was small. The patients were also divided into four groups based on the ZJ-DRG grouping scheme piloted in Zhejiang Province. The RIV using this grouping method was 55.24, the differences between groups were acceptable, the coefficients of variance were 1.00, 0.61, 0.77, and 0.87, respectively, and the intra-group difference was significant. CONCLUSION: When the pulmonary tuberculosis cases were grouped according to the duration of hospital stay, respiratory failure, and age, the results were rather reasonable, providing a reference for DRG management and cost control of this disease.

Allergens grouping names on packaging: Are the substances similar enough to ensure safety? The worst case scenarios question this.

OBJECTIVE: European Commission Regulation (EU) n°2023/1545 introduced the concept of grouping names in the cosmetics sector in July 2023. These groups bring together allergenic substances with the same level of skin sensitization. Their purpose is to lighten the list of ingredients on cosmetic packaging, by grouping together substances deemed to be similar under the same name. As this classification is based on a single toxic effect - skin sensitization - the present study aims to analyse the relevance of these groupings with regard to other toxic effects of substances in the same group. METHODS: This study was carried out by consulting an available database, various reports from 5 committees, 2 books and 5 articles in order to complete the toxicological profile of each substance. Then, in order to highlight any discrepancies within the classification, the worst cases were identified. For this purpose, the data for each substance in a group were compared, and in the event of greater criticality for a toxic effect, this was qualified as a worst case. In addition, similar toxic effects between several substances within the same group were also recorded. The aim of this additional research was to validate the definition of the grouping name and the similarities between substances in the same group. RESULTS: From the 17 grouping names, 5 presented worst cases. Two groups had 2 worst cases and the others only one. In total, from the 7 worst cases detected, 3 were due to the toxic effect "skin irritation". In most cases, the substances in the groupings shared the presence or absence of risk. Only the degree of risk criticality varied. CONCLUSION: Classification by grouping names appears justified regarding the similarities between substances, particularly in terms of skin sensitization. However, the presence of worst cases qualifies it and highlights the importance of being vigilant when assessing the risk of cosmetic products including these grouping names in their list of ingredients.

OBJECTIF: Le règlement (UE) n°2023/1545 de la Commission européenne a introduit la notion de « grouping names ¼ dans le domaine des cosmétiques en juillet 2023. Ces groupes rassemblent des substances allergènes ayant le même niveau de sensibilisation cutanée. Ils ont pour objectif d'alléger la liste des ingrédients figurant sur les emballages des produits cosmétiques, en regroupant sous un même nom des substances jugées similaires. Cette classification étant fondée sur un seul effet toxique ­ la sensibilisation cutanée ­ la présente étude vise à analyser la pertinence de ces regroupements au regard des autres effets toxiques des substances d'un même groupe. MÉTHODES: Cette étude a été réalisée en consultant une base de données disponible, différents rapports de 5 comités, 2 livres et 5 articles afin de compléter le profil toxicologique de chaque substance. Ensuite, afin de mettre en évidence les divergences au sein de la classification, les cas de criticité plus importante ont été identifiés. Pour ce faire, les données de chaque substance d'un groupe ont été comparées, et en cas de criticité supérieure d'un effet toxique, celui­ci a été qualifié de « worst case ¼. En outre, les effets toxiques similaires entre plusieurs substances d'un même groupe ont également été enregistrés. L'objectif de cette recherche complémentaire était de valider la définition du « grouping name ¼ et les similitudes entre les substances d'un même groupe. RÉSULTATS: Sur les 17 « grouping names ¼, 5 présentaient des « worst cases ¼. Deux groupes présentaient deux « worst cases ¼ et les autres un seul. Au total, sur les 7 « worst cases ¼ détectés, 3 étaient dus à l'effet toxique "irritation cutanée". Dans la plupart des cas, les substances des groupes partagent la présence ou l'absence de risque. Seul le degré de criticité du risque variait. CONCLUSION: La classification par « grouping names ¼ semble justifiée au regard des similitudes entre les substances, notamment en termes de sensibilisation cutanée. Cependant, la présence de « worst cases ¼ la nuance et souligne l'importance d'être vigilant lors de l'évaluation du risque des produits cosmétiques incluant ces « grouping names ¼ dans leur liste d'ingrédients.

Multi-omics bioactivity profile-based chemical grouping and read-across: a case study with Daphnia magna and azo dyes.

Grouping/read-across is widely used for predicting the toxicity of data-poor target substance(s) using data-rich source substance(s). While the chemical industry and the regulators recognise its benefits, registration dossiers are often rejected due to weak analogue/category justifications based largely on the structural similarity of source and target substances. Here we demonstrate how multi-omics measurements can improve confidence in grouping via a statistical assessment of the similarity of molecular effects. Six azo dyes provided a pool of potential source substances to predict long-term toxicity to aquatic invertebrates (Daphnia magna) for the dye Disperse Yellow 3 (DY3) as the target substance. First, we assessed the structural similarities of the dyes, generating a grouping hypothesis with DY3 and two Sudan dyes within one group. Daphnia magna were exposed acutely to equi-effective doses of all seven dyes (each at 3 doses and 3 time points), transcriptomics and metabolomics data were generated from 760 samples. Multi-omics bioactivity profile-based grouping uniquely revealed that Sudan 1 (S1) is the most suitable analogue for read-across to DY3. Mapping ToxPrint structural fingerprints of the dyes onto the bioactivity profile-based grouping indicated an aromatic alcohol moiety could be responsible for this bioactivity similarity. The long-term reproductive toxicity to aquatic invertebrates of DY3 was predicted from S1 (21-day NOEC, 40 µg/L). This prediction was confirmed experimentally by measuring the toxicity of DY3 in D. magna. While limitations of this 'omics approach are identified, the study illustrates an effective statistical approach for building chemical groups.

The Potential of Multi-Task Learning in CFDST Design: Load-Bearing Capacity Design with Three MTL Models.

Concrete-filled double steel tubes (CFDSTs) are a load-bearing structure of composite materials. By combining concrete and steel pipes in a nested structure, the performance of the column will be greatly improved. The performance of CFDSTs is closely related to their design. However, existing codes for CFDST design often focus on how to verify the reliability of a design, but specific design parameters cannot be directly provided. As a machine learning technique that can simultaneously learn multiple related tasks, multi-task learning (MTL) has great potential in the structural design of CFDSTs. Based on 227 uniaxial compression cases of CFDSTs collected from the literature, this paper utilized three multi-task models (multi-task Lasso, VSTG, and MLS-SVR) separately to provide multiple parameters for CFDST design. To evaluate the accuracy of models, four statistical indicators were adopted (R2, RMSE, RRMSE, and ρ). The experimental results indicated that there was a non-linear relationship among the parameters of CFDSTs. Nevertheless, MLS-SVR was still able to provide an accurate set of design parameters. The coefficient matrices of two linear models, multi-task Lasso and VSTG, revealed the potential connection among CFDST parameters. The latent-task matrix V in VSTG divided the prediction tasks of inner tube diameter, thickness, strength, and concrete strength into three groups. In addition, the limitations of this study and future work are also summarized. This paper provides new ideas for the design of CFDSTs and the study of related codes.

Estimating the serological underrecognition of patients with weak or partial RHD variants.

BACKGROUND: For patients with weak or discrepant RhD RBC phenotypes, RHD genotyping is employed to determine need for RhD-negative management. However, many RHD variants are type D-negative or D-positive. Serological recognition rates (RRs) of weak and partial RHD variants are poorly characterized. STUDY DESIGN AND METHODS: Four US studies employing RHD genotyping for weak or discrepant RhD phenotypes provided data for race/ethnicity-specific serological recognition. Three studies used microplate, and 1 used gel and tube; 2 had anti-D data. We obtained White and Hispanic/Latino allele frequencies (AFs) of weak D types 1, 2, and 3 single-nucleotide variants (SNVs) from the Genome Aggregation Database (gnomAD, v4.0.0) and devised Hardy-Weinberg-based formulas to correct for gnomAD's overcount of hemizygous RHD SNVs as homozygous. We compiled common partial RHD AF from genotyped cohorts of US Black or sickle cell disease subjects. From variant AF, we calculated hemizygous-plus-homozygous genetic prevalences. Serological prevalence: genetic prevalence ratios yielded serological RRs. RESULTS: Overall RRs of weak D types 1-3 were 17% (95% confidence interval 12%-24%) in Whites and 12% (5%-27%) in Hispanics/Latinos. For eight partial RHD variants in Blacks, overall RR was 11% (8%-14%). However, DAR RR was 80% (38%-156%). Compared to microplate, gel-tube recognition was higher for type 2 and DAU5 and lower for type 4.0. Anti-D was present in 6% of recognized partial RHD cases, but only in 0.7% of estimated total genetic cases. DISCUSSION: Based on AF, >80% of patients with weak or partial RHD variants were unrecognized serologically. Although overall anti-D rates were low, better detection of partial RHD variants is desirable.