A WKNN Indoor Fingerprint Localization Technique Based on Improved Discrimination Capability of RSS Similarity.

There are various indoor fingerprint localization techniques utilizing the similarity of received signal strength (RSS) to discriminate the similarity of positions. However, due to the varied states of different wireless access points (APs), each AP's contribution to RSS similarity varies, which affects the accuracy of localization. In our study, we analyzed several critical causes that affect APs' contribution, including APs' health states and APs' positions. Inspired by these insights, for a large-scale indoor space with ubiquitous APs, a threshold was set for all sample RSS to eliminate the abnormal APs dynamically, a correction quantity for each RSS was provided by the distance between the AP and the sample position to emphasize closer APs, and a priority weight was designed by RSS differences (RSSD) to further optimize the capability of fingerprint distances (FDs, the Euclidean distance of RSS) to discriminate physical distance (PDs, the Euclidean distance of positions). Integrating the above policies for the classical WKNN algorithm, a new indoor fingerprint localization technique is redefined, referred to as FDs' discrimination capability improvement WKNN (FDDC-WKNN). Our simulation results showed that the correlation and consistency between FDs and PDs are well improved, with the strong correlation increasing from 0 to 76% and the high consistency increasing from 26% to 99%, which confirms that the proposed policies can greatly enhance the discrimination capabilities of RSS similarity. We also found that abnormal APs can cause significant impact on FDs discrimination capability. Further, by implementing the FDDC-WKNN algorithm in experiments, we obtained the optimal K value in both the simulation scene and real library scene, under which the mean errors have been reduced from 2.2732 m to 1.2290 m and from 4.0489 m to 2.4320 m, respectively. In addition, compared to not using the FDDC-WKNN, the cumulative distribution function (CDF) of the localization errors curve converged faster and the error fluctuation was smaller, which demonstrates the FDDC-WKNN having stronger robustness and more stable localization performance.

A Novel Registration Framework for Aligning Longitudinal Infant Brain Tensor Images.

Registering longitudinal infant brain images is challenging, as the infant brain undergoes rapid changes in size, shape and tissue contrast in the first months and years of life. Diffusion tensor images (DTI) have relatively consistent tissue properties over the course of infancy compared to commonly used T1 or T2-weighted images, presenting great potential for infant brain registration. Moreover, groupwise registration has been widely used in infant neuroimaging studies to reduce bias introduced by predefined atlases that may not be well representative of samples under study. To date, however, no methods have been developed for groupwise registration of tensor-based images. Here, we propose a novel registration approach to groupwise align longitudinal infant DTI images to a sample-specific common space. Longitudinal infant DTI images are first clustered into more homogenous subgroups based on image similarity using Louvain clustering. DTI scans are then aligned within each subgroup using standard tensor-based registration. The resulting images from all subgroups are then further aligned onto a sample-specific common space. Results show that our approach significantly improved registration accuracy both globally and locally compared to standard tensor-based registration and standard fractional anisotropy-based registration. Additionally, clustering based on image similarity yielded significantly higher registration accuracy compared to no clustering, but comparable registration accuracy compared to clustering based on chronological age. By registering images groupwise to reduce registration bias and capitalizing on the consistency of features in tensor maps across early infancy, our groupwise registration framework facilitates more accurate alignment of longitudinal infant brain images.

Endotypes of severe neutrophilic and eosinophilic asthma from multi-omics integration of U-BIOPRED sputum samples.

BACKGROUND: Clustering approaches using single omics platforms are increasingly used to characterise molecular phenotypes of eosinophilic and neutrophilic asthma. Effective integration of multi-omics platforms should lead towards greater refinement of asthma endotypes across molecular dimensions and indicate key targets for intervention or biomarker development. OBJECTIVES: To determine whether multi-omics integration of sputum leads to improved granularity of the molecular classification of severe asthma. METHODS: We analyzed six -omics data blocks-microarray transcriptomics, gene set variation analysis of microarray transcriptomics, SomaSCAN proteomics assay, shotgun proteomics, 16S microbiome sequencing, and shotgun metagenomic sequencing-from induced sputum samples of 57 severe asthma patients, 15 mild-moderate asthma patients, and 13 healthy volunteers in the U-BIOPRED European cohort. We used Monti consensus clustering algorithm for aggregation of clustering results and Similarity Network Fusion to integrate the 6 multi-omics datasets of the 72 asthmatics. RESULTS: Five stable omics-associated clusters were identified (OACs). OAC1 had the best lung function with the least number of severe asthmatics with sputum paucigranulocytic inflammation. OAC5 also had fewer severe asthma patients but the highest incidence of atopy and allergic rhinitis, with paucigranulocytic inflammation. OAC3 comprised only severe asthmatics with the highest sputum eosinophilia. OAC2 had the highest sputum neutrophilia followed by OAC4 with both clusters consisting of mostly severe asthma but with more ex/current smokers in OAC4. Compared to OAC4, there was higher incidence of nasal polyps, allergic rhinitis, and eczema in OAC2. OAC2 had microbial dysbiosis with abundant Moraxella catarrhalis and Haemophilus influenzae. OAC4 was associated with pathways linked to IL-22 cytokine activation, with the prediction of therapeutic response to anti-IL22 antibody therapy. CONCLUSION: Multi-omics analysis of sputum in asthma has defined with greater granularity the asthma endotypes linked to neutrophilic and eosinophilic inflammation. Modelling diverse types of high-dimensional interactions will contribute to a more comprehensive understanding of complex endotypes. KEY POINTS: Unsupervised clustering on sputum multi-omics of asthma subjects identified 3 out of 5 clusters with predominantly severe asthma. One severe asthma cluster was linked to type 2 inflammation and sputum eosinophilia while the other 2 clusters to sputum neutrophilia. One severe neutrophilic asthma cluster was linked to Moraxella catarrhalis and to a lesser extent Haemophilus influenzae while the second cluster to activation of IL-22.

Et tu Brute? The Roles of Subordinate-Supervisor Similarities on the Relationship between Abusive Supervision and Employee Silence Behavior: A Study from the Subordinates' Perspectives.

The current study examined the moderating effects of subordinate-supervisor similarities on abusive supervision and employee silence relationships. We addressed the question of whether employees' silence reactions are alleviated or aggravated when the abuse comes from a supervisor who shares a similar gender and other sociodemographic attributes with the employee. The results indicated that abusive supervision led to more silence behavior and supported the moderating effect of perceived sociodemographic similarity on this relationship. However, regardless of gender similarities with their supervisors, the findings postulated that employees experiencing abusive supervision were more likely to remain silent at work. When there is a perceived sociodemographic similarity between the employee and the supervisor, abusive supervision has been found to have a harsher influence on employee's silence behavior. These findings help us better understand the antecedents of employee silence behavior and provide important implications for subordinate-supervisor similarity dynamics in exposure to abusive supervision.

A Comparison of Measures for Assessing Profile Similarity in Dyads.

Profile similarity measures are used to quantify the similarity of two sets of ratings on multiple variables. Yet, it remains unclear how different measures are distinct or overlap and what type of information they precisely convey, making it unclear what measures are best applied under varying circumstances. With this study, we aim to provide clarity with respect to how existing measures interrelate and provide recommendations for their use by comparing a wide range of profile similarity measures. We have taken four steps. First, we reviewed 88 similarity measures by applying them to multiple cross-sectional and intensive longitudinal data sets on emotional experience and retained 43 useful profile similarity measures after eliminating duplicates, complements, or measures that were unsuitable for the intended purpose. Second, we have clustered these 43 measures into similarly behaving groups, and found three general clusters: one cluster with difference measures, one cluster with product measures that could be split into four more nuanced groups and one miscellaneous cluster that could be split into two more nuanced groups. Third, we have interpreted what unifies these groups and their subgroups and what information they convey based on theory and formulas. Last, based on our findings, we discuss recommendations with respect to the choice of measure, propose to avoid using the Pearson correlation, and suggest to center profile items when stereotypical patterns threaten to confound the computation of similarity.

Exploring climate change discourse on social media and blogs using a topic modeling analysis.

Climate change is one of the most pressing global issues of our time, and understanding public perception and awareness of the topic is crucial for developing effective policies to mitigate its effects. While traditional survey methods have been used to gauge public opinion, advances in natural language processing (NLP) and data visualization techniques offer new opportunities to analyze user-generated content from social media and blog posts. In this study, a new dataset of climate change-related texts was collected from social media sources and various blogs. The dataset was analyzed using BERTopic and LDA to identify and visualize the most important topics related to climate change. The study also used sentence similarity to determine the similarities in the comments written and which topic categories they belonged to. The performance of different techniques for keyword extraction and text representation, including OpenAI, Maximal Marginal Relevance (MMR), and KeyBERT, was compared for topic modeling with BERTopic. It was seen that the best coherence score and topic diversity metric were obtained with OpenAI-based BERTopic. The results provide insights into the public's attitudes and perceptions towards climate change, which can inform policy development and contribute to efforts to reduce activities that cause climate change.

Assessment of the genetic diversity of the alleles of gliadin-coding loci in common wheat (Triticum aestivum L.) collections in Kazakhstan and Russia.

The study of genetic resources using prolamin polymorphism in wheat cultivars from countries with different climatic conditions makes it possible to identify and trace the preference for the selection of the alleles of gliadine-coding loci characteristic of specific conditions. The aim of the study was to determine the "gliadin profile" of the collection of common wheat (Triticum aestivum L.) from breeding centers in Russia and Kazakhstan by studying the genetic diversity of allelic variants of gliadin-coding loci. Intrapopulation (µ ± Sµ) and genetic (H) diversity, the proportion of rare alleles (h ± Sh), identity criterion (I) and genetic similarity (r) of common wheat from eight breeding centers in Russia and Kazakhstan have been calculated. It has been ascertained that the samples of common wheat bred in Kostanay region (Karabalyk Agricultural Experimental Station, Kazakhstan) and Chelyabinsk region (Chelyabinsk Research Institute of Agriculture, Russia) had the highest intrapopulation diversity of gliadin alleles. The proportion of rare alleles (h) at Gli-B1 and Gli-D1 loci was the highest in the wheat cultivars bred by the Federal Center of Agriculture Research of the South-East Region (Saratov region, Russia), which is explained by a high frequency of occurrence of Gli-B1e (86 %) and Gli-D1a (89.9 %) alleles. Based on identity criterion (I), the studied samples of common wheat from different regions of Kazakhstan and Russia have differences in gliadin-coding loci. The highest value of I = 619.0 was found when comparing wheat samples originated from Kostanay and Saratov regions, and the lowest I = 114.4, for wheat cultivars from Tyumen and Chelyabinsk regions. Some region-specific gliadin alleles in wheat samples have been identified. A combination of Gli-A1f, Gli-B1e and Gli-Da alleles has been identified in the majority of wheat samples from Kazakhstan and Russia. Alleles (Gli-A1f, Gli-A1i, Gli-A1m, Gli-A1o, Gli-B1e, Gli-D1a, Gli-D1f, Gli-A2q, Gli-B2o, and Gli-D2a) turned out to be characteristic and were found with varying frequency in wheat cultivars in eight regions of Russia and Kazakhstan. The highest intravarietal polymorphism (51.1 %) was observed in wheat cultivars bred in Omsk region (Russia) and the lowest (16.6 %), in Pavlodar region (Kazakhstan). On the basis of the allele frequencies, a "gliadin profile" of wheat from various regions and breeding institutions of Russia and Kazakhstan was compiled, which can be used for the selection of parent pairs in the breeding process, the control of cultivars during reproduction, as well as for assessing varietal purity.

KLSD: a kinase database focused on ligand similarity and diversity.

Due to the similarity and diversity among kinases, small molecule kinase inhibitors (SMKIs) often display multi-target effects or selectivity, which have a strong correlation with the efficacy and safety of these inhibitors. However, due to the limited number of well-known popular databases and their restricted data mining capabilities, along with the significant scarcity of databases focusing on the pharmacological similarity and diversity of SMIKIs, researchers find it challenging to quickly access relevant information. The KLIFS database is representative of specialized application databases in the field, focusing on kinase structure and co-crystallised kinase-ligand interactions, whereas the KLSD database in this paper emphasizes the analysis of SMKIs among all reported kinase targets. To solve the current problem of the lack of professional application databases in kinase research and to provide centralized, standardized, reliable and efficient data resources for kinase researchers, this paper proposes a research program based on the ChEMBL database. It focuses on kinase ligands activities comparisons. This scheme extracts kinase data and standardizes and normalizes them, then performs kinase target difference analysis to achieve kinase activity threshold judgement. It then constructs a specialized and personalized kinase database platform, adopts the front-end and back-end separation technology of SpringBoot architecture, constructs an extensible WEB application, handles the storage, retrieval and analysis of the data, ultimately realizing data visualization and interaction. This study aims to develop a kinase database platform to collect, organize, and provide standardized data related to kinases. By offering essential resources and tools, it supports kinase research and drug development, thereby advancing scientific research and innovation in kinase-related fields. It is freely accessible at: http://ai.njucm.edu.cn:8080.

The Structural Biology of Catalase Evolution.

Catalases are essential enzymes for removal of hydrogen peroxide, enabling aerobic and anaerobic metabolism in an oxygenated atmosphere. Monofunctional heme catalases, catalase-peroxidases, and manganese catalases, evolved independently more than two billion years ago, constituting a classic example of convergent evolution. Herein, the diversity of catalase sequences is analyzed through sequence similarity networks, providing the context for sequence distribution of major catalase families, and showing that many divergent catalase families remain to be experimentally studied.

A new approach to comparing the demands of small-sided games and soccer matches.

To improve soccer performance, coaches should be able to replicate the match's physical efforts during the training sessions. For this goal, small-sided games (SSGs) are widely used. The main purpose of the current study was to develop similarity and overload scores to quantify the degree of similarity and the extent to which the SSG was able to replicate match intensity. GPSs were employed to collect external load and were grouped in three vectors (kinematic, metabolic, and mechanical). Euclidean distance was used to calculate the distance between training and match vectors, which was subsequently converted into a similarity score. The average of the pairwise difference between vectors was used to develop the overload scores. Three similarity (Simkin, Simmet, Simmec) and three overload scores (OVERkin, OVERmet, OVERmec) were defined for kinematic, metabolic, and mechanical vectors. Simmet and OVERmet were excluded from further analysis, showing a very large correlation (r > 0.7, p < 0.01) with Simkin and OVERkin. The scores were subsequently analysed considering teams' level (First team vs. U19 team) and SSGs' characteristics in the various playing roles. The independent-sample t-test showed (p < 0.01) that the First team presented greater Simkin (d = 0.91), OVERkin (d = 0.47), and OVERmec (d = 0.35) scores. Moreover, a generalized linear mixed model (GLMM) was employed to evaluate differences according to SSG characteristics. The results suggest that a specific SSG format could lead to different similarity and overload scores according to the playing position. This process could simplify data interpretation and categorize SSGs based on their scores.

Attention module incorporated transfer learning empowered deep learning-based models for classification of phenotypically similar tropical cattle breeds (Bos indicus).

Accurate breed identification in dairy cattle is essential for optimizing herd management and improving genetic standards. A smart method for correctly identifying phenotypically similar breeds can empower farmers to enhance herd productivity. A convolutional neural network (CNN) based model was developed for the identification of Sahiwal and Red Sindhi cows. To increase the classification accuracy, first, cows's pixels were segmented from the background using CNN model. Using this segmented image, a masked image was produced by retaining cows' pixels from the original image while eliminating the background. To improve the classification accuracy, models were trained on four different images of each cow: front view, side view, grayscale front view, and grayscale side view. The masked images of these views were fed to the multi-input CNN model which predicts the class of input images. The segmentation model achieved intersection-over-union (IoU) and F1-score values of 81.75% and 85.26%, respectively with an inference time of 296 ms. For the classification task, multiple variants of MobileNet and EfficientNet models were used as the backbone along with pre-trained weights. The MobileNet model achieved 80.0% accuracy for both breeds, while MobileNetV2 and MobileNetV3 reached 82.0% accuracy. CNN models with EfficientNet as backbones outperformed MobileNet models, with accuracy ranging from 84.0% to 86.0%. The F1-scores for these models were found to be above 83.0%, indicating effective breed classification with fewer false positives and negatives. Thus, the present study demonstrates that deep learning models can be used effectively to identify phenotypically similar-looking cattle breeds. To accurately identify zebu breeds, this study will reduce the dependence of farmers on experts.

Quality space computations for consciousness.

The quality space hypothesis about conscious experience proposes that conscious sensory states are experienced in relation to other possible sensory states. For instance, the colour red is experienced as being more like orange, and less like green or blue. Recent empirical findings suggest that subjective similarity space can be explained in terms of similarities in neural activation patterns. Here, we consider how localist, workspace, and higher-order theories of consciousness can accommodate claims about the qualitative character of experience and functionally support a quality space. We review existing empirical evidence for each of these positions, and highlight novel experimental tools, such as altering local activation spaces via brain stimulation or behavioural training, that can distinguish these accounts.

The neural representation of stereotype content.

Judgments about social groups are characterized by their position in a representational space defined by two axes, warmth and competence. We examined serial dependence (SD) in evaluations of warmth and competence while measuring participants' electroencephalographic (EEG) activity, as a means to address the independence between these two psychological axes. SD is the attraction of perceptual reports towards things seen in the recent past and has recently been intensely investigated in vision. SD occurs at multiple levels of visual processing, from basic features to meaningful objects. The current study aims to (1) measure whether SD occurs between non-visual objects, in particular social groups and (2) uncover the neural correlates of social group evaluation and SD using EEG. Participants' judgments about social groups such as "nurses" or "accountants" were serially dependent, but only when the two successive groups were close in representational space. The pattern of results argues in favor of a non-separability between the two axes, because groups nearby on one dimension but far on the other were not subject to SD, even though that other dimension was irrelevant to the task at hand. Using representational similarity analysis, we found a brain signature that differentiated social groups as a function of their position in the representational space. Our results thus argue that SD may be a ubiquitous cognitive phenomenon, that social evaluations are serially dependent, and that reproducible neural signatures of social evaluations can be uncovered.

Validation of the number of pulses required for TMS-EEG in the prefrontal cortex considering test feasibility.

BACKGROUND: Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG), TMS-EEG, is a useful neuroscientific tool for the assessment of neurophysiology in the human cerebral cortex. Theoretically, TMS-EEG data is expected to have a better data quality as the number of stimulation pulses increases. However, since TMS-EEG testing is a modality that is examined on human subjects, the burden on the subject and tolerability of the test must also be carefully considered. METHOD: In this study, we aimed to determine the number of stimulation pulses that satisfy the reliability and validity of data quality in single-pulse TMS (spTMS) for the dorsolateral prefrontal cortex (DLPFC). TMS-EEG data for (1) 40-pulse, (2) 80-pulse, (3) 160-pulse, and (4) 240-pulse conditions were extracted from spTMS experimental data for the left DLPFC of 20 healthy subjects, and the similarities between TMS-evoked potentials (TEP) and oscillations across the conditions were evaluated. RESULTS: As a result, (2) 80-pulse and (3) 160-pulse conditions showed highly equivalent to the benchmark condition of (4) 240-pulse condition. However, (1) 40-pulse condition showed only weak to moderate equivalence to the (4) 240-pulse condition. Thus, in the DLPFC TMS-EEG experiment, 80 pulses of stimulations was found to be a reasonable enough number of pulses to extract reliable TEPs, compared to 160 or 240 pulses. CONCLUSIONS: This is the first substantial study to examine the appropriate number of stimulus pulses that are reasonable and feasible for TMS-EEG testing of the DLPFC.

Differences in endocrine and reproductive responses to substance exposure across generations: highlighting the importance of complementary findings.

This article analyzes the results from 112 Extended One-Generation Reproductive Toxicity studies. The objective was to determine if test animals show consistent endocrine and reproductive effects within the same and across different generations and life stages. The analysis, grounded in a comprehensive Binary Matrix, included 530 observed effects and 193 unique, statistically significant associations. Associations' strength was quantified using Jaccard (J) coefficients to measure effect co-occurrence in the same study. Associated effects co-occur infrequently across the whole dataset (median J = 0.231). However, specific patterns emerged: associations of same effects across generations exhibited a higher strength (median J = 0.400) compared to associations of different effects (median J = 0.222). Notably, associations with effects observed in both the parental animals of the adult first filial generation (P1) and developing second filial generations (dF2) demonstrated J coefficients (with medians ranging from 0.300 to 0.430) that were approximately twofold higher than those of other associations. Consistently, equivalent life stage associations across generations revealed statistically significant higher association strengths for the P1 and dF2 generations (medians of 0.375 and 0.333, respectively) compared to other generations (medians of 0.200 and 0.174), possibly due to longer exposure duration and altered cross-talk between pregnant P1 dam and its conceptus. Overall, it is concluded that co-occurrence of associated effects in the same study is rather infrequent and that associations with effects in P1 and dF2 are stronger than all other associations. In general, the findings underscore the importance of independently analyzing each effect per generation due to the generally low co-occurrence rates of associated effects, challenging traditional expectations of generational continuity in toxic effects.

Computational identification of potential tau tubulin kinase 1 (TTBK1) inhibitors: a structural analog approach.

Abnormal deposition or aggregation of protein alpha-synuclein and tau in the brain leads to neurodegenerative disorders. Excessive hyperphosphorylation of tau protein and aggregations destroys the microtubule structure resulting in neurofibrillary tangles in neurons and affecting cytoskeleton structure, mitochondrial axonal transport, and loss of synapses in neuronal cells. Tau tubulin kinase 1 (TTBK1), a specific neuronal kinase is a potential therapeutic target for neurodegenerative disorders as it is involved in hyperphosphorylation and aggregation of tau protein. TTBK inhibitors are now the subject of intense study, but limited numbers are found. Hence, this study involves structure-based virtual screening of TTBK1 inhibitor analogs to obtain efficient compounds targeting the TTBK1 using docking, molecular dynamics simulation and protein-ligand interaction profile. The initial analogs set containing 3884 compounds was subjected to Lipinski rule and the non-violated compounds were selected. Docking analysis was done on 2772 compounds through Autodock vina and Autodock 4.2. Data Warrior and SwissADME was utilized to filter the toxic compounds. The stability and protein-ligand interaction of the docked complex was analyzed through Gromacs and VMD. Molecular simulation results such as RMSD, Rg, and hydrogen bond interaction along with pharmacokinetic properties showed CID70794974 as the potential hit targeting TTBKl prompting the need for further experimental investigation to evaluate their potential therapeutic efficacy in Alzheimer's disease. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-024-00242-z.

Skin Test Reactivity Patterns in Patients Allergic to Iodinated Contrast Media: A Refined View.

BACKGROUND: Two-dimensional (2D) classifications of iodinated contrast media (ICM) are insufficient to explain the observed skin test (ST) reactivity patterns in patients with drug hypersensitivity reactions (DHRs) to ICM. OBJECTIVE: To refine the current view on allergic DHRs to ICM by analyzing ST reactivity patterns in patients with previous reactions to ICM. METHODS: Patients with a history of DHR to ICM and positive STs, who presented at the University Hospital of Montpellier between 2004 and 2022, were included in the study. The relative difference between every two ICM products was measured by Manhattan distance and odds ratios were computed for all pairs of products in the immediate reaction (IR) and non-immediate reaction (NIR) ST groups. RESULTS: A total of 181 patients were included in the study. Odds ratio analysis identified significant associations between classical cross-reactive ICM, such as iohexol-ioversol, iohexol-iomeprol, iomeprol-ioversol, and iohexol-iodixanol in the IR ST group and iohexol-ioversol, iopromide-iohexol, and iomeprol-ioversol in the NIR ST group. We also identified uncommon associations, such as ioxitalamate-amidotrizoate in the IR ST group and amidotrizoate-iopamidol and amidotrizoate-ioxitalamate in the NIR ST group. The results were reflected by the Manhattan distance, which suggested the existence of clusters containing the same classically associated ICM as well as uncommon associations, which we hypothesize to be related to similarities in the 3D structure of the respective ICM. CONCLUSIONS: Current chemical (2D) classifications cannot explain all observed ST reactivity patterns. Whether the 3D structure can be integrated into the current classifications to interpret the observed ST reactivity patterns and predict tolerance to alternative ICM requires further research.

Deep Efficient Data Association for Multi-Object Tracking: Augmented with SSIM-Based Ambiguity Elimination.

Recently, to address the multiple object tracking (MOT) problem, we harnessed the power of deep learning-based methods. The tracking-by-detection approach to multiple object tracking (MOT) involves two primary steps: object detection and data association. In the first step, objects of interest are detected in each frame of a video. The second step establishes the correspondence between these detected objects across different frames to track their trajectories. This paper proposes an efficient and unified data association method that utilizes a deep feature association network (deepFAN) to learn the associations. Additionally, the Structural Similarity Index Metric (SSIM) is employed to address uncertainties in the data association, complementing the deep feature association network. These combined association computations effectively link the current detections with the previous tracks, enhancing the overall tracking performance. To evaluate the efficiency of the proposed MOT framework, we conducted a comprehensive analysis of the popular MOT datasets, such as the MOT challenge and UA-DETRAC. The results showed that our technique performed substantially better than the current state-of-the-art methods in terms of standard MOT metrics.

Genetic distance and ancestry proportion modify the association between maternal genetic risk score of type 2 diabetes and fetal growth.

BACKGROUND: Maternal genetic risk of type 2 diabetes (T2D) has been associated with fetal growth, but the influence of genetic ancestry is not yet fully understood. We aimed to investigate the influence of genetic distance (GD) and genetic ancestry proportion (GAP) on the association of maternal genetic risk score of T2D (GRST2D) with fetal weight and birthweight. METHODS: Multi-ancestral pregnant women (n = 1,837) from the NICHD Fetal Growth Studies - Singletons cohort were included in the current analyses. Fetal weight (in grams, g) was estimated from ultrasound measurements of fetal biometry, and birthweight (g) was measured at delivery. GRST2D was calculated using T2D-associated variants identified in the latest trans-ancestral genome-wide association study and was categorized into quartiles. GD and GAP were estimated using genotype data of four reference populations. GD was categorized into closest, middle, and farthest tertiles, and GAP was categorized as highest, medium, and lowest. Linear regression analyses were performed to test the association of GRST2D with fetal weight and birthweight, adjusted for covariates, in each GD and GAP category. RESULTS: Among women with the closest GD from African and Amerindigenous ancestries, the fourth and third GRST2D quartile was significantly associated with 5.18 to 7.48 g (weeks 17-20) and 6.83 to 25.44 g (weeks 19-27) larger fetal weight compared to the first quartile, respectively. Among women with middle GD from European ancestry, the fourth GRST2D quartile was significantly associated with 5.73 to 21.21 g (weeks 18-26) larger fetal weight. Furthermore, among women with middle GD from European and African ancestries, the fourth and second GRST2D quartiles were significantly associated with 117.04 g (95% CI = 23.88-210.20, p = 0.014) and 95.05 g (95% CI = 4.73-185.36, p = 0.039) larger birthweight compared to the first quartile, respectively. The absence of significant association among women with the closest GD from East Asian ancestry was complemented by a positive significant association among women with the highest East Asian GAP. CONCLUSIONS: The association between maternal GRST2D and fetal growth began in early-second trimester and was influenced by GD and GAP. The results suggest the use of genetic GD and GAP could improve the generalizability of GRS.