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Illusory contours demonstrate an important function of the visual system-object inference from incomplete boundaries, which can arise from factors such as low luminance, camouflage, or occlusion. Illusory contours can be perceived with varying degrees of clarity depending on the features of their inducers. The present study aimed to evaluate whether illusory contour clarity influences visual search efficiency. Experiment 1 compared visual search performance for Kanizsa illusory stimuli and nonillusory inducer stimuli when manipulating inducer size as a clarity factor. Experiment 2 examined the effects of illusory contour clarity on visual search by manipulating the number of rings with missing arcs (i.e., line ends) comprising the inducers, for both illusory and nonillusory stimuli. To investigate whether surface alterations had an impact on visual search in Experiment 1, Experiment 3 examined search performance for Kanizsa-like stimuli formed from "smoothed" inducers compared with standard Kanizsa figures. The results of Experiments 1 and 2 indicated that while Kanizsa produced inefficient search, this was not contingent on the clarity of the illusory contours. Experiment 3 suggested that surface alterations of Kanizsa figures did impact visual search performance. Together, the results indicated that illusory contour clarity did not have much bearing on search performance. In certain conditions, Kanizsa figures even facilitated search compared with nonillusory stimuli, suggesting that rather than contour inference, surface features might have greater relevance in guiding visual attention.

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In order to analyze the growth pattern of tree height of planted Pinus koraiensis and screen the provenances with fastest growth, we grouped the provenances using the differences in tree height, diameter at breast height (DBH) and volume of timber of 234 individuals of planted P. koraiensis from 26 provenances in Maoershan Experimental Forest Farm. We constructed the growth equation for tree height by combining the base models of Gompertz, Korf, Richards, Logistic, and Schumacher, and then selected the optimal one. We introduced the prove-nance grouping as a dummy variable into the base model, and evaluated the optimal tree height growth equation by a comprehensive evaluation of the model according to the coefficient of determination (R2), the root-mean-square error (RMSE), the Akaikei Information Criterion (AIC), and the model's predictive precision (FP). The results showed that the growth traits of the 26 provenances had significant difference among the groups, and that tree height and DBH showed significant differences among the provenances. According to the comprehensive consideration of different growth traits, the four groups of provenance growth were divided into group A (Wuying, Hebei, Linjiang, Dongfanghong, Huanan, Lushuihe, Fangzheng) >group B (Aihuisanzhan, Liangshui, Tieli, Qinghe) > group C (Wuyiling, Zhanhe, Liangzihe, Baihe, Chaihe, Caohekou, Bajiazi) >group D (Tongzigou, Dashitou, Wangqing, Helong, Yanshou, Dahailin, Xiaobeihu, Muling). The optimal base tree height growth model of the four groups was the Gompertz model, and the fitting accuracy of the model after the introduction of dummy variables (R2=0.9353) was higher than that of the base model (R2=0.9303), and the model prediction accuracy was also improved. The tree height growth curves of each provenance group conformed to the "S"-shaped rule of change. There were obvious differences among the groups, with the best performance of the provenances in group A. The growth of P. koraiensis from different provenances was different, and the tree height growth model with dummy variables of provenance groups could effectively improve the prediction accuracy of the model, reflect the differences in height growth of P. koraiensis of different provenances, which could provide the scientific basis for the selection and cultivation of P. koraiensis plantations.

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In the realm of computer vision, object detection holds significant importance and has demonstrated commendable performance across various scenarios. However, it typically requires favorable visibility conditions within the scene. Therefore, it is imperative to explore methodologies for conducting object detection under low-visibility circumstances. With its balanced combination of speed and accuracy, the state-of-the-art YOLOv8 framework has been recognized as one of the top algorithms for object detection, demonstrating outstanding performance results across a range of standard datasets. Nonetheless, current YOLO-series detection algorithms still face a significant challenge in detecting objects under low-light conditions. This is primarily due to the significant degradation in performance when detectors trained on illuminated data are applied to low-light datasets with limited visibility. To tackle this problem, we suggest a new model named Grouping Offset and Isolated GiraffeDet Target Detection-YOLO based on the YOLOv8 architecture. The proposed model demonstrates exceptional performance under low-light conditions. We employ the repGFPN feature pyramid network in the design of the feature fusion layer neck to enhance hierarchical fusion and deepen the integration of low-light information. Furthermore, we refine the repGFPN feature fusion layer by introducing a sampling map offset to address its limitations in terms of weight and efficiency, thereby better adapting it to real-time applications in low-light environments and emphasizing the potential features of such scenes. Additionally, we utilize group convolution to isolate interference information from detected object edges, resulting in improved detection performance and model efficiency. Experimental results demonstrate that our GOI-YOLO reduces the parameter count by 11% compared to YOLOv8 while decreasing computational requirements by 28%. This optimization significantly enhances real-time performance while achieving a competitive increase of 2.1% in Map50 and 0.6% in Map95 on the ExDark dataset.

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Non-orthogonal Multiple Access (NOMA) techniques offer potential enhancements in spectral efficiency for 5G and 6G wireless networks, facilitating broader network access. Central to realizing optimal system performance are factors like joint power control, user grouping, and decoding order. This study investigates power control and user grouping to optimize spectral efficiency in NOMA uplink systems, aiming to reduce computational difficulty. While previous research on this integrated optimization has identified several near-optimal solutions, they often come with considerable system and computational overheads. To address this, this study employed an improved Grey Wolf Optimizer (GWO), a nature-inspired metaheuristic optimization method. Although GWO is effective, it can sometimes converge prematurely and might lack diversity. To enhance its performance, this study introduces a new version of GWO, integrating Competitive Learning, Q-learning, and Greedy Selection. Competitive learning adopts agent competition, balancing exploration and exploitation and preserving diversity. Q-learning guides the search based on past experiences, enhancing adaptability and preventing redundant exploration of sub-optimal regions. Greedy selection ensures the retention of the best solutions after each iteration. The synergistic integration of these three components substantially enhances the performance of the standard GWO. This algorithm was used to manage power and user-grouping in NOMA systems, aiming to strengthen system performance while restricting computational demands. The effectiveness of the proposed algorithm was validated through numerical evaluations. Simulated outcomes revealed that when applied to the joint challenge in NOMA uplink systems, it surpasses the spectral efficiency of conventional orthogonal multiple access. Moreover, the proposed approach demonstrated superior performance compared to the standard GWO and other state-of-the-art algorithms, achieving reduced system complexity under identical constraints.

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Introduction: The relationship between herpes zoster recurrence and the gut microbiome was not studied. We analyzed data on the gut microbiome and herpes zoster from the Large-Scale Genome-Wide Association Study (GWAS) database using bidirectional Mendelian randomization. For the first time, we identified a potentially bidirectional causal relationship between the gut microbiome and herpes zoster (HZ). These findings are groundbreaking and hold promise for new directions in the treatment of HZ, a global disease. Background and aims: HZ had a high global incidence, characterized by shingled blisters, blood blisters, and neuropathic pain, and could develop in various parts of the body, including the ear and throat. It was believed its onset was closely related to old age and infirmity. Some studies reported that the incidence of herpes zoster in patients with inflammatory intestinal diseases (such as Crohn's disease and ulcerative colitis) was higher than in the general population. Existing studies attributed this to the reactivation of varicella-zoster virus (VZV) due to autoinflammatory attacks and immunosuppressive drugs. This provided a basis for exploring the new pathogenesis of HZ and investigating whether there was a relationship between intestinal auto-flora and the development of HZ. This study aimed to examine this potential relationship using bidirectional Mendelian analyses. Methods: GWAS data on HZ and gut microbiota were obtained from FinnGen, the Mibiogen consortium, and HZ meta-analysis data from the IEU Open GWAS Project. These data were subjected to two-sample Mendelian randomization (MR) analysis to determine if there is a causal relationship between gut microbiota and HZ. Additionally, bidirectional Mendelian analyses were conducted to identify the direction of causality and to clarify any potential interactions. Results: In our Mendelian Randomization (MR) analysis, we identified, for the first time, two gut microbes that might be associated with HZ reactivation. In the reverse MR analysis, four gut microbiota showed a potential association between the genetic susceptibility of gut microbiota and HZ reactivation. We found that genus Tyzzerella3 (OR: 1.42, 95% CI: 1.17-1.72, FDR < 0.1) may be strongly correlated with an increased probability of HZ (ICD-10: B02.901) reactivation. Additionally, phylum Cyanobacteria was identified as a potential risk factor for the onset of HZ rekindling (OR: 1.42, 95% CI: 1.09-1.87). Analyzing the results of the reverse MR, we also identified a potential inhibitory effect (OR: 0.91, 95% CI: 0.84-0.99) of HZ onset on the genus Eubacteriumhallii group in the gut, suggesting that HZ might reduce its abundance. However, genus Escherichia/Shigella (OR: 1.11, 95% CI: 1.01-1.22), genus Veillonella (OR: 1.16, 95% CI: 1.04-1.30), and phylum Proteobacteria (OR: 1.09, 95% CI: 1.01-1.18) appeared to act as potential protective factors, indicating that the relative abundance and viability of these three bacteria increased in the HZ state. Conclusion: We identified the influence of gut flora as a new causative factor for HZ reactivation. Additionally, we found that individuals suffering from HZ might potentially impact their gut flora. Specific bacterial taxa that could influence the onset and progression of HZ were identified, potentially providing new directions for HZ treatment.

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Two-stage stratified sampling is a complex design that involves nested sampling units and stratification. This complexity increases when the strata have too few sampled units for variance estimation, necessitating the use of collapsed strata, where multiple strata are combined to ensure an adequate sample size. When collapsing strata, two cases can be distinguished depending on whether a size variable associated with the variable of interest is available at the stratum level.•We present computer-implementable formulas for total, mean, and ratio estimators, along with their corresponding sampling variance estimators, for stratified two-stage simple random sampling without replacement, and we provide ready-to-use algorithms.•We introduce two methods for grouping strata: (1) a deterministic approach that uses stratum codes to define an ordinal variable, which orders the strata, and (2) a stochastic method that aims to minimize within-group inertia, which measures the heterogeneity within the newly formed groups of strata.•We emphasize that, unlike the correlation between a size variable and the variable of interest at the stratum level, the bias of the sampling variance estimator for the collapsed strata technique is not invariant to linear transformations. It follows that a high correlation does not ensure a low-bias estimator of the sampling variance.

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Cashmere and wool fibers have similar chemical compositions, making them difficult to distinguish based on their absorption peaks and band positions in near-infrared spectroscopy. Existing studies commonly use wavelength selection or feature extraction algorithms to obtain significant spectral features, but traditional algorithms often overlook the correlations between wavelengths, resulting in weak adaptability and local optimum issues. To address this problem, this paper proposes a recognition algorithm based on optimal wavelength selection, which can remove redundant information and make the model effective in capturing patterns and key features of the data. The wavelengths are rearranged by computing the information gain ratio for each wavelength. Then, the sorted wavelengths are grouped based on equal density, which ensures that all wavelengths within each group have equal information and avoids over-focusing on individual groups. Meanwhile, the group genetic algorithm is used to find the wavelengths with highly informative and search optimal grouped combinations, in order to explore the entire spectrum wavelength. Finally, combined with a partial least squares discriminant analysis(PLS-DA) model, the recognition accuracy reached 97.3 %. The results indicate that, compared to traditional methods such as CARS, SPA, and GA, our method effectively reduces redundant information, selects fewer but more informative wavelengths, and improves classification accuracy and model adaptability.

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Population-based studies of human mitochondrial genetic diversity often require the classification of mitochondrial DNA (mtDNA) haplotypes into more than 5400 described haplogroups, and further grouping those into hierarchically higher haplogroups. Such secondary haplogroup groupings (e.g., "macro-haplogroups") vary across studies, as they depend on the sample quality, technical factors of haplogroup calling, the aims of the study, and the researchers' understanding of the mtDNA haplogroup nomenclature. Retention of historical nomenclature coupled with a growing number of newly described mtDNA lineages results in increasingly complex and inconsistent nomenclature that does not reflect phylogeny well. This "clutter" leaves room for grouping errors and inconsistencies across scientific publications, especially when the haplogroup names are used as a proxy for secondary groupings, and represents a source for scientific misinterpretation. Here we explore the effects of phylogenetically insensitive secondary mtDNA haplogroup groupings, and the lack of standardized secondary haplogroup groupings on downstream analyses and interpretation of genetic data. We demonstrate that frequency-based analyses produce inconsistent results when different secondary mtDNA groupings are applied, and thus allow for vastly different interpretations of the same genetic data. The lack of guidelines and recommendations on how to choose appropriate secondary haplogroup groupings presents an issue for the interpretation of results, as well as their comparison and reproducibility across studies. To reduce biases originating from arbitrarily defined secondary nomenclature-based groupings, we suggest that future updates of mtDNA phylogenies aimed for the use in mtDNA haplogroup nomenclature should also provide well-defined and standardized sets of phylogenetically meaningful algorithm-based secondary haplogroup groupings such as "macro-haplogroups", "meso-haplogroups", and "micro-haplogroups". Ideally, each of the secondary haplogroup grouping levels should be informative about different human population history events. Those phylogenetically informative levels of haplogroup groupings can be easily defined using TreeCluster, and then implemented into haplogroup callers such as HaploGrep3. This would foster reproducibility across studies, provide a grouping standard for population-based studies, and reduce errors associated with haplogroup nomenclatures in future studies.

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The concept of precision nutrition highlights the customization of nutrition to specific needs, emphasizing that a one-size-fits-all approach is not sufficient for either optimal nutrition or optimal health. Precision nutrition encompasses a range of factors, from broad strata of age and sex categories to personal characteristics such as lifestyle to an individual's unique genotype. This breadth of scope requires us to consider how precision nutrition can be implemented in an inclusive and appropriate way for individuals and groups within real-life populations. In this narrative review, we explore the potential of precision nutrition through a life-stage approach that emphasizes age- and gender-specific nutritional needs as these change across the lifespan. Focusing on adult life stages, we delineated trends in age-related conditions and health needs among Korean adults based on national-level survey data (KNHANES 2019-2021). We also reviewed the intake of nutrients associated with these health needs to better understand how life-stage guided approaches to nutrition and supplementation could support optimal health. Looking beyond preventing deficiency or disease, we discuss how tailored supplementation of essential vitamins, minerals, and certain bioactive substances could promote healthy functioning. Finally, we discuss the complexities and challenges of developing multivitamin/multimineral supplements (MVMS) to support life-stage appropriate nutrition while maximizing adherence. Future prospects include leveraging advancements in intelligent technologies and dietary assessments for tracking nutrient intake and health indicators and using these to optimize MVMS formulations in ways that are sensitive to a person's needs and priorities/preferences at different life stages. By adopting a life-stage guided approach to nutrition, we can better support health and well-being across the lifespan.

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Aim of the study: Complementary and integrative medicine (CIM) has been increasingly recognized as offering promising treatment adjunctions in various clinical settings, even amongst patients with serious, chronic, or recurrent illness. Today, only few tertiary care facilities in Switzerland offer dedicated CIM services for inpatients. The aim of the present study was to evaluate whether CIM services for complex medical conditions are adequately valued by the national inpatient SwissDRG reimbursement system. Methods: A simulation was performed by adding a specific code of the Swiss classification of interventions (CHOP) to the list of codes of each patient who received CIM therapies at the Lausanne University Hospital (CHUV) in 2021. This code is to be used when CIM services are provided. Hitherto, it was not entered due to a lack of specific documents justifying the resources used. The analysis focused on the impact of adding this CIM CHOP code on the Swiss Diagnosis Related Group (DRG) reimbursement. Results: In total, 275 patients received a CIM therapy in 2021. The addition of the CIM CHOP code 99.BC.12 (10-25 CIM sessions per stay) resulted in a simulated loss of income of CHF 766 630 for the hospital, while the net real result is already negative by more than CHF 6 million. The DRGs positively impacted by the addition of CIM CHOP code 99.BC.12 had a mean (SD) cost weight (CW) of 1.014 (0.620), while the DRGs negatively impacted had a mean (SD) CW of 3.97 (2.764) points. Conclusion: It is necessary to quickly react and improve the incentives contained in the grouping algorithm of the prospective payment system, whose effects can threaten the provision of adequate medical care to the patients despite suitable indications and potential for cost-savings.

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Background: The global outbreak of coronavirus disease 2019 (COVID-19) presents numerous obstacles for healthcare professionals. The present study aimed to evaluate and compare the role of serum biomarkers like- C-reactive protein (CRP), interleukin-6 (IL-6), and D-dimers in the severity of COVID-19 infection. Methodology: A cross-sectional, observational retrospective pilot study was conducted in Udaipur, Rajasthan, wherein data was collected from 250 subjects, out of which, data of 100 subjects were included as per the inclusion criteria. The data was recorded retrospectively among the health professionals via Google Forms in Udaipur, Rajasthan. Results: There were 1 (1%), 3 (3%), 31 (31%) and 65 (65%) participants with minor elevation (0.3-1.0), moderate elevation (1-10), marked elevation (10-50) and severe elevation (>50) of CRP respectively. The difference between the groups was statistically highly significant with a significantly higher number of study participants with a severe elevation of CRP levels (χ2 = 107.84, P < 0.001). The results showed that there was a significant difference between the groups with IL6 in 0-7 range while 96 (96%) study participants had >7 IL6, and the difference was statistically highly significant (2 = 84.640, P 0.001). Conclusion: In conclusion, the existing body of research indicates a discernible correlation between COVID-19 infection and the fluctuation of biomarker levels. This supplement has the potential to be utilised in clinical practice as a means of informing treatment decisions and determining the necessity of admission to the intensive care unit (ICU).

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Perception has long been envisioned to use an internal model of the world to explain the causes of sensory signals. However, such accounts have historically not been testable, typically requiring intractable search through the space of possible explanations. Using auditory scenes as a case study, we leveraged contemporary computational tools to infer explanations of sounds in a candidate internal generative model of the auditory world (ecologically inspired audio synthesizers). Model inferences accounted for many classic illusions. Unlike traditional accounts of auditory illusions, the model is applicable to any sound, and exhibited human-like perceptual organization for real-world sound mixtures. The combination of stimulus-computability and interpretable model structure enabled 'rich falsification', revealing additional assumptions about sound generation needed to account for perception. The results show how generative models can account for the perception of both classic illusions and everyday sensory signals, and illustrate the opportunities and challenges involved in incorporating them into theories of perception.

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Perceptual grouping, a fundamental mechanism in our visual system, significantly influences our interpretation of and interaction with the surrounding world. This study explores the impact of the proximity principle from the perspective of the Two Visual Systems (TVS) model. The TVS model argues that the visual system comprises two distinct streams: the ventral stream, which forms the neural basis for "vision-for-perception," and the dorsal stream, which underlies "vision-for-action." We designed a perceptual grouping task using dot lattices as well as a line-orientation discrimination task. Data were collected using vocal and mouse methods for the vision-for-perception mode, and joystick and pen-paper methods for the vision-for-action mode. Each method, except for vocal, included separate blocks for right and left hands. The proximity data were fitted using exponential and power models. Linear mixed-effects models were used for the statistical analyses. The results revealed similar line-orientation discrimination accuracy across all conditions. The exponential model emerged as the best fit, demonstrating adherence to the Pure Distance Law in both perceptual modes. Sensitivity to the proximity principle was higher in the vision-for-action mode compared to the vision-for-perception. In terms of orientation biases, a strong preference for vertical orientation was observed in the vision-for-perception mode, whereas a noticeable preference toward either of the oblique orientations was detected in the vision-for-action mode. Analysis of free-drawn lines demonstrated an affordance bias in the vision-for-action mode. This suggests a remarkable tendency to perceive organizations within specific orientations that offer more affordances due to the interaction between the body postures and tools.

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Terahertz (THz) wireless communication is a promising technology that will enable ultra-high data rates, and very low latency for future wireless communications. Intelligent Reconfigurable Surfaces (IRS) aiding Unmanned Aerial Vehicle (UAV) are two essential technologies that play a pivotal role in balancing the demands of Sixth-Generation (6G) wireless networks. In practical scenarios, mission completion time and energy consumption serve as crucial benchmarks for assessing the efficiency of UAV-IRS enabled THz communication. Achieving swift mission completion requires UAV-IRS to fly at maximum speed above the ground users it serves. However, this results in higher energy consumption. To address the challenge, this paper studies UAV-IRS trajectory planning problems in THz networks. The problem is formulated as an optimization problem aiming to minimize UAVs-IRS mission completion time by optimizing the UAV-IRS trajectory, considering the energy consumption constraint for UAVs-IRS. The proposed optimization algorithm, with low complexity, is well-suited for applications in THz communication networks. This problem is a non-convex, optimization problem that is NP-hard and presents challenges for conventional optimization techniques. To overcome this, we proposed a Deep Q-Network (DQN) reinforcement learning algorithm to enhance performance. Simulation results show that our proposed algorithm achieves performance compared to benchmark schemes.

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With the increased availability of chemical data in public databases, innovative techniques and algorithms have emerged for the analysis, exploration, visualization, and extraction of information from these data. One such technique is chemical grouping, where chemicals with common characteristics are categorized into distinct groups based on physicochemical properties, use, biological activity, or a combination. However, existing tools for chemical grouping often require specialized programming skills or the use of commercial software packages. To address these challenges, we developed a user-friendly chemical grouping workflow implemented in KNIME, a free, open-source, low/no-code, data analytics platform. The workflow serves as an all-encompassing tool, expertly incorporating a range of processes such as molecular descriptor calculation, feature selection, dimensionality reduction, hyperparameter search, and supervised and unsupervised machine learning methods, enabling effective chemical grouping and visualization of results. Furthermore, we implemented tools for interpretation, identifying key molecular descriptors for the chemical groups, and using natural language summaries to clarify the rationale behind these groupings. The workflow was designed to run seamlessly in both the KNIME local desktop version and KNIME Server WebPortal as a web application. It incorporates interactive interfaces and guides to assist users in a step-by-step manner. We demonstrate the utility of this workflow through a case study using an eye irritation and corrosion dataset.Scientific contributionsThis work presents a novel, comprehensive chemical grouping workflow in KNIME, enhancing accessibility by integrating a user-friendly graphical interface that eliminates the need for extensive programming skills. This workflow uniquely combines several features such as automated molecular descriptor calculation, feature selection, dimensionality reduction, and machine learning algorithms (both supervised and unsupervised), with hyperparameter optimization to refine chemical grouping accuracy. Moreover, we have introduced an innovative interpretative step and natural language summaries to elucidate the underlying reasons for chemical groupings, significantly advancing the usability of the tool and interpretability of the results.

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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.

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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.

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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.

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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.