A Wireless Sensor Network (WSN) is usually made up of a large number of discrete sensor nodes, each of which requires restricted resources, including memory, computing power, and energy. To extend the network lifetime, these limited resources must be used effectively. In WSN, clustering constitutes one of the best methods for optimizing network longevity and energy conservation. In this work, we proposed a novel Energy and Throughput Aware Adaptive Routing (ETAAR) algorithm based on Cooperative Game Theory (CGT). To achieve the energy efficient and improved data rate routing in WSN, we are applied two game theories of CGT and coalition game. The main part of this routing mechanism is cluster head selection and clustering the nodes to perform energy efficient and throughput effective communication between the nodes. In first stage, CGT based utility function which adopts both energy and throughput is utilized to handpick the CH nodes. In the second stage, along with the energy and throughput, average end-to-end delay is considered for the adaptive time slot transmission to avoid collision in the coalition game approach. MATLAB tool is used for simulation. The simulation results shows that the proposed ETAAR protocol is outperforms than earlier works of routing in terms of residual energy, PDR, energy due ratio, average end-to-end delay, dead nodes. The network lifetime of 48% extension, energy saving of 60% and 52.5% of delay shortage attained in ETAAR.

In dairy farms, cows are commonly fed a mixture of forages and concentrates ad libitum. To improve the energetic status and productivity of dairy cows, individualized feeding strategies have been proposed. One of this strategy is providing supplemental concentrates to adjust the forage-to-concentrate ratio based on factors like individual milk yield or calculated energy balance. This strategy can affect milk production and cow health, though consistent rules for adjustment are lacking. The objectives of this study were to evaluate the effects of an individualized feeding strategy, adjusted weekly based on the body weight gain of dairy cows, on production performance; and to determine if the metabolic status of the cows could be predicted early in lactation to take it into account into the decisions rules of the strategy. A total of 40 multiparous Holstein cows were involved in a 4-mo trial. The cows entered the experiment individually after calving and were initially fed a standard ration with a fixed 3 kg of extra concentrate per day for the first 8 d (on average). The cows were then paired based on calving date, parity (2 or 3), and body weight gain over the initial week. One cow from each pair was assigned to the Standard Feeding (SF) strategy, which continued receiving the fixed ration, while the other was assigned to the Precision Feeding (PF) strategy, which received a variable amount of extra concentrate adjusted weekly based on body weight gain. Measurements included weekly body weight, daily milk yield, and daily intakes of concentrates and forages. Blood samples were collected to measure metabolites (glucose, BHB, NEFA) for metabolic profiling. The results showed no significant differences in overall body weight gain, milk yield, or intakes (concentrates and/or forages). Two metabolic clusters were identified based on blood metabolites (glucose, BHB, NEFA), predicting cows\' metabolic status with 90% accuracy. The balanced cluster had higher milk production, feed intake, and lost more body weight than the imbalanced cluster. Alternative variables like body weight gain and total feed intake can be used to predict metabolic clusters, achieving up to 70% accuracy. To conclude, cows fed this precision feeding strategy had similar performances than those fed the standard feeding strategy. Long-term effect of this strategy should be studied. Metabolic profiling predicted cows\' metabolic status suggesting its potential for enhancing individualized feeding decisions.

Identifying meaningful patterns in data is crucial for understanding complex biological processes, particularly in transcriptomics, where genes with correlated expression often share functions or contribute to disease mechanisms. Traditional correlation coefficients, which primarily capture linear relationships, may overlook important nonlinear patterns. We introduce the clustermatch correlation coefficient (CCC), a not-only-linear coefficient that utilizes clustering to efficiently detect both linear and nonlinear associations. CCC outperforms standard methods by revealing biologically meaningful patterns that linear-only coefficients miss and is faster than state-of-the-art coefficients such as the maximal information coefficient. When applied to human gene expression data from genotype-tissue expression (GTEx), CCC identified robust linear relationships and nonlinear patterns, such as sex-specific differences, that are undetectable by standard methods. Highly ranked gene pairs were enriched for interactions in integrated networks built from protein-protein interactions, transcription factor regulation, and chemical and genetic perturbations, suggesting that CCC can detect functional relationships missed by linear-only approaches. CCC is a highly efficient, next-generation, not-only-linear correlation coefficient for genome-scale data. A record of this paper\'s transparent peer review process is included in the supplemental information.

UNASSIGNED: Ovarian cancer patients\' resistance to first-line treatment posed a significant challenge, with approximately 70% experiencing recurrence and developing strong resistance to first-line chemotherapies like paclitaxel.
UNASSIGNED: Within the framework of predictive, preventive, and personalized medicine (3PM), this study aimed to use artificial intelligence to find drug resistance characteristics at the single cell, and further construct the classification strategy and deep learning prognostic models based on these resistance traits, which can better facilitate and perform 3PM.
UNASSIGNED: This study employed \"Beyondcell,\" an algorithm capable of predicting cellular drug responses, to calculate the similarity between the expression patterns of 21,937 cells from ovarian cancer samples and the signatures of 5201 drugs to identify drug-resistance cells. Drug resistance features were used to perform 10 multi-omics clustering on the TCGA training set to identify patient subgroups with differential drug responses. Concurrently, a deep learning prognostic model with KAN architecture which had a flexible activation function to better fit the model was constructed for this training set. The constructed patient subtype classifier and prognostic model were evaluated using three external validation sets from GEO: GSE17260, GSE26712, and GSE51088.
UNASSIGNED: This study identified that endothelial cells are resistant to paclitaxel, doxorubicin, and docetaxel, suggesting their potential as targets for cellular therapy in ovarian cancer patients. Based on drug resistance features, 10 multi-omics clustering identified four patient subtypes with differential responses to four chemotherapy drugs, in which subtype CS2 showed the highest drug sensitivity to all four drugs. The other subtypes also showed enrichment in different biological pathways and immune infiltration, allowing for targeted treatment based on their characteristics. Besides, this study applied the latest KAN architecture in artificial intelligence to replace the MLP structure in the DeepSurv prognostic model, finally demonstrating robust performance on patients\' prognosis prediction.
UNASSIGNED: This study, by classifying patients and constructing prognostic models based on resistance characteristics to first-line drugs, has effectively applied multi-omics data into the realm of 3PM.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s13167-024-00374-4.

BACKGROUND: Adverse childhood experiences (ACEs) affect up to half the general population, they are known to co-occur, and are particularly common among those experiencing poverty. Yet, there are limited studies examining specific patterns of ACE co-occurrence considering their developmental timing.
OBJECTIVE: To examine the longitudinal co-occurrence patterns of ACEs across childhood and adolescence, and to examine the role of poverty in predicting these.
METHODS: The sample was 8859 children from the Avon Longitudinal Study of Parents and Children, a longitudinal prospective population-based UK birth cohort.
METHODS: Repeated measures of ten ACEs were available, occurring in early childhood (birth-5 years), mid-childhood (6-10 years), and adolescence (11-16 years). Latent class analysis was used to identify groups of children with similar developmental patterns of ACEs. Multinomial regression was used to examine the association between poverty during pregnancy and ACE classes.
RESULTS: Sixteen percent of parents experienced poverty. A five-class latent model was selected: \"Low ACEs\" (72·0 %), \"Early and mid-childhood household disharmony\" (10·6 %), \"Persistent parental mental health problems\" (9·7 %), \"Early childhood abuse and parental mental health problems\" (5·0 %), and \"Mid-childhood and adolescence ACEs\" (2·6 %). Poverty was associated with a higher likelihood of being in each of the ACE classes compared to the low ACEs reference class. The largest effect size was seen for the \"Early and mid-childhood household disharmony\" class (OR 4·70, 95 % CI 3·68-6·00).
CONCLUSIONS: A multifactorial approach to preventing ACEs is needed - including support for parents facing financial and material hardship, at-risk families, and timely interventions for those experiencing ACEs.

BACKGROUND: Every hospital manager aims to build harmonious, mutually beneficial, and steady-state departments. Therefore, it is important to explore a hospital department development assessment model based on objective hospital data.
OBJECTIVE: This study aims to use a novel machine learning algorithm to identify key evaluation indexes for hospital departments, offering insights for strategic planning and resource allocation in hospital management.
METHODS: Data related to the development of a hospital department over the past 3 years were extracted from various hospital information systems. The resulting data set was mined using neural machine algorithms to assess the possible role of hospital departments in the development of a hospital. A questionnaire was used to consult senior experts familiar with the hospital to assess the actual work in each hospital department and the impact of each department\'s development on overall hospital discipline. We used the results from this questionnaire to verify the accuracy of the departmental risk scores calculated by the machine learning algorithm.
RESULTS: Deep machine learning was performed and modeled on the hospital system training data set. The model successfully leveraged the hospital\'s training data set to learn, predict, and evaluate the working and development of hospital departments. A comparison of the questionnaire results with the risk ranking set from the departments machine learning algorithm using the cosine similarity algorithm and Pearson correlation analysis showed a good match. This indicates that the department development assessment model and risk score based on the objective data of hospital systems are relatively accurate and objective.
CONCLUSIONS: This study demonstrated that our machine learning algorithm provides an accurate and objective assessment model for hospital department development. The strong alignment of the model\'s risk assessments with expert opinions, validated through statistical analysis, highlights its reliability and potential to guide strategic hospital management decisions.

BACKGROUND: Motor difficulties are common in many, but not all, autistic individuals. These difficulties can co-occur with other problems, such as delays in language, intellectual, and adaptive functioning. Biological mechanisms underpinning such difficulties are less well understood. Poor motor skills tend to be more common in individuals carrying highly penetrant rare genetic mutations. Such mechanisms may have downstream consequences of altering neurophysiological excitation-inhibition balance and lead to enhanced behavioral motor noise.
METHODS: This study combined publicly available and in-house datasets of autistic (n = 156), typically-developing (TD, n = 149), and developmental coordination disorder (DCD, n = 23) children (age 3-16 years). Autism motor subtypes were identified based on patterns of motor abilities measured from the Movement Assessment Battery for Children 2nd edition. Stability-based relative clustering validation was used to identify autism motor subtypes and evaluate generalization accuracy in held-out data. Autism motor subtypes were tested for differences in motor noise, operationalized as the degree of dissimilarity between repeated motor kinematic trajectories recorded during a simple reach-to-drop task.
RESULTS: Relatively \'high\' (n = 87) versus \'low\' (n = 69) autism motor subtypes could be detected and which generalize with 89% accuracy in held-out data. The relatively \'low\' subtype was lower in general intellectual ability and older at age of independent walking, but did not differ in age at first words or autistic traits or symptomatology. Motor noise was considerably higher in the \'low\' subtype compared to \'high\' (Cohen\'s d = 0.77) or TD children (Cohen\'s d = 0.85), but similar between autism \'high\' and TD children (Cohen\'s d = 0.08). Enhanced motor noise in the \'low\' subtype was also most pronounced during the feedforward phase of reaching actions.
CONCLUSIONS: The sample size of this work is limited. Future work in larger samples along with independent replication is important. Motor noise was measured only on one specific motor task. Thus, a more comprehensive assessment of motor noise on many other motor tasks is needed.
CONCLUSIONS: Autism can be split into at least two discrete motor subtypes that are characterized by differing levels of motor noise. This suggests that autism motor subtypes may be underpinned by different biological mechanisms.

Microelectrode arrays (MEAs) enable simultaneous measurement of spike trains from numerous neurons, owing to advancements in microfabrication technology. These probes are highly valuable for comprehending the intricate dynamics of neuronal networks. Spike sorting is a pivotal step in comprehensively analyzing the activity of neuronal networks from extracellular recordings. However, the accuracy of spike sorting is relatively low due to the dense sampling of spikes in MEAs. Here, we propose an unsupervised pipeline named UMAP-COM method, which utilizes combined features to address this problem. These combined features comprise dominant spike shape features extracted by the uniform manifold approximation and projection (UMAP), as well as spike locations estimated by the center of mass (COM). We validate the UMAP-COM method on publicly available datasets from different kinds of probes, demonstrating that it is more accurate than other spike sorting methods. Furthermore, we conduct separate evaluations of spike shape feature extraction methods and spike localization methods. In this comparison, UMAP emerges as the superior feature extraction method, demonstrating its effectiveness in accurately representing spike shapes. Additionally, we find that the COM method outperforms other spike localization methods, highlighting its ability to enhance the accuracy of spike sorting.

This research addresses the presence of substances of very high concern (SVHCs) confronting the drinking water sector. Responding adequately to the potential hazards by SVHCs, knowledge of emission pathways, toxicity, presence in drinking water sources, and removability during water production is crucial. As this information cannot be received for each compound individually, we employed a detailed clustering approach based on chemical properties and structures of SVHCs from lists with over 1,000 compounds. Through this process, 915 substances were divided into 51 clusters. We tested this clustering in risk assessment. To assess the risks, we developed toxicity prediction models utilizing random forests and multiple linear regression. These models were applied to make toxicity predictions for the list of compounds. This study shows that clustering is a viable approach to reducing sample size. In addition, the toxicity models provide insights into the potential human health risks. This research contributes to more informed decision-making and improved risk assessment in the drinking water sector, aiding in the protection of human health and the environment. This principle is generally applicable. If in a group a suitable representative is found, data from experiments with this compound can be used to gauge the behaviour of chemicals in this group.

Several methods have been developed to computationally predict cell-types for single cell RNA sequencing (scRNAseq) data. As methods are developed, a common problem for investigators has been identifying the best method they should apply to their specific use-case. To address this challenge, we present CHAI (consensus Clustering tHrough similArIty matrix integratIon for single cell-type identification), a wisdom of crowds approach for scRNAseq clustering. CHAI presents two competing methods which aggregate the clustering results from seven state-of-the-art clustering methods: CHAI-AvgSim and CHAI-SNF. CHAI-AvgSim and CHAI-SNF demonstrate superior performance across several benchmarking datasets. Furthermore, both CHAI methods outperform the most recent consensus clustering method, SAME-clustering. We demonstrate CHAI\'s practical use case by identifying a leader tumor cell cluster enriched with CDH3. CHAI provides a platform for multiomic integration, and we demonstrate CHAI-SNF to have improved performance when including spatial transcriptomics data. CHAI overcomes previous limitations by incorporating the most recent and top performing scRNAseq clustering algorithms into the aggregation framework. It is also an intuitive and easily customizable R package where users may add their own clustering methods to the pipeline, or down-select just the ones they want to use for the clustering aggregation. This ensures that as more advanced clustering algorithms are developed, CHAI will remain useful to the community as a generalized framework. CHAI is available as an open source R package on GitHub: https://github.com/lodimk2/chai.