Complex structural and functional changes occurring in typical and atypical development necessitate multidimensional approaches to better understand the risk of developing psychopathology. Here, we simultaneously examined structural and functional brain network patterns in relation to dimensions of psychopathology in the Adolescent Brain Cognitive Development dataset. Several components were identified, recapitulating the psychopathology hierarchy, with the general psychopathology (p) factor explaining most covariance with multimodal imaging features, while the internalizing, externalizing, and neurodevelopmental dimensions were each associated with distinct morphological and functional connectivity signatures. Connectivity signatures associated with the p factor and neurodevelopmental dimensions followed the sensory-to-transmodal axis of cortical organization, which is related to the emergence of complex cognition and risk for psychopathology. Results were consistent in two separate data subsamples, supporting generalizability, and robust to variations in analytical parameters. Our findings help in better understanding biological mechanisms underpinning dimensions of psychopathology, and could provide brain-based vulnerability markers.

Multivariate disease mapping is important for public health research, as it provides insights into spatial patterns of health outcomes. Geostatistical methods that are widely used for mapping spatially correlated health data encounter challenges when dealing with spatial count data. These include heterogeneity, zero-inflated distributions and unreliable estimation, and lead to difficulties when estimating spatial dependence and poor predictions. Variability in population sizes further complicates risk estimation from the counts. This study introduces multivariate Poisson cokriging for predicting and filtering out disease risk. Pairwise correlations between the target variable and multiple ancillary variables are included. By means of a simulation experiment and an application to human immunodeficiency virus incidence and sexually transmitted diseases data in Pennsylvania, we demonstrate accurate disease risk estimation that captures fine-scale variation. This method is compared with ordinary Poisson kriging in prediction and smoothing. Results of the simulation study show a reduction in the mean square prediction error when utilizing auxiliary correlated variables, with mean square prediction error values decreasing by up to 50%. This gain is further evident in the real data analysis, where Poisson cokriging yields a 74% drop in mean square prediction error relative to Poisson kriging, underscoring the value of incorporating secondary information. The findings of this work stress on the potential of Poisson cokriging in disease mapping and surveillance, offering richer risk predictions, better representation of spatial interdependencies, and identification of high-risk and low-risk areas.

Gathered from a real-world discrete manufacturing floor, this dataset features measurements of pneumatic pressure and electrical current during production. Spanning 7 days and encompassing approximately 150 processed units, the data is organized into time series sampled at 100 Hz. The observed machine performs 24 steps to process each unit. Each measurement in the time series, is annotated, linking it to one of the 24 processing steps performed by the machine for processing of a single piece. Segmenting the time series into contiguous regions of constant processing step labels results in 3674 labeled segments, each encompassing one part of the production process. The dataset enriched with labels facilitates the use of supervised learning techniques, like time series classification, and supports the testing of unsupervised methods, such as clustering of time series data. The focus of this dataset is on an end-of-line testing machine for small consumer-grade electric drive assemblies (device under test - DUT). The machine performs multiple actions in the process of evaluating each DUT, with the dataset capturing the pneumatic pressures and electrical currents involved. These measurements are segmented in alignment with the testing machine\'s internal state transitions, each corresponding to a distinct action undertaken in manipulating the device under observation. The included segments offer distinct signatures of pressure and current for each action, making the dataset valuable for developing algorithms for the non-invasive monitoring of industrial (specifically discrete) processes.

BACKGROUND: The occurrence of liver metastasis significantly affects the prognosis of colorectal cancer (CRC). Existing research indicates that primary tumor location, vascular invasion, lymph node metastasis, and abnormal preoperative tumor markers are risk factors for CRC liver metastasis. Positive expression of programmed cell death ligand 1 (PD-L1) may serve as a favorable prognostic marker for nasopharyngeal and gastric cancers, in which combined positive score (CPS) quantifies the level of PD-L1 expression. This study aimed to explore CPS as a potential risk factor for CRC liver metastasis and integrate other independent risk factors to establish a novel predictive model for CRC liver metastasis.
METHODS: A retrospective analysis was conducted on 437 patients with CRC pathologically diagnosed at The Second Xiangya Hospital of Central South University from January 1, 2019, to December 31, 2021. Data were collected, including CPS, age, gender (male and female), primary tumor location, Ki-67 expression, pathologic differentiation, neural invasion, vascular invasion, lymph node metastasis, and preoperative tumor markers. The optimal cutoff point for the continuous variable CPS was determined using the Youden index, and all CPSs were dichotomized into high- and low-risk groups based on this threshold (scores below the threshold were considered high risk, and score above the threshold were considered low risk). Univariate logistic regression analysis was employed to identify risk factors for CRC liver metastasis, followed by multivariate logistic regression analysis to integrate the selected risk factors. The predictive model was validated through the construction of receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). A nomogram was constructed for visualization.
RESULTS: The determined cutoff point for PD-L1 CPS was 4.5, with scores below this threshold indicating a high risk of CRC liver metastasis. In addition, primary tumor origin other than the rectum, presence of pericolonic lymph node metastasis, and abnormal levels of tumor markers carcinoembryonic antigen and cancer antigen 19-9 were identified as independent risk factors for CRC liver metastasis. The constructed clinical prediction model demonstrated good predictive ability and accuracy, with an area under the ROC curve of 0.871 (95% CI, 0.838-0.904).
CONCLUSIONS: The exploration and validation of CPS as a novel predictor of CRC liver metastasis were performed. Based on these findings, a new clinical prediction model for CRC liver metastasis was developed by integrating other independent risk factors. The DCA, clinical impact curve, and nomogram graph constructed on the basis of this model have significant clinical implications and guide clinical practice.

BACKGROUND: Functional connectivity has garnered interest as a potential biomarker of psychiatric disorders including borderline personality disorder (BPD). However, small sample sizes and lack of within-study replications have led to divergent findings with no clear spatial foci.
OBJECTIVE: Evaluate discriminative performance and generalizability of functional connectivity markers for BPD.
METHODS: Whole-brain fMRI resting state functional connectivity in matched subsamples of 116 BPD and 72 control individuals defined by three grouping strategies. We predicted BPD status using classifiers with repeated cross-validation based on multiscale functional connectivity within and between regions of interest (ROIs) covering the whole brain-global ROI-based network, seed-based ROI-connectivity, functional consistency, and voxel-to-voxel connectivity-and evaluated the generalizability of the classification in the left-out portion of non-matched data.
RESULTS: Full-brain connectivity allowed classification (∼70 %) of BPD patients vs. controls in matched inner cross-validation. The classification remained significant when applied to unmatched out-of-sample data (∼61-70 %). Highest seed-based accuracies were in a similar range to global accuracies (∼70-75 %), but spatially more specific. The most discriminative seed regions included midline, temporal and somatomotor regions. Univariate connectivity values were not predictive of BPD after multiple comparison corrections, but weak local effects coincided with the most discriminative seed-ROIs. Highest accuracies were achieved with a full clinical interview while self-report results remained at chance level.
CONCLUSIONS: The accuracies vary considerably between random sub-samples of the population, global signal and covariates limiting the practical applicability.
CONCLUSIONS: Spatially distributed functional connectivity patterns are moderately predictive of BPD despite heterogeneity of the patient population.

The intention to act influences the computations of various task-relevant features. However, little is known about the time course of these computations. Furthermore, it is commonly held that these computations are governed by conjunctive neural representations of the features. But, support for this view comes from paradigms arbitrarily combining task features and affordances, thus requiring representations in working memory. Therefore, the present study used electroencephalography and a well-rehearsed task with features that afford minimal working memory representations to investigate the temporal evolution of feature representations and their potential integration in the brain. Female and male human participants grasped objects or touched them with a knuckle. Objects had different shapes and were made of heavy or light materials with shape and weight being relevant for grasping, not for \"knuckling.\" Using multivariate analysis showed that representations of object shape were similar for grasping and knuckling. However, only for grasping did early shape representations reactivate at later phases of grasp planning, suggesting that sensorimotor control signals feed back to the early visual cortex. Grasp-specific representations of material/weight only arose during grasp execution after object contact during the load phase. A trend for integrated representations of shape and material also became grasp-specific but only briefly during the movement onset. These results suggest that the brain generates action-specific representations of relevant features as required for the different subcomponents of its action computations. Our results argue against the view that goal-directed actions inevitably join all features of a task into a sustained and unified neural representation.

Data analysis methods for the study of treatments or exposures in relation to a clinical outcome in the presence of competing risks have a long history, often with inference targets that are hypothetical, thereby requiring strong assumptions for identifiability with available data. Here data analysis methods are considered that are based on single and higher dimensional marginal hazard rates, quantities that are identifiable under standard independent censoring assumptions. These lead naturally to joint survival function estimators for outcomes of interest, including competing risk outcomes, and provide the basis for addressing a variety of data analysis questions. These methods will be illustrated using simulations and Women\'s Health Initiative cohort and clinical trial data sets, and additional research needs will be described.

In order to enhance the performance of Transformer models for long-term multivariate forecasting while minimizing computational demands, this paper introduces the Joint Time-Frequency Domain Transformer (JTFT). JTFT combines time and frequency domain representations to make predictions. The frequency domain representation efficiently extracts multi-scale dependencies while maintaining sparsity by utilizing a small number of learnable frequencies. Simultaneously, the time domain (TD) representation is derived from a fixed number of the most recent data points, strengthening the modeling of local relationships and mitigating the effects of non-stationarity. Importantly, the length of the representation remains independent of the input sequence length, enabling JTFT to achieve linear computational complexity. Furthermore, a low-rank attention layer is proposed to efficiently capture cross-dimensional dependencies, thus preventing performance degradation resulting from the entanglement of temporal and channel-wise modeling. Experimental results on eight real-world datasets demonstrate that JTFT outperforms state-of-the-art baselines in predictive performance.

Sleep is essential for optimal functioning and health. Interconnected to multiple biological, psychological and socio-environmental factors (i.e., biopsychosocial factors), the multidimensional nature of sleep is rarely capitalized on in research. Here, we deployed a data-driven approach to identify sleep-biopsychosocial profiles that linked self-reported sleep patterns to inter-individual variability in health, cognition, and lifestyle factors in 770 healthy young adults. We uncovered five profiles, including two profiles reflecting general psychopathology associated with either reports of general poor sleep or an absence of sleep complaints (i.e., sleep resilience) respectively. The three other profiles were driven by sedative-hypnotics-use and social satisfaction, sleep duration and cognitive performance, and sleep disturbance linked to cognition and mental health. Furthermore, identified sleep-biopsychosocial profiles displayed unique patterns of brain network organization. In particular, somatomotor network connectivity alterations were involved in the relationships between sleep and biopsychosocial factors. These profiles can potentially untangle the interplay between individuals\' variability in sleep, health, cognition and lifestyle - equipping research and clinical settings to better support individual\'s well-being.

Buffalo bull semen traits are economically important traits that influence farm fertility and profitability. Genetic improvement of semen characteristics is an important detail of the genetic improvement. This study was conducted to assess the relationship between the breeding values as well as the phenotypic values for semen traits (VOL, MM, LS, AS and CONC) of the Egyptian buffalo bulls. A total of 7761 normal semen ejaculates were collected and characterized at ILMTC laboratory from 26 bulls from 2009 to 2019. For VOL, MM, LS, AS, and CONC, the actual means were 3.89 mL, 62.37%, 60.64%, 3.94%, and 0.67 × 109 sperm/mL, respectively. The prediction of breeding values for semen traits was estimated using a Bayesian procedure. The estimated standardized EBVs and phenotypic values were used in the principal component analysis (PCA). Of five PCs, one PC (PC1) had > 1 eigenvalues that was responsible for 87.19% of the total variation of SEBV, and two PCs had > 1 eigenvalues that were responsible for 59.61% and 21.35% of the total variation of the phenotypic values. Together, PC1 and PC2 accounted for 97.97% of the total variance of SEBV and 80.96% of the total variance of phenotypic values. A graphs of the first two components showed the traits separated into two different directions by group. This indicates each group was under similar genetic influence. Therefore, selection can be done separately for each group without influencing the other. Principal component analysis reduced variables to describe the key information in buffalo semen data.