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Stress is a nonspecific response that occurs when the body is stimulated by internal and external stimuli. When the stress levels exceed the body´s tolerance threshold, physiological and biochemical changes occur that affect homeostasis within the internal environment. Takifugu fasciatus is an important cultured fish species in China, and its demand in the aquatic market has increased in recent years. Stress is extremely harmful to T. fasciatus farming, and cortisol is considered an essential marker for determining the stress response of fish, as it changes significantly within the organism under stress. The intestine is a contact medium between fish and the external environment and can respond to physiological stress levels. Although the study of fish intestinal responses to stress is a global research focus, the role of the T. fasciatus intestinal tract in response to stress remains unclear. In this study, intestinal epithelial cells of T. fasciatus were cultured using four different methods: Trypsin (0.25%) with DMEM, trypsin (0.25%) with RPMI 1640, typeⅠcollagenase (1 mg/mL) in DMEM, and typeⅠcollagenase (1 mg/mL) in RPMI 1640 to establish an in vitro culture method for primary intestinal epithelial cells of T. fasciatus. The effects of different concentrations of cortisol on oxidative stress, apoptosis, and lipid metabolism were investigated. The study methodology involved disinfecting of T. fasciatus with 75% alcohol and subsequently anesthetizing with MS-222. Furthermore, the intestines were removed and cleansed using PBS containing 100 IU/mL penicillin and 100 μg/mL streptomycin. The intestines were then sectioned into 1.5 mL centrifuge tubes with digestive solutions and digested at 28 ℃ for 30 min. Cells were subsequently centrifuged at 1 000 r/min for 5 min at 4 ℃, and resuspended in a new complete medium (DMEM containing 20% fetal bovine serum, 100 IU/mL penicillin, and 100 μg/mL streptomycin). The proliferation rate of intestinal epithelial cells under the four culture methods was determined within 5 d using the CCK-8 method, and cell growth was observed under a microscope after 24 h of culture. Cortisol solutions at varying concentrations were diluted with complete medium, and the cell viability of T. fasciatus was measured using the CCK-8 method after cortisol treatment (0, 100, 1 000, 2 000, 3 000, and 5 000 nmol/L) for 24 h. The morphological structure of intracellular mitochondria was observed through transmission electron microscopy after cortisol treatment (0, 10, 100, and 1 000 nmol/L) for 24 h. After cortisol treatment (0, 10, 100, and 1 000 nmol/L) for 3, 6, 12, and 24 h, the expression patterns of oxidative stress-related genes, apoptosis-related genes, and lipid metabolism-related genes in intestinal epithelial cells were measured by real-time fluorescence-based quantitative PCR (qRT-PCR). Cell apoptosis was measured using the Annexin V-FITC/PI method, and the contents of triglycerides, total cholesterol, and free fatty acids were measured using kits from Nanjing Jiancheng Co. The results showed that the intestinal epithelial cells cultured with trypsin (0.25%) digestion and DMEM had the highest cell proliferation rate among the four culture methods, with predominantly fibroblast-like cell morphology and the best apposition. At a cortisol concentration of 1 000 nmol/L, the intestinal epithelial cell viability was 0.8 times that of the control group, with no significant difference between the 1 000 nmol/L cortisol-treated group and the 2 000 nmol/L cortisol-treated group. However, the cell viability significantly decreased when the cortisol concentration exceeded 2 000 nmol/L (P < 0.05). Thus, 1 000 nmol/L or lower was selected for cortisol treatment. Transmission electron microscopy revealed that the mitochondrial structure of intestinal epithelial cells was altered in all the cortisol-treated groups, with an increase in the number of mitochondria. The expression of oxidative stress-related genes (sod, cat, and gsh-px) and apoptotic index significantly increased with increasing cortisol treatment concentration and time. Pro-apoptotic genes (caspase-3, caspase-7, caspase-9, bax and p53) increased significantly, while anti-apoptotic gene bcl-2 expression decreased significantly with the increase of cortisol treatment concentration (P < 0.05). The expression of lipid synthesis-related genes (g6pd, 6gpd, pparγ, fas and acc) decreased significantly and lipolysis-related genes (hsl, pparα, lpl and cpt-1) increased significantly with increasing cortisol treatment concentration (P < 0.05). Triglyceride and total cholesterol contents decreased significantly, whereas the free fatty acids content increased significantly with increasing cortisol treatment concentration and time (P < 0.05). These results indicate that trypsin (0.25%) digestion with DMEM is optimal for culture of primary intestinal epithelial cells of T. fasciatus. Cortisol promotes oxidative stress, apoptosis, and lipid decomposition but suppresses lipid synthesis in intestinal epithelial cells of T. fasciatus. This study established the optimal isolation and culture method for primary intestinal epithelial cells of T. fasciatus and investigated the mechanism of the intestinal response to cortisol stress in T. fasciatus, thus providing a theoretical basis for the subsequent anti-stress culture of fish.

ObjectivesTo identify age-related plasma extracellular vehicle (EVs) phenotypes in healthy adults.MethodsEV proteomics by high-resolution mass spectrometry to evaluate EV protein stability and discover age-associated EV proteins (n=4 with 4 serial freeze-thaws each); validation by high-resolution flow cytometry and EV cytokine quantification by multiplex ELISA (n=28 healthy donors, aged 18-83 years); quantification of WI-38 fibroblast cell proliferation response to co-culture with PKH67-labeled young and old plasma EVs. The EV samples from these plasma specimens were previously characterized for bilayer structure, intra-vesicle mitochondria and cytokines, and hematopoietic cell-related surface markers.ResultsCompared with matched exo-EVs (EV-depleted supernatants), endo-EVs (EV-associated) had higher mean TNF-α and IL-27, lower mean IL-6, IL-11, IFN-γ, and IL-17A/F, and similar mean IL-1β, IL-21, and IL-22 concentrations. Some endo-EV and exo-EV cytokine concentrations were correlated, including TNF-α, IL-27, IL-6, IL-1β, and IFN-γ, but not IL-11, IL-17A/F, IL-21 or IL-22. Endo-EV IFN-γ and exo-EV IL-17A/F and IL-21 declined with age. By proteomics and confirmed by flow cytometry, we identified age-associated decline of fibrinogen (FGA, FGB and FGG) in EVs. Age-related EV proteins indicated predominant origins in the liver and innate immune system. WI-38 cells (>95%) internalized similar amounts of young and old plasma EVs, but cells that internalized PKH67-EVs, particularly young EVs, underwent significantly greater cell proliferation.ConclusionEndo-EV and exo-EV cytokines function as different biomarkers. The observed healthy aging EV phenotype reflected a downregulation of EV fibrinogen subpopulations consistent with the absence of a pro-coagulant and pro-inflammatory condition common with age-related disease.

The knowledge of the causal mechanisms underlying one single system may not be sufficient to answer certain questions. One can gain additional insights from comparing and contrasting the causal mechanisms underlying multiple systems and uncovering consistent and distinct causal relationships. For example, discovering common molecular mechanisms among different diseases can lead to drug repurposing. The problem of comparing causal mechanisms among multiple systems is non-trivial, since the causal mechanisms are usually unknown and need to be estimated from data. If we estimate the causal mechanisms from data generated from different systems and directly compare them (the naive method), the result can be sub-optimal. This is especially true if the data generated by the different systems differ substantially with respect to their sample sizes. In this case, the quality of the estimated causal mechanisms for the different systems will differ, which can in turn affect the accuracy of the estimated similarities and differences among the systems via the naive method. To mitigate this problem, we introduced the bootstrap estimation and the equal sample size resampling estimation method for estimating the difference between causal networks. Both of these methods use resampling to assess the confidence of the estimation. We compared these methods with the naive method in a set of systematically simulated experimental conditions with a variety of network structures and sample sizes, and using different performance metrics. We also evaluated these methods on various real-world biomedical datasets covering a wide range of data designs.

Abstract Cannabis Use Disorder (CUD) has been linked to a complex set of neuro-behavioral risk factors. While many studies have revealed sex and gender differences, the relative importance of these risk factors by sex and gender has not been described. We used an “explainable” machine learning approach that combined decision trees [gradient tree boosting, XGBoost] with factor ranking tools [SHapley’s Additive exPlanations (SHAP)] to investigate sex and gender differences in CUD. We confirmed that previously identified environmental, personality, mental health, neurocognitive, and brain factors highly contributed to the classification of cannabis use levels and diagnostic status. Risk factors with larger effect sizes in men included personality (high openness), mental health (high externalizing, high childhood conduct disorder, high fear somaticism), neurocognitive (impulsive delay discounting, slow working memory performance) and brain (low hippocampal volume) factors. Conversely, risk factors with larger effect sizes in women included environmental (low education level, low instrumental support) factors. In summary, environmental factors contributed more strongly to CUD in women, whereas individual factors had a larger importance in men.

Abstract Heating, ventilation, and air conditionings (HVACs) of commercial buildings bear a large amount of weight in total load and have emerged as a major flexible resource participating in power systems. HVACs can be aggregated in an entity to participate in power system dispatching. First, refrigeration and air systems modelling of HVACs is conducted. Second, commercial buildings with HVACs are encapsulated into a power‐smoothed and independent virtual generation unit (VGU) by using an internal integrated coordination control method, which characterizes the virtual generation capability of commercial buildings. Third, the dynamic modelling method of virtual power plant (VPP) is presented, and a local‐autonomous optimal control strategy of HVACs is proposed via centralized‐distributed architectures. The results of the analysis indicate that HVACs can be adequately regulated to participate in power system peak‐shaving.

Objective: To investigate how medical students process Western medicine (WM) terms vs. traditional Chinese medicine (TCM) terms and how this is impacted by analogical priming from the perspective of cognitive neuroscience. Methods: The experiments were designed as a 2 (TCM terms vs. WM terms) × 2 (correct terms vs. incorrect terms) × 2 (analogical priming task vs. non-priming task) scheme. A total of 26 medical students completed a non-priming judgment task and an analogical priming judgment task on medical terms. During the tasks, the participants were asked to make correct/incorrect judgments on WM terms and TCM terms, and their behavioral data and event-related potentials (ERPs) were recorded. Results: Behaviorally, the response speed and accuracy of WM terms were higher than those of TCM terms (both P

IntroductionApproximately half of individuals with posttraumatic stress disorder (PTSD) may meet criteria for other psychiatric disorders, and PTSD symptoms are associated with diminished health and psychosocial functioning. However, few studies examine the longitudinal progression of PTSD symptoms concurrent with related symptom domains and functional outcomes, such that may neglect important longitudinal patterns of symptom progression beyond PTSD specifically.MethodsTherefore, we used longitudinal causal discovery analysis to examine the longitudinal interrelations among PTSD symptoms, depressive symptoms, substance abuse, and various other domains of functioning in five longitudinal cohorts representing veterans (n = 241), civilians seeking treatment for anxiety disorders (n = 79), civilian women seeking treatment for post-traumatic stress and substance abuse (n = 116), active duty military members assessed 0–90 days following TBI (n = 243), and civilians with a history of TBI (n = 43).ResultsThe analyses revealed consistent, directed associations from PTSD symptoms to depressive symptoms, independent longitudinal trajectories of substance use problems, and cascading indirect relations from PTSD symptoms to social functioning through depression as well as direct relations from PTSD symptoms to TBI outcomes.DiscussionOur findings suggest PTSD symptoms primarily drive depressive symptoms over time, tend to show independence from substance use symptoms, and may cascade into impairment in other domains. The results have implications for refining conceptualization of PTSD co-morbidity and can inform prognostic and treatment hypotheses about individuals experiencing PTSD symptoms along with co-occurring distress or impairment.

Abstract Modern AI-based clinical decision support models owe their success in part to the very large number of predictors they use. Safe and robust decision support, especially for intervention planning, requires causal, not associative, relationships. Traditional methods of causal discovery, clinical trials and extracting biochemical pathways, are resource intensive and may not scale up to the number and complexity of relationships sufficient for precision treatment planning. Computational causal structure discovery (CSD) from electronic health records (EHR) data can represent a solution, however, current CSD methods fall short on EHR data. This paper presents a CSD method tailored to the EHR data. The application of the proposed methodology was demonstrated on type-2 diabetes mellitus. A large EHR dataset from Mayo Clinic was used as development cohort, and another large dataset from an independent health system, M Health Fairview, as external validation cohort. The proposed method achieved very high recall (.95) and substantially higher precision than the general-purpose methods (.84 versus .29, and .55). The causal relationships extracted from the development and external validation cohorts had a high (81%) overlap. Due to the adaptations to EHR data, the proposed method is more suitable for use in clinical decision support than the general-purpose methods.

Nine hundred and seventy million individuals across the globe are estimated to carry the burden of a mental disorder. Limited progress has been achieved in alleviating this burden over decades of effort, compared to progress achieved for many other medical disorders. Progress on outcome improvement for all medical disorders, including mental disorders, requires research capable of discovering causality at sufficient scale and speed, and a diagnostic nosology capable of encoding the causal knowledge that is discovered. Accordingly, the field’s guiding paradigm limits progress by maintaining: (a) a diagnostic nosology (DSM-5) with a profound lack of causality; (b) a misalignment between mental health etiologic research and nosology; (c) an over-reliance on clinical trials beyond their capabilities; and (d) a limited adoption of newer methods capable of discovering the complex etiology of mental disorders. We detail feasible directions forward, to achieve greater levels of progress on improving outcomes for mental disorders, by: (a) the discovery of knowledge on the complex etiology of mental disorders with application of Causal Data Science methods; and (b) the encoding of the etiological knowledge that is discovered within a causal diagnostic system for mental disorders.

Summary: Background: The hard endpoint of death is one of the most significant outcomes in both clinical practice and research settings. Our goal was to discover direct causes of longevity from medically accessible data. Methods: Using a framework that combines local causal discovery algorithms with discovery of maximally predictive and compact feature sets (the “Markov boundaries” of the response) and equivalence classes, we examined 186 variables and their relationships with survival over 27 years in 1507 participants, aged ≥71 years, of the longitudinal, community-based D-EPESE study. Findings: As few as 8-15 variables predicted longevity at 2-, 5- and 10-years with predictive performance (area under receiver operator characteristic curve) of 0·76 (95% CIs 0·69, 0·83), 0·76 (0·72, 0·81) and 0·66 (0·61, 0·71), respectively. Numbers of small high-density lipoprotein particles, younger age, and fewer pack years of cigarette smoking were the strongest determinants of longevity at 2-, 5- and 10-years, respectively. Physical function was a prominent predictor of longevity at all time horizons. Age and cognitive function contributed to predictions at 5 and 10 years. Age was not among the local 2-year prediction variables (although significant in univariable analysis), thus establishing that age is not a direct cause of 2-year longevity in the context of measured factors in our data that determine longevity. Interpretation: The discoveries in this study proceed from causal data science analyses of deep clinical and molecular phenotyping data in a community-based cohort of older adults with known lifespan. Funding: NIH/NIA R01AG054840, R01AG12765, and P30-AG028716, NIH/NIA Contract N01-AG-12102 and NCRR 1UL1TR002494-01.

We demonstrate a data-driven approach for calculating a “causal connectome” of directed connectivity from resting-state fMRI data using a greedy adjacency search and pairwise non-Gaussian edge orientations. We used this approach to construct n = 442 causal connectomes. These connectomes were very sparse in comparison to typical Pearson correlation-based graphs (roughly 2.25% edge density) yet were fully connected in nearly all cases. Prominent highly connected hubs of the causal connectome were situated in attentional (dorsal attention) and executive (frontoparietal and cingulo-opercular) networks. These hub networks had distinctly different connectivity profiles: attentional networks shared incoming connections with sensory regions and outgoing connections with higher cognitive networks, while executive networks primarily connected to other higher cognitive networks and had a high degree of bidirected connectivity. Virtual lesion analyses accentuated these findings, demonstrating that attentional and executive hub networks are points of critical vulnerability in the human causal connectome. These data highlight the central role of attention and executive control networks in the human cortical connectome and set the stage for future applications of data-driven causal connectivity analysis in psychiatry.

Pontederia cordata is previously demonstrated a cadmium (Cd) tolerant plant, and also a candidate for the phytoremediation of heavy-metal-contaminated wetlands. A hydroponic experiment was used to investigate variations in photosynthetic gas exchange parameters, antioxidative activities, chlorophyll and secondary metabolite contents, and transcriptome in leaves of the plant exposed to 0.44 mM Cd2+ for 0 h, 24 h, and 48 h. Under Cd2+ exposure for 24 h, the plant presented a favorable photosynthesis by maintaining relatively higher antioxidant activity. Cd2+ exposure for 48 h accelerated membrane peroxidation, declined photosynthetic pigment content, and increased polyphenol oxidase activity, thus interfering with photosynthesis. The phenylpropane pathway served as a chemical rather than physical defense against Cd2+ in the plant leaves. A total of 20,998, 4743, and 4413 differentially expressed genes (DEGs) were identified in the groups of 0 h vs 24 h, 0 h vs 48 h, and 24 h vs 48 h, respectively. The primary metabolic pathways of the DEGs were mainly enriched in nitrogen metabolism, starch and sucrose metabolism, fructose and mannose metabolism, as well as pentose-phosphate pathway, contributing to a stable cell structure and function. Flavonoid biosynthesis directly or indirectly played an antioxidative role against Cd2+ in the leaves. Forty-nine transcription factor (TF) families were identified, and 8 TF families were shared among the three groups. The present study provides a theoretical foundation for investigating tolerance mechanisms of wetland plants to Cd stress in terms of secondary metabolism and transcriptional regulation.

Apolipoprotein C2 (ApoC2) is a key activator of lipoprotein lipase for plasma triglyceride metabolism. ApoC2-deficient patients present with severe hypertriglyceridemia and recurrent acute pancreatitis, for whom the only effective treatment is the infusion of normal plasma containing ApoC2. However, since ApoC2 has a fast catabolic rate, a repeated infusion is required, which limits its clinical use. To explore a safe and efficient approach for ApoC2 deficiency, we herein established an adeno-associated virus expressing human ApoC2 (AAV-hApoC2) to evaluate the efficacy and safety of gene therapy in ApoC2-deficient hypertriglyceridemic hamsters. Administration of AAV-hApoC2 via jugular or orbital vein in adult and neonatal ApoC2-deficient hamsters, respectively, could prevent the neonatal death and effectively improve severe hypertriglyceridemia of ApoC2-deficient hamsters without side effects in a long-term manner. Our novel findings in the present study demonstrate that AAV-hApoC2-mediated gene therapy will be a promising therapeutic approach for clinical patients with severe hypertriglyceridemia caused by ApoC2 deficiency.

Objective: Qualitative research on therapeutic components is necessary to evaluate the efficacy of complex interventions in healthcare. As few qualitative syntheses have been conducted, this study aimed to derive a new conceptual framework for understanding the components of complex interventions and provide evidence for the implementation and evaluation of complex healthcare interventions. Methods: A systematic search of seven databases was conducted to identify qualitative studies that explored components of complex healthcare interventions. Meta-ethnography was used to analyze the data and thematic analysis was used to build the conceptual framework. Results: Of the 35 included studies, most complex interventions were non-pharmacological, with cancer accounting for 22%, mental health for 14%, and stroke for 8%. Half of the studies were conducted in the United Kingdom. Three main categories emerged: what should healthcare workers do? what qualifications should they have? and what should patients do? Five main themes were identified: psychological, biological, cognitive and behavioral, environmental, and social support. Conclusion: This analysis provides a reference for designing components of complex interventions in further studies.

Clinical trials represent a critical milestone of translational and clinical sciences. However, poor recruitment to clinical trials has been a long standing problem affecting institutions all over the world. One way to reduce the cost incurred by insufficient enrollment is to minimize initiating trials that are most likely to fall short of their enrollment goal. Hence, the ability to predict which proposed trials will meet enrollment goals prior to the start of the trial is highly beneficial. In the current study, we leveraged a data set extracted from ClinicalTrials.gov that consists of 46,724 U.S. based clinical trials from 1990 to 2020. We constructed 4,636 candidate predictors based on data collected by ClinicalTrials.gov and external sources for enrollment rate prediction using various state-of-the-art machine learning methods. Taking advantage of a nested time series cross-validation design, our models resulted in good predictive performance that is generalizable to future data and stable over time. Moreover, information content analysis revealed the study design related features to be the most informative feature type regarding enrollment. Compared to the performance of models built with all features, the performance of models built with study design related features is only marginally worse (AUC = 0.78 ± 0.03 vs. AUC = 0.76 ± 0.02). The results presented can form the basis for data-driven decision support systems to assess whether proposed clinical trials would likely meet their enrollment goal.

Our goal was to understand the complex relationship between age, sex, midlife risk factors, and early white matter changes measured by diffusion tensor imaging (DTI) and their role in the evolution of longitudinal white matter hyperintensities (WMH). We identified 1564 participants (1396 cognitively unimpaired, 151 mild cognitive impairment and 17 dementia participants) with age ranges of 30–90 years from the population-based sample of Mayo Clinic Study of Aging. We used computational causal structure discovery and regression analyses to evaluate the predictors of WMH and DTI, and to ascertain the mediating effect of DTI on WMH. We further derived causal graphs to understand the complex interrelationships between midlife protective factors, vascular risk factors, diffusion changes, and WMH. Older age, female sex, and hypertension were associated with higher baseline and progression of WMH as well as DTI measures (P ≤ 0.003). The effects of hypertension and sex on WMH were partially mediated by microstructural changes measured on DTI. Higher midlife physical activity was predictive of lower WMH through a direct impact on better white matter tract integrity as well as an indirect effect through reducing the risk of hypertension by lowering BMI. This study identified key risks factors, early brain changes, and pathways that may lead to the evolution of WMH.

H emolysis, e levated l iver enzymes, and l ow p latelets (HELLP) syndrome is a serious complication of pregnancy. Postpartum hemorrhage indicates poor prognosis of pregnant women with HELLP syndrome. The aim of our study is to investigate the predictive value of coagulation markers for postpartum hemorrhage of pregnant women with HELLP syndrome. In a retrospective cohort study, 106 patients who were diagnosed as pregnant women with HELLP syndrome in Peking University Third Hospital from August 2010 to January 2017 were analyzed. The demographic characters of maternal and fetus, days of hospital stay, postpartum complications, and the laboratory tests of coagulation markers within 3 days before delivery were collected. In addition, 100 healthy pregnant women were collected as a control group. The result showed that the incidence of preeclampsia in pregnant women with postpartum hemorrhage was higher than that in pregnant women without hemorrhage ( P = .011). The level of fibrinogen (FIB) in postpartum hemorrhage pregnant women with HELLP syndrome was lower than that in nonpostpartum hemorrhage pregnant women with HELLP syndrome and healthy pregnant women (2.3 [1.68-2.81] vs 3.64 ± 0.95, P = .000; 2.3 [1.68-2.81] vs 4.48 ± 0.62, P = .000). Multivariate analysis showed that decreased FIB levels independently predicted the postpartum hemorrhage of pregnant women with HELLP syndrome (odds ratio = 7.374, 95% confidence interval [CI], 1.551-35.05, P = .012). The receiver operating characteristic curve showed that the area under the curve of FIB level when predicting postpartum hemorrhage is 0.841 (95% CI, 0.708-0.976). When the cutoff value of FIB was 3.04 g/L, the sensitivity was 90.90% and the specificity was75.80%. Therefore, the low level of prenatal FIB is a reliable biomarker to predict postpartum hemorrhage of pregnant women with HELLP syndrome, which make it useful for pregnant women with HELLP syndrome in guiding surveillance therapy and prognosis assessment.

Abstract Background Cardiac Resynchronization Therapy (CRT) is an established pacing therapy for heart failure patients. The New York Heart Association (NYHA) class is often used as a measure of a patient’s response to CRT. Identifying NYHA class for heart failure (HF) patients in an electronic health record (EHR) consistently, over time, can provide better understanding of the progression of heart failure and assessment of CRT response and effectiveness. Though NYHA is rarely stored in EHR structured data, such information is often documented in unstructured clinical notes. Methods We accessed HF patients’ data in a local EHR system and identified potential sources of NYHA, including local diagnosis codes, procedures, and clinical notes. We further investigated and compared the performances of rule-based versus machine learning-based natural language processing (NLP) methods to identify NYHA class from clinical notes. Results Of the 36,276 patients with a diagnosis of HF or a CRT implant, 19.2% had NYHA class mentioned at least once in their EHR. While NYHA class existed in descriptive fields association with diagnosis codes (31%) or procedure codes (2%), the richest source of NYHA class was clinical notes (95%). A total of 6174 clinical notes were matched with hospital-specific custom NYHA class diagnosis codes. Machine learning-based methods outperformed a rule-based method. The best machine-learning method was a random forest with n-gram features (F-measure: 93.78%). Conclusions NYHA class is documented in different parts in EHR for HF patients and the documentation rate is lower than expected. NLP methods are a feasible way to extract NYHA class information from clinical notes.

Predicting treatment outcomes in psychiatric populations remains a challenge, but is increasingly important in the pursuit of personalized medicine. Patients with schizophrenia have deficits in cognition, and targeted cognitive training (TCT) of auditory processing and working memory has been shown to improve some of these impairments; but little is known about the baseline patient characteristics predictive of cognitive improvement. Here we use a model selection and regression approach called least absolute shrinkage and selection operator (LASSO) to examine predictors of cognitive improvement in response to TCT for patients with recent onset schizophrenia. Forty-three individuals with recent onset schizophrenia randomized to undergo TCT were assessed at baseline on measures of cognition, symptoms, functioning, illness duration, and demographic variables. We carried out 10-fold cross-validation of LASSO for model selection and regression. We followed up on these results using linear models for statistical inference. No individual variable was found to correlate with improvement in global cognition using a Pearson correlation approach, and a linear model including all variables was also found not to be significant. However, the LASSO model identified baseline global cognition, education, and gender in a model predictive of improvement on global cognition following TCT. These findings offer guidelines for personalized approaches to cognitive training for patients with schizophrenia.

Abstract Background The care of traumatized children would benefit significantly from accurate predictive models for Posttraumatic Stress Disorder (PTSD), using information available around the time of trauma. Machine Learning (ML) computational methods have yielded strong results in recent applications across many diseases and data types, yet they have not been previously applied to childhood PTSD. Since these methods have not been applied to this complex and debilitating disorder, there is a great deal that remains to be learned about their application. The first step is to prove the concept: Can ML methods – as applied in other fields – produce predictive classification models for childhood PTSD? Additionally, we seek to determine if specific variables can be identified – from the aforementioned predictive classification models - with putative causal relations to PTSD. Methods ML predictive classification methods – with causal discovery feature selection – were applied to a data set of 163 children hospitalized with an injury and PTSD was determined three months after hospital discharge. At the time of hospitalization, 105 risk factor variables were collected spanning a range of biopsychosocial domains. Results Seven percent of subjects had a high level of PTSD symptoms. A predictive classification model was discovered with significant predictive accuracy. A predictive model constructed based on subsets of potentially causally relevant features achieves similar predictivity compared to the best predictive model constructed with all variables. Causal Discovery feature selection methods identified 58 variables of which 10 were identified as most stable. Conclusions In this first proof-of-concept application of ML methods to predict childhood Posttraumatic Stress we were able to determine both predictive classification models for childhood PTSD and identify several causal variables. This set of techniques has great potential for enhancing the methodological toolkit in the field and future studies should seek to replicate, refine, and extend the results produced in this study.

"Research Findings:" The present study aims to investigate the relationship between physical fitness and visuomotor integration in young children. A total of 242 three- to six-year-old preschoolers were randomly selected from a preschool in Shenzhen, China and their visuomotor integration and physical fitness were evaluated using the Beery Buktenica Developmental Test of Visual Motor Integration (VMI) and the preschool physical fitness test, respectively. Descriptive and standard-based analysis, t-test, analysis of variances (ANOVAs), correlation analysis, and mediation analysis were conducted using SPSS. Results showed that children with different ages had significant differences in their standing long jump and visuomotor integration ability (F = 7.56, p < 0.001; F = 34.36, p < 0.001) while gender difference was found in children's throwing (t = 4.18, p < 0.001). Furthermore, children's visuomotor integration significantly correlated with their 20 m fast run and throwing (r = 0.31 and 0.19, p < 0.01). Finally, children's performance in 20 m fast run and standing long partially mediated the relationship between age and visuomotor integration. "Practice or Policy:" The findigs highlighted the importance of strengthening young children's physical fitness and its relationship to their visuomotor integration. Implications for theory and practice are also included.

De-novo reverse-engineering of genome-scale regulatory networks is a fundamental problem of biological and translational research. One of the major obstacles in developing and evaluating approaches for de-novo gene network reconstruction is the absence of high-quality genome-scale gold-standard networks of direct regulatory interactions. To establish a foundation for assessing the accuracy of de-novo gene network reverse-engineering, we constructed high-quality genome-scale gold-standard networks of direct regulatory interactions in Saccharomyces cerevisiae that incorporate binding and gene knockout data. Then we used 7 performance metrics to assess accuracy of 18 statistical association-based approaches for de-novo network reverse-engineering in 13 different datasets spanning over 4 data types. We found that most reconstructed networks had statistically significant accuracies. We also determined which statistical approaches and datasets/data types lead to networks with better reconstruction accuracies. While we found that de-novo reverse-engineering of the entire network is a challenging problem, it is possible to reconstruct sub-networks around some transcription factors with good accuracy. The latter transcription factors can be identified by assessing their connectivity in the inferred networks. Overall, this study provides the gene network reverse-engineering community with a rigorous assessment of the accuracy of S. cerevisiae gene network reconstruction and variability in performance of various approaches for learning both the entire network and sub-networks around transcription factors.

BACKGROUND:Cocaine addiction is characterized as a chronically relapsing disorder. It is believed that cues present during self-administration become learned and increase the probability that relapse will occur when they are confronted during abstinence. However, the way in which relapse-inducing cues are interpreted by the user has remained elusive. Recent theories of addiction posit that relapse-inducing cues cause relapse habitually or automatically, bypassing processing information related to the consequences of relapse. Alternatively, other theories hypothesize that relapse-inducing cues produce an expectation of the drug's consequences, designated as goal-directed relapse. Discrete discriminative stimuli signaling the availability of cocaine produce robust cue-induced responding after thirty days of abstinence. However, it is not known whether cue-induced responding is a goal-directed action or habit. METHODOLOGY/PRINCIPAL FINDINGS:We tested whether cue-induced responding is a goal-directed action or habit by explicitly pairing or unpairing cocaine with LiCl-induced sickness (n = 7/group), thereby decreasing or not altering the value of cocaine, respectively. Following thirty days of abstinence, no difference in responding between groups was found when animals were reintroduced to the self-administration environment alone, indicating habitual behavior. However, upon discriminative stimulus presentations, cocaine-sickness paired animals exhibited decreased cue-induced responding relative to unpaired controls, indicating goal-directed behavior. In spite of the difference between groups revealed during abstinent testing, no differences were found between groups when animals were under the influence of cocaine. CONCLUSIONS/SIGNIFICANCE:Unexpectedly, both habitual and goal-directed responding occurred during abstinent testing. Furthermore, habitual or goal-directed responding may have been induced by cues that differed in their correlation with the cocaine infusion. Non-discriminative stimulus cues were weak correlates of the infusion, which failed to evoke a representation of the value of cocaine and led to habitual behavior. However, the discriminative stimulus-nearly perfectly correlated with the infusion-likely evoked a representation of the value of the infusion and led to goal-directed behavior. These data indicate that abstinent cue-induced responding is multifaceted, dynamically engendering habitual or goal-directed behavior. Moreover, since goal-directed behavior terminated habitual behavior during testing, therapeutic approaches aimed at reducing the perceived value of cocaine in addicted individuals may reduce the capacity of cues to induce relapse.

We developed a method of using the Clinically Aligned Pain Assessment (CAPA) measures to reconstruct the Numeric Rating System (NRS). We used an observational retrospective cohort study design with prospective validation using de-identified adult patient data derived from a major health system. Data between 2011-2017 were used for development and 2018-2020 for validation. All included patients had at least one NRS and CAPA measurement at the same time. An ordinal regression model was built with CAPA components to predict NRS scores. We identified 6,414 and 3,543 simultaneous NRS-CAPA pairs in the development and validation dataset, respectively. All CAPA components were significantly related to NRS, with RMSE of 1.938 and Somers' D of 0.803 on the development dataset, and RMSE of 2.1 and Somers' D of 0.74 when prospectively validated. Our model was capable of accurately reconstructing NRS based on CAPA and was exact when the NRS was [0,7]. [ABSTRACT FROM AUTHOR]