Miracle-dz/UniFAR-FTUs huggingface dataset:
Child Maltreatment and Aggressive Behaviors in Early Adolescence: Evidence of Moderation by Parent/Child Relationship Quality

The cycle of violence suggests that maltreatment increases children’s aggression, but research shows that many children are resilient to the harms caused by maltreatment. This study examines whether or not parent/child relationship quality accounts for variation in the impact of maltreatment on aggression and hypothesizes that the effect will be weaker for children who have better relationships with their caregivers. Race differences in these effects are also examined. Based on prospective data from a high-risk sample of 620 (207 White and 413 Black) families in the Longitudinal Studies of Child Abuse and Neglect, ordinary least squares regression analyses indicated that youth with at least one official allegation of maltreatment before age 10 had significantly more frequent aggressive behaviors at age 12. The direct effect of maltreatment on aggression did not vary for Black and White youth. However, a significant three-way interaction indicated that parent/child relationship quality buffered the relationship between maltreatment and aggressive behaviors for White but not Black children. Although additional research is required to identify factors that ameliorate the impact of maltreatment for Black youth, the findings support the need for interventions to help children cope with maltreatment and to strengthen parent/child relationships.
Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-Based Semantic Segmentation

Automatic segmentation of retinal images is an important task in computer-assisted medical image analysis for the diagnosis of diseases such as hypertension, diabetic and hypertensive retinopathy, and arteriosclerosis. Among the diseases, diabetic retinopathy, which is the leading cause of vision detachment, can be diagnosed early through the detection of retinal vessels. The manual detection of these retinal vessels is a time-consuming process that can be automated with the help of artificial intelligence with deep learning. The detection of vessels is difficult due to intensity variation and noise from non-ideal imaging. Although there are deep learning approaches for vessel segmentation, these methods require many trainable parameters, which increase the network complexity. To address these issues, this paper presents a dual-residual-stream-based vessel segmentation network (Vess-Net), which is not as deep as conventional semantic segmentation networks, but provides good segmentation with few trainable parameters and layers. The method takes advantage of artificial intelligence for semantic segmentation to aid the diagnosis of retinopathy. To evaluate the proposed Vess-Net method, experiments were conducted with three publicly available datasets for vessel segmentation: digital retinal images for vessel extraction (DRIVE), the Child Heart Health Study in England (CHASE-DB1), and structured analysis of retina (STARE). Experimental results show that Vess-Net achieved superior performance for all datasets with sensitivity (Se), specificity (Sp), area under the curve (AUC), and accuracy (Acc) of 80.22%, 98.1%, 98.2%, and 96.55% for DRVIE; 82.06%, 98.41%, 98.0%, and 97.26% for CHASE-DB1; and 85.26%, 97.91%, 98.83%, and 96.97% for STARE dataset.
Evaluation of a mindfulness-based mobile application with college students: A pilot study

Abstract Objective: This pilot study assessed the feasibility and acceptability of The Mindfulness App with college students and the utility of daily text-message reminders for increasing adherence. Participants: Students from a mid-sized Midwestern University (N = 59) during the spring and fall of 2016. Methods: Using a pretest–posttest design, a group of students used The Mindfulness App for 5 weeks. All students were sent text-message reminders to submit daily practice time and completed a series of online measures. Results: Participants consistently used the application as intended, and reported positive attitudes toward the text-message reminders and the quality of the application. Conclusions: The Mindfulness App is a feasible, well-accepted tool for delivering mindfulness interventions to college students.
Robust Reflectance Estimation for Projection-Based Appearance Control in a Dynamic Light Environment

We present a novel method that robustly estimates the reflectance, even in an environment with dynamically changing light. To control the appearance of an object by using a projector–camera system, an appropriate estimate of the object's reflectance is vital to the creation of an appropriate projection image. Most conventional estimation methods assume static light conditions; however, in practice, the appearance is affected by both the reflectance and environmental light. In an environment with dynamically changing light, conventional reflectance estimation methods require calibration every time the conditions change. In contrast, our method requires no additional calibration because it simultaneously estimates both the reflectance and environmental light. Our method is based on the concept of creating two different light conditions by switching the projection at a rate higher than that perceived by the human eye and captures the images of a target object separately under each condition. The reflectance and environmental light are then simultaneously estimated by using the pair of images acquired under these two conditions. We implemented a projector–camera system that switches the projection on and off at 120 Hz. Experiments confirm the robustness of our method when changing the environmental light. Further, our method can robustly estimate the reflectance under practical indoor lighting conditions.
ERS guidelines on the diagnosis and treatment of chronic cough in adults and children

These guidelines incorporate the recent advances in chronic cough pathophysiology, diagnosis and treatment. The concept of cough hypersensitivity has allowed an umbrella term that explains the exquisite sensitivity of patients to external stimuli such a cold air, perfumes, smoke and bleach. Thus, adults with chronic cough now have a firm physical explanation for their symptoms based on vagal afferent hypersensitivity. Different treatable traits exist with cough variant asthma (CVA)/eosinophilic bronchitis responding to anti-inflammatory treatment and non-acid reflux being treated with promotility agents rather the anti-acid drugs. An alternative antitussive strategy is to reduce hypersensitivity by neuromodulation. Low-dose morphine is highly effective in a subset of patients with cough resistant to other treatments. Gabapentin and pregabalin are also advocated, but in clinical experience they are limited by adverse events. Perhaps the most promising future developments in pharmacotherapy are drugs which tackle neuronal hypersensitivity by blocking excitability of afferent nerves by inhibiting targets such as the ATP receptor (P2X3). Finally, cough suppression therapy when performed by competent practitioners can be highly effective. Children are not small adults and a pursuit of an underlying cause for cough is advocated. Thus, in toddlers, inhalation of a foreign body is common. Persistent bacterial bronchitis is a common and previously unrecognised cause of wet cough in children. Antibiotics (drug, dose and duration need to be determined) can be curative. A paediatric-specific algorithm should be used. New ERS guideline on chronic cough details the paradigm shift in our understanding. In adults, cough hypersensitivity has become the overarching diagnosis, and in children, persistent bacterial bronchitis explains most wet cough, changing treatment advice. http://bit.ly/2kycX8D
Tissue-specific telomere dynamics in hibernating arctic ground squirrels (Urocitellus parryii)

ABSTRACT Hibernation is used by a variety of mammals to survive seasonal periods of resource scarcity. Reactive oxygen species (ROS) released during periodic rewarming throughout hibernation, however, may induce oxidative damage in some tissues. Telomeres, which are the terminal sequences of linear chromosomes, may shorten in the presence of ROS, and thus the telomere length of an individual reflects the degree of accrued oxidative damage. This study quantified telomere length dynamics throughout hibernation in arctic ground squirrels (Urocitellus parryii). We hypothesized that telomere dynamics are tissue specific and predicted that telomere shortening would be most pronounced in brown adipose tissue (BAT), the organ that directly supports non-shivering thermogenesis during arousals. We used qPCR to determine relative telomere length (RTL) in DNA extracted from liver, heart, skeletal muscle (SM) and BAT of 45 juvenile and adult animals sampled either at mid- or late hibernation. Age did not have a significant effect on RTL in any tissue. At mid-hibernation, RTL of juvenile females was longer in BAT and SM than in liver and heart. In juvenile females, RTL in BAT and SM, but not in liver and heart, was shorter at late hibernation than at mid-hibernation. At late hibernation, juvenile males had longer RTL in BAT than did juvenile females, perhaps due to the naturally shorter hibernation duration of male arctic ground squirrels. Finally, BAT RTL at late hibernation negatively correlated with arousal frequency. Overall, our results suggest that, in a hibernating mammal, telomere shortening is tissue specific and that metabolically active tissues might incur higher levels of molecular damage. Summary: Telomere length in hibernating arctic ground squirrels shows a decrease in brown adipose tissue and skeletal muscle, suggesting that rewarming over periodic arousals may cause localized attrition.
The IL‐6 signaling complex is a critical driver, negative prognostic factor, and therapeutic target in diffuse large B‐cell lymphoma

Interleukin‐6 (IL‐6) is a growth factor for normal B cells and plasma cell‐derived malignancies. Here, we show that the IL‐6 signaling pathway is also active in a subset of diffuse large B‐cell lymphoma (DLBCL) patients with particularly poor prognosis. Primary DLBCL cells and DLBCL cell lines expressing IL‐6R engraft and form orthotopic lymphomas in humanized mice that ectopically produce human IL‐6, and in mice reconstituted with a human immune system. We show that a subset of DLBCL cases have evolved mechanisms that ensure constitutive activation of the IL‐6 signaling pathway, i.e., the expression of both chains of the IL‐6R, the expression of the cytokine itself, and the mutational inactivation of a negative regulator of IL‐6 signaling, SOCS1. IL‐6 signaling promotes MYC‐driven lymphomagenesis in a genetically engineered model, and treatment with the IL‐6R‐specific antibody tocilizumab reduces growth of primary DLBCL cells and of DLBCL cell lines in various therapeutic settings. The combined results uncover the IL‐6 signaling pathway as a driver and negative prognosticator in aggressive DLBCL that can be targeted with a safe and well‐tolerated biologic.
Clinical Practice Guidelines and Consensus Statements About Pain Management in Critically Ill End-of-Life Patients: A Systematic Review.

OBJECTIVES To identify and synthesize available recommendations from scientific societies and experts on pain management at the end-of-life in the ICU. DATA SOURCES We conducted a systematic review of PubMed, EMBASE, the Cochrane Database of Systematic Reviews, and Biblioteca Virtual en Salud from their inception until March 28, 2019. STUDY SELECTION We included all clinical practice guidelines, consensus statements, and benchmarks for quality. DATA EXTRACTION Study selection, methodological quality, and data extraction were performed independently by two investigators. A quality assessment was performed by four investigators using the Appraisal of Guidelines for Research and Evaluation II instrument. The recommendations were then synthesized and categorized. DATA SYNTHESIS Ten publications were included. The Appraisal of Guidelines for Research and Evaluation II statement showed low scores in various quality domains, especially in the applicability and rigor of development. Most documents were in agreement on five topics: 1) using a quantitative tool for pain assessment; 2) administering narcotics for pain relief and benzodiazepines for anxiety relief; 3) against prescribing neuromuscular blockers during withdrawal of life support to assess pain; 4) endorsing the use of high doses of opioids and sedatives for pain control, regardless of the risk that they will hasten death; and 5) using quality indicators to improve pain management during end-of-life in the ICU. CONCLUSIONS In spite of the lack of high-quality evidence, recommendations for pain management at the end-of-life in the ICU are homogeneous and are justified by ethical principles and agreement among experts. Considering the growing demand for the involvement of palliative care teams in the management of the dying patients in the ICU, there is a need to clearly define their early involvement and to further develop comprehensive evidence-based pain management strategies. Based on the study findings, we propose a management algorithm to improve the overall care of dying critically ill patients.
Genomic and Mitochondrial Data Identify Different Species Boundaries in Aposematically Polymorphic Eniclases Net-Winged Beetles (Coleoptera: Lycidae)

Species delineation is essential for any evolutionary and biodiversity research, and recent advances in genomic sequencing have made it possible to robustly define species boundaries and detect hidden diversity. Here, we studied 14 species of aposematically colored New Guinean Eniclases (Coleoptera: Lycidae) whose conventional morphology- and single-locus mtDNA-based taxonomy has been contentious. We analyzed mitochondrial and restriction site associated DNA fragments to obtain a phylogenetic hypothesis and compared relationships recovered by the RAD analysis with species limits based on other information. The results show the presence of cryptic diversity and common mitonuclear discordance when over 30% of individuals were incorrectly assigned to species if only mitogenomic markers were considered. Nuclear data falsified the species rank of one species and identified one earlier unrecognized lineage deserving species rank. Further, our analyses demonstrate a highly variable phenotypic differentiation, with several pairs of cryptic species standing in contrast with genetically close but phenotypically highly divergent lineages. We show that morphological and mitogenomic analyses produce reliable information for taxonomy in most cases. Nevertheless, the species boundaries among closely related species should be based on all lines of evidence, including nuclear markers.
Location Specific Temperature Compensation of Guided Wave Signals in Structural Health Monitoring

In guided wave structural health monitoring, defects are typically detected by identifying high residuals obtained through the baseline subtraction method, where an earlier measurement is subtracted from the “current” signal. Unfortunately, varying environmental and operational conditions (EOCs), such as temperature, also produce signal changes and hence, potentially, high residuals. While the majority of the temperature compensation methods that have been developed target the changed wave speed induced by varying temperature, a number of other effects are not addressed, such as the changes in attenuation, the relative amplitudes of different modes excited by the transducer, and the transducer frequency response. A temperature compensation procedure is developed, whose goal is to correct any spatially dependent signal change that is a systematic function of temperature. At each structural position, a calibration function that models the signal variation with temperature is computed and is used to correct the measurements, so that in the absence of a defect the residual is reduced to close to zero. This new method was applied to a set of guided wave signals collected in a blind trial of a guided wave pipe monitoring system using the T(0, 1) mode, yielding residuals de-coupled from temperature and reduced by at least 50% as compared with those obtained using the standard approach at positions away from structural features, and by more than 90% at features such as the pipe end. The method, therefore, promises a substantial improvement in the detectability of small defects, particularly at the existing pipe features.
Structure-based identification of potential novel inhibitors targeting FAM3B (PANDER) causing type 2 diabetes mellitus through virtual screening

Abstract Type 2 diabetes mellitus is a metabolic disorder that requires potent therapeutic approaches. The FAM3B is a cytokine-like protein also referred to as PANcreatic-DERrived factor (PANDER) which mainly exists in pancreatic islets. In the process of identifying potential inhibitors with the aid of structure-based method PANDER protein is identified as a novel therapeutic target against type 2 diabetes mellitus as it involved in the development of type 2 diabetes by negatively regulating the pancreatic β-cell function and insulin sensitivity in the liver. In the present study, the 3d model of target protein FAM3B was generated by homology modeling technique using the MODELLER9.9 program. The assessment of the structural stability of the 3d model was established by energy minimization technique. The structural quality was evaluated with standard validating protocols. Binding regions of the target protein has been determined by literature and SiteMap tool. In the current study of research, the FAM3B model was subjected to molecular screening with the Asinex-elite database of 14849 output molecules using the Glide virtual screening module in the Schrodinger suite. The final XP descriptor output of 14 molecules was analyzed and prioritized based on molecular interactions at the FAM3B active site. The docking score, binding free energies (Prime MM/GBSA) and bioavailability were undertaken into the consideration to identify lead inhibitors. The identified lead compounds were checked for ADME properties all falling within the permeable ranges. The analysis of results gave the insight to develop the novel therapeutic strategies against type 2 diabetes mellitus.
Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score

OBJECTIVE To develop and validate a novel, machine learning–derived model to predict the risk of heart failure (HF) among patients with type 2 diabetes mellitus (T2DM). RESEARCH DESIGN AND METHODS Using data from 8,756 patients free at baseline of HF, with <10% missing data, and enrolled in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, we used random survival forest (RSF) methods, a nonparametric decision tree machine learning approach, to identify predictors of incident HF. The RSF model was externally validated in a cohort of individuals with T2DM using the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT). RESULTS Over a median follow-up of 4.9 years, 319 patients (3.6%) developed incident HF. The RSF models demonstrated better discrimination than the best performing Cox-based method (C-index 0.77 [95% CI 0.75–0.80] vs. 0.73 [0.70–0.76] respectively) and had acceptable calibration (Hosmer-Lemeshow statistic χ2 = 9.63, P = 0.29) in the internal validation data set. From the identified predictors, an integer-based risk score for 5-year HF incidence was created: the WATCH-DM (Weight [BMI], Age, hyperTension, Creatinine, HDL-C, Diabetes control [fasting plasma glucose], QRS Duration, MI, and CABG) risk score. Each 1-unit increment in the risk score was associated with a 24% higher relative risk of HF within 5 years. The cumulative 5-year incidence of HF increased in a graded fashion from 1.1% in quintile 1 (WATCH-DM score ≤7) to 17.4% in quintile 5 (WATCH-DM score ≥14). In the external validation cohort, the RSF-based risk prediction model and the WATCH-DM risk score performed well with good discrimination (C-index = 0.74 and 0.70, respectively), acceptable calibration (P ≥0.20 for both), and broad risk stratification (5-year HF risk range from 2.5 to 18.7% across quintiles 1–5). CONCLUSIONS We developed and validated a novel, machine learning–derived risk score that integrates readily available clinical, laboratory, and electrocardiographic variables to predict the risk of HF among outpatients with T2DM.
Identification of targetable BRAF ΔN486_P490 variant by whole-genome sequencing leading to dabrafenib-induced remission of a BRAF-mutant pancreatic adenocarcinoma

The tumor genome of a patient with advanced pancreatic cancer was sequenced to identify potential therapeutic targetable mutations after standard of care failed to produce any significant overall response. Matched tumor-normal whole-genome sequencing revealed somatic mutations in BRAF, TP53, CDKN2A, and a focal deletion of SMAD4. The BRAF variant was an in-frame deletion mutation (ΔN486_P490), which had been previously demonstrated to be a kinase-activating alteration in the BRAF kinase domain. Working with the Novartis patient assistance program allowed us to treat the patient with the BRAF inhibitor, dabrafenib. The patient's overall clinical condition improved dramatically with dabrafenib. Levels of serum tumor marker dropped immediately after treatment, and a subsequent CT scan revealed a significant decrease in the size of both primary and metastatic lesions. The dabrafenib-induced remission lasted for 6 mo. Preclinical studies published concurrently with the patient's treatment showed that the BRAF in-frame mutation (ΔNVTAP) induces oncogenic activation by a mechanism distinct from that induced by V600E, and that this difference dictates the responsiveness to different BRAF inhibitors. This study describes a dramatic instance of how high-level genomic technology and analysis was necessary and sufficient to identify a clinically logical treatment option that was then utilized and shown to be of clinical value for this individual.
Prenatal Dexamethasone Exposure Induced Alterations in Neurobehavior and Hippocampal Glutamatergic System Balance in Female Rat Offspring.

Epidemiological investigations have suggested that periodic use of dexamethasone during pregnancy is a risk factor for abnormal behavior in offspring, but the potential mechanism remains unclear. In this study, we investigated the changes in the glutamatergic system and neurobehavior in female offspring with prenatal dexamethasone exposure (PDE) to explore intrauterine programing mechanisms. Compared with the control group, rat offspring with PDE exhibited spatial memory deficits and anxiety-like behavior. The expression of hippocampal glucocorticoid receptors (GR) and histone deacetylase 2 (HDAC2) increased, whereas histone H3 lysine 14 acetylation (H3K14ac) of brain-derived neurotrophic factor (BDNF) exon IV (BDNF IV) and expression of BDNF decreased. The glutamatergic system also changed. We further observed that changes in the fetal hippocampus were consistent with those in adult offspring. In vitro, the administration of 0.5 μM dexamethasone to the H19-7 fetal hippocampal neuron cells directly led to a cascade of changes in the GR/HDAC2/BDNF pathway, whereas the GR antagonist RU486 and the HDAC2 inhibitor romidepsin (Rom) reversed changes caused by dexamethasone to the H3K14ac level of BDNF IV and to the expression of BDNF. The increase in HDAC2 can be reversed by RU486, and the changes in the glutamatergic system can be partially reversed after supplementation with BDNF. It is suggested that PDE increases the expression of HDAC2 by activating GR, reducing the H3K14ac level of BDNF IV, inducing alterations in neurobehavior and hippocampal glutamatergic system balance. The findings suggest that BDNF supplementation and glutamatergic system improvement are potential therapeutic targets for the fetal origins of abnormal neurobehavior.