Miracle-dz/UniFAR-FTUs huggingface dataset:
Antidepressant-like effect induced by P2X7 receptor blockade in FSL rats is associated with BDNF signalling activation

Background: P2X7 receptors (P2X7R) are ligand-gated ion channels activated by adenosine 5’-triphosphate (ATP), which are involved in processes that are dysfunctional in stress response and depression, such as neurotransmitter release, and neuroimmune response. Genetic and pharmacological inhibition of the P2X7R induce antidepressant-like effects in animals exposed to stress. However, the effect of P2X7R antagonism in an animal model of depression based on selective breeding has not previously been studied, and the mechanism underling the antidepressant-like effect induced by the P2X7R blockade remains unknown. Aims: The present study aimed to: (1) determine whether P2X7R blockade induces antidepressant-like effects in the Flinders Sensitive Line (FSL) rats and, (2) investigate whether brain-derived neurotrophic factor (BDNF) signalling in the frontal cortex and hippocampus is involved in this effect. Methods: FSL and the control Flinders Resistant Line (FRL) rats were treated with vehicle or the P2X7R antagonist A-804598 (3, 10 or 30 mg/Kg/day) for 1 or 7 days before being exposed to the forced swim test (FST). After the behavioural test, animals were decapitated, their brains were removed and the frontal cortex, ventral and dorsal hippocampus were dissected for BDNF signalling analysis. Results: We found that repeated treatment with A-804598 (30 mg/Kg) reduced the immobility time in the FST and activated the BDNF signalling in the ventral hippocampus of FSL rats. Conclusions: P2X7R blockade induces an antidepressant-like effect associated with increased levels of BDNF-AKT-p70 S6 kinase in the ventral hippocampus, which may be mediated by tropomyosin-related kinase B (TRKB) receptor activation supporting the notion of P2X7R antagonism as a potential new antidepressant strategy.
Dilution of Molecular–Pathologic Gene Signatures by Medically Associated Factors Might Prevent Prediction of Resection Status After Debulking Surgery in Patients With Advanced Ovarian Cancer

Purpose: Predicting surgical outcome could improve individualizing treatment strategies for patients with advanced ovarian cancer. It has been suggested earlier that gene expression signatures (GES) might harbor the potential to predict surgical outcome. Experimental Design: Data derived from high-grade serous tumor tissue of FIGO stage IIIC/IV patients of AGO-OVAR11 trial were used to generate a transcriptome profiling. Previously identified molecular signatures were tested. A theoretical model was implemented to evaluate the impact of medically associated factors for residual disease (RD) on the performance of GES that predicts RD status. Results: A total of 266 patients met inclusion criteria, of those, 39.1% underwent complete resection. Previously reported GES did not predict RD in this cohort. Similarly, The Cancer Genome Atlas molecular subtypes, an independent de novo signature and the total gene expression dataset using all 21,000 genes were not able to predict RD status. Medical reasons for RD were identified as potential limiting factors that impact the ability to use GES to predict RD. In a center with high complete resection rates, a GES which would perfectly predict tumor biological RD would have a performance of only AUC 0.83, due to reasons other than tumor biology. Conclusions: Previously identified GES cannot be generalized. Medically associated factors for RD may be the main obstacle to predict surgical outcome in an all-comer population of patients with advanced ovarian cancer. If biomarkers derived from tumor tissue are used to predict outcome of patients with cancer, selection bias should be focused on to prevent overestimation of the power of such a biomarker. See related commentary by Handley and Sood, p. 9
Expression of ZNF695 Transcript Variants in Childhood B-Cell Acute Lymphoblastic Leukemia

B-cell acute lymphoblastic leukemia is the most commonly diagnosed childhood malignancy worldwide; more than 50% of these cases are diagnosed in Mexico. Although the five-year survival rate is >80%, 30% of patients experience relapse with poor prognosis. Cancer-associated gene expression profiles have been identified in several malignancies, and some transcripts have been used to predict disease prognosis. The human transcriptome is incompletely elucidated; moreover, more than 80% of transcripts can be processed via alternative splicing (AS), which increases transcript and protein diversity. The human transcriptome is divided; coding RNA accounts for ~2%, and the remaining 98% is noncoding RNA. Noncoding RNA can undergo AS, promoting the diversity of noncoding transcripts. We designed specific primers to amplify previously reported alternative transcript variants of ZNF695 and showed that six ZNF695 transcript variants are co-expressed in cancer cell lines. The amplicons were sequenced and identified. Additionally, we analyzed the expression of these six transcript variants in bone marrow from B-cell acute lymphoblastic leukemia patients and observed that ZNF695 transcript variants one and three were the predominant variants expressed in leukemia. Moreover, our results showed the co-expression of coding and long noncoding RNA. Finally, we observed that long noncoding RNA ZNF695 expression predicted survival rates.
The role of fibrinogen and fibrinogen concentrate in cardiac surgery: an international consensus statement from the Haemostasis and Transfusion Scientific Subcommittee of the European Association of Cardiothoracic Anaesthesiology

To date, data regarding the efficacy and safety of administering fibrinogen concentrate in cardiac surgery are limited. Studies are limited by their low sample size and large heterogeneity with regard to the patient population, by the timing of fibrinogen concentrate administration, and by the definition of transfusion trigger and target levels. Assessment of fibrinogen activity using viscoelastic point‐of‐care testing shortly before or after weaning from cardiopulmonary bypass in patients and procedures with a high risk of bleeding appears to be a rational strategy. In contrast, the use of Clauss fibrinogen test for determination of plasma fibrinogen level can no longer be recommended without restrictions due to its long turnaround time, high inter‐assay variability and interference with high heparin levels and fibrin degradation products. Administration of fibrinogen concentrate for maintaining physiological fibrinogen activity in the case of microvascular post‐cardiopulmonary bypass bleeding appears to be indicated. The available evidence does not suggest aiming for supranormal levels, however. Use of cryoprecipitate as an alternative to fibrinogen concentrate might be considered to increase plasma fibrinogen levels. Although conclusive evidence is lacking, fibrinogen concentrate does not seem to increase adverse outcomes (i.e., thromboembolic events). Large prospective multi‐centre studies are needed to better define the optimal perioperative monitoring tool, transfusion trigger and target levels for fibrinogen replacement in cardiac surgery.
Vapor deposition of a nonmesogen prepares highly structured organic glasses

Significance Organic glasses are useful, versatile materials for current and future organic electronic technologies. They are nonequilibrium materials, so a single-component system can be processed in different ways to create a wide range of solids with diverse structural and optical properties. In this work, we use physical vapor deposition to prepare glasses with structure similar to that of an aligned smectic liquid crystal from a molecule that does not have any liquid crystal phases. This shows the potential to create highly structured glasses from a wide range of molecules. We show that glasses with aligned smectic liquid crystal-like order can be produced by physical vapor deposition of a molecule without any equilibrium liquid crystal phases. Smectic-like order in vapor-deposited films was characterized by wide-angle X-ray scattering. A surface equilibration mechanism predicts the highly smectic-like vapor-deposited structure to be a result of significant vertical anchoring at the surface of the equilibrium liquid, and near-edge X-ray absorption fine structure (NEXAFS) spectroscopy orientation analysis confirms this prediction. Understanding of the mechanism enables informed engineering of different levels of smectic order in vapor-deposited glasses to suit various applications. The preparation of a glass with orientational and translational order from a nonliquid crystal opens up an exciting paradigm for accessing extreme anisotropy in glassy solids.
Cognitive abilities underlying performance on the modified card sorting test: novel and traditional scores

Abstract The Modified Card Sorting Test (MCST) is a widely used variation of the Wisconsin Card Sorting Test. It is faster to administer, less frustrating for respondents and less ambiguous in its scoring but has been criticized for its task impurity and low discriminability between control participants and clinical groups prone to executive dysfunction. This study aimed to examine the executive functions (EF) underlying traditional (number of categories completed, perseverative errors) and novel scores for the MCST, and compare their ability to differentiate between control and clinical samples. Novel and traditional MCST scores were compared between 94 control participants, 87 with bipolar disorder and 64 with major depression. The relationship between MCST scores and traditional EF tasks was examined through correlation and regression analyses. All MCST scores were associated with at least one measure of EF, the most common of which were the Trail Making or Hayling Tests. IQ predicted most scores on the MCST, save for nonperseverative errors and categorizing efficiency. Traditional and novel scores differentiated between clinical and control groups. These findings support the utility of the MCST in detecting executive dysfunction and highlight the importance of new scoring methods in increasing the specificity and interpretability of this task.
The relationship between depressive symptoms and negative symptoms in people with non-affective psychosis: a meta-analysis

Abstract The negative symptoms of psychosis and depressive symptomatology share several features, e.g. low motivation, apathy and reduced activity. Understanding the associations between these two sets of symptoms will support improved assessment and the development of interventions targeting these difficulties in people with psychosis. This is the first large systematic review and meta-analysis to quantify the relationship between these two categories of symptoms, as measured in studies to date. PsycInfo, Embase and Medline were systematically searched to identify eligible studies. Inclusion criteria ensured the studies measured both depression and negative symptoms using validated measures in a sample of over 8000 participants with non-affective psychosis diagnoses. The search led to 2020 records being screened and 56 included in the meta-analysis and review. Both meta-analyses and meta-regressions were conducted to explore the main effect and potential moderating variables. A clear pattern emerges showing that higher ratings of negative symptoms are associated with higher levels of depressive symptoms, with a small effect [standardised effect size = 0.19, p < 0.05). This did not vary greatly with the measures used (SES = 0.19–0.26) and was not moderated by demographic variables or quality ratings. Interestingly, higher depressive symptoms predict a significant relationship with co-occurring negative symptoms. However, higher negative symptoms predict that it is less likely there will be a relationship with co-occurring depressive symptoms. Heterogeneity was high across these analyses. The findings support the adoption of a symptom-specific approach to understanding the interplay between negative and depressive symptoms in psychosis, to improve assessment and intervention.
Structural insights into diverse modes of ICAM-1 binding by Plasmodium falciparum-infected erythrocytes

Significance Malaria is one of the deadliest infectious diseases to affect humans, causing over 200 million cases and hundreds of thousands of deaths annually. Its fatal symptoms occur when parasites cause infected human red blood cells to stick to human tissue surfaces, blocking blood flow and causing inflammation. This stickiness is caused by parasite PfEMP1 proteins, which interact with different human receptors, such as ICAM-1. In this paper, we demonstrate how PfEMP1 proteins bind to ICAM-1. We find that this can happen in 2 different but related ways, perhaps influencing which additional receptors PfEMP1 can bind. We show how the parasite can adapt to allow it to stick tightly, while reducing the chance that it is detected and destroyed. A major determinant of pathogenicity in malaria caused by Plasmodium falciparum is the adhesion of parasite-infected erythrocytes to the vasculature or tissues of infected individuals. This occludes blood flow, leads to inflammation, and increases parasitemia by reducing spleen-mediated clearance of the parasite. This adhesion is mediated by PfEMP1, a multivariant family of around 60 proteins per parasite genome which interact with specific host receptors. One of the most common of these receptors is intracellular adhesion molecule-1 (ICAM-1), which is bound by 2 distinct groups of PfEMP1, A-type and B or C (BC)-type. Here, we present the structure of a domain from a B-type PfEMP1 bound to ICAM-1, revealing a complex binding site. Comparison with the existing structure of an A-type PfEMP1 bound to ICAM-1 shows that the 2 complexes share a globally similar architecture. However, while the A-type PfEMP1 bind ICAM-1 through a highly conserved binding surface, the BC-type PfEMP1 use a binding site that is more diverse in sequence, similar to how PfEMP1 interact with other human receptors. We also show that A- and BC-type PfEMP1 present ICAM-1 at different angles, perhaps influencing the ability of neighboring PfEMP1 domains to bind additional receptors. This illustrates the deep diversity of the PfEMP1 and demonstrates how variations in a single domain architecture can modulate binding to a specific ligand to control function and facilitate immune evasion.
In vivo identification of small molecules mediating Gpr126/Adgrg6 signaling during Schwann cell development

Gpr126/Adgrg6, an adhesion family G protein–coupled receptor (aGPCR), is required for the development of myelinating Schwann cells in the peripheral nervous system. Myelin supports and insulates vertebrate axons to permit rapid signal propagation throughout the nervous system. In mammals and zebrafish, mutations in Gpr126 arrest Schwann cells at early developmental stages. We exploited the optical and pharmacological tractability of larval zebrafish to uncover drugs that mediate myelination by activating Gpr126 or functioning in parallel. Using a fluorescent marker of mature myelinating glia (Tg[mbp:EGFP‐CAAX]), we screened hypomorphic gpr126 mutant larvae for restoration of myelin basic protein (mbp) expression along peripheral nerves following small molecule treatment. Our screens identified five compounds sufficient to promote mbp expression in gpr126 hypomorphs. Using an allelic series of gpr126 mutants, we parsed the ability of small molecules to restore mbp, suggesting differences in drug efficacy dependent on Schwann cell developmental state. Finally, we identify apomorphine hydrochloride as a direct small molecule activator of Gpr126 using combined in vivo/in vitro assays and show that aporphine class compounds promote Schwann cell development in vivo. Our results demonstrate the utility of in vivo screening for aGPCR modulators and identify small molecules that interact with the gpr126‐mediated myelination program.
The relationship between obstructive sleep apnoea and postoperative delirium and pain: an observational study of a surgical cohort

Patients with obstructive sleep apnoea are at increased risk of adverse postoperative outcomes, such as cardiac and respiratory complications. It has been hypothesised that obstructive sleep apnoea also increases the risk for postoperative delirium and acute postoperative pain. We conducted a retrospective, observational study investigating the relationship of obstructive sleep apnoea with postoperative delirium and acute postoperative pain severity. Patients were classified as being at high risk for obstructive sleep apnoea if they had been diagnosed with this condition, or if they were positive for more than four factors using the ‘STOP‐BANG’ screening tool. Adjusted logistic regression was used to investigate the association between obstructive sleep apnoea and postoperative delirium, and multivariable linear regression to study the relationship between obstructive sleep apnoea and postoperative pain severity. The incidence of postoperative delirium was 307 in 1441 patients (21.3%; 95%CI 19.2–23.5%). In unadjusted analysis, high risk for obstructive sleep apnoea was associated with delirium, with an odds ratio (95%CI) of 1.77 (1.22–2.57; p = 0.003). After adjustment for pre‐specified variables, the association was not statistically significant with odds ratio 1.34 (0.80–2.23; p = 0.27). The mean (SD) maximum pain (resting or provoked) reported for the entire cohort was 63.8 (27.9) mm on a 0–100 mm visual analogue scale. High risk for obstructive sleep apnoea was not associated with postoperative pain severity (β‐coefficient 2.82; 95%CI, −2.34–7.97; p = 0.28). These findings suggest that obstructive sleep apnoea is unlikely to be a strong risk factor for postoperative delirium or acute postoperative pain severity.
A Wavelet approach to extract main features from indirect immunofluorescence images

A number of previous studies have shown that IIF image analysis requires complex and sometimes heterogeneous and diversified methods. Robust solutions can be proposed but they need to orchestrate several methods from low-level analysis up to more complex neural networks or SVM for data classification. The contribution intends to highlight the versatility of Wavelet Transform (WT) and their use in various levels of analysis for the classification of IIF images in order to develop a system capable of performing: image enhancement, ROI segmentation and object classification. Therefore, WT was adopted in the de-noise section, segmentation and classification. This analysis allows frequencies characterization (low/high) and with the statistical distributions of the wavelet coefficients will be able to support the medical diagnosis process. In particular, the robustness and the goodness of the segmentation phase must be highlighted and its validation was reported in the section 3.2. 1. From the depicted data it is possible to assert that the validation with ground truths produced an accuracy of 90% and that the method, to the best of our knowledge, is superior to other methods, which do not support WT (see Table 1). The advantage of using WT in all levels of abstraction of IIF data analysis lies robustness of the method, and in the rapid understanding, by the end user, of a single method that shows good average results in all levels of analysis.
How Does Online Word-of-Mouth Impact Movie Box Office: An Empirical Investigation of China

The movie's online word of mouth (WOM) currently is a vital factor affecting the movie box office. To prove this relationship in empirical way, this paper selects 150 released movies in mainland China as the research sample and the movie box office, volume of WOM and valence of WOM are the main variables. Granger causality test and simultaneous equation model are applied as the main method. Combining the theory with the empirical study results, the following conclusions can be made. First, the volume significant promoted the box office Second, in the first week after the film is released, the valence of WOM, so as the box office does. Second, in the first week after the film is released, the valence of WOM will inhibit movie box office significantly. However, in the second week, this effect is no longer significant.
Recognizing Deceptive Reviews Based on Weighted Multi-Instance Unbalanced Support Vector Machine

Recognition performance of detecting deceptive reviews model depends heavily on quality of manual annotated training data. However, manual label of reviews often has misclassification. Considering characteristics of training data that deceptive reviews are less than true ones and misclassified reviews in training data, a model of recognizing deceptive reviews based on weighted multi-instance unbalanced support vector machine is proposed. Theoretical analysis and experimental results showed detection model effectively reduce impact of misclassified data in training data and improve recognition performance of deceptive reviews in unbalanced reviews dataset.
Unsupervised Learning for Log Data Analysis Based on Behavior and Attribute Features

In some special application environments, network fault can lead to loss of important information or even mission failures, resulting in unpredictable losses. Therefore, it has certain research significance and practical value to evaluate the network status and predict the possible faults before performing the key tasks. Based on the logs collected by the router board in the real network, this paper analyses the behavior type, attribute information and the corresponding status value, and detects the hidden fault or network attack, so as to provide early warning information for operators. We propose a deep neural network model utilizing Long Short-Term Memory (LSTM) to predict the current number of level-1 logs. By comparing the predicted number of level-1 logs, it can detect abnormal behavior such as a surge in the number of logs. What's more, we perform semantic analysis on attribute information to construct attribute syntax forest, which assists maintenance staff to monitor the system through key fingerprint information in the log. In addition, we adopt attribute information and status value to train the unsupervised learning algorithm models such as Isolation Forest, OneClassSVM and LocalOutlierFactor. What's more, this paper analyses the results to find out the causes of log surge, and to assist operators in subsequent maintenance of the system.
Security Modeling of Autonomous Systems

Autonomous systems will soon be integrating into our lives as home assistants, delivery drones, and driverless cars. The implementation of the level of automation in these systems from being manually controlled to fully autonomous would depend upon the autonomy approach chosen to design these systems. This article reviews the historical evolution of autonomy, its approaches, and the current trends in related fields to build robust autonomous systems. Toward such a goal and with the increased number of cyberattacks, the security of these systems needs special attention from the research community. To gauge the extent to which research has been done in this area, we discuss the cybersecurity of these systems. It is essential to model the system from a security perspective, identify the threats and vulnerabilities, and then model the attacks. A survey in this direction explores the theoretical/analytical system and attack models that have been proposed over the years and identifies the research gap that needs to be addressed by the research community.