Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic Review and Meta-Analysis of Randomized Controlled Trials of Liangxue Tongyu Formula on Patients With Acute Intracerebral Hemorrhage

Background As a traditional Chinese medicine (TCM) prescription for acute stroke, Liangxue Tongyu formula (LXTYF) was widely used as auxiliary treatment measure in some clinical practice. This study aimed to evaluate the clinical efficacy and safety of LXTYF combined western conventional medicine (WCM) with WCM only for acute intracerebral hemorrhage (ICH). Methods We systematically searched PubMed, Embase, Cochrane Library, CMB (Chinese biomedicine database), CNKI (China National Knowledge Infrastructure), WanFang, and VIP until August 2019 to confirm relevant randomized controlled trials (RCTs) compared the combination of LXTYF and WCM with WCM alone for the treatment of acute ICH. Two investigators independently assessed the risk of bias, and extracted and analyzed the data from the identified studies using RevMan 5.3. 0 software following Cochrane’s standard and PRISMA guidelines. The herbal compositions of LXTYF were also assessed. Results 15 RCTs were identified, totally recruiting 1648 patients with acute intracerebral hemorrhage. Compared with the WCM alone, the combination therapy of LXTYF with WCM could improve the clinical effective rate (RR, 1.21; 95% CI, 1.15–1.25, P < 0.05) and ADL score (MD, 18.09; 95% CI, 12.11–24.07; P < 0.05), and reduce syndrome scores of the TCM (MD, −4.11; 95% CI, −4.69 to −3.53; P < 0.05) and the Glasgow outcome score(GOS) (MD=0.43, 95%CI: 0.06 to 0.79, P=0.02) Moreover, there was no sufficient evidence to indicate the adverse effects would increase compared with WCM alone. Conclusion Based on current evidence, we concluded that the combined therapy had some benefits in treating acute intracerebral hemorrhage. However, considering the potential biases and limitations of our study, additional large, high-quality RCTs are required in the future to confirm or refute the effects of LFTYF combined with WCM in acute stroke.
The Anaerobic Capacity of Cross-Country Skiers: The Effect of Computational Method and Skiing Sub-technique

Anaerobic capacity is an important performance-determining variable of sprint cross-country skiing. Nevertheless, to date, no study has directly compared the anaerobic capacity, determined using the maximal accumulated oxygen deficit (MAOD) method and gross efficiency (GE) method, while using different skiing sub-techniques. Purpose: To compare the anaerobic capacity assessed using two different MAOD approaches (including and excluding a measured y-intercept) and the GE method during double poling (DP) and diagonal stride (DS) cross-country skiing. Methods: After an initial familiarization trial, 16 well-trained male cross-country skiers performed, in each sub-technique on separate occasions, a submaximal protocol consisting of eight 4-min bouts at intensities between ~47–78% of V.O2peak followed by a 4-min roller-skiing time trial, with the order of sub-technique being randomized. Linear and polynomial speed-metabolic rate relationships were constructed for both sub-techniques, while using a measured y-intercept (8+YLIN and 8+YPOL) or not (8–YLIN and 8–YPOL), to determine the anaerobic capacity using the MAOD method. The average GE (GEAVG) of all eight submaximal exercise bouts or the GE of the last submaximal exercise bout (GELAST) were used to calculate the anaerobic capacity using the GE method. Repeated measures ANOVA were used to test differences in anaerobic capacity between methods/approaches. Results: A significant interaction was found between computational method and skiing sub-technique (P < 0.001, η2 = 0.51) for the anaerobic capacity estimates. The different methodologies resulted in significantly different anaerobic capacity values in DP (P < 0.001, η2 = 0.74) and in DS (P = 0.016, η2 = 0.27). The 8-YPOL model resulted in the smallest standard error of the estimate (SEE, 0.24 W·kg−1) of the MAOD methods in DP, while the 8-YLIN resulted in a smaller SEE value than the 8+YLIN model (0.17 vs. 0.33 W·kg−1) in DS. The 8-YLIN and GELAST resulted in the closest agreement in anaerobic capacity values in DS (typical error 2.1 mL O2eq·kg−1). Conclusions: It is discouraged to use the same method to estimate the anaerobic capacity in DP and DS sub-techniques. In DP, a polynomial MAOD method (8-YPOL) seems to be the preferred method, whereas the 8-YLIN, GEAVG, and GELAST can all be used for DS, but not interchangeable, with GELAST being the least time-consuming method.
Autophagy Is a Potential Therapeutic Target Against Duck Tembusu Virus Infection in vivo

Duck tembusu virus (DTMUV) is newly emerged in poultry and causes great losses to the breeding industry in China and neighboring countries. Effective antiviral strategies are still being studied. Autophagy is a cellular degradative pathway, and our lab's previous data show that autophagy promotes DTMUV replication in vitro. To study the role of autophagy further in vivo, we utilized ducks as the animal model to investigate the autophagy responses in DTMUV-targeted tissues. And also, we utilized autophagy regulators, including Rapamycin (Rapa) as the autophagy enhancer, 3-Methyladenine (3-MA) and Chloroquine (CQ) as the autophagy inhibitors, to adjust the host autophagic levels and then study the effects of autophagy on tissue damages and virus replication. As a result, we first found DTMUV infection trigged autophagy and autophagy regulator treatments regulated autophagy levels successfully in duck spleens and brains. Next, we found that autophagy inhibitors inhibited DTMUV replication and alleviated DTMUV-induced pathological symptoms, whereas the autophagy inducer treatment led to the opposite effects. And we also found that autophagic regulation was correlated with the expression of innate immune genes, including pattern recognition receptors, type I interferons, and cytokines, and caused different effects in different tissues. In summary, we demonstrated that autophagy facilitated DTMUV replication, aggravated the developments of pathological symptoms and possibly counteracts the host's innate immunity response in vivo.
Tolerance to FVIII: Role of the Immune Metabolic Enzymes Indoleamine 2,3 Dyoxigenase-1 and Heme Oxygenase-1

The occurrence of neutralizing anti-FVIII antibodies is a major complication in the treatment of patients affected by hemophilia A. The immune response to FVIII is a complex, multi-factorial process that has been extensively studied for the past two decades. The reasons why only a proportion of hemophilic patients treated with FVIII concentrates develop a clinically significant immune response is incompletely understood. The “danger theory” has been proposed as a possible explanation to interpret the findings of some observational clinical studies highlighting the possible detrimental impact of inflammatory stimuli at the time of replacement therapy on inhibitor development. The host immune system is often challenged to react to FVIII under steady state or inflammatory conditions (e.g., bleeding, infections) although fine tuning of mechanisms of immune tolerance can control this reactivity and promote long-term unresponsiveness to the therapeutically administered factor. Recent studies have provided evidence that multiple interactions involving central and peripheral mechanisms of tolerance are integrated by the host immune system with the environmental conditions at the time of FVIII exposure and influence the balance between immunity and tolerance to FVIII. Here we review evidences showing the involvement of two key immunoregulatory oxygenase enzymes (IDO1, HO-1) that have been studied in hemophilia patients and pre-clinical models, showing that the ability of the host immune system to induce such regulatory proteins under inflammatory conditions can play important roles in the balance between immunity and tolerance to exogenous FVIII.
The Relationship Between Aggregation and Deformability of Red Blood Cells in Health and Disease

The molecular organization of the membrane of the red blood cell controls cell morphology and function and is thereby a main determinant of red blood cell homeostasis in the circulation. The role of membrane organization is prominently reflected in red blood cell deformation and aggregation. However, there is little knowledge on whether they are controlled by the same membrane property and if so, to what extent. To address the potential interdependence of these two parameters, we measured deformation and aggregation in a variety of physiological as well as pathological conditions. As a first step, we correlated a number of deformability and aggregation parameters in red blood cells from healthy donors, which we obtained in the course of our studies on red blood cell homeostasis in health and disease. This analysis yielded some statistically significant correlations. Also, we found that most of these correlations were absent in misshapen red blood cells that have an inborn defect in the interaction between the membrane and the cytoskeleton. The observations suggest that deformability and aggregation share at least one common, membrane-related molecular mechanism. Together with data obtained after treatment with various agents known to affect membrane organization in vitro, our findings suggest that a phosphorylation-controlled interaction between the cytoskeleton and the integral membrane protein band 3 is part of the membrane-centered mechanism that plays a role in deformability as well as aggregation.
Progress of Research on the Regulatory Pathway of the Plant Shade-Avoidance Syndrome

When subject to vegetational shading, shade-avoiding plants detect neighbors by perceiving reduced light quantity and altered light quality. The former includes decreases in the ratio of red to far-red wavelengths (low R:FR) and low blue light ratio (LBL) predominantly detected by phytochromes and cryptochromes, respectively. By integrating multiple signals, plants generate a suite of responses, such as elongation of a variety of organs, accelerated flowering, and reduced branching, which are collectively termed the shade-avoidance syndrome (SAS). To trigger the SAS, interactions between photoreceptors and phytochrome-interacting factors are the general switch for activation of downstream signaling pathways. A number of transcription factor families and phytohormones, especially auxin, gibberellins, ethylene, and brassinosteroids, are involved in the SAS processes. In this review, shade signals, the major photoreceptors involved, and the phenotypic characteristics of the shade-intolerant plant Arabidopsis thaliana are described in detail. In addition, integration of the signaling mechanisms that link photoreceptors with multiple hormone signaling pathways is presented and future research directions are discussed.
Modulation of Titin-Based Stiffness in Hypertrophic Cardiomyopathy via Protein Kinase D

The giant protein titin performs structure-preserving functions in the sarcomere and is important for the passive stiffness (Fpassive) of cardiomyocytes. Protein kinase D (PKD) enzymes play crucial roles in regulating myocardial contraction, hypertrophy, and remodeling. PKD phosphorylates myofilament proteins, but it is not known whether the giant protein titin is also a PKD substrate. Here, we aimed to determine whether PKD phosphorylates titin and thereby modulates cardiomyocyte Fpassive in normal and failing myocardium. The phosphorylation of titin was assessed in cardiomyocyte-specific PKD knock-out mice (cKO) and human hearts using immunoblotting with a phosphoserine/threonine and a phosphosite-specific titin antibody. PKD-dependent site-specific titin phosphorylation in vivo was quantified by mass spectrometry using stable isotope labeling by amino acids in cell culture (SILAC) of SILAC-labeled mouse heart protein lysates that were mixed with lysates isolated from hearts of either wild-type control (WT) or cKO mice. Fpassive of single permeabilized cardiomyocytes was recorded before and after PKD and HSP27 administration. All-titin phosphorylation was reduced in cKO compared to WT hearts. Multiple conserved PKD-dependent phosphosites were identified within the Z-disk, A-band and M-band regions of titin by quantitative mass spectrometry, and many PKD-dependent phosphosites detected in the elastic titin I-band region were significantly decreased in cKO. Analysis of titin site-specific phosphorylation showed unaltered or upregulated phosphorylation in cKO compared to matched WT hearts. Fpassive was elevated in cKO compared to WT cardiomyocytes and PKD administration lowered Fpassive of WT and cKO cardiomyocytes. Cardiomyocytes from hypertrophic cardiomyopathy (HCM) patients showed higher Fpassive compared to control hearts and significantly lower Fpassive after PKD treatment. In addition, we found higher phosphorylation at CaMKII-dependent titin sites in HCM compared to control hearts. Expression and phosphorylation of HSP27, a substrate of PKD, were elevated in HCM hearts, which was associated with increased PKD expression and phosphorylation. The relocalization of HSP27 in HCM away from the sarcomeric Z-disk and I-band suggested that HSP27 failed to exert its protective action on titin extensibility. This protection could, however, be restored by administration of HSP27, which significantly reduced Fpassive in HCM cardiomyocytes. These findings establish a previously unknown role for PKDin regulating diastolic passive properties of healthy and diseased hearts.
Cellular Analysis and Comparative Transcriptomics Reveal the Tolerance Mechanisms of Candida tropicalis Toward Phenol

Phenol is a ubiquitous pollutant and can contaminate natural water resources. Hence, the removal of phenol from wastewater is of significant importance. A series of biological methods were used to remove phenol based on the natural ability of microorganisms to degrade phenol, but the tolerance mechanism of phenol-degraded strains to phenol are not very clear. Morphological observation on Candida tropicalis showed that phenol caused the reactive oxygen species (ROS) accumulation, damaging the mitochondrial and the endoplasmic reticulum. On the basis of transcriptome data and cell wall susceptibility analysis, it was found that C. tropicalis prevented phenol-caused cell damage through improvement of cell wall resistance, maintenance of high-fidelity DNA replication, intracellular protein homeostasis, organelle integrity, and kept the intracellular phenol concentration at a low level through cell-wall remodeling and removal of excess phenol via MDR/MXR transporters. The knowledge obtained will promote the genetic modification of yeast strains in general to tolerate the high concentrations of phenol and improve their efficiency of phenol degradation.
Prognostic Significance of Baseline Neutrophil Count and Lactate Dehydrogenase Level in Patients With Esophageal Squamous Cell Cancer Treated With Radiotherapy

Background: This present study aimed to explore the prognostic value of pretreatment neutrophil and lactate dehydrogenase (LDH) and to develop a prognostic risk scoring model to predict prognosis in esophageal squamous cell cancer (ESCC) patients treated with definitive radiotherapy. Methods: Retrospectively collected data of patients who received definitive radiotherapy for ESCC at Shantou Central Hospital between January 2009 and December 2015 were included for the analysis. The association between the level of LDH and neutrophil and clinicopathological characteristics were analyzed. We performed univariate and multivariate analyses to identify the prognostic predictors for patients with ESCC. Based on the results, we also developed a prognostic risk scoring model and assessed its predictive ability in the subgroups. Results: A total of 567 patients who received definitive radiotherapy for ESCC were included in the present study. The optimal cutoff values were 4.5 × 109/L, 3.25, and 220 U/L for neutrophil, neutrophil-to-lymphocyte ratio (NLR), and LDH, respectively. A high level of LDH was significantly associated with advanced N stage (p = 0.031), and neutrophil count was significantly associated with gender (p = 0.001), T stage (p < 0.001), N stage (p = 0.019), clinical stage (p < 0.001), and NLR (p < 0.001). Multivariate survival analysis identified gender (p = 0.006), T stage (p < 0.001), N stage (p = 0.008), treatment modality (p < 0.001), LDH level (p = 0.012), and neutrophil count (p = 0.038) as independent prognostic factors for overall survival. Furthermore, a new prognostic risk scoring (PRS) model based on six prognostic factors was developed, in which the patients were divided into three groups with distinct prognosis (χ2 = 67.94, p < 0.0001). Conclusions: Elevated baseline LDH level and neutrophil count predicted poor prognosis for ESCC patients treated with definitive radiotherapy. A PRS model comprised of LDH, neutrophil count, and other prognostic factors would help identify the patients who would benefit the most from definitive radiotherapy.
Loss of Arabidopsis β-COP Function Affects Golgi Structure, Plant Growth and Tolerance to Salt Stress

The early secretory pathway involves bidirectional transport between the endoplasmic reticulum (ER) and the Golgi apparatus and is mediated by coat protein complex I (COPI)-coated and coat protein complex II (COPII)-coated vesicles. COPII vesicles are involved in ER to Golgi transport meanwhile COPI vesicles mediate intra-Golgi transport and retrograde transport from the Golgi apparatus to the ER. The key component of COPI vesicles is the coatomer complex, that is composed of seven subunits (α/β/β’/γ/δ/ε/ζ). In Arabidopsis two genes coding for the β-COP subunit have been identified, which are the result of recent tandem duplication. Here we have used a loss-of-function approach to study the function of β-COP. The results we have obtained suggest that β-COP is required for plant growth and salt tolerance. In addition, β-COP function seems to be required for maintaining the structure of the Golgi apparatus.
Progressive Training in Recurrent Neural Networks for Chord Progression Modeling

: Recurrent neural networks (RNNs) can be trained to process sequences of tokens as they show impressive results in several sequence prediction. In general, when RNNs are trained, their goals are to maximize the likelihood of each token in the sequence where each token could be represented as a one-hot representation. That is, the model learns for its sequence prediction from true class labels. However, this creates a potential drawback, i.e. , the model cannot learn from the mistakes. In this work, we propose a progressive learning strategy that can mitigate the mistakes by using domain knowledge. Our strategy gently changes the training process from using the class labels guiding scheme to the similarity distribution of class labels instead. Our experiments on chord progression modeling show that this training paradigm yields signiﬁcant improvements.
A Fast Algorithm for Unsupervised Feature Value Selection

: The difﬁculty of unsupervised feature selection results from the fact that many local solutions can exist simul-taneously in the same dataset. No objective measure exists for judging the appropriateness of a particular local solution, because every local solution may reﬂect some meaningful but different interpretation of the dataset. On the other hand, known accurate feature selection algorithms perform slowly, which limits the number of local solutions that can be obtained using these algorithms. They have a small chance of producing a feature set that can explain the phenomenon being studied. This paper presents a new method for searching many local solutions using a signiﬁcantly fast and accurate algorithm. In fact, our feature value selection algorithm (UFVS) requires only a few tens of milliseconds for datasets with thousands of features and instances, and includes a parameter that can change the local solutions to select. It changes the scale of the problem, allowing a user to try many different solutions and pick the best one. In experiments with labeled datasets, UFVS found feature value sets that explain the labels, and also, with different parameter values, it detected relationships between feature value sets that did not line up with the given labels.
Quantifying the Significance of Cybersecurity Text through Semantic Similarity and Named Entity Recognition

: In order to proactively mitigate the risks of cybersecurity, security analysts have to continuously monitor threat information sources. However, the sheer amount of textual information that needs to be processed is overwhelming and requires a great deal of mundane labor. We propose a novel approach to automate this process by analyzing the text document using semantic similarity and Named Entity Recognition (NER) methods. The semantic representation of the given text has been compared with pre-deﬁned “signiﬁcant” text and, by using a NER model, the assets relevant to the organization are identiﬁed. The analysis results then act as features of the linear classiﬁer to generate the signiﬁcance score. The experimental result shows that the overall system could determine the signiﬁcance of the text with 78% accuracy.
D3P: Data-driven Demand Prediction for Fast Expanding Electric Vehicle Sharing Systems

The future of urban mobility is expected to be shared and electric. It is not only a more sustainable paradigm that can reduce emissions, but can also bring societal benefits by offering a more affordable on-demand mobility option to the general public. Many car sharing service providers as well as automobile manufacturers are entering the competition by expanding both their EV fleets and renting/returning station networks, aiming to seize a share of the market and to bring car sharing to the zero emissions level. During their fast expansion, one determinant for success is the ability of predicting the demand of stations as the entire system is growing continuously. There are several challenges in this demand prediction problem: First, unlike most of the existing work which predicts demand only for static systems or at few stages of expansion, in the real world we often need to predict the demand as or even before stations are being deployed or closed, to provide information and decision support. Second, for the new stations to be deployed, there is no historical data available to help the prediction of their demand. Finally, the impact of deploying/closing stations on the other stations in the system can be complex. To address these challenges, we formulate the demand prediction problem in the context of fast expanding electric vehicle sharing systems, and propose a data-driven demand prediction approach which aims to model the expansion dynamics directly from the data. We use a local temporal encoding process to handle the historical data for each existing station, and a dynamic spatial encoding process to take correlations between stations into account with Graph Convolutional Neural Networks (GCN). The encoded features are fed to a multi-scale predictor, which forecasts both the long-term expected demand of the stations and their instant demand in the near future. We evaluate the proposed approach with real-world data collected from a major EV sharing platform for one year. Experimental results demonstrate that our approach significantly outperforms the state of the art, showing up to three-fold performance gain in predicting demand for the expanding EV sharing systems.