Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep learning analysis of intracranial EEG for recognizing drug effects and mechanisms of action

Drug-target interaction (DTI) prediction has become a foundational task in drug repositioning, polypharmacology, drug discovery, as well as drug resistance and side-effect prediction. DTI identification using machine learning is gaining popularity in these research areas. Through the years, numerous deep learning methods have been proposed for DTI prediction. Nevertheless, prediction accuracy and efficiency remain key challenges. Pharmaco-electroencephalogram (pharmaco-EEG) is considered valuable in the development of central nervous system-active drugs. Quantitative EEG analysis demonstrates high reliability in studying the effects of drugs on the brain. Earlier preclinical pharmaco-EEG studies showed that different types of drugs can be classified according to their mechanism of action on neural activity. Here, we propose a convolutional neural network for EEG-mediated DTI prediction. This new approach can explain the mechanisms underlying complicated drug actions, as it allows the identification of similarities in the mechanisms of action and effects of psychotropic drugs.
Visual Steering for One-Shot Deep Neural Network Synthesis

Recent advancements in the area of deep learning have shown the effectiveness of very large neural networks in several applications. However, as these deep neural networks continue to grow in size, it becomes more and more difficult to configure their many parameters to obtain good results. Presently, analysts must experiment with many different configurations and parameter settings, which is labor-intensive and time-consuming. On the other hand, the capacity of fully automated techniques for neural network architecture search is limited without the domain knowledge of human experts. To deal with the problem, we formulate the task of neural network architecture optimization as a graph space exploration, based on the one-shot architecture search technique. In this approach, a super-graph of all candidate architectures is trained in one-shot and the optimal neural network is identified as a sub-graph. In this paper, we present a framework that allows analysts to effectively build the solution sub-graph space and guide the network search by injecting their domain knowledge. Starting with the network architecture space composed of basic neural network components, analysts are empowered to effectively select the most promising components via our one-shot search scheme. Applying this technique in an iterative manner allows analysts to converge to the best performing neural network architecture for a given application. During the exploration, analysts can use their domain knowledge aided by cues provided from a scatterplot visualization of the search space to edit different components and guide the search for faster convergence. We designed our interface in collaboration with several deep learning researchers and its final effectiveness is evaluated with a user study and two case studies.
ECGDetect: Detecting Ischemia via Deep Learning

Coronary artery disease(CAD) is the most common type of heart disease and the leading cause of death worldwide[1]. A progressive state of this disease marked by plaque rupture and clot formation in the coronary arteries, also known as an acute coronary syndrome (ACS), is a condition of the heart associated with sudden, reduced blood flow caused due to partial or full occlusion of coronary vasculature that normally perfuses the myocardium and nerve bundles, compromising the proper functioning of the heart. Often manifesting with pain or tightness in the chest as the second most common cause of emergency department visits in the United States, it is imperative to detect ACS at the earliest. This is particularly relevant to diabetic patients at home, that may not feel classic chest pain symptoms, and are susceptible to silent myocardial injury. In this study, we developed the RCE- ECG-Detect algorithm, a machine learning model to detect the morphological patterns in significant ST change associated with myocardial ischemia. We developed the RCE- ECG-Detect using data from the LTST database which has a sufficiently large sample set to train a reliable model. We validated the predictive performance of the machine learning model on a holdout test set collected using RCE's ECG wearable. Our deep neural network model, equipped with convolution layers, achieves 90.31% ROC-AUC, 89.34% sensitivity, 87.81% specificity.
Fancy Man Lauches Zippo at WNUT 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity Extraction

Automatic or semi-automatic conversion of protocols specifying steps in performing a lab procedure into machine-readable format benefits biological research a lot. These noisy, dense, and domain-specific lab protocols processing draws more and more interests with the development of deep learning. This paper presents our teamwork on WNUT 2020 shared task-1: wet lab entity extract, that we conducted studies in several models, including a BiLSTM CRF model and a Bert case model which can be used to complete wet lab entity extraction. And we mainly discussed the performance differences of \textbf{Bert case} under different situations such as \emph{transformers} versions, case sensitivity that may don't get enough attention before.
Relaxing Common Belief for Social Networks

We propose a relaxation of common belief called factional belief that is suitable for the analysis of strategic coordination on social networks. We show how this definition can be used to analyze revolt games on general graphs, including by giving an efficient algorithm that characterizes a structural result about the possible equilibria of such games. This extends prior work on common knowledge and common belief, which has been too restrictive for use in understanding strategic coordination and cooperation in social network settings.
Precision Orbital Dynamics from Interstellar Scintillation Arcs for PSR J0437–4715

Intensity scintillations of radio pulsars are known to originate from interference between waves scattered by the electron density irregularities of interstellar plasma, often leading to parabolic arcs in the two-dimensional power spectrum of the recorded dynamic spectrum. The degree of arc curvature depends on the distance to the scattering plasma and its transverse velocity with respect to the line of sight. We report the observation of annual and orbital variations in the curvature of scintillation arcs over a period of 16 yr for the bright millisecond pulsar, PSR J0437−4715. These variations are the signature of the relative transverse motions of Earth, the pulsar, and the scattering medium, which we model to obtain precise measurements of parameters of the pulsar’s binary orbit and the scattering medium itself. We observe two clear scintillation arcs in most of our >5000 observations, and we show that they originate from scattering by thin screens located at distances D1 = 89.8 ± 0.4 pc and D2 = 124 ± 3 pc from Earth. The best-fit scattering model we derive for the brightest arc yields the pulsar’s orbital inclination angle, i = 137. °1 ± 0. °3, and longitude of ascending node, Ω = 206. °3 ± 0. °4. Using scintillation arcs for precise astrometry and orbital dynamics can be superior to modeling variations in the diffractive scintillation timescale, because the arc curvature is independent of variations in the level of turbulence of interstellar plasma. This technique can be used in combination with pulsar timing to determine the full three-dimensional orbital geometries of binary pulsars and provides parameters essential for testing theories of gravity and constraining neutron star masses.
Interpretable Detail-Fidelity Attention Network for Single Image Super-Resolution

Benefiting from the strong capabilities of deep CNNs for feature representation and nonlinear mapping, deep-learning-based methods have achieved excellent performance in single image super-resolution. However, most existing SR methods depend on the high capacity of networks that are initially designed for visual recognition, and rarely consider the initial intention of super-resolution for detail fidelity. To pursue this intention, there are two challenging issues that must be solved: (1) learning appropriate operators which is adaptive to the diverse characteristics of smoothes and details; (2) improving the ability of the model to preserve low-frequency smoothes and reconstruct high-frequency details. To solve these problems, we propose a purposeful and interpretable detail-fidelity attention network to progressively process these smoothes and details in a divide-and-conquer manner, which is a novel and specific prospect of image super-resolution for the purpose of improving detail fidelity. This proposed method updates the concept of blindly designing or using deep CNNs architectures for only feature representation in local receptive fields. In particular, we propose a Hessian filtering for interpretable high-profile feature representation for detail inference, along with a dilated encoder-decoder and a distribution alignment cell to improve the inferred Hessian features in a morphological manner and statistical manner respectively. Extensive experiments demonstrate that the proposed method achieves superior performance compared to the state-of-the-art methods both quantitatively and qualitatively. The code is available at github.com/YuanfeiHuang/DeFiAN.
The Secular Evolution of a Uniform Density Star Cluster Immersed in a Compressible Galactic Tidal Field

Nuclear stellar clusters are common in the center of galaxies. We consider the possibility that their progenitors assumed to be globular clusters may have formed elsewhere, migrated to, and assembled near their present location. The main challenge for this scenario is whether globular clusters can withstand the tidal field of their host galaxies. Our analysis suggests that provided the mass-density distribution of background potential is relatively shallow, as in some galaxies with relatively flat surface brightness profiles, the tidal field near the center of galaxies may be shown to be able to compress rather than disrupt a globular cluster at a distance from the center much smaller than the conventionally defined “tidal disruption radius” r t . To do so, we adopt a previously constructed formalism and consider the secular evolution of star clusters with a homogeneous mass-density distribution. We analytically solve the secular equations in the limit that the mass density of stars in the galactic center approaches a uniform distribution. Our model indicates that a star cluster could travel to distances much smaller than r t without disruption, thus potentially contributing to the formation of the nuclear cluster. However, appropriate numerical N-body simulations are needed to confirm our analytic findings.
SUPER STARCHY1/ONAC025 participates in rice grain filling

Abstract NAC transcription factors (TFs) are known for their role in development and stress. This article attempts to functionally validate the role of rice SS1/ ONAC025 (LOC_Os11g31330) during seed development. The gene is seed‐specific and its promoter directs reporter expression in the developing endosperm and embryo in rice transgenic plants. Furthermore, rice transgenic plants ectopically expressing SS1/ ONAC025 have a plantlet lethal phenotype with hampered vegetative growth, but increased tillers and an altered shoot apical meristem structure. The vegetative cells of these plantlets are filled with distinct starch granules. RNAseq analysis of two independent plantlets reveals the differential expression of reproductive and photosynthetic genes. A comparison with seed development transcriptome indicates differential regulation of many seed‐related genes by SS1/ ONAC025. Genes involved in starch biosynthesis, especially amylopectin and those encoding seed storage proteins, and regulating seed size are also differentially expressed. In conjunction, SS1/ ONAC025 shows highest expression in japonica rice. As a TF, SS1/ ONAC025 is a transcriptional repressor localized to endoplasmic reticulum and nucleus. The article shows that SS1/ ONAC025 is a seed‐specific gene promoting grain filling in rice, and negatively affecting vegetative growth.
Hierarchical Sparse Variational Autoencoder for Text Encoding

In this paper we focus on unsupervised representation learning and propose a novel framework, Hierarchical Sparse Variational Autoencoder (HSVAE), that imposes sparsity on sentence representations via direct optimisation of Evidence Lower Bound (ELBO). Our experimental results illustrate that HSVAE is flexible and adapts nicely to the underlying characteristics of the corpus which is reflected by the level of sparsity and its distributional patterns.
Clinical and Structural Outcomes After Rotator Cuff Repair in Patients With Diabetes: A Meta-analysis

Background: The impact of diabetes on clinical and structural outcomes after rotator cuff repair remains controversial. Purpose/Hypothesis: The purpose of this study was to compare clinical outcomes and retear rates after rotator cuff repair in patients with and without diabetes. Our hypotheses were that adequate control of diabetes would decrease the retear rate after rotator cuff repair and that patients with diabetes would have worse clinical outcomes. Study Design: Systematic review; Level of evidence, 3. Methods: The PubMed, Embase, and Cochrane Library databases were searched for studies comparing outcomes in patients with and without diabetes after full-thickness rotator cuff repair. Clinical outcome analysis included the Constant score, the American Shoulder and Elbow Surgeons (ASES) score, and the University of California–Los Angeles shoulder rating scale; we compared preoperative, postoperative, and change in functional scores from baseline to final follow-up among the included studies. The pooled relative risk was calculated using a random-effects model for retear rates. Clinical outcomes were also pooled using a random-effects model. Results: Overall, 10 studies were included. Compared with patients without diabetes, patients with diabetes had a worse preoperative ASES score (P = .009) as well as worse postoperative Constant score (final follow-up range, 9-103 months; P = .0003). However, there was no significant difference in the absolute mean change in clinical outcomes between patients with and without diabetes. Diabetes was associated with a higher retear rate (19.3% in patients without diabetes vs 28.2% in patients with diabetes; P < .0001). The retear rate according to the severity of sustained hyperglycemia in the subgroup analysis was 14.6% in patients without diabetes, versus 22.7% in patients with well-controlled diabetes (<7.0% of preoperative serum HbA1c level; P = .12) and 40.0% in patients with uncontrolled diabetes (HbA1c level ≥7.0%; P < .00001). Conclusion: This meta-analysis suggests that diabetes mellitus is associated with an increased risk of retears after rotator cuff repair, and improved blood glucose control may reduce the risk of retears in patients with diabetes mellitus. Although effective glycemic control was associated with a decreased risk of retears in patients with diabetes, we could not prove causation because of potential bias and confounding in the included studies.
Social Support, Everyday Discrimination, and Depressive Symptoms Among Older African Americans: A Longitudinal Study

Abstract Background and Objectives The purpose of the study was to explore the long-term effects of everyday discrimination on depressive symptoms among older African Americans, as well as the moderating role of social support in this association. Research Design and Methods Mixed-effects negative binomial regression analyses were performed on data selected from 6 waves of the Health and Retirement Study (2006–2016; baseline N = 1,144). The number of depressive symptoms was calculated based on an 8-item Center for Epidemiologic Studies Depression measure. Everyday discrimination was measured using a 6-item scale. Contact with and perceived support from extended family and friends were assessed. Results Older African Americans who experienced more frequent perceived discrimination had more depressive symptoms over time. Significant interactions between discrimination and perceived support from extended family and friends were found, indicating that among older African Americans who reported higher support from extended family and friends, perceived discrimination was positively associated with depressive symptoms over time. However, perceived discrimination and depressive symptoms were not longitudinally related among those who reported lower levels of perceived support. Discussion and Implications This is one of the few studies to examine the effects of discrimination on depressive symptoms over time and the first longitudinal study to test the role of social support in coping with discrimination in older African Americans. This study extends cross-sectional works on discrimination and mental health, indicating that experiences of discrimination can result in worse mental health over time. The significant interactions are consistent with the resource mobilization framework, which suggests that individuals who are more negatively affected by discrimination (more depressive symptoms) are more likely to reach out to friends and family to cope with discrimination.
Airborne Lead (Pb) From Abandoned Mine Waste in Northeastern Oklahoma, USA

Active and abandoned mines pose serious health threats to humans, aquatic, and terrestrial biota. Northeastern Oklahoma, home to a number of Native American Tribes, is part of the well‐known Tri‐State Mining District. More than 100 years of mining production in this area has left numerous, large chat piles in the surrounding environment. Despite numerous studies and efforts on the restoration of metal contamination in this area, no studies have attempted to distinguish the contributions of different sources, particularly from the atmospheric deposition, of metals to the aquatic environment. Here, we analyzed the atmospheric deposition of Pb from Picher, a town surrounded by chat piles, and Tulsa, a primary metropolitan area in northeastern Oklahoma, from 2010 to 2016. We found that chat piles in Picher contain ~20% and 6% of fine particles that are subjective to windborne transport and human inhalation, respectively, and these fine particles contain disproportionally high concentrations of Pb. Despite the absence of industrial and human activities, airborne Pb in Picher is 2–5 times higher than that of Tulsa. A conservative estimate showed that airborne Pb may contribute up to 10% of annual Pb mass flux to a lake 18 km away from the chat piles in Picher and probably a much higher contribution for soil and water located adjacent to Picher. Despite known limitations, our study represents the first attempt to evaluate the significance of Pb‐laden airborne particulate matter from a large‐scale abandoned mining area where the humans are particularly vulnerable to metal exposure.
Both Magnetic Resonance Imaging and Computed Tomography Are Reliable and Valid in Evaluating Cystic Osteochondral Lesions of the Talus

Background: Compared with computed tomography (CT), magnetic resonance imaging (MRI) might overestimate the condition of osteochondral lesions of the talus (OLTs) owing to subchondral bone marrow edema and the overlying cartilage defect. However, no study has compared MRI and CT directly in evaluating OLTs with subchondral cysts. Purpose: To compare the reliability and validity of MRI and CT in evaluating OLTs with subchondral cysts. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: An institutional radiology database was queried for inpatients diagnosed with OLTs with subchondral cysts who had undergone surgical treatment between May 2015 and October 2019. A total of 48 patients met the inclusion criteria. Based on our measurement method, 2 experienced observers who were blinded to the study independently measured the length, width, and depth of the cysts using MRI and CT. The classification of cystic lesions was also performed based on MRI and CT findings. Results: Interobserver reliability was almost perfect, with intraclass correlation coefficients (ICCs) ranging from 0.935 to 0.999. ICCs for intraobserver reliability ranged from 0.944 to 0.976. The mean size of cysts measured on MRI (length, 13.38 ± 4.23 mm; width, 9.28 ± 2.28 mm; depth, 11.54 ± 3.69 mm) was not significantly different to that evaluated on CT (length, 13.40 ± 4.08 mm; width, 9.25 ± 2.34 mm; depth, 11.32 ± 3.54 mm). The size of subchondral cysts was precisely estimated on both MRI and CT. The MRI classification and CT classification revealed almost perfect agreement (kappa = 0.831). Conclusion: With our measurement method, both MRI and CT were deemed to be reliable and valid in evaluating the size of subchondral cysts of OLTs, and the MRI classification was well-correlated with the CT classification. The presented measurement method and classification systems could provide more accurate information before surgery.
Phytochemical profile, cytotoxic, antioxidant, and allelopathic potentials of aqueous leaf extracts of Olea europaea

Abstract Although bioactivities of Olea europaea (OE) have been widely described, most of them were related to its methanolic extracts or its essential oils, While data related to aqueous extracts still very scarce. Thus, in this study, the phytochemical composition, the antioxidant activity, the cytotoxic potential, and the allelopathic potential of aqueous leaf extracts from two varieties of Olea europaea were investigated and compared. High‐performance liquid chromatography (HPLC) was used to identify and quantify the constituents of the tested plants, and spectrophotometric methods to evaluate antioxidant activities. The cytotoxic potential was investigated using murine oligodendrocytes (158N) while germination seeds’ test was used for allelopathic activity. HPLC analysis showed the presence of 10 phenolic compounds in both extracts. Chemlali variety showed the highest antioxidant and allelopathic activities. Regarding the cytotoxicity effect, a significant increase in cell viability was observed with both of our extracts compared to untreated cells. These results confirm that aqueous extracts from OE produce a range of substances with potential antioxidant, antifungal, and allelopathic effects without toxic effects. Thus, they could be used as an alternative of chemical compounds.