Miracle-dz/UniFAR-FTUs huggingface dataset:
Activation of ATF4 triggers trabecular meshwork cell dysfunction and apoptosis in POAG

Primary open angle glaucoma (POAG) is the leading cause of irreversible blindness. Dysfunction of the trabecular meshwork (TM), resulting in decreased outflow of aqueous humor and increased intraocular pressure (IOP), plays an important role in the pathogenesis of POAG. However, the underlying mechanisms still remain unclear. In this study, we demonstrated that the eIF2-α/ATF4/CHOP branch of unfolded protein response (UPR) was activated in human trabecular meshwork cells (HTMCs) upon tert-butyl hydroperoxide (TBHP) exposure. Inhibition of ATF4 ameliorated TBHP-induced apoptosis and inflammatory cytokine production, while ectopic expression of ATF4 increased the expression of endothelial leukocyte adhesion molecule (ELAM)-1 and IL-8 in HTMCs. Furthermore, we found that ATF4 inhibition reduced tunicamycin-induced caspase-3 activation, ROS production, ELAM-1 expression, and HTMCs phagocytosis impairment. By an in vivo study in mice, we showed that overexpression of ATF4 in the TM induced C/EBP homologous protein (CHOP) expression and TM cells apoptosis, contributing to inflammatory cytokine production, and probably IOP elevation. More importantly, upregulation of ATF4 and CHOP, and colocalization of ATF4 with ELAM-1 were found in the TM of POAG patients. These results suggest that ATF4 is a critical mediator of oxidative stress and ER stress-induced TM cell dysfunction and apoptosis in POAG.
Predicting rejection attitudes toward utilizing formal mental health services in Muslim women in the US: Results from the Muslims’ perceptions and attitudes to mental health study

Background: The underutilization of mental health services is a recognized problem for the growing number of Muslims living in the West. Despite their unique mental health risk factors and the pivotal role they play in determining mental health discourse in their families and in society, Muslim women in particular have not received sufficient study. Aim: To help remedy this research gap, we examined factors that may impact the rejection attitudes of Muslim women toward professional mental health care using the first psychometrically validated scale of its kind; the M-PAMH (Muslims’ Perceptions and Attitudes to Mental Health). Methods: A total of 1,222 Muslim women responded to questions about their cultural and religious beliefs about mental health, stigma associated with mental health, and familiarity with formal mental health services in an anonymous online survey. Results: Hierarchical multiple regression analysis revealed that higher religious and cultural beliefs, higher societal stigma, and lower familiarity with professional mental health services were associated with greater rejection attitudes toward professional mental healthcare. The final model was statistically significant, F (5, 1,216) = 73.778; p < . 001, and explained 23% of the variance in rejection attitudes with stigma accounting for the most (12.3%) variance, followed by cultural and religious mental health beliefs (6%), and familiarity with mental health services (2.7%). Conclusions: Findings suggest that although the examined factors contributed significantly to the model, they may not be sufficient in the explanation of Muslim women's rejection attitudes toward mental health services. Future research may explore additional variables, as well as predictive profiles for Muslim women’s perceptions and attitudes of mental health based on a combination of these factors.
Structure-based design of small molecule and peptide inhibitors for selective targeting of ROCK1: an integrative computational approach

Abstract Rho-associated, coiled-coil-containing protein kinase (ROCK1) regulates cell contraction, morphology, and motility by phosphorylating its downstream targets. ROCK1 is a proven target for many pathological conditions like cancer, atherosclerosis, glaucoma, neuro-degeneration, etc. Though many kinase inhibitors are available, there is a dearth of studies on repurposing approved drugs and novel peptide inhibitors that could potentially target ROCK1. Hence, in this study, an extensive integration of open-source pipelines was employed to probe the potential inhibitors (ligand/peptide) for targeting ROCK1. To start with, a systematic enrichment analysis was performed to delineate the most optimal ROCK1 crystal structure that can be harnessed for drug design. A comparative analysis of conformational flexibility between monomeric and dimeric forms was also performed to prioritize the optimal assembly for structural studies. Subsequently, Virtual screening of FDA-approved drugs in Drugbank was performed using POAP pipeline. Further, the top hits were probed for binding affinity, crucial interaction fingerprints, and complex stability during MD simulation. In parallel, a combinatorial tetrapeptide library was also virtually screened against ROCK1 using the PepVis pipeline. Following which, all these shortlisted inhibitors (compounds/peptides) were subjected to Kinomerun analysis to infer other potential kinase targets. Finally, Polydatin and conivaptan were prioritized as the most potential repurposable inhibitors, and WWWF, WWVW as potential inhibitory peptides for targeting ROCK1. The prioritized inhibitors are highly promising for use in therapeutics, as these are resultants of the multilevel stringent filtration process. The computational strategies implemented in this study could potentially serve as a scaffold towards selective inhibitor design for other kinases. Communicated by Ramaswamy H. Sarma
Comprehensive evaluation of the clinical utility of plasma EGFR test in non-small cell lung cancer patients with acquired resistance to first-line EGFR inhibitors

Background Plasma epidermal growth factor receptor (EGFR) mutation tests are widely used when non-small cell lung cancer (NSCLC) patients acquire resistance to EGFR inhibitors. We comprehensively evaluated the clinical utility of plasma EGFR test. Methods We screened NSCLC patients who had a plasma EGFR test upon acquiring resistance to first- or second-generation EGFR inhibitors. Plasma EGFR tests were performed with the EGFR mutation test. Results A total of 355 patients were tested for plasma EGFR mutations, and T790M was detected in 83 patients (23%). Of 79 patients who were tested multiple times, T790M was newly detected in 13 subsequent plasma tests. When initial plasma tests did not detect any EGFR mutation types, the detection rate of T790M in subsequent tests was very low (9%, 5/56), while detection rates of T790M in subsequent tests increased (35%, 8/23) in those individuals in whom sensitizing mutations had been detected in the initial plasma test (P=0.005). Paired plasma and tissue EGFR test results were available for 235 patients. Sensitivity and specificity of the plasma tests for T790M were 14% and 87%, respectively. Among 235 patients, 140 patients had tissue EGFR tests performed after T790M-negative plasma results were reported. The subsequent tissue test detected T790M in 61% (44/72) of these patients when any EGFR mutations were not detected in prior plasma tests, while the detection rate of T790M in subsequent tissue tests was 37% (25/68) when sensitizing mutations were detected in prior plasma tests (P=0.004). Conclusions Because the sensitivity of plasma EGFR test for T790M is low, follow-up tissue or plasma tests are necessary. Presence or absence of a sensitizing mutation in the initial plasma tests can be used to determine which samples (tissue or plasma) should be submitted for further testing.
The intersection of potential energy surfaces in polyatomic molecules

It is proved that if the wave function of a given electronic state changes sign when transported adiabatically round a loop in nuclear configuration space, then the state must become degenerate with another one at some point within the loop. I t is further shown that this condition is satisfied by certain unsymmetrical triatomic systems, thereby disposing of a recent claim that the non-crossing rule for diatomic molecules applies also to polyatomic molecules.
Helios-expressing CD8+ T cells are decreased in patients with systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a chronic, systemic autoimmune disorder with multiorgan inflammation, predominantly affecting women of child-bearing age. SLE is characterized by the production of autoantibodies directed against endogenous nuclear antigens, reflecting a global loss of self-tolerance. A central factor of SLE pathogenesis is the defective clearance of immune-complexes and apoptotic debris, leading to the release of autoantigens by apoptotic cells and subsequent presentation by dendritic cells driving T cell activation. These T cells in turn activate B cells by secreting cytokines such as IL-10 and IL-23 and by cell surface molecules resulting in autoantibody production and end-organ injury. In animals engineered to have defective immune tolerance mechanisms, the development of lupus-like diseases has been documented. Functional or numerical alterations of CD4þ CD25þFoxP3þ regulatory T cells (Treg) have been linked to the pathogenesis of Lupus (and lupus-like diseases). Recently, a novel Heliosþ CD8þ regulatory T cell population has been identified and linked to the
Direct electrochemistry and electrocatalysis of glucose oxidase based poly( l -arginine)-multi-walled carbon nanotubes

This paper reports a simple and fast approach for the construction of a novel glucose biosensor based on the electro polymerization of a poly-L-arginine film (P-L-Arg) onto functionalized multiwalled carbon nanotubes (f-MWCNTs)/glassy carbon electrode (GCE) via electrostatic attraction with glycoprotein glucose oxidase (GOx). The morphological and electrochemical properties of the prepared composite were studied by scanning electron microscopy, electrochemical impedance spectroscopy, cyclic voltammetry, X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy. The prepared GOx/P-L-Arg/f-MWCNTs film modified electrode shows a well defined reversible redox couple at a formal potential of −0.45 V (vs. AgCl) with significantly enhanced peak currents corresponding to the direct electrochemistry of the GOx. The direct electron transfer process is a surface-controlled electrode process with a high heterogeneous electron transfer rate constant (ks) of 5.16 s−1. Moreover, this modified electrode was demonstrated to be an efficient glucose biosensor by means of the reductive detection of oxygen consumption. The proposed biosensor possesses excellent analytical parameters for a wide linear range of glucose from 4.0 μM to 6 mM (a correlation coefficient of R2 = 0.9918) with a 5 s response time. The sensitivity of the biosensor is found to be 48.86 μA cm−2 mM−1, its experimental detection limit is 0.1 μM (S/N = 3), the apparent Km is calculated to be 2.2 mM and it also exhibits good storage stability for 25 days. The novel biosensor developed in this study exhibits a fast response, high stability and very good reproducibility.
Quantitative systems pharmacology in neuroscience: Novel methodologies and technologies

The development and application of quantitative systems pharmacology models in neuroscience have been modest relative to other fields, such as oncology and immunology, which may reflect the complexity of the brain. Technological and methodological advancements have enhanced the quantitative understanding of brain physiology and pathophysiology and the effects of pharmacological interventions. To maximize the knowledge gained from these novel data types, pharmacometrics modelers may need to expand their toolbox to include additional mathematical and statistical frameworks. A session was held at the 10th annual American Conference on Pharmacometrics (ACoP10) to highlight several recent advancements in quantitative and systems neuroscience. In this mini‐review, we provide a brief overview of technological and methodological advancements in the neuroscience therapeutic area that were discussed during the session and how these can be leveraged with quantitative systems pharmacology modeling to enhance our understanding of neurological diseases. Microphysiological systems using human induced pluripotent stem cells (IPSCs), digital biomarkers, and large‐scale imaging offer more clinically relevant experimental datasets, enhanced granularity, and a plethora of data to potentially improve the preclinical‐to‐clinical translation of therapeutics. Network neuroscience methodologies combined with quantitative systems models of neurodegenerative disease could help bridge the gap between cellular and molecular alterations and clinical end points through the integration of information on neural connectomics. Additional topics, such as the neuroimmune system, microbiome, single‐cell transcriptomic technologies, and digital device biomarkers, are discussed in brief.
TOXICITY OF SILVER NANOPARTICLES INCREASES DURING STORAGE BECAUSE OF SLOW DISSOLUTION UNDER RELEASE OF SILVER IONS

The dissolution of citrate-stabilized and poly(vinylpyrrolidone)-stabilized silver nanoparticles in water was studied by dialysis for up to 125 days at 5, 25, and 37 °C. The particles slowly dissolve into ions on a time scale of several days. However, in all cases, a limiting value of the released silver was observed, i.e., the particles did not completely dissolve. In some cases, the nanoparticles released up to 90% of their weight. Formal kinetic data were computed. Rate and degree of dissolution depended on the functionalization as well as on the storage temperature. The release of silver led to a considerably increased toxicity of silver nanoparticles which had been stored in dispersion for several weeks toward human mesenchymal stem cells due to the increased concentration of silver ions. Consequently, “aged” (i.e., immersed) silver nanoparticles are much more toxic to cells than freshly prepared silver nanoparticles.
Age-specific association between invasively measured central blood pressure and left ventricular mass index

ABSTRACT Background: The impact of age on the association between central aortic hemodynamics and left ventricular (LV) remodeling has not been well elucidated. We compared the relationship between measurements of central blood pressure (CBP) and LV mass index (LVMI) according to their ages (<50 years versus ≥50 years). Methods: A total of 305 consecutive subjects (64.4 ± 10.9 years, 60.7% males) who underwent invasive coronary angiography (ICA) for the evaluation of coronary artery disease were prospectively enrolled. Just before ICA, CBP was measured at the aortic root using a pig-tail catheter, and CBP indices, including aortic systolic blood pressure (aSBP), aortic pulse pressure (aPP), aortic fractional pulse pressure (=aPP/mean aortic pressure), and aortic pulsatility index (=aPP/diastolic aortic pressure), were recorded. All subjects underwent transthoracic echocardiography, and LVMI was measured on the same day of ICA. Results: In simple linear correlation analyses, LVMI was associated with all CBP indices in subjects aged <50 years (n = 29) (P < .05 for each), but not in those aged ≥50 years (n = 276) (P > .05 for each). In the younger age group (≤50 years), multivariable analysis showed that aSBP (β = 0.457, P= .021) and aPP (β = 0.610, P= .006) had a significant association with LVMI after adjusting for possible confounding factors. The results remained consistent even when analyzed in a 1:1 propensity score-matched cohort. In conclusion, invasively measured aPP showed the closest association with LVMI in subjects aged <50 years, but not those aged ≥50 years. Conclusion: Aortic pulsatile hemodynamic status appears to have a greater effect on LV remodeling in younger people than in older people.
Untrained networks for compressive lensless photography

Compressive lensless imagers enable novel applications in an extremely compact device, requiring only a phase or amplitude mask placed close to the sensor. They have been demonstrated for 2D and 3D microscopy, single-shot video, and single-shot hyperspectral imaging; in each case, a compressive-sensing-based inverse problem is solved in order to recover a 3D data-cube from a 2D measurement. Typically, this is accomplished using convex optimization and hand-picked priors. Alternatively, deep learning-based reconstruction methods offer the promise of better priors, but require many thousands of ground truth training pairs, which can be difficult or impossible to acquire. In this work, we propose an unsupervised approach based on untrained networks for compressive image recovery. Our approach does not require any labeled training data, but instead uses the measurement itself to update the network weights. We demonstrate our untrained approach on lensless compressive 2D imaging, single-shot high-speed video recovery using the camera's rolling shutter, and single-shot hyperspectral imaging. We provide simulation and experimental verification, showing that our method results in improved image quality over existing methods.
Image Segmentation Methods for Non-destructive testing Applications

In this paper, we present new image segmentation methods based on hidden Markov random fields (HMRFs) and cuckoo search (CS) variants. HMRFs model the segmentation problem as a minimization of an energy function. CS algorithm is one of the recent powerful optimization techniques. Therefore, five variants of the CS algorithm are used to compute a solution. Through tests, we conduct a study to choose the CS variant with parameters that give good results (execution time and quality of segmentation). CS variants are evaluated and compared with non-destructive testing (NDT) images using a misclassification error (ME) criterion.
A two-way factor model for high-dimensional matrix data

In this article, we introduce a two-way factor model for a high-dimensional data matrix and study the properties of the maximum likelihood estimation (MLE). The proposed model assumes separable effects of row and column attributes and captures the correlation across rows and columns with low-dimensional hidden factors. The model inherits the dimension-reduction feature of classical factor models but introduces a new framework with separable row and column factors, representing the covariance or correlation structure in the data matrix. We propose a block alternating, maximizing strategy to compute the MLE of factor loadings as well as other model parameters. We discuss model identifiability, obtain consistency and the asymptotic distribution for the MLE as the numbers of rows and columns in the data matrix increase. One interesting phenomenon that we learned from our analysis is that the variance of the estimates in the two-way factor model depends on the distance of variances of row factors and column factors in a way that is not expected in classical factor analysis. We further demonstrate the performance of the proposed method through simulation and real data analysis.
Modelling Human Kinetics and Kinematics during Walking using Reinforcement Learning

In this work, we develop an automated method to generate 3D human walking motion in simulation which is comparable to real-world human motion. At the core, our work leverages the ability of deep reinforcement learning methods to learn high-dimensional motor skills while being robust to variations in the environment dynamics. Our approach iterates between policy learning and parameter identification to match the real-world bio-mechanical human data. We present a thorough evaluation of the kinematics, kinetics and ground reaction forces generated by our learned virtual human agent. We also show that the method generalizes well across human-subjects with different kinematic structure and gait-characteristics.
Online Double Oracle

Solving strategic games with huge action space is a critical yet under-explored topic in economics, operations research and artificial intelligence. This paper proposes new learning algorithms for solving two-player zero-sum normal-form games where the number of pure strategies is prohibitively large. Specifically, we combine no-regret analysis from online learning with Double Oracle (DO) methods from game theory. Our method -- \emph{Online Double Oracle (ODO)} -- is provably convergent to a Nash equilibrium (NE). Most importantly, unlike normal DO methods, ODO is \emph{rationale} in the sense that each agent in ODO can exploit strategic adversary with a regret bound of $\mathcal{O}(\sqrt{T k \log(k)})$ where $k$ is not the total number of pure strategies, but rather the size of \emph{effective strategy set} that is linearly dependent on the support size of the NE. On tens of different real-world games, ODO outperforms DO, PSRO methods, and no-regret algorithms such as Multiplicative Weight Update by a significant margin, both in terms of convergence rate to a NE and average payoff against strategic adversaries.
Ensemble Approach for Detection of Depression Using EEG Features

Depression is a public health issue that severely affects one’s well being and can cause negative social and economic effects to society. To raise awareness of these problems, this research aims at determining whether the long-lasting effects of depression can be determined from electroencephalographic (EEG) signals. The article contains an accuracy comparison for SVM, LDA, NB, kNN, and D3 binary classifiers, which were trained using linear (relative band power, alpha power variability, spectral asymmetry index) and nonlinear (Higuchi fractal dimension, Lempel–Ziv complexity, detrended fluctuation analysis) EEG features. The age- and gender-matched dataset consisted of 10 healthy subjects and 10 subjects diagnosed with depression at some point in their lifetime. Most of the proposed feature selection and classifier combinations achieved accuracy in the range of 80% to 95%, and all the models were evaluated using a 10-fold cross-validation. The results showed that the motioned EEG features used in classifying ongoing depression also work for classifying the long-lasting effects of depression.
Rapidity gap distribution in diffractive dissociation: Predictions for future electron-ion colliders

We present predictions for the distribution of rapidity gaps in realistic kinematics of future electron-ion colliders, based on numerical solutions of the original Kovchegov-Levin equation and of its next-to-leading extension taking into account the running of the strong coupling. We find that for the rapidities we have considered, the fixed and the running coupling equations lead to different distributions, rather insensitive to the chosen prescription in the running coupling case. The obtained distributions for the fixed coupling framework exhibit a shape characteristic of a recently proposed partonic picture of diffractive dissociation already at rapidities accessible at future electron-ion colliders. The modification of this shape in the running coupling case can also be understood qualitatively from that picture. Our results confirm the relevance of measurements of such observables for the microscopic understanding of diffractive dissociation in the framework of quantum chromodynamics.