Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative docking analysis of cholesterol analogs to ion channels to discriminate between stereospecific binding vs. stereospecific response

ABSTRACT Cholesterol is a major component of the membrane and a key regulator of many ion channels. Multiple studies showed that cholesterol regulates ion channels in a stereospecific manner, with cholesterol but not its chiral isomers having a functional effect. This stereospecificity has been universally attributed to the specificity of cholesterol binding, with the assumption that only native cholesterol binds to the channels whereas its isomers do not. In this study, we challenge this paradigm by docking analyses of cholesterol and its chiral isomers to five ion channels whose response to cholesterol was shown to be stereospecific, Kir2.2, KirBac1.1, TRPV1, GABAA and BK. The analysis is performed using AutoDock Vina to predict the binding poses and energies of the sterols to the channels and identify amino acids interacting with the sterol molecules. We found that for every ion channel tested herein all three sterols showed similar binding poses and significant overlap in the set of the amino acids that comprise the predicted binding sites, along with similar energetic favorability to these overlapping sites. We also found, however, that specific orientations of the three sterols within the binding sites of the channels are distinct, so that a subset of the interacting amino acids is unique to each sterol. We propose therefore, that contrary to previous thought, stereospecific effects of cholesterol should be attributed not to the lack of binding of the stereoisomers but to specific, unique interactions between the cholesterol molecule and the residues within the binding sites of the channels.
Baculovirus PTP2 Functions as a Pro-Apoptotic Protein

The family Baculoviridae encompasses a large number of invertebrate viruses, mainly infecting caterpillars of the order Lepidoptera. The baculovirus Spodoptera exigua multiple nucleopolyhedrovirus (SeMNPV) induces physiological and behavioral changes in its host Spodoptera exigua, as well as immunological responses, which may affect virus transmission. Here we show that the SeMNPV-encoded protein tyrosine phosphatase 2 (PTP2) induces mild apoptosis in Spodoptera frugiperda (Sf) 21 cells upon transient expression. Transient expression of a catalytic-site mutant of ptp2 did not lead to apoptosis, indicating that the phosphatase activity of PTP2 is needed to induce apoptosis. We also found that the caspase level (indicator of apoptosis) was higher in cells transfected with the ptp2 gene than in cells transfected with the catalytic mutant. Adding a caspase inhibitor reduced the level of ptp2-induced apoptosis. Moreover, deletion of the ptp2 gene from the viral genome prevented the induction of apoptosis in S. exigua hemocytes. The virus titer and virulence indices (the viral infectivity and the time to death) were not affected by deletion of the ptp2 gene. However, the viral occlusion body yield from S. exigua larvae infected with the mutant virus lacking the ptp2 gene was much lower than the yield from larvae infected with the wild-type (WT) virus. We hypothesize that the observed pro-apoptotic effects of PTP2 are the result of PTP2-mediated immune suppression in larvae, which consequently leads to higher viral occlusion body yields.
Accurate MapReduce Algorithms for $k$-median and $k$-means in General Metric Spaces

Center-based clustering is a fundamental primitive for data analysis and becomes very challenging for large datasets. In this paper, we focus on the popular $k$-median and $k$-means variants which, given a set $P$ of points from a metric space and a parameter $k<|P|$, require to identify a set $S$ of $k$ centers minimizing, respectively, the sum of the distances and of the squared distances of all points in $P$ from their closest centers. Our specific focus is on general metric spaces, for which it is reasonable to require that the centers belong to the input set (i.e., $S \subseteq P$). We present coreset-based 3-round distributed approximation algorithms for the above problems using the MapReduce computational model. The algorithms are rather simple and obliviously adapt to the intrinsic complexity of the dataset, captured by the doubling dimension $D$ of the metric space. Remarkably, the algorithms attain approximation ratios that can be made arbitrarily close to those achievable by the best known polynomial-time sequential approximations, and they are very space efficient for small $D$, requiring local memory sizes substantially sublinear in the input size. To the best of our knowledge, no previous distributed approaches were able to attain similar quality-performance guarantees in general metric spaces.
Semantic Matching of Documents from Heterogeneous Collections: A Simple and Transparent Method for Practical Applications

We present a very simple, unsupervised method for the pairwise matching of documents from heterogeneous collections. We demonstrate our method with the Concept-Project matching task, which is a binary classification task involving pairs of documents from heterogeneous collections. Although our method only employs standard resources without any domain- or task-specific modifications, it clearly outperforms the more complex system of the original authors. In addition, our method is transparent, because it provides explicit information about how a similarity score was computed, and efficient, because it is based on the aggregation of (pre-computable) word-level similarities.
Attribute Guided Unpaired Image-to-Image Translation with Semi-supervised Learning

Unpaired Image-to-Image Translation (UIT) focuses on translating images among different domains by using unpaired data, which has received increasing research focus due to its practical usage. However, existing UIT schemes defect in the need of supervised training, as well as the lack of encoding domain information. In this paper, we propose an Attribute Guided UIT model termed AGUIT to tackle these two challenges. AGUIT considers multi-modal and multi-domain tasks of UIT jointly with a novel semi-supervised setting, which also merits in representation disentanglement and fine control of outputs. Especially, AGUIT benefits from two-fold: (1) It adopts a novel semi-supervised learning process by translating attributes of labeled data to unlabeled data, and then reconstructing the unlabeled data by a cycle consistency operation. (2) It decomposes image representation into domain-invariant content code and domain-specific style code. The redesigned style code embeds image style into two variables drawn from standard Gaussian distribution and the distribution of domain label, which facilitates the fine control of translation due to the continuity of both variables. Finally, we introduce a new challenge, i.e., disentangled transfer, for UIT models, which adopts the disentangled representation to translate data less related with the training set. Extensive experiments demonstrate the capacity of AGUIT over existing state-of-the-art models.
Stability of stochastic impulsive differential equations: integrating the cyber and the physical of stochastic systems

According to Newton's second law of motion, we humans describe a dynamical system with a differential equation, which is naturally discretized into a difference equation whenever a computer is used. The differential equation is the continuous-time model in human brains and the difference equation the disceret-time model in computers for the dynamical system. This paper formulates a hybrid model with impulsive differential equations for the dynamical system, which integrates its continuous-time model in human brains and its discrete-time counterpart in computers. The presented results establish a theoretic foundation for the scientific study of control and communication in the animal/human and the machine (Norbert Wiener) in the era of rise of the machines.
Selective inhibition of intestinal guanosine 3′,5′-cyclic monophosphate signaling by small-molecule protein kinase inhibitors

The guanosine 3′,5′-cyclic monophosphate (cGMP)-dependent protein kinase II (cGKII) serine/threonine kinase relays signaling through guanylyl cyclase C (GCC) to control intestinal fluid homeostasis. Here, we report the discovery of small-molecule inhibitors of cGKII. These inhibitors were imidazole-aminopyrimidines, which blocked recombinant human cGKII at submicromolar concentrations but exhibited comparatively little activity toward the phylogenetically related protein kinases cGKI and cAMP-dependent protein kinase (PKA). Whereas aminopyrimidyl motifs are common in protein kinase inhibitors, molecular modeling of these imidazole-aminopyrimidines in the ATP-binding pocket of cGKII indicated an unconventional binding mode that directs their amine substituent into a narrow pocket delineated by hydrophobic residues of the hinge and the αC-helix. Crucially, this set of residues included the Leu-530 gatekeeper, which is not conserved in cGKI and PKA. In intestinal organoids, these compounds blocked cGKII-dependent phosphorylation of the vasodilator-stimulated phosphoprotein (VASP). In mouse small intestinal tissue, cGKII inhibition significantly attenuated the anion secretory response provoked by the GCC-activating bacterial heat-stable toxin (STa), a frequent cause of infectious secretory diarrhea. In contrast, both PKA-dependent VASP phosphorylation and intestinal anion secretion were unaffected by treatment with these compounds, whereas experiments with T84 cells indicated that they weakly inhibit the activity of cAMP-hydrolyzing phosphodiesterases. As these protein kinase inhibitors are the first to display selective inhibition of cGKII, they may expedite research on cGMP signaling and may aid future development of therapeutics for managing diarrheal disease and other pathogenic syndromes that involve cGKII.
Atypical clinical features associated with mixed pathology in a case of non-fluent variant primary progressive aphasia

ABSTRACT A 66-year-old woman presented with agrammatism and apraxia of speech, meeting criteria for non-fluent/agrammatic variant primary progressive aphasia (nfvPPA). However, three years later, she developed frontal/executive, short-term phonological memory, visuospatial, and visual memory deficits suggesting involvement of multiple brain networks. Multimodal neuroimaging showed damage of both fronto-striatal and posterior brain regions. She was found to have multiple pathological processes: corticobasal degeneration (CBD), Alzheimer’s disease (AD), and TAR DNA-binding protein (TDP)-43 type A. We hypothesize that cognitive and neuroimaging findings consistent with damage to multiple brain networks, each associated with vulnerability to certain molecular disease subtypes, could indicate mixed pathology.
Quantifying the magnitude and potential influence of missing data in campus sexual assault surveys: A systematic review of surveys, 2010–2016

ABSTRACT Objective: To understand how missing data may influence conclusions drawn from campus sexual assault surveys. Methods: We systematically reviewed 40 surveys from 2010–2016. We constructed a pseudo-population of the total population targeted across schools, creating records proportional to the respective response rate and reported sexual assault prevalence. We simulated the effects of 9 scenarios where the sexual assault prevalence among nonresponders differed from that of responders. Results: The surveys represented a total female undergraduate population of 317,387 with only 77,966 (24.6%) survey responses. Among responders, 20.4% reported experiences of sexual assault. However, prevalence of sexual assault could theoretically range from 5.0 to 80.4% under extreme assumptions about prevalence in nonresponders. Smaller, but still significant differences were observed with less extreme assumptions. Conclusions: Missing data are widespread in campus sexual assault surveys. Conclusions drawn from these incomplete data are highly sensitive to assumptions about the sexual assault prevalence among nonresponders.
Quantization of massive spinor fields around global monopoles

The renormalized quantum stress energy tensor $\left _{ren}$ for a massive spinor field around global monopoles is constructed within the framework of Schwinger-DeWitt approximation, valid whenever the Compton length of the quantum field is much less than the characteristic radius of the curvature of the background geometry. The results obtained shows that the quantum massive spinor field in the global monopole spacetime violates all the pointwise energy conditions.
Data-efficient Analytics for Optimal Human-Cyber-Physical Systems

Author(s): Jin, Ming | Advisor(s): Spanos, Costas | Abstract: The goal of this research is to enable optimal human-cyber-physical systems (h-CPS) by data-efficient analytics. The capacities of societal-scale infrastructures such as smart buildings and power grids are rapidly increasing, becoming physical systems capable of cyber computation that can deliver human-centric services while enhancing efficiency and resilience. Because people are central to h-CPS, the first part of this thesis is dedicated to learning about the human factors, including both human behaviors and preferences. To address the central challenge of data scarcity, we propose physics-inspired sensing by proxy and a framework of "weak supervision" to leverage high-level heuristics from domain knowledge. To infer human preferences, our key insight is to learn a functional abstraction that can rationalize people's behaviors. Drawing on this insight, we develop an inverse game theory framework that determines people's utility functions by observing how they interact with one another in a social game to conserve energy. We further propose deep Bayesian inverse reinforcement learning, which simultaneously learns a motivator representation to expand the capacity of modeling complex rewards and rationalizes an agent's sequence of actions to infer its long-term goals. Enabled by this contextual awareness of the human, cyber, and physical states, we introduce methods to analyze and enhance system-level efficiency and resilience. We propose an energy retail model that enables distributed energy resource utilization and that exploits demand-side flexibility. The synergy that naturally emerges from integrated optimization of thermal and electrical energy provision substantially improves efficiency and economy. While data empowers the aforementioned h-CPS learning and control, malicious attacks can pose major security threats. The cyber resilience of power system state estimation is analyzed. The envisioning process naturally leads to a power grid resilience metric to guide "grid hardening. " While the methods introduced in the thesis can be applied to many h-CPS systems, this thesis focuses primarily on the implications for smart buildings and smart grid.
tsmp: An R Package for Time Series with Matrix Profile

This article describes tsmp, an R package that implements the matrix profile concept for time series. The tsmp package is a toolkit that allows all-pairs similarity joins, motif, discords and chains discovery, semantic segmentation, etc. Here we describe how the tsmp package may be used by showing some of the use-cases from the original articles and evaluate the algorithm speed in the R environment. This package can be downloaded at this https URL.
A Plant Extract of Ribes nigrum folium Possesses Anti-Influenza Virus Activity In Vitro and In Vivo by Preventing Virus Entry to Host Cells

Infections with influenza A viruses (IAV) are still amongst the major causes of highly contagious severe respiratory diseases not only bearing a devastating effect to human health, but also significantly impact the economy. Besides vaccination that represents the best option to protect from IAV infections, only two classes of anti-influenza drugs, inhibitors of the M2 ion channel and the neuraminidase, often causing resistant IAV variants have been approved. That is why the need for effective and amply available antivirals against IAV is of high priority. Here we introduce LADANIA067 from the leaves of the wild black currant (Ribes nigrum folium) as a potent compound against IAV infections in vitro and in vivo. LADANIA067 treatment resulted in a reduction of progeny virus titers in cell cultures infected with prototype avian and human influenza virus strains of different subtypes. At the effective dose of 100 µg/ml the extract did not exhibit apparent harming effects on cell viability, metabolism or proliferation. Further, viruses showed no tendency to develop resistance to LADANIA067 when compared to amantadine that resulted in the generation of resistant variants after only a few passages. On a molecular basis the protective effect of LADANIA067 appears to be mainly due to interference with virus internalisation. In the mouse infection model LADANIA067 treatment reduces progeny virus titers in the lung upon intranasal application. In conclusion, an extract from the leaves of the wild black currant might be a promising source for the development of new antiviral compounds to fight IAV infections.
Generating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis

In this article, we present an open source neuroinformatics platform for exploring, analyzing, and validating distilled graphical representations of high-dimensional neuroimaging data extracted using topological data analysis (TDA). TDA techniques like Mapper have been recently applied to examine the brain’s dynamical organization during ongoing cognition without averaging data in space, in time, or across participants at the outset. Such TDA-based approaches mark an important deviation from standard neuroimaging analyses by distilling complex high-dimensional neuroimaging data into simple—yet neurophysiologically valid and behaviorally relevant—representations that can be interactively explored at the single-participant level. To facilitate wider use of such techniques within neuroimaging and general neuroscience communities, our work provides several tools for visualizing, interacting with, and grounding TDA-generated graphical representations in neurophysiology. Through Python-based Jupyter notebooks and open datasets, we provide a platform to assess and visualize different intermittent stages of Mapper and examine the influence of Mapper parameters on the generated representations. We hope this platform could enable researchers and clinicians alike to explore topological representations of neuroimaging data and generate biological insights underlying complex mental disorders.Author SummaryTo develop biologically grounded psychiatric diagnosis, researchers and clinicians need tools for distilling complex high-dimensional neuroimaging data into simple yet interactive and clinically relevant representations. Further, for translational outcomes, these representations should be conceivable at the single-participant level. Topological data analysis techniques such as Mapper allow generation of these representations. Here, we introduce a set of tools that can facilitate wider acceptance of Mapper within the neuroscience community and provide a series of easy-to-follow steps for visualizing Mapper-generated graphical representations. We provide detailed examples to reveal the “under-the-hood” workings of Mapper.
Patterns of Dual-Specific Phosphatase 4 mRNA Expression Before and after Neoadjuvant Chemotherapy in Breast Cancer

Objective: Evaluation of the neoadjuvant chemotherapy response can be performed by comparing the breast cancer burden and pathobiology before and after treatment. This study was aimed to investigate the pattern of dual-specific phosphatase 4 (DUSP4) mRNA expression in breast cancer cells before and after neoadjuvant chemotherapy. Methods: This was a longitudinal study. Twenty samples of matched breast cancer tissue taken from biopsy before and after chemotherapy were subjected to qRT-PCR to detect DUSP4 mRNA expression. Results: The mean value of DUSP4 mRNA expression in prechemotherapy breast cancer patients was 9.906±0.333 and that in breast cancer patients postchemotherapy was 10.016±1.062. In the responsive group, the rate of DUSP4 mRNA expression increased by 0.476 after chemotherapy. In the nonresponsive group, the proportion of DUSP4 mRNA expression likely decreased by 1.012. Statistical analysis found no significant correlation between DUSP4 mRNA expression prechemotherapy and the clinical chemotherapeutic response with p-value = 0.994 (p ≥0.05). A significant correlation was found between the postchemotherapy DUSP4 mRNA expression and the clinical chemotherapeutic response with p-value = 0.003 (p < 0.5). Conclusion: No significant difference was found in the mRNA expression of DUSP4 in pre- and post-neoadjuvant chemotherapy specimens. High DUSP4 expression postchemotherapy shows a substantial correlation with the chemotherapeutic response.
A Novel Dual-Lidar Calibration Algorithm Using Planar Surfaces

Multiple lidars are used on mobile vehicles for rendering a broad view to enhance the performance of perception systems. However, precise calibration of multiple lidars is challenging since the feature correspondences in scan points are sparse for providing enough constraints. To address this problem, existing methods require fixed calibration targets in scenes or rely exclusively on additional sensors. In this paper, we present a novel method that enables automatic lidar calibration without these restrictions. Three linearly independent planar surfaces appearing in surroundings is utilized to find correspondences. Two components are developed to ensure the extrinsic parameters to be found: a closed-form solver for initialization and an optimizer for refinement by minimizing a nonlinear cost function. Simulation and experimental results demonstrate the accuracy of our calibration approach with the rotation and translation errors smaller than 0.05rad and 0.1m respectively.
Donors and Founders on Charter School Boards and Their Impact on Financial and Academic Outcomes

This study provides the first systematic analysis of the composition of charter school governing boards. We assemble a dataset of charter school boards in Massachusetts from 2001 to 2013 and investigate the consequences of donor and founder representation on governing boards. We find that the presence of donors on the charter school boards is positively related to financial performance and attribute this result to the donors' strong monitoring incentives because of their financial stakes in the school. We also show that financial outcomes are not generated at the expense of academic outcomes, as the presence of donors on the boards is also associated with higher student achievement. Founder representation on charter school boards, on the other hand, is associated with lower financial performance but higher academic achievement.