Miracle-dz/UniFAR-FTUs huggingface dataset:
A human-specific, truncated α7 nicotinic receptor subunit assembles with full-length α7 and forms functional receptors with different stoichiometries

The cholinergic α7 nicotinic receptor gene, CHRNA7, encodes a subunit that forms the homopentameric α7 receptor, involved in learning and memory. In humans, exons 5–10 in CHRNA7 are duplicated and fused to the FAM7A genetic element, giving rise to the hybrid gene CHRFAM7A. Its product, dupα7, is a truncated subunit lacking part of the N-terminal extracellular ligand-binding domain and is associated with neurological disorders, including schizophrenia, and immunomodulation. We combined dupα7 expression on mammalian cells with patch clamp recordings to understand its functional role. Transfected cells expressed dupα7 protein, but they exhibited neither surface binding of the α7 antagonist α-bungarotoxin nor responses to acetylcholine (ACh) or to an allosteric agonist that binds to the conserved transmembrane region. To determine whether dupα7 assembles with α7, we generated receptors comprising α7 and dupα7 subunits, one of which was tagged with conductance substitutions that report subunit stoichiometry and monitored ACh-elicited channel openings in the presence of a positive allosteric α7 modulator. We found that α7 and dupα7 subunits co-assemble into functional heteromeric receptors, which require at least two α7 subunits for channel opening, and that dupα7's presence in the pentameric arrangement does not affect the duration of the potentiated events compared with that of α7. Using an α7 subunit mutant, we found that activation of (α7)2(dupα7)3 receptors occurs through ACh binding at the α7/α7 interfacial binding site. Our study contributes to the understanding of the modulation of α7 function by the human specific, duplicated subunit, associated with human disorders.
Learning Options in Multiobjective Reinforcement Learning

Reinforcement Learning (RL) is a successful technique to train autonomous agents. However, the classical RL methods take a long time to learn how to solve tasks. Option-based solutions can be used to accelerate learning and transfer learned behaviors across tasks by encapsulating a partial policy into an action. However, the literature report only single-agent and single-objective option-based methods, but many RL tasks, especially real-world problems, are better described through multiple objectives. We here propose a method to learn options in Multiobjective Reinforcement Learning domains in order to accelerate learning and reuse knowledge across tasks. Our initial experiments in the Goldmine Domain show that our proposal learn useful options that accelerate learning in multiobjective domains. Our next steps are to use the learned options to transfer knowledge across tasks and evaluate this method with stochastic policies.
Energy Storage Analysis of a Mixed R161/MOF-5 Nanoparticle Nanofluid Based on Molecular Simulations

The thermal properties of refrigerants can be modified by adding porous nanoparticles into them. Here, molecular simulations, including molecular dynamics and grand canonical Monte Carlo, were employed to study the thermal energy storage properties of an R161/MOF-5 nanofluid. The results show that the thermodynamic energy change of MOF-5 nanoparticles is linear to the temperature. The adsorption heat calculated by grand canonical Monte Carlo is close to that calculated by the Clausius–Clapeyron equation. Additionally, a negative enhancement of the thermal energy storage capacity of the R161/MOF-5 nanofluid is found near the phase transition area.
Interplay between ER Ca2+ Binding Proteins, STIM1 and STIM2, Is Required for Store-Operated Ca2+ Entry

Store-operated calcium entry (SOCE), a fundamentally important homeostatic and Ca2+ signaling pathway in many types of cells, is activated by the direct interaction of stromal interaction molecule 1 (STIM1), an endoplasmic reticulum (ER) Ca2+-binding protein, with Ca2+-selective Orai1 channels localized in the plasma membrane. While much is known about the regulation of SOCE by STIM1, the role of stromal interaction molecule 2 (STIM2) in SOCE remains incompletely understood. Here, using clustered regularly interspaced short palindromic repeats -CRISPR associated protein 9 (CRISPR-Cas9) genomic editing and molecular imaging, we investigated the function of STIM2 in NIH 3T3 fibroblast and αT3 cell SOCE. We found that deletion of Stim2 expression reduced SOCE by more than 90% in NIH 3T3 cells. STIM1 expression levels were unaffected in the Stim2 null cells. However, quantitative confocal fluorescence imaging demonstrated that in the absence of Stim2 expression, STIM1 did not translocate or form punctae in plasma membrane-associated ER membrane (PAM) junctions following ER Ca2+ store depletion. Fluorescence resonance energy transfer (FRET) imaging of intact, living cells revealed that the formation of STIM1 and Orai1 complexes in PAM nanodomains was significantly reduced in the Stim2 knockout cells. Our findings indicate that STIM2 plays an essential role in regulating SOCE in NIH 3T3 and αT3 cells and suggests that dynamic interplay between STIM1 and STIM2 induced by ER Ca2+ store discharge is necessary for STIM1 translocation, its interaction with Orai1, and activation of SOCE.
Testing Alignment of Node Attributes with Network Structure Through Label Propagation

Attributed network data is becoming increasingly common across fields, as we are often equipped with information about nodes in addition to their pairwise connectivity patterns. This extra information can manifest as a classification, or as a multidimensional vector of features. Recently developed methods that seek to extend community detection approaches to attributed networks have explored how to most effectively combine connectivity and attribute information to identify quality communities. These methods often rely on some assumption of the dependency relationships between attributes and connectivity. In this work, we seek to develop a statistical test to assess whether node attributes align with network connectivity. The objective is to quantitatively evaluate whether nodes with similar connectivity patterns also have similar attributes. To address this problem, we use a node sampling and label propagation approach. We apply our method to several synthetic examples that explore how network structure and attribute characteristics affect the empirical p-value computed by our method. Finally, we apply the test to a network generated from a single cell mass cytometry (CyTOF) dataset and show that our test can identify markers associated with distinct sub populations of single cells.
Symposium on Particle Methods in Fluid Mechanics High order Poisson solver for unbounded flows

This paper presents a high order method for solving the unbounded Poisson equation on a regular mesh using a Green’s function solution. The high order convergence was achieved by formulating mollified integration kernels, that were derived from a filter regularisation of the solution field. The method was implemented on a rectangular domain using fast Fourier transforms (FFT) to increase computational efficiency. The Poisson solver was extended to directly solve the derivatives of the solution. This is achieved either by including the differential operator in the integration kernel or by performing the differentiation as a multiplication of the Fourier coefficients. In this way, differential operators such as the divergence or curl of the solution field could be solved to the same high order convergence without additional computational effort. The method was applied and validated using the equations of fluid mechanics as an example, but can be used in many physical problems to solve the Poisson equation on a rectangular unbounded domain. For the two-dimensional case we propose an infinitely smooth test function which allows for arbitrary high order convergence. Using Gaussian smoothing as regularisation we document an increased convergence rate up to tenth order. The method however, can easily be extended well beyond the tenth order. To show the full extend of the method we present the special case of a spectrally ideal regularisation of the velocity formulated integration kernel, which achieves an optimal rate of convergence. ©
Uncovering universal rules governing the selectivity of the archetypal DNA glycosylase TDG

Significance The most prominent epigenetic modification in mammalian genomes is cytosine methylation at position 5 on the pyrimidine ring. Thymine DNA glycosylase (TDG) plays a central role in the pathways for 5-methyl cytosine removal and thus influences gene silencing, stem cell differentiation, and alterations in normal development. Additionally, methylation abnormalities in DNA are often observed in diseases, specifically cancer. Here we examine the mechanisms by which TDG detects, extrudes, and excises modified bases in DNA. Using path-sampling methodologies, we compute minimum free energy paths for TDG base extrusion. The computed paths reveal a unique mechanism underpinning TDG selectivity for DNA lesions or modified bases, which involves DNA sculpting, global protein dynamics, conformational gating, and specific protein–nucleic acid interactions. Thymine DNA glycosylase (TDG) is a pivotal enzyme with dual roles in both genome maintenance and epigenetic regulation. TDG is involved in cytosine demethylation at CpG sites in DNA. Here we have used molecular modeling to delineate the lesion search and DNA base interrogation mechanisms of TDG. First, we examined the capacity of TDG to interrogate not only DNA substrates with 5-carboxyl cytosine modifications but also G:T mismatches and nonmismatched (A:T) base pairs using classical and accelerated molecular dynamics. To determine the kinetics, we constructed Markov state models. Base interrogation was found to be highly stochastic and proceeded through insertion of an arginine-containing loop into the DNA minor groove to transiently disrupt Watson–Crick pairing. Next, we employed chain-of-replicas path-sampling methodologies to compute minimum free energy paths for TDG base extrusion. We identified the key intermediates imparting selectivity and determined effective free energy profiles for the lesion search and base extrusion into the TDG active site. Our results show that DNA sculpting, dynamic glycosylase interactions, and stabilizing contacts collectively provide a powerful mechanism for the detection and discrimination of modified bases and epigenetic marks in DNA.
Identification of an Anti‐Inflammation Protein, Annexin A1, in Tendon Derived Stem Cells (TDSCs) of Cystic Fibrosis Mice: A Comparative Proteomic Analysis

Purpose: A previous study reported an elevated inflammation during tendon injury in mice with cystic fibrosis (CF), indicating the inadequate management of inflammation due to dysfunction of the cystic fibrosis transmembrane conductance regulator (CFTR). The objective of this study is to identify the targets of CFTR that contribute to the abnormal inflammation during tendon injury.
A Deep Structure of Person Re-Identification Using Multi-Level Gaussian Models

Person re-identification is being widely used in the forensic, and security and surveillance system these days. However, it is still a challenging task in a real life scenario. Hence, in this work, a new feature descriptor model has been proposed using a multilayer framework of the Gaussian distribution model on pixel features, which include color moments, color space values, gradient information, and Schmid filter responses. An image of a person usually consists of distinct body regions, usually with differentiable clothing followed by local colors and texture patterns. Thus, the image is evaluated locally by dividing the image into overlapping regions. Each region is further fragmented into a set of local Gaussians on small patches. A global Gaussian encodes these local Gaussians for each region, creating a multi-level structure. Hence, the global picture of a person is described by local level information present in it, which is often ignored. Also, we have analyzed the efficiency of some existing metric learning methods on this descriptor. The performance of the descriptor is evaluated on four publicly available challenging datasets and the highest accuracy achieved on these datasets are compared with similar state-of-the-art works. It clearly demonstrates the superior performance of the proposed descriptor.
Lipoxin A4 Ameliorates Lipopolysaccharide-Induced A549 Cell Injury through Upregulation of N-myc Downstream-Regulated Gene-1

Background: Lipoxin A4 (LXA4) can alleviate lipopolysaccharide (LPS)-induced acute lung injury (ALI) and acute respiratory distress syndrome through promoting epithelial sodium channel (ENaC) expression in lung epithelial cells. However, how LXA4 promote ENaC expression is still largely elusive. The present study aimed to explore genes and signaling pathway involved in regulating ENaC expression induced by LXA4. Methods: A549 cells were incubated with LPS and LXA4, or in combination, and analyzed by quantitative real-time polymerase chain reaction (qRT-PCR) of ENaC-&agr;/&ggr;. Candidate genes affected by LXA4 were explored by transcriptome sequencing of A549 cells. The critical candidate gene was validated by qRT-PCR and Western blot analysis of A549 cells treated with LPS and LXA4 at different concentrations and time intervals. LXA4 receptor (ALX) inhibitor BOC-2 was used to test induction of candidate gene by LXA4. Candidate gene siRNA was adopted to analyze its influence on A549 viability and ENaC-&agr; expression. Phosphoinositide 3-kinase (PI3K) inhibitor LY294002 was utilized to probe whether the PI3K signaling pathway was involved in LXA4 induction of candidate gene expression. Results: The A549 cell models of ALI were constructed and subjected to transcriptome sequencing. Among candidate genes, N-myc downstream-regulated gene-1 (NDRG1) was validated by real-time-PCR and Western blot. NDRG1 mRNA was elevated in a dose-dependent manner of LXA4, whereas BOC-2 antagonized NDRG1 expression induced by LXA4. NDRG1 siRNA suppressed viability of LPS-treated A549 cells (treatment vs. control, 0.605 ± 0.063 vs. 0.878 ± 0.083, P = 0.040) and ENaC-&agr; expression (treatment vs. control, 0.458 ± 0.038 vs. 0.711 ± 0.035, P = 0.008). LY294002 inhibited NDRG1 (treatment vs. control, 0.459 ± 0.023 vs. 0.726 ± 0.020, P = 0.001) and ENaC-&agr; (treatment vs. control, 0.236 ± 0.021 vs. 0.814 ± 0.025, P < 0.001) expressions and serum- and glucocorticoid-inducible kinase 1 phosphorylation (treatment vs. control, 0.442 ± 0.024 vs. 1.046 ± 0.082, P = 0.002), indicating the PI3K signaling pathway was involved in regulating NDRG1 expression induced by LXA4. Conclusion: Our research uncovered a critical role of NDRG1 in LXA4 alleviation of LPS-induced A549 cell injury through mediating PI3K signaling to restore ENaC expression.
Advances in MCMC Methods with Applications to Particle Filtering, DSMC, and Bayesian Networks

Markov Chain Monte Carlo (MCMC) methods are a class of algorithms for sampling from a desired probability distribution. While there exist many algorithms that attempt to be somewhat universal, these algorithms can struggle for tractability in specific applications. The work in this dissertation is focused on improving MCMC methods in three application areas: Particle Filtering, Direct Simulation Monte Carlo, and Bayesian Networks. In particle filtering, the dimension of the target distribution grows as more data is obtained. As such, sequential sampling methods are necessary in order to have an efficient algorithm. In this thesis, we develop a "windowed" rejection sampling procedure to get more accurate algorithms while still preserving the necessary sequential structure. Direct Simulation Monte Carlo is a Monte Carlo algorithm for simulating rarefied gas flows. In this dissertation, we review the derivation of the Kac master equation model for 1-dimensional flows. From this, we show how the Poisson process can be exploited to construct a more accurate algorithm for simulating the Kac model. We then develop an epsilon-perfect proof of concept algorithm for the limiting velocity distribution as time goes to infinity. Bayesian Networks (BNs) are graphical models used to represent high dimensional probability distributions. There has been a great deal of interest in learning the structure of a BN from observed data. Here, we do so by walking through the space of graphs by modeling the appearance and disappearance of edges as a birth and death process. We give empirical evidence that this novel jump process approach exhibits better mixing properties than the commonly used Metropolis-Hastings algorithm.
Ergodic Coverage and Active Search in Constrained Environments

In this thesis, we explore sampling-based trajectory planning applied to the task of searching for objects of interest in constrained environments (e.g., a UAV searching for a target in the presence of obstacles). We consider two search scenarios: in the first scenario, accurate prior information distribution of the possible locations of the objects of interest is available, thus we cover this information distribution until we find the target. In the second scenario, no or crude prior information distribution exists about the locations of the objects of interest, thus we actively search for those objects by updating the information distribution. For the first search scenario we explore ergodic coverage. Ergodic coverage is an approach for trajectory planning in which a robot is directed such that the percentage of time spent in a region is in proportion to the probability of locating targets in that region. We extend the ergodic coverage algorithm to robots operating in constrained environments and present a formulation that can capture sensor footprints and avoid obstacles in the domain. We demonstrate through simulation that our formulation easily extends to coordination of multiple robots equipped with different sensing capabilities to perform ergodic coverage of a domain. For the second search scenario, we investigate palpation-based tumor search and develop an approach that guides robots to automatically localize and find the shapes of tumors and other stiff inclusions present in anatomies. Our approach uses Gaussian processes to model the stiffness distribution, and active learning integrated with sampling-based trajectory planning to direct the palpation path of the robot. Our approach provides the flexibility to avoid obstacles in the search domain, incorporate uncertainties in robot position and sensor measurements, and include prior information about the locations of stiff inclusions while respecting the robot kinematic constraints. The proposed framework is evaluated via simulation and experimentation on three different robot platforms: 6-DoF industrial arm, da Vinci Research Kit (dVRK), and the Insertable Robotic Effector
VisBIA 2018: workshop on Visual Interfaces for Big Data Environments in Industrial Applications

Industrial applications can benefit considerably from the overwhelming amount of still growing resources such as websites, images, texts, and videos that the internet offers today. The resulting Big Data Problem does not only consist of handling this immense volume of data. Moreover, data needs to be processed, cleaned, and presented in a user-friendly, intuitive, and interactive way. This workshop addresses visualization and user interaction challenges posed by the four V's: Volume (huge data amounts in the range of tera and petabytes), Velocity (the speed in which data is created, processed, and analysed), Variety (the different heterogeneous data types, sources, and formats), and Veracity (authenticity and validity of data). Big Data driven interfaces combine suitable backend and frontend technologies as well as automatic and semi-automatic approaches in order to analyze data in various business contexts. An important aspect is human intervention in developing and training data-driven applications (human in the loop). Our focus is on Visual Big Data Interfaces in industrial contexts such as e-commerce, e-learning and business intelligence. We address interfaces for three important user groups: data scientists, data workers and end users.
Three‐Dimensional Printing of Macropore Networks of an Undisturbed Soil Sample

Macropore systems predominantly determine rapid water flow and solute transport in undisturbed soils. Repeated experiments are needed to investigate the relationship between the nature of the macropore network and the resulting water and solute transport under different hydraulic initial and boundary conditions. However, the large heterogeneity in soil macropore network structures renders each soil sample unique and multiple identical samples impossible. In addition, the fragile nature of soil strongly limits the possible number of repeated experiments on one individual sample. Micromodels that mimic the precise shape and location of the macropores in undisturbed soil are therefore necessary to allow repeated experiments. In this study we investigated whether such micromodels can be obtained using contemporary three‐dimensional (3‐D) printing techniques and materials. We used X‐ray computed tomography to digitize the 3‐D macropore structure of an undisturbed soil sample. We printed a subsection of this macropore system in five different materials. Four out of the five investigated materials had essential parts of their macropore system clogged with residual printing or printing‐aid material. Only one reprint, namely the prime‐gray sample that was printed using stereo lithography, exhibited no pore clogging and had the largest hydraulic conductivity of all investigated reprints. Prime gray showed subcritical water repellency with a medium contact angle of approximately 65°, which is similar to contact angles found in natural soil. We conclude that the 3‐D printing of undisturbed soil macropore systems is in principle possible with contemporary 3‐D printing systems.
Extracting Highly Effective Features for Supervised Learning via Simultaneous Tensor Factorization

Real world data is usually generated over multiple time periods associated with multiple labels, which can be represented as multiple labeled tensor sequences. These sequences are linked together, sharing some common features while exhibiting their own unique features. Conventional tensor factorization techniques are limited to extract either common or unique features, but not both simultaneously. However, both types of these features are important in many machine learning systems as they inherently affect the systems' performance. In this paper, we propose a novel supervised tensor factorization technique which simultaneously extracts ordered common and unique features. Classification results using features extracted by our method on CIFAR-10 database achieves significantly better performance over other factorization methods, illustrating the effectiveness of the proposed technique.
Learning Sparse Task Relations in Multi-Task Learning

In multi-task learning, when the number of tasks is large, pairwise task relations exhibit sparse patterns since usually a task cannot be helpful to all of the other tasks and moreover, sparse task relations can reduce the risk of overfitting compared with the dense ones. In this paper, we focus on learning sparse task relations. Based on a regularization framework which can learn task relations among multiple tasks, we propose a SParse covAriance based mulTi-taSk (SPATS) model to learn a sparse covariance by using the ℓl regularization. The resulting objective function of the SPATS method is convex, which allows us to devise an alternating method to solve it. Moreover, some theoretical properties of the proposed model are studied. Experiments on synthetic and real-world datasets demonstrate the effectiveness of the proposed method.