Miracle-dz/UniFAR-FTUs huggingface dataset:
clustQ: Efficient Protein Decoy Clustering Using Superposition-free Weighted Internal Distance Comparisons

Structure of a protein largely determines its functional properties. Hence, the knowledge of the protein's 3D structure is an important aspect in determining solutions to fundamental biological problems. Structure prediction algorithms generally employ clustering algorithm to select the optimal model for a target from a large number of predicted confirmations (a.k.a. decoy). Despite significant advancement in clustering-based optimal decoy selection methods, these approaches often cannot deliver high performance in terms of the time taken to cluster large number of protein structures owing to the computational cost associated with pairwise structural superpositions. Here, we propose a superposition-free approach to protein decoy clustering, called clustQ, based on weighted internal distance comparisons. Experimental results suggest that the novel weighing scheme is helpful in both reproducing the decoy-native similarity score and estimating pairwise clustering based predicted quality score in a computationally efficient manner. clustQ attains performance comparable to the state-of-the-art multi-model decoy quality estimation methods participating in the latest Critical Assessment of protein Structure Prediction (CASP) experiments irrespective of target difficulty. Moreover, clustQ predicted score offers a unique way to reliably estimate target difficulty without the knowledge of the experimental structure. clustQ is freely available at http://watson.cse. eng.auburn. edu/clustQ/.
How do Convolutional Neural Networks Learn Design?

In this paper, we aim to understand the design principles in book cover images which are carefully crafted by experts. Book covers are designed in a unique way, specific to genres which convey important information to their readers. By using Convolutional Neural Networks (CNN) to predict book genres from cover images, visual cues which distinguish genres can be highlighted and analyzed. In order to understand these visual clues contributing towards the decision of a genre, we present the application of Layer-wise Relevance Propagation (LRP) on the book cover image classification results. We use LRP to explain the pixel-wise contributions of book cover design and highlight the design elements contributing towards particular genres. In addition, with the use of state-of-the-art object and text detection methods, insights about genre-specific book cover designs are discovered.
Inhibin B suppresses anoikis resistance and migration through the transforming growth factor‐β signaling pathway in nasopharyngeal carcinoma

Inhibin B (INHBB), a heterodimer of a common α‐subunit and a βB‐subunit, is a glycoprotein belonging to the transforming growth factor‐β (TGF‐β) family. In this study, we observed INHBB expression was reduced in nasopharyngeal carcinoma (NPC) tissues compared to non‐tumor nasopharyngeal epithelium tissues, and INHBB was associated with lymph node metastasis, stage of disease, and clinical progress. Positive expression of INHBB in NPC predicted a better prognosis (overall survival, P = 0.038). However, the molecular mechanisms of INHBB have not been addressed in NPC. We induced anoikis‐resistant cells in NPC cell lines under anchorage‐independent conditions, then found epithelial‐mesenchymal transition markers changed, cell apoptosis decreased, cell cycle was modified, and invasion strengthened in anoikis‐resistant NPC cells. These anoikis‐resistant NPC cells showed decreased expression of INHBB compared with adhesion cells. Furthermore, INHBB was found to influence the above‐mentioned changes. In the anoikis‐resistant NPC cells with INHBB overexpression, apoptotic cells increased, S phase cells weakened, vimentin, matrix metallopeptidase‐9, and vascular endothelial growth factor A expression were downregulated, and E‐cadherin expression was upregulated, and vice versa in knockdown of INHBB (INHBB shRNA) anoikis‐resistant NPC cells. Diminished INHBB expression could activate the TGF‐β pathway to phosphorylate Smad2/3 and form complexes in the nucleus, which resulted in the above changes. Thus, our results revealed for the first time that INHBB could suppress anoikis resistance and migration of NPC cells by the TGF‐β signaling pathway, decrease p53 overexpression, and could serve as a potential biomarker for NPC metastasis and prognosis as well as a therapeutic application.
Seq3seq Fingerprint: Towards End-to-end Semi-supervised Deep Drug Discovery

Observing the recent progress in Deep Learning, the employment of AI is surging to accelerate drug discovery and cut R&D costs in the last few years. However, the success of deep learning is attributed to large-scale clean high-quality labeled data, which is generally unavailable in drug discovery practices. In this paper, we address this issue by proposing an end-to-end deep learning framework in a semi-supervised learning fashion. That is said, the proposed deep learning approach can utilize both labeled and unlabeled data. While labeled data is of very limited availability, the amount of available unlabeled data is generally huge. The proposed framework, named as seq3seq fingerprint, automatically learns a strong representation of each molecule in an unsupervised way from a huge training data pool containing a mixture of both unlabeled and labeled molecules. In the meantime, the representation is also adjusted to further help predictive tasks, e.g., acidity, alkalinity or solubility classification. The entire framework is trained end-to-end and simultaneously learn the representation and inference results. Extensive experiments support the superiority of the proposed framework.
Phospholipid synthesis fueled by lipid droplets drives the structural development of poliovirus replication organelles

Rapid development of complex membranous replication structures is a hallmark of picornavirus infections. However, neither the mechanisms underlying such dramatic reorganization of the cellular membrane architecture, nor the specific role of these membranes in the viral life cycle are sufficiently understood. Here we demonstrate that the cellular enzyme CCTα, responsible for the rate-limiting step in phosphatidylcholine synthesis, translocates from the nuclei to the cytoplasm upon infection and associates with the replication membranes, resulting in the rerouting of lipid synthesis from predominantly neutral lipids to phospholipids. The bulk supply of long chain fatty acids necessary to support the activated phospholipid synthesis in infected cells is provided by the hydrolysis of neutral lipids stored in lipid droplets. Such activation of phospholipid synthesis drives the massive membrane remodeling in infected cells. We also show that complex membranous scaffold of replication organelles is not essential for viral RNA replication but is required for protection of virus propagation from the cellular anti-viral response, especially during multi-cycle replication conditions. Inhibition of infection-specific phospholipid synthesis provides a new paradigm for controlling infection not by suppressing viral replication but by making it more visible to the immune system.
A novel architectureof medical image fusion based on YCbCr-DWT transform

Image fusion is one of the most modern, accurate and useful diagnostic techniques in medical imaging. Mainly, image fusion tries to offer a method for solving the problem that no system is able to integrate functional and anatomical information. Multiple image fusion of brain is very important for clinical applications. Positron Emission Tomography (PET) image indicates the brain function and Single-Photon Emission Computerized Tomography (SPECT) indicates local performance in the internal organs like heart and brain imaging. Both of these images are multi-spectral images and have a low spatial resolution. The Magnetic Resonance Imaging (MRI) image shows the brain tissue anatomy and contains no functional information. A good fusion scheme should preserve the spectral characteristics of the source multispectral image as well as the high spatial resolution characteristics of the source panchromatic image. There are many methods for image fusion but each of them has certain limitations. The studies have shown that YCbCr preserves spatial information and Discrete Wavelet Transforms (DWT) preserves spectral information without distortion. The proposed method contains the advantages of both methods and it preserves spatial and spectral information without distortion. Visual and statistical analyses show that the results of our algorithm considerably enhance the fusion quality in connection with: discrepancy, average gradient and Mutual information; compared to fusion methods including, Hue-Intensity-Saturation (HIS), YCbCr, Brovey, Laplacianpyramid, Contourlet and DWT.
Aspect-Aware Target Detection and Localization by Wireless Sensor Networks

This paper considers the active detection of a stealth target with aspect dependent reflection (e.g., submarine, aircraft, etc.) using wireless sensor networks (WSNs). When the target is detected, its localization is also of interest. Due to stringent bandwidth and energy constraints, sensor observations are quantized into few-bit data individually and then transmitted to a fusion center (FC), where a generalized likelihood ratio test (GLRT) detector is employed to achieve target detection and maximum likelihood estimation of the target location simultaneously. In this context, we first develop a GLRT detector using one-bit quantized data which is shown to outperform the typical counting rule and the detection scheme based on the scan statistic. We further propose a GLRT detector based on adaptive multi-bit quantization, where the sensor observations are more precisely quantized, and the quantized data can be efficiently transmitted to the FC. The Cramer-Rao lower bound (CRLB) of the estimate of target location is also derived for the GLRT detector. The simulation results show that the proposed GLRT detector with adaptive 2-bit quantization achieves much better performance than the GLRT based on one-bit quantization, at the cost of only a minor increase in communication overhead.
On a simple method for testing independencies in Bayesian networks

Testing independencies is a fundamental task in reasoning with Bayesian networks (BNs). In practice, d‐separation is often used for this task, since it has linear‐time complexity. However, many have had difficulties understanding d‐separation in BNs. An equivalent method that is easier to understand, called m‐separation, transforms the problem from directed separation in BNs into classical separation in undirected graphs. Two main steps of this transformation are pruning the BN and adding undirected edges.
Don't let spurious accusations of pseudoreplication limit our ability to learn from natural experiments (and other messy kinds of ecological monitoring)

Abstract Pseudoreplication is defined as the use of inferential statistics to test for treatment effects where treatments are not replicated and/or replicates are not statistically independent. It is a genuine but controversial issue in ecology particularly in the case of costly landscape‐scale manipulations, behavioral studies where ethics or other concerns may limit sample sizes, ad hoc monitoring data, and the analysis of natural experiments where chance events occur at a single site. Here key publications on the topic are reviewed to illustrate the debate that exists about the conceptual validity of pseudoreplication. A survey of ecologists and case studies of experimental design and publication issues are used to explore the extent of the problem, ecologists’ solutions, reviewers’ attitudes, and the fate of submitted manuscripts. Scientists working across a range of ecological disciplines regularly come across the problem of pseudoreplication and build solutions into their designs and analyses. These include carefully defining hypotheses and the population of interest, acknowledging the limits of statistical inference and using statistical approaches including nesting and random effects. Many ecologists face considerable challenges getting their work published if accusations of pseudoreplication are made – even if the problem has been dealt with. Many reviewers reject papers for pseudoreplication, and this occurs more often if they haven't experienced the issue themselves. The concept of pseudoreplication is being applied too dogmatically and often leads to rejection during review. There is insufficient consideration of the associated philosophical issues and potential statistical solutions. By stopping the publication of ecological studies, reviewers are slowing the pace of ecological research and limiting the scope of management case studies, natural events studies, and valuable data available to form evidence‐based solutions. Recommendations for fair and consistent treatment of pseudoreplication during writing and review are given for authors, reviewers, and editors.
Supplemental Oxygen Protects Heart Against Acute Myocardial Infarction

Myocardial infarction (MI), which occurs often due to acute ischemia followed by reflow, is associated with irreversible loss (death) of cardiomyocytes. If left untreated, MI will lead to progressive loss of viable cardiomyocytes, deterioration of cardiac function, and congestive heart failure. While supplemental oxygen therapy has long been in practice to treat acute MI, there has not been a clear scientific basis for the observed beneficial effects. Further, there is no rationale for the amount or duration of administration of supplemental oxygenation for effective therapy. The goal of the present study was to determine an optimum oxygenation protocol that can be clinically applicable for treating acute MI. Using EPR oximetry, we studied the effect of exposure to supplemental oxygen cycling (OxCy) administered by inhalation of 21–100% oxygen for brief periods (15–90 min), daily for 5 days, using a rat model of acute MI. Myocardial oxygen tension (pO2), cardiac function and pro-survival/apoptotic signaling molecules were used as markers of treatment outcome. OxCy resulted in a significant reduction of infarct size and improvement of cardiac function. An optimal condition of 30-min OxCy with 95% oxygen + 5% CO2 under normobaric conditions was found to be effective for cardioprotection.
The working set model for program behavior

Probably the most basic reason behind the absence of a general treatment of resource allocation in modern computer systems is an adequate model for program behavior. In this paper a new model is developed, the “working set model”, which enables us to decide which information is in use by a running program and which is not. Such knowledge is vital for dynamic management of paged memories. The working set of pages associated with a process, defined to be the collection of its most recently used pages, is a useful allocation concept. A proposal for an easy-to-implement allocation policy is set forth; this policy is unique, inasmuch as it blends into one decision function the heretofore independent activities of process-scheduling and memory-management.
One-Electron Reduction Potentials: Calibration of Theoretical Protocols for Morita–Baylis–Hillman Nitroaromatic Compounds in Aprotic Media

Nitroaromatic compounds—adducts of Morita–Baylis–Hillman (MBHA) reaction—have been applied in the treatment of malaria, leishmaniasis, and Chagas disease. The biological activity of these compounds is directly related to chemical reactivity in the environment, chemical structure of the compound, and reduction of the nitro group. Because of the last aspect, electrochemical methods are used to simulate the pharmacological activity of nitroaromatic compounds. In particular, previous studies have shown a correlation between the one-electron reduction potentials in aprotic medium (estimated by cyclic voltammetry) and antileishmanial activities (measured by the IC50) for a series of twelve MBHA. In the present work, two different computational protocols were calibrated to simulate the reduction potentials for this series of molecules with the aim of supporting the molecular modeling of new pharmacological compounds from the prediction of their reduction potentials. The results showed that it was possible to predict the experimental reduction potential for the calibration set with mean absolute errors of less than 25 mV (about 0.6 kcal·mol−1).
Systemic Upregulation of MTP2- and HMA2-Mediated Zn Partitioning to the Shoot Supplements Local Zn Deficiency Responses[OPEN]

The physiological Zn status of the shoot controls root transcript levels of MTP2 and HMA2, which encode membrane transport proteins and act in Zn partitioning to the shoot of Zn-deficient Arabidopsis. Low bioavailable concentrations of the micronutrient zinc (Zn) limit agricultural production on 40% of cultivated land. Here, we demonstrate that plant acclimation to Zn deficiency involves systemic regulation. Physiological Zn deficiency of Arabidopsis thaliana shoots results in increased root transcript levels of the membrane transport protein-encoding genes METAL TRANSPORT PROTEIN2 (MTP2) and HEAVY METAL ATPASE2 (HMA2), which are unresponsive to the local Zn status of roots. MTP2 and HMA2 act additively in the partitioning of Zn from roots to shoots. Chimeric GFP fusion proteins of MTP2 complement an mtp2 mutant and localize in the endoplasmic reticulum (ER) membrane of the outer cell layers from elongation to root hair zone of lateral roots. MTP2 restores Zn tolerance in a hypersensitive yeast mutant. These results are consistent with cell-to-cell movement of Zn toward the root vasculature inside the ER-luminal continuum through the desmotubules of plasmodesmata, under Zn deficiency. The previously described Zn deficiency response comprises transcriptional activation of target genes, including ZINC-REGULATED TRANSPORTER IRON-REGULATED TRANSPORTER PROTEIN genes ZIP4 and ZIP9, by the F-group bZIP transcription factors bZIP19 and bZIP23. We show that ZIP4 and ZIP9 respond to the local Zn status in both roots and shoots, in contrast to the systemic regulation identified here. Our findings are relevant for crop management and improvement toward combating human nutritional Zn deficiency that affects 30 to 50% of the world’s population.
Linear Bound for Majority Colourings of Digraphs

Given $\eta \in [0, 1]$, a colouring $C$ of $V(G)$ is an $\eta$-majority colouring if at most $\eta d^+(v)$ out-neighbours of $v$ have colour $C(v)$, for any $v \in V(G)$. We show that every digraph $G$ equipped with an assignment of lists $L$, each of size at least $k$, has a $2/k$-majority $L$-colouring. For even $k$ this is best possible, while for odd $k$ the constant $2/k$ cannot be replaced by any number less than $2/(k+1)$. This generalizes a result of Anholcer, Bosek and Grytczuk, who proved the cases $k=3$ and $k=4$ and claim a weaker result for general $k$.
H∞ Optimal Performance Design of an Unstable Plant under Bode Integral Constraint

This paper proposed the H∞ state feedback and H∞ output feedback design methods for unstable plants, which improved the original H∞ state feedback and H∞ output feedback. For the H∞ state feedback design of unstable plants, it presents the complete robustness constraint which is based on solving Riccati equation and Bode integral. For the H∞ output feedback design of unstable plants, the medium-frequency band should be considered in particular. Besides, this paper presents the method to select weight function or coefficients in the H∞ design, which employs Bode integral to optimize the H∞ design. It takes a magnetic levitation system as an example. The simulation results demonstrate that the optimal performance of perturbation suppression is obtained with the design of robustness constraint. The presented method is of benefit to the general H∞ design.
Post-transcriptional Processing of mRNA in Neurons: The Vestiges of the RNA World Drive Transcriptome Diversity

Neurons are morphologically complex cells that rely on the compartmentalization of protein expression to develop and maintain their extraordinary cytoarchitecture. This formidable task is achieved, at least in part, by targeting mRNA to subcellular compartments where they are rapidly translated. mRNA transcripts are the conveyor of genetic information from DNA to the translational machinery, however, they are also endowed with additional functions linked to both the coding sequence (open reading frame, or ORF) and the flanking 5′ and 3′ untranslated regions (UTRs), that may harbor coding-independent functions. In this review, we will highlight recent evidences supporting new coding-dependent and -independent functions of mRNA and discuss how nuclear and cytoplasmic post-transcriptional modifications of mRNA contribute to localization and translation in mammalian cells with specific emphasis on neurons. We also describe recently developed techniques that can be employed to study RNA dynamics at subcellular level in eukaryotic cells in developing and regenerating neurons.
Fractal Method for Modeling the Peculiar Dynamics of Transient Carbon Plasma Generated by Excimer Laser Ablation in Vacuum

Carbon plasmas generated by excimer laser ablation are often applied for deposition (in vacuum or under controlled atmosphere) of high-technological interest nanostructures and thin films. For specific excimer irradiation conditions, these transient plasmas can exhibit peculiar behaviors when probed by fast time- and space-resolved optical and electrical methods. We propose here a fractal approach to simulate this peculiar dynamics. In our model, the complexity of the interactions between the transient plasma particles (in the Euclidean space) is substituted by the nondifferentiability (fractality) of the motion curves of the same particles, but in a fractal space. For plane symmetry and particular boundary conditions, stationary geodesic equations at a fractal scale resolution give a fractal velocity field with components expressed by means of nonlinear solutions (soliton type, kink type, etc.). The theoretical model successfully reproduces the (surprising) formation of V-like radiating plasma structures (consisting of two lateral arms of high optical emissivity and a fast-expanding central part of low emissivity) experimentally observed.
BézierGAN: Automatic Generation of Smooth Curves from Interpretable Low-Dimensional Parameters

Many real-world objects are designed by smooth curves, especially in the domain of aerospace and ship, where aerodynamic shapes (e.g., airfoils) and hydrodynamic shapes (e.g., hulls) are designed. To facilitate the design process of those objects, we propose a deep learning based generative model that can synthesize smooth curves. The model maps a low-dimensional latent representation to a sequence of discrete points sampled from a rational B\'ezier curve. We demonstrate the performance of our method in completing both synthetic and real-world generative tasks. Results show that our method can generate diverse and realistic curves, while preserving consistent shape variation in the latent space, which is favorable for latent space design optimization or design space exploration.