Miracle-dz/UniFAR-FTUs huggingface dataset:
Sulfite Oxidase Catalyzes Single-Electron Transfer at Molybdenum Domain to Reduce Nitrite to Nitric Oxide.

AIMS Recent studies suggest that the molybdenum enzymes xanthine oxidase, aldehyde oxidase, and mARC exhibit nitrite reductase activity at low oxygen pressures. However, inhibition studies of xanthine oxidase in humans have failed to block nitrite-dependent changes in blood flow, leading to continued exploration for other candidate nitrite reductases. Another physiologically important molybdenum enzyme—sulfite oxidase (SO)—has not been extensively studied. RESULTS Using gas-phase nitric oxide (NO) detection and physiological concentrations of nitrite, SO functions as nitrite reductase in the presence of a one-electron donor, exhibiting redox coupling of substrate oxidation and nitrite reduction to form NO. With sulfite, the physiological substrate, SO only facilitates one turnover of nitrite reduction. Studies with recombinant heme and molybdenum domains of SO indicate that nitrite reduction occurs at the molybdenum center via coupled oxidation of Mo(IV) to Mo(V). Reaction rates of nitrite to NO decreased in the presence of a functional heme domain, mediated by steric and redox effects of this domain. Using knockdown of all molybdopterin enzymes and SO in fibroblasts isolated from patients with genetic deficiencies of molybdenum cofactor and SO, respectively, SO was found to significantly contribute to hypoxic nitrite signaling as demonstrated by activation of the canonical NO-sGC-cGMP pathway. INNOVATION Nitrite binds to and is reduced at the molybdenum site of mammalian SO, which may be allosterically regulated by heme and molybdenum domain interactions, and contributes to the mammalian nitrate-nitrite-NO signaling pathway in human fibroblasts. CONCLUSION SO is a putative mammalian nitrite reductase, catalyzing nitrite reduction at the Mo(IV) center.
Traffic Sign Detection Based on Faster R-CNN in Scene Graph

The use of intelligent detection and identification software for traffic signs have been an indispensable part of the advancement of transportation systems and networked cars into an intelligent system. As neural networks become more effective in image recognition and classification testing, they are being applied through the use of traffic sign detection and recognition, gradually deepening the quality of research. Founded on the thoughts of deep learning and transfer learning, this paper uses the method of Faster Region-based Convolutional Neural Networks (Faster R-CNN) and the pre-trained neural network model, Alex net, to detect traffic signs in scene graph. Building off the positive reviews of the Alex Net neural network model in image classification recognition, a target recognition neural network model was trained in the framework of Faster R-CNN neural network using scene image data sets. According to the mark on the test data set, the results indicate that using the pre-trained neural network model can quickly build a traffic sign detection model.
Análisis genético de marcadores microsatélites en dos poblaciones de la raza bovina Berrenda en Negro

La raza Bovina Berrenda en Negro se ha utilizado clasicamente en el manejo del toro de lidia y actualmente se encuentra proxima a su extincion. Para contribuir a los planes de recuperacion genetica de esta raza, se caracteriza una muestra de 32 animales con un panel de 15 microsatelites y se proporcionan datos que permitan, en un futuro, comparar su variabilidad con otras razas bovinas.
UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization

In this paper, we study an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) architecture, in which a UAV roaming around the area may serve as a computing server to help user equipment (UEs) compute their tasks or act as a relay for further offloading their computation tasks to the access point (AP). We aim to minimize the weighted sum energy consumption of the UAV and UEs subject to the task constraints, the information-causality constraints, the bandwidth allocation constraints and the UAV’s trajectory constraints. The required optimization is nonconvex, and an alternating optimization algorithm is proposed to jointly optimize the computation resource scheduling, bandwidth allocation, and the UAV’s trajectory in an iterative fashion. The numerical results demonstrate that significant performance gain is obtained over conventional methods. Also, the advantages of the proposed algorithm are more prominent when handling computation-intensive latency-critical tasks.
Osteoprotegerin polymorphisms in Chinese Han patients with rheumatoid arthritis.

Abstract In order to investigate the association between osteoprotegerin (OPG) gene polymorphisms and rheumatoid arthritis (RA), we studied OPG rs3102735 T/C and rs2073618 G/C polymorphisms in a Chinese Han population comprising 574 patients with RA and 804 controls. Genotyping by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) was conducted. Our data indicated that OPG rs3102735 T/C and rs2073618 G/C polymorphisms were not associated with the risk of RA. However, among older patients (≥55 years), patients with the OPG rs3102735 TC (TC vs TT: OR = 0.68, 95%CI = 0.49‑0.96, P = 0.029) and TC/CC (TC+CC vs TT: OR = 0.69, 95%CI = 0.49‑0.96, P = 0.026) genotypes showed a significantly lower risk of RA than patients with the TT genotype, while patients with the OPG rs2073618 GC (GC vs GG: OR = 1.53, 95%CI = 1.13‑2.07, P = 0.006) and GC/CC (GC+CC vs GG: OR = 1.43, 95%CI = 1.07‑1.92, P = 0.015) genotypes showed a significantly higher risk of RA than patients with the GG genotype. We also found a significantly increased risk of RA associated with the OPG rs2073618 GC (GC vs GG: OR = 1.44, 95%CI = 1.07‑1.93, P = 0.018) and GC/CC (GC+CC vs GG: OR = 1.39, 95%CI = 1.04‑1.86, P = 0.024) genotypes among functional class III+IV patients. Our results were obtained from only a moderate-sized sample and, thus, a larger study with a more diverse ethnic population is needed to confirm these results.
Migration through Machine Learning Lens - Predicting Sexual and Reproductive Health Vulnerability of Young Migrants

In this paper, we have discussed initial findings and results of our experiment to predict sexual and reproductive health vulnerabilities of migrants in a data-constrained environment. Notwithstanding the limited research and data about migrants and migration cities, we propose a solution that simultaneously focuses on data gathering from migrants, augmenting awareness of the migrants to reduce mishaps, and setting up a mechanism to present insights to the key stakeholders in migration to act upon. We have designed a webapp for the stakeholders involved in migration: migrants, who would participate in data gathering process and can also use the app for getting to know safety and awareness tips based on analysis of the data received; public health workers, who would have an access to the database of migrants on the app; policy makers, who would have a greater understanding of the ground reality, and of the patterns of migration through machine-learned analysis. Finally, we have experimented with different machine learning models on an artificially curated dataset. We have shown, through experiments, how machine learning can assist in predicting the migrants at risk and can also help in identifying the critical factors that make migration dangerous for migrants. The results for identifying vulnerable migrants through machine learning algorithms are statistically significant at an alpha of 0.05.
Multi-hop Question Answering via Reasoning Chains

Multi-hop question answering requires models to gather information from different parts of a text to answer a question. Most current approaches learn to address this task in an end-to-end way with neural networks, without maintaining an explicit representation of the reasoning process. We propose a method to extract a discrete reasoning chain over the text, which consists of a series of sentences leading to the answer. We then feed the extracted chains to a BERT-based QA model to do final answer prediction. Critically, we do not rely on gold annotated chains or "supporting facts:" at training time, we derive pseudogold reasoning chains using heuristics based on named entity recognition and coreference resolution. Nor do we rely on these annotations at test time, as our model learns to extract chains from raw text alone. We test our approach on two recently proposed large multi-hop question answering datasets: WikiHop and HotpotQA, and achieve state-of-art performance on WikiHop and strong performance on HotpotQA. Our analysis shows the properties of chains that are crucial for high performance: in particular, modeling extraction sequentially is important, as is dealing with each candidate sentence in a context-aware way. Furthermore, human evaluation shows that our extracted chains allow humans to give answers with high confidence, indicating that these are a strong intermediate abstraction for this task.
Nonparametric principal subspace regression

In scientific applications, multivariate observations often come in tandem with temporal or spatial covariates, with which the underlying signals vary smoothly. The standard approaches such as principal component analysis and factor analysis neglect the smoothness of the data, while multivariate linear or nonparametric regression fail to leverage the correlation information among multivariate response variables. We propose a novel approach named nonparametric principal subspace regression to overcome these issues. By decoupling the model discrepancy, a simple and general two-step framework is introduced, which leaves much flexibility in choice of model fitting. We establish theoretical property of the general framework, and offer implementation procedures that fulfill requirements and enjoy the theoretical guarantee. We demonstrate the favorable finite-sample performance of the proposed method through simulations and a real data application from an electroencephalogram study.
Arabic stemmers and their effectiveness on the information retrieval system

Arabic Stemmers and tbeir Effectiveness on tbe Information Retrieval System by Rania Elkhoury Dr. Kazem Taghva, Examination Committee Chair Professor o f Computer Science University o f Nevada, Las Vegas Arabic is a Semitic language that has a complex morphology. Therefore, using a stemmer algorithm in an information retrieval system is almost always beneficial. An Arabic stemmer has been implemented and included in the information retrieval system developed at the Information Science Research Institute at the University o f Nevada Las Vegas. The Arabic stemmer is written in the Ruby Language and removes affixes then matches the remaining word against patterns of the same length. The retrieval experiment uses the TREC collection which consists o f over a m illion documents. We w ill test the effectiveness o f the Arabic stemmer using recall/precision measurement and compare the result to other stemmers.
Estimating Unknown Cycles in Geophysical Data

Examples of cyclic (periodic) behavior in geophysical data abound. In many cases the primary period is known, such as in daily measurements of rain, temperature, and sea level. However, many time series of measurements contain cycles of unknown or varying length. We consider the problem of estimating the unknown period in a time series. We review the basic methods, compare their performance through a simulation study using observed sea level data, apply them to an astronomical data set, and discuss generalizations of the methods.
Clear Grasp: 3D Shape Estimation of Transparent Objects for Manipulation

Transparent objects are a common part of everyday life, yet they possess unique visual properties that make them incredibly difficult for standard 3D sensors to produce accurate depth estimates for. In many cases, they often appear as noisy or distorted approximations of the surfaces that lie behind them. To address these challenges, we present ClearGrasp – a deep learning approach for estimating accurate 3D geometry of transparent objects from a single RGB-D image for robotic manipulation. Given a single RGB-D image of transparent objects, ClearGrasp uses deep convolutional networks to infer surface normals, masks of transparent surfaces, and occlusion boundaries. It then uses these outputs to refine the initial depth estimates for all transparent surfaces in the scene. To train and test ClearGrasp, we construct a large-scale synthetic dataset of over 50,000 RGB-D images, as well as a real-world test benchmark with 286 RGB-D images of transparent objects and their ground truth geometries. The experiments demonstrate that ClearGrasp is substantially better than monocular depth estimation baselines and is capable of generalizing to real-world images and novel objects. We also demonstrate that ClearGrasp can be applied out-of-the-box to improve grasping algorithms’ performance on transparent objects. Code, data, and benchmarks will be released. Supplementary materials: https://sites.google. com/view/cleargrasp1
Transfer Brain MRI Tumor Segmentation Models Across Modalities with Adversarial Networks

In this work, we present an approach to brain cancer segmentation in Magnetic Resonance Images (MRI) using Adversarial Networks, that have been successfully applied to several complex image processing problems in recent years. Most of the segmentation approaches presented in the literature exploit the data from all the contrast modalities typically acquired in the clinical practice: T1-weighted, T1-weighted contrast-enhanced, T2-weighted, and T2-FLAIR. Unfortunately, often not all these modalities are available for each patient. Accordingly, in this paper, we extended a previous segmentation approach based on Adversarial Networks to deal with this issue. In particular, we trained a segmentation model for each modality at once and evaluated the performances of these models. Thus, we investigated the possibility of transferring the best among these single-modality models to the other modalities. Our results suggest that such a transfer learning approach allows achieving better performances for almost all the target modalities.
DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images

Purpose To remove blood vessel shadows from optical coherence tomography (OCT) images of the optic nerve head (ONH). Methods Volume scans consisting of 97 horizontal B-scans were acquired through the center of the ONH using a commercial OCT device for both eyes of 13 subjects. A custom generative adversarial network (named DeshadowGAN) was designed and trained with 2328 B-scans in order to remove blood vessel shadows in unseen B-scans. Image quality was assessed qualitatively (for artifacts) and quantitatively using the intralayer contrast—a measure of shadow visibility ranging from 0 (shadow-free) to 1 (strong shadow). This was computed in the retinal nerve fiber layer (RNFL), the inner plexiform layer (IPL), the photoreceptor (PR) layer, and the retinal pigment epithelium (RPE) layer. The performance of DeshadowGAN was also compared with that of compensation, the standard for shadow removal. Results DeshadowGAN decreased the intralayer contrast in all tissue layers. On average, the intralayer contrast decreased by 33.7 ± 6.81%, 28.8 ± 10.4%, 35.9 ± 13.0%, and 43.0 ± 19.5% for the RNFL, IPL, PR layer, and RPE layer, respectively, indicating successful shadow removal across all depths. Output images were also free from artifacts commonly observed with compensation. Conclusions DeshadowGAN significantly corrected blood vessel shadows in OCT images of the ONH. Our algorithm may be considered as a preprocessing step to improve the performance of a wide range of algorithms including those currently being used for OCT segmentation, denoising, and classification. Translational Relevance DeshadowGAN could be integrated to existing OCT devices to improve the diagnosis and prognosis of ocular pathologies.
Impulsive Noise Detection for Intelligibility and Quality Improvement of Speech Enhancement Methods Applied in Time-Domain

This letter introduces a novel speech enhancement method in the Hilbert-Huang Transform domain to mitigate the effects of acoustic impulsive noises. The estimation and selection of noise components is based on the impulsiveness index of decomposition modes. Speech enhancement experiments are conducted considering five acoustic noises with different impulsiveness index and non-stationarity degrees under various signal-to-noise ratios. Three speech enhancement algorithms are adopted as baseline in the evaluation analysis considering spectral and time domains. The proposed solution achieves the best results in terms of objective quality measures and similar speech intelligibility rates to the competitive methods.
Thin Loewner Carpets and Their Quasisymmetric Embeddings in S2

A carpet is a metric space that is homeomorphic to the standard Sierpiński carpet in ℝ2 , or equivalently, in S2. A carpet is called thin if its Hausdorff dimension is <2 . A metric space is called Q‐Loewner if its Q‐dimensional Hausdorff measure is Q‐Ahlfors regular and if it satisfies a 1,Q ‐Poincaré inequality. As we will show, Q‐Loewner planar metric spaces are always carpets, and admit quasisymmetric embeddings into the plane.
The impact of multiplicative noise in SPDEs close to bifurcation via amplitude equations

This article deals with the approximation of a stochastic partial differential equation (SPDE) via amplitude equations. We consider an SPDE with a cubic nonlinearity perturbed by a general multiplicative noise that preserves the constant trivial solution and we study the dynamics around it for the deterministic equation being close to a bifurcation. Based on the separation of time-scales close to a change of stability, we rigorously derive an amplitude equation describing the dynamics of the bifurcating pattern.This allows us to approximate the original infinite dimensional dynamics by a simpler effective dynamics associated with the solution of the amplitude equation. To illustrate the abstract result we apply it to a simple one-dimensional stochastic Ginzburg–Landau equation.
Growth and Strain Engineering of Trigonal Te for Topological Quantum Phases in Non-Symmorphic Chiral Crystals

Strained trigonal Te has been predicted to host Weyl nodes supported by a non-symmorphic chiral symmetry. Using low-pressure physical vapor deposition, we systematically explored the growth of trigonal Te nanowires with naturally occurring strain caused by curvature of the wires. Raman spectra and high mobility electronic transport attest to the highly crystalline nature of the wires. Comparison of Raman spectra for both straight and curved nanowires indicates a breathing mode that is significantly broader and shifted in frequency for the curved wires. Strain induced by curvature during growth therefore may provide a simple pathway to investigate topological phases in trigonal Te.
Two New “Turn-off” Changing-look Active Galactic Nuclei and Implication on “Partially Obscured” AGNs

We report a spectroscopic identification of two new changing-look active galactic nuclei (CL-AGNs): SDSS J104705.16+544405.8 and SDSS J120447.91+170256.8, both with a “turn-off” type transition from type-1 to type-1.8/1.9. The identification is arrived at through a follow-up spectroscopic observation of the five CL-AGNs candidates that are extracted from the sample recently released in Macleod et al. The candidates are extracted by the authors from the Sloan Digit Sky Survey Data Release 7, and are spectroscopically confirmed quasars with large amplitude variability. By compiling a sample of 26 previously identified CL-AGNs, we confirm the claim by Macleod et al. that CL-AGNs tend to be biased against a low Eddington ratio, and identify an overlap between the CL-AGNs at their dim state and the so-called intermediate-type AGNs. The overlap implies that there two populations of the intermediate-type AGNs with different origins. One is due to the torus orientation effect, and the other to the intrinsic change of the accretion rate of the central supermassive black holes.
Cable manipulation with a tactile-reactive gripper

Cables are complex, high-dimensional, and dynamic objects. Standard approaches to manipulate them often rely on conservative strategies that involve long series of very slow and incremental deformations, or various mechanical fixtures such as clamps, pins, or rings. We are interested in manipulating freely moving cables, in real time, with a pair of robotic grippers, and with no added mechanical constraints. The main contribution of this paper is a perception and control framework that moves in that direction, and uses real-time tactile feedback to accomplish the task of following a dangling cable. The approach relies on a vision-based tactile sensor, GelSight, that estimates the pose of the cable in the grip, and the friction forces during cable sliding. We achieve the behavior by combining two tactile-based controllers: (1) cable grip controller, where a PD controller combined with a leaky integrator regulates the gripping force to maintain the frictional sliding forces close to a suitable value; and (2) cable pose controller, where an linear–quadratic regulator controller based on a learned linear model of the cable sliding dynamics keeps the cable centered and aligned on the fingertips to prevent the cable from falling from the grip. This behavior is possible with the use of reactive gripper fitted with GelSight-based high-resolution tactile sensors. The robot can follow 1 m of cable in random configurations within two to three hand regrasps, adapting to cables of different materials and thicknesses. We demonstrate a robot grasping a headphone cable, sliding the fingers to the jack connector, and inserting it. To the best of the authors’ knowledge, this is the first implementation of real-time cable following without the aid of mechanical fixtures. Videos are available at http://gelsight.csail. mit.edu/cable/
Gaussian-Process-Based Dynamic Embedding for Textual Networks

Textual network embedding aims to learn low-dimensional representations of textannotated nodes in a graph. Prior works have typically focused on fixed graph structures. However, real-world networks are often dynamic. We address this challenge with a novel end-to-end node-embedding model, called Dynamic Embedding for Textual Networks with a Gaussian Process (DetGP). Because the structure is allowed to be dynamic, our method uses the Gaussian process to take advantage of its non-parametric properties. After training, DetGP can be applied efficiently to dynamic graphs without re-training or backpropagation. To use both local and global graph structure, diffusion is used to model multiple hops between neighbors. The relative importance of global versus local structure for the embeddings is learned automatically. With the non-parametric nature of the Gaussian process, updating the embeddings for a changed graph structure requires only a forward pass through the learned model. Experiments demonstrate the empirical effectiveness of our method compared to baseline approaches, on link prediction and node classification. We further show that DetGP can be straightforwardly and efficiently applied to dynamic textual networks.