Miracle-dz/UniFAR-FTUs huggingface dataset:
Involvement of NYD‐SP15 in growth and oxidative‐stress responses of ARPE‐19

The aim of this study was to investigate the role of NYD‐SP15 in the growth and oxidative‐stress responses of ARPE‐19 cells. ARPE‐19 cell lines overexpressing wild type or RNA interference against NYD‐SP15 were established via lentivirus transfection. Cell growth and proliferation, migration, apoptosis, and cell cycle progression were monitored using the Cell Counting Kit‐8 assay, the wound scratch assay, and flow cytometry, respectively. Caspase‐3/8/9 activity was examined using the caspase‐3/8/9 assay kit. An hydrogen peroxide (H 2O 2)–induced oxidative‐stress damage model was used to study the effect of NYD‐SP15 knockdown by examining the activity of reactive oxygen species (ROS). Expressions of Kelch‐like ECH‐associated protein 1 (Keap‐1)/heme oxygenase‐1 (HO‐1)/nuclear factor erythroid 2–related factor 2 (Nrf2), mitogen‐activated protein kinase (MAPK), and Akt were detected by Western blot analysis. The mRNA chip of NYD‐SP15 overexpressed ARPE‐19 cells as well as controls were performed by one array plus process. Overexpression (OE) of NYD‐SP15 inhibited the proliferation and migration of ARPE‐19 cells, and led to apoptosis and caspase‐3/9 activation. OE of NYD‐SP15 inhibited MAPKs and Akt signaling. Downregulation of NYD‐SP15 had no effect on the growth of normally cultured ARP19 cells with 10% fetal bovine serum, but promoted the growth of ARP19 cells in the presence of starvation challenge. Gene chip showed that OE of NYD‐SP15 led to downregulation of 254 genes and upregulation of 57 genes. Downregulation of NYD‐SP15 also exerted a protective effect on H 2O 2‐induced cell apoptosis and ROS. NYD‐SP15 downregulation led to increments in the expression of Nrf2, Keap‐1, and HO‐1 in response to 200 μM H 2O 2. NYD‐SP15 might inhibit the growth, proliferation, and migration and promote apoptosis of ARPE‐19 cells via MAPK and Akt signaling. Downregulation of NYD‐SP15 could protect ARPE‐19 cells from H 2O 2‐induced oxidative damage by active Keap‐1/HO‐1/Nrf2, Akt, and MAPK signaling.
Analysis of the Technical Feasibility of Network Parallelism for Load Transfers in Contingency Situations

Electricity suppliers have increasingly invested in technology solutions, especially in the automation area, to improve the electricity supply to their consumers. In contingency situations, the process of restoring the electric energy consists of identifying and isolating the fault, quickly restoring the power supply to unaffected areas, according to the reconfiguration of the network through the opening or closing of the maneuver equipment. After fault correction, new maneuvers are performed to return to the normal state of the networks. Hence, the reactive characteristics of network elements such as line inductances and the loads of capacitor banks, can originate undesirable electromagnetic transients during the switching maneuvers. Therefore, this paper aims to present the development of a methodology for the analysis of the technical viability of parallelism in the distribution networks with capacitor banks for the reestablishment of electric energy, considering the cascade of loads between the network feeders. The proposed study is, therefore, a particularity of the distribution systems and the validation of the operational feasibility of the maneuvers will be verified from computational simulations performed with the ATPDraw software.
Selection Expressions for Procedural Modeling

We introduce a new approach for procedural modeling. Our main idea is to select shapes using selection-expressions instead of simple string matching used in current state-of-the-art grammars like CGA shape and CGA++. A selection-expression specifies how to select a potentially complex subset of shapes from a shape hierarchy, e.g., ”select all tall windows in the second floor of the main building facade”. This new way of modeling enables us to express modeling ideas in their global context rather than traditional rules that operate only locally. To facilitate selection-based procedural modeling we introduce the procedural modeling language SelEx. An important implication of our work is that enforcing important constraints, such as alignment and same size constraints can be done by construction. Therefore, our procedural descriptions can generate facade and building variations without violating alignment and sizing constraints that plague the current state of the art. While the procedural modeling of architecture is our main application domain, we also demonstrate that our approach nicely extends to other man-made objects.
Ambient concentrations of NO2 and hospital admissions for schizophrenia

Objectives Schizophrenia is a chronic and severe mental disorder affecting more than 21 million people worldwide. Short-term exposure to nitrogen dioxide (NO2) has been associated with hospital admissions (HAs) for mental disorders, but no study has evaluated the specific association of NO2 and schizophrenia. Additionally, the shape of the concentration–response (C–R) curve has not yet been assessed at present. This study aims to investigate the relationship between short-term exposure to NO2 and HAs for schizophrenia in Hefei, from 2014 to 2016. We also attempt to explore the C–R and the underlying effect modifiers of the association. Methods Daily number of HAs for schizophrenia was derived from the computerised medical record system of Anhui Mental Health Center. We used a time-series Poisson generalised linear regression combined with distributed lag non-linear models to model the NO2–schizophrenia relationship. Results A total of 11 373 HAs were identified during the study period. An increase in levels of NO2 was significantly associated with elevated schizophrenia HAs. The estimated relative risk per IQR increase in NO2 at lag 01 was 1.10 (95% CI 1.01 to 1.18). Greater association was observed in young patients (relative risk: 1.11, 95% CI 1.02 to 1.19). The modelled C–R curves of the NO2–schizophrenia relationship suggested possible threshold effects of NO2 for all ages combined, young patients, men and both seasons. Conclusions Short-term exposure to NO2 may be associated with increased schizophrenia HAs. Findings indicated potential threshold effects of NO2, which has important implications for health-based risk assessments.
Tractography Analysis for Electroconvulsive Therapy

Computational human head models have been used in electrophysiological studies, and they have been able to provide useful information that is unable or difficult to acquire from experimental or imaging studies. However, most of these models are purely volume conductor models that overlooked the electric excitability of axons in the white matter of the brain. This study combined a finite element (FE) model of electroconvulsive therapy (ECT) with a whole-brain tractography analysis as well as the cable theory of neuronal excitation. We have reconstructed a whole-brain tractogram with 500 neural fibres from the diffusion-weighted magnetic resonance scans, and extracted the information on electrical potential from the FE ECT model of the same head. We then calculated the first and second spatial derivatives of the electrical potential, which describes the activating function for homogenous axons and investigated sensitive regions of white matter activation.
Multiple Kernel Learning Based Classification of Parkinson’s Disease With Multi-Modal Transcranial Sonography

Parkinson’s Disease (PD) is the most common motor neurodegenerative disease in elderly population. Transcranial sonography (TCS) has become a popular imaging tool for diagnosis of PD in clinical practice. Moreover, several pioneering work have developed the computer-aided diagnosis (CAD) for PD with the transcranial B-mode sonography (TBS). It is worth noting that TCS not only has the TBS modality, but also can image the blood flow of major cerebral arteries, which is named transcranial Doppler sonography (TDS). TDS also has been applied to evaluate PD patients with orthostatic hypotension. However, the TDS-based CAD for PD has not been investigated. Since TBS and TDS provide the complementary structural and functional information about brain, it is feasible to develop a multi-modal TCS-based CAD for PD by combining both TBS and TDS. Therefore, in this work, we propose a multiple kernel learning (MKL) based CAD for PD with multi-modal TCS imaging. Particularly, the statistical and texture features are extracted from the midbrain region from TBS images, and the features about blood flow are calculated from the spectrum curves in TDS. The multi-modal features are then fed to a MKL classifier for classification of PD. The experimental results show that the multi-modal TCS-based method outperforms both the single-modal TBS- and TDS-based algorithm, which suggests the feasibility and effectiveness of combining TBS and TDS for diagnosis of PD.
The Dominant Role of Forkhead Box Proteins in Cancer

Forkhead box (FOX) proteins are multifaceted transcription factors that are significantly implicated in cancer, with various critical roles in biological processes. Herein, we provide an overview of several key members of the FOXA, FOXC, FOXM1, FOXO and FOXP subfamilies. Important pathophysiological processes of FOX transcription factors at multiple levels in a context-dependent manner are discussed. We also specifically summarize some major aspects of FOX transcription factors in association with cancer research such as drug resistance, tumor growth, genomic alterations or drivers of initiation. Finally, we suggest that targeting FOX proteins may be a potential therapeutic strategy to combat cancer.
Psychosocial Stress Reactivity Is Associated With Decreased Whole-Brain Network Efficiency and Increased Amygdala Centrality

Cognitive and emotional functions are supported by the coordinated activity of a distributed network of brain regions. This coordinated activity may be disrupted by psychosocial stress, resulting in the dysfunction of cognitive and emotional processes. Graph theory is a mathematical approach to assess coordinated brain activity that can estimate the efficiency of information flow and determine the centrality of brain regions within a larger distributed neural network. However, limited research has applied graph-theory techniques to the study of stress. Advancing our understanding of the impact stress has on global brain networks may provide new insight into factors that influence individual differences in stress susceptibility. Therefore, the present study examined the brain connectivity of participants that completed the Montreal Imaging Stress Task (Goodman et al., 2016; Wheelock et al., 2016). Salivary cortisol, heart rate, skin conductance response, and self-reported stress served as indices of stress, and trait anxiety served as an index of participant’s disposition toward negative affectivity. Psychosocial stress was associated with a decrease in the efficiency of the flow of information within the brain. Further, the centrality of brain regions that mediate emotion regulation processes (i.e., hippocampus, ventral prefrontal cortex, and cingulate cortex) decreased during stress exposure. Interestingly, individual differences in cortisol reactivity were negatively correlated with the efficiency of information flow within this network, whereas cortisol reactivity was positively correlated with the centrality of the amygdala within the network. These findings suggest that stress reduces the efficiency of information transfer and leaves the function of brain regions that regulate the stress response vulnerable to disruption.
Learning Comment Generation by Leveraging User-generated Data

Existing models on open-domain comment generation are difficult to train, and they produce repetitive and uninteresting responses. The problem is due to multiple and contradictory responses from a single article, and by the rigidity of retrieval methods. To solve this problem, we propose a combined approach to retrieval and generation methods. We propose an attentive scorer to retrieve informative and relevant comments by leveraging user-generated data. Then, we use such comments, together with the article, as input for a sequence-to-sequence model with copy mechanism. We show the robustness of our model and how it can alleviate the aforementioned issue by using a large scale comment generation dataset. The result shows that the proposed generative model significantly outperforms strong baseline such as Seq2Seq with attention and Information Retrieval models by around 27 and 30 BLEU-1 points respectively.
Nonvolatile Circuits-Devices Interaction for Memory, Logic and Artificial Intelligence

Emerging nonvolatile memory (eNVM) have aroused extensive attention due to their low power and high speed. Recent advances have further moved eNVM to the forefront as key enablers of nonvolatile logics (nvLogics) for IoT devices and computing-in-memory (CIM) for AI chips. In this paper, we firstly examine the circuit-device-interaction (CDI) issues to implement high-performance memory macro. Then we review examples of emerging eNVM-based nvLogics for nonvolatile processors and CIM macro for AI chips with an emphasis on the challenges required CDI.
A stringent fiber distance measure for dMRI tractography clustering and segmentation*

Most analysis and segmentation methods for diffusion MRI tractography datasets require a fiber distance measure able to determine the similarity between a pair of fibers. We present a stringent fiber distance measure able to perform a good discrimination between fiber shapes and lengths. It uses three terms: (i) a fiber maximum Euclidean distance, (ii) a fiber shape distance, and (iii) a fiber length distance. The distance was evaluated applying a hierarchical clustering of fibers connecting the pre-and post-central gyri of a subject. Results where compared with other known fiber distance measures. A better sensitivity to differences in fiber shape and length was found for the proposed distance. This will be very useful for the detailed study and description of white matter bundles. Known bundles will be better decomposed into sub-bundles, with more precision on the bundle shape and on the regions connected by the fibers. For short association bundles, this distance will be a real improvement, as even the most stringent distance used until now shows some limitations when evaluating the similarity of these fibers.
Opportunistic bacteria confer the ability to ferment prebiotic starch in the adult cystic fibrosis gut

ABSTRACT Chronic disruption of the intestinal microbiota in adult cystic fibrosis (CF) patients is associated with local and systemic inflammation, and has been linked to the risk of serious comorbidities. Supplementation with high amylose maize starch (HAMS) might provide clinical benefit by promoting commensal bacteria and the biosynthesis of immunomodulatory metabolites. However, whether the disrupted CF gut microbiota has the capacity to utilise these substrates is not known. We combined metagenomic sequencing, in vitro fermentation, amplicon sequencing, and metabolomics to define the characteristics of the faecal microbiota in adult CF patients and assess HAMS fermentation capacity. Compared to healthy controls, the faecal metagenome of adult CF patients had reduced bacterial diversity and prevalence of commensal fermentative clades. In vitro fermentation models seeded with CF faecal slurries exhibited reduced acetate levels compared to healthy control reactions, but comparable levels of butyrate and propionate. While the commensal genus Faecalibacterium was strongly associated with short chain fatty acid (SCFA) production by healthy microbiota, it was displaced in this role by Clostridium sensu stricto 1 in the microbiota of CF patients. A subset of CF reactions exhibited enterococcal overgrowth, resulting in lactate accumulation and reduced SCFA biosynthesis. The addition of healthy microbiota to CF faecal slurries failed to displace predominant CF taxa, or substantially influence metabolite biosynthesis. Despite significant microbiota disruption, the adult CF gut microbiota retains the capacity to exploit HAMS. Our findings highlight the potential for taxa associated with the altered CF gut microbiotato mediate prebiotic effects in microbial systems subject to ongoing perturbation, irrespective of the depletion of common commensal clades.
Novel Lexicographic MPC for Loss Optimized Torque Control of Nonlinear PMSM

In electrical drive applications torque tracking is conflicting with the subsidiary goal of loss minimization. In this Paper a lexicographic optimization is proposed to solve this conflict by stringent prioritization. A novel model predictive control (MPC) with a single objective function is presented which is proved to be equal to lexicographic optimization in steady state. The loss weighting factor does not affect the steady state tracking offset and no current set points are needed. Results are confirmed by a simulation with an experimental validated machine model. The torque of an anisotropic permanent magnet synchronous machine (PMSM) fed by a voltage source inverter (VSI) is controlled with minimal ohmic losses. The optimization under input constraints is done using the projected fast gradient method (PFGM).
A Nonlocal InSAR Filter for High-Resolution DEM Generation From TanDEM-X Interferograms

This paper presents a nonlocal interferometric synthetic aperture radar (InSAR) filter with the goal of generating digital elevation models (DEMs) of higher resolution and accuracy from bistatic TanDEM-X strip map interferograms than with the processing chain used in production. The currently employed boxcar multilooking filter naturally decreases the resolution and has inherent limitations on what level of noise reduction can be achieved. The proposed filter is specifically designed to account for the inherent diversity of natural terrain by setting several filtering parameters adaptively. In particular, it considers the local fringe frequency and scene heterogeneity, ensuring proper denoising of interferograms with considerable underlying topography as well as urban areas. A comparison using synthetic and TanDEM-X bistatic strip map data sets with existing InSAR filters shows the effectiveness of the proposed techniques, most of which could readily be integrated into existing nonlocal filters. The resulting DEMs outclass the ones produced with the existing global TanDEM-X DEM processing chain by effectively increasing the resolution from 12 to 6 m and lowering the noise level by roughly a factor of two.
Robust Attitude Control Design of Quad-Tilt-Wing UAV: A Structured $\mu$-Synthesis Approach

The problem of designing the flight controller for a quad-tilt-wing UAV to achieve robust performance under constraints on the controller structure is considered. An approach based on $\mu$-synthesis is proposed: a non-parametric inverse multiplicative uncertainty description is employed to account for the uncertainties of the aircraft attitude dynamics at each operating condition. The resulting structured $\mu$-synthesis problem is reformulated as a structured $H_{\infty}$ problem and solved by means of available MATLAB® software. Simulation results are presented in order to show the effectiveness of the approach.
Universal Dependency Parsing from Scratch

This paper describes Stanford’s system at the CoNLL 2018 UD Shared Task. We introduce a complete neural pipeline system that takes raw text as input, and performs all tasks required by the shared task, ranging from tokenization and sentence segmentation, to POS tagging and dependency parsing. Our single system submission achieved very competitive performance on big treebanks. Moreover, after fixing an unfortunate bug, our corrected system would have placed the 2nd, 1st, and 3rd on the official evaluation metrics LAS, MLAS, and BLEX, and would have outperformed all submission systems on low-resource treebank categories on all metrics by a large margin. We further show the effectiveness of different model components through extensive ablation studies.
Merits and Challenges of Ruddlesden–Popper Soft Halide Perovskites in Electro‐Optics and Optoelectronics

Following the rejuvenation of 3D organic–inorganic hybrid perovskites, like CH3NH3PbI3, (quasi)‐2D Ruddlesden–Popper soft halide perovskites R2An−1PbnX3n+1 have recently become another focus in the optoelectronic and photovoltaic device community. Although quasi‐2D perovskites were first introduced to stabilize optoelectronic/photovoltaic devices against moisture, more interesting properties and device applications, such as solar cells, light‐emitting diodes, white‐light emitters, lasers, and polaritonic emission, have followed. While delicate engineering design has pushed the performance of various devices forward remarkably, understanding of the fundamental properties, especially the charge‐transfer process, electron–phonon interactions, and the growth mechanism in (quasi)‐2D halide perovskites, remains limited and even controversial. Here, after reviewing the current understanding and the nexus between optoelectronic/photovoltaic properties of 2D and 3D halide perovskites, the growth mechanisms, charge‐transfer processes, vibrational properties, and electron–phonon interactions of soft halide perovskites, mainly in quasi‐2D systems, are discussed. It is suggested that single‐crystal‐based studies are needed to deepen the understanding of the aforementioned fundamental properties, and will eventually contribute to device performance.
Optimizing test prioritization via test distribution analysis

Test prioritization aims to detect regression faults faster via reordering test executions, and a large number of test prioritization techniques have been proposed accordingly. However, test prioritization effectiveness is usually measured in terms of the average percentage of faults detected concerned with the number of test executions, rather than the actual regression testing time, making it unclear which technique is optimal in actual regression testing time. To answer this question, this paper first conducts an empirical study to investigate the actual regression testing time of various prioritization techniques. The results reveal a number of practical guidelines. In particular, no prioritization technique can always perform optimal in practice. To achieve the optimal prioritization effectiveness for any given project in practice, based on the findings of this study, we design learning-based Predictive Test Prioritization (PTP). PTP predicts the optimal prioritization technique for a given project based on the test distribution analysis (i.e., the distribution of test coverage, testing time, and coverage per unit time). The results show that PTP correctly predicts the optimal prioritization technique for 46 out of 50 open-source projects from GitHub, outperforming state-of-the-art techniques significantly in regression testing time, e.g., 43.16% to 94.92% improvement in detecting the first regression fault. Furthermore, PTP has been successfully integrated into the practical testing infrastructure of Baidu (a search service provider with over 600M monthly active users), and received positive feedbacks from the testing team of this company, e.g., saving beyond 2X testing costs with negligible overheads.