Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcriptional Identification of Related Proteins in the Immune System of the Crayfish Procambarus clarkii

The freshwater crayfish Procambarus clarkii is an animal model employed for physiological and immunological studies and is also of great economic importance in aquaculture. Although it is a species of easy husbandry, a high percentage of its production is lost annually as a result of infectious diseases. Currently, genetic information about the immune system of crustaceans is limited. Therefore, we used the abdominal nerve cord from P. clarkii to obtain its transcriptome using Next Generation Sequencing (NGS) to identify proteins that participate in the immune system. The reads were assembled de novo and consensus sequences with more than 3000 nucleotides were selected for analysis. The transcripts of the sequences of RNA were edited for annotation and sent to the GenBank database of the National Center for Biotechnology Information (NCBI). We made a list of accession numbers of the sequences which were organized by the putative role of the immune system pathway in which they participate. In this work, we report on 80 proteins identified from the transcriptome of crayfish related to the immune system, 74 of them being the first reported for P. clarkii. We hope that the knowledge of these sequences will contribute significantly to the development of future studies of the immune system in crustaceans.
Grain Boundary Segregation in Pd-Cu-Ag Alloys for High Permeability Hydrogen Separation Membranes

Dense metal membranes that are based on palladium (Pd) are promising for hydrogen separation and production due to their high selectivity and permeability. Optimization of alloy composition has normally focused on bulk properties, but there is growing evidence that grain boundaries (GBs) play a crucial role in the overall performance of membranes. The present study provides parameters and analyses of GBs in the ternary Pd-Ag-Cu system, based on first-principles electronic structure calculations. The segregation tendency of Cu, Ag, and vacancies towards 12 different coherent ∑ GBs in Pd was quantified using three different procedures for relaxation of supercell lattice constants, representing the outer bounds of infinitely elastic and stiff lattice around the GBs. This demonstrated a clear linear correlation between the excess volume and the GB energy when volume relaxation was allowed for. The point defects were attracted by most of the GBs that were investigated. Realistic atomic-scale models of binary Pd-Cu and ternary Pd-Cu-Ag alloys were created for the ∑5(210) boundary, in which the strong GB segregation tendency was affirmed. This is a starting point for more targeted engineering of alloys and grain structure in dense metal membranes and related systems.
The cytochrome P450 CYP77A4 is involved in auxin-mediated patterning of the Arabidopsis thaliana embryo

ABSTRACT Metabolism often plays an important role in developmental control, in addition to supporting basal physiological requirements. However, our understanding of this interaction remains limited. Here, we performed quantitative phenome analysis of Arabidopsis thaliana cytochrome P450 mutants to identify a novel interaction between development and metabolism. We found that cyp77a4 mutants exhibit specific defects in cotyledon development, including asymmetric positioning and cup-shaped morphology, which could be rescued by introducing the CYP77A4 gene. Microscopy revealed that the abnormal patterning was detected at least from the 8-cell stage of the cyp77a4 embryos. We next analysed auxin distribution in mutant embryos, as the phenotypes resembled those of auxin-related mutants. We found that the auxin response pattern was severely perturbed in the cyp77a4 embryos owing to an aberrant distribution of the auxin efflux carrier PIN1. CYP77A4 intracellularly localised to the endoplasmic reticulum, which is consistent with the notion that this enzyme acts as an epoxidase of unsaturated fatty acids in the microsomal fraction. We propose that the CYP77A4-dependent metabolic pathway is an essential element for the establishment of polarity in plant embryos. Summary: Systematic quantification of morphological traits in Arabidopsis thaliana cytochrome P450 mutants shows that CYP77A4 is a novel element for embryo development through polar transport of the phytohormone auxin.
Distilled Wasserstein Learning for Word Embedding and Topic Modeling

We propose a novel Wasserstein method with a distillation mechanism, yielding joint learning of word embeddings and topics. The proposed method is based on the fact that the Euclidean distance between word embeddings may be employed as the underlying distance in the Wasserstein topic model. The word distributions of topics, their optimal transport to the word distributions of documents, and the embeddings of words are learned in a unified framework. When learning the topic model, we leverage a distilled ground-distance matrix to update the topic distributions and smoothly calculate the corresponding optimal transports. Such a strategy provides the updating of word embeddings with robust guidance, improving algorithm convergence. As an application, we focus on patient admission records, in which the proposed method embeds the codes of diseases and procedures and learns the topics of admissions, obtaining superior performance on clinically-meaningful disease network construction, mortality prediction as a function of admission codes, and procedure recommendation.
Robust Pixel-wise Dehazing Algorithm based on Advanced Haze-Relevant Features

The dehazing algorithm aims to remove haze from an image and has been widely used as a pre-processing step in several computer vision applications. The performance of the computer vision algorithm, however, is significantly affected by weather conditions. Hence, conventional algorithms do not work well when weather conditions vary severely over time. This paper proposes an effective haze removal algorithm based on a single image, which is robust to the varying weather conditions. Unlike conventional methods, which estimate "global" atmospheric light, we find the "local" atmospheric light by assuming that it is absorbed and emitted differently at each atmospheric particle. We divide the image into two components, namely, illumination and reflection, according to the retinex theory, and obtain the pixel-wise atmospheric light from the illumination. To estimate an accurate transmission, we initialize it by using a combination of haze-relevant features that have been proven experimentally to be highly correlated with haze. The aforementioned processes interact with each other in producing accurate dehazing results. We solve the dehazing problem via convex optimization and obtain the optimal solution. The experimental results demonstrate that the proposed algorithm outperforms state-of-the-art methods, using the benchmark dataset with regard to contrast, detail, and visibility. Quantitatively, our method produces better dehazing results than other conventional methods.
Drowsiness Detection and Warning in Manual and Automated Driving: Results from Subjective Evaluation

Drowsiness is a main cause of serious traffic accidents, and problematic within the ongoing automation of the driving task. Several approaches for drowsiness detection have been published and are in operation in production cars for manual driving. To assess differences in the development of drowsiness between manual and automated driving, and to further investigate the potential of subjective ratings, we conducted a driving simulator study (N=30). The self-assessment was based on the Karolinska Sleepiness Scale (KSS), during and after driving. Furthermore, we examined the impact of travel time and driver age (20-25, 65-70 years). Results confirm that driving mode and travel time have a significant effect on the development of drowsiness. In both age groups, self-ratings were higher for automated driving and particularly by younger subjects. All subjects estimated themselves drowsier during driving. The gained knowledge can be helpful for the development of future driver-vehicle interfaces in driver drowsiness detection.
A hexagonal Fourier model of grid cells

Grid cells in rat medial entorhinal cortex are widely thought to play a major role in spatial behavior. However, the exact computational role of the population of grid cells is not known. Here we provide a descriptive model, which nonetheless considers biologically feasible mechanisms, whereby the grid cells are viewed as a two‐dimensional Fourier basis set, in hexagonal coordinates, with restricted availability of basis functions. With known properties imposed in the model parameters, we demonstrate how various empirical benchmark findings are straight‐forward to understand in this model. We also explain how complex computations, inherent in a Fourier model, are feasible in the medial entorhinal cortex with simple mechanisms. We further suggest, based on model experiments, that grid cells may support a form of lossy compression of contextual information, enabling its representation in an efficient manner. In sum, this hexagonal Fourier model suggests how the entire population of grid cells may be modeled in a principled way, incorporates biologically feasible mechanisms and provides a potentially powerful interpretation of the relationship between grid‐cell activity and contextual information beyond spatial knowledge. This enables various phenomena to be modeled with relatively simple mechanisms, and leads to novel and testable predictions.
Extrinsic Calibration of 2D Laser Rangefinders Based on a Mobile Sphere

In the fields of autonomous vehicles, virtual reality and three-dimensional (3D) reconstruction, 2D laser rangefinders have been widely employed for different purposes, such as localization, mapping, and simultaneous location and mapping. However, the extrinsic calibration of multiple 2D laser rangefinders is a fundamental prerequisite for guaranteeing their performance. In contrast to existing calibration methods that rely on manual procedures or suffer from low accuracy, an automatic and high-accuracy solution is proposed in this paper for the extrinsic calibration of 2D laser rangefinders. In the proposed method, a mobile sphere is used as a calibration target, thereby allowing the automatic extrapolation of a spherical center and the automatic matching of corresponding points. Based on the error analysis, a matching machine of corresponding points with a low error is established with the restriction constraint of the scan circle radius, thereby achieving the goal of high-accuracy calibration. Experiments using the Hokuyo UTM-30LX sensor show that the method can increase the extrinsic orientation accuracy to a sensor intrinsic accuracy of 10 mm without requiring manual measurements or manual correspondence among sensor data. Therefore, the calibration method in this paper is automatic, highly accurate, and highly effective, and it meets the requirements of practical applications.
Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree-Grass Ecosystems

Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP.
Revisiting Loop Tiling for Datacenters: Live and Let Live

As DNNs gain popularity in modern datacenters, it becomes imperative to revisit compiler optimizations for DNNs in a colocation scenario. Loop tiling turns out to be the most significant compiler optimization, since DNNs typically apply a series of matrix computations iteratively to a massive amount of data. We introduce a reuse-pattern-centric approach to obtaining a peer-aware TSS (Tile Size Selection) model for a matrix-based application A. Our key insight is that the co-running cache behavior of A (once tiled) can be determined by its data reuse patterns, together with the cache pressure exerted by its co-running peers, without actually the need for analyzing the code of its co-runners. Compared with static tiling (that determines a tile size for A statically without considering its co-running peers), our peer-aware tiling enables compilers to generate either faster peer-aware efficient code for A (by optimizing the performance of A) or faster peer-aware nice code for A (by optimizing the performance of its co-runners). In addition, our peer-aware tiling also enables library developers to improve the performance of library routines (more effectively than static tiling).
The Effect of Road Bumps on Touch Interaction in Cars

Touchscreens are a common fixture in current vehicles. With autonomous driving, we can expect touch interaction with such in-vehicle media systems to exponentially increase. In spite of vehicle suspension systems, road perturbations will continue to exert forces that can render in-vehicle touch interaction challenging. Using a motion simulator, we investigate how different vehicle speeds interact with road features (i.e., speed bumps) to influence touch interaction. We determine their effect on pointing accuracy and task completion time. We show that road bumps have a significant effect on touch input and can decrease accuracy by 19%. In light of this, we developed a Random Forest (RF) model that improves touch accuracy by 32.0% on our test set and by 22.5% on our validation set. As the lightweight model uses only features that can easily be determined through inertial measurement units, this model could be easily deployed in current automobiles.
Genetic manipulation of CCN2/CTGF unveils cell‐specific ECM‐remodeling effects in injured skeletal muscle

In skeletal muscle, extracellular matrix (ECM) remodeling can either support the complete regeneration of injured muscle or facilitate pathologic fibrosis and muscle degeneration. Muscular dystrophy (MD) is a group of genetic disorders that results in a progressive decline in muscle function and is characterized by the abundant deposition of fibrotic tissue. Unlike acute injury, where ECM remodeling is acute and transient, in MD, remodeling persists until fibrosis obstructs the regenerative efforts of diseased muscles. Thus, understanding how ECM is deposited and organized is critical in the context of muscle repair. Connective tissue growth factor (CTGF or CCN2) is a matricellular protein expressed by multiple cell types in response to tissue injury. Although used as a general marker of fibrosis, the cell type–dependent role of CTGF in dystrophic muscle has not been elucidated. To address this question, a conditional Ctgf myofiber and fibroblast‐knockout mouse lines were generated and crossed to a dystrophic background. Only myofiber‐selective inhibition of CTGF protected δ‐sarcoglycan–null (Sgcd−/−) mice from the dystrophic phenotype, and it did so by affecting collagen organization in a way that allowed for improvements in dystrophic muscle regeneration and function. To confirm that muscle‐specific CTGF functions to mediate collagen organization, we generated mice with transgenic muscle‐specific overexpression of CTGF. Again, genetic modulation of CTGF in muscle was not sufficient to drive fibrosis, but altered collagen content and organization after injury. Our results show that the myofibers are critical mediators of the deleterious effects associated with CTGF in MD and acutely injured skeletal muscle. —Petrosino, J. M. , Leask, A. , Accornero, F. Genetic manipulation of CCN2/CTGF unveils cell‐specific ECM‐remodeling effects in injured skeletal muscle. FASEB J. 33, 2047–2057 (2019). www.fasebj. org
Eliciting Driver Stress Using Naturalistic Driving Scenarios on Real Roads

We propose a novel method for reliably inducing stress in drivers for the purpose of generating real-world participant data for machine learning, using both scripted in-vehicle stressor events and unscripted on-road stressors such as pedestrians and construction zones. On-road drives took place in a vehicle outfitted with an experimental display that lead drivers to believe they had prematurely ran out of charge on an isolated road. We describe the elicitation method, course design, instrumentation, data collection procedure and the post-hoc labeling of unplanned road events to illustrate how rich data about a variety of stress-related events can be elicited from study participants on-road. We validate this method with data including psychophysiological measurements, video, voice, and GPS data from (N=20) participants. Results from algorithmic psychophysiological stress analysis were validated using participant self-reports. Results of stress elicitation analysis show that our method elicited a stress-state in 89% of participants.
Generic and Automatic Markov Random Field-Based Registration for Multimodal Remote Sensing Image Using Grayscale and Gradient Information

The automatic image registration serves as a technical prerequisite for multimodal remote sensing image fusion. Meanwhile, it is also the technical basis for change detection, image stitching and target recognition. The demands of subpixel level registration accuracy can be rarely satisfied with a multimodal image registration method based on feature matching. In light of this, we propose a Generic and automatic Markov Random Field (MRF)-based registration framework of multimodal image using grayscale and gradient information. The proposed approach performs non-rigid registration and formulates an MRF model while grayscale and gradient statistical information of a multimodal image is employed for the evaluation of similarity while the spatial weighting function is optimized simultaneously. Besides, the value space is discretized to improve the convergence speed. The developed automatic approach was validated both qualitatively and quantitatively, demonstrating its potential for a variety of multimodal remote sensing datasets and scenes. As for the registration accuracy, the average target registration error of the proposed framework is less than 1 pixel, while the maximum displacement error is less than 1 pixel. Compared with the polynomial model registration based on manual selection, the registration accuracy has been significantly improved. In the meantime, the proposed approach had the partial applicability for the multimodal image registration of large deformation scenes. It is also proved that the proposed registration framework using grayscale and gradient information outperforms the MRF-based registration using only grayscale information and only gradient information while the proposed registration framework using Gaussian function as spatial weighting function is superior to that using distance inverse weight method.
Filtering Stems and Branches from Terrestrial Laser Scanning Point Clouds Using Deep 3-D Fully Convolutional Networks

Terrestrial laser scanning (TLS) can produce precise and detailed point clouds of forest environment, thus enabling quantitative structure modeling (QSM) for accurate tree morphology and wood volume allocation. Applying QSM to plot-scale wood delineation is highly dependent on wood visibility from forest scans. A common problem is to filter wood point from noisy leafy points in the crowns and understory. This study proposed a deep 3-D fully convolution network (FCN) to filter both stem and branch points from complex plot scans. To train the 3-D FCN, reference stem and branch points were delineated semi-automatically for 14 sampled areas and three common species. Among seven testing areas, agreements between reference and model prediction, measured by intersection over union (IoU) and overall accuracy (OA), were 0.89 (stem IoU), 0.54 (branch IoU), 0.79 (mean IoU), and 0.94 (OA). Wood filtering results were further incorporated to a plot-scale QSM to extract individual tree forms, isolated wood, and understory wood from three plot scans with visual assessment. The wood filtering experiment provides evidence that deep learning is a powerful tool in 3-D point cloud processing and parsing.
MicroRNA‐351‐5p mediates skeletal myogenesis by directly targeting lactamase‐β and is regulated by lnc‐mg

Skeletal muscle is an important and complex organ with a variety of functions in humans and animals. Skeletal myogenesis is a multistep and complex process, and increasing evidence suggests that microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) play critical roles in skeletal myogenesis. In this study the expression of miR‐351‐5p is dynamically regulated during skeletal myogenesis in vitro and in vivo. Cell‐counting kit‐8, qRT‐PCR, and EdU immunofluorescence analysis showed that miR‐351‐5p overexpression promoted the proliferation and inhibited the differentiation of C2C12 myoblast, whereas inhibition of miR‐351‐5p had the opposite effect. In addition, miR‐351‐5p mediated the regulation of muscle fiber type transition in vivo. In vitro, loss of miR‐351‐5p in muscle tissues promoted muscle hypertrophy and increased slow‐twitch fibers in the gastrocnemius muscles of mice. Luciferase reporter assay and functional analyses demonstrated that lactamase β (LACTB) is a direct target of miR‐351‐5p involved in the regulation of skeletal myogenesis. Expression levels of a myogenesis‐associated lncRNA (lnc‐mg) correlated negatively with miR‐351‐5p and positively with LACTB during C2C12 myoblast proliferation and differentiation. Further analyses showed that lnc‐mg acted as a molecular sponge for miR‐351‐5p, demonstrating its involvement in the negative regulation of LACTB by miR‐351‐5p during skeletal myogenesis. These findings indicate that miRNA‐351‐5p functions in skeletal myogenesis by targeting LACTB and is regulated by lnc‐mg, supporting the role of the competing endogenous RNA network in skeletal myogenesis. —Du, J., Zhang, P., Zhao, X. , He, J., Xu, Y. , Zou, Q. , Luo, J., Shen, L. , Gu, H. , Tang, Q. , Li, M. , Jiang, Y. , Tang, G. , Bai, L. , Li, X. , Wang, J., Zhang, S. , Zhu, L. MicroRNA‐351‐5p mediates skeletal myogenesis by directly targeting lactamase β and is regulated by lnc‐mg. FASEB J. 33, 1911–1926 (2019). www.fasebj. org