Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular alterations in the extracellular matrix in the brains of newborns with congenital Zika syndrome

Multiomics analysis reveals the changes that occur in the extracellular matrix in the brains of Zika virus–infected newborns. How Zika affects the extracellular matrix In some cases, Zika virus (ZIKV) infection during pregnancy leads to a series of severe defects in the fetus collectively known as congenital Zika syndrome (CZS). These include microcephaly, defective neuronal migration, and impaired cortical development. Aguiar et al. combined genomic, transcriptomic, and proteomic analyses of blood and postmortem brains and demonstrated that ZIKV-infected neonates showed a reduction in collagen expression and an increase in adhesion factor expression, alterations in the extracellular matrix consistent with the brain defects seen in CZS. Together, these datasets form a useful resource for those investigating the molecular mechanisms underlying CZS in humans. Zika virus (ZIKV) infection during pregnancy can cause a set of severe abnormalities in the fetus known as congenital Zika syndrome (CZS). Experiments with animal models and in vitro systems have substantially contributed to our understanding of the pathophysiology of ZIKV infection. Here, to investigate the molecular basis of CZS in humans, we used a systems biology approach to integrate transcriptomic, proteomic, and genomic data from the postmortem brains of neonates with CZS. We observed that collagens were greatly reduced in expression in CZS brains at both the RNA and protein levels and that neonates with CZS had several single-nucleotide polymorphisms in collagen-encoding genes that are associated with osteogenesis imperfecta and arthrogryposis. These findings were validated by immunohistochemistry and comparative analysis of collagen abundance in ZIKV-infected and uninfected samples. In addition, we showed a ZIKV-dependent increase in the expression of cell adhesion factors that are essential for neurite outgrowth and axon guidance, findings that are consistent with the neuronal migration defects observed in CZS. Together, these findings provide insights into the underlying molecular alterations in the ZIKV-infected brain and reveal host genes associated with CZS susceptibility.
Combining High Oleic Acid Trait and Resistance to Late Leaf Spot and Rust Diseases in Groundnut (Arachis hypogaea L.)

High oleic trait, resistance to rust and late leaf spot (LLS) are important breeding objectives in groundnut. Rust and LLS cause significant economic loss, and high oleic trait is an industry preferred trait that enhances economic returns. This study reports marker-assisted selection to introgress high oleic content, resistance to LLS and rust into Kadiri 6 (K 6), a popular cultivar. The alleles for target traits were selected using linked allele-specific, simple sequence repeats and single nucleotide polymorphic markers. The F1s (384), intercrossed F1s (441), BC1F1s (380), BC1F2s (195), and BC1F3s (343) were genotyped to obtain desired allelic combination. Sixteen plants were identified with homozygous high oleic, LLS and rust resistance alleles in BC1F2, which were advanced to BC1F3 and evaluated for disease resistance, yield governing and nutritional quality traits. Phenotyping with Near-Infrared Reflectance Spectroscopy identified three lines (BC1F3-76, BC1F3-278, and BC1F3-296) with >80% oleic acid. The identified lines exhibit high levels of resistance to LLS and rust diseases (score of 3.0–4.0) with preferred pod and kernel features. The selected lines are under yield testing trials in multi-locations for release and commercialization. The lines reported here demonstrated combining high oleic trait with resistance to LLS and rust diseases.
LPAR1, Correlated With Immune Infiltrates, Is a Potential Prognostic Biomarker in Prostate Cancer

Prostate cancer is a common malignancy in men worldwide. Lysophosphatidic acid receptor 1 (LPAR1) is a critical gene and it mediates diverse biologic functions in tumor. However, the correlation between LPAR1 and prognosis in prostate cancer, as well as the potential mechanism, remains unclear. In the present study, LPAR1 expression analysis was based on The Cancer Genome Atlas (TCGA) and the Oncomine database. The correlation of LPAR1 on prognosis was also analyzed based on R studio. The association between LPAR1 and tumor-infiltrating immune cells were evaluated in the Tumor Immune Estimation Resource site, ssGSEA, and MCPcounter packages in R studio. Gene Set Enrichment Analysis and Gene Ontology analysis were used to analyze the function of LPAR1. TCGA datasets and the Oncomine database revealed that LPAR1 was significantly downregulated in prostate cancer. High LPAR1 expression was correlated with favorable overall survival. LPAR1 was involved in the activation, proliferation, differentiation, and migration of immune cells, and its expression was positively correlated with immune infiltrates, including CD4+ T cells, B cells, CD8+ T cells, neutrophils, macrophages, dendritic cells, and natural killer cells. Moreover, LPAR1 expression was positively correlated with those chemokine/chemokine receptors, indicating that LPAR1 may regulate the migration of immune cells. In summary, LPAR1 is a potential prognostic biomarker and plays an important part in immune infiltrates in prostate cancer.
An Output Feedback Predictive Control for Stochastic Stabilization of a System With Multiple Fixed State Delays and Multiple Markov Input Delays

This paper studies the stochastic stabilization with an output feedback predictive control for a linear system with multiple fixed state delays and multiple Markov input delays. The two equivalent systems are developed through two steps. The original system is transformed to a system with augmented states in a state delay free form. Then, it is further transformed into a system with a predictive state in an input delay free form. The stochastic stability condition of the equivalent system is shown to be a sufficient condition for the stochastic stability of the original system. The stability condition is given as a set of linear matrix inequalities of which matrix size depends only on the maximum state delay, not on the maximum input delay. Simulation results show that the proposed methods provide similar or better performance than the conventional method while their complexities are much smaller in most cases.
Conflict Mediation, Emotional Regulation and Coping Strategies in the Educational Field

Teachers who present high emotional skills and knowledge of strategies to mediate the conflicts generated in the classroom, are able to exercise a better management of both the teaching tasks that correspond to them and to establish an emotionally nutritive and productive educational climate for the students. This research aims to study and analyze, under an integrative conceptual revision methodology, the theories and models that consider individual, group and social aspects of the origin, development and attention of conflicts, as well as emotional elements that underlie people’s behavior. All this within interpersonal relationships and in the different areas of action; in order to synthesize the results of each, establishing points of agreement and complementarity that serve to be adapted to the educational field. Educational mediation and emotional regulation are two constructs that have been previously studied separately, or in conjunction with others, but not in a complementary way between them. The presentation of these conceptual discussions suggests the formulation of new theoretical proposals, aimed at improving interpersonal relationships, the environment and dynamics of teaching–learning, focusing efforts on the teachers’ collective.
Identification and dynamic analysis of crime hot-spots in Hungary by a complex Computer Intelligence approach

In the field of forensic science, crime maps are widely used. The representation of the data and analysis could offer some steps forward for crime prevention. Clustering is able to help identify criminal hot-spots and further analysis designate which require intervention. The aim of this study is to present a first step in the analysis of Hungary-related criminal information.
Chronicity and Sex Affect Genetic Risk Prediction in Schizophrenia

Schizophrenia (SCZ) is a severe mental disorder with immense personal and societal costs; identifying individuals at risk is therefore of utmost importance. Genomic risk profile scores (GRPS) have been shown to significantly predict cases-control status. Making use of a large-population based sample from Sweden, we replicate a previous finding demonstrating that the GRPS is strongly associated with admission frequency and chronicity of SCZ. Furthermore, we were able to show a substantial gap in prediction accuracy between males and females. In sum, our results indicate that prediction accuracy by GRPS depends on clinical and demographic characteristics.
Coprologic survey of endoparasites from Darwin's fox (Pseudalopex fulvipes) in Chiloé, Chile

Un total de 189 heces del zorro de Darwin (Pseudalopex fulvipes [Martin, 1837]) en peligro critico de extincion, de diferentes localidades de la isla de Chiloe en el sur de Chile, se examinaron en busca de huevos de parasitos y oocitos usando la tecnica de flotacion en azucar. Los resultados mostraron que el 21,2% de las muestras fue positivo a nueve helmintos y/o a un protozoo. Los parasitos con la mayor prevalencia fueron nematodos del orden Ascaridida y un cestodo Spirometra. Otros endoparasitos identificados incluyeron Capillaria sp. , Toxocara canis, Toxascaris leonina, Filaroides osleri, nematodos ancylostomatidos, Trichuris sp. , Taenia sp. , e Isospora sp. Las cargas parasitarias fueron mayores durante el invierno y en areas con mas perros domesticos. Todas estas especies y generos son reportados por primera vez en el zorro de Darwin.
Genetic Organization of the aprX-lipA2 Operon Affects the Proteolytic Potential of Pseudomonas Species in Milk

Psychrotolerant Pseudomonas species are a main cause of proteolytic spoilage of ultra-high temperature (UHT) milk products due to the secretion of the heat-resistant metallopeptidase AprX, which is encoded by the first gene of the aprX-lipA2 operon. While the proteolytic property has been characterized for many different Pseudomonas isolates, the underlying aprX-lipA2 gene organization was only described for a few strains so far. In this study, the phylogenomic analysis of 185 Pseudomonas type strains revealed that the presence of aprX is strongly associated to a monophylum composed of 81 species, of which 83% carried the aprX locus. Furthermore, almost all type strains of known milk-relevant species were shown to be members of the three monophyletic groups P. fluorescens, P. gessardii, and P. fragi. In total, 22 different types of aprX-lipA2 genetic organizations were identified in the genus, whereby 31% of the species tested carried the type 1 operon structure consisting of eight genes (aprXIDEF prtAB lipA2). Other genetic structures differed from type 1 mainly in the presence and location of genes coding for two lipases (lipA1 and lipA2) and putative autotransporters (prtA and prtB). The peptidase activity of 129 strains, as determined on skim milk agar and in UHT-milk, correlated largely with different aprX-lipA2 gene compositions. Particularly, isolates harboring the type 1 operon were highly proteolytic, while strains with other operon types, especially ones lacking prtA and prtB, exhibited significantly lower peptidase activities. In conclusion, the phylogenomic position and the aprX-lipA2 gene organization specify the proteolytic potential of Pseudomonas isolates. In addition, however, an interplay of several environmental factors and intrinsic traits influences production and activity of AprX, leading to strain-specific proteolytic phenotypes.
Enhancement of autonomous vehicle control via the contributions of big data analysis

The paper presents a novel road surface estimation method using a big-data analysis approach. The generation of the estimation model is based on the pace regression technique, which utilizes the large amount of data provided by the sensors of autonomous vehicles. The result of the estimation is used in the lateral control design of vehicles. The control design is based on the LPV method, in which the result of the road surface estimation is used as a scheduling variable. The effectiveness and the performances of the proposed control system are examined through a comprehensive simulation which is conducted in a vehicle simulation environment.
Periocular Biometrics in Head-Mounted Displays: A Sample Selection Approach for Better Recognition

Virtual and augmented reality technologies are increasingly used in a wide range of applications. Such technologies employ a Head Mounted Display (HMD) that typically includes an eye-facing camera and is used for eye tracking. As some of these applications require accessing or transmitting highly sensitive private information, a trusted verification of the operator’s identity is needed. We investigate the use of HMD-setup to perform verification of operator using periocular region captured from inbuilt camera. However, the uncontrolled nature of the periocular capture within the HMD results in images with a high variation in relative eye location and eye-opening due to varied interactions. Therefore, we propose a new normalization scheme to align the ocular images and then, a new reference sample selection protocol to achieve higher verification accuracy. The applicability of our proposed scheme is exemplified using two handcrafted feature extraction methods and two deep-learning strategies. We conclude by stating the feasibility of such a verification approach despite the uncontrolled nature of the captured ocular images, especially when proper alignment and sample selection strategy is employed.
Wavelength-dependent xanthophyll cycle activity in marine microalgae exposed to natural ultraviolet radiation

The wavelength dependency of xanthophyll cycling in two marine microalgae (Thalassiosira weissflogii and Dunaliella tertiolecta) was studied by establishing biological weighting functions (BWFs) during exposure to natural ultraviolet radiation. High-(HL) and low-(LL) light-acclimated cultures of both species were exposed outdoors for up to 60 min under a series of UVR (280–400 nm) cut-off filters, after which the de-epoxidation state of xanthophyll cycle pigments, radiocarbon assimilation and photochemical quantum yield were measured. Exposures were repeated 4–8 times during the daily cycle to create exposure–response curves for each wavelength condition. UVR affected the three target processes significantly in both species and biological weights increased with decreasing wavelength, particularly in the UVBR region (280–315 nm). Minor wavelength dependency was observed between 315 and 360 nm. After BWF normalization to 300 nm, the LL cultures showed highly similar responses when comparing the three target processes, while the BWFs for the HL cultures differed significantly. The observed enhanced xanthophyll cycling activity in the UVR region implied that xanthophylls had an active role in diminishing UVR stress. However, this enhancement seems to be an indirect effect of damage within the dark reactions of photosynthesis. Hence, another vital target process further downstream in the photosynthetic process, possibly involved in the dark reactions, seems to be responsible for the high similarity in BWFs.
ICD-10 Coding of Spanish Electronic Discharge Summaries: An Extreme Classification Problem

Objective: Medical coding is used to identify and standardize clinical concepts in the records collected from healthcare services. The tenth revision of the International Classification of Diseases (ICD-10) is the most widely-used coding with more than 11,000 different diagnoses, affecting research, reporting, and funding. Unfortunately, ICD-10 code sets tend to follow biased, unbalanced, and scattered distributions. These distribution attributes, along with high lexical variability, severely restrict performance when coded clinical records are used to infer code sets in uncoded records. To improve that inference, we explore a combination of example-based methods optimized to capture codes with different appearance frequencies in data sets. Materials and Methods: The proposed exploration has been carried out on Spanish hospital discharge reports coded by experts, excluding all sentences without any biomedical concept. Representations based on semantic and lexical features are explored, using both global and label-specific attributes. In turn, algorithms based on binary outputs, groups of subsets and extreme classification are compared. Lists of codes together with their confidence values (certainty probabilities) are suggested by each method. Results: Diverse spectral behaviors are shown for each method. Binary classifiers seem to maximize the capture of more popular codes, while extreme classifiers promote infrequent ones. In order to exploit such differences, ensemble approaches are proposed by weighting every output code according to the method, confidence value and appearance frequency. The rule-based combination reaches a 46% Precision at 10 ( $P \text{@} 10$ ), which means a 15% improvement over the best individual proposal. Conclusion: Assembling methods based on weighting each code according to training frequency and performance can achieve better overall Precision scores on extreme distributions, such as ICD-10 coding.
Reliability Evaluation of Turbo Decoders Implemented on SRAM-FPGAs

Turbo codes are widely used in satellite communications. When a Turbo decoder is implemented on a Field Programmable Gate Array (FPGA) in a space platform, it will suffer Single Event Upsets (SEUs) that can cause failures and disrupt communications. In this paper, the reliability of Turbo decoders implemented on FPGAs is evaluated. The Turbo decoder with Log-MAP algorithm is implemented on an SRAM-FPGA. Then, fault injection experiments are conducted to simulate the effects of SEU on the user memory and on the configuration memory of the Turbo decoder. Experimental results show that, for user memory, the SEU tolerance rate is over 95%, and the effect of SEU is related to the iteration period, bit position and Signal to Noise Ratio (SNR). In particular, SEUs on the control/address registers and on the interleaving table have a larger impact than on other registers or memories. For the configuration memory, the SEU tolerance rate is higher than 86%, and decreases as SNR increases. In general, the Turbo decoder exhibits a high reliability against SEUs, and the user memory is more reliable than the configuration memory.
Switch Level Time Simulation of CMOS Circuits with Adaptive Voltage and Frequency Scaling

Design and test validation of systems with adaptive voltage-and frequency scaling (AVFS) requires timing simulation with accurate timing models under multiple operating points. Such models are usually located at logic level and compromise accuracy and simulation speed due to the runtime complexity.This paper presents the first massively parallel time simulator at switch level that uses parametric delay modeling for efficient timing-accurate validation of systems with AVFS. It provides full glitch-accurate switching activity information of designs under varying supply voltage and temperature. Offline statistical learning with regression analysis is employed to generate polynomials for dynamic delay modeling by approximation of the first-order electrical parameters of CMOS standard cells. With the parallelization on graphics processing units and simultaneous exploitation of multiple dimensions of parallelism the simulation throughput is maximized and scalable-design space exploration of AVFS-based systems is enabled. Results demonstrate the accuracy and efficiency with speedups of up to 159× over conventional logic level time simulation with static delays.
32 Gbps Data Transmission With 2D Beam-Steering Using a Silicon Optical Phased Array

We demonstrate optical wireless transmission with two-dimensional (2D) beam-steering using an optical phased array (OPA). The OPA consists of 64-elements of electro-optic p-i-n phase shifters and thermo-optic tunable n-i-n grating radiators, employed for 2D beam-steering in the transversal and longitudinal directions. The design of the transmitter and receiver device parameters allowed the error-free transmission of 32 Gbps data over a distance of 3 m. The beam-steering range covered 46.0°/10.2° in the transversal/longitudinal direction with a beam divergence of 0.7°/0.9°. When compared with a transmission over fiber, it was confirmed that free-space transmission through the OPA did not degrade the quality of the tens of Gbps signals.
A Survey on Sequential Recommendation

In modern e-commerce, the user’s sequential behavior contains a wealth of information. According to the user’s historical interaction behavior information, modeling the user’s preferences to predict the continuous items that the user may interact with is the main task of the sequential recommendation system. In this article, we conducted a systematic and comprehensive review of sequential recommendation systems based on user interaction behavior. Specifically, first of all, we briefly introduce the existing general recommendation methods, and point out the shortcomings of the general recommendation method, and then systematically expound the sequence recommendation, and finally summarize and forecast.
Imitation Learning for High Precision Peg-in-Hole Tasks

Industrial robot manipulators are not able to match the precision and speed with which humans are able to execute contact rich tasks even to this day. Therefore, as a means to overcome this gap, we demonstrate generative methods for imitating a peg-in-hole insertion task in a 6-DOF robot manipulator. In particular, generative adversarial imitation learning (GAIL) is used to successfully achieve this task with a $6 \mu\mathrm{m}$ peg-hole clearance on the Yaskawa GP8 industrial robot. Experimental results show that the policy successfully learns within 20 episodes from a handful of human expert demonstrations on the robot (i.e., < 10 tele-operated robot demonstrations). The insertion time improves from > 20 seconds (which also includes failed insertions) to < 15 seconds, thereby validating the effectiveness of this approach.
A Scan Matching Method For Quadruped Robots In Outdoor Environment

In the outdoor unstructured environment, the relative displacement and rotation angle of the continuous laser scans are large due to the dynamic motion of the quadruped robots. Without good initial value, widely-used scan matching method ICP is prone to converge to a local optimum. In this paper, an improved Super 4PCS method based on fast and robust point cloud segmentation is proposed. Through point cloud segmentation preprocessing, unstable obstacles such as vegetation and leaves are removed and only large object point clouds with obvious features are extracted. More efficient point clouds to be registered are provided to Super 4PCS registration algorithm. Experiments prove that the improved method can be applied to quadruped robots in outdoor unstructured environment, speed up the matching and improve the accuracy of point cloud registration.