Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural and Functional Characterization of a Unique AP Endonuclease From Deinococcus radiodurans

Various endogenous and exogenous agents cause DNA damage, including apurinic/apyrimidinic (AP) sites. Due to their cytotoxic effects, AP sites are usually cleaved by AP endonuclease through the base excision repair (BER) pathway. Deinococcus radiodurans, an extraordinary radiation-resistant bacterium, is known as an ideal model organism for elucidating DNA repair processes. Here, we have investigated a unique AP endonuclease (DrXth) from D. radiodurans and found that it possesses AP endonuclease, 3′-phosphodiesterase, 3′-phosphatase, and 3′–5′ exonuclease but has no nucleotide incision repair (NIR) activity. We also found that Mg2+ and Mn2+ were the preferred divalent metals for endonuclease and exonuclease activities, respectively. In addition, DrXth were crystallized and the crystals diffracted to 1.5 Å. Structural and biochemical analyses demonstrated that residue Gly198 is the key residue involved in the substrate DNA binding and cleavage. Deletion of the drxth gene in D. radiodurans caused elevated sensitivity to DNA damage agents and increased spontaneous mutation frequency. Overall, our results indicate that DrXth is an important AP endonuclease involved in BER pathway and functions in conjunction with other DNA repair enzymes to maintain the genome stability.
Feed efficiency and maternal productivity of Bos indicus beef cows

This study evaluated 53 primiparous cows (36.8±1.23 months old and 484±40.9 kg of body weight) performance tested (GrowSafe® System) from 22±5 to 190±13 days of lactation in order to obtain daily dry matter intake (DMI). The animals received a high-forage diet (forage-to-concentrate ratio of 90:10). Milk production of the cows was evaluated three times by mechanical milking and the energy-corrected milk yield (ECMY) was calculated. Energy status (through the indicators glucose, cholesterol, triglycerides, and β-hydroxybutyrate), protein status (indicators albumin, urea, and creatinine), mineral status (indicators calcium, phosphorus, and magnesium), and hormonal status (indicators insulin and cortisol) were estimated four times throughout lactation. The residual feed intake (RFI) of cows was calculated considering DMI, average daily gain (ADG) and mid-test metabolic weight (BW0.75) obtained in early lactation (from 22±5 to 102±7 days), and the animals were classified as negative (most efficient) or positive RFI (least efficient). The RFI model explained 53% of the variation in DMI. The mean DMI, ADG, ECMY, and calf weight as a percentage of cow weight were 12.47±2.70 kg DM/day, 0.632±0.323 kg/day, 10.47±3.23 kg/day, and 36.6±5.39%, respectively. Negative RFI cows consumed 11.5% less DM than positive RFI cows, with performance and metabolic profile being similar to those of positive RFI cows, except for a lower milk protein content and higher blood cholesterol concentration. In conclusion, negative (most efficient) and positive RFI (least efficient) Nellore cows, fed an ad libitum high-forage diet, produced similar amounts of milk, fat and lactose and had similar subcutaneous fat thickness, weight, calf weight as a percentage of cow weight, and blood metabolite concentrations (except for cholesterol). Therefore, there are economic benefits to utilizing RFI in a cow herd since cattle had decreased DMI with similar overall performance, making them more profitable due to lower input costs.
Energy-Efficient Monitoring of Fire Scenes for Intelligent Networks

In the current surveillance networks, a vast amount of data is generated from different sources, resulting in big data. Such data need intelligent technologies and big data analytics for its realtime analysis to provide different services. This article describes an efficient artificial intelligence and big data analytics-assisted system for real-time fire scene analysis in surveillance networks. The proposed system is based on intelligent independent and subordinate agents, where each agent has a different task to report to the fire brigade and disaster management instantly. A deep yet efficient CNN is utilized in the system for feature extraction, classification, localization, and detection of fire in video frames. When the fire is detected in the frames, a fire alert is instantly sent to the emergency department and all agents immediately start their processing for checking the severity and growth rate of the fire, recognizing the scene and all objects on fire, and evacuation monitoring. Each agent of the system instantly sends information to disaster management to stop the loss of precious human lives and minimizes other economic and ecological loss. Experimental validation shows the promising results compared to existing systems. It is believed that using such a system is the demand of the time to save humanity from massive fire disasters and can make the current surveillance networks more intelligent.
Substation Equipment Thermal Fault Diagnosis Model Based on ResNet and Improved Bayesian Optimization

At present, infrared detection technology is widely used to diagnose the thermal fault of substation equipment, and it is needed to use computers to intelligently handle the progressively increasing infrared images. A model based on improved Bayesian optimization to diagnose thermal fault is proposed in this paper. Firstly, in order to utilize the effective network structure of image classification, ResNet10 and ResNet14 with much smaller parameter size are created based on CNN and residual block. Secondly, Bayesian optimization based on Gauss process and expected improvement is adopted to select hyperparameter, and the constrains of validation accuracy and parameter size are set to improve the performance of the algorithm. Experiment result shows that the improved Bayesian optimization can converge faster and obtain a smaller converge value. Compared with Alexnet, VGG-16, ResNet18, the proposed model has a highest accuracy and smallest parameter size, which can diagnose the thermal fault more effectively.
Presentation and Outcome of Acute Necrotizing Encephalopathy of Childhood: A 10-Year Single-Center Retrospective Study From Hong Kong

Acute necrotizing encephalopathy (ANE) is a rare disease in childhood. We reviewed the 10-year data from a local pediatric department, reported the clinical characteristics, laboratory tests, neuroimaging findings, and outcome of the acute necrotizing encephalopathy cases and identified the potential factors affecting the outcome. Eight episodes of acute necrotizing encephalopathy among 7 patients were recorded, in which all of them had an initial presentation of fever and seizure. We identified that acute necrotizing encephalopathy patients with a severe score of Glasgow Coma Scale (GCS) on presentation, brainstem involvement in magnetic resonance imaging (MRI) of the brain, and higher MR imaging scores were associated with worse outcome. Association of outcome with acute necrotizing encephalopathy severity score, platelet count, and serum alanine aminotransferase level did not reach a statistically significant level. These results highlight the importance of combined clinical, laboratory, and neuroimaging findings in determining the prognostic outcome of acute necrotizing encephalopathy patients.
Adapting Neuro Fuzzy System for Oil Spills Detection From Sea Sar Images

Marine oil slicks represent both an environmental and Economical concern since they seriously effect fragile marine and coastal ecosystem. Accurate detection of oil slicks can help identification and management of polluted areas and assessment of oil slicks drift in order to protect the coasts. Today, Synthetic Aperture Radar (SAR) satellites are the main data sources to detect the oil spills discharged into the sea with sufficient accuracies. In this study, we present SAR image classification process to extract dark spots using neuro-fuzzy systems. Thus, we first performed multi-scale analysis image to extract textural descriptors that can characterize the surface roughness. For this purpose, we used the distance measure similarity of kullback-leibler (KLD). Then, we integrated these textual descriptors into Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier to extract dark spots. The proposed process was tested on SAR image covering Algerian coasts and gave good spots detection with minimum false alarms.
The chloroplasts of Euglena may have evolved from symbiotic green algae

It is proposed that the chloroplasts of Euglena have arisen from the progressive reduction of endosymbiotic green algae. The theory is supported by the presence of a third membrane around the chloroplasts of Euglena which is not endoplasmic reticulum (ER) in origin and may be derived from the plasmalemma of the original symbiont. In addition, Euglena is the only organism in which chloroplast loss can be induced experimentally. Dinoflagellate chloroplasts are also surrounded by three membranes, and it is proposed that they too evolved from symbiotic eucaryotic algae.
Assessment of cognitive load from bio-potentials measured using wearable endosomatic device

Cognitive load is the amount of mental resources required to execute a task. Psychophysiological researches show variations in the electrodermal activity of the skin with variations in cognitive load. State of the art studies mostly use exosomatic approach like Galvanic Skin Resistance (GSR) for measuring electrodermal activity. However, recent advances made in implementation of wearable endosomatic devices motivated us to use endosomatic bio-potentials measured using a wearable device for assessing cognitive load. In the present study, we have studied the effect of cognitive load on the endosomatic human bio-potentials. We have used a 4-channel high-resolution wearable device for measuring the bio-potentials during the execution of low and high cognitive load versions of mental addition tasks. Results suggest that there is significant variation in endosomatic electrodermal activity for various levels of cognitive load which can be used to classify low and high mental workload successfully. We have observed and analyzed bio-potentials recorded from both hands and feet. Combining potentials measured from all 4 locations, the classification accuracy obtained using a random forest classifier is approximately 83.4% and the corresponding f-score is 0.83. An optimum set of features selected increases the accuracy further to 86.5%. Moreover, a feature level fusion of GSR and endosomatic signal features gives an accuracy of 89.4%. Thus, the proposed approach provides immense scope for assessing mental workload based on bio-potentials recorded using a wearable device.
Framework for Sentiment-Driven Evaluation of Customer Satisfaction With Cosmetics Brands

Cosmetics brand managers’ efforts in monitoring customer satisfaction and service quality have suffered due to the lack of effective analysis methods. In order to derive more comprehensive and objective insights into customer opinions on product quality and preferences for cosmetics brands, this study derived an online-review-based process for evaluation of customer satisfaction. The present study developed a systematic approach to the evaluation of relative customer satisfaction with cosmetics brands via sentiment analysis and statistical data analysis, and interpreted the determinants of positive and negative opinions via Term Frequency-Inverse Document Frequency (TF-IDF) analysis. To illustrate the efficacy and applicability of the proposed approach, an empirical case study applying it to the global top 26 cosmetics brands was conducted, which evaluated relative customer satisfaction with brands and examined the main causes of positive and negative opinions. The proposed approach is expected to be employed by cosmetics companies to realize or improve satisfaction with the brands that customers evaluate. Furthermore, we hope that it can be used as a source of fundamental data that could be applied to efforts to improve both brand competitiveness and provision of systematic services.
Active Discriminative Cross-Domain Alignment for Low-Resolution Face Recognition

In real application scenarios, the face images captured by cameras often incur blur, illumination variation, occlusion, and low-resolution (LR), which leads to a challenging problem for many real-time face recognition systems due to a big distribution difference between the captured degraded images and the high-resolution (HR) gallery images. As widespread application of transfer learning in across-visual recognition, we propose a novel active discriminative cross-domain alignment (ADCDA) technique for LR face recognition method by jointly exploring both geometrical and statistical properties of the source domain and the target domain in a unique way. Specifically, the proposed ADCDA-based method contains three key components: 1) it simultaneously reduces the domain shift in both marginal distribution and conditional distribution between the source domain and the target domain; 2) it aligns the data of two domains in the common latent subspace by discriminant locality alignment (DLA); 3) it selects the representative and the diverse samples with an active learning strategy to further improve classification performance. Extensive experiments on six benchmark databases verify that the proposed method significantly outperforms other state-of-the-art predecessors.
Recruitment of Stat4 to the Human Interferon-α/β Receptor Requires Activated Stat2*

Stat4 activation is involved in differentiation of type 1 helper (Th1) T cells. Although Stat4 is activated by interleukin (IL)-12 in both human and murine T cells, Stat4 is activated by interferon (IFN)-α only in human, but not murine, CD4+ T cells. This species-specific difference in cytokine activation of Stat4 underlies critical differences in Th1 development in response to cytokines and is important to the interpretation of murine models of immunopathogenesis. Here, we sought to determine the mechanism of Stat4 recruitment and activation by the human IFN-α receptor. Analysis of phosphopeptide binding analysis suggests that Stat4 does not interact directly with tyrosine-phosphorylated amino acid residues within the cytoplasmic domains of either of the subunits of the IFN-α receptor complex. Expression of murine Stat4 in the Stat1-deficient U3A and the Stat2-deficient U6A cell lines shows that IFN-α-induced Stat4 phosphorylation requires the presence of activated Stat2 but not Stat1. Thus, in contrast to the direct recruitment of Stat4 by the IL-12 receptor, Stat4 activation by the human IFN-α receptor occurs through indirect recruitment by intermediates involving Stat2.
INFLUENCE OF SEED SIZE, PROTEIN CONTENT AND CULTIVAR ON EARLY SEEDLING VIGOR EM WHEAT

Four greenhouse experiments were conducted to study the influence of seed size, protein content and cultivar on seedling vigor of wheat measured as seedling dry weight at 20 days. The simple and partial correlation coefficients among the variables were all positive and significant. No significant interaction occurred between seed size and genotype. The genotypic differences in seedling vigor may lead to its use as a selection criterion in wheat breeding programs.
Multi-Topology Based QoS-Differentiation in RPL for Internet of Things Applications

The rapid development of the Internet of Things (IoT) concept has promoted the presence of routing protocol for low power and lossy network (RPL). Unlike traditional applications, many applications envisioned for IoT networks may have different and sometimes conflicting requirements. In this context, the underlying routing protocol requires to provide quality of service (QoS) for multipurpose IoT and is inevitable. However, the routing approach in RPL is not efficient because default objective functions (OFs) rely on a single metric, which can result in a tradeoff in routing performance, particularly for multipurpose IoT that enchant different QoS requirements in the same network. Although RPL specification allows the use of multiple metrics for parent selection, however, no specific guideline is defined for metric combinations. Besides, many studies have revealed that RPL encounters severe problems in large scale networks as it was mainly designed for low data traffic network. To address these problems, in this paper, we primarily focus on QoS differentiation by exploiting the multi-topology routing feature of the RPL standard. For this, we propose different OFs, which ensures the QoS differentiation at the network level by splitting the physical network virtually into multiple RPL Instances. Each Instance can incorporate different traffic by associating with differing OFs, and routed it through the corresponding virtual network topology. We also present a new parent selection framework based on a multi-attribute decision-making approach that addresses the single routing metric problem in RPL. The extensive simulation results verify that our multi-topology routing approach can support the QoS provisioning and is suitable for large scale networks as compared with standard RPL.
A Novel Nanoprobe Based on Core–Shell Au@Pt@Mesoporous SiO2 Nanozyme With Enhanced Activity and Stability for Mumps Virus Diagnosis

Nanoporous materials with core-shell structure have received lots of attention owing to the great versatility of the functional cores and shells and their potential application in catalysis and biological applications. In this work, a facile method has been developed to synthesize uniform Au@Pt@mesoporous SiO2 nanostructures with high peroxidase-like activity, which had a well-defined core–shell structure with Au nanorods@Pt nanodots as a core and mesoporous SiO2 as a shell. The mesoporous SiO2 shell can not only provide convenient transmission channels but offer a substantial location for accommodation of large biomolecules, such as antibodies and antigens. Here a novel nanoprobe based on Au@Pt@mesoporous SiO2 nanozyme modified with mumps antigens was reported. Notably, the encapsulation of Au@Pt nanorod in mesoporous SiO2 shell was able to hinder the interaction between catalytical nanoparticles and recognition antigens, retaining the catalytic activity of the inner active nanoparticle core. Furthermore, this nanoprobe exhibited an extraordinarily stability and showed excellent activity. As a result, we presented an enzyme linked immunosorbant assay (ELISA) for the diagnosis of mumps virus; this proposed method exhibited good sensitivity to mumps-specific IgM antibodies. The limit of detection can be as low as 10 ng/mL, which was more sensitive compared to the conventional immunoassay. Our results indicated that this nanoprobe hold great promise with opportunities for applications of biosensors, catalysis and biotechnology.
COVID‐19 patients benefit from early antiviral treatment: A comparative, retrospective study

The outbreak of COVID‐19, caused by severe acute respiratory syndrome coronavirus 2, started in December 2019, Wuhan, China. We aimed to figure out the time‐point and duration of using antiviral drugs for receiving the maximal effects in patients with COVID‐19. In this study, we enrolled 129 confirmed COVID‐19 mild to moderate patients who had been treated with antiviral drugs during their hospitalization in Wuhan Union Hospital China. The patients were divided into an early antiviral treatment group and late antiviral treatment group. The demographic data, laboratory tests, the virus clearance time, chest computed tomography scans, and so forth were extracted, calculated, and compared between two groups. Our data showed that the median time from illness onset to initiation of antiviral treatment was 6 days in all patients. The group with early antiviral treatment demonstrated 7 days shorter in the virus clearance time when compared to the group with late antiviral treatment. After virus clearance, the group with early antiviral treatment showed milder illness than the group with late antiviral treatment. Early antiviral treatment could effectively shorten the virus clearance time, and prevent the rapid progression of COVID‐19. Therefore, the COVID‐19 patients should receive combined therapies with antiviral treatment at an early stage.
A histological study of the response of the interrenal cells of the goldfish (Carassius auratus) to treatment with sodium lauryl sulphate

Treatment of goldfish (Carassius auratus) with sub-lethal concentrations of an anionic detergent sodium lauryl sulphate for 1, 2 or 4 weeks produced histological changes in the interrenal (steroidogenic) cells of the head kidney which are indicative of a cellular activation. Significant increases in both the nuclear diameter of these cells and the number of nucleoli contained in the nuclei occurred after 1 week's treatment, although these effects were more pronounced after 2 and 4 weeks. There were little associated alterations in chromaffin cell activity. Preliminary experiments with another pollutant, zinc sulphate, suggest that it exerts a similar action to sodium lauryl sulphate on the interrenal cells. It is suggested the increases in corticosteroid production which occur after exposure of fish to sub-lethal concentrations of pollutants may be important in the development of a generalized disease syndrome and to the long-term success of fish populations.
Peer-to-Peer Energy Sharing Model Between Grid-Connected Residential Wind and Commercial Photovoltaic Prosumers

In this paper, a model to optimise the operation of two prosumers exchanging electricity under the Peer-to-Peer concept is presented. One prosumer is a residential type using a wind energy conversion system, while the second one is a commercial type using a photovoltaic system. The city of Durban, where both wind and solar resources are abundant, is identified to analyse the power flow between the prosumers. The created model intends to optimise the energy exchanged between the micro renewables-based energy generators while minimizing the amount of energy purchased from the grid operating with the Time of Use pricing structure. The simulation results have showed that the created model to optimally dispatch the power exchanged has reduced the prosumers’ combined daily cost of electricity from the grid by 84% while making best use of the on-site renewable energy productions and storage management.
Evaluation of Three Different Vaccination Protocols against EHV1/EHV4 Infection in Mares: Double Blind, Randomized Clinical Trial

EHV1 and EHV4 are the most important herpesviruses in horses. Repeated cases of abortion in mares regularly vaccinated, prompted us to investigate the immune response after vaccination with the same inactivated vaccine, but with three different protocols. Eighteen mares were chosen and randomly divided in three study groups (G1-G2-G3) and a control group (Ctrl). For serologic and PCR investigations nasal swabs, sera and blood were collected. The protocol used in G3 (4 doses) increased the titer recorded by ELISA and seroneutralization (SN). Poor agreement and no correlation were observed in titer values between ELISA and SN and between SN and PCR. A very weak positive correlation between ELISA and PCR was obtained. Seven out of 18 nasal swabs were positive by PCR; none showed viremia and no abortion occurred, regardless of vaccination status and despite active circulation of EHV-1 in the farm at the time of the study. The study was conducted in field conditions, in a susceptible population with a known history of infection and abortion, and among the three protocols, the one proposed in the G1 was the least efficient while the one proposed for the G3, seems to have induced a higher antibody titer in both SN and ELISA.