Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome‐wide association studies identify putative pleiotropic locus mediating drought tolerance in sorghum

Abstract Drought is a key constraint on plant productivity and threat to food security. Sorghum ( Sorghum bicolor L. Moench), a global staple food and forage crop, is among the most drought‐adapted cereal crops, but its adaptation is not yet well understood. This study aims to better understand the genetic basis of preflowering drought in sorghum and identify loci underlying variation in water use and yield components under drought. A panel of 219 diverse sorghum from West Africa was phenotyped for yield components and water use in an outdoor large‐tube lysimeter system under well‐watered (WW) versus a preflowering drought water‐stressed (WS) treatment. The experimental system was validated based on characteristic drought response in international drought tolerant check genotypes and genome‐wide association studies (GWAS) that mapped the major height locus at QHT7.1 and Dw3. GWAS further identified marker trait associations (MTAs) for drought‐related traits (plant height, flowering time, forage biomass, grain weight, water use) that each explained 7–70% of phenotypic variance. Most MTAs for drought‐related traits correspond to loci not previously reported, but some MTA for forage biomass and grain weight under WS co‐localized with staygreen post‐flowering drought tolerance loci (Stg3a and Stg4). A globally common allele at S7_50055849 is associated with several yield components under drought, suggesting that it tags a major pleiotropic variant controlling assimilate partitioning to grain versus vegetative biomass. The GWAS revealed oligogenic variants for drought tolerance in sorghum landraces, which could be used as trait predictive markers for improved drought adaptation.
Multiclass classification for DDoS attacks using LSTM time-series model

As the penetration of the Internet into the interior of the third world keeps on increasing, more people come onto the Internet to communicate and globalize this behemoth. Thus, generating and consuming large amounts of data. To service the increasing number of customers and their data needs, we need to set up new servers increasingly. Having many people using the Internet can also be a problem as they might be a part of a DDoS attack unknowingly. In Botnet, a Bot-Herder infects many computers with Botnet malware, remotely controlling multiple devices, and makes them accomplices in DDoS attacks. People are getting included in a botnet sphere unknowingly. The traditional cyber security paradigm has become obsolete because of the conventional approaches. Thus, machine learning has become a necessity in DDoS intrusion detection. In DDoS attacks, multiple malicious packets are sent to the server to stagger its bandwidth, buffer, and CPU power. This paper proposes a Time-Series-based Recurrent Neural Network (RNN) model using the LSTM network employed on the CICDDoS2019 dataset. The model outperforms the previous benchmarked models as it achieves maximum value with a 1-layer LSTM model in multiclass grouped classification. The 1- layer LSTM values have F1-Score, Recall, and Precision are 0.980, 0.975, and 0.988, respectively.
Laterality and Sex Differences of Human Lateral Habenula Afferent and Efferent Fiber Tracts

Introduction The lateral habenula (LHb) is an epithalamic nucleus associated with negative valence and affective disorders. It receives input via the stria medullaris (SM) and sends output via the fasciculus retroflexus (FR). Here, we use tractography to reconstruct and characterize this pathway. Methods Multi-shell human diffusion magnetic resonance imaging (dMRI) data was obtained from the human connectome project (HCP) (n = 20, 10 males) and from healthy controls (n = 10, 6 males) scanned at our institution. We generated LHb afferents and efferents using probabilistic tractography by selecting the pallidum as the seed region and the ventral tegmental area as the output target. Results We were able to reconstruct the intended streamlines in all individuals from the HCP dataset and our dataset. Our technique also aided in identification of the LHb. In right-handed individuals, the streamlines were significantly more numerous in the left hemisphere (mean ratio 1.59 ± 0.09, p = 0.04). In left-handed individuals, there was no hemispheric asymmetry on average (mean ratio 1.00 ± 0.09, p = 1.0). Additionally, these streamlines were significantly more numerous in females than in males (619.9 ± 159.7 vs. 225.9 ± 66.03, p = 0.04). Conclusion We developed a method to reconstruct the SM and FR without manual identification of the LHb. This technique enables targeting of these fiber tracts as well as the LHb. Furthermore, we have demonstrated that there are sex and hemispheric differences in streamline number. These findings may have therapeutic implications and warrant further investigation.
Enhancing sentiment analysis performance with blend of features and deep learning algorithms

In Sentiment analysis, writer's view about products, events, government policies, services, topics, organizations, and individuals is determined through the text written by them on social media platforms like Twitter, and Facebook. Previous researchers focused on getting sentiment either from syntactic and lexical features or by using pre-trained word embeddings. We have combined GloVe pre-trained word embedding,lexicon based features, and Bag of words to form an ensemble feature(FE). We have considered two Twitter datasets namely, US Airlines Twitter, and Stanford Twitter Sentiment Gold (STSGd). We have applied three machine learning algorithms on TF-IDF features, three deep learning algorithms on GloVe word embedding feature. We have proposed a hybrid model which is a combination of LSTM and CNN model with ensemble feature. The proposed model has performed around 2% better on US Airlines Twitter dataset and around 8% better on STS-Gold dataset than state-of-the-art-results. It is observed that ensemble feature improved the performance of algorithms.
Wrinkling of Elastic Cylinders With Material Properties Varying in Radial Direction

Although the instability of graded elastic cylinders has been analyzed by many researchers, most of them focused on the core-shell cylinders and film-substrate structures with inhomogeneous Young’s modulus. For a radially graded elastic cylinder subjected to the axial compression, the variation of Poisson’s ratio may result in the radial and circumferential stresses and thereby affects the critical condition of instability. By assuming linear elasticity with nonlinear kinematics, the governing equation for the incremental stress field is developed for instability analysis of the cylinder with radially graded material properties (Young’s modulus and Poisson’s ratio). Considering the arbitrariness of material properties, the state space technique is implemented and a semi-analytical solution is acquired. The obtained solution is validated by the finite element results. Numerical examples show that the critical condition of instability for graded elastic cylinders is related to whether Poisson’s ratio is assumed to be constant.
Seasonal and Semi‐Diurnal Variations in Cloud‐Phase Characteristics Over the Southern Himalayas and Adjacent Regions as Observed by the Himawari‐8 Satellite

Investigated in this study are the seasonal and semi‐diurnal variations of the cloud‐phase climatology over the southern Himalayas and adjacent regions by using 5 years of Himawari‐8 cloud‐product data (2016–2020). The four selected regions from south to north are the Gangetic Plains (I), Himalayan foothills (II), southern slope of the Himalayas (III), and the Himalayan–Tibetan Plateau region (IV), respectively. Results showed that the cloud frequency of cloud gradually increases from region I via II to III, then sharply decreases as the altitude increases up to IV. Ice‐phase clouds occurs more frequently over regions I/II (III/IV) during boreal summer (winter), while water‐phase cloud has a higher frequency over region III/IV (I/II) in summer (winter), respectively. All of the different cloud phases have a clear seasonal cycle. Furthermore, the semi‐diurnal variation shows that water‐phase clouds tends to have an occurrence frequency peak around noon (in the later afternoon) over regions I/II/III in summer (winter), whereas over region IV it only occurs more in the morning in both seasons. Ice‐phase cloud generally occurs more in the late afternoon in both boreal summer and winter, especially over region IV. Further analysis showed that the differences in the cloud‐phase characteristics over the southern Himalayas and adjacent regions are likely related to the interactions of the synoptic‐scale circulation, the topography, and the water vapor transport.
Bacterial Enoyl-Reductases: The Ever-Growing List of Fabs, Their Mechanisms and Inhibition

Enoyl-ACP reductases (ENRs) are enzymes that catalyze the last step of the elongation cycle during fatty acid synthesis. In recent years, new bacterial ENR types were discovered, some of them with structures and mechanisms that differ from the canonical bacterial FabI enzymes. Here, we briefly review the diversity of structural and catalytic properties of the canonical FabI and the new FabK, FabV, FabL, and novel ENRs identified in a soil metagenome study. We also highlight recent efforts to use the newly discovered Fabs as targets for drug development and consider the complex evolutionary history of this diverse set of bacterial ENRs.
Three-dimensional flow and load characteristics of flexible revolving wings at low Reynolds number

This study explores the flow field and fluid-dynamic loads generated by revolving low-aspect-ratio flat plate wings undergoing a revolving motion starting from rest. Three wings with different degree of chordwise flexural stiffness (i.e., rigid, moderate flexibility and high flexibility) have been tested in order to investigate the influence of the wing flexibility. The wings have an angle of attack of 45 deg in their undeformed condition. The measurements have been performed in a water tank at a Reynolds number of 10,000 based on the chord length and terminal velocity at the 75% span position. The experimental campaign consists of phase-locked tomographic particle image velocimetry measurements complemented with simultaneous force measurements The three-dimensional velocity fields are captured in three measurement volumes positioned side-by-side along the span of the wing for different phases of the revolving motion, generating a time-resolved volumetric velocity field data set. Subsequently, from the velocity data the pressure fields are reconstructed as well as the loads acting on the wing.
Study on the parameter evaluation of dielectric relaxation and electric conduction for the thermal aged xlpe cables by DCIC-Q(T) method

This paper focuses on the evaluation of dielectric relaxation and electric conduction of XLPE insulation by using a new diagnosis technique-direct current integrated charge (DCIC-Q(t)) method. Thermal aged full-sized cables were studied under different voltages with a long duration time. The selected time benefits to distinguish the relaxation and conduction processes from charge carrier transports. Relaxation time τ of different aged cables are calculated by the analysis of space charges and conduction charges from measurements. Through comparison of charge ratio k and τ, the determination of relaxation by space charges and conduction behaviours can be obtained. The results pointed out that the measured charges increase with the time, especially for high voltage measurements (4 kV and 6 kV). Dielectric relaxation plays an important role in the slight increase of charges within 1000 s. While, charge injection and conduction can lead to the obvious increase of charges under high voltages after 1000 s. In addition, the τ decreases with the increase in voltage. Severe thermal aged cables present a high charge ratio k, permittivity and conductivity. However, the mild aged cables indicate a slight decrease of high charge ratio k, permittivity and conductivity. The measurements and analysis of charges under long time are of importance for dielectric parameter and aging evaluation of XLPE cables.
Absolute Reliability of Gait Parameters Acquired With Markerless Motion Capture in Living Domains

Purpose: To examine the between-day absolute reliability of gait parameters acquired with Theia3D markerless motion capture for use in biomechanical and clinical settings. Methods: Twenty-one (7 M,14 F) participants aged between 18 and 73 years were recruited in community locations to perform two walking tasks: self-selected and fastest-comfortable walking speed. Participants walked along a designated walkway on two separate days.Joint angle kinematics for the hip, knee, and ankle, for all planes of motion, and spatiotemporal parameters were extracted to determine absolute reliability between-days. For kinematics, absolute reliability was examined using: full curve analysis [root mean square difference (RMSD)] and discrete point analysis at defined gait events using standard error of measurement (SEM). The absolute reliability of spatiotemporal parameters was also examined using SEM and SEM%. Results: Markerless motion capture produced low measurement error for kinematic full curve analysis with RMSDs ranging between 0.96° and 3.71° across all joints and planes for both walking tasks. Similarly, discrete point analysis within the gait cycle produced SEM values ranging between 0.91° and 3.25° for both sagittal and frontal plane angles of the hip, knee, and ankle. The highest measurement errors were observed in the transverse plane, with SEM >5° for ankle and knee range of motion. For the majority of spatiotemporal parameters, markerless motion capture produced low SEM values and SEM% below 10%. Conclusion: Markerless motion capture using Theia3D offers reliable gait analysis suitable for biomechanical and clinical use.
The Effects of Nonstate Shareholder on the Excess Perquisites: Evidence from Chinese Mixed Ownership Reform

ABSTRACT Excess perquisites have hindered the high-quality development of state-owned enterprises, while mixed ownership reform can improve corporate governance by acquiring shareholdings and appointing directors, supervisors, and managers. This paper empirically tests the impact of mixed ownership reform on excess perquisites and the path. The results show that mixed ownership reform can restrain excess perquisites, and this restraining effect is more significant in competitive industries and local SOEs. Further research shows that mixed ownership reforms can strengthen shareholders’ supervisory power and weaken executives’ control power. The relevant findings still hold after a series of robustness tests and endogeneity tests.
High Diversity of Novel Viruses in the Tree Pathogen Phytophthora castaneae Revealed by High-Throughput Sequencing of Total and Small RNA

Phytophthora castaneae, an oomycete pathogen causing root and trunk rot of different tree species in Asia, was shown to harbor a rich diversity of novel viruses from different families. Four P. castaneae isolates collected from Chamaecyparis hodginsii in a semi-natural montane forest site in Vietnam were investigated for viral presence by traditional and next-generation sequencing (NGS) techniques, i.e., double-stranded RNA (dsRNA) extraction and high-throughput sequencing (HTS) of small RNAs (sRNAs) and total RNA. Genome organization, sequence similarity, and phylogenetic analyses indicated that the viruses were related to members of the order Bunyavirales and families Endornaviridae, Megabirnaviridae, Narnaviridae, Totiviridae, and the proposed family “Fusagraviridae. ” The study describes six novel viruses: Phytophthora castaneae RNA virus 1–5 (PcaRV1-5) and Phytophthora castaneae negative-stranded RNA virus 1 (PcaNSRV1). All six viruses were detected by sRNA sequencing, which demonstrates an active RNA interference (RNAi) system targeting viruses in P. castaneae. To our knowledge, this is the first report of viruses in P. castaneae and the whole Phytophthora major Clade 5, as well as of the activity of an RNAi mechanism targeting viral genomes among Clade 5 species. PcaRV1 is the first megabirnavirus described in oomycetes and the genus Phytophthora.
The Effect of Estrogen Receptor Status on Survival in Breast Cancer Patients in Ethiopia. Retrospective Cohort Study

Background There is wide global variance in survival from breast cancer, both in developed and developing country. However, the effect of estrogen receptor status has not been widely evaluated in Ethiopia where the incidence of breast cancer is rapidly increasing. Hence, the current study aimed to determine the effect of estrogen receptor status on the overall survival of breast cancer patients who were treated at Black Lion Specialized Hospital, Ethiopia. Methods In this institution-based retrospective cohort study a total of 368 study participants were included with a one-to-one ratio of estrogen receptor negative to estrogen receptor positive. The main outcome of interest for this study was death due to breast cancer. The authors compared the women with estrogen receptor-positive and estrogen receptor-negative breast cancer for overall survival rate using log rank test. The incidence density rate of mortality was calculated for each exposed and non-exposed variable. The effect of estrogen receptor status on breast cancer mortality was estimated using the Cox proportional hazards model. Results The incidence density rate of mortality among breast cancer patients for estrogen receptor positive were found to be 5.48 (95% CI=3.94–7.64) per 1,000 person years observation; while for estrogen negative receptor status the mortality rate was found to be 10.47 (95% CI=8.19–13.37) per 1,000 person years observation. In the Cox regression analysis after ful adjustments for confounder variables, the mortality event risk was 32% higher among estrogen receptor negative (HR=1.32; 95% CI=1.08–2.91) as compared to estrogen receptor positive breast cancer patients. Conclusion We have found that the incidence density rate of mortality among breast cancer patients was significantly higher in the estrogen receptor negative groups. Therefore, clinicians should give careful attention to the impact of estrogen receptor negative status on the overall outcome of clients.
Voltage/Frequency Regulation With Optimal Load Dispatch in Microgrids using SMC based Distributed Cooperative Control

Majority of the contemporary hierarchical control strategies for microgrids are either centralized or distributed, relying on leader-follower (or master-slave) consensus for secondary frequency and/or voltage regulation. Thus, in either case, these strategies are susceptible to single-point-failure (SPF). This potential research gap motivated the authors to propose a distributed three-layered hierarchical control strategy applicable to droop-controlled islanded AC microgrids. The proposed strategy can simultaneously ensure (i) frequency and voltage regulation of DGs within the prescribed frequency and voltage deviation limits as per IEC 60034-1 standard (i.e., ±2% and ±5%, respectively) without relying on leader-follower consensus at the secondary level, (ii) distributed economic dispatch of active power with minimum error, and (iii) distributed reactive power dispatch with plausible error. The proposed technique is fully distributed and shares the computation and communication burden among the neighboring nodes using a sparse communication network, thus, it is insusceptible to SPF. The feasibility of the proposed technique is guaranteed through various time-domain based numerical simulations executed in Matlab/Simulink under different loading conditions and microgrid expansion.
Theophylline Induces Remyelination and Functional Recovery in a Mouse Model of Peripheral Neuropathy

Charcot-Marie-Tooth disease (CMT) is a large group of inherited peripheral neuropathies that are primarily due to demyelination and/or axonal degeneration. CMT type 1A (CMT1A), which is caused by the duplication of the peripheral myelin protein 22 (PMP22) gene, is a demyelinating and the most frequent CMT subtype. Hypermyelination, demyelination, and secondary loss of large-caliber axons are hallmarks of CMT1A, and there is currently no cure and no efficient treatment to alleviate the symptoms of the disease. We previously showed that histone deacetylases 1 and 2 (HDAC1/2) are critical for Schwann cell developmental myelination and remyelination after a sciatic nerve crush lesion. We also demonstrated that a short-term treatment with Theophylline, which is a potent activator of HDAC2, enhances remyelination and functional recovery after a sciatic nerve crush lesion in mice. In the present study, we tested whether Theophylline treatment could also lead to (re)myelination in a PMP22-overexpressing mouse line (C22) modeling CMT1A. Indeed, we show here that a short-term treatment with Theophylline in C22 mice increases the percentage of myelinated large-caliber axons and the expression of the major peripheral myelin protein P0 and induces functional recovery. This pilot study suggests that Theophylline treatment could be beneficial to promote myelination and thereby prevent axonal degeneration and enhance functional recovery in CMT1A patients.
Selection of Beamforming in 5G MIMO scenarios using Machine Learning approach

In new generation networks, 5G and 6G networks, intelligent mechanisms based on artificial intelligence algorithms are playing a relevant role in the performance improvement at different network levels. In 5G networks, different techniques are used, such as MIMO systems, and its network infrastructure is utilized by diverse services, for instance, vehicular communications. Thus, the description and the tracing of a communication scenario needs a large volume of data. Because of the difficulty to implement actual 5G networks, there is a lack of datasets containing complete 5G scenarios, the data is not enough or contain imbalanced classes to properly train machine learning (ML) algorithms. In this context, we propose a method to increase the amount of data to improve the machine learning performance of some classification models, specifically Random Forest, Multilayer Perceptron, and k-Nearest Neighbors. In the experimental results of the test phase, considering the inclusion of synthetic data, Random Forest, Multilayer Perceptron and k-Nearest Neighbors reached macro F1 scores of 0.9341, 0.9241 and 0.9456, respectively, which are superior to the results obtained when training with the original data only.
Indoxyl Sulfate Induces Oxidative Changes in Plasma and Hemolysate

The deteriorating function of the kidneys in chronic kidney disease (CKD) is associated, among other things, with the retention of many unnecessary metabolic products in the body. Indoxyl sulfate (IS) belongs to the group of uremic toxins with a high protein binding affinity. Moreover, this compound can generate oxidative stress. We hypothesized that a high concentration of IS might induce oxidative changes in erythrocytes and plasma components, and could therefore contribute to CKD progression. In this study, we evaluated the influence of IS on the oxidative stress parameters in plasma and hemolysate. Moreover, as a result of the action of IS, we observed a decrease in the total antioxidant capacity and a change in the activity of catalase and superoxide dismutase in hemolysate and plasma. The obtained results indicate that IS induces oxidative damage to hemolysate and plasma components. Greater changes in the parameters of oxidative stress were observed in hemolysate than in plasma treated with indoxyl sulfate. The obtained results suggest that the increased concentration of IS in patients with chronic kidney disease may lead to a decrease in the lifespan of erythrocytes in their bloodstream.