Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Transcriptomics and Methylomics Reveal Adaptive Responses of Digestive and Metabolic Genes to Dietary Shift in Giant and Red Pandas

Both the giant panda (Ailuropoda melanoleuca) and red panda (Ailurus fulgens) belong to the order Carnivora, but have changed their dietary habits to eating bamboo exclusively. The convergent evolution characteristics of their morphology, genome and gut flora have been found in the two pandas. However, the research on the convergent adaptation of their digestion and metabolism to the bamboo diet, mediated by the dietary shift of the two pandas at the gene-expression and epigenetic regulation levels, is still lacking. We therefore used RNA sequencing among five species (two pandas and three non-herbivore mammals) and bisulfite sequencing among three species (two pandas and a carnivore ferret) to sequence key digestion and metabolism tissues (stomach and small intestine). Our results provide evidence that the convergent differentially expressed genes (related to carbohydrate utilization, bile secretion, Lys and Arg metabolism, vitamin B12 utilization and cyanide detoxification) of the two pandas are adaptive responses to the bamboo diet containing low lipids, low Lys and Arg, low vitamin B12 and high cyanide. We also profiled the genome-wide methylome maps of giant panda, red panda and ferret, and the results indicated that the promoter methylation of the two pandas may regulate digestive and metabolic genes to adapt to sudden environmental changes, and then, transmit genetic information to future generations to evolve into bamboo eaters. Taken together, our study provides new insights into the molecular mechanisms of the dietary shift and the adaptation to a strict bamboo diet in both pandas using comparative transcriptomics and methylomics.
Data Integrity Attack Detection Using Ensemble-Based Learning for Cyber–Physical Power Systems

The modern power system has evolved into a cyber-physical system with the integration of PMUs, smart meters, substation automation, and wide-area monitoring and control systems. This increases attack surface area and cybersecurity issues, but we can also use these technologies to prevent such scenarios. This paper provides a novel technique to generate and detect data integrity attacks in smart grids. It also gives an optimization algorithm for generating FDIA against state estimation algorithms present at the control center. The formulation for generating AC state estimation attack with full information and limited information along with DC state estimation attacks is given. It further proposes a combining technique for the voting based ensemble learning technique (MVCC) to detect FDIA in smartgrids. The model is then tested on an IEEE 24 bus system and 39 bus new England system by generating false data injection attacks and detecting them. The detection strategy is compared with most of the existing state of the art Machine learning algorithms, ensemble algorithms and conventional weighted least square algorithm and is found to have a better performance.
Prediction and control of transient stability using system integrity protection schemes

Prediction of the transient stability in real-time can be a boon in preventing the occurrence of catastrophic events in a particular area that could potentially affect the entire grid and cause blackouts. Synchronised voltage angle measurements procured from the installed phasor measurement units in the network have been used to devise an index for assessment of transient stability in the system. The behaviour of the trend of the acceleration at the instant of the critical clearing angle has been used in this study as the basis for differentiating between a stable and an unstable power swing. Upon detection of an unstable condition, the generator, which is most likely to be going out of step is detected, and appropriate control action is suggested for mitigating the predicted out of step condition. Moreover, the distance relay maloperations caused during a stable power swing can also be a reason for triggering cascading outages. Suitable remedial measures to avoid such maloperations of the distance relay have been proposed. The efficacy of the proposed philosophy has been tested on a single machine infinite bus system, WSCC-9 bus system and New England 39 bus system modelled in Power Factory/DIgSILENT.
Self-Supervised Locality Preserving Low-Pass Graph Convolutional Embedding for Large-Scale Hyperspectral Image Clustering

Due to prior knowledge deficiency, large spectral variability, and high dimension of hyperspectral image (HSI), HSI clustering is extremally a fundamental but challenging task. Deep clustering methods have achieved remarkable success and have attracted increasing attention in unsupervised HSI classification (HSIC). However, the poor robustness, adaptability, and feature presentation limit their practical applications to complex large-scale HSI datasets. Thus, this article introduces a novel self-supervised locality preserving low-pass graph convolutional embedding method (L2GCC) for large-scale hyperspectral image clustering. Specifically, a spectral–spatial transformation HSI preprocessing mechanism is introduced to learn superpixel-level spectral–spatial features from HSI and reduce the number of graph nodes for subsequent network processing. In addition, locality preserving low-pass graph convolutional embedding autoencoder is proposed, in which the low-pass graph convolution and layerwise graph attention are designed to extract the smoother features and preserve layerwise locality features, respectively. Finally, we develop a self-training strategy, in which a self-training clustering objective employs soft labels to supervise the clustering process and obtain appropriate hidden representations for node clustering. L2GCC is an end-to-end training network, which is jointly optimized by graph reconstruction loss and self-training clustering loss. On Indian Pines, Salinas, and University of Houston 2013 datasets, the clustering accuracy overall accuracies (OAs) of the proposed L2GCC are 73.51%, 83.15%, and 64.12%, respectively.
AGFA-Net: Adaptive Global Feature Augmentation Network for Point Cloud Completion

Completing shapes of point clouds from partial scans is a fundamental problem for 3-D vision and remote sensing. However, recent methods mainly relied on K-nearest neighbors (KNN) operations to extract local features of point clouds, which are susceptible to outliers and have limited ability to capture features from long-range context information. In this letter, we propose a new framework with an encoder–decoder architecture, named adaptive global feature augmentation network (AGFA-Net) for point cloud completion. The network mainly consists of spatial and channel attention blocks. Spatial attention blocks are used to replace KNN operations and aggregate global features adaptively by calculating per-point attention values, and channel attention blocks are used to augment useful features of geometric details. Meanwhile, several skip connections are added between different attention blocks to selectively convey geometric features from local regions of partial point clouds to the completion process. Experimental results and analyses demonstrate that our method can generate finer shapes of point clouds and outperforms other state-of-the-art methods under widely used benchmark point completion network (PCN) dataset and several terrestrial laser scanning (TLS) data.
A Multiclass Approach to Estimating Software Vulnerability Severity Rating with Statistical and Word Embedding Methods

The analysis and grading of software vulnerabilities is an important process that is done manually by experts today. For this reason, there are time delays, human errors, and excessive costs involved with the process. The final result of these software vulnerability reports created by experts is the calculation of a severity score and a severity rating. The severity rating is the first and foremost value of the software’s vulnerability. The vulnerabilities that can be exploited are only 20% of the total vulnerabilities. The vast majority of exploitations take place within the first two weeks. It is therefore imperative to determine the severity rating without time delays. Our proposed model uses statistical methods and deep learning-based word embedding methods from natural language processing techniques, and machine learning algorithms that perform multi-class classification. Bag of Words, Term Frequency Inverse Document Frequency and Ngram methods, which are statistical methods, were used for feature extraction. Word2Vec, Doc2Vec and Fasttext algorithms are included in the study for deep learning based Word embedding. In the classification stage, Naive Bayes, Decision Tree, K-Nearest Neighbors, Multi-Layer Perceptron, and Random Forest algorithms that can make multi-class classification were preferred. With this aspect, our model proposes a hybrid method. The database used is open to the public and is the most reliable data set in the field. The results obtained in our study are quite promising. By helping experts in this field, procedures will speed up. In addition, our study is one of the first studies containing the latest version of the data size and scoring systems it covers.
Generalized second law of thermodynamics in f(T) gravity

We investigate the validity of the generalized second law (GSL) of gravitational thermodynamics in the framework of f(T) modified teleparallel gravity. We consider a spatially flat FRW universe containing only the pressureless matter. The boundary of the universe is assumed to be enclosed by the Hubble horizon. For two viable f(T) models containing f(T) = T+μ1{(−T)}n and f(T) = T−μ2T(1−eβT0/T), we first calculate the effective equation of state and deceleration parameters. Then, {we investigate the null and strong energy conditions and conclude that a sudden future singularity appears in both models. Furthermore, using a cosmographic analysis we check the viability of two models. Finally, we examine the validity of the GSL and find that for both models it} is satisfied from the early times to the present epoch. But in the future, the GSL is violated for the special ranges of the torsion scalar T.
Analysis of Pictorial Analogy Needs for Continuous Arabic Consonant Recognition among Kadazandusun Students

of Analogy Needs for Continuous Arabic Consonant Recognition Abstract The analogy approach in consonant learning can help the process of recognising Arabic consonants in foreign language learning so that students can easily understand and master them. Learning the Arabic alphabet and consonants was found to be difficult for Kadazandusun students as they had never been exposed to them before entering first grade. Thus, this study aims to look at the needs of Kadazandusun students towards an analogy approach for the recognition of continuous Arabic consonants (KAB). The research questions for this study are, 1) what is the level of students’ need for learning Ar abic consonants in terms of interests, problems, and desires? and 2) what is the level of students’ recognition of continuous Arabic consonants? This study is a survey study involving a study sample of 350 people from a total population of 3161 students of level two Kadazandusun, involving students in years 4, 5 and 6 in primary schools in Sabah. This study uses a survey-based quantitative approach. However, to further clarify the descriptive data, an interview session with two Arabic language teachers was conducted. This study uses a questionnaire instrument adapted from the study of Ridhuan (2017) to look at the learning needs of Arabic consonants in terms of interests, problems, and desires. Qualitative survey data were obtained through semi-structured in terviews. Bogdan and Biklen’s (1992) coding strategies i.e., activity codes and situation codes were used to help categorise themes that emerged from the qualitative data. The findings of the study as a whole show that the level of students’ needs in terms of interest, problems and desires for learning Arabic consonants is high and there is a need to use an analogy approach in learning KAB. The test results showed that the students chose a picture that represented their understanding of the shape of KAB. The implications of the study show that the analogy approach in KAB learning among Kadazandusun students helps teachers and students in learning Arabic consonants.
ZERO CURVATURE AND GEL'FAND-DIKII FORMALISMS

ZERO CURVATURE AND GEL’FAND-DIKII FORMALISMS Burcu Silindir M.S. in Mathematics Supervisor: Prof. Dr. Metin Gurses September, 2004 In soliton theory, integrable nonlinear partial differential equations play an important role. In that respect such differential equations create great interest in many research areas. There are several ways to obtain these differential equations; among them zero curvature and Gel’fand-Dikii formalisms are more effective. In this thesis, we studied these formalisms and applied them to explicit examples.
Superpixel Spectral–Spatial Feature Fusion Graph Convolution Network for Hyperspectral Image Classification

Recently, convolutional neural networks (CNNs) have demonstrated impressive capabilities in the representation and classification of hyperspectral remote sensing images. Traditional CNNs require massive data to sufficiently train the network. To tackle this problem, graph convolutional network (GCN) has been introduced for hyperspectral image classification. GCN methods usually construct the graph from either spectral or spatial domain, which has not adequately explored the information in the joint spectral–spatial domain. In this article, we propose a superpixel spectral–spatial feature fusion graph convolution network for hyperspectral image classification (S3FGCN). S3FGCN can comprehensively use information in spectral, spatial, and spectral–spatial domains with limited data. Moreover, to enhance the performance, we explore a shared weights’ GCN in the spectral–spatial domain. To further improve the efficiency, superpixels are used to construct the adjacency matrix. Finally, dynamic sampling is adopted to make the model focus more on difficult samples. In the experiments on four datasets, S3FGCN demonstrates better accuracy compared with the state-of-the-art hyperspectral image classification methods.
Approximate Controllability of Non-Instantaneous Impulsive Stochastic Evolution Systems Driven by Fractional Brownian Motion with Hurst Parameter H∈(0,12)

This paper initiates a study on the existence and approximate controllability for a type of non-instantaneous impulsive stochastic evolution equation (ISEE) excited by fractional Brownian motion (fBm) with Hurst index 0<H<1/2. First, to overcome the irregular or singular properties of fBm with Hurst parameter 0<H<1/2, we define a new type of control function. Then, by virtue of the stochastic analysis theory, inequality technique, the semigroup approach, Krasnoselskii’s fixed-point theorem and Schaefer’s fixed-point theorem, we derive two new sets of sufficient conditions for the existence and approximate controllability of the concerned system. In the end, a concrete example is worked out to demonstrate the applicability of our obtained results.
When Inclusion and Diversity Are Not Enough: A Case Study of Instructional Practices and Barriers to Success for First-Generation Engineering Students

ABSTRACT There is a need for effective online learning strategies for First-Generation (FG) college students. Equity and efficacy can build academic attainment, a higher quality of life, advancement opportunities, and lifelong learning. A qualitative analysis of FG engineering students at a Tier 1 university in the Southwestern United States resulted in the identification of four concepts: (1) environmental factors, (2) self-directed learning, (3) multimedia incorporation, and (4) a sense of belonging. Lessons learned can inform educators and institutions regarding student control, modality of course design, assessment, and organizational change to enhance the FG experience and success.
How Hydroxyurea Alters the Gut Microbiome: A Longitudinal Study Involving Angolan Children with Sickle Cell Anemia

Sickle cell anemia (SCA) is an inherited hematological disorder and a serious global health problem, especially in Sub-Saharan Africa. Although hydroxyurea (HU) is the leading treatment for patients with SCA, its effects on the gut microbiome have not yet been explored. In this context, the aim of this study was to investigate this association by characterizing the gut microbiome of an Angolan SCA pediatric population before and after 6 months of HU treatment. A total of 66 stool samples were obtained and sequenced for the 16S rRNA gene (V3-V4 regions). Significant associations were observed in alpha and beta-diversity, with higher values of species richness for the children naïve for HU. We also noticed that children after HU had higher proportions of several beneficial bacteria, mostly short-chain fatty acids (SCFAs) producing species, such as Blautia luti, Roseburia inulinivorans, Eubacterium halli, Faecalibacterium, Ruminococcus, Lactobacillus rogosae, among others. In addition, before HU there was a higher abundance of Clostridium_g24, which includes C. bolteae and C. clostridioforme, both considered pathogenic. This study provides the first evidence of the HU effect on the gut microbiome and unravels several microorganisms that could be considered candidate biomarkers for disease severity and HU efficacy.
Evaluation of Effecting Factors About Exercise Motivation in Patients with Parkinson’s Disease

AIM: The aim of this study was to explore the factors that affect exercise motivation in patients with Parkinson’s disease. Methods: Data collected in Taiwan from March 2016 to January 2017. Using convenience sampling, a total of 49 participants were recruited, and participants who usually regularly exercise ≥ 150 minutes per week. Using the 8-item Parkinson’s Disease Questionnaire (PDQ-8), Geriatric Depression Scale (GDS), Unified Parkinson’s Disease Rating Scale part II & III (UPDRS part II & III), Fatigue Severity Scale (FSS), Pittsburg Sleep Quality Index (PSQI), and Hospital Anxiety Scale (HAS). Descriptive statistics, Pearson correlation analysis, and multiple linear regression analysis were adopted for data analysis. Results: Pearson correlation analysis showed L-Dopa dosage, UPDRS part II & III, FSS, PSQI, GDS-15, Hospital Anxiety Scale, and PDQ-8 were significantly correlated with exercise motivation. Fatigue was the strongest factor related to exercise motivation and explained 42.52% of the total variances; moreover, motor ability and activity of daily living explained 24.6% and 12.0% of the total variances, respectively. Conclusion: Motor ability, the activity of daily living and fatigue were related factors of Parkinson’s disease patients’ exercise motivation.
Effects of Human Activity on Markers of Oxidative Stress in the Intestine of Holothuria tubulosa, with Special Reference to the Presence of Microplastics

Pollution in the seas and oceans is a global problem, which highlights emerging pollutants and plastics, specifically microplastics (MPs), which are tiny (1 μm to 5 mm) ubiquitous plastic particles present in marine environments that can be ingested by a wide range of organisms. Holothurians are benthic organisms that feed on sediment; therefore, they can be exposed to contaminants present in the particles they ingest. The objective was to evaluate the effects of human activity on Holothuria tubulosa through the study of biomarkers. Specimens were collected in three different areas throughout the island of Eivissa, Spain: (1) a highly urbanized area, with tourist uses and a marina; (2) an urbanized area close to the mouth of a torrent; (3) an area devoid of human activity and considered clean. The results showed a higher presence of microplastics (MPs) in the sediments from the highly urbanized area in relation to the other two areas studied. Similarly, a higher number of MPs were observed in the digestive tract of H. tubulosa from the most affected area, decreasing with the degree of anthropic influence. Both in the sediment and in the holothurians, fibers predominated with more than 75% of the items. In the three areas, mesoplastics were analyzed by means of FTIR, showing that the main polymer was polypropylene (27%) followed by low-density polyethylene (17%) and polystyrene (16%). Regarding the biomarkers of oxidative stress, the intestine of H. tubulosa from the most impacted areas showed higher catalase, superoxide dismutase (SOD), glutathione reductase (GRd), and glutathione S-transferase (GST) activities and reduced glutathione (GSH) levels compared to the control area. The intermediate area only presented significant differences in GRd and GST with respect to the clean area. The activities of acetylcholinesterase and the levels and malondialdehyde presented similar values in all areas. In conclusion, human activity evaluated with the presence of MPs induced an antioxidant response in H. tubulosa, although without evidence of oxidative damage or neurotoxicity. H. tubulosa, due to its benthic animal characteristics and easy handling, can be a useful species for monitoring purposes.
Subspecies Classification and Comparative Genomic Analysis of Lactobacillus kefiranofaciens HL1 and M1 for Potential Niche-Specific Genes and Pathways

(1) Background: Strains HL1 and M1, isolated from kefir grains, have been tentatively identified, based on their partial 16S rRNA gene sequences, as Lactobacillus kefiranofaciens. The two strains demonstrated different health benefits. Therefore, not only the genetic factors exerting diverse functionalities in different L. kefiranofaciens strains, but also the potential niche-specific genes and pathways among the L. kefiranofaciens strains, should be identified. (2) Methods: Phenotypic and genotypic approaches were employed to identify strains HL1 and M1 at the subspecies level. For the further characterization of the probiotic properties of both strains, comparative genomic analyses were used. (3) Results: Both strains were identified as L. kefiranofaciens subsp. kefirgranum. According to the COG function category, dTDP-rhamnose and rhamnose-containing glycans were specifically detected in the L. kefiranofaciens subsp. Kefirgranum genomes. Three unique genes (epsI, epsJ, and epsK) encoding glycosyltransferase in the EPS gene cluster, and the ImpB/MucB/SamB family protein encoding gene were found in HL1 and M1. The specific ability to degrade arginine via the ADI pathway was found in HL1. The presence of the complete glycogen metabolism (glg) operon in the L. kefiranofaciens strains suggested the importance of glycogen synthesis to enable colonization in kefir grains and extend survival under environmental stresses. (4) Conclusions: The obtained novel information on the potential genes and pathways for polysaccharide synthesis and other functionalities in our HL1 and M1 strains could be applied for further functionality predictions for potential probiotic screening.
Synergistic potential of microorganisms (bio-fertilizer) on growth performance of Meliadubia

The objective of the study was to test the synergistic effect of microorganisms (bio-fertilizer) on the growthof Meliadubia. For this study the bio-fertilizer Azospirillum, Azotobacter, Phosphobacteria, AM fungi, andPotash Mobilizer developed by Institute of Forest Genetics and Tree Breeding (IFGTB) Coimbatore, wereused. The synergistic efficacy of bio-fertilizer was assessed in different forms viz., dual, triple, quadrupleand pentuple. The growth parameters viz., maximum shoot length (94.3 cm), root length (24.5cm), collardiameter (12.85mm), shoot and root dry weights (23.27g and 31.47g), total dry weights (54.73g) revealedplants receiving bio-fertilizer consortia (pentuple) performed well, which was further intrigued by thegrowth indices and quality indices. This study highlighted the potential efficacy of bio-fertilizer incombination i.e., triple, quadruple and pentuple was more efficient than single inoculants.