Miracle-dz/UniFAR-FTUs huggingface dataset:
An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree Dropout

Identifying and monitoring students who are likely to dropout is a vital issue for universities. Early detection allows institutions to intervene, addressing problems and retaining students. Prior research into the early detection of at-risk students has opted for the use of predictive models, but a comprehensive assessment of the suitability of different algorithms and approaches is complicated by the large number of variable features that constitute a student's educational experience. Predictive models vary in terms of their amplitude, temporality and the learning algorithms employed. While amplitude refers to the ability of the model to operate on multiple degrees, temporality is often considered due to the natural temporal aspect of the data. In the absence of a comparative framework of learning algorithms, the aim of this paper has been to provide such an analysis, based on a proposed classification of strategies for predicting dropouts in Higher Education Institutions. Three different student representations are implemented (namely Global Feature-Based, Local Feature-Based, and Time Series) in conjunction with the appropriate learning algorithms for each of them. A description of each approach, as well as its implementation process, are presented in this paper as technical contributions. An experiment based on a dataset of student information from two degrees, namely Business Administration and Architecture, acquired through an automated management system from a university in Brazil is used. Our findings can be summarized as: (i) of the three proposed student representations, the Local Feature-Based was the most suitable approach for predicting dropout. In addition to providing high quality results, the Local Feature-Based representations are simple to build, and the construction of the model is less expensive when compared to more complex ones; (ii) as a conclusion of the results obtained via Local Feature-Based, dropout can be said to be accurately predicted using grades of a few core courses, so there is no need for a complex features extraction process; (iii) considering temporal aspects of the data does not seem to contribute to the prediction performance although it increases computational costs as the model complexity increases.
Altered Brain Signal Variability in Patients With Generalized Anxiety Disorder

Generalized anxiety disorder (GAD) is characterized by a chronic, continuous symptom of worry and exaggerated startle response. Although functional abnormality in GAD has been widely studied using functional magnetic resonance imaging (fMRI), the dynamic signatures of GAD are not fully understood. As a vital index of brain function, brain signal variability (BSV) reflects the capacity of state transition of neural activities. In this study, we recruited 47 patients with GAD and 38 healthy controls (HCs) to investigate whether or not BSV is altered in patients with GAD by measuring the standard deviation of fMRI signal of each voxel. We found that patients with GAD exhibited decreased BSV in widespread regions including the visual network, sensorimotor network, frontoparietal network, limbic system, and thalamus, indicating an inflexible brain state transfer pattern in these systems. Furthermore, the correlation between BSV and trait anxiety score was prone to be positive in patients with GAD but negative in HCs. The opposite relationships between BSV and anxiety level in the two groups indicate that the brain with moderate anxiety level may stay in the most stable rather than in the flexible state. As the first study of BSV in GAD, we revealed extensively decreased BSV in patients with GAD similar to that in other mental disorders but with a non-linear relationship between BSV and anxiety level indicating a novel neurodynamic mechanism of the anxious brain.
Analytics of Learning Strategies: Associations with Academic Performance and Feedback

Learning analytics has the potential to detect and explain characteristics of learning strategies through analysis of trace data and communicate the findings via feedback. However, the role of learning analytics-based feedback in selection and regulation of learning strategies is still insufficiently explored and understood. This research aims to examine the sequential and temporal characteristics of learning strategies and investigate their association with feedback. Three years of trace data were collected from online pre-class activities of a flipped classroom, where different types of feedback were employed in each year. Clustering, sequence mining, and process mining were used to detect and interpret learning tactics and strategies. Inferential statistics were used to examine the association of feedback with the learning performance and the detected learning strategies. The results suggest a positive association between the personalised feedback and the effective strategies.
Detection of phishing attacks in financial and e-banking websites using link and visual similarity relation

Today, phishing is one of the biggest problems faced by the cyber-world. In this paper, we present an approach that can detect phishing attacks in commercial and e-banking websites using the link and visual similarity relations. Phisher always tries to mimic the visual design of the webpage and the fake webpage contains identity keywords and hyperlinks that point to the corresponding legitimate webpage to trap internet users. Therefore, our proposed approach analyse the keywords, hyperlinks and CSS layout of the webpage to detect phishing attack. In the proposed approach, we make a set of associate domains with the suspicious webpage and explore the link and similarity relation to identifying phishing webpages. Also, we use the login form and whitelist based filtering to increase the running time of the proposed approach. Our proposed approach is not only able to detect phishing webpages accurately but its source webpage also. Moreover, it does not require any prior training to detect zero hour phishing attack. Experiments are conducted over a 6,616 phishing and legitimate webpages and the proposed approach gives approximately 99.72% true positive rate and less than 1.89% false negative rate.
DMfold: A Novel Method to Predict RNA Secondary Structure With Pseudoknots Based on Deep Learning and Improved Base Pair Maximization Principle

While predicting the secondary structure of RNA is vital for researching its function, determining RNA secondary structure is challenging, especially for that with pseudoknots. Typically, several excellent computational methods can be utilized to predict the secondary structure (with or without pseudoknots), but they have their own merits and demerits. These methods can be classified into two categories: the multi-sequence method and the single-sequence method. The main advantage of the multi-sequence method lies in its use of the auxiliary sequences to assist in predicting the secondary structure, but it can only successfully predict in the presence of multiple highly homologous sequences. The single-sequence method is associated with the major merit of easy operation (only need the target sequence to predict secondary structure), but its folding parameters are the common features of diversity RNA, which cannot describe the unique characteristics of RNA, thus potentially resulting in the low prediction accuracy in some RNA. In this paper, “DMfold,” a method based on the Deep Learning and Improved Base Pair Maximization Principle, is proposed to predict the secondary structure with pseudoknots, which fully absorbs the advantages and avoids some disadvantages of those two methods. Notably, DMfold could predict the secondary structure of RNA by learning similar RNA in the known structures, which uses the similar RNA sequences instead of the highly homogeneous sequences in the multi-sequence method, thereby reducing the requirement for auxiliary sequences. In DMfold, it only needs to input the target sequence to predict the secondary structure. Its folding parameters are fully extracted automatically by deep learning, which could avoid the lack of folding parameters in the single-sequence method. Experiments show that our method is not only simple to operate, but also improves the prediction accuracy compared to multiple excellent prediction methods. A repository containing our code can be found at https://github.com/linyuwangPHD/RNA-Secondary-Structure-Database.
Modulation of Autophagy Influences the Function and Survival of Human Pancreatic Beta Cells Under Endoplasmic Reticulum Stress Conditions and in Type 2 Diabetes

Autophagy is the major mechanism involved in degradation and recycling of intracellular components, and its alterations have been proposed to cause beta cell dysfunction. In this study, we explored the effects of autophagy modulation in human islets under conditions associated to endoplasmic reticulum (ER) stress. Human pancreatic islets were isolated by enzymatic digestion and density gradient purification from pancreatic samples of non-diabetic (ND; n = 17; age 65 ± 21 years; gender: 5 M/12 F; BMI 23.4 ± 3.3 kg/m2) and T2D (n = 9; age 76 ± 6 years; 4 M/5 F; gender: BMI 25.4 ± 3.7 kg/m2) organ donors. Nine ND organ donors were treated for hypertension and 1 for both hypertension and hypercholesterolemia. T2D organ donors were treated with metformin (1), oral hypoglycemic agents (2), diet + oral hypoglycemic agents (3), insulin (3) or insulin plus metformin (3) as for antidiabetic therapy and, of these, 3 were treated also for hypertension and 6 for both hypertension and hypercholesterolemia. Two days after isolation, they were cultured for 1–5 days with 10 ng/ml rapamycin (autophagy inducer), 5 mM 3-methyladenine or 1.0 nM concanamycin-A (autophagy blockers), either in the presence or not of metabolic (0.5 mM palmitate) or chemical (0.1 ng/ml brefeldin A) ER stressors. In ND islets palmitate exposure induced a 4 to 5-fold increase of beta cell apoptosis, which was significantly prevented by rapamycin and exacerbated by 3-MA. Similar results were observed with brefeldin treatment. Glucose-stimulated insulin secretion from ND islets was reduced by palmitate (−40 to 50%) and brefeldin (−60 to 70%), and rapamycin counteracted palmitate, but not brefeldin, cytotoxic actions. Both palmitate and brefeldin induced PERK, CHOP and BiP gene expression, which was partially, but significantly prevented by rapamycin. With T2D islets, rapamycin alone reduced the amount of p62, an autophagy receptor that accumulates in cells when macroautophagy is inhibited. Compared to untreated T2D cells, rapamycin-exposed diabetic islets showed improved insulin secretion, reduced proportion of beta cells showing signs of apoptosis and better preserved insulin granules, mitochondria and ER ultrastructure; this was associated with significant reduction of PERK, CHOP and BiP gene expression. This study emphasizes the importance of autophagy modulation in human beta cell function and survival, particularly in situations of ER stress. Tuning autophagy could be a tool for beta cell protection.
Weighted Kernel joint sparse representation for hyperspectral image classification

Kernel joint sparse representation (KJSR) performs joint sparse representation in the feature space and has shown good performance for the hyperspectral image (HSI) classification. In order to distinguish spatial neighbouring pixels in the feature space, we propose two weighted KJSR (WKJSR) methods in this paper. The first one computes the weight directly based on the kernel similarity between neighbouring pixels. The second weighted scheme uses a nearest regularisation strategy to simultaneously optimise the weights of projected neighbouring pixels and joint sparse representation coefficients. The proposed WKJSR methods can exploit the similarities and differences among neighbouring pixels to obtain accurate weights for the joint sparse representation and classification. Experimental results on two benchmark HSI data sets demonstrate the effectiveness of the proposed methods.
Identification of Important Effector Proteins in the FOXJ1 Transcriptional Network Associated With Ciliogenesis and Ciliary Function

Developmental defects in motile cilia, arising from genetic abnormalities in one or more ciliary genes, can lead to a common ciliopathy known as primary ciliary dyskinesia (PCD). Functional studies in model organisms undertaken to understand PCD or cilia biogenesis have identified 100s of genes regulated by Foxj1, the master regulator of motile ciliogenesis. However, limited systems based studies have been performed to elucidate proteins or network/s crucial to the motile ciliary interactome, although this approach holds promise for identification of multiple cilia-associated genes, which, in turn, could be utilized for screening and early diagnosis of the disease. Here, based on the assumption that FOXJ1-mediated regulatory and signaling networks are representative of the motile cilia interactome, we have constructed and analyzed the gene regulatory and protein–protein interaction network (PPIN) mediated by FOXJ1. The predicted FOXJ1 regulatory network comprises of 424 directly and 148 indirectly regulated genes. Additionally, based on gene ontology analysis, we have associated 17 directly and 6 indirectly regulated genes with possible ciliary roles. Topological and perturbation analyses of the PPIN (6927 proteins, 40,608 interactions) identified 121 proteins expressed in ciliated cells, which interact with multiple proteins encoded by FoxJ1 induced genes (FIG) as important interacting proteins (IIP). However, it is plausible that IIP transcriptionally regulated by FOXJ1 and/or differentially expressed in PCD are likely to have crucial roles in motile cilia. We have found 20 de-regulated topologically important effector proteins in the FOXJ1 regulatory network, among which some (PLSCR1, SSX2IP, ACTN2, CDC42, HSP90AA1, PIAS4) have previously reported ciliary roles. Furthermore, based on pathway enrichment of these proteins and their primary interactors, we have rationalized their possible roles in the ciliary interactome. For instance, 5 among these novel proteins that are involved in cilia associated signaling pathways (like Notch, Wnt, Hedgehog, Toll-like receptor etc.) could be ‘topologically important signaling proteins. ’ Therefore, based on this FOXJ1 network study we have predicted important effectors in the motile cilia interactome, which are possibly associated with ciliary biology and/or function and are likely to further our understanding of the pathophysiology in ciliopathies like PCD.
Multisource Energy Harvesting System for a Wireless Sensor Network Node in the Field Environment

This paper presents the design, implementation, and characterization of a hardware platform applicable to a self-powered wireless sensor network (WSN) node. Its primary design objective is to devise a hybrid energy harvesting system to extend the operational lifetime of WSN node after they are deployed in the field environment. Besides the implementation of optimal components (microcontroller, sensor, radio frequency (RF) transceiver, and others) to achieve the lowest power consumption, it is also necessary to consider the sources of energy instead of the frequent recharging or replacement of batteries. Therefore, the platform incorporates a multisource energy harvesting module to collect energy from the surrounding environment, including wind, solar radiation, and thermal energy. The platform also includes an energy storage module through a super-capacitor, RF transceiver module, and the primary microcontroller module. Experimental results showed that the WSN node system with appropriate integration will reserve sufficient energy and meet the long-term power supply requirements of the WSN node without batteries in the field environment. The experimental results and empirical measurements taken over nine days demonstrated that the average daily generating capacity was 7805.09 J, which is far more than the energy consumption of the WSN node (about 2972.88 J).
On the convergence rate of the augmented Lagrangian-based parallel splitting method

The augmented Lagrangian method (ALM) is a well-regarded algorithm for solving convex optimization problems with linear constraints. Recently, in He et al. [On full Jacobian decomposition of the augmented Lagrangian method for separable convex programming, SIAM J. Optim. 25(4) (2015), pp. 2274–2312], it has been demonstrated that a straightforward Jacobian decomposition of ALM is not necessarily convergent when the objective function is the sum of functions without coupled variables. Then, Wang et al. [A note on augmented Lagrangian-based parallel splitting method, Optim. Lett. 9 (2015), pp. 1199–1212] proved the global convergence of the augmented Lagrangian-based parallel splitting method under the assumption that all objective functions are strongly convex. In this paper, we extend these results and derive the worst-case convergence rate of this method under both ergodic and non-ergodic conditions, where t represents the number of iterations. Furthermore, we show that the convergence rate can be improved from to , and finally, we also demonstrate that this method can achieve global linear convergence, when the involved functions satisfy some additional conditions.
Nutrient Regulation of Endocrine Factors Influencing Feeding and Growth in Fish

Endocrine factors regulate food intake and growth, two interlinked physiological processes critical for the proper development of organisms. Somatic growth is mainly regulated by growth hormone (GH) and insulin-like growth factors I and II (IGF-I and IGF-II) that act on target tissues, including muscle, and bones. Peptidyl hormones produced from the brain and peripheral tissues regulate feeding to meet metabolic demands. The GH-IGF system and hormones regulating appetite are regulated by both internal (indicating the metabolic status of the organism) and external (environmental) signals. Among the external signals, the most notable are diet availability and diet composition. Macronutrients and micronutrients act on several hormone-producing tissues to regulate the synthesis and secretion of appetite-regulating hormones and hormones of the GH-IGF system, eventually modulating growth and food intake. A comprehensive understanding of how nutrients regulate hormones is essential to design diet formulations that better modulate endogenous factors for the benefit of aquaculture to increase yield. This review will discuss the current knowledge on nutritional regulation of hormones modulating growth and food intake in fish.
Metformin Protects the Heart Against Hypertrophic and Apoptotic Remodeling After Myocardial Infarction

Cardiovascular complications are the most prevalent cause of morbidity and mortality in diabetic patients. Metformin is currently the first-line blood glucose-lowering agent with potential relevance to cardiovascular diseases. However, the underpinning mechanisms of action remain elusive. Here, we report that metformin represses cardiac apoptosis at least in part through inhibition of Forkhead box O1 (FoxO1) pathway. In a mouse model of ischemia-reperfusion (I/R), treatment with metformin attenuated cardiac and hypertrophic remodeling after 14 days of post-reperfusion. Additionally, cardiac expression of brain-like natriuretic peptide (BNP) was significantly reduced in metformin-treated mice after 14 days of cardiac I/R. In cultured H9C2 cells, metformin counteracted hypertrophic and apoptotic responses to metabolic or hypoxic stress. FoxO1 silencing by siRNA abolished anti-apoptotic effect of metformin under hypoxic stress in H9C2 cells. Taken together, these results suggest that metformin protects the heart against hypertrophic and apoptotic remodeling after myocardial infarction.
The After-Effects of Theta Burst Stimulation Over the Cortex of the Suprahyoid Muscle on Regional Homogeneity in Healthy Subjects

Theta burst stimulation (TBS) is a powerful variant of repetitive transcranial magnetic stimulation (rTMS), making it potentially useful for the treatment of swallowing disorders. However, how dose TBS modulate human swallowing cortical excitability remains unclear. Here, we aim to measure the after-effects of spontaneous brain activity at resting-state using the regional homogeneity (ReHo) approach in healthy subjects who underwent different TBS protocols over the suprahyoid muscle cortex. Sixty healthy subjects (23.45 ± 2.73 years, 30 males) were randomized into three groups which completed different TBS protocols. The TMS coil was applied over the cortex of the suprahyoid muscles. Data of resting-state functional MRI (Rs-fMRI) of the subjects were acquired before and after TBS. The ReHo was compared across sessions [continuous TBS (cTBS), intermittent TBS (iTBS) and cTBS/iTBS] and runs (pre/post TBS). In the comparison between pre- and post-TBS, increased ReHo was observed in the right lingual gyrus and right precuneus and decreased ReHo in the left cingulate gyrus in the cTBS group. In the iTBS group, increased ReHo values were seen in the pre-/postcentral gyrus and cuneus, and decreased ReHo was observed in the left cerebellum, brainstem, bilateral temporal gyrus, insula and left inferior frontal gyrus. In the cTBS/iTBS group, increased ReHo was found in the precuneus and decreased ReHo in the right cerebellum posterior lobe, left anterior cerebellum lobe, and right inferior frontal gyrus. In the post-TBS inter-groups comparison, increased ReHo was seen in right middle occipital gyrus and decreased ReHo in right middle frontal gyrus and right postcentral gyrus (cTBS vs. cTBS/iTBS). Increased ReHo was shown in left inferior parietal lobule and left middle frontal gyrus (cTBS vs. iTBS). Increased ReHo was shown in right medial superior frontal gyrus and decreased ReHo in right cuneus (cTBS/iTBS vs. iTBS). Our findings indicate cTBS had no significant influence on ReHo in the primary sensorimotor cortex, iTBS facilitates an increased ReHo in the bilateral sensorimotor cortex and a decreased ReHo in multiple subcortical areas, and no reverse effect exhibits when iTBS followed the contralateral cTBS over the suprahyoid motor cortex. The results provide a novel insight into the neural mechanisms of TBS on swallowing cortex.
Design and Analysis of a Distributed and Demand-Based Backscatter MAC Protocol for Internet of Things Networks

Backscatter communication is a new wireless communication technology, which can enable battery-free devices to communicate with others by backscattering ambient radio-frequency signals, and therefore has great potential to be deployed in the future Internet of Things (IoT) networks. In the existing related studies, the adopted centralized approach might be not suitable for a large-scale IoT network with sporadic backscatter communication, while the adopted distributed approach ignored the demand of the access point (AP). In this paper, we consider a large-scale IoT network consisting of the legacy Wi-Fi communication and the backscatter communication, and aim to propose an efficient distributed backscatter medium access control protocol that takes into account of the demand of the AP. In our protocol, when the AP has a demand to collect the information of backscatter devices, it enables backscatter devices to contend for the channel with Wi-Fi devices in a separate manner, where the backscatter devices are only allowed to participate in the contention in a limited time period. We then characterize this constrained contention process and develop a theoretical model to analyze the performance of the proposed protocol. With this model, we can exactly express the per-node throughput of Wi-Fi and backscatter devices. Finally, extensive simulations verify that our model is much accurate, and our protocol can outperform the related protocol in terms of both per-node throughput and system throughput.