Miracle-dz/UniFAR-FTUs huggingface dataset:
Crosstalk Between Mammalian Autophagy and the Ubiquitin-Proteasome System

Autophagy and the ubiquitin–proteasome system (UPS) are the two major intracellular quality control and recycling mechanisms that are responsible for cellular homeostasis in eukaryotes. Ubiquitylation is utilized as a degradation signal by both systems, yet, different mechanisms are in play. The UPS is responsible for the degradation of short-lived proteins and soluble misfolded proteins whereas autophagy eliminates long-lived proteins, insoluble protein aggregates and even whole organelles (e.g., mitochondria, peroxisomes) and intracellular parasites (e.g., bacteria). Both the UPS and selective autophagy recognize their targets through their ubiquitin tags. In addition to an indirect connection between the two systems through ubiquitylated proteins, recent data indicate the presence of connections and reciprocal regulation mechanisms between these degradation pathways. In this review, we summarize these direct and indirect interactions and crosstalks between autophagy and the UPS, and their implications for cellular stress responses and homeostasis.
Altered spontaneous brain activity patterns in strabismus with amblyopia patients using amplitude of low-frequency fluctuation: a resting-state fMRI study

Objective Previous studies have demonstrated that strabismus or amblyopia can result in markedly brain function and anatomical alterations. However, the differences in spontaneous brain activities of strabismus with amblyopia (SA) patients still remain unclear. This current study intended to use the amplitude of low-frequency fluctuation (ALFF) technique to investigate the intrinsic brain activity changes in SA subjects. Patients and methods A total of 16 patients with SA (6 males and 10 females) and 16 healthy controls (HCs; 6 males and 10 females) similarly matched in age, gender, and education status were recruited and examined with the resting-state functional MRI. The spontaneous brain activity changes were investigated using the ALFF technique. The receiver operating characteristic curve was performed to classify the mean ALFF signal values of the SA patients from HCs. The correlations between the ALFF values of distinct brain regions and the clinical manifestations in SA patients were evaluated in terms of the Pearson’s correlation analysis. Results Compared with HCs, SA patients had significantly decreased ALFF in the left cerebellum posterior lobe, left middle frontal gyrus, and bilateral thalamus. In contrast, SA patients showed increased ALFF values in the right superior frontal gyrus, right precuneus, left cuneus, and bilateral precentral gyrus. Nonetheless, there was no linear correlation between the mean ALFF values in brain regions and clinical features. Conclusion Diverse brain regions including vision-related and motion-related areas exhibited aberrant intrinsic brain activity patterns, which imply the neuropathologic mechanisms of oculomotor disorder and vision deficit in the SA patients.
The Untapped Potential of the Gaming Community: Narrative Review

Background Video gamers are a population at heightened risk of developing obesity due to the sedentary nature of gaming, increased energy intake, and the disruption caused to their sleep. This increases their risk of developing a number of noncommunicable diseases. To date, research seeking to improve health behaviors has focused on developing novel video games to promote behavior change. Although positive results have emerged from this research, large-scale success has been limited due to the lack of transferability to mainstream games and the focus on children and adolescents. The gaming community has a number of unique aspects, which have received comparatively less attention than the development of new video games. Objective The purpose of this paper is to highlight under-researched areas that have the potential to encourage positive health behavior among this community. Methods A narrative review of the lay and academic literature was conducted to provide context and support to our claims that further research could be beneficial in this area. Results Research has found that advertising can have implicit effects on an individual’s memories, which could influence later decisions. However, the effect of the exponential growth of in-game advertisements and the brand sponsorship of gaming events and professional gamers have not been explored in the gaming community. The possibility of using advertising techniques to encourage positive health behaviors within games or at these events has also not been explored. Research suggests that virtual communities can be effective at disseminating health information, but the efficacy of this needs to be explored using known community influencers within the gaming community. Conclusions This paper has highlighted a number of potential avenues for the development of interventions within the gaming community. Further research must be conducted alongside game developers to ensure that any in-game developed interventions do not deter gameplay and gamers to ensure that potential approaches are acceptable.
Detecting Satire in the News with Machine Learning

We built models with Logistic Regression and linear Support Vector Machines on a large dataset consisting of regular news articles and news from satirical websites, and showed that such linear classifiers on a corpus with about 60,000 articles can perform with a precision of 98.7% and a recall of 95.2% on a random test set of the news. On the other hand, when testing the classifier on "publication sources" which are completely unknown during training, only an accuracy of 88.2% and an F1-score of 76.3% are achieved. As another result, we showed that the same algorithm can distinguish between news written by the news agency itself and paid articles from customers. Here the results had an accuracy of 99%.
Endovascular therapy for acute ischemic stroke: The standard of care

Acute ischemic stroke continues to be a major cause of permanent disability and death worldwide. Outcomes are particularly poor in patients presenting with large vessel occlusive disease with resultant ischemia and tissue injury in large and eloquent territories. Intravenous thrombolysis has been the mainstay of medical therapy, however treatment is limited to a subset of patients and many patients continue to have poor outcomes. Three trials in 2013 investigating the benefit of intra-arterial therapy failed to demonstrate benefit over medical therapy alone. More recently, five trials in 2015 were completed demonstrating superior outcomes with intra-arterial therapy with improved results attributed to higher and faster rates of recanalization in a select patient population. These trials have introduced a new standard of care in the management of acute ischemic stroke patients.
Applications of Artificial Neural Networks in Process Control Applications: A Review

This work provides an overview of applications of Artificial Neural Networks and related solutions such as Neuro-Fuzzy within different control loops such as the Network Predictive Control regarding energy saving. It also shows such applications in the Chemical Industry and points out why further research on these applications regarding the control of distillation columns might be economical promising. The application of a Generic Algorithm in combination with an Artificial Neural Network in an optimal control configuration shows up to almost 20% possible improvement in an absorption chiller system compared to the normalized system. Applications of Artificial Neural Networks in distillation columns have been found and in combination with conventional control strategies energy saving possibilities seem promising but need further investigation and research.
A Machine Learning-Based Security Vulnerability Study on XOR PUFs for Resource-Constraint Internet of Things

Physical unclonable functions (PUFs) are emerging as a promising class of hardware primitives for delivering security for IoT devices. Cryptographic key-based security mechanisms are heavyweight by demanding resources more than many resource-constraint IoT devices can provide, and are also vulnerable to side-channel invasive attacks. PUFs utilize integrated circuits' manufacturing variations to produce responses unique for individual devices, and hence cannot be reproduced. An important goal of security research is to discover all possible insecure risks, which can provide secure application developers useful information so that they can avoid the risk-containing components or mechanisms. While physically unclonable, some PUFs have been found to be mathematically clonable by machine learning methods. Large XOR arbiter PUFs is one group of PUFs that were shown to withstand existing attack methods unless long training time is used in the machine learning process. In this paper, we investigate the effectiveness of a neural network method in attacking large XOR PUFs, a neural network method modified to handle training datasets possibly larger than memory capacity. Our study shows that the modified neural network method attains high prediction accuracy while consuming substantially less time for large XOR PUFs than the fastest machine learning code used in all earlier studies known to us. Some of the large XOR PUFs that took existing machine learning codes several days of parallel computing time on high-performance computing servers have been broken by our method in less than two hours, indicating vulnerability of even large XOR PUFs. Discovery of all potential vulnerabilities of a PUF is important since secure application developers need such information for deciding which PUF to choose, and an unidentified vulnerability can lead to security risks for IoT devices.
Handwritten Character Recognition Using Deep-Learning

In this paper we present an innovative method for offline handwritten character detection using deep neural networks. In today world it has become easier to train deep neural networks because of availability of huge amount of data and various Algorithmic innovations which are taking place. Now-a-days the amount of computational power needed to train a neural network has increased due to the availability of GPU's and other cloud based services like Google Cloud platform and Amazon Web Services which provide resources to train a Neural network on the cloud. We have designed a image segmentation based Handwritten character recognition system. In our system we have made use of OpenCV for performing Image processing and have used Tensorflow for training a the neural Network. We have developed this system using python programming language.
A Probabilistic-driven Ensemble Approach to Perform Event Classification in Intrusion Detection System

: Nowadays, it is clear how the network services represent a widespread element, which is absolutely essential for each category of users, professional and non-professional. Such a scenario needs a constant research activity aimed to ensure the security of the involved services, so as to prevent any fraudulently exploitation of the related network resources. This is not a simple task, because day by day new threats arise, forcing the research community to face them by developing new speciﬁc countermeasures. The Intrusion Detection System (IDS) covers a central role in this scenario, as its main task is to detect the intrusion attempts through an evaluation model designed to classify each new network event as normal or intrusion. This paper introduces a Probabilistic-Driven Ensemble (PDE) approach that operates by using several classiﬁcation algorithms, whose effectiveness has been improved on the basis of a probabilistic criterion. A series of experiments, performed by using real-world data, show how such an approach outperforms the state-of-the-art competitors, proving its better capability to detect intrusion events with regard to the canonical solutions.
Directional Analysis of Stochastic Gradient Descent via von Mises-Fisher Distributions in Deep learning

Although stochastic gradient descent (SGD) is a driving force behind the recent success of deep learning, our understanding of its dynamics in a high-dimensional parameter space is limited. In recent years, some researchers have used the stochasticity of minibatch gradients, or the signal-to-noise ratio, to better characterize the learning dynamics of SGD. Inspired from these work, we here analyze SGD from a geometrical perspective by inspecting the stochasticity of the norms and directions of minibatch gradients. We propose a model of the directional concentration for minibatch gradients through von Mises-Fisher (VMF) distribution, and show that the directional uniformity of minibatch gradients increases over the course of SGD. We empirically verify our result using deep convolutional networks and observe a higher correlation between the gradient stochasticity and the proposed directional uniformity than that against the gradient norm stochasticity, suggesting that the directional statistics of minibatch gradients is a major factor behind SGD.
An Effective and Robust Self-Training Algorithm Using k-Means and Random Forest Models for Program-Level Student Classification

In educational data mining, program-level student classification is a popular task focusing on predicting the final study status of a student at the end of the permitted period of study time with a program. In a practical context where the task is applied to predict students early in their learning path, two issues have to be addressed. The first issue is the completeness of data in both model construction and prediction of new data, while the latter refers to the number of labeled data in model construction. Nonetheless, a few works considered this practical context thoroughly. In order to manage these issues, we propose a self-training algorithm using $k$-means and random forest models, named $k$-meansTriForest. Our algorithm is regarded as a solution to the program-level student classification task in the aforementioned practical context. $k$-means helps us form a cluster-based incomplete data imputation method. Random forests play an important role of base classifiers of an ensemble in our algorithm. Besides, the Tri-training styled unlabeled instance selection is defined so that our algorithm can remove the burden of probability threshold settings from its users. Moreover, in our algorithm, the diversity of the base classifiers can be preserved when the unlabeled data set can be exploited and the labeled data set can be enhanced over time. As a result, our algorithm outperforms the previous ones in the related works on three real educational data sets, in order to classify students into the groups with different final study status at three different points in time. In particular, our improvements in Accuracy are ranged from about 1.5% to about 7%. Such higher accuracy in an empirical study with many various incomplete data and unlabeled data percentages has confirmed our algorithm's effectiveness and robustness.
Esrrb extinction triggers dismantling of naïve pluripotency and marks commitment to differentiation

Self‐renewal of embryonic stem cells (ESCs) cultured in LIF/fetal calf serum (FCS) is incomplete with some cells initiating differentiation. While this is reflected in heterogeneous expression of naive pluripotency transcription factors (TFs), the link between TF heterogeneity and differentiation is not fully understood. Here, we purify ESCs with distinct TF expression levels from LIF/FCS cultures to uncover early events during commitment from naïve pluripotency. ESCs carrying fluorescent Nanog and Esrrb reporters show Esrrb downregulation only in Nanoglow cells. Independent Esrrb reporter lines demonstrate that Esrrbnegative ESCs cannot effectively self‐renew. Upon Esrrb loss, pre‐implantation pluripotency gene expression collapses. ChIP‐Seq identifies different regulatory element classes that bind both OCT4 and NANOG in Esrrbpositive cells. Class I elements lose NANOG and OCT4 binding in Esrrbnegative ESCs and associate with genes expressed preferentially in naïve ESCs. In contrast, Class II elements retain OCT4 but not NANOG binding in ESRRB‐negative cells and associate with more broadly expressed genes. Therefore, mechanistic differences in TF function act cumulatively to restrict potency during exit from naïve pluripotency.
A Meta-Model for Information Systems Quality

Information Systems Quality (ISQ) is a critical source of competitive advantages for organizations. In a scenario of increasing competition on digital services, ISQ is a competitive differentiation asset. In this regard, managing, maintaining, and evolving IT infrastructures have become a primary concern of organizations. Thus, a technical perspective on ISQ provides useful guidance to meet current challenges. The financial sector is paradigmatic, since it is a traditional business, with highly complex business-critical legacy systems, facing a tremendous change due to market and regulation drivers. We carried out a Mixed-Methods study, performing a Delphi-like study on the financial sector. We developed a specific research framework to pursue this vertical study. Data were collected in four phases starting with a high-level randomly stratified panel of 13 senior managers and then a target panel of 124 carefully selected and well-informed domain experts. We have identified and dealt with several quality factors; they were discussed in a comprehensive model inspired by the ISO 25010, 42010, and 12207 standards, corresponding to software quality, software architecture, and software process, respectively. Our results suggest that the relationship among quality, architecture, and process is a valuable technical perspective to explain the quality of an information system. Thus, we introduce and illustrate a novel meta-model, named SQuAP (Software Quality, Architecture, Process), which is intended to give a comprehensive picture of ISQ by abstracting and connecting detailed individual ISO models.
Effects of Ability and Effort Praise on Children’s Failure Attribution, Self-Handicapping, and Performance

Previous research has suggested that children praised for ability are more likely to attribute their failure to low ability compared to those who are praised for effort. At the same time, self-worth theory suggests that when an individual’s self-worth is threatened, they are likely to use a self-serving attributional strategy and self-handicapping. From the perspective of self-worth theory, the present study investigated how ability and effort praise influenced children’s failure attribution, self-handicapping, and their subsequent performance compared to simple informational feedback. Fifth graders (N = 103, average age = 11.2 years, SD = 0.71) were randomly assigned to three praise conditions (ability, effort, or no praise). The results revealed that children praised for ability were more likely to attribute their subsequent failure to non-ability factors and indicate more claimed and behavioral self-handicapping than children who were praised for effort or not praised at all. As behavioral self-handicapping created actual obstacles to progress, children praised for ability made significantly less improvement in their performance than those in the other two groups. In addition, the findings showed that children praised for effort also adopted the claimed self-handicapping and defensive attributional strategies compared to those in the no-praise conditions. These results indicate that parents and teachers should not haphazardly administer praise. Implications for parents, teachers, and future research directions, including the replication of this study in diverse cultural settings, conditions of effort praise, and effects of other types of praise, are discussed.
Thermal conductivity of silicon nanowire forests

A large amount of parallel silicon nanowires, placed perpendicularly to a silicon substrate (silicon nanowire forests), have been contacted and assembled in order to fabricate legs of a thermoelectric generator. This paper reports the measurement of the main parameter for thermoelectric applications, which is the thermal conductivity. The reported value, which confirms the strong reduction of the thermal conductivity in nanostructures, is measured on a large amount (>107) of parallel nanowires with a diameter variable in the range 60–120 nm, and takes into account eventual non-uniformities which are unavoidable on surfaces of several mm2. As silicon nanowire forests are very thin, it has been necessary to develop a suitable measurement apparatus. The fabrication of devices based on silicon nanowire forests, the apparatus and the measurement procedure, as well as the the results, are illustrated and discussed.
Loss of murine Paneth cell function alters the immature intestinal microbiome and mimics changes seen in neonatal necrotizing enterocolitis

Necrotizing enterocolitis (NEC) remains the leading cause of gastrointestinal morbidity and mortality in premature infants. Human and animal studies suggest a role for Paneth cells in NEC pathogenesis. Paneth cells play critical roles in host-microbial interactions and epithelial homeostasis. The ramifications of eliminating Paneth cell function on the immature host-microbial axis remains incomplete. Paneth cell function was depleted in the immature murine intestine using chemical and genetic models, which resulted in intestinal injury consistent with NEC. Paneth cell depletion was confirmed using histology, electron microscopy, flow cytometry, and real time RT-PCR. Cecal samples were analyzed at various time points to determine the effects of Paneth cell depletion with and without Klebsiella gavage on the microbiome. Deficient Paneth cell function induced significant compositional changes in the cecal microbiome with a significant increase in Enterobacteriacae species. Further, the bloom of Enterobacteriaceae species that occurs is phenotypically similar to what is seen in human NEC. This further strengthens our understanding of the importance of Paneth cells to intestinal homeostasis in the immature intestine.
Correlation of In Vitro Susceptibility Based on MICs and Squalene Epoxidase Mutations with Clinical Response to Terbinafine in Patients with Tinea Corporis/Cruris

Recalcitrant dermatophytoses are on the rise in India. High MICs of terbinafine (TRB) and squalene epoxidase (SQLE) gene mutations conferring resistance in Trichophyton spp. ABSTRACT Recalcitrant dermatophytoses are on the rise in India. High MICs of terbinafine (TRB) and squalene epoxidase (SQLE) gene mutations conferring resistance in Trichophyton spp. have been recently documented. However, studies correlating laboratory data with clinical response to TRB in tinea corporis/cruris are lacking. For this study, we investigated the clinicomycological profile of 85 tinea corporis/cruris patients and performed antifungal susceptibility testing by CLSI microbroth dilution and SQLE mutation analysis of the isolates obtained and correlated these with the responses to TRB. Patients confirmed by potassium hydroxide (KOH) mounting of skin scrapings were started on TRB at 250 mg once a day (OD). If >50% clinical clearance was achieved by 3 weeks, the same dose was continued (group 1). If response was <50%, the dose was increased to 250 mg twice a day (BD) (group 2). If the response still remained below 50% after 3 weeks of BD, the patients were treated with itraconazole (ITR; group 3). Overall, skin scrapings from 64 (75.3%) patients yielded growth on culture. Strikingly, all isolates were confirmed to be Trichophyton interdigitale isolates by internal transcribed spacer (ITS) sequencing. Thirty-nine (61%) of the isolates had TRB MICs of ≥1 µg/ml. Complete follow-up data were available for 30 culture-positive patients. A highly significant difference in modal MICs to TRB among the three treatment response groups was noted (P = 0.009). Interestingly, 8 of the 9 patients in group 3 harbored isolates exhibiting elevated TRB MICs (8 to 32 µg/ml) and SQLE mutations. The odds of achieving cure with TRB MIC < 1 µg/ml strains were 2.5 times the odds of achieving cure with the strain exhibiting MIC ≥1 µg/ml.