Miracle-dz/UniFAR-FTUs huggingface dataset:
Increased mitochondrial surface area and cristae density in the skeletal muscle of strength athletes

Mitochondria are the cellular organelles responsible for resynthesising the majority of ATP. In skeletal muscle, there is an increased ATP turnover during resistance exercise to sustain the energetic demands of muscle contraction. Despite this, little is known regarding the mitochondrial characteristics of chronically strength-trained individuals and any potential pathways regulating the strength-specific mitochondrial remodelling. Here, we investigated the mitochondrial structural characteristics in skeletal muscle of strength athletes and age-matched untrained controls. The mitochondrial pool in strength athletes was characterised by increased mitochondrial cristae density, decreased mitochondrial size, and increased surface-to-volume ratio, despite similar mitochondrial volume density. We also provide a fibre-type and compartment specific assessment of mitochondria morphology in human skeletal muscle, which reveals across groups a compartment-specific influence on mitochondrial morphology that is largely independent of fibre-type. Furthermore, we show that resistance exercise leads to signs of mild mitochondrial stress, without an increase in the number of damaged mitochondria. Using publicly available transcriptomic data we show that acute resistance exercise increases the expression of markers of mitochondrial biogenesis, fission, and mitochondrial unfolded protein responses (UPRmt). Further, we observed an enrichment of the UPRmt in the basal transcriptome of strength-trained individuals. Together, these findings show that strength athletes possess a unique mitochondrial remodelling, which minimises the space required for mitochondria. We propose that the concurrent activation of markers of mitochondrial biogenesis and mitochondrial remodelling pathways (fission and UPRmt) with resistance exercise may be partially responsible for the observed mitochondrial phenotype of strength athletes.
The chromatin modulating NSL complex regulates genes and pathways genetically linked to Parkinson’s disease

Genetic variants conferring risk for Parkinson’s disease have been highlighted through genome-wide association studies, yet exploration of their specific disease mechanisms is lacking. Two Parkinson’s disease candidate genes, KAT8 and KANSL1, identified through genome-wide studies and a PINK1-mitophagy screen, encode part of the histone acetylating non-specific lethal complex. This complex localises to the nucleus, where it has a role in transcriptional activation, and to mitochondria, where it has been suggested to have a role in mitochondrial transcription. In this study, we sought to identify whether the non-specific lethal complex has potential regulatory relationships with other genes associated with Parkinson’s disease in human brain. Correlation in the expression of non-specific lethal genes and Parkinson’s disease-associated genes was investigated in primary gene co-expression networks utilising publicly available transcriptomic data from multiple brain regions (provided by the Genotype-Tissue Expression Consortium and UK Brain Expression Consortium), whilst secondary networks were used to examine cell-type specificity. Reverse engineering of gene regulatory networks generated regulons of the complex, which were tested for heritability using stratified linkage disequilibrium score regression and then validated in vitro using the QuantiGene multiplex assay. Significant clustering of non-specific lethal genes was revealed alongside Parkinson’s disease-associated genes in frontal cortex primary co-expression modules. Both primary and secondary co-expression modules containing these genes were enriched for mainly neuronal cell types. Regulons of the complex contained Parkinson’s disease-associated genes and were enriched for biological pathways genetically linked to disease. When examined in a neuroblastoma cell line, 41% of prioritised gene targets showed significant changes in mRNA expression following KANSL1 or KAT8 perturbation. In conclusion, genes encoding the non-specific lethal complex are highly correlated with and regulate genes associated with Parkinson’s disease. Overall, these findings reveal a potentially wider role for this protein complex in regulating genes and pathways implicated in Parkinson’s disease.
MRI-assessed locus coeruleus contrast and functional response are not associated in young and late middle-aged individuals

The brainstem locus coeruleus (LC) influences a broad range of brain processes, including cognition. The so-called LC contrast is an accepted marker of the integrity of the LC that consists of a local hyperintensity on specific Magnetic Resonance Imaging (MRI) structural images. The small size of the LC has, however, rendered its functional characterization difficult in humans, including in aging. A full characterization of the structural and functional characteristics of the LC in healthy young and late middle-aged individuals is needed to determine to potential roles of the LC in different medical conditions. Here, we wanted to determine whether the activation of the LC in a mismatch negativity task changes in aging and whether the LC functional response was associated to the LC contrast. We used Ultra-High Field (UHF) 7-Tesla functional MRI (fMRI) to record brain response during an auditory oddball task in 53 healthy volunteers, including 34 younger (age: 22.15y ± 3.27; 29 women) and 19 late middle-aged (age: 61.05y ± 5.3; 14 women) individuals. Whole-brain analyses confirmed brain responses in the typical cortical and subcortical regions previously associated with mismatch negativity. When focusing on the brainstem, we found a significant response in the rostral part of the LC probability mask generated based on individual LC images. Although bilateral, the activation was more extensive in the left LC. Individual LC activity was not significantly different between young and late middle-aged individuals. Critically, while the LC contrast was higher in older individuals, the functional response of the LC was not associated with its contrast. These findings show that the age-related alterations of the LC structural integrity may not necessarily be related to changes in its functional response. The results further indicate that LC responses could remain stable in healthy individuals aged 20 to 70.
Fatty liver-mediated glycine restriction impairs glutathione synthesis and causes hypersensitization to acetaminophen

Non-alcoholic fatty liver disease (NAFLD) affects nearly one third of the population worldwide. Understanding metabolic pathways involved can provide insights into disease progression. Untargeted metabolomics of livers from mice with early-stage steatosis indicated a decrease in methylated metabolites suggesting altered one carbon metabolism. The levels of glycine, a central component of one carbon metabolism, were lower in steatotic mice, in line with clinical evidence. Isotope tracing studies demonstrated that increased synthesis of serine from glycine is the underlying cause for glycine limitation in fatty livers. Consequently, the low glycine availability in steatotic livers impaired glutathione (GSH) synthesis under oxidative stress induced by acetaminophen (APAP), enhancing hepatic toxicity. Glycine supplementation mitigated acute liver damage and overall toxicity caused by APAP in fatty livers by supporting de novo GSH synthesis. Thus, early metabolic changes in NAFLD that lead to glycine depletion sensitize mice to xenobiotic toxicity even at a reversible stage of NAFLD.
A partially shared joint clustering framework for detecting protein complexes from multiple state-specific signed interaction networks

Detecting protein complexes is critical for studying cellular organizations and functions. The accumulation of protein-protein interaction (PPI) data enables the identification of protein complexes computationally. Although various computational approaches have been proposed to detect protein complexes from PPI networks, most of them ignore the signs of PPIs that reflect the ways proteins interact (activation or inhibition). As not all PPIs imply cocomplex relationships, taking into account the signs of PPIs can benefit the detection of protein complexes. Moreover, PPI networks are not static, but vary with the change of cell states or environments. However, existing protein complex identification algorithms are primarily designed for single-network clustering, and rarely consider joint clustering of multiple PPI networks. In this study, we propose a novel partially shared signed network clustering model (PS-SNC) for detecting protein complexes from multiple state-specific signed PPI networks jointly. PS-SNC can not only consider the signs of PPIs, but also identify the common and unique protein complexes in different states. Experimental results on synthetic and real datasets show that PS-SNC outperforms other state-of-the-art protein complex detection methods. Extensive analysis on real datasets demonstrate the effectiveness of PS-SNC in revealing novel insights about the underlying patterns of different cell lines.
Sex differences in neurotransmitter levels in different brain regions after acute and chronic morphine treatment in mice

Background and Purpose Pain management is a major health burden. Pain results from the integration of the nociceptive message and neuronal communication relying on neurotransmitters such as glutamate, γ-aminobutyric acid (GABA), dopamine, noradrenaline and serotonin in brain regions including the periaqueductal gray (PAG), the nucleus accumbens (Nac), the caudate-putamen (Cpu) and the amygdala. Morphine remains the gold standard painkiller for severe pain via the activation of the mu opioid receptors. However, among side effects, morphine chronic treatment lead to antinociceptive tolerance. As antinociceptive tolerance might be linked to neurotransmission dysregulation, we have compared various neurotransmitter concentrations in acute vs chronic morphine conditions in the amygdala, PAG, Cpu, and the Nac of male and female mice. Experimental approach Sex differences in morphine antinociception and tolerance were assessed using the tail-immersion test. The behavioural effects of acute and chronic morphine treatments, as well as sex differences in the levels of dopamine, serotonin, noradrenaline, glutamate and GABA in the amygdala, PAG, Cpu, and the Nac were determined by an absolute quantification LC-MS/MS approach using the isotopic dilution method. Key results This study indicates, as previously reported, that female mice are less sensitive to morphine and develop morphine antinociceptive tolerance earlier than males (ED50 of 5.5±0.24 days vs 1.54±0.11 days, respectively). However, the rate at which the tolerance developed did not differ between both sex. We have found major differences in dopamine, serotonin, noradrenaline, glutamate and GABA levels between female and male mice in the amygdala, PAG, Cpu, and the Nac. Finally, no major effect of anti-nociceptive tolerance induced by chronic morphine was observed compared to acute administration of morphine. Conclusion Neurotransmitter differences are attributable mainly to sex differences in pain-related CNS regions. However, the impacts of morphine anti-nociceptive tolerance on dopamine, serotonin, noradrenaline, glutamate and GABA contents appeared to be limited.
Reduction of stimulated Brillouin scattering threshold through pump recycling technique

We demonstrate a simple method of stimulated Brillouin scattering (SBS) threshold reduction through a procedure of Brillouin pump recycling technique. High reflectivity optical mirror was incorporated onto our experimental structure in order to recycle the forward transmitted Brillouin pump signal back into the Brillouin gain medium thereby increasing its effective gain. In a 5.0 km single mode fiber spool, our technique reduced SBS threshold by over 48% (measured at 8.5 mW of input signal against 16.5 mW in the conventional technique under same input signal conditions). In addition, the Stokes power was amplified to 9.2 dBm by our method against 4.3 dBm measured in the conventional technique in the 5.0 km single mode fiber spool. Result of different lengths has proved that our technique considerably reduced SBS threshold compared to the conventional method of SBS characterization under same pumping schemes.
Definition and Implementation of W-STEM Mentoring Network

The lack of women in STEM careers is a problem that affects all higher education institutions, not only in Latin America but also in Europe and other regions of the world. Improving access, retention, and orientation mechanisms in universities is fundamental to ensure that more women interested in these areas effectively pursue a degree in science, technology, engineering, or mathematics. The W-STEM project seeks to establish strategies to improve these mechanisms in Latin American institutions. Since 2019, it has addressed the improvement of attraction, access, retention, and orientation mechanisms. The W-STEM Mentoring Network is part of these strategies. Specifically, it aims to empower women and encourage their active participation in STEM programmes. The mentoring model implemented uses a three-way relationship: a mentor teacher, a mentor-student and the mentee. The Network provides guidelines for the implementation of mentoring programmes, but each institution involved has adapted them to their contexts and needs according to their Gender Equality Action Plan. This paper describes the guidelines developed through a collaborative co-creation process involving all project institutions under the coordination of the Universidad Técnica Particular de Loja (Ecuador) and the University of Salamanca (Spain).
Comparison of Multi Layer Perceptron, Random Forest & Logistic Regression on Students Performance Test

The test is one thing that can be taken to measure a person's ability to understand a material or a competency. In general, there is a final test taken by students at the school level, before reaching the final test, usually students will take a series of preparatory tests. In reality, of course, not all students can take the test preparation well. therefore of course the school has data related to test preparation. From this test preparation data, a classification technique can be used to classify the data of students who have completed the preparatory test and students who have not completed the preparatory test, so that schools can prepare the best strategy. To assist in classifying data, data classification techniques are needed, in this study the Multi-Layer Perceptron, Random Forest and Simple Logistics algorithms were used. These three methods produce different accuracy when used for the data classification process. For testing the data, scenarios are used using cross-validation. The results of this test scenario show that the Logistic Regression method is superior to the Random Forest and Multi-Layer Perceptron methods with an accuracy of 73.9%. The best Root Mean Square Error results are in the Multi-Layer Perceptron method with the smallest value of 0.363.
Towards Smart Traffic Lights based on Deep Learning and Traffic Flow Information

Traffic congestion is a significant cause hindering development and adversely affecting socio-economic life; mean-while, traditional traffic light systems have become obsolete. Therefore, the application of machine learning to enhance the effectiveness of these systems has received much attention from the research community. However, their practical application is limited because of the lack of training datasets and high computational requirements. In this paper, we propose a lightweight approach that can dynamically control traffic lights at intersections based on current traffic situation. To do this, we design a deep learning model based on the Bidirectional LSTM architecture to estimate the appropriate duration of traffic lights by learning traffic flow information. Our model achieves high accuracy and is lightweight enough to deploy resource-constrained IoT devices. In addition, we introduce an algorithm to generate data about traffic flow information from a well-known traffic simulation framework. The evaluation results show that the model could accurately estimate the duration of the traffic light with a low mean square error and outperformed other machine learning models.
FFM-SVD: A Novel Approach for Personality-aware Recommender Systems

This paper addresses and evaluates approaches to incorporating personality data into a recommender system. Automatic personality recognition is enabled by the LIWC dictionary. Personality-aware pre-filtering techniques are developed and discussed, with the introduced non-targeted stratified personality sampling performing the best. A novel personality-aware model, FFM-SVD, is proposed and shown to outperform alternative models in prediction accuracy.
A Web-based Application for Snake Species Identification using Vision Transformer and CNN-based Ensemble Meta Classifier

Being a tropical country, Sri Lanka has one of the highest snakebite rates in the world. In 2019, 50 snake bite fatalities have been reported in Sri Lanka. Therefore, the accurate identification of the snake category is crucial for healthcare workers to diagnose and treat the victims as well as to save the snake from being killed. In this paper, we present a web-based application based on Convolutional Neural Network and Vision Transformer architectures to classify between the Russell's viper and the Indian Rock Python. Five different image classification models were trained using the pre-trained architectures ResNet-50, ResNet-100, EfficientNet B0, EfficientNet B7 and Data-Efficient Image Transformers. We were able to gain a testing accuracy of 94.5% by using an ensemble approach for the mentioned classifiers. Furthermore, this study presents the first web-based application in Sri Lanka enabling the automatic identification between Russell's Viper and the Indian Rock Python.
A Deep Learning post-processor with a perceptual loss function for video compression artifact removal

While video compression is necessary for large scale video streaming services, compression at low bitrate can degrade the original video and negatively affect the end user’s quality of experience. Deep Neural Networks (DNNs) are actively researched with respect to artifact removal, however the loss functions that are typically employed follows a derivation of a pixel-wise Lp norm. In this paper we consider a DNN as a post-processor for video compression artifact removal. The DNN is trained using a composite perceptual loss that combines a traditional Lp norm loss and a VMAF proxy network based on the Video Multimethod Assessment Function (VMAF). Results show an improvement in VMAF score over both the training and testing sets.
Successive Cancellation Decoding of Single Parity-Check Product Codes: Analysis and Improved Decoding

A product code with single parity-check component codes can be described via the tools of a multi-kernel polar code, where the rows of the generator matrix are chosen according to the constraints imposed by the product code construction. Following this observation, successive cancellation decoding of such codes is introduced. In particular, the error probability of single parity-check product codes over binary memoryless symmetric channels under successive cancellation decoding is characterized. A bridge with the analysis of product codes introduced by Elias is also established for the binary erasure channel. Successive cancellation list decoding of single parity-check product codes is then described. For the provided example, simulations over the binary input additive white Gaussian channel show that successive cancellation list decoding outperforms belief propagation decoding applied to the code graph. Finally, the performance of the concatenation of a product code with a high-rate outer code is investigated via distance spectrum analysis. Examples of concatenations performing within 0.7 dB from the random coding union bound are provided.
Analysis of SMEs Consideration in Adopting New Technology Using Technology Acceptance Model

Nowadays, a business cannot be separated from optimizing technology. Many businesses implement technology to enhance and support their business performance. However, many software developers in the market create similar applications to help SMEs perform. On the other hand, SMEs have limited resources and time to learn and adopt those applications. This study aims to assess the factors that become a consideration for SMEs if they want to adopt new technology for their business using the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT) approach. Using the quantitative method with a purposive sampling approach, we get 225 representative respondents. We analyzed using Structured Equation Models (SEM) with PLS-SEM analysis tools. Our findings show that all variables, including perceived usefulness, perceived ease of use, compatibility, and cost-effectiveness, significantly influence the attitude and intention to use applications. The variable compatibility and cost-effectiveness have the highest score in this model, meaning that it is an essential consideration for SMEs to choose the application before selecting the fitness of the application for the company.
A Fatigue Detection Model Based on Convolutional Neural Network

The use of vehicles has grown steadily during the past few years. This rise in vehicle usage does not correspond to the degree of driving safety. This is seen in Indonesia's 4% average annual increase in accidents. A drowsy driver was one of the human variables that contributed most to this event. In this study, we address this issue utilizing our CNN-based model that was developed using the National Tsing Hua University Driver Drowsiness (NTHU DD) dataset. The model was trained using approximately 600,000 images extracted from the video dataset. The CNN-based model can obtain a 70% accuracy rate for the validation and a 56% accuracy rate for testing.
Supporting the Monitoring of Institutional Competency in Learning Innovation: The PROF-XXI Tool

The transition from the 20th- to 21st-century education is a vital and complex process for Higher Education Institutions (HEIs). It requires constant updating of their strategic plan, ensuring all internal competencies necessary for improving and adapting their teaching and learning processes are in place. This paper presents the design and evaluation of the PROF-XXI tool, a web-based dashboard designed to help HEIs make such transformation sustainable and support their Teaching and Learning Centres (TLCs) in decision making about teaching and learning strategies. The tool is based on the PROF-XXI competency framework, which offers a holistic perspective on the competencies that TLCs should develop to support deep and sustainable transformations. Through a structured interview carried out with 17 participants from 8 HEIs, this study evaluates the extent to which the proposed tool assists users with identifying the required competencies and monitoring their evolution. The results show the value of the tool to improve and monitor the institutions' innovative teaching and learning competencies, the most appropriate indicators for this purpose and suggestions on adaptations to the existing features of the tool.