Miracle-dz/UniFAR-FTUs huggingface dataset:
Specific Strains of Faecalibacterium prausnitzii Ameliorate Nonalcoholic Fatty Liver Disease in Mice in Association with Gut Microbiota Regulation

Evidence linking Faecalibacterium prausnitzii abundance to nonalcoholic fatty liver disease (NAFLD) is accumulating; however, the causal relationship remains obscure. In this study, 12 F. prausnitzii strains were orally administered to high fat diet fed C57BL/6J mice for 12 weeks to evaluate the protective effects of F. prausnitzii on NAFLD. We found that five F. prausnitzii strains, A2-165, LB8, ZF21, PL45, and LC49, significantly restored serum lipid profiles and ameliorated glucose intolerance, adipose tissue dysfunction, hepatic steatosis, inflammation, and oxidative stress in a mouse model of NAFLD. Moreover, two strains, LC49 and LB8, significantly enhanced short-chain fatty acid (SCFA) production and modulated the gut microbiota. Based on the combined analysis of linear discriminant analysis effect size and microbial communities, the core microbiome related to NAFLD comprised Odoribacter, Roseburia, Erysipelatoclostridium, Tyzzerella, Faecalibaculum, Blautia, and Acetatifactor, and the last five genera can be reversed by treatment with the LC49 and LB8 strains. Additionally, the LC49 and LB8 strains enriched Lactobacillus, Ileibacterium, Faecalibacterium, Dubosiella, and Bifidobacterium and downregulated pathways involving carbohydrate metabolism, amino acid metabolism, and fatty acid biosynthesis. Interestingly, LC49 supplementation also upregulated tryptophan metabolism, glutathione metabolism, and valine, leucine, and isoleucine degradation, which might be related to NAFLD prevention. Collectively, F. prausnitzii LC49 and LB8 exerted considerable anti-NAFLD and microbiota-regulating effects, indicating their potential as probiotic agents for NAFLD treatment.
Relaxation behaviour of Pb(Ni1/3Nb2/3)O3 ceramics

Polycrystalline samples of a cubic perovskite structure with an estimated 2.9% of a minor pyrochlore phase of Pb(Ni1/3,Nb2/3)O3 were synthesized by a high-temperature solid-state reaction technique. Dielectric studies show that the material is a good relaxor with a high dielectric constant. Diffuse phase transition through a wide Curie-temperature range was observed. The activation energy in a specified range of temperature for the material is estimated.
Treinamento em natação atenua a disfução contrátil de cardiomiócitos de ratos diabéticos

FUNDAMENTO: O diabete experimental promove disfuncao contratil em cardiomiocitos, mas os efeitos do treinamento em natacao nesta disfuncao nao sao conhecidos. OBJETIVO: Testar os efeitos de um programa de treino em natacao (PTN) sobre a disfuncao contratil de cardiomiocitos de ratos com diabete experimental. METODOS: Ratos Wistar (idade: 30 dias; peso corporal medio: 84,19 g) com diabete induzida por estreptozotocina (60 mg/kg de peso corporal; glicemia > 300 mg/dl) foram alocados em diabeticos sedentarios (DS, n = 10) e diabeticos exercitados (DE, n = 13). Animais da mesma idade e peso serviram de controles sedentarios (CS, n = 10) e controles exercitados (CE, n = 06). Os animais DE e CE foram submetidos a um PTN (05 dias/semana, 90 min/dia), por 08 semanas. Os miocitos do ventriculo esquerdo (VE) foram isolados e estimulados eletricamente a 3,0 Hz em temperatura ambiente (∼ 25o C). RESULTADOS: O diabete reduziu a funcao contratil nos cardiomiocitos dos animais em relacao aos controles (i.e., menor amplitude de contracao, maior tempo de contracao e relaxamento). O PTN atenuou a reducao na amplitude de contracao (CS, 11 ± 0,2% vs DE, 11,6 ± 0,2%), o tempo para o pico de contracao (CS, 319 ± 5,8 ms vs DE, 333 ± 4,8 ms) e o tempo para 50% de relaxamento (CS, 619 ± 22,2 ms vs DE, 698 ± 18,6 ms) dos cardiomiocitos dos animais diabeticos. O diabete reduziu as dimensoes dos cardiomiocitos, porem, o PTN minimizou a reducao da largura e volume celular, sem alterar o comprimento. CONCLUSAO: O programa de treino em natacao atenuou a disfuncao contratil dos miocitos do VE de ratos com diabete experimental.
Aspects algorithmiques des triangulations minimales des graphes

Nous nous sommes interesses dans cette these au calcul de la largeur arborescente des graphes, qui consiste a trouver une triangulation minimale du graphe, tout en minimisant la taille de la clique maximum de la triangulation. Ce probleme est np-difficile en general. Les triangulations minimales des graphes sont fortement liees aux separateurs minimaux, mais les caracterisations des triangulations minimales en termes de separateurs minimaux sont trop globales pour avoir des consequences algorithmiques. Il existe toutefois des algorithmes polynomiaux specifiques, calculant la largeur arborescente pour certaines classes de graphes ayant un nombre polynomial de separateurs minimaux. Nous avons introduit la notion de famille maximale de separateurs voisins, qui est une facon de regrouper les separateurs minimaux induisant des cliques dans les triangulations minimales. Nous avons defini ensuite les cliques maximales potentielles, qui permettent de caracteriser localement et algorithmiquement toutes les cliques maximales de toutes les triangulations minimales d'un graphe. Nous avons prouve que si l'on sait calculer en temps polynomial toutes les cliques maximales potentielles d'un graphe, la largeur arborescente de ce graphe se calcule en temps polynomial. Nous avons montre que pour toutes les classes de graphes pour lesquelles il existait des algorithmes calculant la largeur arborescente, les algorithmes respectifs calculent de maniere implicite les cliques maximales potentielles des graphes donnes. Nous unifions ainsi tous ces algorithmes. Nous avons donne un algorithme enumerant en temps polynomial les cliques maximales potentielles des graphes faiblement triangules, classe pour laquelle le calcul de la largeur arborescente etait un probleme ouvert. Tous ces algorithmes se generalisent au calcul d'un autre parametre de graphes, a savoir la completion minimale.
The Problem of Wound Healing in Diabetes—From Molecular Pathways to the Design of an Animal Model

Chronic wounds are becoming an increasingly common clinical problem due to an aging population and an increased incidence of diabetes, atherosclerosis, and venous insufficiency, which are the conditions that impair and delay the healing process. Patients with diabetes constitute a group of subjects in whom the healing process is particularly prolonged regardless of its initial etiology. Circulatory dysfunction, both at the microvascular and macrovascular levels, is a leading factor in delaying or precluding wound healing in diabetes. The prolonged period of wound healing increases the risk of complications such as the development of infection, including sepsis and even amputation. Currently, many substances applied topically or systemically are supposed to accelerate the process of wound regeneration and finally wound closure. The role of clinical trials and preclinical studies, including research based on animal models, is to create safe medicinal products and ensure the fastest possible healing. To achieve this goal and minimize the wide-ranging burdens associated with conducting clinical trials, a correct animal model is needed to replicate the wound conditions in patients with diabetes as closely as possible. The aim of the paper is to summarize the most important molecular pathways which are impaired in the hyperglycemic state in the context of designing an animal model of diabetic chronic wounds. The authors focus on research optimization, including economic aspects and model reproducibility, as well as the ethical dimension of minimizing the suffering of research subjects according to the 3 Rs principle (Replacement, Reduction, Refinement).
Automated Heart Sound Activity Detection From PCG Signal Using Time–Frequency-Domain Deep Neural Network

The phonocardiogram (PCG) signal deciphers the mechanical activity of the heart, and it consists of the fundamental heart sounds (FHSs) (S1 and S2), murmurs, and other associated sounds (S3 and S4). Detection of FHS activity (FHSA) is vital for the automated analysis of PCG signals to diagnose various heart valve diseases. This article proposes a time–frequency-domain (TFD) deep neural network (DNN) approach for automated FHSA detection using PCG signals. The modified Gaussian window-based Stockwell transform (MGWST) is used to obtain the time–frequency representation (TFR) of PCG signals. The Shannon–Teager–Kaiser energy (STKE), smoothing, and thresholding techniques are then employed to evaluate the segmented heart sound components. The TFD Shannon entropy (TFDSE) features are computed from the segmented heart sound components of the PCG signal. The DNN developed based on the stacked autoencoders (SAEs) is used for the automated identification of FHSA components. The performance of the proposed approach is evaluated using two publicly available standard databases (Database 1: Michigan heart sound and murmur database and Database 2: PhysioNet Computing in Cardiology Challenge 2016). The results demonstrate that the proposed approach has achieved the accuracy, sensitivity, specificity, and precision values of 99.55%, 99.93%, 99.26%, and 99.02% for Database 1 and 95.43%, 97.92%, 98.32%, and 97.60% for Database 2, respectively. It is shown that the proposed FHSA detection approach has obtained better accuracy than existing methods.
Framing Professional Learning Analytics as Reframing Oneself

Central to imagining the future of technology-enhanced professional learning is the question of how data are gathered, analyzed, and fed back to stakeholders. The field of learning analytics (LA) has emerged over the last decade at the intersection of data science, learning sciences, human-centered and instructional design, and organizational change, and so could in principle inform how data can be gathered and analyzed in ways that support professional learning. However, in contrast to formal education where most research in LA has been conducted, much work-integrated learning is experiential, social, situated, and practice-bound. Supporting such learning exposes a significant weakness in LA research, and to make sense of this gap, this article proposes an adaptation of the Knowledge-Agency Window framework. It draws attention to how different forms of professional learning locate on the dimensions of learner agency and knowledge creation. Specifically, we argue that the concept of “reframing oneself” holds particular relevance for informal, work-integrated learning. To illustrate how this insight translates into LA design for professionals, three examples are provided: first, analyzing personal and team skills profiles (skills analytics); second, making sense of challenging workplace experiences (reflective writing analytics); and third, reflecting on orientation to learning (dispositional analytics). We foreground professional agency as a key requirement for such techniques to be used effectively and ethically.
A Study of Adversarial Attacks on Malaria Cell Image Classification

In all mosquito borne diseases in the world no other disease is as dangerous Malaria. According to the WHO 409,000 people died of this disease in 2020. Early detection and diagnosis can aid in saving precious human lives. In order to identify Malaria infected cells properly, expertise is needed from people. Deep learning approaches can aid in such tasks since they can accurately identify these cell images in large-scale scenarios without any fatigue. In recent studies, it has been shown that, in spite of the success that deep learning has achieved in varying applications, it is unreliable. Adversarial perturbations when added to the input data forces the model to provide wrong answers. We used VGG16, ResNet18 and Wide-ResNet-28-10 model to correctly classify infected Malaria cells from normal images. We also performed Fast Gradient Sign Method (FGSM) attack to evaluate the robustness of these models. FGSM adversarial training was performed on these models to defend it from such attacks as well. We studied in depth the effects of this attack against these models by varying maximum allowed perturbations. We achieved 95.74%, 95.94% and 95.30% accuracy on clean data and 92.71%, 90.64%, and 88.73% accuracy on adversarial images after performing adversarial training for VGG16, ResNet and Wide-ResNet respectively.
PENGUATAN PERAN KADER KESEHATAN DALAM DETEKSI DINI RESIKO PENYAKIT JANTUNG

Latar Belakang: Penyakit jantung merupakan istilah umum dari semua penyakit yang menyerang jantung sebagai sistem kardiovaskuler, seperti Angina Pectoris (Nyeri dada), Acut Miocard Infarck (Serangan Jantung) dan Congestive Heart Failure (Gagal Jantung). Penyakit jantung di setiap negara menduduki peringkat pertama sebagai penyakit tidak menular (PTM) penyebab kematian nomor satu di dunia, dengan jumlah korban 17 juta setiap tahunnya. Penyakit jantung tersebut dapat dicegah dan dapat dideteksi lebih awal jika diketahui faktor resiko pada setiap orang yang dapat menyebabkan penyakit jantung tersebut. Tujuan dari pengabdian masyarakat ini adalah meningkatkan pengetahuan kader tentang promosi kesehatan dan deteksi dini resiko penyakit jantung. Meningkatkan keterampilan kader dalam melakukan intervensi deteksi dini resiko penyakit jantung. Metode: Metode kegiatan bertahap dengan tahapan kegiatan sebagai berikut: 1) transfer pengetahuan dan demonstrasi penentuan factor resiko penyakit jantung, 2) Evaluasi tindakan penyuluhan. Hasil: Hasil yang diperoleh adalah kader dan warga mampu mengetahui cara deteksi dini gangguan penyakit jantung berdasarkan kuesioner skrining deteksi dini penyakit jantung. Setelah pengabdian ini dilakukan, kader diharapkan dapat menerapkan ke warga yang beresiko penyakit jantung.
Investigation and Analysis of 49343 Case Women’s Vaginal Microecology

Background: We aimed to investigate the pathogen infection in vaginal secretions of women in Linyi area. Methods: From October 2016 to September 2018, a total of 49,343 vaginal secretion specimens from women who attended Women and Children’s Health Care Hospital of Linyi District, Shandong Province, China were used to detect the cleanliness, Candida, clue cells, Trichomonas, etc. with Ultra-high power microscopy. Results: Among the 49343 patients, 6377 had vaginal cleanliness of degree I∼II, the detection rate was 37.89%; 10455 cases of III∼IV degree, the detection rate was 62.11%; 13193 cases of simple vaginal pathogen infection, the detection rate was 26.74%. Among them, 9256 cases of vaginal Candida (VVC) had a detection rate of 18.76%; 3176 cases of Bacterial vaginosis (BV) had a detection rate of 6.44%; and 761 cases of Trichomonas infection (TV) had a detection rate 1.54%; 899 cases of mixed infection. The detection rate was 1.82% and the detection rate of each pathogen in the 18–30 year old group was the highest. The detection rates of VVC, BV, TV and MVI were 10.80%, 3.25%, 0.65%, 1.00%, repsectively. Conclusion: The incidence of VVC women with vaginitis in Linyi was the highest, and the incidence was mainly between 18 and 40 years old. The infection rate of VVC, BV and MVI pathogens was the highest in summer, and the infection rate of TV was the highest in autumn.
Annual simulation of outdoor thermal comfort with TRNSYS

This paper presents the modelling enhancements of the multizone model of TRNSYS and its GUI TRNLizard for the dynamic evaluation of the thermal behaviour of outdoor urban spaces. The developed approaches for detailed radiation modelling based on surface discretization and raytracing in 3-dimensional space extended the capabilities of the thermal building model to consider interior and exterior vegetation volumes. For the implementation special care was taken to enable both, the application of empirical correlations/assumptions for a simplified and fast modelling as well as the integration/coupling to detailed simulation results from other domains such as vegetation models and CFD. The validation showed that the improvements were successfully integrated. A comparison with measured data is still pending. The urban space model has been applied to the Bordeauxplatz in Munich. The case study showed that dynamic annual simulation results like the frequency distribution of comfort conditions provide very useful information for urban planning.
A Research Based on Machine Learning of Satellite Data Modelling and Image Deep Convolution Learning Model

The issue of fires causing damage in the life of people has been especially prevalent in the past year around the world in larger events and through this project computer learning models are able to detect and recognize them with high accuracy and functions as a potential solution towards the reduction of damage caused by fires. During the first stage we use satellite imaging in order to detect the areas where a wildfire could be started and then after the areas are identified we move to stage 2 of the program where we would use closer monitors over the area in the form of images in order to identify whether or not there is a fire in the area or not. Once the it is confirmed that there the area the local fire department could then be contacted to resolve the issue.
Parametric Analysis on Non-Lubricated Wear Over Hybrid Aluminium Metal Matrix Composite Material by Rsm

The primary focus of this research was to investigate the parameters that influence the wear rate of a hybrid composite material. The parameters used are the percentages of two reinforcements used, applied weight, and sliding velocity. The methodology involves the production of the hybrid composite utilizing the stir casting process. The ASTM G99 standard is used to evaluate the produced composite materials utilizing a pin-on-disc equipment. The testing settings include changing weights of 10 [N], 20 [N], and 30 [N], as well as sliding velocities of 1 [m.s -1 ], 2 [m.s -1 ], and 3 [m.s -1 ] using RSM technique. The obtained results of wear rate for the composite materials were analyzed for the most influencing parameter and it was found that the sliding velocity is the major factor influencing the wear rate followed by applied load. The weight percentage of cenosphere and weight percentage of MoS 2 are least influencing factors in linear terms and the applied load factor was the only factor having influence in square term also. Interaction plot shows the combinational effect of the weight percentage of cenosphere - weight percentage of MoS 2 , weight percentage of MoS 2 – applied load, weight percentage of MoS 2 – sliding velocity and applied load – sliding velocity majorly influencing the wear rate. The weight percentage of cenosphere and MoS 2 found to have a significant influence on the wear behaviour of the composite material even though their percentage contribution is relatively low and the wear rate decreases as the weight percentage of cenosphere and MoS 2 tends to increase.
Negative polarization properties of regolith simulants. Systematic experimental evaluation of composition and geometry effects

Polarization phase curves of asteroids and other small airless bodies are influenced by the compositional and physical properties of their regolith. The mixing of minerals composing the regolith influences the negative polarization at small phase angles because it changes the multiple scattering properties of the medium. This work aims to demonstrate experimentally how the mixing effect influences the polarization phase curve at small phase angles for different mineralogies relevant for asteroids, and to determine how different aggregate sizes affect the negative polarization. We prepared a set of binary and ternary mixtures with different common minerals on asteroids and one set of the same mixture with different aggregate sizes. We measured their reflected light at 530 nm with full Stokes polarimetry at phase angles ranging from 0.8{\deg} to 30{\deg}. The mixing effect of the mixtures with both bright and dark minerals significantly changes the behavior of the phase curves in terms of minimum polarization, phase angle of the minimum, and inversion angle with respect to the mineral components that are mixed together. The changes in phase curve could explain the polarization observation of particular classes of asteroids (F and L class) and other asteroids with peculiar polarization curves or photometric properties. Furthermore, we demonstrate that the negative polarization is invariant to the presence of dust aggregates up to centimeter sizes.
Visualization of profiling and tracing in CPU‐GPU programs

As the complexity of the toolchain increases for heterogeneous CPU‐GPU systems, the needs for comprehensive tracing and debugging tools also grows. Heterogeneous platforms bring new possibilities but also new performance issues that are hard to detect. Some techniques that were used on CPU programs are now adapted to GPUs. However, there are some concepts specific to GPUs, like SIMD processing, and the effects of the close interactions between the CPUs and the GPUs, with shared virtual memory and user‐level queues. Multiple sources of data need to be extracted and correlated to obtain a more global view of the performance. In this article, we introduce a novel approach for measuring and visualizing performance defects inside CPU‐GPU programs by combining kernel events, compute kernel events, user API calls and memory transfers. We created two new views that combine this information, to help provide a global view. This framework uses the open source user queue system described in the HSA standard. It can easily be adapted to any user queue system for heterogeneous computing devices. We compare this framework with current existing tools and test it against the Rodinia benchmark. We look at how the execution behavior affects the tracing and profiling overhead and we use Trace Compass to visualize the resulting trace.
Price and Risk Awareness for Data Offloading Decision-Making in Edge Computing Systems

The proliferation of multiaccess edge computing (MEC) paradigm has created a challenging multiuser–multiserver–multiaccess edge computing competitive environment, which brings the problem of data offloading decision-making to the forefront of research. In this article, we address this issue while jointly studying the impact of the user behavioral characteristics and the MEC servers pricing policies on determining the optimal user data offloading strategies. Prospect theory is exploited to reflect the user satisfaction and subjectivity from the data offloading, while the MEC servers’ probability of failure owing to the potential overexploitation by the users, is modeled via the theory of tragedy of the commons. A multileader multifollower Stackelberg game is formulated among the MEC servers (leaders) and the users (followers), to determine the servers’ optimal pricing policies and the users’ optimal data offloading strategies. The users’ data offloading decision-making is formulated as a noncooperative game among them and a Nash equilibrium is determined, while the MEC servers’ optimal computing service prices are obtained either through a semiautonomous game-theoretic approach, or through a fully autonomous reinforcement learning-based approach. The performance evaluation and demonstration of the superiority of the proposed framework against other benchmarking alternatives is achieved via modeling and simulation.