Miracle-dz/UniFAR-FTUs huggingface dataset:
Commensal microbiome dysbiosis elicits IL-8 signaling to drive fibrotic skin disease

Commensal bacteria are core players in wound healing whose function in the opposite pathophysiological process-scarring is presently unclear. Here, we document the association between bacteria and a specific skin fibrotic disease-keloid, which might offer a promising avenue for translational practice. ABSTRACT Wound healing is an intensely studied topic involved in many relevant pathophysiological processes, including fibrosis. Despite the large interest in fibrosis, the network that related to commensal microbiota and skin fibrosis remain mysterious. Here, we pay attention to keloid, a classical yet intractable skin fibrotic disease to establish the association between commensal microbiota to scaring tissue. Our histological data reveal the presence of microbiota in the keloids. 16S rRNA sequencing characterize microbial composition and divergence between the pathological and normal skin tissue. Moreover, the data show elevation of interleukin-8 both in the circulation and keloid tissue, which elicited the collagen accumulation and migratory program of dermal fibroblasts via CXCR1/2 receptor. Our research provides insights into the pathology of human fibrotic diseases, advocating commensal bacteria and IL-8 signaling as useful targets in future interventions of recurrent keloid disease.
Comparative genomics reveals the emergence of an outbreak-associated Cryptosporidium parvum population in Europe and its spread to the USA

The zoonotic parasite Cryptosporidium parvum is a global cause of gastrointestinal disease in humans and ruminants. Sequence analysis of the highly polymorphic gp60 gene enabled the classification of C. parvum isolates into multiple groups (e.g. IIa, IIc, Id) and a large number of subtypes. In Europe, subtype IIaA15G2R1 is largely predominant and has been associated with many water-and food-borne outbreaks. In this study, we generated new whole genome sequence (WGS) data from 123 human-and ruminant-derived isolates collected in 13 European countries and included other available WGS data from Europe, Egypt, China and the USA (n=72) in the largest comparative genomics study to date. We applied rigorous filters to exclude mixed infections and analysed a dataset from 141 isolates from the zoonotic groups IIa (n=119) and IId (n=22). Based on 28,047 high quality, biallelic genomic SNPs, we identified three distinct and strongly supported populations: isolates from China (IId) and Egypt (IIa and IId) formed population 1, a minority of European isolates (IIa and IId) formed population 2, while the majority of European (IIa, including all IIaA15G2R1 isolates) and all isolates from the USA (IIa) clustered in population 3. Based on analyses of the population structure, population genetics and recombination, we show that population 3 has recently emerged and expanded throughout Europe to then, possibly from the UK, reach the USA where it also expanded. In addition, genetic exchanges between different populations led to the formation of mosaic genomes. The reason(s) for the successful spread of population 3 remained elusive, although genes under selective pressure uniquely in this population were identified.
Artificial neural network language models predict human brain responses to language even after a developmentally realistic amount of training

Artificial neural networks have emerged as computationally plausible models of human language processing. A major criticism of these models is that the amount of training data they receive far exceeds that of humans during language learning. Here, we use two complementary approaches to ask how the models’ ability to capture human fMRI responses to sentences is affected by the amount of training data. First, we evaluate GPT-2 models trained on 1 million, 10 million, 100 million, or 1 billion words against an fMRI benchmark. We consider the 100-million-word model to be developmentally plausible in terms of the amount of training data given that this amount is similar to what children are estimated to be exposed to during the first 10 years of life. Second, we test the performance of a GPT-2 model trained on a 9-billion-token dataset to reach state-of-the-art next-word prediction performance on the human benchmark at different stages during training. Across both approaches, we find that (i) the models trained on a developmentally plausible amount of data already achieve near-maximal performance in capturing fMRI responses to sentences. Further, (ii) lower perplexity—a measure of next-word prediction performance—is associated with stronger alignment with human data, suggesting that models that have received enough training to achieve sufficiently high next-word prediction performance also acquire representations of sentences that are predictive of human fMRI responses. In tandem, these findings establish that although some training is necessary for the models’ predictive ability, a developmentally realistic amount of training (∼100 million words) may suffice.
Triggering cascades and statistical properties of aftershocks

Applying a simple general procedure for identifying aftershocks, we investigate their statistical properties for a high‐resolution earthquake catalog covering Southern California. We compare our results with those obtained by using other methods in order to show which features truly characterize aftershock sequences and which depend on the definition of aftershocks. Features robust across methods include the p value in the Omori‐Utsu law for large main shocks, Båth's law, and the productivity law with an exponent smaller than the b value in the Gutenberg‐Richter law. The identification of a typical aftershock distance with the rupture length is a feature we confirm as well as a power law decay in the spatial distribution of aftershocks with an exponent less than 2. Other results we obtain, but not common to all other works including Marsan and Lengliné (2008), Hainzl and Marsan (2008), and Zhuang et al. (2008), are (a) p values that do not increase with the main shock magnitude; (b) the duration of bare aftershock sequences that scales with the main shock magnitude; (c) an additional power law in the temporal variation, at intermediate times, in the rate of aftershocks for main shocks of small and intermediate magnitude; and (d) a b value for the Gutenberg‐Richter law of background events that is sensibly larger than that of aftershocks. Tests on synthetic catalogs generated by the epidemic‐type aftershock sequence model corroborate the validity of our approach.
Pengembangan Desain Media Pembelajaran Fisika SMA Berbasis Video pada Materi Gelombang Bunyi

Penelitian ini merupakan penelitian research and development yang bertujuan untuk mengembangkan desain media pembelajaran fisika SMA berbasis video pada pokok bahasan gelombang bunyi yang valid dan reliabel. Tahap dalam penelitian ini meliputi potensi dan masalah, studi literatur dan pengumpulan informasi, desain produk, validasi desain, dan desain teruji. Media pembelajaran yang valid dan reliabel dihasilkan melalui validasi desain yang merupakan tahap pengujian produk secara internal melalui validasi isi oleh pendapat ahli dengan tiga aspek validasi yaitu, kualitas isi dan tujuan, kualitas instruksional, dan kualitas teknis dan kemudian diuji reliabilitasnya. Hasil penelitian menunjukkan bahwa, desain media pembelajaran yang dikembangkan dikategorikan sangat valid (89,59%) dan rebilitas tinggi dengan koefisien 0,76 pada aspek kualitas isi dan tujaan, pada aspek kualitas instruksional dikategorikan sangat valid (92,71%) dan reliabilitas sedang dengan koefisien 0,65, dan pada aspek kualitas teknis dkategorikan sangat valid (90,47%) dan reliabilitas sedang dengan koefisien 0,67. Hasil total validasi dari tiga aspek sebesar 91% dengan kategori sangat valid. Kata kunci : Research and Development, Media Pembelajaran Video, Gelombang Bunyi
Nighttime Sleep in Parkinson`s disease – the potential use of Parkinson’s KinetiGraph: A prospective comparative study

motor states and periods of immobility may reflect episodes of sleep. We aimed to evaluate whether PKG can be used as an objective marker of disturbed nighttime sleep in PD. Methods: In this prospective comparative study, data from PKG recordings over six consecutive 24-hour periods are compared with Hauser-diaries and scales focusing on motor state, sleep and HRQoL in PD-patients. 33 “non-sleepy” PD-patients (PD-NS) were compared with 30 PD-patients presenting with excessive daytime sleepiness (PD-EDS). The groups were matched for age, gender and Hoehn and Yahr state. Results: In the PD-EDS group, subjective sleep reports correlated with the PKG’s parameters for quantity and quality nighttime sleep but not in the PD-NS group. There were no significant correlations of the nighttime sleep quantity parameters of Hauser-diary with subjective sleep perception, neither in the PD-EDS nor in the PD-NS group. Conclusions: This first PKG based study of nighttime sleep in PD suggests that PKG could be used to provide an easy to use and rough evaluation of aspects of nighttime sleep and one that could flag patients where polysomnography may be required. In sleepy PD-patients, for instance quantity and quality PKG parameters correlate with different aspects of sleep like insomnia, parasomnia, restless legs syndrome.
The Effective Pre-school Provision Northern Ireland (EPPNI) Project: Technical Paper 13: Identifying and Monitoring Changes in Special Educational Needs in the Early Years.

This longitudinal study assesses the attainment and development of children followed from the age of 3 until the end of Key Stage 1 (age 8). Over 700 children were recruited to the study during 1998 and 1999 from 80 pre-school centres in Northern Ireland. Both qualitative and quantitative methods are used to explore the effects of pre-school experience on children's cognitive attainment and social/behavioural development at entry to school and any continuing effects on such outcomes up to 8 years of age. In addition to the effects of pre-school experience, the study investigates the contribution to children's development of individual and family characteristics such as gender, family size, parental education and employment. This overview describes the research design and discusses a variety of research issues (methodological and practical) in investigating the impact of pre-school provision on children's developmental progress. A parallel study is being carried out in England (EPPE).
Independent and relative effects of stress, depressive symptoms, and affect on college students' daily health behaviors.

Functional genetic polymorphisms associated with Brain-Derived Neurotrophic Factor (BDNF) and serotonin (5-HTTLPR) have demonstrated associations with depression in interaction with environmental stressors. In light of evidence for biological connections between BDNF and serotonin, it is prudent to consider genetic epistasis between variants in these genes in the development of depressive symptoms. The current study examined the effects of val66met, 5-HTTLPR, and family environment quality on youth depressive symptoms in adolescence and young adulthood in a longitudinal sample oversampled for maternal depression history. A differential susceptibility model was tested, comparing the effects of family environment on depression scores across different levels of a cumulative plasticity genotype, defined as presence of both, either, or neither plasticity alleles (defined here as val66met Met and 5-HTTLPR ‘S’). Cumulative plasticity genotype interacted with family environment quality to predict depression among males and females at age 15. After age 15, however, the interaction of cumulative plasticity genotype and early family environment quality was only predictive of depression among females. Results supported a differential susceptibility model at age 15, such that plasticity allele presence was associated with more or less depressive symptoms depending on valence of the family environment, and a diathesis-stress model of gene-environment interaction after age 15. These findings, although preliminary because of the small sample size, support prior results indicating interactive effects of 5-HTTLPR, val66met, and environmental stress, and suggest that family environment may have a stronger influence on genetically susceptible women than men. significantly from the 0 allele group, t (66) (cid:3) (cid:4) 2.56, p (cid:3) . 01, and trended toward differing significantly from the 1 allele group, t (98) (cid:3) (cid:4) 1.79, p (cid:3) . 08. These results indicate significant effects of family environment on depressive symptoms with presence of either or both susceptibility alleles, with stronger effects in the presence of both alleles. The PoI, . 72, indicated support for diathesis-stress over differential susceptibility. The observed p value (.007) remained significant with the adjusted threshold (.05). Nonlinear terms nonsignificant.
Al-Qadisiyah Journal of Pure Science Al-Qadisiyah Journal of Pure Science

The aim of this study was to conduct a survey of the Candida species associated with COVID-19 viral infection in 150 patients who were admitted to the intensive care unit (ICU) in Al-Diwaniyah Teaching Hospital in Al-Diwaniyah City, Iraq, for a ﬁ ve-month period from October 2021 to February 2022. The results indicated the dominance of Candida spp . over the rest of the isolated fungal species, with 97 isolates (64.66%). Candida albicans was shown to be the most abundant species with a percentage of 55.67 percent (P 0.05), compared to the other Candida species that were isolated ( Candida tropicalis 13.4 percent, Candida glabrata 12 percent, Candida krusie and Candida parapsilosis with 9.28 percent). In addition, we brought attention to the excellent action of the antifungals amphotericin B, itraconazole, and voriconazole, all of which have a high susceptibility rate.
Assessment of Muscle Activation of Caregivers Performing Dependent Transfers With a Novel Robotic-Assisted Transfer Device Compared With the Hoyer Advance

Objective: The purpose of this study was to compare muscle activity in caregivers while using a novel robotic-assisted transfer device (Strong Arm) to a clinical standard of care (Hoyer Advance). Design: A quasi-experimental design was used in which 20 caregivers (33 ± 15 yrs old) performed transfers with three surfaces (toilet, bench, and shower chair) with the Strong Arm and Hoyer Advance. Transfer completion time (seconds), peak percentage surface electromyography (EMG), and integrated EMG of the bilateral erector spinae, latissimus dorsi, pectoralis major and anterior deltoid were measured. Results: Caregivers required less transfer time when transferring from wheelchair to surface using the Hoyer Advance ( P = 0.011, f = 0.39). For the lower back, significantly lower peak percentage EMGs were found using Strong Arm in 50% and for the integrated EMG in 25% of the cases, with the remaining cases showing no significant differences. For the shoulder, significantly lower peak percentage EMG values were found using Strong Arm in 19% of transfers and lower integrated EMG was found in 25% of transfers when using the Hoyer Advance, withthe remaining cases showing no significant differences. Conclusion: Although back muscle activation during Strong Arm transfers is statistically, but not clinically, lower, additional features that couple with significantly lower muscle activation make it an alternative to the clinical standard for further research and possible clinical applicability.
Burgess Rosemary (Orcid ID: 0000-0002-2664-4395) Scheffer Ingrid (Orcid ID: 0000-0002-2311-2174) Berkovic Samuel (Orcid ID: 0000-0003-4580-841X) SCN1A variants in vaccine-related febrile seizures: a prospective study

1. Department of Medicine, University of Melbourne, Austin Hospital, Heidelberg, Australia 2. National Centre for Immunisation Research and Surveillance, The Children’s Hospital at Westmead, Sydney, Australia 3. Children’s Hospital Westmead Clinical School, The University of Sydney, Sydney, Australia; 4. Department of Neurology, Children`s Hospital of Fudan University, Shanghai, China 5. Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Parkville, Australia 6. Murdoch Children’s Research Institute, Parkville, Australia 7. Infection and Immunity, Monash Children’s Hospital, Department of Paediatrics Monash Centre for Health Care Research and Implementation , Monash University, Clayton, Australia 8. Discipline of Paediatrics, School of Medicine, Women’s and Children’s Hospital, University of Adelaide, Adelaide, Australia 9. Vaccine Trials Group, Wesfarmer’s Centre of Vaccines and Infectious Disease, Telethon Kids Institute, and Dept of General Paediatrics, Perth Children’s Hospital, Nedlands, Australia 10. Division of Paediatrics, School of Medicine, University of Western Australia, Perth, Australia 11. The Florey Institute of Neurosciences and Mental Health, Melbourne, Victoria, Australia *These authors contributed equally to this study
Properties of the subauroral electron temperature peak observed by langmuir-probe measurements on board intercosmos-18

Il a deja ete observe dans les annees 60 un accroissement anormal de Te dans la zone de projection de la plasmapause sur l'ionosphere en connexion avec des arcs SAR. On presente ici une analyse statistique des altitudes des pics de Te (entre 400 et 1480 km) en relation avec le T.L. , l'anneau de courant et l'activite geomagnetique sur la base de 249 traversees de la depression ionospherique par des satellites
Deep Optimization based Routing Protocol for Energy Efficient Routing in MANET-IoT

The mobile ad hoc network (MANET) is a decentralized, self-organizing system. Integration, MANETs, and the Internet of Things (IoT) have all become increasingly important in today's technology climate. The majority of current MANET-IoT research has either an efficiency or security focus. However, MANET-IoT relies heavily on both of these factors. The primary goal of this study is to improve energy efficiency while maintaining a high level of security by employing a decentralized trust architecture. A tree-block design, including the Portable Node Block (Block 1), the Blockchain Block (Block 2), and the Cloud Block (Block 3) is presented as the basis for this new method of organizing the work. Mobile nodes are clustered together in the first block. Similarity metrics between nodes are taken into account throughout the cluster creation process. The route selection in this section is handled by the Deep Optimization Model (DeepOpt), which combines the DNN with the AFO to optimize for numerous factors. The level of trust is the most important factor there. Here, with the help of Blockchain Block, they developed a Decentralized Trust Model. DeepOpt chooses the best possible path by applying this combined trust model. The results of the experiments show that the proposed approach improves the network in terms of energy efficiency, longevity, residual energy, security, productivity, route overhead, and the ratio of packet delivery (PDR).
Toward Digital Twin: Leveraging Pre-training Approaches for Multivariate Time Series Forecasting

Time series forecasting has been an active research area, particularly in the context of Digital Twin (DT) systems. Despite the excellent results yielded by pre-trained models in Natural Language Processing (NLP) and Computer Vision (CV), only a few studies have researched pre-training strategies for time series forecasting networks within DT systems. Recent studies demonstrate that transfer learning across multiple time series datasets does not always provide promising results, making self-supervised pre-training directly on the downstream dataset a temporarily considered optimal solution. To the best of our knowledge, the only general pre-training task in the time series field is the Masked Autoencoder. However, this approach may lead to redundant representations for downstream forecasting within DT systems. Therefore, we propose three pre-training tasks specially designed for time series forecasting within DT frameworks: Inverse Forecasting (IF), Coarser Forecasting (CF), and Anomaly Forecasting (AF). These tasks are respectively designed to capture bidirectional dependency, reduce noise, and augment data, all crucial aspects in enhancing the predictive capabilities of DT systems. By integrating the three tasks, we obtain a composite pre-training task which generally improves the forecasting results of time series models within DT systems across multiple datasets. This work contributes to the ongoing efforts to improve the accuracy and efficiency of DT systems, paving the way for more robust and reliable digital representations of physical systems.
Revolutionizing Crop Management: An Emphasis on Ginger Leaf Disease Detection Techniques Using Machine Learning and IoT

Ginger is the most crucial spice used in cuisines worldwide. The increasing demand for ginger has necessitated the need to optimize crop management practices. One such practice is ginger leaf disease detection, which is essential in determining the health and growth of ginger plants. In recent years, machine learning and the Internet of Things (IoT) have revolutionized crop management practices. In this article, this study reviewed the profuse techniques to identify ginger leaf spotting, including traditional image processing techniques and deep learning-based approaches. This paper also explores the use of IoT in ginger crop management and the challenges faced by researchers in developing effective solutions for ginger leaf disease detection and collaboration statics. Finally, this study discusses the future of ginger leaf Diseases using machine learning and IoT and its potential impact on the ginger industry.
Prediction of Solar Energy from Sky Images and Meteorological Data Using Neural Networks

Many of the renewable energy generation methods suffer from the variable power output they produce. This is because they depend on elements with a substantial stochastic nature. To mitigate the variability different controlling mechanisms must be deployed. For thorough and optimal control, it is a necessity to predict the generated power in the near or more distant future. In this article, we explore the possibility of predicting electricity production by photovoltaic panels based on sky images. In addition, we also tested selected meteorological data that are relevant and available. For this purpose, we used SkyCam dataset that contains required information. We have proposed 3 scenarios combining block-based and sequential processing of sky images and selected meteorological data using CNN and LSTM networks. The best results were obtained by a combination of sky images and meteorological data processed as sequence of frames by LSTM. The achieved mean absolute error was 44.3 W/m2 for 15-minute intervals.