Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of nine microsatellite loci for Trypanosoma lewisi, a potential human pathogen in Western Africa and South-East Asia, and preliminary population genetics analyses

Trypanosoma lewisi belongs to the so-called atypical trypanosomes that occasionally aﬀect humans. It shares the same hosts and ﬂea vector of other medically relevant pathogenic agents as Yersinia pestis, the agent of plague. Increasing knowledge on the population structure (reproductive mode, population size, dispersal) of this parasite thus represents a challenging but important issue. The use of polymorphic genetic markers, together with suitable population genetics tools, is a convenient way to achieve such objectives. To date, the population biology of T. lewisi is poorly known and, to our knowledge, no population genetics studies have ever been conducted. Here, we present the development of nine microsatellite markers of this species. We investigated their polymorphism in diﬀerent countries from Africa and South-East Asia from DNAs extracted from the spleen of their rodent reservoirs (essentially rat species). Several ampliﬁcation problems arose, especially with SouthEast Asian individuals. This led to retain only those individuals with complete genotypes (most of them originating from West Africa, notably Cotonou, Benin) to ensure an optimal estimate of heterozygosity. Our results pointed towards a mainly (at least 95-99%) clonal mode of propagation, a strong subdivision at the smallest scale available (i.e., urban neighborhoods, i.e. 0.250 km²), and a generation time most probably shorter than 4 months. In future studies, more extensive sampling at smaller geographic scales (i.e., households), within a one- or two-months window and with improved ampliﬁcation conditions, should lead to a more precise picture of the ﬁne population structure of this parasite.
The effects of human care on the blowhole and gut microbiotas of two cohabiting dolphin species based on a year-round surveillance

Understanding the effects of human care on the dynamics of the host-associated microbiota is critical for the health management of dolphins living in an aquarium. Yet this aspect remains relatively unexplored. Here, by utilizing 16S rRNA gene sequencing, we profiled the blowhole and gut bacterial communities of two bottlenose dolphins (Tursiops truncatus) and a Chinese white dolphin (Sousa chinensis) reared in the same indoor pool, based on year-round surveillance. In addition, we compared these dolphin microbiotas with those previously published datasets from wild dolphins. Our results showed that both the blowhole and the gut of the two dolphin species under human care shared a more similar microbiome than members of the same dolphin species across different habitats (human care vs wild). However, the effects of human care on the dolphin microbiome from the two body sites varied. In the aquarium, bacterial alpha diversities differed significantly between the two body sites, and the seasonal stability of the bacterial community was more evident in the gut than in the blowhole. Additionally, the blowhole bacterial composition and the predicted functional capacity from the two dolphin species showed differences and were less convergent than their gut microbiota over a decade-long cohabitation. Further analyses showed that heterogeneous and homogeneous selections (i.e., deterministic processes) contributed more to the blowhole than to the gut bacterial communities, while a dispersal limitation (i.e., a stochastic process) was more important for the gut microbiota. The present study provides the first comparative evidence that the gut microbiota may be more plastic in response to the human care environment than the blowhole microbiota. This improves our understanding of dolphin health management under human care and helps to predict their microbial responses to environmental changes.
Vesicle-based cell-free synthesis of short and long unspecific peroxygenases

Unspecific peroxygenases (UPOs, EC 1.11.2.1) are fungal enzymes that catalyze the oxyfunctionalization of non-activated hydrocarbons, making them valuable biocatalysts. Despite the increasing interest in UPOs that has led to the identification of thousands of putative UPO genes, only a few of these have been successfully expressed and characterized. There is currently no universal expression system in place to explore their full potential. Cell-free protein synthesis has proven to be a sophisticated technique for the synthesis of difficult-to-express proteins. In this work, we aimed to establish an insect-based cell-free protein synthesis (CFPS) platform to produce UPOs. CFPS relies on translationally active cell lysates rather than living cells. The system parameters can thus be directly manipulated without having to account for cell viability, thereby making it highly adaptable. The insect-based lysate contains translocationally active, ER-derived vesicles, called microsomes. These microsomes have been shown to allow efficient translocation of proteins into their lumen, promoting post-translational modifications such as disulfide bridge formation and N-glycosylations. In this study the ability of a redox optimized, vesicle-based, eukaryotic CFPS system to synthesize functional UPOs was explored. The influence of different reaction parameters as well as the influence of translocation on enzyme activity was evaluated for a short UPO from Marasmius rotula and a long UPO from Agrocybe aegerita. The capability of the CFPS system described here was demonstrated by the successful synthesis of a novel UPO from Podospora anserina, thus qualifying CFPS as a promising tool for the identification and evaluation of novel UPOs and variants thereof.
Universal scaling behavior and Hawkes process of videos’ views on Bilibili.com

Online videos have become the most popular method to obtain information for the public in recent years, such as TikTok and YouTube, and regional sites like Bilibili and Douyin. Compared with its growing influence, the analysis of user behavior on video sites is still less investigated. Herein, we fetch the video data from Bilibili.com and analyze the video views, comments, and other behaviors on the website. We found that the description model based on the Hawkes process can accurately predict the video views, which suggests that on the Bilibili website, the self-incentive mechanism of information cascade diffusion plays a decisive role in online views. Meanwhile, we also found that the view increment of the videos during the same period of time conforms to the general power-law distribution.
Connectivity in graphs

a b x u y w v c d Typical question: Is it possible to get from some node u to another node v ? Example: Train network – if there is path from u to v , possible to take train from u to v and vice versa. If it's possible to get from u to v , we say u and v are connected and there is a path between u and v Paths a b x u y w v c d A path between u and v is a sequence of edges that starts at vertex u, moves along adjacent edges, and ends in v. A simple path is a path that does not repeat any edges What are all the simple paths from z to w ? What are all the simple paths from x to y? How many paths (can be non-simple) are there from x to y? Connectedness a b x u y w v c d A graph is connected if there is a path between every pair of vertices in the graph Example: This graph not connected; e.g., no path from x to d A connected component of a graph G is a maximal connected subgraph of G Example Prove: Suppose graph G has exactly two vertices of odd degree, say u and v. Then G contains a path from u to v .
Editorial: Investigation of brain functional connectivity from electroencephalogram data

Mathematical Biology & Physiology, Department Electronics and Communications, Politecnico di Torino, Turin, Italy, Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand, Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran, Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran, Department of Physiology, McGill University, Montreal, QB, Canada, School of Information and Electronics, Beijing Institute of Technology, Beijing, China
Age-dependent increase of cytoskeletal components in sensory axons in human skin

Aging is a complex process characterized by several molecular and cellular imbalances. The composition and stability of the neuronal cytoskeleton is essential for the maintenance of homeostasis, especially in long neurites. Using human skin biopsies containing sensory axons from a cohort of healthy individuals, we investigate alterations in cytoskeletal content and sensory axon caliber during aging via quantitative immunostainings. Cytoskeletal components show an increase with aging in both sexes, while elevation in axon diameter is only evident in males. Transcriptomic data from aging males illustrate various patterns in gene expression during aging. Together, the data suggest gender-specific changes during aging in peripheral sensory axons, possibly influencing cytoskeletal functionality and axonal caliber. These changes may cumulatively increase susceptibility of aged individuals to neurodegenerative diseases.
COMIRESTROKE—A clinical study protocol for monitoring clinical effect and molecular biological readouts of COMprehensive Intensive REhabilitation program after STROKE: A four-arm parallel-group randomized double blinded controlled trial with a longitudinal design

Introduction While the role of physiotherapy as part of a comprehensive inpatient rehabilitation is indisputable, clear evidence concerning the effectiveness of different rehabilitation managements [interdisciplinary implementing the International Classification of Functioning, disability and health (ICF) vs. multidisciplinary model] and physiotherapy categories (neuroproprioceptive “facilitation, inhibition” vs. motor/skill acquisitions using technologies) are still lacking. In this study, four kinds of comprehensive inpatient rehabilitation with different management and content of physical therapy will be compared. Moreover, focus will be placed on the identification of novel biological molecules reflective of effective rehabilitation. Long non-coding RNAs (lncRNAs) are transcripts (>200 bps) of limited coding potential, which have recently been recognized as key factors in neuronal signaling pathways in ischemic stroke and as such, may provide a valuable readout of patient recovery and neuroprotection during therapeutic progression. Methods and analysis Adults after the first ischemic stroke in an early sub-acute phase with motor disability will be randomly assigned to one of four groups and undergo a 3 weeks comprehensive inpatient rehabilitation of different types: interdisciplinary team work using ICF model as a guide; multidisciplinary teamwork implementing neuroproprioceptive “facilitation and inhibition” physiotherapy; multidisciplinary teamwork implementing technology-based physiotherapy; and standard multidisciplinary teamwork. Primary (the Goal Attainment Scale, the Patient-Reported Outcomes Measurement Information System, and the World Health Organization Disability Assessment Schedule) and secondary (motor, cognitive, psychological, speech and swallowing functions, functional independence) outcomes will be measured. A blood sample will be obtained upon consent (20 mls; representing pre-rehabilitation molecular) before and after the inpatient program. Primary outcomes will be followed up again 3 and 12 months after the end of the program. The overarching aim of this study is to determine the effectiveness of various rehabilitation managements and physiotherapeutic categories implemented by patients post ischemic stroke via analysis of primary, secondary and long non-coding RNA readouts. This clinical trial will offer an innovative approach not previously tested and will provide new complex analysis along with public assessable molecular biological evidence of various rehabilitation methodology for the alleviation of the effects of ischemic stroke. Clinical trial registration NCT05323916, https://clinicaltrials.gov/ct2/show/NCT05323916.
Synergistic effects of multiword sequences structure, function, frequency and association on raters’ evaluations of essay quality

Despite accumulated research findings confirming the link of multiword sequences (MWSs) structures and functions to essay quality, as well as the connection between MWSs statistical features (e.g., their frequency and association strengths in BNC/COCA) and writing quality, to date no study integrated these two separate lines of investigations. It remains to investigate whether and how MWSs structures, functions and their statistical features jointly affect writing quality. Drawing on 900 rated argumentative essays composed by Chinese grade 12 students in National Matriculation Test, the present study employed CollGram to automatically identify the nativelike 4-word sequences in these essays and to analyze their frequency and Mutual Information (MI) scores in COCA. The structures and functions of frequent nativelike 4-word sequences were also analyzed manually. A serial of linear mixed-effect models was constructed to investigate their main effects as well as interaction effects on essay scores. The best fit model revealed the links of higher essay scores to higher MI scores, to more noun-phrase sequences, to more stance sequences, as well as to fewer referential sequences. Additionally, the interaction of prepositional phrase sequences and their frequency in COCA affected essay scores, so did the interaction of verb phrase sequences and their MI in COCA, as well as the interaction of noun phrase sequences and their MI in COCA. The findings provide new insights into the complex interaction between MWSs structures, functions and their statistical features, as well as their joint effects on writing quality.
Simulation of XRD, Raman and IR spectrum for phase identification in doped HfO2 and ZrO2

Fluorite-structured hafnium and zirconia require different, complementary characterization methods to identify the numerous metastable phases. This is because of the many possible positions of the oxygen ions, which are difficult to observe directly. Ab initio simulations are useful to probe the corresponding XRD, Raman, and infrared spectra for fingerprints. However, the predictive power of theoretical methods is limited both by model errors and by boundary conditions such as defects, stresses, and morphology that are difficult to detect. We first consider the calculation of Raman and infrared spectra of the most interesting undoped phases of HfO2 and ZrO2, compare the results with known results, and discuss the uncertainties. Next, we consider the possibilities of classifying the phases using X-ray diffraction. To this end, we introduce the effects of doping, which increases the uncertainty due to structural disorder. For illustration, we examine a large data set of doped structures obtained with ab initio calculations. To make an unbiased assignment of phases, we use machine learning methods with clusters. The limits of X-ray diffraction spectroscopy are reached when phase mixtures are present. Resolution of single-phase polycrystalline samples may only be possible here if these three characterization methods are used.
Effect of the m6ARNA gene on the prognosis of thyroid cancer, immune infiltration, and promising immunotherapy

Background Accumulating evidence suggests that N6-methyladenosine (m6A) RNA methylation plays an important role in tumor proliferation and growth. However, its effect on the clinical prognosis, immune infiltration, and immunotherapy response of thyroid cancer patients has not been investigated in detail. Methods Clinical data and RNA expression profiles of thyroid cancer were extracted from the Cancer Genome Atlas-thyroid carcinoma (TCGA-THCA) and preprocessed for consensus clustering. The risk model was constructed based on differentially expressed genes (DEGs) using Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analyses. The associations between risk score and clinical traits, immune infiltration, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), immune infiltration, and immunotherapy were assessed. Immunohistochemistry was used to substantiate the clinical traits of our samples. Results Gene expression analysis showed that 17 genes, except YHTDF2, had significant differences (vs healthy control, P<0.001). Consensus clustering yielded 2 clusters according to their clinical features and estimated a poorer prognosis for Cluster 1 (P=0.03). The heatmap between the 2 clusters showed differences in T (P<0.01), N (P<0.001) and stage (P<0.01). Based on univariate Cox and LASSO regression, a risk model consisting of three high-risk genes (KIAA1429, RBM15, FTO) was established, and the expression difference between normal and tumor tissues of three genes was confirmed by immunohistochemical results of our clinical tissues. KEGG and GSEA analyses showed that the risk DEGs were related mainly to proteolysis, immune response, and cancer pathways. The levels of immune infiltration in the high- and low-risk groups were different mainly in iDCs (P<0.05), NK cells (P<0.05), and type-INF-II (P<0.001). Immunotherapy analysis yielded 30 drugs associated with the expression of each gene and 20 drugs associated with the risk score. Conclusions Our risk model can act as an independent marker for thyroid cancer and provides promising immunotherapy targets for its treatment.
Neural network learning using non-ideal resistive memory devices

We demonstrate a modified stochastic gradient (Tiki-Taka v2 or TTv2) algorithm for deep learning network training in a cross-bar array architecture based on ReRAM cells. There have been limited discussions on cross-bar arrays for training applications due to the challenges in the switching behavior of nonvolatile memory materials. TTv2 algorithm is known to overcome the device non-idealities for deep learning training. We demonstrate the feasibility of the algorithm for a linear regression task using 1R and 1T1R ReRAM devices. Using the measured device properties, we project the performance of a long short-term memory (LSTM) network with 78 K parameters. We show that TTv2 algorithm relaxes the criteria for symmetric device update response. In addition, further optimization of the algorithm increases noise robustness and significantly reduces the required number of states, thereby drastically improving the model accuracy even with non-ideal devices and achieving the test error close to that of the conventional learning algorithm with an ideal device.
Exploring choices in higher education: Female and male first-generation students’ trajectories from study aspiration to study satisfaction in Germany

Previous studies have pointed out that first-generation students, continuing-generation students and female and male students differ concerning trajectories into and through higher education regarding study program choices and satisfaction. Studies focused on single time points in students’ educational trajectories and mainly emanated from the United States. In order to facilitate a longitudinal comparison of students’ study program aspirations and choices, as well as study satisfaction and dropout intentions, we analyzed panel data from the German National Educational Panel Study (NEPS), consisting of 1,694 students. We modeled the influence of gender (female, male) and generational status (first-generation, continuing-generation) with multinomial logit regression models. Results show that students’ aspirations and choices did not significantly differ concerning generational status whereas gender differences were confirmed once again. Generational status did not reveal significant differences concerning study satisfaction and dropout intentions, whereas differences concerning gender revealed that female students were significantly more satisfied with their studies than male students. Based on our findings, we point out the importance of early counseling and other support to compensate gender differences in study program aspirations and choices. Further research is needed to explore reasons for our findings showing equal aspirations and choices of first- and continuing-generation students, which are opposed to previous studies’ findings.
Lifestyle indices of body composition and obesity risk and prevalence among multi-ethnic adolescents in Malaysia

The prevalence of childhood obesity in Malaysia has doubled in less than a decade. Moreover, being overweight and obese have been associated with immediate and later comorbidities, thus emphasizing the need to prevent obesity from an early age. This cross-sectional study involved 923 multi-ethnic Malaysian adolescents aged between 15 and 17 years old. Body composition was estimated using bioelectrical impedance analysis. Body mass index (BMI) classification was based on the World Health Organization (WHO) growth reference and the International Obesity Task Force (IOTF) cut-off. Meanwhile, the Child Growth Foundation (CGF) body fat reference was used to classify adolescents’ adiposity. Lifestyle indices including physical activity, sedentary behavior, supplement intake, smoking and alcohol status were assessed via questionnaires. A high prevalence of overweight/obesity among the adolescents was observed according to the CGF (26%), followed by that of the WHO (24%) and then the IOTF (23%) cut-off, with high concordance values between each pair. After adjustment, a significant association was found between physical activity (PAQ score) and lean mass (p = 0.027). No lifestyle determinant was found to be a significant predictor of fat percentage. A high sedentary level increased the likelihood of obesity (OR 3.0, p < 0.01), while antioxidant-rich supplements were found to protect against obesity (OR 0.4, p < 0.05). The lifestyle predictors identified in this study may be considered when designing interventions that integrate lifestyle modifications targeting adolescents.
Blur identification using bispectrum

Blur identification methods based on the bispectrum of an observed noisy and blurred image are proposed. Blur identification is addressed, in the case of uniform blurs, by zero-crossing detection in a particular cross section of the bispectrum of the observed image. The identification of general finite-impulse-response blurs is considered. The blurred image is represented by a nonminimum phase ARMA (autoregressive moving average) model whose parameters are identified using the complex bispectrum of the observed image. These methods are superior to previous methods which are based on the second-order statistics of the image, since the bispectrum is insensitive to additive, Gaussian noise in theory, and it contains the phase of the blur transfer function. Simulation results are provided. <<ETX>>