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Complex motor commands for human locomotion are generated through the combination of motor modules representable as muscle synergies. Recent data have argued that muscle synergies are inborn or determined early in life, but development of the neuro-musculoskeletal system and acquisition of new skills may demand fine-tuning or reshaping of the early synergies. We seek to understand how locomotor synergies change during development and training by studying the synergies for running in preschoolers and diverse adults from sedentary subjects to elite marathoners, totaling 63 subjects assessed over 100 sessions. During development, synergies are fractionated into units with fewer muscles. As adults train to run, specific synergies coalesce to become merged synergies. Presences of specific synergy-merging patterns correlate with enhanced or reduced running efficiency. Fractionation and merging of muscle synergies may be a mechanism for modifying early motor modules (Nature) to accommodate the changing limb biomechanics and influences from sensorimotor training (Nurture). Motor commands for human locomotion are generated by combination of muscle synergies. In humans, muscle synergies for running exhibit considerable plasticity during child-to-adult development and adult training to meet the constantly changing biomechanical and efficiency demands.

Breast cancer is the leading type of cancer in women, causing nearly 600,000 deaths every year, globally. Although the tumors can be localized within the breast, they can spread to other body parts, causing more harm. Therefore, early diagnosis can help reduce the risks of this cancer. However, a breast cancer diagnosis is complicated, requiring biopsy by various methods, such as MRI, ultrasound, BI-RADS, or even needle aspiration and cytology with the suggestions of specialists. On certain occasions, such as body examinations of a large number of people, it is also a large workload to check the images. Therefore, in this work, we present an efficient and automatic diagnosis system based on the hierarchical extreme learning machine (H-ELM) for breast cancer ultrasound results with high efficiency and make a primary diagnosis of the images. To make it compatible to use, this system consists of PNG images and general medical software within the H-ELM framework, which is easily trained and applied. Furthermore, this system only requires ultrasound images on a small scale, of 28×28 pixels, reducing the resources and fulfilling the application with low-resolution images. The experimental results show that the system can achieve 86.13% in the classification of breast cancer based on ultrasound images from the public breast ultrasound images (BUSI) dataset, without other relative information and supervision, which is higher than the conventional deep learning methods on the same dataset. Moreover, the training time is highly reduced, to only 5.31 s, and consumes few resources. The experimental results indicate that this system could be helpful for precise and efficient early diagnosis of breast cancers with primary examination results.

The to and fro of RNA polymerase RNA polymerase II (RNA pol II) moves forwards along the DNA strand during gene transcription, synthesizing messenger RNA as it goes. It can also move backwards and stall — a useful property for regulatory purposes or if it hits an obstacle such as a nucleosome. This arrested state is reactivated by transcription factor IIS (TFIIS). Now, the crystal structure of a backtracked yeast RNA pol II complex containing observable backtracked RNA has been determined at 3.3 Å resolution, as well as the structure of a backtracked complex containing TFIIS. The structures reveal possible mechanisms of transcriptional stalling and transcription reactivation. During gene transcription, RNA polymerase (Pol) II moves forward along DNA and synthesizes mRNA. However, Pol II can also move backwards and stall, which is important for regulatory purposes or when the polymerase hits an obstacle such as a nucleosome. This arrested state is reactivated by the transcription factor TFIIS. Here, a crystal structure is presented of a backtracked yeast Pol II complex in which the backtracked RNA can be observed, plus a structure of a backtracked complex that contains TFIIS. A model is presented for Pol II backtracking, arrest and reactivation during transcription elongation. During gene transcription, RNA polymerase (Pol) II moves forwards along DNA and synthesizes messenger RNA. However, at certain DNA sequences, Pol II moves backwards, and such backtracking can arrest transcription. Arrested Pol II is reactivated by transcription factor IIS (TFIIS), which induces RNA cleavage that is required for cell viability 1 . Pol II arrest and reactivation are involved in transcription through nucleosomes 2 , 3 and in promoter-proximal gene regulation 4 , 5 , 6 . Here we present X-ray structures at 3.3 Å resolution of an arrested Saccharomyces cerevisiae Pol II complex with DNA and RNA, and of a reactivation intermediate that additionally contains TFIIS. In the arrested complex, eight nucleotides of backtracked RNA bind a conserved ‘backtrack site’ in the Pol II pore and funnel, trapping the active centre trigger loop and inhibiting mRNA elongation. In the reactivation intermediate, TFIIS locks the trigger loop away from backtracked RNA, displaces RNA from the backtrack site, and complements the polymerase active site with a basic and two acidic residues that may catalyse proton transfers during RNA cleavage. The active site is demarcated from the backtrack site by a ‘gating tyrosine’ residue that probably delimits backtracking. These results establish the structural basis of Pol II backtracking, arrest and reactivation, and provide a framework for analysing gene regulation during transcription elongation.

Gene transcription by RNA polymerase II is regulated by activator proteins that recruit the coactivator complexes SAGA (Spt–Ada–Gcn5–acetyltransferase) 1 , 2 and transcription factor IID (TFIID) 2 – 4 . SAGA is required for all regulated transcription 5 and is conserved among eukaryotes 6 . SAGA contains four modules 7 – 9 : the activator-binding Tra1 module, the core module, the histone acetyltransferase (HAT) module and the histone deubiquitination (DUB) module. Previous studies provided partial structures 10 – 14 , but the structure of the central core module is unknown. Here we present the cryo-electron microscopy structure of SAGA from the yeast Saccharomyces cerevisiae and resolve the core module at 3.3 Å resolution. The core module consists of subunits Taf5, Sgf73 and Spt20, and a histone octamer-like fold. The octamer-like fold comprises the heterodimers Taf6–Taf9, Taf10–Spt7 and Taf12–Ada1, and two histone-fold domains in Spt3. Spt3 and the adjacent subunit Spt8 interact with the TATA box-binding protein (TBP) 2 , 7 , 15 – 17 . The octamer-like fold and its TBP-interacting region are similar in TFIID, whereas Taf5 and the Taf6 HEAT domain adopt distinct conformations. Taf12 and Spt20 form flexible connections to the Tra1 module, whereas Sgf73 tethers the DUB module. Binding of a nucleosome to SAGA displaces the HAT and DUB modules from the core-module surface, allowing the DUB module to bind one face of an ubiquitinated nucleosome. Structural studies on the yeast transcription coactivator complex SAGA (Spt–Ada–Gcn5–acetyltransferase) provide insights into the mechanism of initiation of regulated transcription by this multiprotein complex, which is conserved among eukaryotes.

Bile acids (BAs) play a crucial role in nutrient absorption and act as key regulators of lipid and glucose metabolism and immune homeostasis. Through the enterohepatic circulation, BAs are synthesized, metabolized, and reabsorbed, with a portion entering the vascular circulation and distributing systemically. This allows BAs to interact with receptors in all major organs, leading to organ-organ interactions that regulate both local and global metabolic processes, as well as the immune system. This review focuses on the whole-body effects of BA-mediated metabolic and immunological regulation, including in the brain, heart, liver, intestine, eyes, skin, adipose tissue, and muscle. Targeting BA synthesis and receptor signaling is a promising strategy for the development of novel therapies for various diseases throughout the body.

Sepsis is among the top global health issues, leading to serious health risks with high mortality and morbidity. Recent statistics revealed around 50 million Sepsis cases and 11 million deaths annually. Sepsis arises due to the body’s abnormal response to an infection, leading to severe health conditions such as organ failure. As such, failure to detect Sepsis timely may be fatal. However, early detection of Sepsis onset remains a great challenge. This paper leverages lightweight models and develops an efficient approach and system for accurate, easily affordable, and early diagnosis of Sepsis. The diagnosis uses seven non-invasive vital signs, namely, heart rate, body temperature, systolic, diastolic, and mean arterial blood pressure, oxygen saturation level, and end-tidal carbon dioxide. The proposed system will improve public health and relieve the burden on the ICU by preventing severe Sepsis cases. Our proposed system could also be implemented using low-cost sensors and interfaces, making the system suitable for remote monitoring with the Internet of Things (IoT). Furthermore, a novel method is adopted for handling class imbalance in the dataset. Additionally, Shapley Additive Explanations (SHAP) and Locally Interpretable Model-agnostic Explanations (LIME) were leveraged to gain insight into the system. The system was implemented and evaluated using our evaluation platform consisting of basic sensors and a Raspberry Pi as a point-of-care Sepsis detection tool proof of concept. Our model obtained a utility score of around 46% (set by the Physionet challenge, the database source). The model also achieved better performances of 86.49% accuracy and an AUROC of 0.94 compared to the related Sepsis detection works.

In the United States, non-alcoholic fatty liver disease (NAFLD) is the most common liver disease and associated with higher mortality according to data from earlier National Health and Nutrition Examination Survey (NHANES) 1988-1994. Our goal was to determine the NAFLD prevalence in the recent 1999-2012 NHANES, risk factors for advanced fibrosis (stage 3-4) and mortality. NAFLD was defined as having a United States Fatty Liver Index (USFLI) > 30 in the absence of heavy alcohol use and other known liver diseases. The probability of low/high risk of having advanced fibrosis was determined by the NAFLD Fibrosis Score (NFS). In total, 6000 persons were included; of which, 30.0% had NAFLD and 10.3% of these had advanced fibrosis. Five and eight-year overall mortality in NAFLD subjects with advanced fibrosis was significantly higher than subjects without NAFLD ((18% and 35% vs. 2.6% and 5.5%, respectively) but not NAFLD subjects without advanced fibrosis (1.1% and 2.8%, respectively). NAFLD with advanced fibrosis (but not those without) is an independent predictor for mortality on multivariate analysis (HR = 3.13, 95% CI 1.93-5.08, p<0.001). In conclusion, in this most recent NHANES, NAFLD prevalence remains at 30% with 10.3% of these having advanced fibrosis. NAFLD per se was not a risk factor for increased mortality, but NAFLD with advanced fibrosis was. Mexican American ethnicity was a significant risk factor for NAFLD but not for advanced fibrosis or increased mortality.

The human brain can be seen as one of the most powerful processors in the world, and it has a very complex structure with different kinds of signals for monitoring organics, communicating to neurons, and reacting to different information, which allows large developments in observing human sleeping, revealing diseases, reflecting certain motivations of limbs, and other applications. Relative theory, algorithms, and applications also help us to build brain-computer interface (BCI) systems for different powerful functions. Therefore, we present a fast-reaction framework based on an extreme learning machine (ELM) with multiple layers for the ElectroEncephaloGram (EEG) signals classification in motor imagery, showing the advantages in both accuracy of classification and training speed compared with conventional machine learning methods. The experiments are performed on software with the dataset of BCI Competition II with fast training time and high accuracy. The final average results show an accuracy of 93.90% as well as a reduction of 75% of the training time as compared to conventional deep learning and machine learning algorithms for EEG signal classification, also showing its prospects of the improvement of the performance of the BCI system.

Fecal microbiota transplant (FMT) has emerged as a potential treatment for severe colitis associated with graft-versus-host disease (GvHD) following hematopoietic stem cell transplant. Bacterial engraftment from FMT donor to recipient has been reported, however the fate of fungi and viruses after FMT remains unclear. Here we report longitudinal dynamics of the gut bacteriome, mycobiome and virome in a teenager with GvHD after receiving four doses of FMT at weekly interval. After serial FMTs, the gut bacteriome, mycobiome and virome of the patient differ from compositions before FMT with variable temporal dynamics. Diversity of the gut bacterial community increases after each FMT. Gut fungal community initially shows expansion of several species followed by a decrease in diversity after multiple FMTs. In contrast, gut virome community varies substantially over time with a stable rise in diversity. The bacterium, Corynebacterium jeikeium , and Torque teno viruses, decrease after FMTs in parallel with an increase in the relative abundance of Caudovirales bacteriophages. Collectively, FMT may simultaneously impact on the various components of the gut microbiome with distinct effects. Fecal microbiota transplant (FMT) is emerging as a potential treatment for graft-versus-host disease (GvHD). Here, the authors examine temporal dynamics of the bacteriome, mycobiome, and virome of a patient with GvHD who received multiple FMTs.

SummaryTo determine denosumab’s effectiveness for fracture prevention among postmenopausal women with osteoporosis in East Asia, the risk of fracture was compared between patients continuing denosumab therapy versus patients discontinuing denosumab after one dose. The real-world effectiveness was observed to be consistent with the efficacy demonstrated in the phase III trial.IntroductionAfter therapeutic efficacy is demonstrated for subjects in global clinical trials, real-world evidence may provide complementary knowledge of therapeutic effectiveness in a heterogeneous mix of patients seen in clinical practice. This retrospective cohort study was conducted to compare the fracture risk in real-world clinical care received in Taiwan and Hong Kong between a treatment cohort (patients receiving denosumab 60 mg subcutaneously every 6 months) versus an off-treatment cohort (patients discontinuing after 1 dose of denosumab, which has no known clinical benefit) among real-world postmenopausal women.MethodsThis study included 38,906 and 2,835 postmenopausal women receiving denosumab in Taiwan and Hong Kong, respectively. The primary endpoint was hip fracture, and secondary endpoints were clinical vertebral and nonvertebral fractures. Propensity-score-matched analysis, adjusting for known covariates, was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). The robustness of findings was evaluated with a series of sensitivity and quantitative bias analyses.ResultsIn this study, 554 hip fractures were included in the primary Taiwan population analysis. The crude incidence rate was 0.9 per 100 person-years in the treatment cohort (n = 25,059) and 1.7 per 100 person-years in the off-treatment cohort (n = 13,847). After adjusting for prognostic differences between cohorts, denosumab reduced the risk of hip fractures by 38% (HR = 0.62, CI:0.52–0.75). Risk reductions of similar magnitude were observed for the secondary endpoints and for the analysis of the smaller Hong Kong population.ConclusionThe effectiveness of denosumab for fracture reduction among real-world postmenopausal women with osteoporosis was consistent with the efficacy demonstrated in a global clinical trial.Registration: EnCePP registration number: EUPAS26372; registration date: 12/11/2018.