Miracle-dz/UniFAR-FTUs huggingface dataset:
Commentary: Computational Analysis for ERAS and Other Surgical Processes: Commentary From Clinical Perspective

Nowadays, many diseases need to be treated or diagnosed by surgery (1). Surgical treatment can produce better improvement on body function of patients compared with non-surgical treatment (2). However, surgery often result in a high complication rate, which will affect postoperative recovery and lengthen hospital stay (3, 4). In addition, surgery usually results in psychological problems of patients such as anxiety and depression (5, 6). Therefore, an optimized nursing method with physiological and psychological care is required for patients (7, 8). The theory of enhanced recovery after surgery (ERAS)was first proposed byKehlet (9), which refers to a standardized method of perioperative nursing and a multi-mode approach that combines preoperative and postoperative nursing methods to reduce the stress associated with surgery, thus allowing for accelerated recovery after surgical treatment (10). ERAS nursing has been proved to be a beneficial way of improving patients’ function (11). A large amount of evidences have shown that ERAS pathway can bring various positive effects, including reduce complication rate after surgery and the risk of death, decreasing readmission rate, length of stay and hospitalization expenses (12, 13). Kobayashi’s study indicated that the implement of ERAS pathways can reduce the probability of postoperative complication for each surgical complexity of liver resection of patients (14). Fay and Gwacham’s research have proved that the use of ERAS protocol was associated with significant decrease in hospital stay and discharge expenses (15, 16). It is reported that following ERAS nursing has been proved a better improvement in patients’ psychological status and their quality of life (17, 18). The research results of Li and her team about ERAS nursing are presented in Table 1, which are consistent with the previous studies. Statistically, p < 0.05 means that the result is statistically meaningful, and p < 0.01 means that there is significant difference. This study proved ERAS pathways to be an effective way to improve patients’
Microphysiological Modeling of the Structure and Function of Neuromuscular Transmitter Release Sites

The general mechanism of calcium-triggered chemical transmitter release from neuronal synapses has been intensely studied, is well-known, and highly conserved between species and synapses across the nervous system. However, the structural and functional details within each transmitter release site (or active zone) are difficult to study in living tissue using current experimental approaches owing to the small spatial compartment within the synapse where exocytosis occurs with a very rapid time course. Therefore, computer simulations offer the opportunity to explore these microphysiological environments of the synapse at nanometer spatial scales and on a sub-microsecond timescale. Because biological reactions and physiological processes at synapses occur under conditions where stochastic behavior is dominant, simulation approaches must be driven by such stochastic processes. MCell provides a powerful simulation approach that employs particle-based stochastic simulation tools to study presynaptic processes in realistic and complex (3D) geometries using optimized Monte Carlo algorithms to track finite numbers of molecules as they diffuse and interact in a complex cellular space with other molecules in solution and on surfaces (representing membranes, channels and binding sites). In this review we discuss MCell-based spatially realistic models of the mammalian and frog neuromuscular active zones that were developed to study presynaptic mechanisms that control transmitter release. In particular, these models focus on the role of presynaptic voltage-gated calcium channels, calcium sensors that control the probability of synaptic vesicle fusion, and the effects of action potential waveform shape on presynaptic calcium entry. With the development of these models, they can now be used in the future to predict disease-induced changes to the active zone, and the effects of candidate therapeutic approaches.
Alterations of RNA Modification in Mouse Germ Cell-2 Spermatids Under Hypoxic Stress

Hypoxia is a known stress factor in mammals and has been shown to potentially impair male fertility, which manifests as spermatogenic dysfunction and decreased semen quality. Studies have shown that RNA modifications, the novel post-transcriptional regulators, are involved in spermatogenesis, and hypoxia-induced alterations in RNA modification in testes and sperm cells may be associated with impaired spermatogenesis in mice. However, the molecular mechanisms via which RNA modifications influence spermatogenesis under hypoxic stress conditions are unclear. In this study, we generated a mouse Germ Cell-2 spermatid (GC-2spd) hypoxia model by culturing cells in a 1% O2 incubator for 48 h or treating them with CoCl2 for 24 h. The hypoxia treatment significantly inhibited proliferation and induced apoptosis in GC-2spd cells. The RNA modification signatures of total RNAs (2 types) and differentially sized RNA fragments (7 types of approximately 80 nt-sized tRNAs; 9 types of 17–50 nt-sized sncRNAs) were altered, and tRNA stability was partially affected. Moreover, the expression profiles of sncRNAs, such as microRNAs, tsRNAs, rsRNAs, and ysRNAs, were significantly regulated, and this might be related to the alterations in RNA modification and subsequent transcriptomic changes. We comprehensively analyzed alterations in RNA modification signatures in total RNAs, tRNAs (approximately 80 nt), and small RNAs (17–50 nt) as well as the expression profiles of sncRNAs and transcriptomes in hypoxia-treated GC-2spd cells; our data suggested that RNA modifications may be involved in cellular responses under hypoxic stress conditions and could provide a basis for a better understanding of the molecular mechanisms underlying male infertility.
BUB1 Is Identified as a Potential Therapeutic Target for Pancreatic Cancer Treatment

Pancreatic cancer is one of the most challenging cancer types in clinical treatment worldwide. This study aimed to understand the tumorigenesis mechanism and explore potential therapeutic targets for patients with pancreatic cancer. Single-cell data and expression profiles of pancreatic cancer samples and normal tissues from multiple databases were included. Comprehensive bioinformatics analyses were applied to clarify tumor microenvironment and identify key genes involved in cancer development. Immense difference of cell types was shown between tumor and normal samples. Four cell types (B cell_1, B cell_2, cancer cell_3, and CD1C+_B dendritic cell_3) were screened to be significantly associated with prognosis. Three ligand–receptor pairs, including CD74-MIF, CD74-COPA, and CD74-APP, greatly contributed to tumorigenesis. High expression of BUB1 (BUB1 Mitotic Checkpoint Serine/Threonine Kinase) was closely correlated with worse prognosis. CD1C+_B dendritic cell_3 played a key role in tumorigenesis and cancer progression possibly through CD74-MIF. BUB1 can serve as a prognostic biomarker and a therapeutic target for patients with pancreatic cancer. The study provided a novel insight into studying the molecular mechanism of pancreatic cancer development and proposed a potential strategy for exploiting new drugs.
Acupuncture Regulates Symptoms of Parkinson’s Disease via Brain Neural Activity and Functional Connectivity in Mice

Parkinson’s disease (PD) is a multilayered progressive brain disease characterized by motor dysfunction and a variety of other symptoms. Although acupuncture has been used to ameliorate various symptoms of neurodegenerative disorders, including PD, the underlying mechanisms are unclear. Here, we investigated the mechanism of acupuncture by revealing the effects of acupuncture treatment on brain neural responses and its functional connectivity in an animal model of PD. We observed that destruction of neuronal network between many brain regions in PD mice were reversed by acupuncture. Using machine learning analysis, we found that the key region associated with the improvement of abnormal behaviors might be related to the neural activity of M1, suggesting that the changes of c-Fos in M1 could predict the improvement of motor function induced by acupuncture treatment. In addition, acupuncture treatment was shown to significantly normalize the brain neural activity not only in M1 but also in other brain regions related to motor behavior (striatum, substantia nigra pars compacta, and globus pallidus) and non-motor symptoms (hippocampus, lateral hypothalamus, and solitary tract) of PD. Taken together, our results demonstrate that acupuncture treatment might improve the PD symptoms by normalizing the brain functional connectivity in PD mice model and provide new insights that enhance our current understanding of acupuncture mechanisms for non-motor symptoms.
Long Short-Term Memory-Based Music Analysis System for Music Therapy

Music can express people’s thoughts and emotions. Music therapy is to stimulate and hypnotize the human brain by using various forms of music activities, such as listening, singing, playing and rhythm. With the empowerment of artificial intelligence, music therapy technology has made innovative development in the whole process of “diagnosis, treatment and evaluation. ” It is necessary to make use of the advantages of artificial intelligence technology to innovate music therapy methods, ensure the accuracy of treatment schemes, and provide more paths for the development of the medical field. This paper proposes an long short-term memory (LSTM)-based generation and classification algorithm for multi-voice music data. A Multi-Voice Music Generation system called MVMG based on the algorithm is developed. MVMG contains two main steps. At first, the music data are modeled to the MDPI and text sequence data by using an autoencoder model, including music features extraction and music clip representation. And then an LSTM-based music generation and classification model is developed for generating and analyzing music in specific treatment scenario. MVMG is evaluated based on the datasets collected by us: the single-melody MIDI files and the Chinese classical music dataset. The experiment shows that the highest accuracy of the autoencoder-based feature extractor can achieve 95.3%. And the average F1-score of LSTM is 95.68%, which is much higher than the DNN-based classification model.
Implications of Kappa Suprathermal Halo of the Solar Wind Electrons

The electron velocity distributions measured in-situ in space plasmas reveal two central populations, a low-energy and highly dense (quasi-)thermal core, and a more diffuse but hotter suprathermal halo. Even if the core contributes much more to the total number density than the suprathermal particles, the energetic electrons play an important role in the higher moments. Using a dataset of more than 120,000 solar wind observations of electron distributions, measured in the ecliptic between 0.35 and 3.3 AU, we investigate here the main characteristics of the halo population and its potential influence on the core, and macroscopic properties of electrons, i.e., number density (n), bulk velocity (u), temperature (T) and temperature anisotropy (T⊥/Τ//). The analysis indicates that the parameters exhibit interdependence trends characterized by correlations between certain of these parameters and the kappa exponent (κ) corresponding to the power law of the halo population tail. The links between low kappa and low number densities (of both the core and halo populations) confirm that Coulomb collisions can be quite ineffective even at low radial distances if the density of the plasma is sufficiently low. Moreover, halo populations with lower values of κ are also associated to higher temperature anisotropies, and to higher bulk velocity. An interdependence between core and halo populations is also suggested by an apparent (inverse) correlation between their density and temperature ratios. We further show relations between the parameters fitting the sum of a Maxwellian core and a Kappa halo, and those of a global (single) Kappa fit that incorporates both the core and halo components. Such a global Kappa is used in an exospheric model of the solar wind, to predict the influence of suprathermal electrons on the characteristics of the solar wind. These results should stimulate future detailed analysis of these relationships and correlations, which may contribute to a realistic modeling of the solar wind and the formation and evolution of suprathermal populations.
A Survey of Statistical Methods for Microbiome Data Analysis

In the last decade, numerous statistical methods have been developed for analyzing microbiome data generated from high-throughput next-generation sequencing technology. Microbiome data are typically characterized by zero inflation, overdispersion, high dimensionality, and sample heterogeneity. Three popular areas of interest in microbiome research requiring statistical methods that can account for the characterizations of microbiome data include detecting differentially abundant taxa across phenotype groups, identifying associations between the microbiome and covariates, and constructing microbiome networks to characterize ecological associations of microbes. These three areas are referred to as differential abundance analysis, integrative analysis, and network analysis, respectively. In this review, we highlight available statistical methods for differential abundance analysis, integrative analysis, and network analysis that have greatly advanced microbiome research. In addition, we discuss each method's motivation, modeling framework, and application.
Development of Designer Transcription Activator-Like Effector-Based Plant Growth Regulator for Higher Yield in Rice

Recent studies have shown that reprogramming of gene expression in a genome can induce the production of proteins enabling yield increase. The transcription activator-like effectors (TALEs) from several species of bacterial Xanthomonas have been extensively studied, and a series of research tools, such as genome editing tool TALENs and gene expression activators, have been developed based on the specific protein–nucleic acid recognition and binding mechanisms of TALEs. In this proof-of-principle study, we designed and constructed a designer TALE (dTALE), designated as dTALE-NOG1, to specifically target the promoter of OsNOG1 gene in rice, and demonstrated that this dTALE can be used as a new type of plant growth regulator for better crop growth and harvest. In doing so, the dTALE-NOG1 was transferred into the non-pathogenic Xanthomonas oryzae pv. oryzae (Xoo) strain PH to generate a genetically engineered bacteria (GEB) strain called PH-dtNOG1. Functional verification showed that dTALE-NOG1 could significantly induce the expression of OsNOG1. By spraying cell suspension of PH-dtNOG1 on the rice plants during the tillering stage, the transcription level of OsNOG1 was highly enhanced, the grain number of rice plants was increased by more than 11.40%, and the grain yield per plant increased by more than 11.08%, demonstrating that the dTALE-NOG1 was highly effective in enhancing rice yield. This work provided a new strategy for manipulating agronomical traits by reprogramming gene expression in a crop genome.
Synthesis, Antibacterial and Insecticidal Activities of Novel Capsaicin Derivatives Containing a Sulfonic Acid Esters Moiety

In order to develop an efficient and broad-spectrum bactericide, a series of novel capsaicin derivatives containing a sulfonic acid esters moiety was synthesized. The structure of these compounds were confirmed by nuclear magnetic resonance spectroscopy (NMR) and high-resolution mass spectrum (HRMS). The results of the bioactivities revealed that some target compounds exhibited remarkable antibacterial activity. Compound 3b exhibited the highest activities against Pseudomonas syringae pv. actinidiae (Psa), Xanthomonas oryzae pv. oryzae (Xoo), and Xanthomonas axonopodis pv. citri (Xac), and the values were 86, 54, and 92% at 50 μg/ml, respectively, which were higher than were for thiodiazole copper (87, 34, and 77%) and bismerthiazol (87, 37 and 75%). Although some compounds also showed certain activity against Spodoptera frugiperda, it was weaker than the positive controls monosultap and mulfoxaflor. Thus, the bioassay results recommend that these newly designed and synthesized scaffolds should be used as a bactericide lead compound rather than an insecticide lead compound.
Effect of Athletic Training on Fatigue During Neuromuscular Electrical Stimulation

The purpose of this study was to explore the effect an individual's exercise training type will have on muscle fatigability during repetitive contractions induced by Neuromuscular Electrical Stimulation (NMES). Thirty-four subjects comprising of competitive athletes and controls were recruited into three cohorts: Endurance (runners/cyclists) n = 13; nine male, four female; 27 ± 8 years old, Explosive (Lifters/Sprinters) n = 11; nine male, two female; 30 ± 7 years old, and controls n = 10, six male, four female, 26 ± 4 years old. Subjects were placed in a custom-made leg extension rig, and received NMES against a fixed resistance (NMES-FR), to the Vastus Medialis muscle resulting in isometric leg extensions, at a duty cycle of 1 s on/3 s rest, for 20 min. The force of the isometric contractions was recorded using a Hogan MicroFet2 dynamometer, and three separate fatigue metrics were calculated to compare the different cohorts, sports within each cohort, and gender within each cohort. For every fatigue metric, the endurance group fatigued significantly less than both the explosive and control cohorts, with no difference observed between the explosive and the controls. Within each cohort, no significant difference was observed in any fatigue metric between sport or gender, but these comparisons lacked power. The results show that only high capacity endurance activity will have any effect on reducing one's fatigability during repetitive NMES. The implications of this conclusion can aid in the development of NMES regimens for use in healthy populations, such as athletic training or astronaut musculoskeletal countermeasures, as well as clinical applications when fatigue is to be minimized.
Facing the FACS—Using AI to Evaluate and Control Facial Action Units in Humanoid Robot Face Development

This paper presents a new approach for evaluating and controlling expressive humanoid robotic faces using open-source computer vision and machine learning methods. Existing research in Human-Robot Interaction lacks flexible and simple tools that are scalable for evaluating and controlling various robotic faces; thus, our goal is to demonstrate the use of readily available AI-based solutions to support the process. We use a newly developed humanoid robot prototype intended for medical training applications as a case example. The approach automatically captures the robot’s facial action units through a webcam during random motion, which are components traditionally used to describe facial muscle movements in humans. Instead of manipulating the actuators individually or training the robot to express specific emotions, we propose using action units as a means for controlling the robotic face, which enables a multitude of ways to generate dynamic motion, expressions, and behavior. The range of action units achieved by the robot is thus analyzed to discover its expressive capabilities and limitations and to develop a control model by correlating action units to actuation parameters. Because the approach is not dependent on specific facial attributes or actuation capabilities, it can be used for different designs and continuously inform the development process. In healthcare training applications, our goal is to establish a prerequisite of expressive capabilities of humanoid robots bounded by industrial and medical design constraints. Furthermore, to mediate human interpretation and thus enable decision-making based on observed cognitive, emotional, and expressive cues, our approach aims to find the minimum viable expressive capabilities of the robot without having to optimize for realism. The results from our case example demonstrate the flexibility and efficiency of the presented AI-based solutions to support the development of humanoid facial robots.
Interactive Maneuver Prediction and Planning for Highly Automated Driving Functions

Today, "intelligent systems" are affecting almost every aspect of our lives. At this, highly automated driving offers entirely new possibilities. In this thesis, a novel interactive maneuver prediction and planning for highly automated driving functions for highways is presented. Furthermore, a novel framework for impact assessment based on microscopic traffic simulation is developed.
Disentangling the varying associations between systolic blood pressure and health outcomes in the very old: an individual patient data meta-analysis

Objectives: While randomized controlled trials have proven the benefits of blood pressure (BP) lowering in participating octogenarians, population-based observational studies suggest an association between low systolic blood pressure (SBP) and faster overall decline. This study investigates the effects of BP-lowering treatment, a history of cardiovascular diseases (CVD), and cognitive and physical fitness on the associations between SBP and health outcomes in the very old. Methods: Five cohorts from the Towards Understanding Longitudinal International older People Studies (TULIPS) consortium were included in a two-step individual participant data meta-analysis (IPDMA). We pooled hazard ratios (HR) from Cox proportional-hazards models for 5-year mortality and estimates of linear mixed models for change in cognitive and functional decline. Models were stratified by BP-lowering treatment, history of CVD, Mini-Mental State Examination scores, grip strength (GS) and body mass index (BMI). Results: Of all 2480 participants (59.9% females, median 85 years), median baseline SBP was 149 mmHg, 64.3% used BP-lowering drugs and 47.3% had a history of CVD. Overall, higher SBP was associated with lower all-cause mortality (pooled HR 0.91 [95% confidence interval 0.88–0.95] per 10 mmHg). Associations remained irrespective of BP-lowering treatment, history of CVD and BMI, but were absent in octogenarians with above-median MMSE and GS. In pooled cohorts, SBP was not associated with cognitive and functional decline. Conclusion: While in the very old with low cognitive or physical fitness a higher SBP was associated with a lower all-cause mortality, this association was not evident in fit octogenarians. SBP was not consistently associated with cognitive and functional decline.