Enhancing motion capture technology for youth sports training through decision tree algorithms

With the popularization and specialization of youth sports training, how to accurately capture and evaluate the quality of trainers' movements has become an important topic in sports science research. This study aims to improve the youth sports training motion capture technology, using decision tree algorithm to classify and analyze the movement data in order to improve the training effect of athletes. The traditional motion capture technique has problems such as high subjectivity, low efficiency, and error-prone, while the decision tree algorithm has the advantages of simplicity, fast training speed, and adaptability to small sample data. In this study, the action data of youth athletes were collected and the decision tree algorithm was used to train and predict the athletes' action classification results. The experimental results show that the decision tree algorithm can effectively classify and analyze the action data of adolescent athletes, accurately judge the strengths and weaknesses of athletes' actions, and provide targeted training suggestions and improvement directions. Compared with the traditional manual observation method, the motion capture technology based on the decision tree algorithm has obvious advantages in terms of accuracy and efficiency. Therefore, this technical improvement method provides a new way and method for youth sports training, which is expected to provide important support for improving the training effect and assessment accuracy. (AU)

TCMSSD: A comprehensive database focused on syndrome standardization.

BACKGROUD: Quantitative and standardized research on syndrome differentiation has always been at the forefront of modernizing Traditional Chinese Medicine (TCM) theory. However, the majority of existing databases primarily concentrate on the network pharmacology of herbal prescriptions, and there are limited databases specifically dedicated to TCM syndrome differentiation. PURPOSE: In response to this gap, we have developed the Traditional Chinese Medical Syndrome Standardization Database (TCMSSD, http://tcmssd.ratcm.cn). METHODS: TCMSSD is a comprehensive database that gathers data from various sources, including TCM literature such as TCM Syndrome Studies (Zhong Yi Zheng Hou Xue) and TCM Internal Medicine (Zhong Yi Nei Ke Xue) and various public databases such as TCMID and ETCM. In our study, we employ a deep learning approach to construct the knowledge graph and utilize the BM25 algorithm for syndrome prediction. RESULTS: The TCMSSD integrates the essence of TCM with the modern medical system, providing a comprehensive collection of information related to TCM. It includes 624 syndromes, 133,518 prescriptions, 8,073 diseases (including 1,843 TCM-specific diseases), 8,259 Chinese herbal medicines, 43,413 ingredients, 17,602 targets, and 8,182 drugs. By analyzing input data and comparing it with the patterns and characteristics recorded in the database, the syndrome prediction tool generates predictions based on established correlations and patterns. CONCLUSION: The TCMSSD fills the gap in existing databases by providing a comprehensive resource for quantitative and standardized research on TCM syndrome differentiation and laid the foundation for research on the biological basis of syndromes.

Adaptive Time-Frequency Segment Optimization for Motor Imagery Classification.

Motor imagery (MI)-based brain-computer interface (BCI) has emerged as a crucial method for rehabilitating stroke patients. However, the variability in the time-frequency distribution of MI-electroencephalography (EEG) among individuals limits the generalizability of algorithms that rely on non-customized time-frequency segments. In this study, we propose a novel method for optimizing time-frequency segments of MI-EEG using the sparrow search algorithm (SSA). Additionally, we apply a correlation-based channel selection (CCS) method that considers the correlation coefficient of features between each pair of EEG channels. Subsequently, we utilize a regularized common spatial pattern method to extract effective features. Finally, a support vector machine is employed for signal classification. The results on three BCI datasets confirmed that our algorithm achieved better accuracy (99.11% vs. 94.00% for BCI Competition III Dataset IIIa, 87.70% vs. 81.10% for Chinese Academy of Medical Sciences dataset, and 87.94% vs. 81.97% for BCI Competition IV Dataset 1) compared to algorithms with non-customized time-frequency segments. Our proposed algorithm enables adaptive optimization of EEG time-frequency segments, which is crucial for the development of clinically effective motor rehabilitation.

Factors affecting N-acetyl-ß-D-glucosaminidase as an indicator for mastitis detection in dairy sheep.

The study of new indirect methods for mastitis detection is of great relevance both at the economic level of the farm and dairies, and in terms of consumer health, and animal welfare. These methods help us to monitor the disease and speed up the decision-making process on treatment of the affected animal and the destination of the milk. The main aim of this work was to study the effect of intramammary infection and other non-infectious factors on the activity of the enzyme N-acetyl-ß-D-glucosaminidase (NAGase) in milk, in order to evaluate its use as an indicator for the early diagnosis of mastitis in sheep that could be less expensive, easier to measure and a better marker of inflammation or complementary to existing methods such as somatic cell count (SCC). Seven biweekly samplings were carried out, in which NAGase activity, SCC and milk were analyzed. Glands were classified according to their sanitary status based on the results of the SCC and bacteriological analysis. Non-infectious factors such as lactation stage, parity number and milking session had a statistically significant effect on NAGase values, finding the highest NAGase values at the onset and end of the study, in infectious mastitic glands of multiparous females and at morning milking. However, among the NAGase variation factors studied, the health status of the gland was the factor that caused the highest variation in enzyme levels, with infectious mastitic glands showing higher values than healthy glands. The predictive ability of NAGase was also studied by means of several logistic regression models, with the one that included NAGase together with lactation stage and parity obtaining the best results if sensitivity is to be prioritized, or the model that included NAGase, lactation stage, parity, milking and production if specificity is to be prioritized. From the results obtained, it can be concluded that the use of NAGase as an intramammary infection detection method in sheep can be useful when non-infectious factors that cause changes in the concentration of the enzyme are also considered.

Capacity of Generative AI to Interpret Human Emotions From Visual and Textual Data: Pilot Evaluation Study.

BACKGROUND: Mentalization, which is integral to human cognitive processes, pertains to the interpretation of one's own and others' mental states, including emotions, beliefs, and intentions. With the advent of artificial intelligence (AI) and the prominence of large language models in mental health applications, questions persist about their aptitude in emotional comprehension. The prior iteration of the large language model from OpenAI, ChatGPT-3.5, demonstrated an advanced capacity to interpret emotions from textual data, surpassing human benchmarks. Given the introduction of ChatGPT-4, with its enhanced visual processing capabilities, and considering Google Bard's existing visual functionalities, a rigorous assessment of their proficiency in visual mentalizing is warranted. OBJECTIVE: The aim of the research was to critically evaluate the capabilities of ChatGPT-4 and Google Bard with regard to their competence in discerning visual mentalizing indicators as contrasted with their textual-based mentalizing abilities. METHODS: The Reading the Mind in the Eyes Test developed by Baron-Cohen and colleagues was used to assess the models' proficiency in interpreting visual emotional indicators. Simultaneously, the Levels of Emotional Awareness Scale was used to evaluate the large language models' aptitude in textual mentalizing. Collating data from both tests provided a holistic view of the mentalizing capabilities of ChatGPT-4 and Bard. RESULTS: ChatGPT-4, displaying a pronounced ability in emotion recognition, secured scores of 26 and 27 in 2 distinct evaluations, significantly deviating from a random response paradigm (P<.001). These scores align with established benchmarks from the broader human demographic. Notably, ChatGPT-4 exhibited consistent responses, with no discernible biases pertaining to the sex of the model or the nature of the emotion. In contrast, Google Bard's performance aligned with random response patterns, securing scores of 10 and 12 and rendering further detailed analysis redundant. In the domain of textual analysis, both ChatGPT and Bard surpassed established benchmarks from the general population, with their performances being remarkably congruent. CONCLUSIONS: ChatGPT-4 proved its efficacy in the domain of visual mentalizing, aligning closely with human performance standards. Although both models displayed commendable acumen in textual emotion interpretation, Bard's capabilities in visual emotion interpretation necessitate further scrutiny and potential refinement. This study stresses the criticality of ethical AI development for emotional recognition, highlighting the need for inclusive data, collaboration with patients and mental health experts, and stringent governmental oversight to ensure transparency and protect patient privacy.

[Application strategy of artificial intelligence technology in feed-based development of medicinal and edible homologous traditional Chinese medicine(TCM)resources in era of the "antibiotic ban"].

In the context of the "antibiotic ban" era, the feed conversion of medicinal and edible traditional Chinese medicine(TCM) resources is a research hotspot in the field of antibiotic alternatives development. How to develop feed products that are beneficial to agriculture and livestock while ensuring nutrient balance and precision using medicinal and edible TCM resources as raw materials has become a challenge. Artificial intelligence(AI) technology has unique advantages in feed production and improving the efficiency of intelligent breeding. If AI technology is applied to the feed development of medicinal and edible TCM resources, it is possible to realize feeding and antibiotic-replacement value while ensuring precise nutrition. In order to better apply AI technology in the field of feed development of medicinal and edible TCM resources, this article used CiteSpace software to carry out literature visualization analysis and found that AI technology had a good application in the field of feed formulation optimization in recent years. However, there is still a gap in the research on the intelligent utilization of medicinal and edible TCM resources. Nonetheless, it is feasible for AI technology to be applied to the feed conversion of medicinal and edible TCM resources. Therefore, this article proposed for the first time an intelligent formulation system framework for feed materials derived from medicinal and edible TCM resources to provide new ideas for research in the field of feed development of medicinal and edible TCM resources and the research on the development of antibiotic alternatives. At the same time, it can pave the way for a new green industry chain for contemporary animal husbandry and the TCM industry.

Optimization of ultrasonic-assisted supramolecular solvent microextraction of coumarins from Cortex fraxini using response surface methodology combined with artificial neural network-genetic algorithm.

A simple, fast, and efficient ultrasonic-assisted supramolecular solvent microextraction combined with high performance liquid chromatography method was developed for the determination of coumarins in Cortex fraxini, including esculin, esculetin and fraxetin. In this study, a novel supramolecular solvent was prepared with 1-octanol, tetrahydrofuran and water for the first time, and its composition, viscosity, density, structure, and micromorphology were characterized. The prepared supramolecular solvent exhibited vesicular structures and had the characteristics of low viscosity. Through single-factor experiments, response surface methodology and artificial neural network-genetic algorithm, the optimal extraction conditions were obtained as follows: NaCl concentration of 1 mol mL-1, pH value of 10, solid-liquid ratio of 10:1, vortex time of 30 s, ultrasonic power of 100 W, ultrasonic temperature of 60 °C, ultrasonic time of 15 min, centrifugation speed of 5000 rpm, and centrifugation time of 1 min. The results demonstrated that the artificial neural network model exhibited maximum R-values of 0.98703, 0.97440, 0.99836, and 0.95447 for training, testing, validation, and all dataset, respectively. The minimum mean square errors were 0.75, 10.15, 1.99, and 2.63, respectively. This indicated that the predicted values were almost consistent with the actual values. Under the optimal conditions, the total extraction yields of target analytes reached 2.80 %. The calibration curves for each analyte exhibited excellent linearity within the linear range (r > 0.9993). The limits of detection and quantification ranged from 4.87 to 6.55 ng mL-1 and 16.24 to 21.84 ng mL-1, respectively. The recoveries ranged from 98.71 % to 111.01 % with relative standard deviations of less than 3.6 %. The present method had the advantages of short extraction time (15 min) and less solvent consumption (0.5 mL). The prepared supramolecular solvent was proved to have great potential in extracting coumarins from medicinal plants.

Thalamic segmentation based on diffusion tensor imaging in patients with trigeminal neuralgia.

Classical trigeminal neuralgia (CTN) refers to episodic pain that is strictly confined to the trigeminal distribution area, and the thalamus is an important component of the trigeminal sensory pathway. Probabilistic tracking imaging algorithm was used to identify specific connections between the thalamus and the cortex, in order to identify structural changes in the thalamus of patients with CTN and perform thalamic segmentation. A total of 32 patients with CTN and 32 healthy controls underwent DTI-MRI scanning (3.0 T). Differences in fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) between the groups were studied. Correlation analysis was performed with clinical course and pain level. Compared to the healthy controls, patients in the CTN group had significantly reduced FA, increased AD, RD and MD in somatosensory subregion of the bilateral thalamus, increased RD in frontal subregion, increased RD and MD in motor subregion. Correlation analysis showed that patient history was positively correlated with pain grading, and that medical history was positively correlated with significantly reduced FA in somatosensory subregion, negatively correlated with increased RD and MD in motor subregion. We used DTI-based probabilistic fiber tracking to discover altered structural connectivity between the thalamus and cerebral cortex in patients with CTN and to obtain a thalamic segmentation atlas, which will help to further understand the pathophysiology of CTN and serve as a future reference for thalamic deep brain stimulation electrode implantation for the treatment of intractable pain.

Identification of acute myocardial infarction and stroke events using the National Health Insurance Service database in Korea.

OBJECTIVES: The escalating burden of cardiovascular disease (CVD) is a critical public health issue worldwide. CVD, especially acute myocardial infarction (AMI) and stroke, is the leading contributor to morbidity and mortality in Korea. We aimed to develop algorithms for identifying AMI and stroke events from the National Health Insurance Service (NHIS) database and validate these algorithms through medical record review. METHODS: We first established a concept and definition of "hospitalization episode," taking into account the unique features of health claims-based NHIS database. We then developed first and recurrent event identification algorithms, separately for AMI and stroke, to determine whether each hospitalization episode represents a true incident case of AMI or stroke. Finally, we assessed our algorithms' accuracy by calculating their positive predictive values (PPVs) based on medical records of algorithm- identified events. RESULTS: We developed identification algorithms for both AMI and stroke. To validate them, we conducted retrospective review of medical records for 3,140 algorithm-identified events (1,399 AMI and 1,741 stroke events) across 24 hospitals throughout Korea. The overall PPVs for the first and recurrent AMI events were around 92% and 78%, respectively, while those for the first and recurrent stroke events were around 88% and 81%, respectively. CONCLUSIONS: We successfully developed algorithms for identifying AMI and stroke events. The algorithms demonstrated high accuracy, with PPVs of approximately 90% for first events and 80% for recurrent events. These findings indicate that our algorithms hold promise as an instrumental tool for the consistent and reliable production of national CVD statistics in Korea.

A novel enzyme-based functional correlation algorithm for multi-omics reveals the potential mechanisms of traditional Chinese medicines: Taking Jian-Pi-Yi-Shen formula as an example.

The integrated analysis of host metabolome and intestinal microbiome is an opportunity to explore the complex therapeutic mechanisms of traditional Chinese medicines. Currently, researchers mainly employ various statistical correlation analytical methods to investigate metabolome-microbiome correlations. However, these conventional correlation techniques often focus on statistical correlations and their biological meanings are always ignored, especially the functional relevance between them. Here, we developed a novel enzyme-based functional correlation (EBFC) algorithm to further improve the interpretability and the identified scope of microbe-metabolite correlations based on the conventional Spearman's analysis. The proposed EBFC algorithm is successfully utilized to reveal the therapeutic mechanisms of Jian-Pi-Yi-Shen (JPYS) formula on the treatment of adenine-induced chronic kidney disease (CKD) rats. JPYS, a TCM formula for treating CKD, has beneficial clinical effects. We tentatively revealed the potential mechanism of JPYS for treating CKD rats from the perspective of the serum metabolome, gut microbiome, and their interactions. Specifically, 11 metabolites and 19 bacterial genera in the CKD rats were significantly regulated to approaching normal status after JPYS treatment, suggesting that JPYS could ameliorate the pathological symptoms of CKD rats by reshaping the disturbed metabolome and gut microbiota. Further correlation analysis between the significantly perturbed metabolites, microbiota, and the related enzymes provided more strong evidence for the study of host metabolism-microbiota interactions and the therapeutic mechanism of JPYS on CKD rats. In conclusion, these findings will help us to deeply understand the pathogenesis of CKD and provide new insights into the therapeutic mechanism of JPYS.

An efficient artificial intelligence algorithm for predicting the sensory quality of green and black teas based on the key chemical indices.

The key components dominating the quality of green tea and black tea are still unclear. Here, we respectively produced green and black teas in March and June, and investigated the correlations between sensory quality and chemical compositions of dry teas by multivariate statistics, bioinformatics and artificial intelligence algorithm. The key chemical indices were screened out to establish tea sensory quality-prediction models based on the result of OPLS-DA and random forest, namely 4 flavonol glycosides of green tea and 8 indices of black tea (4 pigments, epigallocatechin, kaempferol-3-O-rhamnosyl-glucoside, ratios of caffeine/total catechins and epi/non-epi catechins). Compared with OPLS-DA and random forest, the support vector machine model had good sensory quality-prediction performance for both green tea and black tea (F1-score > 0.92), even based on the indices of fresh tea leaves. Our study explores the potential of artificial intelligence algorithm in classification and prediction of tea products with different sensory quality.

Optimizing BP neural network algorithm for Pericarpium Citri Reticulatae (Chenpi) origin traceability based on computer vision and ultra-fast gas-phase electronic nose data fusion.

This study utilized computer vision to extract color and texture features of Pericarpium Citri Reticulatae (PCR). The ultra-fast gas-phase electronic nose (UF-GC-E-nose) technique successfully identified 98 volatile components, including olefins, alcohols, and esters, which significantly contribute to the flavor profile of PCR. Multivariate statistical Analysis was applied to the appearance traits of PCR, identifying 57 potential marker-trait factors (VIP > 1 and P < 0.05) from the 118 trait factors that can distinguish PCR from different origins. These factors include color, texture, and odor traits. By integrating multivariate statistical Analysis with the BP neural network algorithm, a novel artificial intelligence algorithm was developed and optimized for traceability of PCR origin. This algorithm achieved a 100% discrimination rate in differentiating PCR samples from various origins. This study offers a valuable reference and data support for developing intelligent algorithms that utilize data fusion from multiple intelligent sensory technologies to achieve rapid traceability of food origins.

Identification of Commercial Antimalarial Herbal Drugs Using Laser-Induced Autofluorescence Technique and Multivariate Algorithms.

In malaria-prone developing countries the integrity of Anti-Malarial Herbal Drugs (AMHDs) which are easily preferred for treatment can be compromised. Currently, existing techniques for identifying AMHDs are destructive. We report on the use of non-destructive and sensitive technique, Laser-Induced-Autofluorescence (LIAF) in combination with multivariate algorithms for identification of AMHDs. The LIAF spectra were recorded from commercially prepared decoction AMHDs purchased from accredited pharmacy shop in Ghana. Deconvolution of the LIAF spectra revealed secondary metabolites belonging to derivatives of alkaloids and classes of phenolic compounds of the AMHDs. Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) were able to discriminate the AMHDs base on their physicochemical properties. Based on two principal components, the PCA- QDA (Quadratic Discriminant Analysis), PCA-LDA (Linear Discriminant Analysis), PCA-SVM (Support Vector Machine) and PCA-KNN (K-Nearest Neighbour) models were developed with an accuracy performance of 99.0, 99.7, 100.0, and 100%, respectively, in identifying AMHDs. PCA-SVM and PCA-KNN provided the best classification and stability performance. The LIAF technique in combination with multivariate techniques may offer a non-destructive and viable tool for AMHDs identification.

Rapid screening, isolation, and activity evaluation of potential xanthine oxidase inhibitors in Polyporus umbellatus and mechanism of action in the treatment of gout.

INTRODUCTION: Studies show that Polyporus umbellatus has some pharmacological effects in enhancing immunity and against gout. OBJECTIVES: We aimed to establish new techniques for extraction, biological activity screening, and preparation of xanthine oxidase inhibitors (XODIs) from P. umbellatus. METHODS: First, the extraction of P. umbellatus was investigated using the back propagation (BP) neural network genetic algorithm mathematical regression model, and the extraction variables were optimised to maximise P. umbellatus yield. Second, XODIs were rapidly screened using ultrafiltration, and the change of XOD activity was tested by enzymatic reaction kinetics experiment to reflect the inhibitory effect of active compounds on XOD. Meanwhile, the potential anti-gout effects of the obtained active substances were verified using molecular docking, molecular dynamics simulations, and network pharmacology analysis. Finally, with activity screening as guide, a high-speed countercurrent chromatography (HSCCC) method combined with consecutive injection and two-phase solvent system preparation using the UNIFAC mathematical model was successfully developed for separation and purification of XODIs, and the XODIs were identified using MS and NMR. RESULTS: The results verified that polyporusterone A, polyporusterone B, ergosta-4,6,8(14),22-tetraen-3-one, and ergosta-7,22-dien-3-one of P. umbellatus exhibited high biological affinity towards XOD. Their structures have been further identified by NMR, indicating that the method is effective and applicable for rapid screening and identification of XODIs. CONCLUSION: This study provides new ideas for the search for natural XODIs active ingredients, and the study provide valuable support for the further development of functional foods with potential therapeutic benefits.

Validation of an algorithm for identifying incident cancer cases based on long-term illness and diagnosis related group program data from the French National Health Insurance Information System (SNDS).

PURPOSE: Three generic claims-based algorithms based on the Illness Classification of Diseases (10th revision- ICD-10) codes, French Long-Term Illness (LTI) data, and the Diagnosis Related Group program (DRG) were developed to identify retirees with cancer using data from the French national health insurance information system (Système national des données de santé or SNDS) which covers the entire French population. The present study aimed to calculate the algorithms' performances and to describe false positives and negatives in detail. METHODS: Between 2011 and 2016, data from 7544 participants of the French retired self-employed craftsperson cohort (ESPrI) were first matched to the SNDS data, and then toFrench population-based cancer registries data, used as the gold standard. Performance indicators, such as sensitivity and positive predictive values, were estimated for the three algorithms in a subcohort of ESPrI. RESULTS: The third algorithm, which combined the LTI and DRG program data, presented the best sensitivities (90.9%-100%) and positive predictive values (58.1%-95.2%) according to cancer sites. The majority of false positives were in fact nearby organ sites (e.g., stomach for esophagus) and carcinoma in situ. Most false negatives were probably due to under declaration of LTI. CONCLUSION: Validated algorithms using data from the SNDS can be used for passive epidemiological follow-up for some cancer sites in the ESPrI cohort.

Analysis on the medication characteristics and compatibility law of ancient prescriptions in the treatment of pediatric epilepsy based on visual network analysis / 国际中医中药杂志

Objective:To analyze the medication characteristics of ancient prescriptions for pediatric epilepsy (PE) through data mining; To summarize the compatibility law; To provide a reference for the treatment selection of Chinese materia medica and the development of patent drugs related to PE in clinic.Methods:Those with definite composition, dosage and efficacy for the treatment of PE was screened from the data of TCM prescription designed by Institute of Traditional Chinese Medicine Information, China Academy of Traditional Chinese Medicine. Excel 2013 was used to analyze the frequency of Chinese materia medica and its flavor and meridian tropism in the included prescriptions. The arules package in R 3.6.3 was used for association analysis based on Apriori algorithm. The sankey package and ggraph package of R 3.6.3 were used to draw the network diagram of the property, taste, meridian tropism and association rules of high-frequency Chinese medicine, so as to realize data visualization.Results:A total of 360 ancient prescriptions for the treatment of PE were included, and the dosage form was mainly pills. Most of the prescriptions were composed of 1 to 10 kinds of Chinese materia medica, with a total of 192 (53.33%, 192/360) prescriptions. 152 kinds of Chinese materia medica were included. The most commonly used types of Chinese materia medica were Glycyrrhizae Radix et Rhizoma, Moschus, Saposhnikoviae Radix, Gastrodiae Rhizoma, and Aconiti Lateralis Radix Praeparata. The properties of high-frequency Chinese materia medica (frequency≥30) were characterized by warm and mild, and the tastes were mainly pungent, bitter and sweet, and the meridians were mainly spleen and liver meridians. Through Apriori association analysis, the commonly used combination drugs were Bovis Calculus-Moschus, Ginseng Radix et Rhizoma-Poria and Saposheikovize Radix-Glycyrrhizae Radix et Rhizoma. Similarly, the commonly used triple drugs included Gastrodiae Rhizoma-Aconiti Lateralis Radix Praeparata-Bombyx Batryticatus, Poria-Glycyrrhizae Radix et Rhizoma-Ginseng Radix et Rhizoma, and Moschus-Bovis Calculus-Realgar.Conclusions:The ancient prescriptions for the treatment of PE is mainly composed of wind-calming, resuscitation and tonifying drug. The core prescription ideas of the ancient prescriptions are as follows: dispelling phlegm and dispelling wind, warming the meridian and dispelling yang, resuscitating and relieving spasms, clearing heat and reducing depression, and tonifying qi and blood.

Study on The Toxicity of Strychnos nux-vomica L. in vivo in Rats：Application of Bagging Algorithm and 16S rRNA Gene Sequencing Technology in Toxicology Research / 生物化学与生物物理进展

ObjectiveThe traditional Chinese medicine Strychnos nux-vomica L. (SN) has the clinical effect of reducing swelling and relieving pain; however, SN is toxic due to its alkaloid components. Little is known about the endogenous metabolic changes induced by SN toxicity in rats and their potential effects on the metabolic dysregulation of intestinal microbiota. Therefore, toxicological investigation of SN is of great significance to its safety assessment. In this study, the toxic mechanisms of SN were explored using a combination of metabonomics and 16S rRNA gene sequencing. MethodsThe toxic dose, intensity, and target organ of SN were determined in rats using acute, cumulative, and subacute toxicity tests. UHPLC-MS was used to analyze the serum, liver, and renal samples of rats after intragastric SN administration. The decision tree and K Nearest Neighbor (KNN) model were established based on the bootstrap aggregation (bagging) algorithm to classify the omics data. After samples were extracted from rat feces, the high-throughput sequencing platform was used to analyze the 16S rRNA V3-V4 region of bacteria. ResultsThe bagging algorithm improved the accuracy of sample classification. Twelve biomarkers were identified, where their metabolic dysregulation may be responsible for SN toxicity in vivo. Several types of bacteria such as Bacteroidetes, Anaerostipes, Oscillospira and Bilophila, were demonstrated to be closely related to physiological indices of renal and liver function, indicating that SN-induced liver and kidney damage may be related to the disturbance of these intestinal bacteria. ConclusionThe toxicity mechanism of SN was revealed in vivo, which provides a scientific basis for the safe and rational clinical use of SN.

MATLAB-based innovative 3D finite element method simulator for optimized real-time hyperthermia analysis.

BACKGROUND AND OBJECTIVE: Owing to the significant role of hyperthermia in enhancing the efficacy of chemotherapy or radiotherapy for treating malignant tissues, this study introduces a real-time hyperthermia simulator (RTHS) based on the three-dimensional finite element method (FEM) developed using the MATLAB App Designer. METHODS: The simulator consisted of operator-defined homogeneous and heterogeneous phantom models surrounded by an annular phased array (APA) of eight dipole antennas designed at 915 MHz. Electromagnetic and thermal analyses were conducted using the RTHS. To locally raise the target temperature according to the tumor's location, a convex optimization algorithm (COA) was employed to excite the antennas using optimal values of the phases to maximize the electric field at the tumor and amplitudes to achieve the required temperature at the target position. The performance of the proposed RTHS was validated by comparing it with similar hyperthermia setups in the FEM-based COMSOL software and finite-difference time-domain (FDTD)-based Sim4Life software. RESULTS: The simulation results obtained using the RTHS were consistent, both for the homogeneous and heterogeneous models, with those obtained using commercially available tools, demonstrating the reliability of the proposed hyperthermia simulator. The effectiveness of the simulator was illustrated for target positions in five different regions for both homogeneous and heterogeneous phantom models. In addition, the RTHS was cost-effective and consumed less computational time than the available software. The proposed method achieved 94% and 96% accuracy for element sizes of λ/26 and λ/36, respectively, for the homogeneous model. For the heterogeneous model, the method demonstrated 93% and 95% accuracy for element sizes of λ/26 and λ/36, respectively. The accuracy can be further improved by using a more refined mesh at the cost of a higher computational time. CONCLUSIONS: The proposed hyperthermia simulator demonstrated reliability, cost-effectiveness, and reduced computational time compared to commercial software, making it a potential tool for optimizing hyperthermia treatment.

Enhanced Detection Precision of the Taiji Program by Frequency Setting Strategy Based on a Hierarchical Optimization Algorithm.

For space-based gravitational wave detection, a laser interferometric measurement system composed of a three-spacecraft formation offers the most rewarding bandwidth of astrophysical sources. There are no oscillators available that are stable enough so that each spacecraft could use its own reference frequency. The conversion between reference frequencies and their distribution between all spacecrafts for the synchronization of the different metrology systems is the job of the inter-spacecraft frequency setting strategy, which is important for continuously acquiring scientific data and suppressing measurement noise. We propose a hierarchical optimization algorithm to solve the frequency setting strategy. The optimization objectives are minimum total readout displacement noise and maximum beat-note frequency feasible range. Multiple feasible parameter combinations were obtained for the Taiji program. These optimized parameters include lower and upper bounds of the beat note, sampling frequency, pilot tone signal frequency, ultrastable clock frequencies, and modulation depth. Among the 20 Pareto optimal solutions, the minimum total readout displacement noise was 4.12 pm/Hz, and the maximum feasible beat-note frequency range was 23 MHz. By adjusting the upper bound of beat-note frequency and laser power transmitted by the telescope, we explored the effects of these parameters on the minimum total readout displacement noise and optimal local laser power in greater depth. Our results may serve as a reference for the optimal design of laser interferometry system instrument parameters and may ultimately improve the detection performance and continuous detection time of the Taiji program.