Bipedal Robot Gait Generation Using Bessel Interpolation.

This paper introduces a novel approach to bipedal robot gait generation by proposing a higher-order form through the parameter equation of first-order Bessel interpolation. The trajectory planning for the bipedal robot, specifically for stepping up or down stairs, is established based on a three-dimensional interpolation equation. The experimental prototype, Roban, is utilized for the study, and the structural sketch of a single leg is presented. The inverse kinematics expression for the leg is derived using kinematic methods. Employing a position control method, the angle information is transmitted to the robot's joints, enabling the completion of both downstairs simulation experiments and physical experiments with the Roban prototype. The analysis of the experimental process reveals a noticeable phenomenon of hip and ankle joint tilting in the robot. This observation suggests that low-cost bipedal robots driven by servo motors exhibit low stiffness characteristics in their joints.

Online public concern about allergic rhinitis and its association with COVID-19 and air quality in China: an informative epidemiological study using Baidu index.

BACKGROUND: Allergic rhinitis is a common health concern that affects quality of life. This study aims to examine the online search trends of allergic rhinitis in China before and after the COVID-19 epidemic and to explore the association between the daily air quality and online search volumes of allergic rhinitis in Beijing. METHODS: We extracted the online search data of allergic rhinitis-related keywords from the Baidu index database from January 23, 2017 to June 23, 2022. We analyzed and compared the temporal distribution of online search behaviors across different themes of allergic rhinitis before and after the COVID-19 pandemic in mainland China, using the Baidu search index (BSI). We also obtained the air quality index (AQI) data in Beijing and assessed its correlation with daily BSIs of allergic rhinitis. RESULTS: The online search for allergic rhinitis in China showed significant seasonal variations, with two peaks each year in spring from March to May and autumn from August and October. The BSI of total allergic rhinitis-related searches increased gradually from 2017 to 2019, reaching a peak in April 2019, and declined after the COVID-19 pandemic, especially in the first half of 2020. The BSI for all allergic rhinitis themes was significantly lower after the COVID-19 pandemic than before (all p values < 0.05). The results also revealed that, in Beijing, there was a significant negative association between daily BSI and AQI for each allergic rhinitis theme during the original variant strain epidemic period and a significant positive correlation during the Omicron variant period. CONCLUSION: Both air quality and the interventions used for COVID-19 pandemic, including national and local quarantines and mask wearing behaviors, may have affected the incidence and public concern about allergic rhinitis in China. The online search trends can serve as a valuable tool for tracking real-time public concerns about allergic rhinitis. By complementing traditional disease monitoring systems of health departments, these search trends can also offer insights into the patterns of disease outbreaks. Additionally, they can provide references and suggestions regarding the public's knowledge demands related to allergic rhinitis, which can further be instrumental in developing targeted strategies to enhance population-based disease education on allergic diseases.

[Deep learning approach for automatic segmentation of auricular acupoint divisions].

The automatic segmentation of auricular acupoint divisions is the basis for realizing intelligent auricular acupoint therapy. However, due to the large number of ear acupuncture areas and the lack of clear boundary, existing solutions face challenges in automatically segmenting auricular acupoints. Therefore, a fast and accurate automatic segmentation approach of auricular acupuncture divisions is needed. A deep learning-based approach for automatic segmentation of auricular acupoint divisions is proposed, which mainly includes three stages: ear contour detection, anatomical part segmentation and keypoints localization, and image post-processing. In the anatomical part segmentation and keypoints localization stages, K-YOLACT was proposed to improve operating efficiency. Experimental results showed that the proposed approach achieved automatic segmentation of 66 acupuncture points in the frontal image of the ear, and the segmentation effect was better than existing solutions. At the same time, the mean average precision (mAP) of the anatomical part segmentation of the K-YOLACT was 83.2%, mAP of keypoints localization was 98.1%, and the running speed was significantly improved. The implementation of this approach provides a reliable solution for the accurate segmentation of auricular point images, and provides strong technical support for the modern development of traditional Chinese medicine.

A Novel Underwater Image Enhancement Using Optimal Composite Backbone Network.

Continuous exploration of the ocean has made underwater image processing an important research field, and plenty of CNN (convolutional neural network)-based underwater image enhancement methods have emerged over time. However, the feature-learning ability of existing CNN-based underwater image enhancement is limited. The networks were designed to be complicated or embed other algorithms for better results, which cannot simultaneously meet the requirements of suitable underwater image enhancement effects and real-time performance. Although the composite backbone network (CBNet) was introduced in underwater image enhancement, we proposed OECBNet (optimal underwater image-enhancing composite backbone network) to obtain a better enhancement effect and shorten the running time. Herein, a comprehensive study of different composite architectures in an underwater image enhancement network was carried out by comparing the number of backbones, connection strategies, pruning strategies for composite backbones, and auxiliary losses. Then, a CBNet with optimal performance was obtained. Finally, cross-sectional research of the obtained network with the state-of-the-art underwater enhancement network was performed. The experiments showed that our optimized composite backbone network achieved better-enhanced images than those of existing CNN-based methods.

An AAM-Based Identification Method for Ear Acupoint Area.

Ear image segmentation and identification is for the "observation" of TCM (traditional Chinese medicine), because disease diagnoses and treatment are achieved through the massaging of or pressing on some corresponding ear acupoints. With the image processing of ear image positioning and regional segmentation, the diagnosis and treatment of intelligent traditional Chinese medicine ear acupoints is improved. In order to popularize ear acupoint therapy, image processing technology has been adopted to detect the ear acupoint areas and help to gradually replace well-trained, experienced doctors. Due to the small area of the ear and the numerous ear acupoints, it is difficult to locate these acupoints based on traditional image recognition methods. An AAM (active appearance model)-based method for ear acupoint segmentation was proposed. The segmentation was illustrated as 91 feature points of a human ear image. In this process, the recognition effects of the ear acupoints, including the helix, antihelix, cymba conchae, cavum conchae, fossae helicis, fossae triangularis auriculae, tragus, antitragus, and earlobe, were divided precisely. Besides these, specially appointed acupoints or acupoint areas could be prominent in ear images. This method made it possible to partition and recognize the ear's acupoints through computer image processing, and maybe own the same abilities as experienced doctors for observation. The method was proved to be effective and accurate in experiments and can be used for the intelligent diagnosis of diseases.

LncRNA5251 inhibits spermatogenesis via modification of cell-cell junctions.

BACKGROUND: Male factors-caused decline in total fertility has raised significant concern worldwide. LncRNAs have been identified to play various roles in biological systems, including spermatogenesis. This study aimed to explore the role of lncRNA5251 in mouse spermatogenesis. METHODS: The expression of lncRNA5251 was modulated in mouse testes in vivo or spermatogonial stem cells (C18-4 cells) in vitro by shRNA. RESULTS: The sperm motility in two generations mice after modulation of lncRNA5251 (muF0 and muF1) was decreased significantly after overexpression of lncRNA5251. GO enrichment analysis found that knockdown lncRNA5251 increased the expression of genes related to cell junctions, and genes important for spermatogenesis in mouse testes. Meanwhile, overexpressing lncRNA5251 decreased the gene and/or protein expression of important genes for spermatogenesis and immune pathways in mouse testes. In vitro, knockdown lncRNA5251 increased the expression of genes for cell junction, and the protein levels of some cell junction proteins such as CX37, OCLN, JAM1, VCAM1 and CADM2 in C18-4 cells. LncRNA5251 is involved in spermatogenesis by modulation of cell junctions. CONCLUSION: This will provide a theoretical basis for improving male reproductive ability via lncRNA.

A Spring Compensation Method for a Low-Cost Biped Robot Based on Whole Body Control.

At present, the research and application of biped robots is more and more popular. The popularity of biped robots can be better promoted by improving the motion performance of low-cost biped robots. In this paper, the method of the Linear Quadratic Regulator (LQR) is used to track a robot's center of mass (COM). At the same time, the whole-body-control method and value function generated in the process of tracking COM are used to construct the quadratic programming (QP) model of a biped robot. Through the above method, the torque feedforward of the robot is obtained in the Drake simulation platform. The torque feedforward information of the robot is transformed into position feedforward information by spring compensation. In this paper, open loop control and spring compensation are used, respectively, to make the robot perform simple actions. Generally, after the compensation method of spring compensation is adopted, the roll angle and pitch angle of the upper body of the robot are closer to 0 after the robot performs an action. However, as the selected motion can introduce more forward and lateral motions, the robot needs more spring clearance compensation to improve performance. For improving the motion performance of a low-cost biped robot, the experimental results show that the spring compensation method is both reasonable and effective.

Gut-Spleen Axis: Microbiota via Vascular and Immune Pathways Improve Busulfan-Induced Spleen Disruption.

Fecal microbiota transplantation (FMT) is an effective means of modulating gut microbiota for the treatment of many diseases, including Clostridioides difficile infections. The gut-spleen axis has been established, and this is involved in the development and function of the spleen. However, it is not understood whether gut microbiota can be used to improve spleen function, especially in spleens disrupted by a disease or an anti-cancer treatment. In the current investigation, we established that alginate oligosaccharide (AOS)-improved gut microbiota (A10-FMT) can rescue anticancer drug busulfan-disrupted spleen vasculature and spleen function. A10-FMT improved the gene and/or protein expression of genes involved in vasculature development, increased the cell proliferation rate, enhanced the endothelial progenitor cell capability, and elevated the expression of the cell junction molecules to increase the vascularization of the spleen. This investigation found for the first time that the reestablishment of spleen vascularization restored spleen function by improving spleen immune cells and iron metabolism. These findings may be used as a strategy to minimize the side effects of anti-cancer drugs or to improve spleen vasculature-related diseases. IMPORTANCE Alginate oligosaccharide (AOS)-improved gut microbiota (A10-FMT) can rescue busulfan disrupted spleen vasculature. A10-FMT improved the cell proliferation rate, endothelial progenitor cell capability, and cell junction molecules to increase vasculature formation in the spleen. This reestablishment restored spleen function by improving spleen immune cells and iron metabolism. These findings are useful for the treatment of spleen vasculature-related diseases.

Gut-Testis Axis: Microbiota Prime Metabolome To Increase Sperm Quality in Young Type 2 Diabetes.

Young type 2 diabetes (T2D) affects 15% of the population, with a noted increase in cases, and T2D-related male infertility has become a serious issue in recent years. The current study aimed to explore the improvements of alginate oligosaccharide (AOS)-modified gut microbiota on semen quality in T2D. The T2D was established in young mice of 5 weeks of age with a blood glucose level of 21.2 ± 2.2 mmol/L, while blood glucose was 8.7 ± 1.1 mM in control animals. We discovered that fecal microbiota transplantation (FMT) of AOS-improved microbiota (A10-FMT) significantly decreased blood glucose, while FMT of gut microbiota from control animals (Con-FMT) did not. Sperm concentration and motility were decreased in T2D to 10% to 20% of those in the control group, while A10-FMT brought about a recovery of around 5- to 10-fold. A10-FMT significantly increased small intestinal Allobaculum, while it elevated small intestinal and cecal Lactobacillus in some extent, blood butyric acid and derivatives and eicosapentaenoic acid (EPA), and testicular docosahexaenoic acid (DHA), EPA, and testosterone and its derivatives. Furthermore, A10-FMT improved liver functions and systemic antioxidant environments. Most importantly, A10-FMT promoted spermatogenesis through the improvement in the expression of proteins important for spermatogenesis to increase sperm concentration and motility. The underlying mechanisms may be that A10-FMT increased gut-beneficial microbes Lactobacillus and Allobaculum to elevate blood and/or testicular butyric acid, DHA, EPA, and testosterone to promote spermatogenesis and thus to ameliorate sperm concentration and motility. AOS-improved gut microbes could emerge as attractive candidates to treat T2D-diminished semen quality. IMPORTANCE A10-FMT benefits gut microbiota, liver function, and systemic environment via improvement in blood metabolome, consequently to favor the testicular microenvironment to improve spermatogenesis process and to boost T2D-diminished semen quality. We established that AOS-improved gut microbiota may be used to boost T2D-decreased semen quality and metabolic disease-related male subfertility.

Medical Knowledge Extraction and Analysis from Electronic Medical Records Using Deep Learning.

Objectives Medical knowledge extraction (MKE) plays a key role in natural language processing (NLP) research in electronic medical records (EMR), which are the important digital carriers for recording medical activities of patients. Named entity recognition (NER) and medical relation extraction (MRE) are two basic tasks of MKE. This study aims to improve the recognition accuracy of these two tasks by exploring deep learning methods. Methods This study discussed and built two application scenes of bidirectional long short-term memory combined conditional random field (BiLSTM-CRF) model for NER and MRE tasks. In the data preprocessing of both tasks, a GloVe word embedding model was used to vectorize words. In the NER task, a sequence labeling strategy was used to classify each word tag by the joint probability distribution through the CRF layer. In the MRE task, the medical entity relation category was predicted by transforming the classification problem of a single entity into a sequence classification problem and linking the feature combinations between entities also through the CRF layer. Results Through the validation on the I2B2 2010 public dataset, the BiLSTM-CRF models built in this study got much better results than the baseline methods in the two tasks, where the F1-measure was up to 0.88 in NER task and 0.78 in MRE task. Moreover, the model converged faster and avoided problems such as overfitting. Conclusion This study proved the good performance of deep learning on medical knowledge extraction. It also verified the feasibility of the BiLSTM-CRF model in different application scenarios, laying the foundation for the subsequent work in the EMR field.