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A neighborhood’s walkability is associated with public health, economic and environmental benefits. The state of the walking surface on sidewalks is a key factor in assessing walkability, as it promotes pedestrian movement and exercise. Yet, conventional practices for assessing sidewalks are labor-intensive and rely on subject-matter experts, rendering them subjective, inefficient and ineffective. Wearable sensors can be utilized to address these limitations. This study proposes a novel classification method that employs a long short-term memory (LSTM) network to analyze gait data gathered from a single wearable accelerometer to automatically identify irregular walking surfaces. Three different input modalities—raw acceleration data, single-stride and multi-stride hand-crafted accelerometer-based gait features—were explored and their effects on the classification performance of the proposed method were compared and analyzed. To verify the effectiveness of the proposed approach, we compared the performance of the LSTM models to the traditional baseline support vector machine (SVM) machine learning method presented in our previous study. The results from the experiment demonstrated the effectiveness of the proposed framework, thereby validating its feasibility. Both LSTM networks trained with single-stride and multi-stride gait feature modalities outperformed the baseline SVM model. The LSTM network trained with multi-stride gait features achieved the highest average AUC of 83%. The classification performance of the LSTM model trained with single-stride gait features further improved to an AUC of 88% with post-processing, making it the most effective model. The proposed classification framework serves as an unbiased, user-oriented tool for conducting sidewalk surface condition assessments.

Lu Liu,1 Ruting Hui,2 Tianyang Zeng,3 Xuetao Yang,3 Qingchen Wu,3 Tao Yang3,4 1Intensive Care Unit of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China; 2Department of Rehabilitation Medicine, Chengdu First People’s Hospital, Chengdu, 61007, People’s Republic of China; 3Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China; 4Department of Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, CT, 06520, USACorrespondence: Tao Yang, Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China, Email xiangfei654@hotmail.comBackground: Malignant pleural mesothelioma (MPM) is a particularly fatal cancer with a median survival of less than one year. The value of single-agent checkpoint inhibitors is still obscure in MPM. We aim to reveal CUL4B prognostic role and immune infiltrates in MPM patients.Methods: CUL4B expression profile and clinical information of malignant pleura mesothelioma individuals were collected from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) dataset. Quantitative real-time PCR (qRT-PCR) analysis measured CUL4B mRNA expression in epithelioid, biphasic, sarcomatoid, and normal pleural cell lines.Results: CUL4B expression elevated in MPM and had a high diagnostic value with AUC = 0.772. Additionally, our results showed that CUL4B high expression significantly correlated with poorer outcomes in MPM. Moreover, GSEA revealed 15 KEGG pathways enriched in the CUL4B high-expression group, and 22 were exhibited in the CUL4B low-expression group. Otherwise, our results showed that CUL4B was relevant to Wnt antagonistic factors (BARX2), Insulin-like growth factor binding protein 3 (IGFBP3), and Phosphatase and tensin homolog (PTEN). In addition, our results revealed that CUL4B expression was positively linked with four types of immune cells, whereas CUL4B expression was negatively linked with three types of immune cells. Additionally, our results showed that CUL4B expression regulates T helper cells, Tcm, and Th2 cells infiltration MPM microenvironment. Finally, our results identified CUL4B high expression in MPM cell line NCI-H2052 (epithelioid), MSTO-211H (biphasic), and NCI-H28 (sarcomatoid).Conclusion: CUL4B is a valuable prognostic biomarker and a critical immune cell infiltration regulator in MPM.Keywords: malignant pleural mesothelioma, CUL4B, prognosis, tumorigenesis, immune infiltrates

The walkability of a neighborhood impacts public health and leads to economic and environmental benefits. The condition of sidewalks is a significant indicator of a walkable neighborhood as it supports and encourages pedestrian travel and physical activity. However, common sidewalk assessment practices are subjective, inefficient, and ineffective. Current alternate methods for objective and automated assessment of sidewalk surfaces do not consider pedestrians’ physiological responses. We developed a novel classification framework for the detection of irregular walking surfaces that uses a machine learning approach to analyze gait parameters extracted from a single wearable accelerometer. We also identified the most suitable location for sensor placement. Experiments were conducted on 12 subjects walking on good and irregular walking surfaces with sensors attached at three different locations: right ankle, lower back, and back of the head. The most suitable location for sensor placement was at the ankle. Among the five classifiers trained with gait features from the ankle sensor, Support Vector Machine (SVM) was found to be the most effective model since it was the most robust to subject differences. The model’s performance was improved with post-processing. This demonstrates that the SVM model trained with accelerometer-based gait features can be used as an objective tool for the assessment of sidewalk walking surface conditions.

Li Cheng,1 Doris Yin-ping Leung,1 Janet Wing-hung Sit,1 Xiao-mei Li,2 Yu-ning Wu,3 Miao-yan Yang,4 Cui-xia Gao,5 Rong Hui6 1The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Ma Liu Shui, Shatin, New Territories, Hong Kong; 2The Department of Nursing, Faculty of Medicine, The Xi’an Jiaotong University, 3The Department of Endocrinology, The Ninth Affiliated Hospital of Xi’an Jiaotong University, 4The Department of Endocrinology, The Second Affiliated Hospital of Xi’an Jiaotong University, 5The Department of Endocrinology, The First Affiliated Hospital of Xi’an Jiaotong University, 6The Department of Endocrinology, Shaanxi Provincial People’s Hospital, Xi’an, People’s Republic of China Background: The study was conducted to investigate the diet barriers perceived by patients with poorly controlled type 2 diabetes and examine the associations between diet barriers and sociodemographic characteristics, medical condition, and patient-centered variables. Methods: Secondary subgroup analyses were conducted based on the responses of 246 adults with poorly controlled type 2 diabetes from a multicenter, cross-sectional study. Diet barriers were captured by the Diet Barriers subscale of the Personal Diabetes Questionnaire. Participants also completed validated measures of diet knowledge, empowerment level, and appraisal of diabetes. Multiple regression techniques were used for model building, with a hierarchical block design to determine the separate contribution of sociodemographic characteristics, medical condition, and patient-centered variables to diet barriers. Results: Diet barriers were moderately evident (2.23±0.86) among Chinese patients with poorly controlled type 2 diabetes. The feeling of deprivation as a result of complying with a diet was the most recognized diet barrier (3.24±1.98), followed by “eating away from home” (2.79±1.82). Significantly higher levels of diet barriers were observed among those with lower levels of diet knowledge (β=-0.282, P

China has the largest number of managed honey bee colonies globally, but there is currently no data on viral infection in diseased A. mellifera L. colonies in China. In particular, there is a lack of data on chronic bee paralysis virus (CBPV) in Chinese honey bee colonies. Consequently, the present study investigated the occurrence and frequency of several widespread honey bee viruses in diseased Chinese apiaries, and we used the reverse transcription-polymerase chain reaction (RT-PCR) assay. Described was the relationship between the presence of CBPV and diseased colonies (with at least one of the following symptoms: depopulation, paralysis, dark body colorings and hairless, or a mass of dead bees on the ground surrounding the beehives). Phylogenetic analyses of CBPV were employed. The prevalence of multiple infections of honey bee viruses in diseased Chinese apiaries was 100%, and the prevalence of infections with even five and six viruses were higher than expected. The incidence of CBPV in diseased colonies was significantly higher than that in apparently healthy colonies in Chinese A. mellifera aparies, and CBPV isolates from China can be separated into Chinese-Japanese clade 1 and 2. The results indicate that beekeeping in China may be threatened by colony decline due to the high prevalence of multiple viruses with CBPV.

The walkability of a neighborhood impacts public health and leads to economic and environmental benefits. The condition of sidewalks is a significant indicator of a walkable neighborhood as it supports and encourages pedestrian travel and physical activity. However, common sidewalk assessment practices are subjective, inefficient, and ineffective. Current alternate methods for objective and automated assessment of sidewalk surfaces do not consider pedestrians' physiological responses. We developed a novel classification framework for the detection of irregular walking surfaces that uses a machine learning approach to analyze gait parameters extracted from a single wearable accelerometer. We also identified the most suitable location for sensor placement. Experiments were conducted on 12 subjects walking on good and irregular walking surfaces with sensors attached at three different locations: right ankle, lower back, and back of the head. The most suitable location for sensor placement was at the ankle. Among the five classifiers trained with gait features from the ankle sensor, Support Vector Machine (SVM) was found to be the most effective model since it was the most robust to subject differences. The model's performance was improved with post-processing. This demonstrates that the SVM model trained with accelerometer-based gait features can be used as an objective tool for the assessment of sidewalk walking surface conditions. [ABSTRACT FROM AUTHOR]

Objective To assess the feasibility of fetoscopy-guided bipolar umbilical cord coagulation for selective fetal reduction in complicated monochorionic diamniotic (MCDA) twin pregnancies. Methods MCDA twins undergoing fetoscopy-guided bipolar cord coagulation (BCC) were enrolled prospectively between December 2015 to March 2020 in a fetal medicine center. Results Twenty-three cases undergoing fetoscopy-guided BCC were finally analyzed, including 11 cases for type 2 selective intrauterine growth restriction, 4 cases for twin–twin transfusion syndrome, and 8 cases for a severe discordant anomaly. The overall survival rate was 78.3% (18/23). Conclusions Fetoscopy‐guided BCC is effective for selective fetal reduction in complicated monochorionic twin pregnancies. [ABSTRACT FROM AUTHOR]