Association of daily sitting time and leisure-time physical activity with body fat among U.S.adults

Background:Prolonged sitting and reduced physical activity lead to low energy expenditures.However,little is known about the joint impact of daily sitting time and physical activity on body fat distribution.We investigated the independent and joint associations of daily sitting time and physical activity with body fat among adults.Methods:This was a cross-sectional analysis of U.S.nationally representative data from the National Health and Nutrition Examination Survey2011-2018 among adults aged 20 years or older.Daily sitting time and leisure-time physical activity(LTPA)were self-reported using the Global Physical Activity Questionnaire.Body fat(total and trunk fat percentage)was determined via dual X-ray absorptiometry.Results:Among 10,808 adults,about 54.6%spent 6 h/day or more sitting;more than one-half reported no LTPA(inactive)or less than 150 min/week LTPA(insufficiently active)with only 43.3%reported 150 min/week or more LTPA(active)in the past week.After fully adjusting for sociodemographic data,lifestyle behaviors,and chronic conditions,prolonged sitting time and low levels of LTPA were associated with higher total and trunk fat percentages in both sexes.When stratifying by LTPA,the association between daily sitting time and body fat appeared to be stronger in those who were inactive/insuufficiently active.In the joint analyses,inactive/insuufficiently active adults who reported sitting more than 8 h/day had the highest total(female:3.99%(95%confidence interval(95%CI):3.09%-4.88%);male:3.79%(95%CI:2.75%-4.82%))and trunk body fat percentages(female:4.21%(95%CI:3.09%-5.32%);male:4.07%(95%CI:2.95%-5.19%))when compared with those who were active and sitting less than 4 h/day.Conclusion:Prolonged daily sitting time was associated with increased body fat among U.S.adults.The higher body fat associated with 6 h/day sitting may not be offset by achieving recommended levels of physical activity.

Structure and immunomodulatory activity of Lentinus edodes polysaccharides modified by probiotic fermentation

Plant-based fermentations provide an untapped source for novel biotechnological applications.In this study,a probiotic named Lactobacillus fermentum 21828 was introduced to ferment Lentinus edodes.Polysaccharides were extracted from fermented and non-fermented L.edodes and purified via DEAE-52 and Sephadex G-100.The components designated F-LEP-2a and NF-LEP-2a were analyzed by FT-IR,HPGPC,HPAEC,SEM,GC-MS and NMR.The results revealed that probiotic fermentation increased the molecular weight from 1.16×10^(4) Da to 1.87×10^(4) Da and altered the proportions of glucose,galactose and mannose,in which glucose increased from 45.94%to 48.16%.Methylation analysis and NMR spectra indicated that F-LEP-2a and NF-LEP-2a had similar linkage patterns.Furthermore,their immunomodulatory activities were evaluated with immunosuppressive mice.NF-LEP and F-LEP improved immune organ indices,immunoglobulin(Ig G and Ig M)and cytokines concentrations;restored the antioxidation capacity of liver;and maintained the balance of gut microbiota.F-LEP displayed better moderating effects on the spleen index,immunoglobulin,cytokines and the diversity of gut microbiota than NF-LEP(200,400 mg/kg).Our study provides an efficient and environment-friendly way for the structural modification of polysaccharides,which helps to enhance their biological activity and promote their wide application in food,medicine and other fields.

Evaluation of Four Anthropogenic Activity Impacts on Heavy Metal Quality of the Kumba River in the South West Region of Cameroon

Anthropogenic activities have contributed to pollution of water bodies through deposition of diverse pollutants amongst which are heavy metals. These pollutants, which at times are above the maximum concentration levels recommended, are detrimental to the quality of the water, soil and crops (plant) with subsequent human health risks. The objective of the work was to evaluate the impacts of human-based activities on the heavy metal properties of surface water with focus on the Kumba River basin. Field observations, interviews, field measurements and laboratory analyses of different water samples enabled us to collect the different data. The results show four main human-based activities within the river basin (agriculture, livestock production, domestic waste disposal and carwash activities) that pollute surface water. Approximately 20.61 tons of nitrogen and phosphorus from agricultural activities, 156.48 tons of animal wastes, 2517.5 tons of domestic wastes and 1.52 tons of detergent from carwash activities were deposited into the river each year. A highly significant difference at 1% was observed between the upstream and downstream heavy metal loads in four of the five heavy metals tested except for copper that was not significant. Lead concentrations were highest in all the activities with an average of 2.4 mg∙L−1 representing 57.81%, followed by zinc with 1.596 mg∙L−1 (38.45%) and manganese with 0.155 mg∙L−1 (3.74%) for the different anthropogenic activities thus indicating that these activities highly lead to pollution of the Kumba River water. The level of zinc and manganese was significantly influenced at ρ 005 by anthropogenic activities though generally the variations were in the order: carwash (3.196 mg∙L−1) < domestic waste disposal (3.347 mg∙L−1) < agriculture (4.172 mg∙L−1) < livestock (4.886 mg∙L−1) respectively and leading to a total of 14.04 tons of heavy metal pollutants deposited each day.

TransTM:A device-free method based on time-streaming multiscale transformer for human activity recognition

RFID-based human activity recognition(HAR)attracts attention due to its convenience,noninvasiveness,and privacy protection.Existing RFID-based HAR methods use modeling,CNN,or LSTM to extract features effectively.Still,they have shortcomings:1)requiring complex hand-crafted data cleaning processes and 2)only addressing single-person activity recognition based on specific RF signals.To solve these problems,this paper proposes a novel device-free method based on Time-streaming Multiscale Transformer called TransTM.This model leverages the Transformer's powerful data fitting capabilities to take raw RFID RSSI data as input without pre-processing.Concretely,we propose a multiscale convolutional hybrid Transformer to capture behavioral features that recognizes singlehuman activities and human-to-human interactions.Compared with existing CNN-and LSTM-based methods,the Transformer-based method has more data fitting power,generalization,and scalability.Furthermore,using RF signals,our method achieves an excellent classification effect on human behaviorbased classification tasks.Experimental results on the actual RFID datasets show that this model achieves a high average recognition accuracy(99.1%).The dataset we collected for detecting RFID-based indoor human activities will be published.

Association of physical activity with risk of chronic kidney disease in China:A population-based cohort study

Background:Information on the association between physical activity(PA)and the risk of chronic kidney disease(CKD)is limited.We aimed to explore the associations of total,domain-specific,and intensity-specific PA with CKD and its subtypes in China.Methods:The study included 475,376 adults from the China Kadoorie Biobank aged 30-79 years during 2004-2008 at baseline.An interviewer-administered questionnaire was used to collect the information about PA,which was quantified as metabolic equivalent of task hours per day(MET-h/day)and categorized into 4 groups based on quartiles.Cox regression was used to analyze the association between PA and CKD risk.Results:During a median follow-up of 12.1 years,5415 incident CKD cases were documented,including 1159 incident diabetic kidney disease(DKD)cases and 362 incident hypertensive nephropathy(HTN)cases.Total PA was inversely associated with CKD risk,with an adjusted hazard ratio(HR,95%confidence interval(95%CI))of 0.83(0.75-0.92)for incident CKD in the highest quartile of total PA as compared with participants in the lowest quartile.Similar results were observed for risk of DKD and HTN,and the corresponding HRs(95%CIs)were 0.75(0.58-0.97)for DKD risk and 0.56(0.37-0.85)for HTN risk.Increased nonoccupational PA,low-intensity PA,and moderate-to-vigorous-intensity PA were significantly associated with a decreased risk of CKD,with HRs(95%CIs)of 0.80(0.73-0.88),0.85(0.77-0.94),and 0.85(0.76-0.95)in the highest quartile,respectively.Conclusion:PA,including nonoccupational PA,low-intensity PA,and moderate-to-vigorous-intensity PA,was inversely associated with the risk of CKD,including DKD,HTN,and other CKD,and such associations were dose dependent.

Antioxidant Activity and Phytochemical Screening in Acacia rigidula Benth. Leaves

This study aimed to compare the total phenolic content (TPC) and antioxidant activities of A. rigidula extracts. It also aimed to identify phenolic acids present in the extracts. The 2,2-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), 2,2-diphenyl-1-picrylhydrazyl (DPPH), ferric reducing antioxidant power (FRAP), and ferric thiocyanate lipid peroxidation antioxidant assays were performed. High performance liquid chromatography was used to identify phenolic acids. There was no solvent effect on TPC nor on scavenging activities, and inhibition of lipid peroxidation (p > 0.05) among solvent extracts. On the other hand, 1:1:3 water: acetone: methanol extract (10.22 mg GAE/g sample) had significantly higher reducing potential than 50% ethanol extract (EE) (9.259 mg GAE/g sample) (p < 0.05);but EE was not significantly different from 80% methanol extract (9.781 mg GAE/g sample) (p > 0.05). Phenolic fraction designated as fraction 4 had the highest antioxidant activity (p < 0.05) with 69.49% ABTS scavenging activity and FRAP reducing potential, 22.26 mg of GAE/g sample. DPPH scavenging activities of fractions 4 (55.59%) and 5 (55.64%) were significantly higher than the other fractions (p A. rigidula extracts contain gallic, caffeic, vanillic, p-coumaric, salicylic acids and vanillin.

Static Stretching Combined with Conscious Slower Breathing May Increase Parasympathetic Activity and Reduce Stress in Adult Women

Background: Women are thought to be more susceptible to stress than men in a stressful society, and reducing stress is crucial for women to maintain their health. Static stretching (SST) is applied in various fields to not only increase muscle flexibility but also reduce stress. Additionally, conscious slower breathing (CSB) predominates parasympathetic activity, causing a relaxing effect. These results indicate that combining SST and CSB may be more useful in reducing stress. However, to the best of our knowledge, the effect of this combination remains unclear. Objective: This study aimed to elucidate the effects of the combination of SST and CSB on autonomic activity and stress in adult women. Methods: Eleven healthy Japanese adult female participants performed SST with nonconscious natural breathing for 20 min. The same participants performed SST in combination with CSB (2 s inspiratory and 4 s expiratory) for 20 min on another day. Salivary cortisol and chromogranin A levels were measured before and after stretching as stress markers of the hypothalamic-pituitary-adrenal axis and sympathetic nervous system. The coefficient of variation of the R-R interval (CVR-R) and high-frequency component (HF), which reflect parasympathetic nerve activity, and heart rate and low-frequency component (LF)/HF ratio, which reflect sympathetic nerve activity, were measured before, during, and after stretching. Results: SST decreased cortisol levels but with no significant changes in chromogranin A, heart rate, CVR-R, HF, or LF/HF ratio. The combination of SST and CSB increased CVR-R and HF levels in addition to decreasing cortisol levels but with no significant changes in chromogranin A, heart rate, or LF/HF levels. Conclusion: These results indicate that the combination of SST and CSB may increase parasympathetic activity and reduce stress. However, future randomized controlled trials with larger sample sizes should support this conclusion.

Elevated brain temperature under severe heat exposure impairs cortical motor activity and executive function

Background:Excessive heat exposure can lead to hyperthermia in humans,which impairs physical performance and disrupts cognitive function.While heat is a known physiological stressor,it is unclear how severe heat stress affects brain physiology and function.Methods:Eleven healthy participants were subjected to heat stress from prolonged exercise or warm water immersion until their rectal temperatures(T_(re))attained 39.5℃,inducing exertional or passive hyperthermia,respectively.In a separate trial,blended ice was ingested before and during exercise as a cooling strategy.Data were compared to a control condition with seated rest(normothermic).Brain temperature(T_(br)),cerebral perfusion,and task-based brain activity were assessed using magnetic resonance imaging techniques.Results:T_(br)in motor cortex was found to be tightly regulated at rest(37.3℃±0.4℃(mean±SD))despite fluctuations in T_(re).With the development of hyperthermia,T_(br)increases and dovetails with the rising T_(re).Bilateral motor cortical activity was suppressed during high-intensity plantarflexion tasks,implying a reduced central motor drive in hyperthermic participants(T_(re)=38.5℃±0.1℃).Global gray matter perfusion and regional perfusion in sensorimotor cortex were reduced with passive hyperthermia.Executive function was poorer under a passive hyperthermic state,and this could relate to compromised visual processing as indicated by the reduced activation of left lateral-occipital cortex.Conversely,ingestion of blended ice before and during exercise alleviated the rise in both T_(re)and T_(bc)and mitigated heat-related neural perturbations.Conclusion:Severe heat exposure elevates T_(br),disrupts motor cortical activity and executive function,and this can lead to impairment of physical and cognitive performance.

A scoping review of methodologies for applying artificial intelligence to physical activity interventions

Purpose This scoping review aimed to offer researchers and practitioners an understanding of artificial intelligence(AI)applications in physical activity(PA)interventions;introduce them to prevalent machine learning(ML),deep learning(DL),and reinforcement learning(RL)algorithms;and encourage the adoption of AI methodologies.Methods A scoping review was performed in PubMed,Web of Science,Cochrane Library,and EBSCO focusing on AI applications for promoting PA or predicting related behavioral or health outcomes.AI methodologies were summarized and categorized to identify synergies,patterns,and trends informing future research.Additionally,a concise primer on predominant AI methodologies within the realm of PA was provided to bolster understanding and broader application.Results The review included 24 studies that met the predetermined eligibility criteria.AI models were found effective in detecting significant patterns of PA behavior and associations between specific factors and intervention outcomes.Most studies comparing AI models to traditional statistical approaches reported higher prediction accuracy for AI models on test data.Comparisons of different AI models yielded mixed results,likely due to model performance being highly dependent on the dataset and task.An increasing trend of adopting state-of-the-art DL and RL models over standard ML was observed,addressing complex human–machine communication,behavior modification,and decision-making tasks.Six key areas for future AI adoption in PA interventions emerged:personalized PA interventions,real-time monitoring and adaptation,integration of multimodal data sources,evaluation of intervention effectiveness,expanding access to PA interventions,and predicting and preventing injuries.Conclusion The scoping review highlights the potential of AI methodologies for advancing PA interventions.As the field progresses,staying informed and exploring emerging AI-driven strategies is essential for achieving significant improvements in PA interventions and fostering overall well-being.

Preparation,characterization and antioxidant activity analysis of three Maillard glycosylated bone collagen hydrolysates from chicken,porcine and bovine

Bone collagen hydrolysates(peptides)derived from byproduct of animal product processing have been used to produce commercially valuable products due to their potential antioxidant activity.Maillard glycosylated reaction is considered as a promising method to enhance the antioxidant activity of peptides.Hence,this research aims at investigating the Maillard glycosylation activity and antioxidant activity of bone collagen hydrolysates from different sources.In this study,3 glycosylated bone collagen hydrolysates were prepared and characterized,and cytotoxicity and antioxidant activity were analyzed and evaluated.The free amino groups loss,browning intensity,and fluorescence intensity of G-Cbcp(glycosylated chicken bone collagen hydrolysates(peptides))were the heaviest,followed by G-Pbcp(glycosylated porcine bone collagen hydrolysates(peptides))and G-Bbcp(glycosylated bovine bone collagen hydrolysates(peptides)).The results of amino acid analysis showed that amino acid composition of different bone collagen hydrolysates was significantly different and the amino acid decreased to different degrees after Maillard glycosylated reaction,which may lead to differences in Maillard glycosylated reaction activity.Furthermore,the 3 glycosylated hydrolysates showed no significant cytotoxicity.The results showed that glycosylation process significantly increased the antioxidant activity of bone collagen hydrolysates,and G-Cbcp showed the strongest antioxidant activity,followed by G-Pbcp and G-Bbcp.Therefore,compared with the bone collagen hydrolysates,3 glycosylated hydrolysates showed significant characteristic and structural changes,and higher antioxidant activity.

Diabetic Retinopathy: Diagnostic Challenges and Impact of Physical Activity

Introduction: Diabetes represents a major public health challenge, affecting over 463 million people according to the World Health Organization (WHO). Among its complications, diabetic retinopathy stands out as the leading cause of blindness in industrialized countries. Physical activity is now recognized as a cornerstone of diabetes management, and its impact on diabetic retinopathy is of growing interest. Observation: Mr. A.E, a 54-year-old man, presents with progressive deterioration of his vision. Family history of diabetes and hypertension is noted. He engages in one hour of physical activity daily, including treadmill running and indoor cycling. Despite recent optical correction, his visual acuity remains at 3/10 unimproved. Ocular imaging, notably fluorescein retinal angiography and optical coherence tomography, reveals proliferative diabetic retinopathy with bilateral macular edema. Blood tests, performed after physical activity, show normal values of blood glucose and HbA1c, delaying the initiation of treatment and promoting the progression of retinopathy. Despite appropriate management, no significant improvement in visual function is observed during follow-up. Conclusion: The management of diabetic retinopathy requires an approach integrating physical activity. A better understanding of the interaction between physical activity and diabetic retinopathy is needed to optimize prevention and treatment strategies.

Design of a Lightweight Compressed Video Stream-Based Patient Activity Monitoring System

Inpatient falls from beds in hospitals are a common problem.Such falls may result in severe injuries.This problem can be addressed by continuous monitoring of patients using cameras.Recent advancements in deep learning-based video analytics have made this task of fall detection more effective and efficient.Along with fall detection,monitoring of different activities of the patients is also of significant concern to assess the improvement in their health.High computation-intensive models are required to monitor every action of the patient precisely.This requirement limits the applicability of such networks.Hence,to keep the model lightweight,the already designed fall detection networks can be extended to monitor the general activities of the patients along with the fall detection.Motivated by the same notion,we propose a novel,lightweight,and efficient patient activity monitoring system that broadly classifies the patients’activities into fall,activity,and rest classes based on their poses.The whole network comprises three sub-networks,namely a Convolutional Neural Networks(CNN)based video compression network,a Lightweight Pose Network(LPN)and a Residual Network(ResNet)Mixer block-based activity recognition network.The compression network compresses the video streams using deep learning networks for efficient storage and retrieval;after that,LPN estimates human poses.Finally,the activity recognition network classifies the patients’activities based on their poses.The proposed system shows an overall accuracy of approx.99.7% over a standard dataset with 99.63% fall detection accuracy and efficiently monitors different events,which may help monitor the falls and improve the inpatients’health.

Discovery and structure-activity relationship studies of novel tetrahydro-β-carboline derivatives as apoptosis initiators for treating bacterial infections

Developing and excavating new agrochemicals with highly active and safe is an important tactic for protecting crop health and food safety.In this paper,to discover the new bactericide candidates,we designed,prepared a new type of1,2,3,4-tetrahydro-β-carboline(THC)derivatives and evaluated the in vitro and in vivo bioactivities against the Xanthomonas oryzae pv.oryzae(Xoo),Xanthomonas axonopodis pv.citri(Xac),and Pseudomonas syringae pv.actinidiae(Psa).The in vitro bioassay results exhibited that most title molecules possessed good activity toward the three plant pathogenic bacteria,the compound A17 showed the most active against Xoo and Xac with EC50 values of 7.27 and 4.89 mg mL^(-1)respectively,and compound A8 exhibited the best inhibitory activity against Psa with EC50value of 4.87 mg mL^(-1).Pot experiments showed that compound A17 exhibited excellent in vivo antibacterial activities to manage rice bacterial leaf blight and citrus bacterial canker,with protective efficiencies of 52.67 and 79.79%at 200 mgmL^(-1),respectively.Meanwhile,compound A8 showed good control efficiency(84.31%)against kiwifruit bacterial canker at 200 mg mL^(-1).Antibacterial mechanism suggested that these compounds could interfere with the balance of the redox system,damage the cell membrane,and induce the apoptosis of Xoo cells.Taken together,our study revealed that tetrahydro-β-carboline derivatives could be a promising candidate model for novel broadspectrum bactericides.

Strengthening Active Aging through Older People’s Association and Economic Activity of the Older People in Nepal

Aging is a natural lifelong process ending in death. Many older people are living in poverty. Older people are generally considered dependent on others as they grow older. The purpose of this article is to explore the entrepreneurship activities of Nepalese older adults. Data for this study were collected from the project Help Age International (HAI) implemented in Nepal. Qualitative data observations and interviews were used to collect data. The findings of this study show the formation of the Older People’s Association (OPA) has supported many older people to participate outside the home in various social activities. Moreover, regular deposits through OPAs offer little help. OPAs support older people in their need of financial support to implement minor entrepreneurship. Older people who received support were pleased and were actively involved in their activities and also regularly deposited money in them. Subsequently, older people’s participation in social activities has increased and also helped to lower elderly abuse, loneliness, and depression. Local governments should promote such activities which will help with healthy aging.

Dynamics of information diffusion and disease transmission in time-varying multiplex networks with asymmetric activity levels

While the interaction between information and disease in static networks has been extensively investigated,many studies have ignored the characteristics of network evolution.In this study,we construct a new two-layer coupling model to explore the interactions between information and disease.The upper layer describes the diffusion of disease-related information,and the lower layer represents the disease transmission.We then use power-law distributions to examine the influence of asymmetric activity levels on dynamic propagation,revealing a mapping relationship characterizing the interconnected propagation of information and diseases among partial nodes within the network.Subsequently,we derive the disease outbreak threshold by using the microscopic Markov-chain approach(MMCA).Finally,we perform extensive Monte Carlo(MC)numerical simulations to verify the accuracy of our theoretical results.Our findings indicate that the activity levels of individuals in the disease transmission layer have a more significant influence on disease transmission compared with the individual activity levels in the information diffusion layer.Moreover,reducing the damping factor can delay disease outbreaks and suppress disease transmission,while improving individual quarantine measures can contribute positively to disease control.This study provides valuable insights into policymakers for developing outbreak prevention and control strategies.

Activity Data and Emission Factor for Forestry and Other Land Use Change Subsector to Enhance Carbon Market Policy and Action in Malawi

Activity data and emission factors are critical for estimating greenhouse gas emissions and devising effective climate change mitigation strategies. This study developed the activity data and emission factor in the Forestry and Other Land Use Change (FOLU) subsector in Malawi. The results indicate that “forestland to cropland,” and “wetland to cropland,” were the major land use changes from the year 2000 to the year 2022. The forestland steadily declined at a rate of 13,591 ha (0.5%) per annum. Similarly, grassland declined at the rate of 1651 ha (0.5%) per annum. On the other hand, cropland, wetland, and settlements steadily increased at the rate of 8228 ha (0.14%);5257 ha (0.17%);and 1941 ha (8.1%) per annum, respectively. Furthermore, the results indicate that the “grassland to forestland” changes were higher than the “forestland to grassland” changes, suggesting that forest regrowth was occurring. On the emission factor, the results interestingly indicate that there was a significant increase in carbon sequestration in the FOLU subsector from the year 2011 to 2022. Carbon sequestration increased annually by 13.66 ± 0.17 tCO2 e/ha/yr (4.6%), with an uncertainty of 2.44%. Therefore, it can be concluded that there is potential for a Carbon market in Malawi.

Revealing the correlation between adsorption energy and activation energy to predict the catalytic activity of metal oxides for HMX using DFT

Traditional selection of combustion catalysis is time-consuming and labor-intensive.Theoretical calculation is expected to resolve this problem.The adsorption energy of HMX and O atoms on 13 metal oxides was calculated using DMol3,since HMX and O are key substances in decomposition process.And the relationship between the adsorption energy of HMX,O on metal oxides(TiO_(2),Al_(2)O_(3),PbO,CuO,Fe_(2)O_(3),Co_(3)O_(4),Bi_(2)O_(3),NiO)and experimental T30 values(time required for the decomposition depth of HMX to reach 30%)was depicted as volcano plot.Thus,the T30 values of other metal oxides was predicted based on their adsorption energy on volcano plot and validated by previous experimental data.Further,the adsorption energy of HMX on ZrO_(2)and MnO_(2)was predicted based on the linear relationship between surface energy and adsorption energy,and T30 values were estimated based on volcano plot.The apparent activation energy data of HMX/MgO,HMX/SnO_(2),HMX/ZrO_(2),and HMX/MnO_(2)obtained from DSC experiments are basically consistent with our predicted T30 values,indicating that it is feasible to predict the catalytic activity based on the adsorption calculation,and it is expected that these simple structural properties can predict adsorption energy to reduce the large quantities of computation and experiment cost.

Interaction between inflammatory bowel disease,physical activity,and myokines:Assessment of serum irisin levels

Inflammatory bowel disease(IBD),including Crohn’s disease and ulcerative colitis,showed a wide spectrum of intestinal and extra-intestinal manifestations,which rendered the patients physically inactive and impaired their quality of life.It has been found that physical activity is a non-pharmacological intervention that improves the quality of life for those patients.Irisin is one member of the myokines secreted by muscle contraction during exercise and could be used as an antiinflammatory biomarker in assessing the physical activity of IBD patients.In addition,experimental studies showed that exogenous irisin significantly decreased the inflammatory markers and the histological changes of the intestinal mucosa observed in experimental colitis.Furthermore,irisin produces changes in the diversity of the microbiota.Therefore,endogenous or exogenous irisin,via its anti-inflammatory effects,will improve the health of IBD patients and will limit the barriers to physical activity in patients with IBD.

Molecular Characterization and Antibacterial Activity of a G-Type Lysozyme in Yellow Drum(Nibea albiflora)

Lysozyme(EC3.2.1.17)plays an important role in the immune response;as a nonspecific immune factor,it can resist causative agents.Lysozyme can be divided into c-type and g-type in fish.In a previous study,through genome-wide association analysis,the g-type lysozyme gene,which is named NaLyg in yellow drum(Nibea albiflora),was found to be a key candidate gene for disease resistance in response to Vibrio harveyi infection.The cDNA of NaLyg was 1025 bp,including four exons and three introns,and its open reading frame(ORF)had a full-length of 582 bp,encoding 193 amino acids.NaLyg was found to be conserved during evolution through bioinformatic analyses.The NaLyg protein possessed a sugar binding domain and three catalytic sites,including Glu71,Asp84 and Asp101.Quantitative qRT-PCR results confirmed that NaLyg gene mRNA was visibly increased after V.harveyi infection.The NaLyg protein purified by prokaryotic expression killed some gram-negative bacterial pathogens by inducing cell wall destruction,including V.harveyi,Aeromonas hydrophila and Edwardsiella tarda.Moreover,the NaLyg protein killed two gram-positive bacteria,Bacillus subtilis and Staphylococcus aureus.Taken together,the experimental results suggested that the NaLyg protein of N.albiflora played an important role in fighting bacterial infections.

Construction Activity Analysis of Workers Based on Human Posture Estimation Information

Identifying workers’construction activities or behaviors can enable managers to better monitor labor efficiency and construction progress.However,current activity analysis methods for construction workers rely solely on manual observations and recordings,which consumes considerable time and has high labor costs.Researchers have focused on monitoring on-site construction activities of workers.However,when multiple workers are working together,current research cannot accu rately and automatically identify the construction activity.This research proposes a deep learning framework for the automated analysis of the construction activities of multiple workers.In this framework,multiple deep neural network models are designed and used to complete worker key point extraction,worker tracking,and worker construction activity analysis.The designed framework was tested at an actual construction site,and activity recognition for multiple workers was performed,indicating the feasibility of the framework for the automated monitoring of work efficiency.