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Analyzing the labor market and salary determinants for big data talent based on job advertisements in China

The demand for big data talent is rapidly increasing with the growth of the big data industry. However, there has been limited research on what employers seek in recruiting big data talent. This paper aims to apply labor market segmentation theories to the big data labor market and develop a theoretical framework to analyze the distribution of big data talent in different labor market segments. Furthermore, we develop a salary determination model to explain wage differentials. An empirical analysis is conducted using online job advertisements from a Chinese recruitment website to investigate the labor market for big data talent in China. Our findings show that there are significant differences in the demand for big data talent across different types of cities and industries. Different types of enterprises have different requirements for individual characteristics and offer various levels of big data job positions. Furthermore, our results reveal that individual, job-related and organizational characteristics are all significant predictors of salaries. These findings can provide particularly useful insights for organizations and managers in the big data industry.

Correction: The implementation and impact of non-invasive prenatal testing (NIPT) for Down’s syndrome into antenatal screening programmes: A systematic review and meta-analysis

Prediction of mechanical characteristics of shearer intelligent cables under bending conditions

The frequent bending of shearer cables during operation often leads to mechanical fatigue, posing risks to equipment safety. Accurately predicting the mechanical properties of these cables under bending conditions is crucial for improving the reliability and service life of shearers. This paper proposes a shearer optical fiber cable mechanical characteristics prediction model based on Temporal Convolutional Network (TCN), Bidirectional Long Short-Term Memory (BiLSTM), and Squeeze-and-Excitation Attention (SEAttention), referred to as the TCN-BiLSTM-SEAttention model. This method leverages TCN’s causal and dilated convolution operations to capture long-term sequential features, BiLSTM’s bidirectional information processing to ensure the completeness of sequence information, and the SEAttention mechanism to assign adaptive weights to features, effectively enhancing the focus on key features. The model’s performance is validated through comparisons with multiple other models, and the contributions of input features to the model’s predictions are quantified using Shapley Additive Explanations (SHAP). By learning the stress variation patterns between the optical fiber, power conductor, and control conductor in the shearer cable, the model enables accurate prediction of the stress in other cable conductors based on optical fiber stress data. Experiments were conducted using a shearer optical fiber cable bending simulation dataset with traction speeds of 6 m/min, 8 m/min, and 10 m/min. The results show that, compared to other predictive models, the proposed model achieves reductions in Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) to 0.0002, 0.0159, and 0.0126, respectively, with the coefficient of determination (R2) increasing to 0.981. The maximum deviation between predicted and actual values is only 0.86%, demonstrating outstanding prediction accuracy. SHAP feature analysis reveals that the control conductor features have the most substantial influence on predictions, with a SHAP value of 0.095. The research shows that the TCN-BiLSTM-SEAttention model demonstrates outstanding predictive capability under complex operating conditions, providing a novel approach for improving cable management and equipment safety through optical fiber monitoring technology in the intelligent development of coal mines, highlighting the potential of deep learning in complex mechanical predictions.

Effects of high-intensity interval training on physical performance, systolic blood pressure, oxidative stress and inflammatory markers in skeletal muscle of spontaneously hypertensive rats

Aim To investigate whether high-intensity interval training (HIIT) improves physical performance, systolic blood pressure, and markers of oxidative stress and inflammation in skeletal muscle of spontaneously hypertensive rats (SHR). Methods Nineteen male SHR rats were randomly assigned to two groups: sedentary (SHRC) and trained (SHR+T). The SHR+T group trained five times a week for eight weeks on a treadmill, while the SHR group remained without any exercise stimulus throughout the experimental period. Maximum physical performance and systolic blood pressure (SBP) were assessed before and after the training period. The following variables were measured in the tibialis anterior (TA) muscle: gene expression of the NADPH oxidase complex (NOX2, NOX4, p22phox, p47phox) and the NF-kB pathway (NF-kB and Ik-B), lipid peroxidation (malonaldehyde; MDA), protein carbonylation, hydrophilic antioxidant capacity (HAC) and pro-inflammatory cytokines (IL-6 and TNF-α). Results SHR+T rats showed higher physical performance and levels of IL-6, and lower SBP and protein carbonylation (p<0.05), compared with SHRC rats. No significant differences (p>0.05) were observed in the other variables. Significance Our results indicate that HIIT is an effective non-pharmacologic strategy to improve physical performance, reduce SBP, and modulate the skeletal muscle oxidative damage and inflammation in hypertensive rats.

Correction for Wu et al., NR2B subunit of the NMDA glutamate receptor regulates appetite in the parabrachial nucleus

Correction: From colorblind to systemic racism: Emergence of a rhetorical shift in higher education discourse in response to the murder of George Floyd

Correction for He et al., Structural insights into the assembly and energy transfer of haptophyte photosystem I–light-harvesting supercomplex

Fecal bacteria transplantation replicates aerobic exercise to reshape the gut microbiota in mice to inhibit high-fat diet-induced atherosclerosis

Aerobic exercise exerts a significant impact on the gut microbiota imbalance and atherosclerosis induced by a high-fat diet. However, whether fecal microbiota transplantation, based on aerobic exercise, can improve atherosclerosis progression remains unexplored. In this study, we utilized male C57 mice to establish models of aerobic exercise and atherosclerosis, followed by fecal microbiota transplantation(Fig 1a). Firstly, we analyzed the body weight, somatotype, adipocyte area, and aortic HE images of the model mice. Our findings revealed that high-fat diet -induced atherosclerosis mice exhibited elevated lipid accumulation, larger adipocyte area, and more severe atherosclerosis progression. Additionally, we assessed plasma lipid levels, inflammatory factors, and gut microbiota composition in each group of mice. high-fat diet -induced atherosclerosis mice displayed dyslipidemia along with inflammatory responses and reduced gut microbiota diversity as well as abundance of beneficial bacteria. Subsequently performing fecal microbiota transplantation demonstrated that high-fat diet -induced atherosclerosis mice experienced weight loss accompanied by reduced lipid accumulation while normalizing their gut microbiota profile; furthermore it significantly improved blood lipids and inflammation markers thereby exhibiting notable anti- atherosclerosis effects. The findings suggest that aerobic exercise can modify gut microbiota composition and improve high-fat diet-induced atherosclerosis(Fig 1b). Moreover, these beneficial effects can be effectively transmitted through fecal microbiota transplantation, offering a promising therapeutic approach for managing atherosclerosis.

Characteristics of Plasmodium vivax apicomplexan amino acid transporter 8 (PvApiAT8) in the cationic amino acid transport

High mortality rates and long-term complications in children with infectious brainstem encephalitis: A study of sixteen cases

Objective Brainstem encephalitis (BE) can cause sudden death in children. Fewer studies have been conducted on the incidence, clinical manifestations, pathogens and post-infectious sequelae of pediatric infectious BE. Methods Pediatric patients diagnosed with BE in our Medical Center from 01 January 2015 to 31 July 2024 were retrospectively reviewed. The clinical data of these children were obtained from the hospital’s medical database on 15 August 2024. The number of outpatient and inpatient patients at our Medical Center during that period were provided by the hospital data center. Data analysis was conducted using Excel 2019. Results A total of twenty-eight cases were diagnosed with BE in our National Children′s Medical Center over the past decade. Among them, 57.1% (16/28) cases were diagnosed with infectious BE. The incidence of infectious BE was estimated to be 16 cases per 30 million outpatient visits and 13 cases per 500,000 hospitalized patients. Fever, consciousness disorders and seizures were observed in 75.0% (12/16), 68.8% (11/16) and 62.5% (10/16) of the cases, respectively. Among them, 31.3% (5/16) cases were diagnosed as human enterovirus infections, 12.5% (2/16) cases were confirmed to be influenza B virus infections, while one case each was diagnosed with herpes simplex virus 1 and human herpesvirus 6 infection. The mortality rate during hospitalization was 12.5% (2/16). Among the surviving patients, 50.0% (7/14) of them had follow-up records, 85.7% (6/7) of the survivors suffered from sequelae such as motor disorders. Conclusion Fever, consciousness disorders and seizures were the major clinical manifestations in patients with infectious BE visited our Medical Center. These rare cases exhibited a notably high mortality rate and a significant frequency of long-term complications.

Author Correction: Calibration of miniature air quality detector monitoring data with PCA–RVM–NAR combination model

On the road to Mecca: Branding discourses and national identity on coffee shop signage

Commercial branding stands as a discursive and cultural facet of the contemporary global era where competing brands construct their own identities. From a discourse perspective, a brand is discursively constructed on commercial signs. Accordingly, this study examines the interplay between coffee shop branding and national identity in Saudi Arabia. In so doing, the study investigates the competing branding discourses associated with coffee as well as the space given to national identity. To achieve this task, the study developed a conceptual framework grounded on critical discourse analysis (CDA) and linguistic landscape (LL). The data consists of 88 commercial signs of coffee shops collected by driving on a road from Najran to Mecca, Saudi Arabia. The research site was then verified through Google Maps. The data built a communicative event for an empirical mixed-method research design. CDA linguistic and multimodal toolbox was utilized. The analysis showed that three names of coffee are found on the road to Mecca: qahwa (Arabic), coffee (English), and kufi (transliteration). With these names, four discourse are in competition. For globalization, English-Arabic glocal discourse (34%), and English global discourse (8%) are competing to construct coffee branding. For national identity, Arabic local discourse (42%) and Arabic-English glocal discourse (16%) are associated with qahwa; something that gives substantial space (58%) for national identity. These findings enhance our understanding of the linguistic and multimodal dimensions of globalized spaces and their discursive construction of branding at the local scale of globalization. The study recommends further research and suggests some cultural and pedagogical implications for authorities, translation, bilingual awareness, teaching, and learning.

Exploiting question-answer framework with multi-GRU to detect adverse drug reaction on social media

Correction for Humbert et al., Functional SARS-CoV-2 cross-reactive CD4 ⁺ T cells established in early childhood decline with age

The protein interactome of Escherichia coli carbohydrate metabolism

We investigate how protein-protein interactions (PPIs) can regulate carbohydrate metabolism in Escherichia coli. We specifically investigated the stoichiometry of 378 PPIs involving carbohydrate metabolic enzymes. In 48 interactions, the interactors were much more abundant than the enzyme and are thus likely to affect enzyme activity and carbohydrate metabolism. Many of these PPIs are conserved across thousands of bacteria including pathogens and microbial species. E. coli adapts to different cellular environments by adjusting the quantities of the interacting proteins (25 PPIs) in a way that the protein-enzyme interaction (PEI) is a likely mechanism to regulate its metabolism in specific environments. We predict 3 PPIs (RpsB-AdhE, DcyD-NanE and MinE-Yccx) previously not known to regulate metabolism.

PCR-based detection of Botryosphaeria canker pathogens in fig trees

Spatial risk modelling of highly pathogenic avian influenza in France: Fattening duck farm activity matters

In this study, we present a comprehensive analysis of the key spatial risk factors and predictive risk maps for HPAI infection in France, with a focus on the 2016–17 and 2020–21 epidemic waves. Our findings indicate that the most explanatory spatial predictor variables were related to fattening duck movements prior to the epidemic, which should be considered as indicators of farm operational status, e.g., whether they are active or not. Moreover, we found that considering the operational status of duck houses in nearby municipalities is essential for accurately predicting the risk of future HPAI infection. Our results also show that the density of fattening duck houses could be used as a valuable alternative predictor of the spatial distribution of outbreaks per municipality, as this data is generally more readily available than data on movements between houses. Accurate data regarding poultry farm densities and movements is critical for developing accurate mathematical models of HPAI virus spread and for designing effective prevention and control strategies for HPAI. Finally, our study identifies the highest risk areas for HPAI infection in southwest and northwest France, which is valuable for informing national risk-based strategies and guiding increased surveillance efforts in these regions.

Sulfur partitioning between aqueous fluids and felsic melts at high pressures: Implications for sulfur migration in subduction zones

A comprehensive analysis of time investment in skid trail planning for forest access

Properly planned skid trails form an important basis for sustainable timber production. They affect cost-effectiveness and the environmental impact of the harvesting process to a large extent. Here, we conducted an economic analysis to understand the skid trail planning process and to generate an initial model to estimate the time and costs involved. We investigated in detail how the planning process of skid trails is carried out in practice, what time is required for the planning work, and what factors influence its performance. Through an online survey conducted in 2022, we asked practitioners in Germany and Switzerland about their time and effort required for the planning process and the determining factors, such as the planning method and the terrain and stand conditions. Based on this survey, we calculated statistical indicators of time consumption, considered possible rationalization options, and developed an initial estimation model. The effort required to identify and evaluate skid trails planned for distances of 20 to 40 m amounts to around 3 to 4 hours of productive working time per hectare, with deviations expected depending on the specific situation in the forest. The costs corresponding to this investment amount to less than one euro per cubic meter of harvested timber, depending primarily on the extent of wood use. Our in-depth insight into the planning process enables its economic evaluation and the development of improvements.

Automatic cervical lymph nodes detection and segmentation in heterogeneous computed tomography images using deep transfer learning