Use of low-dose computed tomography to assess pulmonary tuberculosis among healthcare workers in a tuberculosis hospital

BACKGROUND: According to the World Health Organization, China is one of 22 countries with serious tuberculosis (TB) infections and one of the 27 countries with serious multidrug-resistant TB strains. Despite the decline of tuberculosis in the overall population, healthcare workers (HCWs) are still at a high risk of infection. Compared with high-income countries, the TB prevalence among HCWs is higher in low- and middle-income countries. Low-dose computed tomography (LDCT) is becoming more popular due to its superior sensitivity and lower radiation dose. However, there have been no reports about active pulmonary tuberculosis (PTB) among HCWs as assessed with LDCT. The purposes of this study were to examine PTB statuses in HCWs in hospitals specializing in TB treatment and explore the significance of the application of LDCT to these workers. METHODS: This study retrospectively analysed the physical examination data of healthcare workers in the Beijing Chest Hospital from September 2012 to December 2015. Low-dose lung CT examinations were performed in all cases. The comparisons between active and inactive PTB according to the CT findings were made using the Pearson chi-square test or the Fisher’s exact test. Comparisons between the incidences of active PTB in high-risk areas and non-high-risk areas were performed using the Pearson chi-square test. Analyses of active PTB were performed according to different ages, numbers of years on the job, and the risks of the working areas. Active PTB as diagnosed by the LDCT examinations alone was compared with the final comprehensive diagnoses, and the sensitivity and positive predictive value were calculated. RESULTS: A total of 1 012 participants were included in this study. During the 4-year period of medical examinations, active PTB was found in 19 cases, and inactive PTB was found in 109 cases. The prevalence of active PTB in the participants was 1.24%, 0.67%, 0.81%, and 0.53% for years 2012 to 2015. The corresponding incidences of active PTB among the tuberculosis hospital participants were 0.86%, 0.41%, 0.54%, and 0.26%. Most HCWs with active TB (78.9%, 15/19) worked in the high-risk areas of the hospital. There was a significant difference in the incidences of active PTB between the HCWs who worked in the high-risk and non-high-risk areas (odds ratio [OR], 14.415; 95% confidence interval (CI): 4.733 – 43.896). Comparisons of the CT signs between the active and inactive groups via chi-square tests revealed that the tree-in-bud, cavity, fibrous shadow, and calcification signs exhibited significant differences (P = 0.000, 0.021, 0.001, and 0.024, respectively). Tree-in-bud and cavity opacities suggest active pulmonary tuberculosis, whereas fibrous shadow and calcification opacities are the main features of inactive pulmonary tuberculosis. Comparison with the final comprehensive diagnoses revealed that the sensitivity and positive predictive value of the diagnoses of active PTB based on LDCT alone were 100% and 86.4%, respectively. CONCLUSIONS: Healthcare workers in tuberculosis hospitals are a high-risk group for active PTB. Yearly LDCT examinations of such high-risk groups are feasible and necessary. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40249-017-0274-6) contains supplementary material, which is available to authorized users

Optimization of Fermentation Medium for the Production of Atrazine Degrading Strain Acinetobacter

Statistical experimental designs provided by statistical analysis system (SAS) software were applied to optimize the fermentation medium composition for the production of atrazine-degrading Acinetobacter sp. DNS32 in shake-flask cultures. A “Plackett-Burman Design” was employed to evaluate the effects of different components in the medium. The concentrations of corn flour, soybean flour, and K2HPO4 were found to significantly influence Acinetobacter sp. DNS32 production. The steepest ascent method was employed to determine the optimal regions of these three significant factors. Then, these three factors were optimized using central composite design of “response surface methodology.” The optimized fermentation medium composition was composed as follows (g/L): corn flour 39.49, soybean flour 25.64, CaCO3 3, K2HPO4 3.27, MgSO4 ·7H2O 0.2, and NaCl 0.2. The predicted and verifiable values in the medium with optimized concentration of components in shake flasks experiments were 7.079×108 CFU/mL and 7.194×108 CFU/mL, respectively. The validated model can precisely predict the growth of atrazine-degraing bacterium, Acinetobacter sp. DNS32

Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition

Emotion recognition based on Electroencephalography (EEG) has gained significant attention and diversified development in fields such as neural signal processing and affective computing. However, the unique brain anatomy of individuals leads to non-negligible natural differences in EEG signals across subjects, posing challenges for cross-subject emotion recognition. While recent studies have attempted to address these issues, they still face limitations in practical effectiveness and model framework unity. Current methods often struggle to capture the complex spatial-temporal dynamics of EEG signals and fail to effectively integrate multimodal information, resulting in suboptimal performance and limited generalizability across subjects. To overcome these limitations, we develop a Pre-trained model based Multimodal Mood Reader for cross-subject emotion recognition that utilizes masked brain signal modeling and interlinked spatial-temporal attention mechanism. The model learns universal latent representations of EEG signals through pre-training on large scale dataset, and employs Interlinked spatial-temporal attention mechanism to process Differential Entropy(DE) features extracted from EEG data. Subsequently, a multi-level fusion layer is proposed to integrate the discriminative features, maximizing the advantages of features across different dimensions and modalities. Extensive experiments on public datasets demonstrate Mood Reader's superior performance in cross-subject emotion recognition tasks, outperforming state-of-the-art methods. Additionally, the model is dissected from attention perspective, providing qualitative analysis of emotion-related brain areas, offering valuable insights for affective research in neural signal processing.Comment: Accepted by International Conference on Neural Computing for Advanced Applications, 202

Sex-Specific Variation of Social Play in Wild Immature Tibetan Macaques, \u3ci\u3eMacaca thibetana\u3c/i\u3e

Theories proposed to explain social play have centered on its function in establishing social relationships critical for adulthood, its function in developing motor skills needed to survive, and promoting cognitive development and social learning. In this study, we compared variations in social play among infant and juvenile male and female Macaca thibetana. Given that this species is characterized by female philopatry and male dispersal, we hypothesized that immature females use social play as a mechanism to develop bonds that persist through adulthood whereas immature males use play to develop social skills needed to successfully enter new groups. The results indicated that social play steadily increased during the infant period and peaked at approximately 12 months of age. There were no significant differences in the frequency or types of social play exhibited between infant males and infant females. During the juvenile period, however, social play was found to decrease with age, with males engaging in social play more frequently than juvenile females. Moreover, whereas juvenile males engaged in more aggressive forms of play, juvenile females engaged in more affiliative forms of play. In addition, juvenile females engaged in higher rates of grooming than juvenile males. These results provide evidence of sex-specific differences and imply the functional variation of social play in Tibetan macaques, with immature males using social play to develop skills needed to enter and enhanced their dominance rank in a new social group and immature females using social play to develop long-term same-sex social bonds in their natal group

Homology-Driven Proteomics of Dinoflagellates with Unsequenced Genomes Using MALDI-TOF/TOF and Automated De Novo Sequencing

This study developed a multilayered, gel-based, and underivatized strategy for de novo protein sequence analysis of unsequenced dinoflagellates using a MALDI-TOF/TOF mass spectrometer with the assistance of DeNovo Explorer software. MASCOT was applied as the first layer screen to identify either known or unknown proteins sharing identical peptides presented in a database. Once the confident identifications were removed after searching against the NCBInr database, the remainder was searched against the dinoflagellate expressed sequence tag database. In the last layer, those borderline and nonconfident hits were further subjected to de novo interpretation using DeNovo Explorer software. The de novo sequences passing a reliability filter were subsequently submitted to nonredundant MS-BLAST search. Using this layer identification method, 216 protein spots representing 158 unique proteins out of 220 selected protein spots from Alexandrium tamarense, a dinoflagellate with unsequenced genome, were confidently or tentatively identified by database searching. These proteins were involved in various intracellular physiological activities. This study is the first effort to develop a completely automated approach to identify proteins from unsequenced dinoflagellate databases and establishes a preliminary protein database for various physiological studies of dinoflagellates in the future

Effects of Hierarchical Steepness on Grooming Patterns in Female Tibetan Macaques (\u3ci\u3eMacaca thibetana\u3c/i\u3e)

Hierarchical steepness, defined as status asymmetries among conspecifics living in the same group, is not only used as a main characteristic of animal social relationships, but also represents the degree of discrepancy between supply and demand within the framework of biological market theory. During September and December 2011, we studied hierarchical steepness by comparing variation in grooming patterns in two groups of Tibetan macaques (Macaca thibetana), a primate species characterized by a linear dominance hierarchy. Using a focal sampling method, we collected behavioral data from two provisioned, free-ranging groups (YA1 and YA2) at Mt. Huangshan, China. We found that female dominance hierarchies were steeper in the YA1 group (0.81 based on the proportion of wins-losses and 0.66 based on dyadic dominance indices) than among members of the YA2 group (0.76 based on the proportion of wins-losses and 0.56 based on dyadic dominance indices). Females in the YA1 group groomed more frequently and for longer duration than females in YA2. Further analysis showed that grooming patterns of high- and low-ranking females did not differ between the two groups. However, middle-ranking females in YA1 groomed conspecifics more frequently and for longer duration than middle-ranking females in YA2. Our results suggest that the steepness of a dominance hierarchy plays an important role in the set of social strategies used by middle-ranking females to avoid a reduction in rank, as well as to increase their rank (the dilemma of middle class hypothesis). We suggest that future studies focus on individuals of middle-rank in order to better understand how the dynamics of rank stability and rank changes influence social relationships, and affiliative and competitive interactions in non-human primates

Multi-level temporal autoregressive modelling of daily activity satisfaction using GPS-integrated activity diary data

In this research, we match web-based activity diary data with daily mobility information recorded by GPS trackers for a sample of 709 residents in a 7-day survey in Beijing in 2012 to investigate activity satisfaction. Given the complications arising from the irregular time intervals of GPS-integrated diary data and the associated complex dependency structure, a direct application of standard (spatial) panel data econometric approaches is inappropriate. This study develops a multi-level temporal autoregressive modelling approach to analyse such data, which conceptualises time as continuous and examines sequential correlations via a time or space-time weights matrix. Moreover, we manage to simultaneously model individual heterogeneity through the inclusion of individual random effects, which can be treated flexibly either as independent or dependent. Bayesian Markov chain Monte Carlo (MCMC) algorithms are developed for model implementation. Positive sequential correlations and individual heterogeneity effects are both found to be statistically significant. Geographical contextual characteristics of sites where activities take place are significantly associated with daily activity satisfaction, controlling for a range of situational characteristics and individual socio-demographic attributes. Apart from the conceivable urban planning and development implications of our study, we demonstrate a novel statistical methodology for analysing semantic GPS trajectory data in general

Characterization of particulate organic matters in the water column of the South China Sea using a shotgun proteomic approach

We characterized particulate organic matter (POM) collected from both the surface (41 m and 200 m) and mesopelagic layers (500 m and 1000 m) in the western South China Sea. By using a shotgun proteomic approach, a total of 3035 proteins matching one or more peptides were detected from four POM samples, 505 of which were identified as high-confidence proteins matching two or more peptides. Cyanobacteria was the largest contributor throughout the water column, while crustaceans and dinophytes were the two major groups contributing to the particulate proteins in the POM collected from 200 m. Subcellular locations and biological processes of particulate proteins varied significantly between the 41-m and 200-m layers: photosynthesis-associated proteins were highly abundant in the 41-m layer while tubulins and actins accumulated in the midwaters, especially at the 200-m layer. Porins, adenosine triphosphate synthases, nutrient transporters, molecular chaperones, and ectoenzymes were frequently detected in the POM samples and presented different distribution patterns within the water column, revealing complex biological processes at the different water layers and/or during the sinking of POM. The sources of surface and midwater particulate proteins are different, and the cellular metabolism, generation of energy, and transport processes in POM are attenuated rapidly down ocean water column. Zooplankton fecal pellet packages and membrane encapsulation might play important roles in protecting particulate proteins from degradation.Ministry of Science and Technology [2009CB421203]; National Natural Science Foundation of China [40821063, 40776068, 40876059]; Program for New Century Excellent Talents in University of Chin

Superconducting switching jump induced missing first Shapiro step in Al-InSb nanosheet Josephson junctions

The absence of odd-order Shapiro steps is one of the predicted signatures for topological superconductors. Experimentally, the missing first-order Shapiro step has been reported in several superconducting systems presumably to be topologically non-trivial, as well as in the topologically trivial regime of superconductor-semiconductor Josephson junctions. In this work, we revisit the missing first Shapiro step signature in the topologically trivial regime of Al-InSb nanosheet Josephson junctions under microwave irradiation. The missing first Shapiro step is found to be accompanied by a sharp voltage jump during the superconducting switching and reappears when the jump is softened by increasing temperature or magnetic field. The missing first Shapiro step also reappears with an increased microwave frequency. The sharp switching jump, existing without microwave irradiation, deviates from the relation given by the standard resistively shunted junction (RSJ) model. Missing Shapiro step signatures are qualitatively captured by introducing the sharp voltage jump into the RSJ model. This work reveals a common, yet overlooked, phenomenon that leads to the missing first Shapiro step, providing a new perspective on fractional Josephson experiments

Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning

The performance of deep learning-based methods strongly relies on the number of datasets used for training. Many efforts have been made to increase the data in the medical image analysis field. However, unlike photography images, it is hard to generate centralized databases to collect medical images because of numerous technical, legal, and privacy issues. In this work, we study the use of federated learning between two institutions in a real-world setting to collaboratively train a model without sharing the raw data across national boundaries. We quantitatively compare the segmentation models obtained with federated learning and local training alone. Our experimental results show that federated learning models have higher generalizability than standalone training.Comment: Accepted by MICCAI DCL Workshop 202