Effects of mask fitness and worker education on the prevention of occupational dust exposure

To decrease the incidence of pneumoconiosis, we examined dust protective mask performance and its relation to pulmonary function as well as the effects of worker education on the proper wearing of masks. One hundred and seventy-eight workers from 15 factories subject to dust exposure participated in this study. All participants were interviewed to obtain relevant personal information and underwent both a mask leakage and a pulmonary function test. The mask leakage was expressed as a percentage, with under 10% leakage indicating that the dust protective mask worked efficiently. In addition, 23 workers from 2 factories were educated on how to wear masks properly. The average mask leakage was 24.3%, and 58% of workers wore ineffective masks. Though pulmonary function was almost normal, the percent vital capacity (%VC) tended to be lower depending on the mask leakage. Mask education, which was very easy and took only a short time, dramatically decreased average mask leakage from 32.1% to 10.5% (p0.001). Educating workers to wear masks properly might prevent the worsening of pulmonary function in response to dust exposure. Appropriate mask fitness by education could be useful in preventing the development of pneumoconiosis.</p

Development of a Vertex Finding Algorithm using Recurrent Neural Network

Deep learning is a rapidly-evolving technology with possibility to significantly improve physics reach of collider experiments. In this study we developed a novel algorithm of vertex finding for future lepton colliders such as the International Linear Collider. We deploy two networks; one is simple fully-connected layers to look for vertex seeds from track pairs, and the other is a customized Recurrent Neural Network with an attention mechanism and an encoder-decoder structure to associate tracks to the vertex seeds. The performance of the vertex finder is compared with the standard ILC reconstruction algorithm.Comment: 8 pages, 8 figures, preliminary version currently under review by IL

Interferon-γ induced expression of MHC antigens facilitates identification of donor cells in chimeric transplant recipients

After whole organ transplantation, donor bone marrow-derived cells migrate out of the graft into the recipient, leading to establishment of chimerism, which is the first step towards the subsequent induction of donor-specific tolerance. In routine immunohistochemical staining, monoclonal antibodies specific for heterotopic MHC alleles are used to identify donor and recipient cells. However, it is difficult to detect these cells using this technique in long-term allograft recipients who have a persistently low donor cell population (microchimerism). Because Interferon-gamma (IFN-γ) is known to induce expression of MHC class I and class II cell surface molecules, we used this cytokine 12-48 h before sacrifice, to facilitate the identification of donor and recipient cells in the tissues of animals transplanted with either liver (B10 → C3H) or bone marrow (LEW → BN). In long-term allograft recipients, the use of IFN-γ for as briefly as 12 h prior to sacrifice, results in marked upregulation of class I and class II antigens, leading to easy identification of ubiquitously distributed low numbers of donor cells. © 1994

The effect of duration of illness and antipsychotics on subcortical volumes in schizophrenia: Analysis of 778 subjects

BackgroundThe effect of duration of illness and antipsychotic medication on the volumes of subcortical structures in schizophrenia is inconsistent among previous reports. We implemented a large sample analysis utilizing clinical data from 11 institutions in a previous meta-analysis.MethodsImaging and clinical data of 778 schizophrenia subjects were taken from a prospective meta-analysis conducted by the COCORO consortium in Japan. The effect of duration of illness and daily dose and type of antipsychotics were assessed using the linear mixed effect model where the volumes of subcortical structures computed by FreeSurfer were used as a dependent variable and age, sex, duration of illness, daily dose of antipsychotics and intracranial volume were used as independent variables, and the type of protocol was incorporated as a random effect for intercept. The statistical significance of fixed-effect of dependent variable was assessed.ResultsDaily dose of antipsychotics was positively associated with left globus pallidus volume and negatively associated with right hippocampus. It was also positively associated with laterality index of globus pallidus. Duration of illness was positively associated with bilateral globus pallidus volumes. Type of antipsychotics did not have any effect on the subcortical volumes.DiscussionA large sample size, uniform data collection methodology and robust statistical analysis are strengths of the current study. This result suggests that we need special attention to discuss about relationship between subcortical regional brain volumes and pathophysiology of schizophrenia because regional brain volumes may be affected by antipsychotic medication

Improving topic modeling through homophily for legal documents

International audienceTopic modeling that can automatically assign topics to legal documents is very important in the domain of computational law. The relevance of the modeled topics strongly depends on the legal context they are used in. On the other hand, references to laws and prior cases are key elements for judges to rule on a case. Taken together, these references form a network, whose structure can be analysed with network analysis. However, the content of the referenced documents may not be always accessed. Even in that case, the reference structure itself shows that documents share latent similar characteristics. We propose to use this latent structure to improve topic modeling of law cases using document homophily. In this paper, we explore the use of homophily networks extracted from two types of references: prior cases and statute laws, to enhance topic modeling on legal case documents. We conduct in detail, an analysis on a dataset consisting of rich legal cases, i.e., the COLIEE dataset, to create these networks. The homophily networks consist of nodes for legal cases, and edges with weights for the two families of references between the case nodes. We further propose models to use the edge weights for topic modeling. In particular, we propose a cutting model and a weighting model to improve the relational topic model (RTM). The cutting model uses edges with weights higher than a threshold as document links in RTM; the weighting model uses the edge weights to weight the link probability function in RTM. The weights can be obtained either from the co-citations or from the cosine similarity based on an embedding of the homophily networks. Experiments show that the use of the homophily networks for topic modeling significantly outperforms previous studies, and the weighting model is more effective than the cutting model