ショウコウ ホウゲン ノ ソンザイ ヒョウゲン Ⅴダ Ｖドン Ｖハン

中国紹興方言において、動作の結果として具体物が特定の空間領域に存在することを表す文法形式は“V带”“V咚”“V亨”の三体系に分かれている。本稿ではその三者の使い分けに関して、新たに「具体物へのアクセスに対する制御権」という基準を提唱する。その基準から見ると、話し手が制御権を持つ領域での存在に“带”、聞き手が制御権を持つ領域での存在に“咚”、話し手も聞き手も制御権を持たない領域での存在に“亨”が用いられているということが分かる。「制御権の帰属」を決定づける要素に対しても考察を行い、「物理的距離」「具体物へのアクセスの主張や付与」「具体物の位置情報の帰属先」の要素を挙げる

KLF13 sustains thymic memory-like CD8+ T cells in BALB/c mice by regulating IL-4–generating invariant natural killer T cells

Transcription factor KLF13 regulates the elevated numbers of iNKT cells in the BALB/c versus C57BL/6 thymus that results in production of sufficient levels of IL-4 to generate memory-like CD8+ T cells

Abnormalities of White Matter Microstructure in Unmedicated Obsessive-Compulsive Disorder and Changes after Medication

BACKGROUND: Abnormalities of myelin integrity have been reported in obsessive-compulsive disorder (OCD) using multi-parameter maps of diffusion tensor imaging (DTI). However, it was still unknown to what degree these abnormalities might be affected by pharmacological treatment. OBJECTIVE: To investigate whether the abnormalities of white matter microstructure including myelin integrity exist in OCD and whether they are affected by medication. METHODOLOGY AND PRINCIPAL FINDINGS: Parameter maps of DTI, including fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD), were acquired from 27 unmedicated OCD patients (including 13 drug-naïve individuals) and 23 healthy controls. Voxel-based analysis was then performed to detect regions with significant group difference. We compared the DTI-derived parameters of 15 patients before and after 12-week Selective Serotonin Reuptake Inhibitor (SSRI) therapies. Significant differences of DTI-derived parameters were observed between OCD and healthy groups in multiple structures, mainly within the fronto-striato-thalamo-cortical loop. An increased RD in combination with no change in AD among OCD patients was found in the left medial superior frontal gyrus, temporo-parietal lobe, occipital lobe, striatum, insula and right midbrain. There was no statistical difference in DTI-derived parameters between drug-naive and previously medicated OCD patients. After being medicated, OCD patients showed a reduction in RD of the left striatum and right midbrain, and in MD of the right midbrain. CONCLUSION: Our preliminary findings suggest that abnormalities of white matter microstructure, particularly in terms of myelin integrity, are primarily located within the fronto-striato-thalamo-cortical circuit of individuals with OCD. Some abnormalities may be partly reversed by SSRI treatment

YesWorkflow:A User-Oriented, Language-Independent Tool for Recovering Workflow Information from Scripts

Scientific workflow management systems offer features for composing complex computational pipelines from modular building blocks, for executing the resulting automated workflows, and for recording the provenance of data products resulting from workflow runs. Despite the advantages such features provide, many automated workflows continue to be implemented and executed outside of scientific workflow systems due to the convenience and familiarity of scripting languages (such as Perl, Python, R, and MATLAB), and to the high productivity many scientists experience when using these languages. YesWorkflow is a set of software tools that aim to provide such users of scripting languages with many of the benefits of scientific workflow systems. YesWorkflow requires neither the use of a workflow engine nor the overhead of adapting code to run effectively in such a system. Instead, YesWorkflow enables scientists to annotate existing scripts with special comments that reveal the computational modules and dataflows otherwise implicit in these scripts. YesWorkflow tools extract and analyze these comments, represent the scripts in terms of entities based on the typical scientific workflow model, and provide graphical renderings of this workflow-like view of the scripts. Future versions of YesWorkflow also will allow the prospective provenance of the data products of these scripts to be queried in ways similar to those available to users of scientific workflow systems

Light-based technologies for management of COVID-19 pandemic crisis.

The global dissemination of the novel coronavirus disease (COVID-19) has accelerated the need for the implementation of effective antimicrobial strategies to target the causative agent SARS-CoV-2. Light-based technologies have a demonstrable broad range of activity over standard chemotherapeutic antimicrobials and conventional disinfectants, negligible emergence of resistance, and the capability to modulate the host immune response. This perspective article identifies the benefits, challenges, and pitfalls of repurposing light-based strategies to combat the emergence of COVID-19 pandemic

Incorporating multimodal context information into traffic speed forecasting through graph deep learning

Accurate traffic speed forecasting is a prerequisite for anticipating future traffic status and increasing the resilience of intelligent transportation systems. However, most studies ignore the involvement of context information ubiquitously distributed over the urban environment to boost speed prediction. The diversity and complexity of context information also hinder incorporating it into traffic forecasting. Therefore, this study proposes a multimodal context-based graph convolutional neural network (MCGCN) model to fuse context data into traffic speed prediction, including spatial and temporal contexts. The proposed model comprises three modules, ie (a) hierarchical spatial embedding to learn spatial representations by organizing spatial contexts from different dimensions, (b) multivariate temporal modeling to learn temporal representations by capturing dependencies of multivariate temporal contexts and (c) attention-based multimodal fusion to integrate traffic speed with the spatial and temporal context representations for multi-step speed prediction. We conduct extensive experiments in Singapore. Compared to the baseline model (spatial-temporal graph convolutional network, STGCN), our results demonstrate the importance of multimodal contexts with the mean-absolute-error improvement of 0.29 km/h, 0.45 km/h and 0.89 km/h in 30-min, 60-min and 120-min speed prediction, respectively. We also explore how different contexts affect traffic speed forecasting, providing references for stakeholders to understand the relationship between context information and transportation systems.ISSN:1362-3087ISSN:1365-881

Identification of Wiener Model with Internal Noise Using a Cubic Spline Approximation-Bayesian Composite Quantile Regression Algorithm

A cubic spline approximation-Bayesian composite quantile regression algorithm is proposed to estimate parameters and structure of the Wiener model with internal noise. Firstly, an ARX model with a high order is taken to represent the linear block; meanwhile, the nonlinear block (reversibility) is approximated by a cubic spline function. Then, parameters are estimated by using the Bayesian composite quantile regression algorithm. In order to reduce the computational burden, the Markov Chain Monte Carlo algorithm is introduced to calculate the expectation of parameters’ posterior distribution. To determine the structure order, the Final Output Error and the Akaike Information Criterion are used in the nonlinear block and the linear block, respectively. Finally, a numerical simulation and an industrial case verify the effectiveness of the proposed algorithm