The Japanese model in retrospective : industrial strategies, corporate Japan and the 'hollowing out' of Japanese industry

This article provides a retrospective look at the Japanese model of industrial development. This model combined an institutional approach to production based around the Japanese Firm (Aoki's, J-mode) and strategic state intervention in industry by the Japanese Ministry of International Trade and Industry (MITI). For a long period, the alignment of state and corporate interests appeared to match the wider public interest as the Japanese economy prospered. However, since the early 1990s, the global ambitions of the corporate sector have contributed to a significant 'hollowing out' of Japan's industrial base. As the world today looks for a new direction in economic management, we suggest the Japanese model provides policy-makers with a salutary lesson in tying the wider public interest with those of the corporate sector

Fertility, Living Arrangements, Care and Mobility

There are four main interconnecting themes around which the contributions in this book are based. This introductory chapter aims to establish the broad context for the chapters that follow by discussing each of the themes. It does so by setting these themes within the overarching demographic challenge of the twenty-first century – demographic ageing. Each chapter is introduced in the context of the specific theme to which it primarily relates and there is a summary of the data sets used by the contributors to illustrate the wide range of cross-sectional and longitudinal data analysed

Positive-strand RNA viruses—a Keystone Symposia report

Positive-strand RNA viruses have been the cause of several recent outbreaks and epidemics, including the Zika virus epidemic in 2015, the SARS outbreak in 2003, and the ongoing SARS-CoV-2 pandemic. On June 18–22, 2022, researchers focusing on positive-strand RNA viruses met for the Keystone Symposium “Positive-Strand RNA Viruses” to share the latest research in molecular and cell biology, virology, immunology, vaccinology, and antiviral drug development. This report presents concise summaries of the scientific discussions at the symposium

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data

Interphase fluid-particle mass transport at low Reynolds numbers

10.1007/BF00813687Catalysis Letters301-4213-217CALE

Geostatistical analysis of the spatial distribution of mycotoxin concentration in bulk cereals

Deoxynivalenol (DON) and ochratoxin A (OTA) in agricultural commodities present hazards to human and animal health. Bulk lots are routinely sampled for their presence, but it is widely acknowledged that designing sampling plans is particularly problematical because of the heterogeneous distribution of the mycotoxins. Previous studies have not take samples from bulk. Sampling plans are therefore designed on the assumption of random distributions. The objective of this study was to analyse the spatial distribution of DON and OTA in bulk commodities with geostatistics. This study was the first application of geostatistical analysis to data on mycotoxins contamination of bulk commodities. Data sets for DON and OTA in bulk storage were collected from the literature and personal communications, of which only one contained data suitable for geostatistical analysis. This data set represented a 26-tonne truck of wheat with a total of 100 sampled points. The mean concentrations of DON and OTA were 1342 and 0.59 mu g kg(-1), respectively. The results showed that DON presented spatial structure, whilst OTA was randomly distributed in space. This difference between DON and OTA probably reflected the fact that DON is produced in the field, whereas OTA is produced in storage. The presence of spatial structure for DON implies that sampling plans need to consider the location of sample points in addition to the number of points sampled in order to obtain reliable estimates of quantities such as the mean contamination