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Reported are results of 2 public opinion polls in the former Soviet Union commissioned by the US Information Agency in Aug 1990 & Feb 1991. The first survey (N = 2,504 respondents [Rs]), conducted a year before the attempted coup by communist hardliners, showed little public confidence for the Soviet regime, disillusionment with the economic system, & wide support for political pluralism & democratic practices. The 1991 poll (N = 2,263 Rs), conducted a few months before Boris Yeltsin's election as president, indicated greater support for Yeltsin over Mikhail Gorbachev, prominently among nonconservative segments of the population. The correlation between these opinion polls & political developments is discussed. 6 Tables, 1 Figure, 1 Appendix, 27 References.

Introduction & BackgroundWith the advent of ubiquitous sensors and mobile technologies, wearables and smartphones offer a cost-effective means for monitoring mental health conditions, particularly depression. These devices enable the continuous collection of behavioral data, providing novel insights into the daily manifestations of depressive symptoms.
Objectives & ApproachThe present study summarizes findings from our five recent investigations that explored the relationships between depression severity and digital biomarkers captured by wearables and smartphones. These studies analyzed data from RADAR-MDD, a multinational mobile health program, involving 623 participants and tracked for up to two years. Participants' depression severity was measured biweekly using the PHQ-8 questionnaire conducted via smartphones. Concurrently, participants' Fitbit and smartphone data were also collected. Given the longitudinal nature and repeated measurements for each participant, multilevel modeling techniques were employed to analyze the data.
Relevance to Digital FootprintsOur approach involved extracting features from passive data that reflect various aspects of daily behavior—such as sleep quality, social interaction, physical activity, and walking patterns—akin to digital footprints.
ResultsWe found several significant links between depression severity and various behavioral biomarkers: elevated depression levels were associated with diminished sleep quality (assessed through Fitbit metrics), reduced sociability (approximated by Bluetooth), decreased levels of physical activity (quantified by step counts and GPS data), a slower cadence of daily walking (captured by smartphone accelerometers), and disturbances in circadian rhythms (analyzed across various data streams).
Conclusions & ImplicationsLeveraging digital biomarkers for assessing and continuously monitoring depression introduces a new paradigm in early detection and development of customized intervention strategies. Findings from these studies not only enhance our comprehension of depression in real-world settings but also underscore the potential of mobile technologies in the prevention and management of mental health issues.

ABSTRACT
ObjectivesClinical text de-identification is a common requirement of the 'enclave' governance model of ethical EHR research. However, there is often little consideration of the engineering task that is required to scale these approaches across the hundreds of millions of clinical documents containing personal identifiers that are resident in the data repositories of a typical NHS Trust. Similarly, natural language processing is an increasingly important field of clinical data science, yet it requires fault tolerant approaches to data processing. This work concerns the development of "turbo-laser" - a distributed document processing architecture based upon the popular 'battle hardened' Spring Batch framework - an industry standard for large scale processing tasks.
ApproachUsing Spring Batch, we developed a highly scalable unstructured data processing framework, using the concept of remote partitioning. Remote partitioning allows us to offload processing tasks to any and all computers in a network. With this approach, it is possible to harness the entire compute available of an organisation, whether it be an office of 15 desktop PCs that go unused overnight, or a compute cluster of a thousand processors. This method is especially valuable in the NHS, where the provision of sufficient compute to make large scale analytics possible are often hindered by the lack of available hardware, or difficulties in navigating technical governance policies ill equipped for the demands of modern data science.
ResultsTurbo-laser was developed in consideration of the processing challenges common in the NHS. Currently, four types of 'job' are available - De-identification, using the Cognition algorithm, generic GATE output, text extraction from binary files such as MS Office, PDF and scanned documents, and a document re-compiler to deal with EHR legacy issues. Examples of turbo-laser usage include processing 9 million binary documents on modest hardware, within 48 hours.
ConclusionTurbo-laser is an enterprise grade processing tool, in keeping with the software engineering pattern of 'batch processing' that has been at the forefront of the informatics movement. An open source project, it is hoped that others may contribute and extend its principles, lowering the barrier of large scale data processing throughout the NHS.

A. R. Myers's research in the history of late medieval England spanned more than forty years. Throughout his academic career 15th-century England, especially the documentary remnants of its administration, held his attention consistently though not exclusively. The relevant studies, fruits of his research in this field which were originally published in periodicals published over five decades, have here been brought together. As a corpus they provide a collection of important documents related to the crown, the royal household and parliament. Complete with a critical introduction by R. B. Dobs

The UK government has recently recognised the need to improve mental health services in the country. Electronic health records provide a rich source of patient data which could help policymakers to better understand needs of the service users. The main objective of this study is to unveil statistics of diagnoses recorded in the Case Register of the South London and Maudsley NHS Foundation Trust, one of the largest mental health providers in the UK and Europe serving a source population of over 1.2 million people residing in south London. Based on over 500,000 diagnoses recorded in ICD10 codes for a cohort of approximately 200,000 mental health patients, we established frequency rate of each diagnosis (the ratio of the number of patients for whom a diagnosis has ever been recorded to the number of patients in the entire population who have made contact with mental disorders). We also investigated differences in diagnoses prevalence between subgroups of patients stratified by gender and ethnicity. The most common diagnoses in the considered population were (recurrent) depression (ICD10 codes F32-33; 16.4% of patients), reaction to severe stress and adjustment disorders (F43; 7.1%), mental/behavioural disorders due to use of alcohol (F10; 6.9%), and schizophrenia (F20; 5.6%). We also found many diagnoses which were more likely to be recorded in patients of a certain gender or ethnicity. For example, mood (affective) disorders (F31-F39); neurotic, stress-related and somatoform disorders (F40-F48, except F42); and eating disorders (F50) were more likely to be found in records of female patients, while males were more likely to be diagnosed with mental/behavioural disorders due to psychoactive substance use (F10-F19). Furthermore, mental/behavioural disorders due to use of alcohol and opioids were more likely to be recorded in patients of white ethnicity, and disorders due to use of cannabinoids in those of black ethnicity.

The UK government has recently recognised the need to improve mental health services in the country. Electronic health records provide a rich source of patient data which could help policymakers to better understand needs of the service users. The main objective of this study is to unveil statistics of diagnoses recorded in the Case Register of the South London and Maudsley NHS Foundation Trust, one of the largest mental health providers in the UK and Europe serving a source population of over 1.2 million people residing in south London. Based on over 500,000 diagnoses recorded in ICD10 codes for a cohort of approximately 200,000 mental health patients, we established frequency rate of each diagnosis (the ratio of the number of patients for whom a diagnosis has ever been recorded to the number of patients in the entire population who have made contact with mental disorders). We also investigated differences in diagnoses prevalence between subgroups of patients stratified by gender and ethnicity. The most common diagnoses in the considered population were (recurrent) depression (ICD10 codes F32-33;16.4% of patients), reaction to severe stress and adjustment disorders (F43;7.1%), mental/behavioural disorders due to use of alcohol (F10;6.9%), and schizophrenia (F20;5.6%). We also found many diagnoses which were more likely to be recorded in patients of a certain gender or ethnicity. For example, mood (affective) disorders (F31-F39); neurotic, stress-related and somatoform disorders (F40-F48, except F42); and eating disorders (F50) were more likely to be found in records of female patients, while males were more likely to be diagnosed with mental/behavioural disorders due to psychoactive substance use (F10-F19). Furthermore, mental/behavioural disorders due to use of alcohol and opioids were more likely to be recorded in patients of white ethnicity, and disorders due to use of cannabinoids in those of black ethnicity.

Background: With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field. Objective: Wide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy. Methods: RADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided. Results: General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts. Conclusions: RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.

Die Einzelbeiträge des Sammelbandes informieren über folgende Themen: über die Charakteristika der westlichen und der sowjetischen Informationsgesellschaft, den Einsatz von Computern im Schul- und Hochschulwesen der UdSSR, die Entwicklungsstadien der sowjetischen Personal-Computer Generationen, die Verwendung von Computer-Networks in der sowjetischen Informationsgesellschaft und von Computer-Simulationsmodellen bei der Formulierung und Implementierung der Gorbachevschen Reformpolitik, den Einsatz computerintegrierter Fertigung in der sowjetischen Industrie, über die Realisierungsstrategien und -chancen der Revolutionierung des Informationssektors im wissenschaftlich-tenchnologischen Komplex der UdSSR sowie über die innen- und außenwirtschaftlichen Optionen hinsichtlich der Realisierung der computergestützeten "dritten industriellen Revolution" und über die politischen Implikationen des Technologietransfers aus US-Sicht. (BIOst-Klk)