Biomarker-assessed passive smoking in relation to cause-specific mortality: pooled data from 12 prospective cohort studies comprising 36 584 individuals

AIMS: While investigators have typically quantified the health risk of passive (secondhand) smoking by using self-reported data, these are liable to measurement error. By pooling data across studies, we examined the prospective relation of a biochemical assessment of passive smoking, salivary cotinine, with mortality from a range of causes. METHODS: We combined data from 12 cohort studies from England and Scotland initiated between 1998 and 2008. A total of 36 584 men and women aged 16-85 years of age reported that they were non-smoking at baseline, provided baseline salivary cotinine and consented to mortality record linkage. RESULTS: A mean of 8.1 years of mortality follow-up of 36 584 non-smokers (16 792 men and 19 792 women) gave rise to 2367 deaths (775 from cardiovascular disease, 779 from all cancers and 289 from smoking-related cancers). After controlling for a range of covariates, a 10 ng/mL increase in salivary cotinine was related to an elevated risk of total (HRs; 95% CI) (1.46; 1.16 to 1.83), cardiovascular disease (1.41; 0.96 to 2.09), cancer (1.49; 1.00 to 2.22) and smoking-related cancer mortality (2.92; 1.77 to 4.83). CONCLUSIONS: Assessed biomedically, passive smoking was a risk factor for a range of health outcomes known to be causally linked to active smoking

The association between parity, CVD mortality, and CVD risk factors among Norwegian women and men

Background Several studies have shown that women and men with two children have lower mortality than the childless, but there is less certainty about mortality, including CVD mortality, at higher parities and meagre knowledge about factors underlying the parity–mortality relationship. Methods The association between parity and CVD mortality was analyzed by estimating discrete-time hazard models for women and men aged 40–80 in 1975–2015. Register data covering the entire Norwegian population were used, and the models included a larger number of relevant sociodemographic control variables than in many previous studies. To analyze the relationship between parity and seven CVD risk factors, logistic models including the same variables as the mortality models were estimated from the CONOR collection of health surveys, linked to the register data. Results Men (but not women) who had four or more children had higher mortality from CVD than those with two, although this excess mortality was not observed for the heart disease sub-group. Overweight, possibly in part a result of less physical activity, seems to play a role in this. All CVD risk factors except smoking and alcohol may contribute to the relatively high CVD mortality among childless. Conclusions Childbearing is related to a number of well-known CVD risk factors, and becoming a parent or having an additional child is, on the whole, associated with lower—or at least not higher—CVD mortality in Norway. However, for men family sizes beyond three children are associated with increased CVD mortality, with risks of overweight one possible pathway

UDAVA: an unsupervised learning pipeline for sensor data validation in manufacturing

Manufacturing has enabled the mechanized mass production of the same (or similar) products by replacing craftsmen with assembly lines of machines. The quality of each product in an assembly line greatly hinges on continual observation and error compensation during machining using sensors that measure quantities such as position and torque of a cutting tool and vibrations due to possible imperfections in the cutting tool and raw material. Patterns observed in sensor data from a (near-)optimal production cycle should ideally recur in subsequent production cycles with minimal deviation. Manually labeling and comparing such patterns is an insurmountable task due to the massive amount of streaming data that can be generated from a production process. We present UDAVA, an unsupervised machine learning pipeline that automatically discovers process behavior patterns in sensor data for a reference production cycle. UDAVA performs clustering of reduced dimensionality summary statistics of raw sensor data to enable high-speed clustering of dense time-series data. It deploys the model as a service to verify batch data from subsequent production cycles to detect recurring behavior patterns and quantify deviation from the reference behavior. We have evaluated UDAVA from an AI Engineering perspective using two industrial case studies.publishedVersio

Differential gene expression in brain tissues of aggressive and non-aggressive dogs

<p>Abstract</p> <p>Background</p> <p>Canine behavioural problems, in particular aggression, are important reasons for euthanasia of otherwise healthy dogs. Aggressive behaviour in dogs also represents an animal welfare problem and a public threat. Elucidating the genetic background of adverse behaviour can provide valuable information to breeding programs and aid the development of drugs aimed at treating undesirable behaviour. With the intentions of identifying gene-specific expression in particular brain parts and comparing brains of aggressive and non-aggressive dogs, we studied amygdala, frontal cortex, hypothalamus and parietal cortex, as these tissues are reported to be involved in emotional reactions, including aggression. Based on quantitative real-time PCR (qRT-PCR) in 20 brains, obtained from 11 dogs euthanised because of aggressive behaviour and nine non-aggressive dogs, we studied expression of nine genes identified in an initial screening by subtraction hybridisation.</p> <p>Results</p> <p>This study describes differential expression of the <it>UBE2V2 </it>and <it>ZNF227 </it>genes in brains of aggressive and non-aggressive dogs. It also reports differential expression for eight of the studied genes across four different brain tissues (amygdala, frontal cortex, hypothalamus, and parietal cortex). Sex differences in transcription levels were detected for five of the nine studied genes.</p> <p>Conclusions</p> <p>The study showed significant differences in gene expression between brain compartments for most of the investigated genes. Increased expression of two genes was associated with the aggression phenotype. Although the <it>UBE2V2 </it>and <it>ZNF227 </it>genes have no known function in regulation of aggressive behaviour, this study contributes to preliminary data of differential gene expression in the canine brain and provides new information to be further explored.</p

Cybersecurity Awareness and Capacities of SMEs

Small and Medium Enterprises (SMEs) are increasingly exposed to cyber risks. Some of the main reasons include budget constraints, the employees’ lack of cybersecurity awareness, cross-sectoral cyber risks, lack of security practices at organizational level, and so on. To equip SMEs with appropriate tools and guidelines that help mitigate their exposure to cyber risk, we must better understand the SMEs’ context and their needs. Thus, the contribution of this paper is a survey based on responses collected from 141 SMEs based in the UK, where the objective is to obtain information to better understand their level of cybersecurity awareness and practices they apply to protect against cyber risks. Our results indicate that although SMEs do apply some basic cybersecurity measures to mitigate cyber risks, there is a general lack of cybersecurity awareness and lack of processes and tools to improve cybersecurity practices. Our findings provide to the cybersecurity community a better understanding of the SME context in terms of cybersecurity awareness and cybersecurity practices, and may be used as a foundation to further develop appropriate tools and processes to strengthen the cybersecurity of SMEs.publishedVersio

Uncertainty-aware Virtual Sensors for Cyber-Physical Systems

Abstract—Virtual sensors in Cyber-Physical Systems (CPS) are AI replicas of physical sensors that can mimic their behavior by processing input data from other sensors monitoring the same system. However, we cannot always trust these replicas due to uncertainty ensuing from changes in environmental conditions, measurement errors, model structure errors, and unknown input data. An awareness of numerical uncertainty in these models can help ignore some predictions and communicate limitations for responsible action. We present a data pipeline to train and deploy uncertainty-aware virtual sensors in CPS. Our virtual sensor based on a Bayesian Neural Network (BNN) predicts the expected values of a physical sensor and a standard deviation indicating the degree of uncertainty in its predictions. We discuss how this uncertainty awareness bolsters trustworthy AI using a vibration-sensing virtual sensor in automotive manufacturing.Acknowledgement The work has been conducted as part of the InterQ project (958357) and the DAT4.ZERO project (958363) funded by the European Commission within the Horizon 2020 research and innovation programme

Intimate partner violence and prescription of potentially addictive drugs: prospective cohort study of women in the Oslo Health Study

Objectives:To investigate the prescription of potentially addictive drugs, including analgesics and central nervous system depressants, to women who had experienced intimate partner violence (IPV). Design: Prospective population-based cohort study. Setting: Information about IPV from the Oslo Health Study 2000/2001 was linked with prescription data from the Norwegian Prescription Database from 1 January 2004 through 31 December 2009. Participants: The study included 6081 women aged 30–60 years. Main outcome measures: Prescription rate ratios (RRs) for potentially addictive drugs derived from negative binomial models, adjusted for age, education, paid employment, marital status, chronic musculoskeletal pain, mental distress and sleep problems. Results: Altogether 819 (13.5%) of 6081 women reported ever experiencing IPV: 454 (7.5%) comprised physical and/or sexual IPV and 365 (6.0%) psychological IPV alone. Prescription rates for potentially addictive drugs were clearly higher among women who had experienced IPV: crude RRs were 3.57 (95% CI 2.89 to 4.40) for physical/sexual IPV and 2.13 (95% CI 1.69 to 2.69) for psychological IPV alone. After full adjustment RRs were 1.83 (1.50 to 2.22) for physical/sexual IPV, and 1.97 (1.59 to 2.45) for psychological IPV alone. Prescription rates were increased both for potentially addictive analgesics and central nervous system depressants. Furthermore, women who reported IPV were more likely to receive potentially addictive drugs from multiple physicians. Conclusions: Women who had experienced IPV, including psychological violence alone, more often received prescriptions for potentially addictive drugs. Researchers and clinicians should address the possible adverse health and psychosocial impact of such prescription and focus on developing evidence-based healthcare for women who have experienced IPV

Does smoking reduction in midlife reduce mortality risk? Results of 2 long-term prospective cohort studies of men and women in Scotland

A long-term cohort study of working men in Israel found that smokers who reduced their cigarette consumption had lower subsequent mortality rates than those who did not. We conducted comparable analyses in 2 populations of smokers in Scotland. The Collaborative Study included 1,524 men and women aged 40–65 years in a working population who were screened twice, in 1970–1973 and 1977. The Renfrew/Paisley Study included 3,730 men and women aged 45–64 years in a general population who were screened twice, in 1972–1976 and 1977–1979. Both groups were followed up through 2010. Subjects were categorized by smoking intensity at each screening as smoking 0, 1–10, 11–20, or ≥21 cigarettes per day. At the second screening, subjects were categorized as having increased, maintained, or reduced their smoking intensity or as having quit smoking between the first and second screenings. There was no evidence of lower mortality in all reducers compared with maintainers. Multivariate adjusted hazard ratios of mortality were 0.91 (95% confidence interval (CI): 0.75, 1.10) in the Collaborative Study and 1.08 (95% CI: 0.97, 1.20) in the Renfrew/Paisley Study. There was clear evidence of lower mortality among quitters in both the Collaborative Study (hazard ratio = 0.66, 95% CI: 0.56, 0.78) and the Renfrew/Paisley Study (hazard ratio = 0.75, 95% CI: 0.67, 0.84). In the Collaborative Study only, we observed lower mortality similar to that of quitters among heavy smokers (≥21 cigarettes/day) who reduced their smoking intensity. These inconclusive results support the view that reducing cigarette consumption should not be promoted as a means of reducing mortality, although it may have a valuable role as a step toward smoking cessation

Taming Data Quality in AI-Enabled Industrial Internet of Things

We address the problem of taming data quality in artificial intelligence (AI)-enabled Industrial Internet of Things systems by devising machine learning pipelines as part of a decentralized edge-to-cloud architecture. We present the design and deployment of our approach from an AI engineering perspective using two industrial case studies.acceptedVersio

Virtual sensors for erroneous data repair in manufacturing a machine learning pipeline

Manufacturing converts raw materials into finished products using machine tools for controlled material removal or deposition. It can be observed using sensors installed within and around machine tools. These sensors measure quantities, such as vibrations, cutting forces, temperature, currents, power consumption, and acoustic emission, to diagnose defects and enable zero-defect manufacturing as part of the Industry 4.0 vision. The continuity of high-quality sensor data streams is fundamental to predicting phenomena, such as geometric deformations, surface roughness, excessive coolant use, and imminent tool wear with adequate accuracy and appropriate timing. However, in practice, data acquired by some sensors can be of poor quality and unsuitable for prediction due to sensor faults stemming from environmental factors. In this paper, we answer if we can repair erroneous data in a faulty sensor based on data simultaneously available in redundant sensors that observe the same process. We present a machine learning pipeline to synthesize virtual sensors that can step in for faulty sensors to maintain reasonable quality and continuity in sensor data streams. We have validated the synthesized virtual sensors in four industrial case studies.publishedVersio