An Evolutionary Algorithm Approach to Link Prediction in Dynamic Social Networks

Many real world, complex phenomena have underlying structures of evolving networks where nodes and links are added and removed over time. A central scientific challenge is the description and explanation of network dynamics, with a key test being the prediction of short and long term changes. For the problem of short-term link prediction, existing methods attempt to determine neighborhood metrics that correlate with the appearance of a link in the next observation period. Recent work has suggested that the incorporation of topological features and node attributes can improve link prediction. We provide an approach to predicting future links by applying the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to optimize weights which are used in a linear combination of sixteen neighborhood and node similarity indices. We examine a large dynamic social network with over $10^6$ nodes (Twitter reciprocal reply networks), both as a test of our general method and as a problem of scientific interest in itself. Our method exhibits fast convergence and high levels of precision for the top twenty predicted links. Based on our findings, we suggest possible factors which may be driving the evolution of Twitter reciprocal reply networks.Comment: 17 pages, 12 figures, 4 tables, Submitted to the Journal of Computational Scienc

The prevalence and nature of cardiac arrhythmias in horses following general anaesthesia and surgery

Background: The prevalence and nature of arrhythmias in horses following general anaesthesia and surgery is poorly documented. It has been proposed that horses undergoing emergency surgery for gastrointestinal disorders may be at particular risk of developing arrhythmias. Our primary objective was to determine the prevalence and nature of arrhythmias in horses following anaesthesia in a clinical setting and to establish if there was a difference in the prevalence of arrhythmias between horses with and without gastrointestinal disease undergoing surgery. Our secondary objective was to assess selected available risk factors for association with the development of arrhythmias following anaesthesia and surgery. Methods: Horses with evidence of gastrointestinal disease undergoing an exploratory laparotomy and horses with no evidence of gastrointestinal disease undergoing orthopaedic surgery between September 2009 and January 2011 were recruited prospectively. A telemetric electrocardiogram (ECG) was fitted to each horse following recovery from anaesthesia and left in place for 24 hours. Selected electrolytes were measured before, during and after surgery and data was extracted from clinical records for analysis. Recorded ECGs were analysed and the arrhythmias characterised. Multivariable logistic regression was used to identify risk factors associated with the development of arrhythmias. Results: Sixty-seven horses with gastrointestinal disease and 37 without gastrointestinal disease were recruited. Arrhythmias were very common during the post-operative period in both groups of horses. Supra-ventricular and bradyarrhythmias predominated in both groups. There were no significant differences in prevalence of any type of arrhythmias between the horses with or without gastrointestinal disease. Post-operative tachycardia and sodium derangements were associated with the development of any type of arrhythmia. Conclusions: This is the first study to report the prevalence of arrhythmias in horses during the post-operative period in a clinical setting. This study shows that arrhythmias are very common in horses following surgery. It showed no differences between those horses with or without gastrointestinal disease. Arrhythmias occurring in horses during the post-anaesthetic period require further investigation

Association of Academic Stress, Anxiety and Depression with Social-Demographic among Medical Students

Background: Medical education programmes aim at producing wholesome of competent and skilled graduates, research have shown that students experience stress which impacts on their health, academic performance and social functioning. This paper aims to determine the extent of academic stress, depression and anxiety among medical undergraduates and to explore the correlation between academic stressors, psychological stress and socio-demographic background among first year medical students at National University of Science and Technology.Method: This descriptive cross-sectional study was undertaken by first-year medical students in 2016 at NUST Division of Social Medical Sciences. A validated and standardised survey Depression Anxiety Stress Scale (DASS 42) questionnaire was used. Data was analysed by SPSS version 21.0.Results: Nineteen first-year midwifery students participated in the study. Males were 63.1% while females were 38.8%. Seventy-three per cent of the participants experienced stress during the programme, of which forty-nine percent were females. Female students showed severe stress of 6±1.15 as compared to their male counterparts who scored extremely severe stress of 3.81±1.53. Academic, health-related and psychosocial problems were the chief sources of stress.Conclusion: Stress impacts negatively on undergraduate students. Midwifery students need guidance, mentorship and educational integration support to identify and monitor their own well-being.  These measures should promote a balance in selection of positive strategies to overcome stress, managing workload and time effectively during study period

Impacts of labour on interactions between economics and animal welfare in extensive sheep farms

This study quantified interactions between animal welfare and farm profitability in British extensive sheep farming systems. Qualitative welfare assessment methodology was used to assess welfare from the animal's perspective in 20 commercial extensive sheep farms and to estimate labour demand for welfare, based on the assessed welfare scores using data collected from farm inventories. The estimated labour demand was then used as a coefficient in a linear program based model to establish the gross margin maximising farm management strategy for given farm situations, subject to constraints that reflected current resource limitations including labour supply. Regression analysis showed a significant relationship between the qualitative welfare assessment scores and labour supply on the inventoried farms but there was no significant relationship between current gross margin and assessed welfare scores. However, to meet the labour demand of the best welfare score, a reduction in flock size and in the average maximum farm gross margin was often required. These findings supported the hypothesis that trade-offs between animal welfare and farm profitability are necessary in providing maximum animal welfare via on-farm labour and sustainable British extensive sheep farming systems.Sheep, Labour, Animal Welfare, Linear Programme, Livestock Production/Industries, C6, Q10, Q19, Q57,

An automatic gait analysis pipeline for wearable sensors: a pilot study in Parkinson’s disease

The use of wearable sensors allows continuous recordings of physical activity from participants in free-living or at-home clinical studies. The large amount of data collected demands automatic analysis pipelines to extract gait parameters that can be used as clinical endpoints. We introduce a deep learning-based automatic pipeline for wearables that processes tri-axial accelerometry data and extracts gait events—bout segmentation, initial contact (IC), and final contact (FC)—from a single sensor located at either the lower back (near L5), shin or wrist. The gait events detected are posteriorly used for gait parameter estimation, such as step time, length, and symmetry. We report results from a leave-one-subject-out (LOSO) validation on a pilot study dataset of five participants clinically diagnosed with Parkinson’s disease (PD) and six healthy controls (HC). Participants wore sensors at three body locations and walked on a pressure-sensing walkway to obtain reference gait data. Mean absolute errors (MAE) for the IC events ranged from 22.82 to 33.09 milliseconds (msecs) for the lower back sensor while for the shin and wrist sensors, MAE ranges were 28.56–64.66 and 40.19–72.50 msecs, respectively. For the FC-event detection, MAE ranges were 29.06–48.42, 40.19–72.70 and 36.06–60.18 msecs for the lumbar, wrist and shin sensors, respectively. Intraclass correlation coefficients, ICC(2,k), between the estimated parameters and the reference data resulted in good-to-excellent agreement (ICC ≥ 0.84) for the lumbar and shin sensors, excluding the double support time (ICC = 0.37 lumbar and 0.38 shin) and swing time (ICC = 0.55 lumbar and 0.59 shin). The wrist sensor also showed good agreements, but the ICCs were lower overall than for the other two sensors. Our proposed analysis pipeline has the potential to extract up to 100 gait-related parameters, and we expect our contribution will further support developments in the fields of wearable sensors, digital health, and remote monitoring in clinical trials

A controlled trial of Partners in Dementia Care: veteran outcomes after six and twelve months

Introduction: “Partners in Dementia Care” (PDC) tested the effectiveness of a care-coordination program integrating healthcare and community services and supporting veterans with dementia and their caregivers. Delivered via partnerships between Veterans Affairs medical centers and Alzheimer’s Association chapters, PDC targeted both patients and caregivers, distinguishing it from many non-pharmacological interventions. Hypotheses posited PDC would improve five veteran self-reported outcomes: 1) unmet need, 2) embarrassment about memory problems, 3) isolation, 4) relationship strain and 5) depression. Greater impact was expected for more impaired veterans. A unique feature was self-reported research data collected from veterans with dementia. Methods and Findings: Five matched communities were study sites. Two randomly selected sites received PDC for 12 months; comparison sites received usual care. Three structured telephone interviews were completed every 6 months with veterans who could participate. Results: Of 508 consenting veterans, 333 (65.6%) completed baseline interviews. Among those who completed baseline interviews, 263 (79.0%) completed 6-month follow-ups and 194 (58.3%) completed 12-month follow-ups. Regression analyses showed PDC veterans had significantly less adverse outcomes than those receiving usual care, particularly for more impaired veterans after 6 months, including reduced relationship strain (B = −0.09; p = 0.05), depression (B = −0.10; p = 0.03), and unmet need (B = −0.28; p = 0.02; and B = −0.52; p = 0.08). PDC veterans also had less embarrassment about memory problems (B = −0.24; p = 0.08). At 12 months, more impaired veterans had further reductions in unmet need (B = −0.96; p < 0.01) and embarrassment (B = −0.05; p = 0.02). Limitations included use of matched comparison sites rather than within-site randomization and lack of consideration for variation within the PDC group in amounts and types of assistance provided. Conclusions: Partnerships between community and health organizations have the potential to meet the dementia-related needs and improve the psychosocial functioning of persons with dementia. Trial Registry NCT0029116

Rapid, ultra low coverage copy number profiling of cell-free DNA as a precision oncology screening strategy.

Current cell-free DNA (cfDNA) next generation sequencing (NGS) precision oncology workflows are typically limited to targeted and/or disease-specific applications. In advanced cancer, disease burden and cfDNA tumor content are often elevated, yielding unique precision oncology opportunities. We sought to demonstrate the utility of a pan-cancer, rapid, inexpensive, whole genome NGS of cfDNA approach (PRINCe) as a precision oncology screening strategy via ultra-low coverage (~0.01x) tumor content determination through genome-wide copy number alteration (CNA) profiling. We applied PRINCe to a retrospective cohort of 124 cfDNA samples from 100 patients with advanced cancers, including 76 men with metastatic castration-resistant prostate cancer (mCRPC), enabling cfDNA tumor content approximation and actionable focal CNA detection, while facilitating concordance analyses between cfDNA and tissue-based NGS profiles and assessment of cfDNA alteration associations with mCRPC treatment outcomes. Therapeutically relevant focal CNAs were present in 42 (34%) cfDNA samples, including 36 of 93 (39%) mCRPC patient samples harboring AR amplification. PRINCe identified pre-treatment cfDNA CNA profiles facilitating disease monitoring. Combining PRINCe with routine targeted NGS of cfDNA enabled mutation and CNA assessment with coverages tuned to cfDNA tumor content. In mCRPC, genome-wide PRINCe cfDNA and matched tissue CNA profiles showed high concordance (median Pearson correlation = 0.87), and PRINCe detectable AR amplifications predicted reduced time on therapy, independent of therapy type (Kaplan-Meier log-rank test, chi-square = 24.9, p &lt; 0.0001). Our screening approach enables robust, broadly applicable cfDNA-based precision oncology for patients with advanced cancer through scalable identification of therapeutically relevant CNAs and pre-/post-treatment genomic profiles, enabling cfDNA- or tissue-based precision oncology workflow optimization