Romantic Theory

Winner of the Jean-Pierre Barricelli Prize given by the International Conference on RomanticismThis original study explores the new idea of theory that emerged in the wake of the French Revolution. Leon Chai sees in the Romantic age a significant movement across several broad fields of intellectual endeavor, from theoretical concepts to an attempt to understand how they arise. He contends that this movement led to a spatial treatment of concepts, the primacy of development over concepts, and the creation of metatheory, or the formal analysis of theory. Chai begins with P. B. Shelley on the need for conceptual framework, or theory. He then considers how Friedrich Wolf and Friedrich Schlegel shift from a preoccupation with antiquity to a heightened self-awareness of Romantic nostalgia for that lost past. He finds a similar reflexivity in Napoleon's battle plan at Jena and, subsequently, in Hegel's move from substance to subject. Chai then turns to the sciences: Xavier Bichat's rejection of the idea of a unitary vital principle for life as process; the chemical theory of matter developed by Humphry Davy; and the work of Évariste Galois, whose proof of the solvability of equations using radicals ushered in the age of metatheory. Chai concludes with reactions to theory: Coleridge's proposal of the conflict between reason and understanding as a model of theory, Mary Shelley's effort to replace theory with a different kind of relationship to external others, and Hölderlin's reflection on the limits of representation and the possibility of fulfillment beyond it

Development of a Human Activity Recognition System for Ballet Tasks

Background: Accurate and detailed measurement of a dancer’s training volume is a key requirement to understanding the relationship between a dancer’s pain and training volume. Currently, no system capable of quantifying a dancer’s training volume, with respect to specific movement activities, exists. The application of machine learning models to wearable sensor data for human activity recognition in sport has previously been applied to cricket, tennis and rugby. Thus, the purpose of this study was to develop a human activity recognition system using wearable sensor data to accurately identify key ballet movements (jumping and lifting the leg). Our primary objective was to determine if machine learning can accurately identify key ballet movements during dance training. The secondary objective was to determine the influence of the location and number of sensors on accuracy. Results: Convolutional neural networks were applied to develop two models for every combination of six sensors (6, 5, 4, 3, etc.) with and without the inclusion of transition movements. At the first level of classification, including data from all sensors, without transitions, the model performed with 97.8% accuracy. The degree of accuracy reduced at the second (83.0%) and third (75.1%) levels of classification. The degree of accuracy reduced with inclusion of transitions, reduction in the number of sensors and various sensor combinations. Conclusion: The models developed were robust enough to identify jumping and leg lifting tasks in real-world exposures in dancers. The system provides a novel method for measuring dancer training volume through quantification of specific movement tasks. Such a system can be used to further understand the relationship between dancers’ pain and training volume and for athlete monitoring systems. Further, this provides a proof of concept which can be easily translated to other lower limb dominant sporting activitie

Dynamic portfolio rebalancing with lag-optimised trading indicators using SeroFAM and genetic algorithms

Some common technical indicators, such as moving average convergence divergence (MACD), relative strength index (RSI), and MACD histogram (MACDH) are used in technical analyses and stock trading. However, some of them are lagging indicators, affecting the effectiveness in the stock trading and portfolio management. A forecasted MACDH (fMACDH) indicator for predicting next day price by a neuro-fuzzy network, Self-reorganizing Fuzzy Associative Machine (SeroFAM) which has been reported in the prior research work. In order to further reduce the lagging effect, two trading indicators are proposed in this paper: the optimised fMACDH indicator and the fMACDH-fRSI indicator. The optimised fMACDH indicator is derived to extend price forecasting to 1-5 days ahead as the prediction depth, using 1-5 days of historical price data as the input depth. The fMACDH-fRSI indicator is derived by combining the optimized fMACDH indicator and the forecasted RSI (fRSI) indicator. A genetic algorithm (GA) and the fitness functions are designed with the SeroFAM in this paper, which are utilised for optimising parameters of these two proposed indicators. Experiments have been conducted to evaluate and benchmark of the proposed trading indicators optimised by the GA. Two rule-based portfolio rebalancing algorithms are then proposed using the optimised fMACDH trading indicator tuned by the GA: the Tactical Buy and Hold (TBH) and the Rule-Based Business Cycle (RBBC) portfolio rebalancing algorithms. The TBH algorithm takes advantage of relative differences in risk levels to perform rebalancing during trend reversals. The RBBC portfolio rebalancing algorithm takes advantage of the offsets between the business cycles of different market sectors. Experiments have been conducted to evaluate the performance of both algorithms using two sets of portfolios consisting of different assets. The TBH portfolio rebalancing algorithm outperforms the equally weighted portfolio strategy by about 26% - 27%; as well outperforms the Buy and Hold strategy by 5% - 40%. The RBBC portfolio rebalancing algorithm outperforms the equally weighted portfolio strategy by 54% - 55%; it also outperforms 12 out of the 13 assets with the Buy and Hold strategy, by an average performance of about 166%. The results are highly encouraging with consistent performances achieved in dynamic portfolio rebalancing

Neurodegenerative disease biomarkers Aβ1–40, Aβ1–42, tau, and p-tau181 in the vervet monkey cerebrospinal fluid: Relation to normal aging, genetic influences, and cerebral amyloid angiopathy

Background:The Caribbean vervet monkey (Chlorocebus aethiops sabaeus) is a potentially valuable animal model of neurodegenerative disease. However, the trajectory of aging in vervets and its relationship to human disease is incompletely understood. Methods:To characterize biomarkers associated with neurodegeneration, we measured cerebrospinal fluid (CSF) concentrations of Aβ1-40, Aβ1-42, total tau, and p-tau181 in 329 members of a multigenerational pedigree. Linkage and genome-wide association were used to elucidate a genetic contribution to these traits. Results:Aβ1-40 concentrations were significantly correlated with age, brain total surface area, and gray matter thickness. Levels of p-tau181 were associated with cerebral volume and brain total surface area. Among the measured analytes, only CSF Aβ1-40 was heritable. No significant linkage (LOD > 3.3) was found, though suggestive linkage was highlighted on chromosomes 4 and 12. Genome-wide association identified a suggestive locus near the chromosome 4 linkage peak. Conclusions:Overall, these results support the vervet as a non-human primate model of amyloid-related neurodegeneration, such as Alzheimer's disease and cerebral amyloid angiopathy, and highlight Aβ1-40 and p-tau181 as potentially valuable biomarkers of these processes

Effect of high-risk sleep apnea on treatment-response to a tailored digital cognitive behavioral therapy for insomnia program : a quasi-experimental trial

Introduction: Therapist-delivered Cognitive Behavioral Therapy for Insomnia (CBTi) is an effective but largely inaccessible treatment for people with Co-Morbid Insomnia and Sleep Apnea (COMISA). To increase CBTi access for COMISA, we aimed to develop a self-guided interactive 5-session digital CBTi program that is appropriate for people with insomnia-alone and COMISA, and compare its effectiveness between people with insomnia-alone, vs. comorbid insomnia and high-risk sleep apnea. Methods: Data from 62 adults with insomnia symptoms were used. High-risk sleep apnea was defined as a score of ≥5 on the OSA50. Participants self-reported symptoms of insomnia (ISI), depression, anxiety, sleepiness (ESS), fatigue, and maladaptive sleep-related beliefs (DBAS-16) at baseline, 8-week, and 16-week follow-up. ESS scores were additionally assessed during each CBTi session. Intent-to-treat mixed models and complete-case chi2 analyses were used. Results: There were more participants with insomnia-alone [n = 43, age M (sd) = 51.8 (17.0), 86.1% female] than suspected COMISA [n = 19, age = 54.0 (14.8), 73.7% female]. There were no between-group differences in baseline questionnaire data, or rates of missing follow-up data. There were no significant group by time interactions on any outcomes. Main effects of time indicated moderate-to-large and sustained improvements in insomnia (d = 3.3), depression (d = 1.2), anxiety (d = 0.6), ESS (d = 0.5), fatigue (d = 1.2), and DBAS-16 symptoms (d = 1.2) at 16-weeks. ESS scores did not increase significantly during any CBTi session. Conclusion: This interactive digital CBTi program is effective in people with insomnia-alone, and people with co-morbid insomnia and high-risk sleep apnea. Further research is required to determine the effectiveness, safety and acceptability of digital CBTi in people with insomnia and confirmed sleep apnea. Clinical Trial Registration: This trial was prospectively registered on the Australian and New Zealand Clinical Trials Registry (ANZCTR, ACTRN12621001395820)

Privacy reinforcement learning for faults detection in the smart grid

Recent anticipated advancements in ad hoc Wireless Mesh Networks (WMN) have made them strong natural candidates for Smart Grid’s Neighborhood Area Network (NAN) and the ongoing work on Advanced Metering Infrastructure (AMI). Fault detection in these types of energy systems has recently shown lots of interest in the data science community, where anomalous behavior from energy platforms is identified. This paper develops a new framework based on privacy reinforcement learning to accurately identify anomalous patterns in a distributed and heterogeneous energy environment. The local outlier factor is first performed to derive the local simple anomalous patterns in each site of the distributed energy platform. A reinforcement privacy learning is then established using blockchain technology to merge the local anomalous patterns into global complex anomalous patterns. Besides, different optimization strategies are suggested to improve the whole outlier detection process. To demonstrate the applicability of the proposed framework, intensive experiments have been carried out on well-known CASAS (Center of Advanced Studies in Adaptive Systems) platform. Our results show that our proposed framework outperforms the baseline fault detection solutions.publishedVersio

Measurement of the Generalized Forward Spin Polarizabilities of the Neutron

The generalized forward spin polarizabilities $\gamma_0$ and $\delta_{LT}$ of the neutron have been extracted for the first time in a $Q^2$ range from 0.1 to 0.9 GeV$^2$. Since $\gamma_0$ is sensitive to nucleon resonances and $\delta_{LT}$ is insensitive to the $\Delta$ resonance, it is expected that the pair of forward spin polarizabilities should provide benchmark tests of the current understanding of the chiral dynamics of QCD. The new results on $\delta_{LT}$ show significant disagreement with Chiral Perturbation Theory calculations, while the data for $\gamma_0$ at low $Q^2$ are in good agreement with a next-to-lead order Relativistic Baryon Chiral Perturbation theory calculation. The data show good agreement with the phenomenological MAID model.Comment: 5 pages, 2 figures, corrected typo in author name, published in PR

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

RET Mutational Spectrum in Hirschsprung Disease: Evaluation of 601 Chinese Patients

Rare (RVs) and common variants of the RET gene contribute to Hirschsprung disease (HSCR; congenital aganglionosis). While RET common variants are strongly associated with the commonest manifestation of the disease (males; short-segment aganglionosis; sporadic), rare coding sequence (CDS) variants are more frequently found in the lesser common and more severe forms of the disease (females; long/total colonic aganglionosis; familial)

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication