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Background: Although we had previously reported the cardiac and neurologic outcomes of Chinese and South Asian Ontarians in wave 1 of COVID-19, data on subsequent waves of COVID-19 remain unexamined. This is an extension study of this cohort in waves 2 and 3. Methods: We identified adult Ontarians with a positive COVID-19 polymerase chain reaction test from January 1, 2020 to June 30, 2021, and they were classified as being Chinese or South Asian using a validated surname algorithm; we compared their outcomes of mortality, and cardiac and neurologic complications with those of the general population using multivariable logistic regression models. Results: Compared to the general population (n = 439,977), the Chinese population (n = 15,208) was older (mean age 44.2 vs 40.6 years, P < 0.001) and the South Asian population (n = 46,333) was younger (39.2 years, P < 0.001). The Chinese population had a higher 30-day mortality (odds ratio [OR] 1.44; 95% confidence interval [CI] 1.28-1.61) and more hospitalization or emergency department visits (OR, 1.14; 95% CI, 1.09-1.28), with a trend toward a higher incidence of cardiac complications (OR, 1.03; 95% CI, 0.87-1.12) and neurologic complications (OR, 1.23; 95% CI, 0.96-1.58). South Asians had a lower 30-day mortality (OR, 0.88; 95% CI, 0.78-0.98) but a higher incidence of hospitalization or emergency department visits (OR, 1.17; 95% CI, 1.14-1.20) with a trend toward a lower incidence of cardiac complications (OR, 0.76; 95% CI, 0.67-0.87) and neurologic complications (OR, 0.89; 95% CI, 0.73-1.09). There was also a significant difference in these outcomes between wave 1, 2 and 3, with a greater mortality in all groups in waves 2 and 3. Conclusions: Ethnicity continues to be an important determinant of mortality, cardiac and neurologic outcomes, and healthcare use among patients with COVID-19, requiring further studies to understand factors driving these differences. Résumé: Contexte: Nous avons déjà présenté les issues cliniques cardiaques et neurologiques chez les Ontariens de descendance chinoise ou sud-asiatique pour la première vague de la pandémie de COVID-19, mais les données au sujet des vagues ultérieures n’avaient pas encore été analysées. Nous présentons ici une prolongation de cette étude de cohortes pour la seconde et la troisième vague de COVID-19. Méthodologie: Notre analyse porte sur des adultes ontariens ayant obtenu un résultat positif à un test de COVID-19 par réaction en chaîne de la polymérase entre le 1er janvier 2020 et le 30 juin 2021. Un algorithme validé pour l’analyse des noms de famille a été utilisé pour isoler les sujets de descendance chinoise ou sud-asiatique, et leur taux de mortalité de même que les complications cardiaques et neurologiques ont été comparés à ceux de la population générale à l’aide de modèles de régression logistique multivariée. Résultats: En comparaison de la population générale (n = 439 977), les personnes de descendance chinoise (n = 15 208) se sont révélées plus âgées (âge moyen de 44,2 ans contre 40,6 ans, P < 0,001), tandis que les personnes de descendance sud-asiatique (n = 46 333) étaient plus jeunes (39,2 ans, P < 0,001). Dans la population de descendance chinoise, le taux de mortalité après 30 jours était plus élevé (rapport de cotes [RC] de 1,44; intervalle de confiance [IC] à 95 % de 1,28 à 1,61), et davantage d’hospitalisations ou de consultations aux urgences sont survenues (RC de 1,14; IC à 95 % de 1,09 à 1,28). L’incidence de complications cardiaques (RC de 1,03; IC à 95 % de 0,87 à 1,12) et de complications neurologiques (RC de 1,23; IC à 95 % de 0,96 à 1,58) avait également tendance à être plus élevée. Chez les personnes de descendance sud-asiatique, le taux de mortalité après 30 jours était plus faible (RC de 0,88; IC à 95 % de 0,78 à 0,98), mais l’incidence d’hospitalisations ou de consultations aux urgences était plus élevée (RC de 1,17; IC à 95 % de 1,14 à 1,20). Elles présentaient également une tendance vers une plus faible incidence de complications cardiaques (RC de 0,76; IC à 95 % de 0,67 à 0,87) et de complications neurologiques (RC de 0,89; IC à 95 % de 0,73 à 1,09). Des différences significatives ont également été observées pour ces paramètres entre les vagues 1, 2 et 3 de la maladie, et le taux de mortalité était plus élevé pour tous les groupes des vagues 2 et 3. Conclusions: L’origine ethnique demeure un déterminant important de la mortalité, des issues cliniques cardiaques et neurologiques ainsi que de l’utilisation des ressources en santé chez les patients atteints de la COVID-19. D’autres études sont toutefois nécessaires pour mieux comprendre les facteurs qui expliquent ces différences.

Background: We previously found large variation among family physicians in adherence to the Canadian Cardiovascular Harmonization of National Guidelines Endeavour (C-CHANGE). We assessed the role of patient- and physician-level factors in the variation in adherence to recommendations for managing cardiovascular disease risk factors. Methods: We conducted a retrospective study using multilevel logistic regression analyses with the Electronic Medical Record Administrative data Linked Database (EMRALD) housed at ICES in Ontario. Five quality indicators based on C-CHANGE guidelines were modelled. Effects of clustering and between-group variation, patient-level (sociodemographics, comorbidities) and physician-level characteristics (demographic and practice information) were assessed to determine odds ratios of receiving C-CHANGE recommended care. Results: In all, 324 Ontario physicians practicing in 41 clinics who provided care to 227,999 adult patients were studied. We found significant variation in quality indicators, with 15% to 39% of the total variation attributable to nonpatient factors. The largest variation was in performing 2-hour plasma glucose testing in prediabetic patients. Patient-level factors most frequently associated with recommendation adherence included sex, age, and multi-comorbidities. Women were more likely than men to have their body mass index measured, and their blood pressure controlled, but less likely to receive antiplatelet medications and liver-enzyme testing if overweight or obese. Conclusions: The majority of variations in adherence were attributable to patient attributes, but a substantial proportion of unexplained variation was due to differences among physicians and clinics. This finding may signal suboptimal processes or structures and warrant further investigation to improve the quality of primary care management of cardiovascular disease in Ontario. Résumé: Contexte: Nous avions déjà constaté que l’observance des recommandations canadiennes en matière de prévention et de gestion des maladies cardiovasculaires de l’initiative C-CHANGE (Canadian Cardiovascular Harmonization of National Guidelines Endeavour) varie beaucoup d’un médecin de famille à l’autre. Nous avons évalué l’effet de caractéristiques des patients et des médecins sur l’observance de ces recommandations pour la gestion des facteurs de risque de maladies cardiovasculaires. Méthodologie: Nous avons mené une étude rétrospective reposant sur des analyses de régression logistique multiniveaux au sein de la base de données liée aux dossiers médicaux électroniques EMRALD (Electronic Medical Record Administrative data Linked Database) qui se trouve à l’ICES, en Ontario. Nous avons modélisé cinq indicateurs de la qualité en nous basant sur les recommandations de l’initiative C-CHANGE. Nous avons évalué les effets de regroupement, de la variation entre les groupes, des caractéristiques des patients (données sociodémographiques, maladies concomitantes), des caractéristiques des médecins (données démographiques et renseignements sur la pratique) afin de déterminer les risques relatifs approchés associés aux soins conformes aux recommandations de l’initiative C-CHANGE. Résultats: L’étude a porté sur un total de 324 médecins ontariens pratiquant dans 41 cliniques et ayant prodigué des soins à 227 999 adultes. Nous avons observé une variation significative entre les indicateurs de qualité, et de 15 % à 39 % de la variation totale était attribuable aux caractéristiques non reliées aux patients. La variation la plus importante concernait le test d’hyperglycémie provoquée (2 heures) chez les patients prédiabétiques. Les caractéristiques des patients qui étaient le plus souvent associées à l’observance des recommandations étaient le sexe, l’âge et la présence de multiples maladies concomitantes. L’indice de masse corporelle et la pression artérielle étaient plus souvent mesurés chez les femmes que chez les hommes, mais les femmes étaient moins susceptibles de recevoir un traitement antiplaquettaire ou de subir une analyse des enzymes hépatiques si elles étaient en surpoids ou obèses. Conclusions: La plus grande partie des variations dans l’observance des recommandations était liée aux caractéristiques des patients, mais une proportion importante de variations injustifiées était associée aux différences entre les médecins et entre les cliniques. Ces observations pourraient indiquer la présence de processus ou de structures sous-optimales et méritent une analyse approfondie qui permettra d’améliorer la qualité de la prise en charge des maladies cardiovasculaires par les médecins de soins primaires en Ontario.

Background Heart failure (HF) with reduced ejection fraction (rEF) is a widely regarded prognosticator after coronary artery bypass grafting. HF with preserved ejection fraction (pEF) accounts for up to half of all HF cases and is associated with considerable morbidity and mortality in hospitalized cohorts. However, HFpEF outcomes have not been elucidated in cardiac surgical patients. We investigated the prevalence and outcomes of HFpEF and HFrEF in women and men following coronary artery bypass grafting. Methods and Results We conducted a retrospective cohort study in Ontario, Canada, between October 1, 2008, and March 31, 2015, using Cardiac Care Network and Canadian Institute of Health Information data. HF is captured through a validated population‐based database of all Ontarians with physician‐diagnosed HF. We defined pEF as ejection fraction ≥50% and rEF as ejection fraction

Abstract The current thyroid ultrasound relies heavily on the experience and skills of the sonographer and the expertise of the radiologist, and the process is physically and cognitively exhausting. In this paper, we report a fully autonomous robotic ultrasound system, which is able to scan thyroid regions without human assistance and identify malignant nod- ules. In this system, human skeleton point recognition, reinforcement learning, and force feedback are used to deal with the difficulties in locating thyroid targets. The orientation of the ultrasound probe is adjusted dynamically via Bayesian optimization. Experimental results on human participants demonstrated that this system can perform high-quality ultrasound scans, close to manual scans obtained by clinicians. Additionally, it has the potential to detect thyroid nodules and provide data on nodule characteristics for American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) calculation.

Technological advances have led to a modern-day lighting and smartphone revolution, with artificial light exposure at night increasing to levels never before seen in the evolutionary history of living systems on Earth. Light as a pollutant, however, remains largely unrecognized, and the reproductive effects of light pollution are mostly if not entirely unconsidered. This is despite the reproductive system being intricately linked to metabolism and the circadian system, both of which can be disturbed even by low levels of light. Here, we aim to change this perspective by reviewing the physiological and pathophysiological mechanisms by which light exposure alters the intricate hormonal, metabolic and reproductive networks that are relevant to reproductive toxicology. Nascent human studies have recently identified the photoreceptors responsible for the light dose relationship with melatonin suppression and circadian re-entrainment, directly shown the association between the alignment of light-dark cycles with activity-rest cycles on metabolic health and provided proof-of-principle that properly timed blue light-enriched and blue light-depleted delivery can accelerate circadian re-entrainment. With these advances, there is now a need to consider testicular effects of light pollution.

Abstract Sleep-disordered breathing (SDB) is a prevalent disorder characterized by recurrent episodic upper airway obstruction. Using data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), we apply principal component analysis (PCA) to seven SDB-related measures. We estimate the associations of the top two SDB PCs with serum levels of 617 metabolites, in both single-metabolite analysis, and a joint penalized regression analysis. The discovery analysis includes 3299 individuals, with validation in a separate dataset of 1522 individuals. Five metabolite associations with SDB PCs are discovered and replicated. SDB PC1, characterized by frequent respiratory events common in older and male adults, is associated with pregnanolone and progesterone-related sulfated metabolites. SDB PC2, characterized by short respiratory event length and self-reported restless sleep, enriched in young adults, is associated with sphingomyelins. Metabolite risk scores (MRSs), representing metabolite signatures associated with the two SDB PCs, are associated with 6-year incident hypertension and diabetes. These MRSs have the potential to serve as biomarkers for SDB, guiding risk stratification and treatment decisions.

This paper introduces a novel error-based adaptive feedback zeroing neural network (EAF-ZNN) to solve the time-varying quadratic programming (TVQP) problem. Compared to existing variable gain ZNNs, the EAF-ZNN dynamically adjusts the parameter to adaptively accelerate without increasing to very large values over time. Unlike adaptive fuzzy ZNN, which only considers the current convergence error, EAF-ZNN ensures regulation by introducing a feedback regulation mechanism between the current convergence error, the historical cumulative convergence error, the change rate of the convergence error, and the model gain parameter. This regulation mechanism promotes effective neural dynamic evolution, which results in high convergence rate and accuracy. This paper provides a detailed analysis of the convergence of the model, utilizing four distinct activation functions. Furthermore, the effect of changes in the proportional, integral, and derivative factors in the EAF-ZNN model on the rate of convergence is explored. To assess the superiority of EAF-ZNN in solving TVQP problems, a comparative evaluation with three existing ZNN models is performed. Simulation experiments demonstrate that the EAF-ZNN model exhibits a superior convergence rate. Finally, the EAF-ZNN model is compared with the other three models through the redundant robotic arms example, which achieves smaller position error.

Summary: Cancer is a leading cause of mortality worldwide. Over 50% of cancers are diagnosed late, rendering many treatments ineffective. Existing liquid biopsy studies demonstrate a minimally invasive and inexpensive approach for disease detection but lack parsimonious biomarker selection, exhibit poor cancer detection performance and lack appropriate validation and testing. We established a tailored machine learning pipeline, DEcancer, for liquid biopsy analysis that addresses these limitations and improved performance. In a test set from a published cohort of 1,005 patients including 8 cancer types and 812 cancer-free individuals, DEcancer increased stage 1 cancer detection sensitivity across cancer types from 48 to 90%. In addition, with a test set cohort of patients from a high dimensional proteomics dataset of 61 lung cancer patients and 80 cancer-free individuals, DEcancer’s performance using a 14-43 protein panel was comparable to 1,000 original proteins. DEcancer is a promising tool which may facilitate improved cancer detection and management.

Abstract Short‐term load forecasting is essential to power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate the intrinsic time‐related dimensions of electric load data and the decomposition methods into machine learning models so that their prediction accuracy and robustness still have much room for improvement. To solve this problem, this paper proposes a decomposition‐based multi‐time dimension long short‐term memory (DB‐MTD‐LSTM) model for short‐term electric load forecasting (STELF). In DB‐MTD‐LSTM, empirical mode decomposition with adaptive noise (CEEMDAN) is first introduced to smooth non‐linear non‐stationary electric load data and constrain the modal aliasing or noise of decomposed electric load data in the traditional decomposed method. A joint relevant time dimensions method (JRTDM) is then developed using autocorrelation analysis to rationally extract the temporal characteristics of decomposed data in multiple time dimensions. An improved LSTM called MTD‐LSTM is developed by combining JRTDM with LSTM, which can effectively apply multi‐dimensional time characteristics of the decomposed load to improve the predictive accuracy and robustness. Several datasets from Australia and China are performed to check the predictive performance of DB‐MTD‐LSTM. Experimental results verify that DB‐MTD‐LSTM has better predictive accuracy and satisfactory robustness compared with state‐of‐the‐art and conventional predictive models.

A novel wearable upper arm tactile display device, which can simultaneously provide three types of tactile stimuli (i.e., squeezing, stretching, and vibration) is presented. The squeezing and stretching stimulation of the skin is generated by two motors simultaneously driving the nylon belt in the opposite and the same direction, respectively. In addition, four evenly spaced vibration motors are fixed around the user’s arm by an elastic nylon band. There is also a unique structural design for assembling the control module and actuator, powered by two lithium batteries, making it portable and wearable. Psychophysical experiments are conducted to investigate the effect of interference on the perception of squeezing and stretching stimulation by this device. Results show that (1) different tactile stimuli actually interfere with the user’s perception compared to the case where only one stimulus is applied to the user; (2) the squeezing has a considerable impact on the stretch just noticeable difference (JND) values when both stimuli are exerted on the user, and when the squeezing is strong, while the impact of stretch on the squeezing JND values is negligible.

Background: Interleukin-2 (IL-2), one of the proinflammatory cytokines, is used in the treatment of certain malignancies. In some studies, transient i ncreases in cortisol and ACTH secretion occurred. Thus, this agent may be used as an exp erimental probe of adrenal cortisol secretion. Objective: This study quantifies the effects of low and moderate doses of I L-2 on cortisol secretion and assesses the modulation by age, dose and body com position. Site: Mayo Clinical Translational Research Unit. Subjects: Study comprised 35 healthy men, 17 young and 18 older. Methods: Randomized prospective double-blind saline-controlled study of IL-2 administration in two doses with concurrent 10-min blood sampli ng for 24 h. Outcome measures: Deconvolution analysis and approximate entropy of cortisol secretion. Results: Low-dose IL-2 administration increased nocturnal pulsatile cor tisol secretion from 1460 ± 160 to 2120 ± 220 nmol/L/8 h in young subjects and from 1680 ± 105 to 1960 ± 125 nmol/L/8 h (treatment P < 0.0001, but more in young than older, P = 0.02). Comparable results were obtained for total cortisol secretion (P treatment

A class of heterogeneous second-order multi-agent consensus problems is studied, in which an event-triggered method is used to improve the feasibility of the control protocol. The sliding mode control method is used to achieve the robustness of the system. A special type of general radial basis function neural network is applied to estimate the uncertainties. The event-triggered mechanism is introduced to reduce the update frequency of the controller and the communication frequency among the agents. Zeno behavior is avoided by ensuring a lower bound between two adjacent trigger instants. Finally, the simulation results are provided to demonstrate that the time evolution of consensus errors eventually approaches zero. The consensus of multi-agent systems is achieved.

Background: The prevalence of coronary artery disease (CAD) with reduced left ventricular ejection fraction (LVEF) is rising, but the optimal treatment remains unclear. The present study aims to investigate the impact of revascularization (coronary artery bypass grafting (CABG), or percutaneous coronary intervention (PCI)) and medical therapy (MT) on LVEF recovery in patients with CAD and LVEF ≤ 40%. Methods: All clinical studies which reported LVEF in patients with CAD and LVEF ≤ 40% undergoing either revascularization or MT were included. A systematic literature search up to May 11, 2017 was performed on MEDLINE, EMBASE and the Cochrane Library in Ovid. Studies were assessed for eligibility and data were extracted by independent reviewers in duplicate with disagreements resolved by a third reviewer. Bias assessment was performed using the Newcastle-Ottawa Scale. Random effect meta-analysis was performed on included studies. The primary outcome was change in LVEF after intervention as compared to baseline. Results: 83 cohort studies with a total of 7,157 patients were included. We observed a statistically significant increase in LVEF following revascularization with CABG (MD 7.76; 95% CI: 6.81 to 8.70; pI 2=99%) and PCI (MD 6.70; 95% CI: 4.71 to 8.70; pI 2=94%) but not after MT (MD 4.06; 95% CI: -2.12 to 10.24; p=0.20; I 2=98%). Conclusion: In patients with CAD and LVEF ≤ 40%, revascularization leads to LVEF recovery as compared to MT. There is continued need for RCT level evidence to enable optimization of treatment selection in this difficult population.

Objectives To examine the temporal trends in mortality and heart failure (HF) hospitalisation in ambulatory patients following a new diagnosis of HF.Design Retrospective cohort studySetting OutpatientParticipants Ontario residents who were diagnosed with HF in an outpatient setting between 1994 and 2013.Primary and secondary outcome measures The primary outcome was all-cause mortality within 1 year of diagnosis and the secondary outcome was HF hospitalisation within 1 year. Risks of mortality and hospitalisation were calculated using the Kaplan-Meier method and the relative hazard of death was assessed using multivariable Cox proportional hazard models.Results A total of 352 329 patients were studied (50% female). During the study period, there was a greater decline in age standardised 1-year mortality rates (AMR) in men (33%) than in women (19%). Specifically, female AMR at 1 year was 10.4% (95% CI 9.1% to 12.0%) in 1994 and 8.5% (95% CI 7.5% to 9.5%) in 2013, and male AMR at 1 year was 12.3% (95% CI 11.1% to 13.7%) in 1994 and 8.3% (95% CI 7.5% to 9.1%) in 2013. Conversely, age standardised HF hospitalisation rates declined in men (11.4% (95% CI 10.1% to 12.9%) in 1994 and 9.1% (95% CI 8.2% to 10.1%) in 2013) but remained unchanged in women (9.7% (95% CI 8.3% to 11.3%) in 1994 and 9.8% (95% CI 8.6% to 11.0%) in 2013).Conclusion Among patients with HF over a 20-year period, there was a greater improvement in the prognosis of men compared with women. Further research should focus on the determinants of this disparity and ways to reduce this gap in outcomes.

We have previously shown that a severely energy-restricted diet leads to greater loss of weight, fat, lean mass and bone mineral density (BMD) at 12 months in postmenopausal women with obesity than a moderately energy-restricted diet. We now aim to evaluate whether these effects are sustained longer term (ie, at 36 months). 101 postmenopausal women were randomized to either 12 months of moderate (25 to 35%) energy restriction with a food-based diet (moderate intervention), or 4 months of severe (65 to 75%) energy restriction with a total meal replacement diet followed by moderate energy restriction for 8 months (severe intervention). Body weight and composition were measured at 0, 24 and 36 months. Participants in the severe intervention lost ~1.5 to 1.7 times as much weight, waist circumference, whole-body fat mass and visceral adipose tissue compared to those in the moderate intervention, and were 2.6 times more likely (42% versus 16%) to have lost 10% or more of their initial body weight at 36 months (P < 0.01 for all). However, those in the severe versus moderate intervention lost ~1.4 times as much whole-body lean mass (P < 0.01), albeit this was proportional to total weight lost and there was no greater loss of handgrip strength, and they also lost ~2 times as much total hip BMD between 0 and 36 months (P < 0.05), with this bone loss occurring in the first 12 months. Thus, severe energy restriction is more effective than moderate energy restriction for reducing weight and adiposity in postmenopausal women in the long term (3 years), but attention to BMD loss in the first year is required. Trial registration: Australian New Zealand Clinical Trials Registry Reference Number: 12612000651886, anzctr.org.au.

Abstract Background Mycobacterium Avium Complex (MAC) is an established microbiologic cause of pulmonary disease, lymphadenitis, and disseminated disease in cases of advanced immune suppression. However, MAC manifesting as vertebral osteomyelitis is less common, and is particularly rare in the absence of Acquired Immunodeficiency Syndrome (AIDS). Prompt diagnosis of MAC vertebral osteomyelitis is challenging, but necessary to prevent serious morbidity or mortality. Case presentation We report a case of MAC osteomyelitis of the lumbar spine in a 70-year-old woman on extended duration corticosteroid therapy for systemic lupus erythematosus who presented with progressive back pain. Upon presentation, imaging revealed osteomyelitis of the lumbar spine with associated paraspinal abscess. Cultures from the surgical evacuation of the paraspinal abscess yielded no pathogen growth and she was therefore treated with empiric antibacterial therapy. Two weeks after her initial hospital discharge she represented with severe back pain and radiologic evidence of progressive disease in her lumbar spine. Two additional vertebral biopsies were required during her first 2 weeks of admission. MAC eventually grew from culture 14 days after collection. She was treated with ethambutol and rifampin and her symptoms resolved in 2 weeks, though therapy was continued for 12 months. Conclusions MAC is an unusual cause of vertebral osteomyelitis in patients with AIDS, but is exceedingly rare in those without severe immune compromise. Despite its rarity, it must be considered in cases of vertebral osteomyelitis that do not respond to empiric antibiotic therapy. Multiple biopsies may be necessary to obtain a diagnosis and avoid destructive infectious complications of an untreated infection.

Antitumor immunity has emerged as a favorable byproduct of radiation therapy (RT), whereby tumor-associated antigens released from irradiated cells unleash innate and adaptive attacks on tumors located both within and outside the radiation field. RT-induced immune responses further provide actionable targets for overcoming tumor resistance to RT (R-RT); immunotherapy (IT) with checkpoint inhibitors or Toll-like receptor (TLR) agonists can markedly improve, if not synergize with, RT in preclinical models, and several of these drugs are currently investigated as radiosensitizers in patients. In an unbiased chemical-genetic screen in a zebrafish model of tumor R-RT, we unexpectedly found that Interleukin 1 Receptor-Associated Kinase 1 (IRAK1), a core effector of TLR-mediated innate immunity, also functions in live fish and human cancer models to counter RT-induced cell death mediated by the PIDDosome complex (PIDD-RAIDD-caspase-2). IRAK1 acting both as a driver of intrinsic tumor R-RT and as an effector of RT-induced antitumor immunity would, at first glance, pose obvious therapeutic conundrums. IRAK1 inhibitors would be expected to sensitize the irradiated tumor to RT but simultaneously thwart RT-induced antitumor immunity as initiated by stromal dendritic cells. Conversely, TLR agonist-based immunotherapy would be expected to intensify RT-induced antitumor immunity but at the expense of fueling IRAK1-mediated cell survival in the irradiated tumor. We discuss how IRAK1's differential reliance on catalytic activity in the radiation vs. TLR responses might help overcome these hurdles, as well as the crucial importance of developing IRAK1 inhibitors that lack activity against IRAK4, the kinase activity of which is essential for IRAK1 activation in both pathways.

The ability to detect and respond to varying oxygen tension is an essential prerequisite to life. Several mechanisms regulate the cellular response to oxygen including the prolyl hydroxylase domain (PHD)/factor inhibiting HIF (FIH)-hypoxia inducible factor (HIF) pathway, cysteamine (2-aminoethanethiol) dioxygenase (ADO) system, and the lysine-specific demethylases (KDM) 5A and KDM6A. Using a systems-based approach we discuss the literature on oxygen sensing pathways in the context of virus replication in different tissues that experience variable oxygen tension. Current information supports a model where the PHD-HIF pathway enhances the replication of viruses infecting tissues under low oxygen, however, the reverse is true for viruses with a selective tropism for higher oxygen environments. Differences in oxygen tension and associated HIF signaling may play an important role in viral tropism and pathogenesis. Thus, pharmaceutical agents that modulate HIF activity could provide novel treatment options for viral infections and associated pathological conditions.

In this paper, a novel constraint-following control for uncertain robot manipulators that is inspired by analytical dynamics is developed. The motion can be regarded as external constraints of the system. However, it is not easy to obtain explicit equations for dynamic modeling of constrained systems. For a multibody system subject to motion constraints, it is a common practice to introduce Lagrange multipliers, but using these to obtain explicit dynamical equations is a very difficult task. In order to obtain such equations more simply, motion constraints are handled here using the Udwadia-Kalaba equation(UKE). Then, considering real-life robot manipulators are usually uncertain(but bounded), by using continuous controllers compensate for the uncertainties. No linearizations/approximations of the robot manipulators systems are made throughout, and the tracking errors are bounds. A redundant manipulator of the SCARA type as the example to illustrates the methodology. Numerical results are demonstrates the simplicity and ease of implementation of the methodology.

Canonical Wnt signaling is controlled intracellularly by the level of β-catenin protein, which is dependent on Axin scaffolding of a complex that phosphorylates β-catenin to target it for ubiquitylation and proteasomal degradation. This function of Axin is counteracted through relocalization of Axin protein to the Wnt receptor complex to allow for ligand-activated Wnt signaling. AXIN1 and AXIN2 protein levels are regulated by tankyrase-mediated poly(ADP-ribosyl)ation (PARsylation), which destabilizes Axin and promotes signaling. Mechanistically, how tankyrase limits Axin protein accumulation, and how tankyrase levels and activity are regulated for this function, are currently under investigation. By RNAi screening, we identified the RNF146 RING-type ubiquitin E3 ligase as a positive regulator of Wnt signaling that operates with tankyrase to maintain low steady-state levels of Axin proteins. RNF146 also destabilizes tankyrases TNKS1 and TNKS2 proteins and, in a reciprocal relationship, tankyrase activity reduces RNF146 protein levels. We show that RNF146, tankyrase, and Axin form a protein complex, and that RNF146 mediates ubiquitylation of all three proteins to target them for proteasomal degradation. RNF146 is a cytoplasmic protein that also prevents tankyrase protein aggregation at a centrosomal location. Tankyrase auto-PARsylation and PARsylation of Axin is known to lead to proteasome-mediated degradation of these proteins, and we demonstrate that, through ubiquitylation, RNF146 mediates this process to regulate Wnt signaling.

Cardiac hypertrophy is a complex pathological process that involves multiple factors including inflammation and apoptosis. Interferon regulatory factor 7 (IRF7) is a multifunctional regulator that participates in immune regulation, cell differentiation, apoptosis, and oncogenesis. However, the role of IRF7 in cardiac hypertrophy remains unclear. We performed aortic banding in cardiac-specific IRF7 transgenic mice, IRF7 knockout mice, and the wild-type littermate of these mice. Our results demonstrated that IRF7 was downregulated in aortic banding-induced animal hearts and cardiomyocytes that had been treated with angiotensin II or phenylephrine for 48 hours. Accordingly, heart-specific overexpression of IRF7 significantly attenuated pressure overload-induced cardiac hypertrophy, fibrosis, and dysfunction, whereas loss of IRF7 led to opposite effects. Moreover, IRF7 protected against angiotensin II-induced cardiomyocyte hypertrophy in vitro. Mechanistically, we identified that IRF7-dependent cardioprotection was mediated through IRF7 binding to inhibitor of κB kinase-β, and subsequent nuclear factor-κB inactivation. In fact, blocking nuclear factor-κB signaling with cardiac-specific inhibitors of κBαS32A/S36A super-repressor transgene counteracted the adverse effect of IRF7 deficiency. Conversely, activation of nuclear factor-vd3 signaling via a cardiac-specific conditional inhibitor of κB kinase-βS177E/S181E (constitutively active) transgene negated the antihypertrophic effect of IRF7 overexpression. Our data demonstrate that IRF7 acts as a novel negative regulator of pathological cardiac hypertrophy by inhibiting nuclear factor-κB signaling and may constitute a potential therapeutic target for pathological cardiac hypertrophy. [ABSTRACT FROM AUTHOR]

Abstract A second‐order adaptive integral terminal sliding mode controller is proposed for the trajectory tracking control of robotic manipulators with uncertainties. A second‐order integral terminal sliding mode surface is designed for which an integral sliding mode (ISM) surface and a fast nonsingular integral terminal sliding mode surface are combined. By using the ISM surface, the reaching phase is removed, which enhances system robustness. The steady‐state error is reduced because of the presence of an error integral term. A fast second‐order nonsingular integral terminal sliding mode surface is employed to ensure that the ISM surface is able to converge to zero rapidly within a finite period of time without leading to a singularity problem. The control input of the proposed controller is continuous. Thus, the chattering phenomenon is removed. An adaptation technique is employed to estimate the upper bound of unknown lumped disturbance. The second‐order derivative of position is calculated using a robust differentiator, making it practical. Simulations and experiments show that the proposed scheme improves the tracking performance and eliminates chattering.

Photonic sensors: trapping analytes by digging into nano-trenches An innovative sensor can make it easier to use infrared light for label-free, picogram-scale detection of amino acids and other molecules. Photonic sensors avoid the need for large quantities of analytes by using nanoscale gap structures to trap targets and subject them to ultrahigh optical fields. Peter Liu and colleagues at the State University of New York at Buffalo in the United States have developed a microchip that naturally drives molecules into nanoscale gap regions. The team fabricated arrays where aluminum ribbons overhang narrower strips of insulating crystals. When a drop of test solution is placed onto the chip and left to evaporate, surface tension creates concave profiles that concentrate molecules under the aluminum layers. The resonant frequencies within this device’s “nano-trenches” can be tuned to specific molecular targets by tweaking the array’s geometric parameters.

The RNase activity of Inositol-Requiring Enzyme 1 (IRE1) can be allosterically regulated by ATP-competitive inhibitors of the IRE1 kinase domain. Here, the authors identify ATP-competitive IRE1 RNase activators with improved selectivity and cellular activity, and elucidate their mechanism of action.

Abstract Background Focal impulse and rotor modulation (FIRM) can cause slowing, organization, and occasionally termination of atrial fibrillation (AF), although results have been mixed. To further characterize changes in AF during rotor ablation, we quantified morphologic and temporal activation changes following FIRM. Methods In patients undergoing FIRM ablation for AF, we retrospectively analyzed coronary sinus bipolar EGMs before and after rotor ablation, including EGM activation frequency and regularity, dominant frequency (DF), and organizational index (OI). Changes in EGM waveform morphology were determined with recurrence quantification analysis (RQA) consisting of recurrence rate (RR), determinism (DET), laminarity (LAM), average diagonal line length (L), and trapping time (TT) using Wilcoxon signed‐rank testing. Results Overall, 36 rotors from 21 patients undergoing FIRM ablation were analyzed. All morphology RQA parameters demonstrated significant organization of atrial activation after rotor ablation (RR P = .03, DET P = .005, LAM P = .03, L P = .005, TT P = .009). The organizational index also showed a significant increase after rotor ablation (P = .01), and the change in OI correlated with changes in all morphology parameters. Of the rotors, 14/36 (39%) rotors showed organizational changes in all morphology parameters and OI, and an additional 5 rotors (19/36, 53%) showed organizational changes in 4 of 5 morphology parameters and OI. Conclusions Coronary sinus EGM waveform morphologies and activation patterns are significantly altered after FIRM ablation even when there is no fibrillatory slowing. RQA morphology analysis and organizational index may impart important information regarding underlying AF organization and may be useful in quantifying the acute response to ablation.

Multipath fingerprinting is a promising indoor location technique, which contains abundant position features of the received array signals. Effectively representing the position information is one critical issue in fingerprinting localization. Meanwhile, positional accuracy is prone to reduction caused by abnormal measurement readings, which are referred to as outliers, and it has received a little attention in the existing literature. A multipath fingerprinting model for indoor single-site localization is proposed. In this model, the location fingerprint is composed of the spatial-temporal covariance matrix of the multipath signals received by the base station antenna array. The low-dimensional linear subspace of the location fingerprinting is introduced as feature descriptors. Based on the fact that the Grassmann manifold maintains the orthogonality of the linear subspace, the Binet-Cauchy kernel is employed to map the multipath fingerprinting to a higher dimensional reproducing kernel Hilbert space. The Euclidean distance of the nearest point between multipath fingerprinting affine hulls is adopted to represent the similarity of the position. Moreover, an augmented Lagrangian and alternating direction solution is given to remove the influence of outliers. We extensively evaluated the proposed method with the indoor multi-scenario benchmark data set. All the results demonstrate that the location accuracy of the proposed positioning model outperforms the existing method in an indoor environment. As the proportion of outliers increases, the positional accuracy loss of the proposed model is negligible.

The estimation of image noise level is a critical task for image denoising or super-resolution reconstruction. Mathematical methods like patch-based or model-based methods, suffer from the sensitivity of the selection of homogeneous regions or the selection of a proper statistic model, leading to inaccurate estimation, especially in signal-dependent noise cases, such as Rice noise. Ordinary, fully connected networks often suffer from the over-fitting problem, restricting their usage for realistic images. This article proposes a deep-learning-based algorithm by building a deep neural network, and train it by using the evolutionary genetic algorithm and extreme learning machine (ELM) algorithm extended into Hinton’s dropout framework. By combining the evolutionary genetic algorithm and the proposed extended ELM algorithm, comparative results are obtained, showing higher accuracy and better stability than several state-of-the-art algorithms.

To obtain accurate trajectory tracking with robustness and faithful force feedback in a practical application, a sliding mode controller (SMC) combined with a compensation controller based on a nonlinear disturbance observer (DOB) is proposed. The DOB estimates the disturbances arising mainly from the uncertain dynamic model of a surgical manipulator, frictional forces and external interaction forces, and compensates for these disturbances in the control law. Accordingly, it alleviates the chattering problem caused by the SMC and improves tracking performance. The surgical teleoperation system using the proposed SMC-DOB is proved to be asymptotically stable using Lyapunov theory. The simulation and experiment results show that the surgical manipulator with the SMC-DOB can better track the trajectory of the master even in the presence of disturbances and the interaction forces between the instrument and the patient's tissues are faithfully presented to the surgeon.

A class-incremental scheme of fisher discriminant analysis is proposed to improve the performance ofprocess fault diagnosis. Fisher discriminant analysis seeks directions which are efficient for discrimination and has excellent fault detection and diagnostic performance for the sample set with the tag. However, due to the property of the model, it has no detection and diagnostic capabilities forun-seen faults. In order to address this issue, the $F$ direction, which is based on a partial $F$ -values with the principle component analysis, is proposed in this paper. After a new fault being detected and added into the known fault collection, a class-incremental scheme is used to update the fisher discriminant analysis model to enhance the model's ability for continuous fault identification. The proposed approach is validated by the Tennessee Eastman process for the fault diagnosis. The results demonstrate that the proposed class-incremental fisher discriminant analysis method outperforms other conventional fisher discriminant analysis methods.

Aiming to dynamic modeling of a three-link manipulator subjected to motion constraints, a novel explicit approach to the dynamical equations based on Udwadia-Kalaba (UK) theory is established. The motion constraints on the three-link manipulator can be regarded as external constraints of the system. However, it is not easy to obtain explicit equations for the dynamic modeling of constrained systems. For a multibody system subjecting to motion constraints, it is common to introduce Lagrange multipliers, but obtaining an explicit dynamical equation using traditional Lagrange multipliers is difficult. In order to obtain such equations more simply, motion constraints are handled using the UK equation. Compared with the Lagrange method, the UK approach can simplify the analysis and solution of a constrained system, without the need to introduce additional auxiliary variables to solve the Lagrange equation. Based on a more real-life nominal system (whose parameters are known) model considering the uncertain environment, this paper develops a nonlinear controller that satisfies the required trajectory. This controller allows the nonlinear nominal system to track the desired trajectory exactly without linearizations or approximations. These continuous controllers compensate extra force to eliminate the errors caused by uncertainties. The controllers are based on a generalization of sliding surfaces. Error bounds on tracking caused by uncertainties are analytically obtained. The numerical results show the simplicity and efficacy of the proposed methodology, and the reliability of the error bounds.

In order to achieve a high degree of visual realism in surgery simulation, we present a virtual surgery system framework, which is based on point primitives for the virtual surgery scene rendering and the biomechanical calculation of the soft tissue. To embody the superiority of this framework, two virtual surgery systems based on point primitives we developed are exhibited in this paper. Six critical functional modules were selected as representative of basic and advanced virtual surgery skill. These modules were: 1) point-based texture mapping; 2) deformation simulation; 3) cutting simulation; 4) tearing simulation; 5) dynamic texture mapping; and 6) 3-D display. These modules were elaborated by including working principle, execution process, and the performance of the algorithm. The experimental results have shown that point primitives-based virtual surgery systems obtained higher performance in terms of computational efficiency and rendering effect than traditional meshes-based virtual surgery system.

The conventional sine cosine algorithm (SCA) does not appropriately balance exploration and exploitation, causing premature convergence, especially for complex optimization problems, such as the complex shifted or shifted rotated problems. To address this issue, this paper proposes an enhanced brain storm SCA (EBS-SCA), where an EBS strategy is employed to improve the population diversity, and by combining it with two different update equations, two new individual update strategies [individual update strategies (IUS): IUS-I and IUS-II] are developed to make effective balance between exploration and exploitation during the entire iterative search process. Double sets of benchmark suites involving 46 popular functions and two real-world problems are employed to compare the EBS-SCA with other metaheuristic algorithms. The experimental results validate that the proposed EBS-SCA achieves the overall best performance including the global search ability, convergence speed, and scalability.

Most machine learning algorithms require a large set of training samples in order to achieve satisfactory performance. However, this requirement may be difficult to satisfy in practice. Take the one-shot learning (OSL) problem on texture recognition for example; the machine learning algorithm is difficult to achieve satisfactory results. In order to solve this problem, a novel multi-modal one-shot learning method for texture recognition is presented. First, in order to improve the robustness of identification and the anti-interference to noise, we addressed the nontravel texture recognition challenges of learn information about object categories from only one training sample by fusing varied modalities data, including image, sound and acceleration, which provides rich information regarding textures. Second, a novel dictionary learning model is designed, which contains the various modalities information, and can simultaneously learn the latent common sparse code for the different modalities. Third, an original regularization term is developed to enhance the degree of distinction of different classes. Furthermore, the common features of the three modalities are evaluated in the case of one-shot learning and used as the basis for feature selection. In the end, experiments were performed based on a data set which was published openly to validate the effectiveness of the presented method.

It is challenging to obtain accurate and efficient predictions in short-term load forecasting (STLF) systems due to the complexity and nonlinearity of the electric load signals. To address these problems, we propose a hybrid predictive model that includes a sliding-window algorithm, a stacking ensemble neural network, and a similar-days predictive method. First, we leverage a sliding-window algorithm to process the time-series electric load data with high nonlinearity and non-stationarity. Second, we propose an ensemble learning scheme of stacking neural networks to improve forecasting performance. Specifically, the stacking neural networks contain two types of networks: the base-layer and the meta-layer networks. During the pre-training process, the base-layer network integrates a radial basis function (RBF), random vector functional link (RVFL), and backpropagation neural network (BPNN) to provide a robust predictive model. The meta-layer network utilizes a deep belief network (DBN) and the improved broad learning system (BLS) to enhance predictive accuracy. Finally, the similar-days prediction method is developed to extract the relationship of electric load data in different time dimensions, further enhancing the robustness and accuracy of the model. To demonstrate the effectiveness of our model, it is evaluated using real data from five regions of the United States in three consecutive years. We compare our method with several state-of-the-art and conventional neural-network-based models. Our proposed algorithm improves the prediction accuracy by 16.08%, 16.83%, and 22.64% compared to DWT-EMD-RVFL, SWT-LSTM, and EMD-BLS, respectively. Empirical results demonstrate that our model achieves better accuracy and robustness compared with the baselines.

A novel hybrid model is proposed to improve the accuracy of ultra-short-term wind speed prediction by combining the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the sample entropy (SE), optimized recurrent broad learning system (ORBLS), and broadened temporal convolutional network (BTCN). First, ICEEMDAN is introduced to smooth the nonlinear part of the wind speed data by decomposing the raw wind speed data into a series of sequences. Second, SE is applied to quantitatively assess the complexity of each sequence. All sequences are divided into simple sequence set and complex sequence set based on the values of SE. Third, based on the typical broad learning system (BLS), we propose ORBLS with cyclically connected enhancement nodes, which can better capture the dynamic characteristics of the wind. The improved particle swarm optimization (PSO) is used to optimize the hyper-parameters of ORBLS. Fourth, we propose BTCN by adding a dilated causal convolution layer in parallel to each residual block, which can effectively alleviate the local information loss of the temporal convolutional network (TCN) in case of insufficient time series data. Note that ORBLS and BTCN can effectively predict the simple and complex sequences, respectively. To validate the performance of the proposed model, we conducted three predictive experiments on four data sets. The experimental results show that our model obtains the best predictive results on all evaluation metrics, which fully demonstrates the accuracy and robustness of the proposed model.

Night shift work is a risk factor for viral infection, suggesting that night shift schedules compromise host defense mechanisms. Prior studies have investigated changes in the temporal profiles of circulating cytokines important for priming and restraining the immune response to infectious challenges from night shift work, but not by way of a 24-h constant routine of continuous wakefulness devoid of behavioral or environmental influences. Hence the true endogenous pattern of cytokines, and the combined effect of sleep loss and circadian misalignment on these cytokines remains unknown. Here, 14 healthy young men and women underwent three days of either a simulated night shift or a simulated day shift schedule under dim light in a controlled in-laboratory environment. This was followed by a 24-h constant routine protocol during which venous blood was collected at 3-h intervals. Those who had been in the night shift schedule showed lower mean circulating TNF-α (t13 = -6.03, p

Short-term wind power forecasting (SWPF) is essential for managing wind power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate multi-view learning technologies with attention mechanisms. In this case, some potential features cannot be fully extracted, degenerating the predictive accuracy and robustness in SWPF. To solve this problem, this paper proposes a multi-view ensemble width-depth neural network (MVEW-DNN) for SWPF. Specifically, MVEW-DNN consists of local and global view learning subnetworks, which can effectively achieve more potential global and local view features of the original wind power data. In MVEW-DNN, the local view learning subnetwork is developed by introducing the deep belief network (DBN) model, which can efficiently extract the local view features. On the other hand, by introducing the attention mechanism, a new deep encoder board learning system (deBLS) is developed as the global view learning subnetwork, which provides more comprehensive global information. Therefore, by rationally learning the effective local and global view features, MVEW-DNN can achieve competitive predictive performance in SWPF. MVEW-DNN is compared with the state-of-the-art models in SWPF. The experiment results indicate that MVEW-DNN can provide competitive predictive accuracy and robustness.

Artificial intelligence: Algorithm predicts clinical outcomes for hospital inpatients Artificial intelligence outperforms traditional statistical models at predicting a range of clinical outcomes from a patient’s entire raw electronic health record (EHR). A team led by Alvin Rajkomar and Eyal Oren from Google in Mountain View, California, USA, developed a data processing pipeline for transforming EHR files into a standardized format. They then applied deep learning models to data from 216,221 adult patients hospitalized for at least 24 h each at two academic medical centers, and showed that their algorithm could accurately predict risk of mortality, hospital readmission, prolonged hospital stay and discharge diagnosis. In all cases, the method proved more accurate than previously published models. The authors provide a case study to serve as a proof-of-concept of how such an algorithm could be used in routine clinical practice in the future.

Traditional tracking-by-detection trackers may fail to track targets owing to interferences, such as deformation, occlusion, fast or irregular motion, and background clutter. To improve the process of feature extraction, sample training, and the performance of the traditional kernelized correlation filter (KCF), this paper proposes a new joint multi-feature and scale-adaptive correlation filter tracking algorithm based on histogram of oriented gradients (HOG) and color-naming features. The new algorithm is composed of a position correlation filter tracker and a scale correlation filter tracker. It first uses a mask matrix to cut the training samples, obtains a higher proportion of real samples, and then trains the two correlation filter trackers. One is a position tracker that uses HOG and color-naming features to train two correlation filters, and then combines the results of the two correlation filters to calculate the target position. The second tracker is used to identify the scale with the maximum response size to select the target scale. The proposed algorithm achieves a competitive performance compared with algorithms, such as KCF, compressive tracking, and circulant structure kernel. The results show that this algorithm achieves a better performance when faced with occlusion, deformation, background clutter, and fast or irregular motion, and is able to track changes in target scale.

Brain storm optimization (BSO) is a young and promising population-based swarm intelligence algorithm inspired by the human process of brainstorming. The BSO algorithm has been successfully applied to both science and engineering issues. However, thus far, most BSO algorithms are prone to fall into local optima when solving complicated optimization problems. In addition, these algorithms adopt complicated clustering strategies, such as K-means clustering, resulting in large computational burdens. This paper proposes a simple BSO algorithm with a periodic quantum learning strategy (SBSO-PQLS), which includes three new strategies developed to improve the defects described above. First, we develop a simple individual clustering strategy that sorts individuals according to their fitness values and then allocates all individuals into different clusters. This reduces computational burdens and resists premature convergence. Second, we present a simple individual updating strategy by simplifying the individual combinations and improving the step size function to enrich the diversity of newly generated individuals and reduce redundancy in the pattern for generating individuals. Third, a quantum-behaved individual updating with periodic learning (QBIU-PL) strategy is developed by introducing a quantum-behaved mechanism into SBSO-PQLS. QBIU-PL provides new momentum, enabling individuals to escape local optima. With the support of these three strategies, SBSO-PQLS effectively improves its global search capability and computational burdens. SBSO-PQLS is compared with seven other BSO variants, particle swarm optimization, and differential evolution on CEC2013 benchmark functions. The results show that SBSO-PQLS achieves a better global search performance than do the other nine algorithms.

The original brain storm optimization (BSO) fails to consider some potential information interactions in its individual update pattern, causing the premature convergence for complex problems. To address this problem, we propose a BSO algorithm with multi-information interactions (MIIBSO). First, a multi-information interaction (MII) strategy is developed, thoroughly considering various information interactions among individuals. Specially, this strategy contains three new MII patterns. The first two patterns aim to reinforce information interaction capability between individuals. The third pattern provides interactions between the corresponding dimensions of different individuals. The collaboration of the above three patterns is established by an individual stagnation feedback mechanism, contributing to preserve the diversity of the population and enhance the global search capability for MIIBSO. Second, a random grouping (RG) strategy is introduced to replace both the K-means algorithm and cluster center disruption of the original BSO algorithm, further enhancing the information interaction capability and reducing the computational cost of MIIBSO. Finally, a dynamic difference step-size (DDS), which can offer individual feedback information and improve search range, is designed to achieve an effective balance between global and local search capability for MIIBSO. By combining the MII strategy, RG, and DDS, MIIBSO achieves the effective improvement in the global search ability, convergence speed, and computational cost. MIIBSO is compared with 11 BSO algorithms and five other algorithms on the CEC2013 test suit. The results confirm that MIIBSO obtains the best global search capability and convergence speed amongst the 17 algorithms.

This paper addresses the robust finite-time trajectory tracking control problem for a rigid three-degrees-of-freedom (3-DOFs) manipulator system, which is subject to model uncertainty and external disturbances. The disturbances are assumed to be upper bounded. The proposed method incorporates a finite-time velocity observer and a disturbance observer into the proposed nonlinear control scheme and the conditions of global finite-time stability are established. The global finite-time stability of the controlled system is proved by using the finite-time Lyapunov stability theory. Simulation results of a 3-DOF manipulator show that the trajectory tracking was achieved with a finite period of settling time.

The original brain storm optimization (BSO) method does not rationally compromise global exploration and local exploitation capability, which results in the premature convergence when solving complicated optimization problems such as the shifted or shifted rotated functions. To address this problem, this paper develops a vector grouping learning BSO (VGLBSO) method. In VGLBSO, the individuals' creation based on a VGL scheme is first developed to improve the population diversity and compromise the global exploration and local exploitation capability. Moreover, a hybrid individuals' update scheme is established by reasonably combing two different individuals' update schemes, which further compromises the global exploration and local exploitation capability. Finally, the random grouping scheme, instead of K-means grouping, is allowed to shrink the computational cost and maintain the diversity of the information exchange between different individuals. Twenty-eight popular benchmark functions are used to compare VGLBSO with 12 BSO and nine swarm intelligence methods. Experimental results present that VGLBSO achieves the best overall performance, including the global search ability, convergence speed, and scalability among all the compared algorithms.

A critical problem associated with surgical simulation is balancing deformation accuracy with a real-time performance. Although the canonical surface mass-spring model (MSM) can provide an excellent real-time performance, it fails to provide effective shape restoration behavior when generating large deformations. This significantly influences its deformation accuracy. To address this problem, this paper proposes a modified surface MSM. In the proposed MSM, a new flexion spring is first developed to oppose bending based on the included angle between the initial position vector and the deformational position vector, improving the shape restoration performance, and enhance the deformational accuracy of MSM; then, a new type of surface triangular topological unit is developed for enhancing the computational efficiency and better adapting to the different topological soft tissue deformational models. In addition, to further improve the accuracy of deformational interactions between the soft tissue and surgical instruments, we also propose two new collision detection algorithms. One is the discrete collision detection with the volumetric structure (DCDVS), applying a volumetric structure to extend the effective range of collision detection and the other is the hybrid collision detection with the volumetric structure (HCDVS), introducing the interpolation techniques of the continuous collision detection to DCDVS. Experimental results show that the proposed MSM with the DCDVS or the HCDVS can achieve accurate and stable shape restoration and show the real-time interactive capability in the virtual artery vessel and heart compared with the canonical surface MSM and new volume MSM.

The original moth-flame optimization (MFO) algorithm neither generates high-performance flames nor utilizes the flames to offer enough effective search guidance for moths in solution spaces, causing the degeneration of the global search capability and convergence speed in confronting complicated problems. To overwhelm those imperfections, this paper proposes a double-evolutionary learning MFO algorithm (DELMFO), where two different evolutionary learning strategies, namely, the differential evolution flame generation (DEFG) and dynamic flame guidance (DFG) strategy, are presented to generate high-performance flames and dynamically guide the search of moths, respectively. By constructing the cascading collaboration between DEFG and DFG, the DELMFO offers a positive feedback channel that makes the personal best historical solutions (PBHSs), flames, and moths promote each other. This improves the global search capability and accelerates convergence speed. The DELMFO is compared with six MFO algorithms and nine popular stochastic optimization algorithms on the CEC2013 test suite. Furthermore, the DELMFO also is further compared with 10 stochastic optimization algorithms on the CEC2017 test suite. Experimental results show that the DELMFO obtains the competitive performance on the global search capability, convergence speed, and scalability among all the algorithms.

Time reversal symmetry breaking gives rise to magnetic circular dichroism and Faraday rotation in graphene. The authors use terahertz magneto-electro-optical spectroscopy to demonstrate that electrostatic doping at a fixed magnetic field allows inversion of magnetic circular dichroism and Faraday rotation.

In this paper, we present a novel interactive cutting simulation model for soft tissue based on the meshless framework. Unlike most existing methods that consider the cutting process of soft tissue in an over simplified manner, the presented model is able to simulate the complete cutting process that includes three stages: deformation before cutting open, cutting open, and deformation after cutting open. To characterize the complicated physical and mechanical properties of soft tissue, both nonlinearity and viscoelasticity were incorporated into the equations governing the motion of soft tissue. A line contact model was used for simulating the cutting process after analyzing the two major types of surgical instruments, i.e., scalpel and electrostick. The cutting speed and angle were taken into account in order to improve haptic rendering. Biomechanical tests and simulation experiments verified the validity of the introduced model. Specifically, the displacement versus cutting force curves can be divided into three segments corresponding to the three stages of the cutting process. The results were also applied in a liver cutting simulating system and satisfactory visual effect and haptic feedback were achieved.

BackgroundMineralocorticoid receptor antagonist (MRA) therapy may be beneficial to patients with atrial fibrillation (AF), but little is known about their use in patients with AF and subsequent outcomes. Methods and ResultsIn order to better understand MRA use and subsequent outcomes, we performed a retrospective cohort study of the contemporary ORBIT‐AF (Outcomes Registry for Better Informed Treatment of Atrial Fibrillation) registry. AF progression and cardiovascular outcomes were compared using propensity‐matched Cox proportional hazards modeling according to MRA use at baseline and new MRA use at follow‐up versus patients with no MRA use. Among 7012 patients with nonpermanent AF, 320 patients were taking MRA at enrollment, and 416 patients initiated MRA use during follow‐up. The mean patient age was 72.5 years, 56.3% were men, and 70.4% had paroxysmal AF. Among all patients taking MRAs, 434 (59.0%) had heart failure, 655 (89.0%) had hypertension, and 380 (51.6%) had both. After adjustment, new MRA use was not associated with reduced AF progression (hazard ratio, 1.18; 95% confidence interval, 0.88–1.58; P=0.27) but showed a trend towards lower risk of stroke, transient ischemic attack, or systemic embolism (hazard ratio, 0.17; 95% confidence interval, 0.02–1.23; P=0.08). Results were similar for a comparison of new MRA users and baseline MRA users compared with nonusers. ConclusionsIn community‐based outpatients with AF, the majority of MRA use was for heart failure and hypertension. MRA use also trended towards lower adjusted stroke risk. Future studies should test the hypothesis that MRA use may decrease the risk of stroke in patients with AF.