Explore the vast range of titles available.

We investigated whether sprint interval training (SIT) was a time-efficient exercise strategy to improve insulin sensitivity and other indices of cardiometabolic health to the same extent as traditional moderate-intensity continuous training (MICT). SIT involved 1 minute of intense exercise within a 10-minute time commitment, whereas MICT involved 50 minutes of continuous exercise per session. Sedentary men (27±8y; BMI = 26±6kg/m2) performed three weekly sessions of SIT (n = 9) or MICT (n = 10) for 12 weeks or served as non-training controls (n = 6). SIT involved 3x20-second 'all-out' cycle sprints (~500W) interspersed with 2 minutes of cycling at 50W, whereas MICT involved 45 minutes of continuous cycling at ~70% maximal heart rate (~110W). Both protocols involved a 2-minute warm-up and 3-minute cool-down at 50W. Peak oxygen uptake increased after training by 19% in both groups (SIT: 32±7 to 38±8; MICT: 34±6 to 40±8ml/kg/min; p<0.001 for both). Insulin sensitivity index (CSI), determined by intravenous glucose tolerance tests performed before and 72 hours after training, increased similarly after SIT (4.9±2.5 to 7.5±4.7, p = 0.002) and MICT (5.0±3.3 to 6.7±5.0 x 10-4 min-1 [μU/mL]-1, p = 0.013) (p<0.05). Skeletal muscle mitochondrial content also increased similarly after SIT and MICT, as primarily reflected by the maximal activity of citrate synthase (CS; P<0.001). The corresponding changes in the control group were small for VO2peak (p = 0.99), CSI (p = 0.63) and CS (p = 0.97). Twelve weeks of brief intense interval exercise improved indices of cardiometabolic health to the same extent as traditional endurance training in sedentary men, despite a five-fold lower exercise volume and time commitment.

Anti-infectives, including antibiotics, are essentially different from all other drugs; they not only affect the individual to whom they are given but also the entire community, through selection for resistance to their own action. Thus, their use resides at the intersection of personal and public health. Antibiotics can be likened to a four-edged sword against bacteria. The first two edges of the antibiotic sword were identified immediately after their discovery and deployment in that they not only benefit an individual in treating their infection but also benefit the community in preventing the spread of that infectious agent. The third edge was already recognized by Alexander Fleming in 1945 in his Nobel acceptance speech, which warned about the cost to the community of antibiotic resistance that would inevitably evolve and be selected for during clinical practice. We have seen this cost mount up, as resistance curtails or precludes the activities of some of our most effective drugs for clinically important infections. But the fourth edge of the antibiotic sword remained unappreciated until recently, i.e., the cost that an antibiotic exerts on an individual's own health via the collateral damage of the drug on bacteria that normally live on or in healthy humans: our microbiota. These organisms, their genes, metabolites, and interactions with one another, as well as with their host collectively, represent our microbiome. Our relationship with these symbiotic bacteria is especially important during the early years of life, when the adult microbiome has not yet formed.

Epithelial cell monolayers show remarkable displacement and velocity correlations over distances of ten or more cell sizes that are reminiscent of supercooled liquids and active nematics. We show that many observed features can be described within the framework of dense active matter, and argue that persistent uncoordinated cell motility coupled to the collective elastic modes of the cell sheet is sufficient to produce swirl-like correlations. We obtain this result using both continuum active linear elasticity and a normal modes formalism, and validate analytical predictions with numerical simulations of two agent-based cell models, soft elastic particles and the self-propelled Voronoi model together with in-vitro experiments of confluent corneal epithelial cell sheets. Simulations and normal mode analysis perfectly match when tissue-level reorganisation occurs on times longer than the persistence time of cell motility. Our analytical model quantitatively matches measured velocity correlation functions over more than a decade with a single fitting parameter. Epithelial cell monolayers show remarkable displacement and velocity correlations over distances of ten or more cell sizes. Here the authors show that cell motility coupled to the collective elastic modes of the cell sheet is sufficient to produce characteristic swirl-like correlations.

The EM algorithm is a widely used tool in maximum-likelihood estimation in incomplete data problems. Existing theoretical work has focused on conditions under which the iterates or likelihood values converge, and the associated rates of convergence. Such guarantees do not distinguish whether the ultimate fixed point is a near global optimum or a bad local optimum of the sample likelihood, nor do they relate the obtained fixed point to the global optima of the idealized population likelihood (obtained in the limit of infinite data). This paper develops a theoretical framework for quantifying when and how quickly EM-type iterates converge to a small neighborhood of a given global optimum of the population likelihood. For correctly specified models, such a characterization yields rigorous guarantees on the performance of certain two-stage estimators in which a suitable initial pilot estimator is refined with iterations of the EM algorithm. Our analysis is divided into two parts: a treatment of the EM and first-order EM algorithms at the population level, followed by results that apply to these algorithms on a finite set of samples. Our conditions allow for a characterization of the region of convergence of EM-type iterates to a given population fixed point, that is, the region of the parameter space over which convergence is guaranteed to a point within a small neighborhood of the specified population fixed point. We verify our conditions and give tight characterizations of the region of convergence for three canonical problems of interest: symmetric mixture of two Gaussians, symmetric mixture of two regressions and linear regression with covariates missing completely at random.

Bevacizumab is frequently used in the treatment of recurrent WHO grade II and III glioma, but without supporting evidence from randomised trials. Therefore, we assessed the use of bevacizumab in patients with first recurrence of grade II or III glioma who did not have 1p/19q co-deletion. The TAVAREC trial was a randomised, open-label phase 2 trial done at 32 centres across Europe in patients with locally diagnosed grade II or III glioma without 1p/19q co-deletion, with a first and contrast-enhancing recurrence after initial radiotherapy or chemotherapy, or both. Previous chemotherapy must have been stopped at least 6 months before enrolment and radiotherapy must have been stopped at least 3 months before enrolment. Random group assignment was done electronically through the European Organisation for Research and Treatment of Cancer web-based system, stratified by a minimisation procedure using institution, initial histology (WHO grade II vs III), WHO performance status (0 or 1 vs 2), and previous treatment (radiotherapy, chemotherapy, or both). Patients were assigned to receive either temozolomide (150–200 mg/m2, orally) monotherapy on days 1–5 every 4 weeks for a maximum of 12 cycles, or the same temozolomide regimen in combination with bevacizumab (10 mg/kg, intravenously) every 2 weeks until progression. The primary endpoint was overall survival at 12 months in the per-protocol population. Safety analyses were done in all patients who started their allocated treatment. The study is registered at EudraCT (2009–017422–39) and ClinicalTrials.gov (NCT01164189), and is complete. Between Feb 8, 2011, and July 31, 2015, 155 patients were enrolled and randomly assigned to receive either monotherapy (n=77) or combination therapy (n=78). Overall survival in the per-protocol population at 12 months was achieved by 44 (61% [80% CI 53–69]) of 72 patients in the temozolomide group and 38 (55% [47–69]) of 69 in the combination group. The most frequent toxicity was haematological: 17 (23%) of 75 patients in the monotherapy group and 25 (33%) of 76 in the combination group developed grade 3 or 4 haematological toxicity. Other than haematological toxicities, the most common adverse events were nervous system disorders (59 [79%] of 75 patients in the monotherapy group vs 65 [86%] of 76 in the combination group), fatigue (53 [70%] vs 61 [80%]), and nausea (39 [52%] vs 43 [56%]). Infections were more frequently reported in the combination group (29 [38%] of 76 patients) than in the monotherapy group (17 [23%] of 75). One treatment-related death was reported in the combination group (infection after intratumoral haemorrhage during a treatment-related grade 4 thrombocytopenia). We found no evidence of improved overall survival with bevacizumab and temozolomide combination treatment versus temozolomide monotherapy. The findings from this study provide no support for further phase 3 studies on the role of bevacizumab in this disease. Roche Pharmaceuticals.

Building on the successful formula of the first edition, Martin Tovée offers a concise but detailed account of how the visual system is organised and functions to produce visual perception. He takes his readers from first principles; the structure and function of the eye and what happens when light enters, to how we see and process images, recognise patterns and faces, and through to the most recent discoveries in molecular genetics and brain imaging, and how they have uncovered a host of new advances in our understanding of how visual information is processed within the brain. Incorporating new material throughout, including almost 50 new images, every chapter has been updated to include the latest research, and culminates in helpful key points, which summarise the lessons learnt. This book is an invaluable course text for students within the fields of psychology, neuroscience, biology and physiology.

In response to major changes in diagnostic algorithms and the publication of mature results from various large clinical trials, the European Association of Neuro-Oncology (EANO) recognized the need to provide updated guidelines for the diagnosis and management of adult patients with diffuse gliomas. Through these evidence-based guidelines, a task force of EANO provides recommendations for the diagnosis, treatment and follow-up of adult patients with diffuse gliomas. The diagnostic component is based on the 2016 update of the WHO Classification of Tumors of the Central Nervous System and the subsequent recommendations of the Consortium to Inform Molecular and Practical Approaches to CNS Tumour Taxonomy — Not Officially WHO (cIMPACT-NOW). With regard to therapy, we formulated recommendations based on the results from the latest practice-changing clinical trials and also provide guidance for neuropathological and neuroradiological assessment. In these guidelines, we define the role of the major treatment modalities of surgery, radiotherapy and systemic pharmacotherapy, covering current advances and cognizant that unnecessary interventions and expenses should be avoided. This document is intended to be a source of reference for professionals involved in the management of adult patients with diffuse gliomas, for patients and caregivers, and for health-care providers.Herein, the European Association of Neuro-Oncology (EANO) provides recommendations for the diagnosis, treatment and follow-up of adult patients with diffuse gliomas. These evidence-based guidelines incorporate major changes in diagnostic algorithms based on the 2016 update of the WHO Classification of Tumors of the Central Nervous System as well as on evidence from recent large clinical trials.

The Michaelis constant K M describes the affinity of an enzyme for a specific substrate and is a central parameter in studies of enzyme kinetics and cellular physiology. As measurements of K M are often difficult and time-consuming, experimental estimates exist for only a minority of enzyme–substrate combinations even in model organisms. Here, we build and train an organism-independent model that successfully predicts K M values for natural enzyme–substrate combinations using machine and deep learning methods. Predictions are based on a task-specific molecular fingerprint of the substrate, generated using a graph neural network, and on a deep numerical representation of the enzyme’s amino acid sequence. We provide genome-scale K M predictions for 47 model organisms, which can be used to approximately relate metabolite concentrations to cellular physiology and to aid in the parameterization of kinetic models of cellular metabolism.

We investigated whether a training protocol that involved 3 min of intense intermittent exercise per week--within a total training time commitment of 30 min including warm up and cool down--could increase skeletal muscle oxidative capacity and markers of health status. Overweight/obese but otherwise healthy men and women (n = 7 each; age = 29±9 y; BMI = 29.8±2.7 kg/m2) performed 18 training sessions over 6 wk on a cycle ergometer. Each session began with a 2 min warm-up at 50 W, followed by 3×20 s \"all-out\" sprints against 5.0% body mass (mean power output: ∼450-500 W) interspersed with 2 min of recovery at 50 W, followed by a 3 min cool-down at 50 W. Peak oxygen uptake increased by 12% after training (32.6±4.5 vs. 29.1±4.2 ml/kg/min) and resting mean arterial pressure decreased by 7% (78±10 vs. 83±10 mmHg), with no difference between groups (both p<0.01, main effects for time). Skeletal muscle biopsy samples obtained before and 72 h after training revealed increased maximal activity of citrate synthase and protein content of cytochrome oxidase 4 (p<0.01, main effect), while the maximal activity of β-hydroxy acyl CoA dehydrogenase increased in men only (p<0.05). Continuous glucose monitoring measured under standard dietary conditions before and 48-72 h following training revealed lower 24 h average blood glucose concentration in men following training (5.4±0.6 vs. 5.9±0.5 mmol/L, p<0.05), but not women (5.5±0.4 vs. 5.5±0.6 mmol/L). This was associated with a greater increase in GLUT4 protein content in men compared to women (138% vs. 23%, p<0.05). Short-term interval training using a 10 min protocol that involved only 1 min of hard exercise, 3x/wk, stimulated physiological changes linked to improved health in overweight adults. Despite the small sample size, potential sex-specific adaptations were apparent that warrant further investigation.

For most proteins annotated as enzymes, it is unknown which primary and/or secondary reactions they catalyze. Experimental characterizations of potential substrates are time-consuming and costly. Machine learning predictions could provide an efficient alternative, but are hampered by a lack of information regarding enzyme non-substrates, as available training data comprises mainly positive examples. Here, we present ESP, a general machine-learning model for the prediction of enzyme-substrate pairs with an accuracy of over 91% on independent and diverse test data. ESP can be applied successfully across widely different enzymes and a broad range of metabolites included in the training data, outperforming models designed for individual, well-studied enzyme families. ESP represents enzymes through a modified transformer model, and is trained on data augmented with randomly sampled small molecules assigned as non-substrates. By facilitating easy in silico testing of potential substrates, the ESP web server may support both basic and applied science. For many enzymes, it is unknown which primary and/or secondary reactions they catalyze. Here, the authors use machine and deep learning to develop a general model for the prediction of enzyme-small molecule substrate pairs and make the resulting model available through a webserver.