scite: A Smart Citation Index that Displays the Context of Citations and Classifies Their Intent Using Deep Learning

Citation indices are tools used by the academic community for research and research evaluation that aggregate scientific literature output and measure impact by collating citation counts. Citation indices help measure the interconnections between scientific papers but fall short because they fail to communicate contextual information about a citation. The use of citations in research evaluation without consideration of context can be problematic because a citation that presents contrasting evidence to a paper is treated the same as a citation that presents supporting evidence. To solve this problem, we have used machine learning, traditional document ingestion methods, and a network of researchers to develop a “smart citation index” called scite, which categorizes citations based on context. Scite shows how a citation was used by displaying the surrounding textual context from the citing paper and a classification from our deep learning model that indicates whether the statement provides supporting or contrasting evidence for a referenced work, or simply mentions it. Scite has been developed by analyzing over 25 million full-text scientific articles and currently has a database of more than 880 million classified citation statements. Here we describe how scite works and how it can be used to further research and research evaluation

Changes in single K+ channel behavior through the lipid phase transition

We show that the activity of an ion channel is strictly related to the phase state of the lipid bilayer hosting the channel. By measuring unitary conductance, dwell times, and open probability of the K+ channel KcsA as a function of temperature in lipid bilayers composed of POPE and POPG in different relative proportions, we obtain that all those properties show a trend inversion when the bilayer is in the transition region between the liquid disordered and the solid ordered phase. These data suggest that the physical properties of the lipid bilayer influence ion channel activity likely via a fine tuning of its conformations. In a more general interpretative framework, we suggest that other parameters such as pH, ionic strength, and the action of amphiphilic drugs can affect the physical behavior of the lipid bilayer in a fashion similar to temperature changes resulting in functional changes of transmembrane proteins

The influence of anesthetics, neurotransmitters and antibiotics on the relaxation processes in lipid membranes

In the proximity of melting transitions of artificial and biological membranes fluctuations in enthalpy, area, volume and concentration are enhanced. This results in domain formation, changes of the elastic constants, changes in permeability and slowing down of relaxation processes. In this study we used pressure perturbation calorimetry to investigate the relaxation time scale after a jump into the melting transition regime of artificial lipid membranes. This time corresponds to the characteristic rate of domain growth. The studies were performed on single-component large unilamellar and multilamellar vesicle systems with and without the addition of small molecules such as general anesthetics, neurotransmitters and antibiotics. These drugs interact with membranes and affect melting points and profiles. In all systems we found that heat capacity and relaxation times are related to each other in a simple manner. The maximum relaxation time depends on the cooperativity of the heat capacity profile and decreases with a broadening of the transition. For this reason the influence of a drug on the time scale of domain formation processes can be understood on the basis of their influence on the heat capacity profile. This allows estimations of the time scale of domain formation processes in biological membranes.Comment: 12 pages, 6 figure

Cardiovascular health and potential cardiovascular risk factors in young athletes

IntroductionCardiovascular disease remains the most common cause of death worldwide, and early manifestations are increasingly identified in childhood and adolescence. With physical inactivity being the most prevalent modifiable risk factor, the risk for cardiovascular disease is deemed low in people engaging in regular physical exercise. The aim of this study was to investigate early markers and drivers of cardiovascular disease in young athletes pursuing a career in competitive sports.MethodsOne hundred and five athletes (65 males, mean age 15.7 ± 3.7 years) were characterized by measurement of body impedance to estimate body fat, blood pressure (BP), carotid femoral pulse wave velocity (PWV) to evaluate arterial elasticity, ergometry to assess peak power output, echocardiography to calculate left ventricular mass, and blood tests.ResultsSystolic BP was elevated in 12.6% and thereby more than twice as high as expected for the normal population. Similarly, structural vascular and cardiac changes represented by elevated PWV and left ventricular mass were found in 9.5% and 10.3%. Higher PWV was independently associated with higher systolic BP (β = 0.0186, p < 0.0001), which in turn was closely correlated to hemoglobin levels (β = 0.1252, p = 0.0435). In this population, increased left ventricular mass was associated with lower resting heart rate (β = −0.5187, p = 0.0052), higher metabolic equivalent hours (β = 0.1303, p = 0.0002), sport disciplines with high dynamic component (β = 17.45, p = 0.0009), and also higher systolic BP (β = 0.4715, p = 0.0354).ConclusionDespite regular physical exercise and in the absence of obesity, we found an unexpected high rate of cardiovascular risk factors. The association of PWV, systolic BP, and hemoglobin suggested a possible link between training-induced raised hemoglobin levels and altered vascular properties. Our results point toward the need for thorough medical examinations in this seemingly healthy cohort of children and young adults. Long-term follow-up of individuals who started excessive physical exercise at a young age seems warranted to further explore the potential adverse effects on vascular health

Phase transitions in biological membranes

Native membranes of biological cells display melting transitions of their lipids at a temperature of 10-20 degrees below body temperature. Such transitions can be observed in various bacterial cells, in nerves, in cancer cells, but also in lung surfactant. It seems as if the presence of transitions slightly below physiological temperature is a generic property of most cells. They are important because they influence many physical properties of the membranes. At the transition temperature, membranes display a larger permeability that is accompanied by ion-channel-like phenomena even in the complete absence of proteins. Membranes are softer, which implies that phenomena such as endocytosis and exocytosis are facilitated. Mechanical signal propagation phenomena related to nerve pulses are strongly enhanced. The position of transitions can be affected by changes in temperature, pressure, pH and salt concentration or by the presence of anesthetics. Thus, even at physiological temperature, these transitions are of relevance. There position and thereby the physical properties of the membrane can be controlled by changes in the intensive thermodynamic variables. Here, we review some of the experimental findings and the thermodynamics that describes the control of the membrane function.Comment: 23 pages, 15 figure

Longitudinal multi-centre brain imaging studies: guidelines and practical tips for accurate and reproducible imaging endpoints and data sharing

Abstract Background Research involving brain imaging is important for understanding common brain diseases. Study endpoints can include features and measures derived from imaging modalities, providing a benchmark against which other phenotypical data can be assessed. In trials, imaging data provide objective evidence of beneficial and adverse outcomes. Multi-centre studies increase generalisability and statistical power. However, there is a lack of practical guidelines for the set-up and conduct of large neuroimaging studies. Methods We address this deficit by describing aspects of study design and other essential practical considerations that will help researchers avoid common pitfalls and data loss. Results The recommendations are grouped into seven categories: (1) planning, (2) defining the imaging endpoints, developing an imaging manual and managing the workflow, (3) performing a dummy run and testing the analysis methods, (4) acquiring the scans, (5) anonymising and transferring the data, (6) monitoring quality, and (7) using structured data and sharing data. Conclusions Implementing these steps will lead to valuable and usable data and help to avoid imaging data wastage