Dirac Electrons on a Sharply Edged Surface of Topological Insulators

An unpaired gapless Dirac electron emergent at the surface of a strong topological insulator (STI) is protected by the bulk-surface correspondence and believed to be immune to backward scattering. It is less obvious, however, and yet to be verified explicitly whether such a gapless Dirac state is smoothly extended over the entire surface when the surface is composed of more than a single facet with different orientations in contact with one another at sharp corner edges (typically forming a steplike structure). In the realistic situation that we consider, the anisotropy of the sample leads to different group velocities in each of such facets. Here, we propose that much insight on this issue can be obtained by studying the electronic states on a hyperbolic surface of an STI. By explicitly constructing the surface effective Hamiltonian, we demonstrate that no backward scattering takes place at a concave $90^\circ$ step edge. A strong renormalization of the velocity in the close vicinity of the step edge is also suggested.Comment: 4 pages, 2 figures, to be published in J. Phys. Soc. Jp

Annexin-A5 assembled into two-dimensional arrays promotes cell membrane repair

Eukaryotic cells possess a universal repair machinery that ensures rapid resealing of plasma membrane disruptions. Before resealing, the torn membrane is submitted to considerable tension, which functions to expand the disruption. Here we show that annexin-A5 (AnxA5), a protein that self-assembles into two-dimensional (2D) arrays on membranes upon Ca2+ activation, promotes membrane repair. Compared with wild-type mouse perivascular cells, AnxA5-null cells exhibit a severe membrane repair defect. Membrane repair in AnxA5-null cells is rescued by addition of AnxA5, which binds exclusively to disrupted membrane areas. In contrast, an AnxA5 mutant that lacks the ability of forming 2D arrays is unable to promote membrane repair. We propose that AnxA5 participates in a previously unrecognized step of the membrane repair process: triggered by the local influx of Ca2+, AnxA5 proteins bind to torn membrane edges and form a 2D array, which prevents wound expansion and promotes membrane resealing

Environmentally friendly analysis of emerging contaminants by pressurized hot water extraction-stir bar sorptive extraction-derivatization and gas chromatography-mass spectrometry

This work describes the development, optimiza- tion, and validation of a new method for the simultaneous determination of a wide range of pharmaceuticals (beta- blockers, lipid regulators ... ) and personal care products (fragrances, UV filters, phthalates ... ) in both aqueous and solid environmental matrices. Target compounds were extracted from sediments using pressurized hot water ex- traction followed by stir bar sorptive extraction. The first stage was performed at 1,500 psi during three static extrac- tion cycles of 5 min each after optimizing the extraction temperature (50 – 150 °C) and addition of organic modifiers (% methanol) to water, the extraction solvent. Next, aqueous extracts and water samples were processed using polydime- thylsiloxane bars. Several parameters were optimized for this technique, including extraction and desorption time, ionic strength, presence of organic modifiers, and pH. Fi- nally, analytes were extracted from the bars by ultrasonic irradiation using a reduced amount of solvent (0.2 mL) prior to derivatization and gas chromatography – mass spectrome- try analysis. The optimized protocol uses minimal amounts of organic solvents (<10 mL/sample) and time ( ≈ 8 h/sam- ple) compared to previous ex isting methodologies. Low standard deviation (usually below 10 %) and limits of de- tection (sub-ppb) vouch for the applicability of the method- ology for the analysis of target compounds at trace levels. Once developed, the method was applied to determin

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

Contrasting mechanisms underlie short‐ and longer‐term soil respiration responses to experimental warming in a dryland ecosystem

Soil carbon losses to the atmosphere through soil respiration are expected to rise with ongoing temperature increases, but available evidence from mesic biomes suggests that such response disappears after a few years of experimental warming. However, there is lack of empirical basis for these temporal dynamics in soil respiration responses, and for the mechanisms underlying them, in drylands, which collectively form the largest biome on Earth and store 32% of the global soil organic carbon pool. We coupled data from a 10 year warming experiment in a biocrust‐dominated dryland ecosystem with laboratory incubations to confront 0–2 years (short‐term hereafter) versus 8–10 years (longer‐term hereafter) soil respiration responses to warming. Our results showed that increased soil respiration rates with short‐term warming observed in areas with high biocrust cover returned to control levels in the longer‐term. Warming‐induced increases in soil temperature were the main drivers of the short‐term soil respiration responses, whereas longer‐term soil respiration responses to warming were primarily driven by thermal acclimation and warming‐induced reductions in biocrust cover. Our results highlight the importance of evaluating short‐ and longer‐term soil respiration responses to warming as a mean to reduce the uncertainty in predicting the soil carbon–climate feedback in drylands.This research was funded by the European Research Council (ERC Grant agreements 242658 [BIOCOM] and 647038 [BIODESERT]). M.D. is supported by an FPU fellowship from the Spanish Ministry of Education, Culture and Sports (FPU-15/00392). P.G.-P. is supported by a Ramón y Cajal grant from the Spanish Ministry of Science and Innovation (RYC2018-024766-I). S.A. acknowledges the Spanish MINECO for financial support via the DIGGING_DEEPER project through the 2015–2016 BiodivERsA3/FACCE-JPI joint call for research proposals. F.T.M. and S.A. acknowledge support from the Generalitat Valenciana (CIDEGENT/2018/041). C.C.-D. acknowledges support from the European Research Council (ERC Grant 647038 [BIODESERT])

Measurement of the muon decay spectrum with the ICARUS liquid Argon TPC

Examples are given which prove the ICARUS detector quality through relevant physics measurements. We study the muon decay energy spectrum from a sample of stopping muon events acquired during the test run of the ICARUS T600 detector. This detector allows the spatial reconstruction of the events with fine granularity, hence, the precise measurement of the range and dE/dx of the muon with high sampling rate. This information is used to compute the calibration factors needed for the full calorimetric reconstruction of the events. The Michel rho parameter is then measured by comparison of the experimental and Monte Carlo simulated muon decay spectra, obtaining rho = 0.72 +/- 0.06(stat.) +/- 0.08(syst.). The energy resolution for electrons below ~50 MeV is finally extracted from the simulated sample, obtaining (Emeas-Emc)/Emc = 11%/sqrt(E[MeV]) + 2%.Comment: 16 pages, 8 figures, LaTex, A4. Some text and 1 figure added. Final version as accepted for publication in The European Physical Journal

A review of estimation of distribution algorithms in bioinformatics

Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain

Association between adiposity levels and cognitive impairment in the Chilean older adult population

Although both obesity and ageing are risk factors for cognitive impairment, there is no evidence in Chile on how obesity levels are associated with cognitive function. Therefore, the aim of the present study was to investigate the association between adiposity levels and cognitive impairment in older Chilean adults. This cross-sectional study includes 1384 participants, over 60 years of age, from the Chilean National Health Survey 2009–2010. Cognitive impairment was evaluated using the Mini-Mental State Examination. BMI and waist circumference (WC) were used as measures of adiposity. Compared with people with a normal BMI, the odds of cognitive impairment were higher in participants who were underweight (OR 4·44; 95 % CI 2·43, 6·45; P &lt; 0·0001), overweight (OR 1·86; 95 % CI 1·06, 2·66; P = 0·031) and obese (OR 2·26; 95 % CI 1·31, 3·21; P = 0·003). The associations were robust after adjustment for confounding variables. Similar results were observed for WC. Low and high levels of adiposity are associated with an increased likelihood of cognitive impairment in older adults in Chile

Regularized logistic regression and multi-objective variable selection for classifying MEG data

This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori