Revealing puddles of electrons and holes in compensated topological insulators

Three-dimensional topological insulators harbour metallic surface states with exotic properties. In transport or optics, these properties are typically masked by defect-induced bulk carriers. Compensation of donors and acceptors reduces the carrier density, but the bulk resistivity remains disappointingly small. We show that measurements of the optical conductivity in BiSbTeSe$_2$ pinpoint the presence of electron-hole puddles in the bulk at low temperatures, which is essential for understanding DC bulk transport. The puddles arise from large fluctuations of the Coulomb potential of donors and acceptors, even in the case of full compensation. Surprisingly, the number of carriers appearing within puddles drops rapidly with increasing temperature and almost vanishes around 40 K. Monte Carlo simulations show that a highly non-linear screening effect arising from thermally activated carriers destroys the puddles at a temperature scale set by the Coulomb interaction between neighbouring dopants, explaining the experimental observation semi-quantitatively. This mechanism remains valid if donors and acceptors do not compensate perfectly.Comment: 11 pages with 7 figures plus supplemental material (3 pages

A kernel method for the two-sample-problem

We propose two statistical tests to determine if two samples are from different dis-tributions. Our test statistic is in both cases the distance between the means of the two samples mapped into a reproducing kernel Hilbert space (RKHS). The first test is based on a large deviation bound for the test statistic, while the second is based on the asymptotic distribution of this statistic. The test statistic can be com-puted in O(m2) time. We apply our approach to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where our test performs strongly. We also demonstrate excellent performance when compar-ing distributions over graphs, for which no alternative tests currently exist

Comparison of nuclear imaging techniques and volumetric imaging for the prediction of postoperative mortality and liver failure in patients undergoing localized liver-directed treatments:a systematic review

BACKGROUND/AIMS: Although volumetric imaging by computed tomography (CT) is the gold standard for preoperative assessment of the future liver remnant, nuclear imaging studies have shown promising data. This systematic review summarized the results from trials investigating volumetric and nuclear medicine imaging for the prediction of postoperative mortality and liver failure (LF). METHODS: MEDLINE and Web of Science were searched for papers investigating nuclear imaging methods for the prediction of postoperative clinical outcomes in patients undergoing local, liver-directed treatments. Only papers investigating both preoperative nuclear imaging and CT or magnetic resonance imaging (MR) for the prediction of postoperative mortality and/or LF were included. RESULTS: Twenty-five trials were qualified for this review. All trials but two used technetium-based tracers for the nuclear imaging examination. Four papers used MR imaging and the remaining used CT for the volumetric evaluation. Overall, the studies were heterogeneous both in terms of methodology and imaging technique. Of the thirteen studies reporting on postoperative mortality, most were descriptive without detailed diagnostic data. A few with detailed data found that nuclear imaging had better predictive value than volumetric imaging. Nineteen studies investigated the prediction of postoperative LF of which seven papers investigated the predictive value of both modalities in multivariable regression analysis. Two papers found that only nuclear imaging parameters were predictive of LF, one paper found that the CT parameter was predictive, and four papers found that combined nuclear and CT/MR imaging parameters were predictive of LF. CONCLUSION: Both methodologies were useful in the preoperative assessment of patients scheduled for liver interventions, especially in combination, but nuclear imaging demonstrated better predictive value for postoperative mortality and LF in a few trials. The overall technical and methodological heterogeneity of the included studies complicates the ability to directly compare the clinical utility of the two imaging techniques

Fine motor function and neuropsychological deficits in individuals at risk for schizophrenia

Deficits in fine motor function and neuropsychological performance have been described as risk factors for schizophrenia. In the Basel FEPSY study (Früherkennung von Psychosen; English: Early Detection of Psychosis) individuals at risk for psychosis were identified in a screening procedure (Riecher-Rössler et al. 2005). As a part of the multilevel assessment, 40 individuals at risk for psychosis and 42 healthy controls matched for age, sex and handedness were investigated with a fine motor function test battery and a neuropsychological test battery. Individuals at risk showed lower performances in all subtests of the fine motor function tests, predominantly in dexterity and velocity (wrist/fingers and arm/hand). In the neuropsychological test battery, individuals at risk performed less well compared to healthy controls regarding sustained attention, working memory and perseveration. The combined evaluation of the two test batteries (neuropsychological and fine motor function) separates the two groups into individuals at risk and healthy controls better than each test battery alone. A multilevel approach might therefore be a valuable contribution to detecting beginning schizophreni

In silico phenotyping via co-training for improved phenotype prediction from genotype

Motivation: Predicting disease phenotypes from genotypes is a key challenge in medical applications in the postgenomic era. Large training datasets of patients that have been both genotyped and phenotyped are the key requisite when aiming for high prediction accuracy. With current genotyping projects producing genetic data for hundreds of thousands of patients, large-scale phenotyping has become the bottleneck in disease phenotype prediction. Results: Here we present an approach for imputing missing disease phenotypes given the genotype of a patient. Our approach is based on co-training, which predicts the phenotype of unlabeled patients based on a second class of information, e.g. clinical health record information. Augmenting training datasets by this type of in silico phenotyping can lead to significant improvements in prediction accuracy. We demonstrate this on a dataset of patients with two diagnostic types of migraine, termed migraine with aura and migraine without aura, from the International Headache Genetics Consortium. Conclusions: Imputing missing disease phenotypes for patients via co-training leads to larger training datasets and improved prediction accuracy in phenotype prediction. Availability and implementation: The code can be obtained at: http://www.bsse.ethz.ch/mlcb/research/bioinformatics-and-computational-biology/co-training.html Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Individualized prediction of psychosis in subjects with an at-risk mental state

Early intervention strategies in psychosis would significantly benefit from the identification of reliable prognostic biomarkers. Pattern classification methods have shown the feasibility of an early diagnosis of psychosis onset both in clinical and familial high-risk populations. Here we were interested in replicating our previous classification findings using an independent cohort at clinical high risk for psychosis, drawn from the prospective FePsy (Fruherkennung von Psychosen) study. The same neuroanatomical-based pattern classification pipeline, consisting of a linear Support Vector Machine (SVM) and a Recursive Feature Selection (RFE) achieved 74% accuracy in predicting later onset of psychosis. The discriminative neuroanatomical pattern underlying this finding consisted of many brain areas across all four lobes and the cerebellum. These results provide proof-of-concept that the early diagnosis of psychosis is feasible using neuroanatomical-based pattern recognition

Hippocampus abnormalities in at risk mental states for psychosis? A cross-sectional high resolution region of interest magnetic resonance imaging study

Background: Hippocampal volume (HV) reduction is well documented in schizophrenia. However, it is still unclear whether this change is a pre-existing vulnerability factor, a sign of disease progression, a consequence of environmental factors, such as drug use, antipsychotic medication, or malnutrition. The timing of HV changes is not well established, but a lack of macrostructural hippocampal brain abnormalities before disease onset would rather support a neuroprogressive illness model. Aim: To investigate the timing of HV changes in emerging psychosis. Methods: A cross-sectional MRI study of manually traced HVs in 37 individuals with an At Risk Mental State (ARMS) for psychosis, 23 individuals with First-Episode Psychosis (FEP), and 22 Healthy Controls (HC) was performed. We compared left and right HVs corrected for whole brain volume across groups using analysis of covariance (ANCOVA) with gender as a covariate. Sixteen of 37 ARMS individuals developed a psychotic disorder during follow up (ARMS-T). The mean duration of follow up in ARMS was 25.1 months. Results: The overall ANCOVA model comparing left HVs across FEP, ARMS and HC indicated a significant general group effect (p < .05) with largest volumes in ARMS and smallest in FEP. ARMS-T subjects had significantly larger left HVs compared to FE but no HV differences compared to HC (p < 0.05). Over all groups, we found an asymmetry between the left and right mean HVs and a strong effect of sex. Discussion: The present study suggests that macrostructural hippocampal abnormalities probably occur in the context of the first psychotic breakdown

Insular volume abnormalities associated with different transition probabilities to psychosis

Background Although individuals vulnerable to psychosis show brain volumetric abnormalities, structural alterations underlying different probabilities for later transition are unknown. The present study addresses this issue by means of voxel-based morphometry (VBM). Method We investigated grey matter volume (GMV) abnormalities by comparing four neuroleptic-free groups: individuals with first episode of psychosis (FEP) and with at-risk mental state (ARMS), with either long-term (ARMS-LT) or short-term ARMS (ARMS-ST), compared to the healthy control (HC) group. Using three-dimensional (3D) magnetic resonance imaging (MRI), we examined 16 FEP, 31 ARMS, clinically followed up for on average 3 months (ARMS-ST, n=18) and 4.5 years (ARMS-LT, n=13), and 19 HC. Results The ARMS-ST group showed less GMV in the right and left insula compared to the ARMS-LT (Cohen's d 1.67) and FEP groups (Cohen's d 1.81) respectively. These GMV differences were correlated positively with global functioning in the whole ARMS group. Insular alterations were associated with negative symptomatology in the whole ARMS group, and also with hallucinations in the ARMS-ST and ARMS-LT subgroups. We found a significant effect of previous antipsychotic medication use on GMV abnormalities in the FEP group. Conclusions GMV abnormalities in subjects at high clinical risk for psychosis are associated with negative and positive psychotic symptoms, and global functioning. Alterations in the right insula are associated with a higher risk for transition to psychosis, and thus may be related to different transition probabilitie