Local density of states and scanning tunneling currents in graphene

We present exact analytical calculations of scanning tunneling currents in locally disordered graphene using a multimode description of the microscope tip. Analytical expressions for the local density of states (LDOS) are given for energies beyond the Dirac cone approximation. We show that the LDOS at the $A$ and $B$ sublattices of graphene are out of phase by $\pi$ implying that the averaged LDOS, as one moves away from the impurity, shows no trace of the $2q_F$ (with $q_F$ the Fermi momentum) Friedel modulation. This means that a STM experiment lacking atomic resolution at the sublattice level will not be able of detecting the presence of the Friedel oscillations [this seems to be the case in the experiments reported in Phys. Rev. Lett. {\bf 101}, 206802 (2008)]. The momentum maps of the LDOS for different types of impurities are given. In the case of the vacancy, $2q_F$ features are seen in these maps. In all momentum space maps, $K$ and $K+K^\prime$ features are seen. The $K+K^\prime$ features are different from what is seen around zero momentum. An interpretation for these features is given. The calculations reported here are valid for chemical substitution impurities, such as boron and nitrogen atoms, as well as for vacancies. It is shown that the density of states close to the impurity is very sensitive to type of disorder: diagonal, non-diagonal, or vacancies. In the case of weakly coupled (to the carbon atoms) impurities, the local density of states presents strong resonances at finite energies, which leads to steps in the scanning tunneling currents and to suppression of the Fano factor.Comment: 21 pages. Figures 6 and 7 are correctly displayed in this new versio

A model for predicting physical function upon discharge of hospitalized older adults in Taiwan—a machine learning approach based on both electronic health records and comprehensive geriatric assessment

BackgroundPredicting physical function upon discharge among hospitalized older adults is important. This study has aimed to develop a prediction model of physical function upon discharge through use of a machine learning algorithm using electronic health records (EHRs) and comprehensive geriatrics assessments (CGAs) among hospitalized older adults in Taiwan.MethodsData was retrieved from the clinical database of a tertiary medical center in central Taiwan. Older adults admitted to the acute geriatric unit during the period from January 2012 to December 2018 were included for analysis, while those with missing data were excluded. From data of the EHRs and CGAs, a total of 52 clinical features were input for model building. We used 3 different machine learning algorithms, XGBoost, random forest and logistic regression.ResultsIn total, 1,755 older adults were included in final analysis, with a mean age of 80.68 years. For linear models on physical function upon discharge, the accuracy of prediction was 87% for XGBoost, 85% for random forest, and 32% for logistic regression. For classification models on physical function upon discharge, the accuracy for random forest, logistic regression and XGBoost were 94, 92 and 92%, respectively. The auROC reached 98% for XGBoost and random forest, while logistic regression had an auROC of 97%. The top 3 features of importance were activity of daily living (ADL) at baseline, ADL during admission, and mini nutritional status (MNA) during admission.ConclusionThe results showed that physical function upon discharge among hospitalized older adults can be predicted accurately during admission through use of a machine learning model with data taken from EHRs and CGAs

Brain computerized tomography reading in suspected acute ischemic stroke patients: what are essentials for medical students?

Background Few systematic methods prioritize the image education in medical students (MS). We hope to develop a checklist of brain computerized tomography (CT) reading in patients with suspected acute ischemic stroke (AIS) for MS and primary care (PC) physicians. Methods Our pilot group generated the items indicating specific structures or signs for the checklist of brain CT reading in suspected AIS patients for MS and PC physicians. These items were used in a modified web-based Delphi process using the online software “SurveyMonkey”. In total 15 panelists including neurologists, neurosurgeons, neuroradiologists, and emergency department physicians participated in the modified Delphi process. Each panelist was encouraged to express feedback, agreement or disagreement on the inclusion of each item using a 9-point Likert scale. Items with median scores of 7–9 were included in our final checklist. Results Fifty-two items were initially provided for the first round of the Delphi process. Of these, 35 achieved general agreement of being an essential item for the MS and PC physicians. The other 17 of the 52 items in this round and another two added items suggested by the panelists were further rated in the next round. Finally, 38 items were included in the essential checklist items of brain CT reading in suspected AIS patients for MS and PC physicians. Conclusions We established a reference regarding the essential items of brain CT reading in suspected AIS patients. We hope this helps to minimize malpractice and a delayed diagnosis, and to improve competency-based medical education for MS and PC physicians

Corrigendum to: The TianQin project: current progress on science and technology

In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article

The draft genome, transcriptome, and microbiome of Dermatophagoides farinae reveal a broad spectrum of dust mite allergens

© 2014 The Authors. Published by Elsevier Inc. Background A sequenced house dust mite (HDM) genome would advance our understanding of HDM allergens, a common cause of human allergies. Objective We sought to produce an annotated Dermatophagoides farinae draft genome and develop a combined genomic-transcriptomic-proteomic approach for elucidation of HDM allergens. Methods A D farinae draft genome and transcriptome were assembled with high-throughput sequencing, accommodating microbiome sequences. The allergen gene structures were validated by means of Sanger sequencing. The mite's microbiome composition was determined, and the predominant genus was validated immunohistochemically. The allergenicity of a ubiquinol-cytochrome c reductase binding protein homologue was evaluated with immunoblotting, immunosorbent assays, and skin prick tests. Results The full gene structures of 20 canonical allergens and 7 noncanonical allergen homologues were produced. A novel major allergen, ubiquinol-cytochrome c reductase binding protein-like protein, was found and designated Der f 24. All 40 sera samples from patients with mite allergy had IgE antibodies against rDer f 24. Of 10 patients tested, 5 had positive skin reactions. The predominant bacterial genus among 100 identified species was Enterobacter (63.4%). An intron was found in the 13.8-kDa D farinae bacteriolytic enzyme gene, indicating that it is of HDM origin. The Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed a phototransduction pathway in D farinae, as well as thiamine and amino acid synthesis pathways, which is suggestive of an endosymbiotic relationship between D farinae and its microbiome. Conclusion An HDM genome draft produced from genomic, transcriptomic, and proteomic experiments revealed allergen genes and a diverse endosymbiotic microbiome, providing a tool for further identification and characterization of HDM allergens and development of diagnostics and immunotherapeutic vaccines.Link_to_subscribed_fulltex

The Energy Landscape Analysis of Cancer Mutations in Protein Kinases

The growing interest in quantifying the molecular basis of protein kinase activation and allosteric regulation by cancer mutations has fueled computational studies of allosteric signaling in protein kinases. In the present study, we combined computer simulations and the energy landscape analysis of protein kinases to characterize the interplay between oncogenic mutations and locally frustrated sites as important catalysts of allostetric kinase activation. While structurally rigid kinase core constitutes a minimally frustrated hub of the catalytic domain, locally frustrated residue clusters, whose interaction networks are not energetically optimized, are prone to dynamic modulation and could enable allosteric conformational transitions. The results of this study have shown that the energy landscape effect of oncogenic mutations may be allosteric eliciting global changes in the spatial distribution of highly frustrated residues. We have found that mutation-induced allosteric signaling may involve a dynamic coupling between structurally rigid (minimally frustrated) and plastic (locally frustrated) clusters of residues. The presented study has demonstrated that activation cancer mutations may affect the thermodynamic equilibrium between kinase states by allosterically altering the distribution of locally frustrated sites and increasing the local frustration in the inactive form, while eliminating locally frustrated sites and restoring structural rigidity of the active form. The energy landsape analysis of protein kinases and the proposed role of locally frustrated sites in activation mechanisms may have useful implications for bioinformatics-based screening and detection of functional sites critical for allosteric regulation in complex biomolecular systems

Trends in template/fragment-free protein structure prediction

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward

Evaluation of Candidate Stromal Epithelial Cross-Talk Genes Identifies Association between Risk of Serous Ovarian Cancer and TERT, a Cancer Susceptibility “Hot-Spot”

We hypothesized that variants in genes expressed as a consequence of interactions between ovarian cancer cells and the host micro-environment could contribute to cancer susceptibility. We therefore used a two-stage approach to evaluate common single nucleotide polymorphisms (SNPs) in 173 genes involved in stromal epithelial interactions in the Ovarian Cancer Association Consortium (OCAC). In the discovery stage, cases with epithelial ovarian cancer (n = 675) and controls (n = 1,162) were genotyped at 1,536 SNPs using an Illumina GoldenGate assay. Based on Positive Predictive Value estimates, three SNPs—PODXL rs1013368, ITGA6 rs13027811, and MMP3 rs522616—were selected for replication using TaqMan genotyping in up to 3,059 serous invasive cases and 8,905 controls from 16 OCAC case-control studies. An additional 18 SNPs with Pper-allele<0.05 in the discovery stage were selected for replication in a subset of five OCAC studies (n = 1,233 serous invasive cases; n = 3,364 controls). The discovery stage associations in PODXL, ITGA6, and MMP3 were attenuated in the larger replication set (adj. Pper-allele≥0.5). However genotypes at TERT rs7726159 were associated with ovarian cancer risk in the smaller, five-study replication study (Pper-allele = 0.03). Combined analysis of the discovery and replication sets for this TERT SNP showed an increased risk of serous ovarian cancer among non-Hispanic whites [adj. ORper-allele 1.14 (1.04–1.24) p = 0.003]. Our study adds to the growing evidence that, like the 8q24 locus, the telomerase reverse transcriptase locus at 5p15.33, is a general cancer susceptibility locus