Electron transport in semiconducting carbon nanotubes with hetero-metallic contacts

We present an atomistic self-consistent study of the electronic and transport properties of semiconducting carbon nanotube in contact with metal electrodes of different work functions, which shows simultaneous electron and hole doping inside the nanotube junction through contact-induced charge transfer. We find that the band lineup in the nanotube bulk region is determined by the effective work function difference between the nanotube channel and source/drain electrodes, while electron transmission through the SWNT junction is affected by the local band structure modulation at the two metal-nanotube interfaces, leading to an effective decoupling of interface and bulk effects in electron transport through nanotube junction devices.Comment: Higher quality figures available at http://www.albany.edu/~yx15212

Scaling analysis of Schottky barriers at metal-embedded semiconducting carbon nanotube interfaces

We present an atomistic self-consistent tight-binding study of the electronic and transport properties of metal-semiconducting carbon nanotube interfaces as a function of the nanotube channel length when the end of the nanotube wire is buried inside the electrodes. We show that the lineup of the nanotube band structure relative to the metal Fermi-level depends strongly on the metal work function but weakly on the details of the interface. We analyze the length-dependent transport characteristics, which predicts a transition from tunneling to thermally-activated transport with increasing nanotube channel length.Comment: To appear in Phys.Rev.B Rapid Communications. Color figures available in PRB online versio

Development of the Reporting Infographics and Visual Abstracts of Comparative studies (RIVA-C) checklist and guide

People often use infographics (also called visual or graphical abstracts) as a substitute for reading the full text of an article. This is a concern because most infographics do not present sufficient information to interpret the research appropriately and guide wise health decisions. The Reporting Infographics and Visual Abstracts of Comparative studies (RIVA-C) checklist and guide aims to improve the completeness with which research findings of comparative studies are communicated and avoid research findings being misinterpreted if readers do not refer to the full text. The primary audience for the RIVA-C checklist and guide is developers of infographics that summarise comparative studies of health and medical interventions. The need for the RIVA-C checklist and guide was identified by a survey of how people use infographics. Possible checklist items were informed by a systematic review of how infographics report research. We then conducted a two-round, modified Delphi survey of 92 infographic developers/designers, researchers, health professionals and other key stakeholders. The final checklist includes 10 items. Accompanying explanation and both text and graphical examples linked to the items were developed and pilot tested over a 6-month period. The RIVA-C checklist and guide was designed to facilitate the creation of clear, transparent and sufficiently detailed infographics which summarise comparative studies of health and medical interventions. Accurate infographics can ensure research findings are communicated appropriately and not misinterpreted. By capturing the perspectives of a wide range of end users (eg, authors, informatics editors, journal editors, consumers), we are hopeful of rapid endorsement and implementation of RIVA-C.</p

Multi-antigen Vaccination With Simultaneous Engagement of the OX40 Receptor Delays Malignant Mesothelioma Growth and Increases Survival in Animal Models

Malignant Mesothelioma (MM) is a rare and highly aggressive cancer that develops from mesothelial cells lining the pleura and other internal cavities, and is often associated with asbestos exposure. To date, no effective treatments have been made available for this pathology. Herein, we propose a novel immunotherapeutic approach based on a unique vaccine targeting a series of antigens that we found expressed in different MM tumors, but largely undetectable in normal tissues. This vaccine, that we term p-Tvax, is comprised of a series of immunogenic peptides presented by both MHC-I and -II to generate robust immune responses. The peptides were designed using in silico algorithms that discriminate between highly immunogenic T cell epitopes and other harmful epitopes, such as suppressive regulatory T cell epitopes and autoimmune epitopes. Vaccination of mice with p-Tvax led to antigen-specific immune responses that involved both CD8+ and CD4+ T cells, which exhibited cytolytic activity against MM cells in vitro. In mice carrying MM tumors, p-Tvax increased tumor infiltration of CD4+ T cells. Moreover, combining p-Tvax with an OX40 agonist led to decreased tumor growth and increased survival. Mice treated with this combination immunotherapy displayed higher numbers of tumor-infiltrating CD8+ and CD4+ T cells and reduced T regulatory cells in tumors. Collectively, these data suggest that the combination of p-Tvax with an OX40 agonist could be an effective strategy for MM treatment

Designed glycopeptidomimetics disrupt protein−protein interactions mediating amyloid β‑peptide aggregation and restore neuroblastoma cell viability

How anti-Alzheimer’s drug candidates that reduce amyloid 1−42 peptide fibrillization interact with the most neurotoxic species is far from being understood. We report herein the capacity of sugar-based peptidomimetics to inhibit both Aβ1−42 early oligomerization and fibrillization. A wide range of bio- and physicochemical techniques, such as a new capillary electrophoresis method, nuclear magnetic resonance, and surface plasmon resonance, were used to identify how these new molecules can delay the aggregation of Aβ1−42. We demonstrate that these molecules interact with soluble oligomers in order to maintain the presence of nontoxic monomers and to prevent fibrillization. These compounds totally suppress the toxicity of Aβ1−42 toward SH-SY5Y neuroblastoma cells, even at substoichiometric concentrations. Furthermore, demonstration that the best molecule combines hydrophobic moieties, hydrogen bond donors and acceptors, ammonium groups, and a hydrophilic β-sheet breaker element provides valuable insight for the future structure-based design of inhibitors of Aβ1−42 aggregation

Planning Technologies for Interactive Storytelling

Since AI planning was first proposed for the task of narrative generation in interactive storytelling (IS), it has emerged as the dominant approach in this field. This chapter traces the use of planning technologies in this area, considers the core issues involved in the application of planning technologies in IS, and identifies some of the remaining challenges

In-House Packed Porous Graphitic Carbon Columns for Liquid Chromatography-Mass Spectrometry Analysis of N-Glycans

Protein glycosylation is a common post-translational modification that modulates biological processes such as the immune response and protein trafficking. Altered glycosylation profiles are associated with cancer and inflammatory diseases, as well as impacting the efficacy of therapeutic monoclonal antibodies. Consisting of oligosaccharides attached to asparagine residues, enzymatically released N-linked glycans are analytically challenging due to the diversity of isomeric structures that exist. A commonly used technique for quantitative N-glycan analysis is liquid chromatography-mass spectrometry (LC-MS), which performs glycan separation and characterization. Although many reversed and normal stationary phases have been utilized for the separation of N-glycans, porous graphitic carbon (PGC) chromatography has become desirable because of its higher resolving capability, but is difficult to implement in a robust and reproducible manner. Herein, we demonstrate the analytical properties of a 15 cm fused silica capillary (75 µm i.d., 360 µm o.d.) packed in-house with Hypercarb PGC (3 µm) coupled to an Agilent 6550 Q-TOF mass spectrometer for N-glycan analysis in positive ion mode. In repeatability and intermediate precision measurements conducted on released N-glycans from a glycoprotein standard mixture, the majority of N-glycans reported low coefficients of variation with respect to retention times (≤4.2%) and peak areas (≤14.4%). N-glycans released from complex samples were also examined by PGC LC-MS. A total of 120 N-glycan structural and compositional isomers were obtained from formalin-fixed paraffin-embedded ovarian cancer tissue sections. Finally, a comparison between early- and late-stage formalin-fixed paraffin-embedded ovarian cancer tissues revealed qualitative changes in the α2,3- and α2,6-sialic acid linkage of a fucosylated bi-antennary complex N-glycan. Although the α2,3-linkage was predominant in late-stage ovarian cancer, the alternate α2,6-linkage was more prevalent in early-stage ovarian cancer. This study establishes the utility of in-house packed PGC columns for the robust and reproducible LC-MS analysis of N-glycans

An intercomparison of measurement systems for vapor and particulate phase concentrations of formic and acetic acids

During June 1986, eight systems for measuring vapor phase and four for measuring particulate phase concentrations of formic acid (HCOOH) and acetic acid (CH_3COOH) were intercompared in central Virginia. HCOOH and CH_3COOH vapors were sampled by condensate, mist, Chromosorb 103 GC resin, NaOH-coated annular denuders, NaOH impregnated quartz filters, K_2CO_3 and Na_2CO_3 impregnated cellulose filters, and Nylasorb membranes. Atmospheric aerosol was collected on Teflon and Nuclepore filters using both hi-vol and lo-vol systems to measure particulate phase concentrations. Samples were collected during 31 discrete day and night intervals of 0.5–2 hour duration over a 4-day period. Performance of the mist chamber and K_2CO_3 impregnated filter techniques were also evaluated using zero air and ambient air spiked with HCOOH_g, CH_3COOH_g, and formaldehyde (CH_2O_g) from permeation sources. Results of this intercomparison show significant systematic and episodic artifacts among many currently deployed measurement systems for HCOOH_g and CH_3COOH_g. The spiking experiments revealed no significant interferences for the mist chamber technique and results generated by the mist chamber and denuder techniques were statistically indistinguishable. The condensate technique showed general agreement with the mist chamber and denuder methods, but episodic bias between these systems was inferred from large and significant differences observed during the first day of sampling. Nylasorb membranes are unacceptable for collecting carboxylic acid vapors as they did not retain HCOOH_g and CH_3COOH_g quantitatively. Strong base impregnated filter and GC resin sampling techniques are prone to large positive interferences apparently resulting, in part, from reactions involving CH_2O_g to generate HCOOH and CH_3COOH subsequent to collection. Significant bias presumably associated with differences in postcollection handling was observed for particulate phase measurements by participating groups. Analytical bias did not contribute significantly to differences in vapor and particulate phase measurements

Large Scale Application of Neural Network Based Semantic Role Labeling for Automated Relation Extraction from Biomedical Texts

To reduce the increasing amount of time spent on literature search in the life sciences, several methods for automated knowledge extraction have been developed. Co-occurrence based approaches can deal with large text corpora like MEDLINE in an acceptable time but are not able to extract any specific type of semantic relation. Semantic relation extraction methods based on syntax trees, on the other hand, are computationally expensive and the interpretation of the generated trees is difficult. Several natural language processing (NLP) approaches for the biomedical domain exist focusing specifically on the detection of a limited set of relation types. For systems biology, generic approaches for the detection of a multitude of relation types which in addition are able to process large text corpora are needed but the number of systems meeting both requirements is very limited. We introduce the use of SENNA (“Semantic Extraction using a Neural Network Architecture”), a fast and accurate neural network based Semantic Role Labeling (SRL) program, for the large scale extraction of semantic relations from the biomedical literature. A comparison of processing times of SENNA and other SRL systems or syntactical parsers used in the biomedical domain revealed that SENNA is the fastest Proposition Bank (PropBank) conforming SRL program currently available. 89 million biomedical sentences were tagged with SENNA on a 100 node cluster within three days. The accuracy of the presented relation extraction approach was evaluated on two test sets of annotated sentences resulting in precision/recall values of 0.71/0.43. We show that the accuracy as well as processing speed of the proposed semantic relation extraction approach is sufficient for its large scale application on biomedical text. The proposed approach is highly generalizable regarding the supported relation types and appears to be especially suited for general-purpose, broad-scale text mining systems. The presented approach bridges the gap between fast, cooccurrence-based approaches lacking semantic relations and highly specialized and computationally demanding NLP approaches