Gas Sensing Properties of Single Conducting Polymer Nanowires and the Effect of Temperature

We measured the electronic properties and gas sensing responses of template-grown poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonate) (PEDOT/PSS)-based nanowires. The nanowires have a "striped" structure (gold-PEDOT/PSS-gold), typically 8um long (1um-6um-1um for each section, respectively) and 220 nm in diameter. Single-nanowire devices were contacted by pre-fabricated gold electrodes using dielectrophoretic assembly. A polymer conductivity of 11.5 +/- 0.7 S/cm and a contact resistance of 27.6 +/- 4 kOhm were inferred from measurements of nanowires of varying length and diameter. The nanowire sensors detect a variety of odors, with rapid response and recovery (seconds). The response (R-R0)/R0 varies as a power law with analyte concentration.Comment: 4 figures 8 pages, add 2 reference

Market Access Analysis of Biologics and Small-Molecule Inhibitors for Inflammatory Bowel Disease Among US Health Insurance Policies.

BACKGROUND AND AIMS:Treatment pathways for ulcerative colitis (UC) and Crohn's disease (CD) are shifting to a more individualized, risk-stratified approach. The perception is that insurance policies may not have implemented this paradigm shift, particularly regarding access to newer agents. We evaluated patient access to advanced therapies by analyzing policy information from the Managed Markets Insight and Technology database. METHODS:Coverage status as of December 2018 for all US lives was queried for adalimumab, infliximab, infliximab-dyyb, tofacitinib, ustekinumab, and vedolizumab by indication (UC and/or CD) and medical or pharmacy coverage benefit. Coverage status was classified by the number of biologic steps before access to specified drug as "No Biologic," "1 Prior Biologic," "2+ Prior Biologics," "Not Covered." Unknown lives were excluded from the analyses. RESULTS:Coverage analysis was available for approximately 302 million lives under each medical and pharmacy benefit. Our analysis indicates that approximately half of covered lives had access to all agents (except tofacitinib) as first-line therapy; two-thirds had access after one biologic exposure. Among newer agents, vedolizumab had the widest coverage. For indications of UC and CD, 81% of known lives had access to vedolizumab with no prior biologic exposure required ("No Biologic"), 95% after "No Biologic" + "1 prior Biologic." Geographic variations were identified for coverage patterns. CONCLUSIONS:This US-based healthcare policy analysis points to an increased access to advanced therapies for UC and CD. An individualized, risk-stratified treatment approach integrating advanced therapies, including those recently approved, into treatment pathways for UC and CD is feasible

Evaluation of the angiotensin II receptor blocker azilsartan medoxomil in African-American patients with hypertension

The efficacy and safety of azilsartan medoxomil (AZL-M) were evaluated in African-American patients with hypertension in a 6-week, double-blind, randomized, placebo-controlled trial, for which the primary end point was change from baseline in 24-hour mean systolic blood pressure (BP). There were 413 patients, with a mean age of 52years, 57% women, and baseline 24-hour BP of 146/91mmHg. Treatment differences in 24-hour systolic BP between AZL-M 40mg and placebo (-5.0mmHg; 95% confidence interval, -8.0 to -2.0) and AZL-M 80mg and placebo (-7.8mmHg; 95% confidence interval, -10.7 to -4.9) were significant (P.001 vs placebo for both comparisons). Changes in the clinic BPs were similar to the ambulatory BP results. Incidence rates of adverse events were comparable among the treatment groups, including those of a serious nature. In African-American patients with hypertension, AZL-M significantly reduced ambulatory and clinic BPs in a dose-dependent manner and was well tolerated

A language and compiler for enabling automatic and parallel chemistry simulations

The modeling of chemical reactions, and the ability to predict the properties of the end- products of a chemical reaction, is of extreme commercial importance. The properties of many compounds have complex dependencies on a variety of additives, in ways that are not well understood. This paper describes a domain specific language, compiler and parallel runtime system that allows chemists to investigate, and understand how, different additives affect these properties. In particular, our system allows the inputs and types of reactions that are possible to be specified in a high level language. It then produces a set of ordinary differential equations (ODEs) that when combined with boundary conditions from quantum chemistry are processed using parallel templates and off-the-shelf solvers to simulate the reaction. This paper describes the complete system, including optimizations to reduce the amount of redundant computation in the ODEs, the parallel templates for simulating the reaction, and experimental data showing the effectiveness of these. Our system saves the chemist from manually developing, testing and debugging systems of hundreds or thousands of ODEs that require weeks or months to develop

UNSEEN: Bewertung der Unsicherheiten in linear optimierenden Energiesystemmodellen unter Zuhilfenahme Neuronaler Netze

Der Einsatz von Modellen zur Erstellung und Untersuchung von Szenarien ist ein wesentliches Instrument der Energiesystemanalyse. Für die Politikberatung ist die Frage nach der Verlässlichkeit von solchen Szenarien von großer Wichtigkeit, da diese mit großen Unsicherheiten behaftet sein können. Diesem Problem wird in UNSEEN begegnet: durch das Abfahren eines sehr großen Parameterraums sollen weit mehr als 1000 Energieszenarien automatisch generiert, berechnet und ausgewertet werden. Hierzu zählen insbesondere auch Extremszenarien. Eine wesentliche Herausforderung ist dabei die Senkung von Modellrechenzeiten zur Lösung gemischt-ganzzahliger Optimierungsprobleme. Im Vorläuferprojekt BEAM-ME wurde mit der Entwicklung und Anwendung des Open Source Solvers PIPS-IPM++ die Voraussetzung für den Einsatz von Hochleistungscomputern zur performanten Lösung dieser Modelle gelegt. Die grundlegende Idee für die Weiterentwicklung ist es eine Methode des Maschinellen Lernens (Reinforcement Learning) zu verwenden, um schnelle Vorhersagen der Ergebnisse eines Optimierungsproblems zu erhalten und diese als Startlösung für einen deterministischen Lösungsalgorithmus zu nutzen. Mittels Modellkopplungen und statistischer Analysen werden ex-post ausführliche Auswertungen des entstehenden Szenarioraums durchgeführt. Hierzu werden multi-kriterielle Indikatoren (u. a. zu Angemessenheit, Betriebssicherheit und Wirtschaftlichkeit) von möglichen, zukünftigen Stromversorgungssystemen ermittelt. Auf dieser Grundlage sollen abschließend Methoden entwickelt werden, um besonders interessante Punkte innerhalb des Szenarioraums gezielt ansteuern können

Development and Validation of Clinical Scoring Tool to Predict Outcomes of Treatment With Vedolizumab in Patients With Ulcerative Colitis.

BACKGROUND & AIMS: We created and validated a clinical decision support tool (CDST) to predict outcomes of vedolizumab therapy for ulcerative colitis (UC). METHODS: We performed logistic regression analyses of data from the GEMINI 1 trial, from 620 patients with UC who received vedolizumab induction and maintenance therapy (derivation cohort), to identify factors associated with corticosteroid-free remission (full Mayo score of 2 or less, no subscore above 1). We used these factors to develop a model to predict outcomes of treatment, which we called the vedolizumab CDST. We evaluated the correlation between exposure and efficacy. We validated the CDST in using data from 199 patients treated with vedolizumab in routine practice in the United States from May 2014 through December 2017. RESULTS: Absence of exposure to a tumor necrosis factor (TNF) antagonist (+3 points), disease duration of 2 y or more (+3 points), baseline endoscopic activity (moderate vs severe) (+2 points), and baseline albumin concentration (+0.65 points per 1 g/L) were independently associated with corticosteroid-free remission during vedolizumab therapy. Patients in the derivation and validation cohorts were assigned to groups of low (CDST score, 26 points or less), intermediate (CDST score, 27-32 points), or high (CDST score, 33 points or more) probability of vedolizumab response. We observed a statistically significant linear relationship between probability group and efficacy (area under the receiver operating characteristic curve, 0.65), as well as drug exposure (P \u3c .001) in the derivation cohort. In the validation cohort, a cutoff value of 26 points identified patients who did not respond to vedolizumab with high sensitivity (93%); only the low and intermediate probability groups benefited from reducing intervals of vedolizumab administration due to lack of response (P = .02). The vedolizumab CDST did not identify patients with corticosteroid-free remission during TNF antagonist therapy. CONCLUSIONS: We used data from a trial of patients with UC to develop a scoring system, called the CDST, which identified patients most likely to enter corticosteroid-free remission during vedolizumab therapy, but not anti-TNF therapy. We validated the vedolizumab CDST in a separate cohort of patients in clinical practice. The CDST identified patients most likely to benefited from reducing intervals of vedolizumab administration due to lack of initial response. ClinicalTrials.gov no: NCT00783718

Phenotypic Pattern-Based Assay for Dynamically Monitoring Host Cellular Responses to Salmonella Infections

The interaction between mammalian host cells and bacteria is a dynamic process, and the underlying pathologic mechanisms are poorly characterized. Limited information describing the host-bacterial interaction is based mainly on studies using label-based endpoint assays that detect changes in cell behavior at a given time point, yielding incomplete information. In this paper, a novel, label-free, real-time cell-detection system based on electronic impedance sensor technology was adapted to dynamically monitor the entire process of intestinal epithelial cells response to Salmonella infection. Changes in cell morphology and attachment were quantitatively and continuously recorded following infection. The resulting impedance-based time-dependent cell response profiles (TCRPs) were compared to standard assays and showed good correlation and sensitivity. Biochemical assays further suggested that TCRPs were correlated with cytoskeleton-associated morphological dynamics, which can be largely attenuated by inhibitions of actin and microtubule polymerization. Collectively, our data indicate that cell-electrode impedance measurements not only provide a novel, real-time, label-free method for investigating bacterial infection but also help advance our understanding of host responses in a more physiological and continuous manner that is beyond the scope of current endpoint assays

The prediction of radiationâ induced liver dysfunction using a local dose and regional venous perfusion model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134805/1/mp1081.pd

Evaluation of uncertainties in linear energy system optimization models using HPC and neural networks

Within the interdisciplinary BMWK-funded project UNSEEN, experts from High Performance Computing, mathematical optimization and energy systems analysis combine strengths to evaluate uncertainties in modeling and planning future energy systems with the aid of High Performance Computing (HPC) and neural networks. Energy System Models (ESM) are central instruments for realizing the energy transition. These models try to optimize complex energy systems in order to ensure security of supply while minimizing costs for power production and transmission. In order to derive reliable and robust policy advice for decision makers, hundreds or even thousands of ESM problems need to be solved in order to address uncertainties in a given model and dataset.Mixed-integer linear programs (MIPs), a direct extension of Linear programs (LPs), can be used to formulate and compute more concrete and realistic energy systems. Since the availability of fast LP solvers is a major prerequisite for optimizing MIPs, the development of an open-source scalable distributed-memory LP solver, called PIPS-IPM++, was started in a preceding project and can already outperform state-of-the-art solvers. A second prerequisite for efficient MIP solving is the availability of MIP heuristics. For this purpose, we develop a generic MIP framework including reinforcement learning methods. Moreover, we aim to implement an efficient automated HPC workflow for generating, solving, and postprocessing numerous ESM problems with a special structure in order to develop new tools for better predictions about the future of our energy system. This novel approach couples multiple existing and new software packages to achieve the project goals