Enhanced hydrogen sensing properties of graphene by introducing a mono-atom-vacancy

To facilitate the dissociative adsorption of H2 molecules on pristine graphene, the addition of a mono-atom-vacancy to graphene is proposed. This leads to reduction of the dissociative energy barrier for a H2 molecule on graphene from 3.097 to 0.805 eV for the first H2 and 0.869 eV for the second, according to first principles calculations. As a result, two H2 molecules can be easily dissociatively adsorbed on this defected graphene at room temperature. The electronic structure and conductivity of the graphene change significantly after H2 adsorption. In addition, the related dissociative adsorption phase diagrams under different temperatures and partial pressures show that this dissociative adsorption at room temperature is very sensitive (10-35 mol L -1). Therefore, this defected graphene is promising for ultra-sensitive room temperature hydrogen sensing. © 2013 the Owner Societies

Adjoint free four-dimensional variational data assimilation for a storm surge model of the German North Sea

In this paper, a data assimilation scheme based on the adjoint free Four-Dimensional Variational(4DVar) method is applied to an existing storm surge model of the German North Sea. To avoid the need of an adjoint model, an ensemble-like method to explicitly represent the linear tangent equation is adopted. Results of twin experiments have shown that the method is able to recover the contaminated low dimension model parameters to their true values. The data assimilation scheme was applied to a severe storm surge event which occurred in the North Sea in December 5, 2013. By adjusting wind drag coefficient, the predictive ability of the model increased significantly. Preliminary experiments have shown that an increase in the predictive ability is attained by narrowing the data assimilation time window

Multicolor Combinatorial Probe Coding for Real-Time PCR

The target volume of multiplex real-time PCR assays is limited by the number of fluorescent dyes available and the number of fluorescence acquisition channels present in the PCR instrument. We hereby explored a probe labeling strategy that significantly increased the target volume of real-time PCR detection in one reaction. The labeling paradigm, termed “Multicolor Combinatorial Probe Coding” (MCPC), uses a limited number (n) of differently colored fluorophores in various combinations to label each probe, enabling one of 2n-1 genetic targets to be detected in one reaction. The proof-of-principle of MCPC was validated by identification of one of each possible 15 human papillomavirus types, which is the maximum target number theoretically detectable by MCPC with a 4-color channel instrument, in one reaction. MCPC was then improved from a one-primer-pair setting to a multiple-primer-pair format through Homo-Tag Assisted Non-Dimer (HAND) system to allow multiple primer pairs to be included in one reaction. This improvement was demonstrated via identification of one of the possible 10 foodborne pathogen candidates with 10 pairs of primers included in one reaction, which had limit of detection equivalent to the uniplex PCR. MCPC was further explored in detecting combined genotypes of five β-globin gene mutations where multiple targets were co-amplified. MCPC strategy could expand the scope of real-time PCR assays in applications which are unachievable by current labeling strategy

Validation and refinement of two interpretable models for coronavirus disease 2019 prognosis prediction

Objective: To validate two proposed coronavirus disease 2019 (COVID-19) prognosis models, analyze the characteristics of different models, consider the performance of models in predicting different outcomes, and provide new insights into the development and use of artificial intelligence (AI) predictive models in clinical decision-making for COVID-19 and other diseases. Materials and Methods: We compared two proposed prediction models for COVID-19 prognosis that use a decision tree and logistic regression modeling. We evaluated the effectiveness of different model-building strategies using laboratory tests and/or clinical record data, their sensitivity and robustness to the timings of records used and the presence of missing data, and their predictive performance and capabilities in single-site and multicenter settings. Results: The predictive accuracies of the two models after retraining were improved to 93.2% and 93.9%, compared with that of the models directly used, with accuracies of 84.3% and 87.9%, indicating that the prediction models could not be used directly and require retraining based on actual data. In addition, based on the prediction model, new features obtained by model comparison and literature evidence were transferred to integrate the new models with better performance. Conclusions: Comparing the characteristics and differences of datasets used in model training, effective model verification, and a fusion of models is necessary in improving the performance of AI models

Characterization of High-Fat, Diet-Induced, Non-alcoholic Steatohepatitis with Fibrosis in Rats

An ideal animal model is necessary for a clear understanding of the etiology, pathogenesis, and mechanisms of human non-alcoholic steatohepatitis (NASH) and for facilitating the design of effective therapy for this condition. We aimed to establish a rat model of NASH with fibrosis by using a high-fat diet (HFD). Male Sprague–Dawley (SD) rats were fed a HFD consisting of 88 g normal diet, 10 g lard oil, and 2 g cholesterol. Control rats were fed normal diet. Rats were killed at 4, 8, 12, 16, 24, 36, and 48 weeks after HFD exposure. Body weight, liver weight, and epididymal fat weight were measured. Serum levels of fasting glucose, triglyceride, cholesterol, alanine aminotransferase (ALT), free fatty acids (FFA), insulin, and tumor necrosis factor-alpha (TNF-α) were determined. Hepatic histology was examined by H&E stain. Hepatic fibrosis was assessed by VG stain and immunohistochemical staining for transforming growth factor beta 1 (TGF-β1), and alpha-smooth-muscle actin (α-SMA). The liver weight and liver index increased from week 4, when hepatic steatosis was also observed. By week 8, the body weight and epididymal fat weight started increasing, which was associated with increased serum levels of FFA, cholesterol, and TNF-α, as well as development of simple fatty liver. The serum ALT level increased from week 12. Steatohepatitis occurred from weeks 12 through 48. Apparent hepatic perisinosodial fibrosis did not occur until week 24, and progressed from week 36 to 48 with insulin resistance. Therefore, this novel model may be potentially useful in NASH study

Ordered Mesostructured CdS Nanowire Arrays with Rectifying Properties

Highly ordered mesoporous CdS nanowire arrays were synthesized by using mesoporous silica as hard template and cadmium xanthate (CdR2) as a single precursor. Upon etching silica, mesoporous CdS nanowire arrays were produced with a yield as high as 93 wt%. The nanowire arrays were characterized by XRD, N2adsorption, TEM, and SEM. The results show that the CdS products replicated from the mesoporous silica SBA-15 hard template possess highly ordered hexagonal mesostructure and fiber-like morphology, analogous to the mother template. The current–voltage characteristics of CdS nanoarrays are strongly nonlinear and asymmetrical, showing rectifying diode-like behavior

Measurement of the matrix element for the decay η′→ηπ +π -

The Dalitz plot of η⊃′→ηπ⊃+π⊃- decay is studied using (225.2±2.8)×106 J/ψ events collected with the BESIII detector at the BEPCII e⊃+e⊃- collider. With the largest sample of η⊃′ decays to date, the parameters of the Dalitz plot are determined in a generalized and a linear representation. Also, the branching fraction of J/ψ→γη⊃′ is determined to be (4.84±0.03±0.24)×10⊃-3, where the first error is statistical and the second systematic. © 2011 American Physical Society.published_or_final_versio