Heterologous boost with mRNA vaccines against SARS-CoV-2 Delta/Omicron variants following an inactivated whole-virus vaccine.

The coronavirus SARS-CoV-2 has mutated quickly and caused significant global damage. This study characterizes two mRNA vaccines ZSVG-02 (Delta) and ZSVG-02-O (Omicron BA.1), and associating heterologous prime-boost strategy following the prime of a most widely administrated inactivated whole-virus vaccine (BBIBP-CorV). The ZSVG-02-O induces neutralizing antibodies that effectively cross-react with Omicron subvariants. In naïve animals, ZSVG-02 or ZSVG-02-O induce humoral responses skewed to the vaccines targeting strains, but cellular immune responses cross-react to all variants of concern (VOCs) tested. Following heterologous prime-boost regimes, animals present comparable neutralizing antibody levels and superior protection against Delta and Omicron BA.1variants. Single-boost only generated ancestral and omicron dual-responsive antibodies, probably by recall and reshape the prime immunity. New Omicron-specific antibody populations, however, appeared only following the second boost with ZSVG-02-O. Overall, our results support a heterologous boost with ZSVG-02-O, providing the best protection against current VOCs in inactivated virus vaccine-primed populations

Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism

Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases

Fast Computation of Singular Oscillatory Fourier Transforms

We consider the problem of the numerical evaluation of singular oscillatory Fourier transforms  ∫ab‍x-aαb-xβf(x)eiωxdx, where α>-1  and  β>-1. Based on substituting the original interval of integration by the paths of steepest descent, if f is analytic in the complex region G containing [a, b], the computation of integrals can be transformed into the problems of integrating two integrals on [0, ∞) with the integrand that does not oscillate and decays exponentially fast, which can be efficiently computed by using the generalized Gauss Laguerre quadrature rule. The efficiency and the validity of the method are demonstrated by both numerical experiments and theoretical results. More importantly, the presented method in this paper is also a great improvement of a Filon-type method and a Clenshaw-Curtis-Filon-type method shown in Kang and Xiang (2011) and the Chebyshev expansions method proposed in Kang et al. (2013), for computing the above integrals

Selective Oxidation of Toluene to Benzaldehyde Using Co-ZIF Nano-Catalyst

Nanometer-size Co-ZIF (zeolitic imidazolate frameworks) catalyst was prepared for selective oxidation of toluene to benzaldehyde under mild conditions. The typical characteristics of the metal-organic frameworks (MOFs) material were affirmed by the XRD, SEM, and TEM, the BET surface area of this catalyst was as high as 924.25 m2/g, and the diameter of particles was near 200 nm from TEM results. The Co metal was coated with 2-methyl glyoxaline, and the crystalline planes were relatively stable. The reaction temperatures, oxygen pressure, mass amount of N-hydroxyphthalimide (NHPI), and reaction time were discussed. The Co-ZIF catalyst gave the best result of 92.30% toluene conversion and 91.31% selectivity to benzaldehyde under 0.12 MPa and 313 K. The addition of a certain amount of NHPI and the smooth oxidate capacity of the catalyst were important factors in the high yield of benzaldehyde. This nanometer-size catalyst showed superior performance for recycling use in the oxidation of toluene. Finally, a possible reaction mechanism was proposed. This new nanometer-size Co-ZIF catalyst will be applied well in the selective oxidation of toluene to benzaldehyde

MetaBoot: a machine learning framework of taxonomical biomarker discovery for different microbial communities based on metagenomic data

As more than 90% of species in a microbial community could not be isolated and cultivated, the metagenomic methods have become one of the most important methods to analyze microbial community as a whole. With the fast accumulation of metagenomic samples and the advance of next-generation sequencing techniques, it is now possible to qualitatively and quantitatively assess all taxa (features) in a microbial community. A set of taxa with presence/absence or their different abundances could potentially be used as taxonomical biomarkers for identification of the corresponding microbial community’s phenotype. Though there exist some bioinformatics methods for metagenomic biomarker discovery, current methods are not robust, accurate and fast enough at selection of non-redundant biomarkers for prediction of microbial community’s phenotype. In this study, we have proposed a novel method, MetaBoot, that combines the techniques of mRMR (minimal redundancy maximal relevance) and bootstrapping, for discover of non-redundant biomarkers for microbial communities through mining of metagenomic data. MetaBoot has been tested and compared with other methods on well-designed simulated datasets considering normal and gamma distribution as well as publicly available metagenomic datasets. Results have shown that MetaBoot was robust across datasets of varied complexity and taxonomical distribution patterns and could also select discriminative biomarkers with quite high accuracy and biological consistency. Thus, MetaBoot is suitable for robustly and accurately discover taxonomical biomarkers for different microbial communities

Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects

Monitoring in situ chlorophyll (Chl) content in agricultural crop leaves is of great importance for stress detection, nutritional state diagnosis, yield prediction and studying the mechanisms of plant and environment interaction. Numerous spectral indices have been developed for chlorophyll estimation from leaf- and canopy-level reflectance. However, in most cases, these indices are negatively affected by variations in canopy structure and soil background. The objective of this study was to develop spectral indices that can reduce the effects of varied canopy structure and growth stages for the estimation of leaf Chl. Hyperspectral reflectance data was obtained through simulation by a radiative transfer model, PROSAIL, and measurements from canopies of barley comprising different cultivars across growth stages using spectroradiometers. We applied a comprehensive band-optimization algorithm to explore five types of spectral indices: reflectance difference (RD), reflectance ratio (RR), normalized reflectance difference (NRD), difference of reflectance ratio (DRR) and ratio of reflectance difference (RRD). Indirectly using the multiple scatter correction (MSC) theory, we hypothesized that RRD can eliminate adverse effects of soil background, canopy structure and multiple scattering. Published indices and multivariate models such as optimum multiple band regression (OMBR), partial least squares regression (PLSR) and support vector machines for regression (SVR) were also employed. Results showed that the ratio of reflectance difference index (RRDI) optimized for simulated data significantly improved the correlation with Chl (R-2 = 0.98, p < 0.0001) and was insensitive to LAI variations (1-8), compared to widely used indices such as MCARI/OSAVI (R-2 = 0.64, p < 0.0001) and TCARI/OSAVI (R-2 = 0.74, p < 0.0001). The RRDI optimized for barley explained 76% of the variation in Chi and outperformed multivariate models. However, the accuracy decreased when employing the indices for individual growth stages (R-2 < 0.59). Accordingly, RRDIs optimized for open and closed canopies improved the estimations of Chl for individual stages before and after canopy closure, respectively, with R-2 of 0.65 (p < 0.0001) and 0.78 (p < 0.0001). This study shows that RRDI can efficiently eliminate the effects of structural properties on canopy reflectance response to canopy biochemistry. The results yet are limited to the datasets used in this study; therefore, transferability of the methods to large scales or other datasets should be further evaluated. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved

Unconventional gas:Experimental study of the influence of subcritical carbon dioxide on the mechanical properties of black shale

An experimental study was performed to investigate the effect of subcritical carbon dioxide (CO2) adsorption on mechanical properties of shales with different coring directions. Uniaxial compressive strength (UCS) tests were conducted on shale samples with different CO2 adsorption time at a pressure of 7 MPa and a temperature of 40 °C. The crack propagation and the failure mechanism of shale samples were recorded by using acoustic emission (AE) sensors together with ARAMIS technology. According to the results, samples with parallel and normal bedding angles present reductions of 26.7% and 3.0% in UCS, 30.7% and 36.7% in Young’s modulus after 10 days’ adsorption of CO2, and 30.3% and 18.4% in UCS, 13.8% and 22.6% in Young’s modulus after 20 days’ adsorption of CO2. Samples with a normal bedding angle presented higher brittleness index than that with a parallel bedding angle. The strain distributions show that longer CO2 adsorption will cause higher axial strains and lateral strains. The AE results show that samples with a parallel angle have higher AE energy release than the samples with a normal angle. Finally, samples with longer CO2 adsorption times present higher cumulative AE energy release

Unconventional Gas: Experimental Study of the Influence of Subcritical Carbon Dioxide on the Mechanical Properties of Black Shale

An experimental study was performed to investigate the effect of subcritical carbon dioxide (CO2) adsorption on mechanical properties of shales with different coring directions. Uniaxial compressive strength (UCS) tests were conducted on shale samples with different CO2 adsorption time at a pressure of 7 MPa and a temperature of 40 °C. The crack propagation and the failure mechanism of shale samples were recorded by using acoustic emission (AE) sensors together with ARAMIS technology. According to the results, samples with parallel and normal bedding angles present reductions of 26.7% and 3.0% in UCS, 30.7% and 36.7% in Young’s modulus after 10 days’ adsorption of CO2, and 30.3% and 18.4% in UCS, 13.8% and 22.6% in Young’s modulus after 20 days’ adsorption of CO2. Samples with a normal bedding angle presented higher brittleness index than that with a parallel bedding angle. The strain distributions show that longer CO2 adsorption will cause higher axial strains and lateral strains. The AE results show that samples with a parallel angle have higher AE energy release than the samples with a normal angle. Finally, samples with longer CO2 adsorption times present higher cumulative AE energy release

Investigation of Leaf Diseases and Estimation of Chlorophyll Concentration in Seven Barley Varieties Using Fluorescence and Hyperspectral Indices

Leaf diseases, such as powdery mildew and leaf rust, frequently infect barley plants and severely affect the economic value of malting barley. Early detection of barley diseases would facilitate the timely application of fungicides. In a field experiment, we investigated the performance of fluorescence and reflectance indices on (1) detecting barley disease risks when no fungicide is applied and (2) estimating leaf chlorophyll concentration (LCC). Leaf fluorescence and canopy reflectance were weekly measured by a portable fluorescence sensor and spectroradiometer, respectively. Results showed that vegetation indices recorded at canopy level performed well for the early detection of slightly-diseased plants. The combined reflectance index, MCARI/TCARI, yielded the best discrimination between healthy and diseased plants across seven barley varieties. The blue to far-red fluorescence ratio (BFRR_UV) and OSAVI were the best fluorescence and reflectance indices for estimating LCC, respectively, yielding R-2 of 0.72 and 0.79. Partial least squares (PLS) and support vector machines (SVM) regression models further improved the use of fluorescence signals for the estimation of LCC, yielding R-2 of 0.81 and 0.84, respectively. Our results demonstrate that non-destructive spectral measurements are able to detect mild disease symptoms before significant losses in LCC due to diseases under natural conditions