A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification

Essential genes contain key information of genomes that could be the key to a comprehensive understanding of life and evolution. Because of their importance, studies of essential genes have been considered a crucial problem in computational biology. Computational methods for identifying essential genes have become increasingly popular to reduce the cost and time-consumption of traditional experiments. A few models have addressed this problem, but performance is still not satisfactory because of high dimensional features and the use of traditional machine learning algorithms. Thus, there is a need to create a novel model to improve the predictive performance of this problem from DNA sequence features. This study took advantage of a natural language processing (NLP) model in learning biological sequences by treating them as natural language words. To learn the NLP features, a supervised learning model was consequentially employed by an ensemble deep neural network. Our proposed method could identify essential genes with sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), and area under the receiver operating characteristic curve (AUC) values of 60.2%, 84.6%, 76.3%, 0.449, and 0.814, respectively. The overall performance outperformed the single models without ensemble, as well as the state-of-the-art predictors on the same benchmark dataset. This indicated the effectiveness of the proposed method in determining essential genes, in particular, and other sequencing problems, in general.     

Significant enhancement of fracture toughness and mechanical properties of epoxy resin using CTBN-grafted epoxidized linseed oil

Carboxyl-terminated poly(acrylonitrile-co-butadiene) (CTBN)-grafted epoxidized linseed oil (ELO) (CTBN-g-ELO) was synthesized and used as an effective toughener to simultaneously enhance the mechanical properties and fracture toughness of epoxy resin (EP). The ELO was fabricated from linseed oil via epoxidation processing. The characteristics of the ELO and CTBN-g-ELO, such as the average molecular weight and chemical structure, were determined using gel permeation chromatography, proton nuclear magnetic resonance, and Fourier transform infrared spectroscopy. The effects of the CTBN-g-ELO loading on the characteristics of the EP were investigated in detail. The test results indicated that by adding 15 phr CTBN-g-ELO, the tensile strength, impact strength, and critical stress intensity factor (K_IC) were significantly increased, by approximately 23.62, 91.8, and 33.8%, respectively, compared with pristine EP. The glass-transition temperature (T_g) and storage modulus, which were examined via dynamic mechanical thermal analysis and differential scanning calorimetry, respectively, exhibited decreasing trends. Scanning electron microscopy revealed that the CTBN-g-ELO existed as spherical particles in the EP, helping to stop the crack growth and change the crack growth directions. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48276.     

Flapping flight in the wake of a leading insect

Journal of Mechanical Science and Technology - This study explores the wake effects of an upstream leading insect on the flight performance of a following one. The potential-flow based aerodynamic...     

Near room temperature dyeing of m-aramid fabrics

Due to their high crystallinity and inertness, the coloration of meta-aramid fiber has been widely confirmed to be difficult. Though substantial improvements have been achieved, the dyeing of this fiber still requires high temperature and long duration for good color strength and shade. In this article, grafting poly(acrylic acid), a polyelectrolyte, onto meta-aramind fibers followed by dyeing via conventional pad-dry-cure technique resulted in good dyeing and a decrease the dyeing time and temperature. Dyed samples that possess industrially acceptable K/S values were produced in 15 minutes at room temperature (25–27 °C) under neutral pH. Dyed samples had good dry and wet crockfastness. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 48190.     

Biodegradable Periodic Mesoporous Organosilica (BPMO) Loaded with Daunorubicin: A Promising Nanoparticle-Based Anticancer Drug

Biodegradable periodic mesoporous organosilica (BPMO) nanoparticles have emerged as a promising type of nanocarrier for drug delivery, given the biodegradable feature is advantageous for clinical translation. In this paper, we report synthesis and characterization of daunorubicin (DNR) loaded BPMO. DNR was loaded onto rhodamine B-labeled BPMO that contain tetrasulfide bonds. Tumor spheroids and chicken egg tumor models were used to characterize the activity in biological settings. In the first experiment we examined the uptake of BPMO into tumor spheroids prepared from ovarian cancer cells. BPMO were efficiently taken up into tumor spheroids and inhibited their growth. In the chicken egg tumor model, intravenous injection of DNR-loaded BPMO led to the elimination of ovarian tumor. Lack of adverse effect on organs such as lung appears to be due to excellent tumor accumulation of BPMO. Thus, DNR-loaded BPMO represents a promising nanodrug compared with free DNR currently used in cancer therapy. OK     

Stability evaluation of ethanol dry reforming on Lanthania‐doped cobalt‐based catalysts for hydrogen‐rich syngas generation

Catalytic stability with time‐on‐stream is an important aspect in ethanol dry reforming (EDR) since catalysts could encounter undesirable deterioration arising from deposited carbon. This work examined the promotional effect of La on 10%Co/Al₂O₃ in terms of activity, stability, and characteristics. Catalysts were characterized by X‐ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), Raman, and X‐ray photoelectron spectroscopy (XPS) measurements whilst catalytic EDR performance of La‐promoted and unpromoted 10%Co/Al₂O₃ prepared via wet impregnation technique was investigated at 973 K for 72 h using a stoichiometric feed ratio (C₂H₅OH/CO₂ = 1/1). La promoter substantially enhanced both metal dispersion and metal surface area from 0.11% to 0.64% and 0.08 to 0.43 m² g^?1, respectively. Ethanol and CO₂ conversions appeared to be stable within 50 to 72 h after experiencing an initial activity drop. The conversion of C₂H₅OH and CO₂ for La‐promoted catalyst was about 1.65 and 1.34 times greater than unpromoted counterpart in this order. The carbonaceous deposition was considerably decreased from 55.6% to 36.8% with La promotion due to La₂O₂CO₃ intermediate formation. Additionally, 3%La‐10%Co/Al₂O₃ possessed greater oxygen vacancies acting as active sites for CO₂ adsorption and hence increasing carbon gasification. Even though graphitic and filamentous carbons were formed on used catalyst surface, La‐addition diminished graphite formation and increased the reactiveness of amorphous carbon.     

Identification of Anti-Mycobacterial Biofilm Agents Based on the 2-Aminoimidazole Scaffold

Tuberculosis (TB) remains a significant global health problem for which new therapeutic options are sorely needed. The ability of the causative agent, Mycobacterium tuberculosis, to reside within host macrophages and form biofilm-like communities contributes to the persistent and drug-tolerant nature of the disease. Compounds that can prevent or reverse the biofilm-like phenotype have the potential to serve alongside TB antibiotics to overcome this tolerance, and decrease treatment duration. Using Mycobacterium smegmatis as a surrogate organism, we report the identification of two new 2-aminoimidazole compounds that inhibit and disperse mycobacterial biofilms, work synergistically with isoniazid and rifampicin to eradicate preformed M. smegmatis biofilms in vitro, are nontoxic toward Galleria mellonella, and exhibit stability in mouse plasma.     

Forecasting Damage Mechanics By Deep Learning

We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics problems. The methodologies that are able to work accurately for less computational and resolving attempts are a significant demand nowadays. Relied on learning an amount of information from given data, the long short-term memory (LSTM) method and multi-layer neural networks (MNN) method are applied to predict solutions. Numerical examples are implemented for predicting fracture growth rates of L-shape concrete specimen under load ratio, single-edge-notched beam forced by 4-point shear and hydraulic fracturing in permeable porous media problems such as storage-toughness fracture regime and fracture-height growth in Marcellus shale. The predicted results by deep learning algorithms are well-agreed with experimental data.     

Critical Aspects and Recent Advances in Structural Engineering of Photocatalysts for Sunlight‐Driven Photocatalytic Reduction of CO2 into Fuels

The catalytic conversion of CO₂ into valuable fuels is a compelling solution for tackling the global warming and fuel crisis. Light absorption and charge separation, as well as adsorption/activation of CO₂ on the photocatalyst surface, are essential steps for this process. This article reviews the CO₂ photoreduction mechanisms and critical aspects that greatly affect the photoreduction efficiency. Additionally, different materials for CO₂ photoreduction are provided, including d⁰ and d¹⁰ metal oxides/mixed oxides, sulfides, polymeric materials, and metal phosphides with visible response, metal‐organic frameworks, and layer double hydroxides. Furthermore, various structural engineering strategies and corresponding state‐of‐the‐art photocatalytic systems are reviewed and discussed, such as bandgap engineering, geometrical nanostructure engineering, and heterostructure engineering. Each strategy has advantages and disadvantages, requiring further adjustment to further improve the photocatalytic performance of the photocatalyst. Based on this review, it is greatly expected that efficiently artificial systems and the breakthrough technologies for CO₂ reduction will be successfully developed in the future to solve the energy shortage as well as the environmental problem.