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Lock-in thermography (LT) is a non-destructive evaluation method capable of inspecting defects in composites by calculating differences of the thermal amplitude and phase between sound and defective regions. This method greatly reduces influences of non-uniform heating and emissivity in detection, while a single excitation frequency limits the depth resolution of the subsurface defects, as defects at a specific depth can only be detected with a specific excitation frequency. In this paper, a multi-frequency fused LT is proposed to improve the depth resolution and obtain better inspection results. Sequential infrared images are acquired under multiple excitation frequencies and then the phase information is extracted along with a specified enhancement scheme. Finally, a fusion image containing defects with multiple depths is constructed by using principal component analysis method. The proposed method has been applied to characterization of the flat-bottomed holes in a carbon fiber reinforced polymer (CFRP) panel and compared with other active thermography techniques. To improve the detection efficiency, an optimal selection method of excitation frequency is also proposed. The experimental results show that the use of the proposed method is able to identify all defects at multiple depths with clear edges and high contrast. [ABSTRACT FROM AUTHOR]

• A thermal signal processing algorithm for defect detection is proposed. • The number of acquisition infrared images are experimentally determined. • Long pulse thermography is applied to evaluate defects with quantitative metrics. Long pulse thermography (LPT) is widely employed as a non-destructive testing technique owing to its broad detection range, cost-effectiveness and user-friendly nature. This method is suitable for detecting materials with low thermal properties, while the image quality is limited by the blurred edges and low contrast of defects. To address these problems, a method for processing infrared image sequence based on Fourier transform, phase integration, and edge-preserving filters has been proposed. Sequential infrared images undergo phase Fourier analysis (PFA) as a first step, followed by integration of phase difference information across frequencies. Subsequently, the integrated phase image is converted into an 8-bit visual image using a designated enhancement scheme. After this processing, the surface temperature during the cooling period is transformed into latent variables that more accurately reflect the defect information within the sample. These variables eliminate the influence of non-uniform heating and improve the visualization of defects. To evaluate the performance of the proposed method, experiments were conducted on two plant fiber composite planes and compared with alternative infrared signal processing methods. The results demonstrate that the proposed method achieves superior quantitative metrics and effectively extracts defect edge features. [ABSTRACT FROM AUTHOR]

We investigate the relation between backbone and side-chain ordering in a small protein. For this purpose, we have performed multicanonical simulations of the villin headpiece subdomain HP-36, an often used toy model in protein studies. Concepts of circular statistics are introduced to analyze side-chain fluctuations. In contrast to earlier studies on homopolypeptides [Wei et al., J. Phys. Chem. B 111, 4244 (2007)], we do not find collective effects leading to a separate transition. Rather, side-chain ordering is spread over a wide temperature range. Our results indicate a thermal hierarchy of ordering events, with side-chain ordering appearing at temperatures below the helix-coil transition but above the folding transition. We conjecture that this thermal hierarchy reflects an underlying temporal order, and that side-chain ordering facilitates the search for the correct backbone topology. [ABSTRACT FROM AUTHOR]

Using multicanonical simulations, the authors study the effect of charged end groups on helix formation in alanine based polypeptides. They confirm earlier reports that neutral polyalanine exhibits a pronounced helix-coil transition in gas phase simulations. Introducing a charged Lys+ at the C terminal stabilizes the α helix and leads to a higher transition temperature. On the other hand, adding the Lys+ at the N terminal inhibits helix formation. Instead, a more globular structure was found. These results are in agreement with recent experiments on alanine based polypeptides in gas phase. They indicate that present force fields describe accurately the intramolecular interactions in proteins. [ABSTRACT FROM AUTHOR]

We report results from multicanonical simulations of polyglutamic acid chains of length of ten residues. For this simple polypeptide we observe a decoupling of backbone and side-chain ordering in the folding process. While the details of the two transitions vary between the peptide in gas phase and in an implicit solvent, our results indicate that, independent of the specific surroundings, upon continuously lowering the temperature side-chain ordering occurs only after the backbone topology is completely formed. [ABSTRACT FROM AUTHOR]

Depending on the nucleation sites, two types of tin dioxide (SnO2) nanostructures were synthesized: (1) single-crystal SnO2 nanowires and (2) novel tri-crystal SnO2 nanobelts with the coexistence of (301) and (1̅01) twins. It is speculated that the different vapor concentrations of Sn/O may lead to the nucleation of distinct structures and morphologies in low-dimensional SnO2. Meanwhile, the growth direction changes in both nanowires (from [1̅01] to [001]) and nanobelts (from [1̅03] to [001]), which could be related to the relatively lower Sn/O ratio during the later growth stages. The thermodynamic growth processes are also discussed in terms of surface energy. These findings contribute to the intuitive understanding of the growth mechanisms in SnO2 which may lead to the property-controlled design of next generation optical and electrical nanodevices. [ABSTRACT FROM AUTHOR]

Hydroxycamptothecin (HCPT) represents a new generation of anticancer drugs, with almost no side effects when used for the treatment of a number of types of cancer. Autophagy is becoming recognized as an important biological mechanism in human cancer, including lung cancer. However, the involvement of autophagy in the antiproliferative effects of HCPT on lung cancer remains unclear. In the present study, A549 cells, an accepted model of non-small cell lung cancer (NSCLC) cells, were employed. It was demonstrated that HCPT was able to suppress proliferation and induce apoptosis and autophagy in A549 cells. The molecular mechanism underlying HCPT-induced cell death was attributed to apoptosis and autophagy. Furthermore, it was demonstrated that an autophagy inhibitor, 3-methyladenine, accelerated HCPT-induced cell death in A549 cells. The results of the present study may lead to a deeper understanding of the molecular mechanism by which HCPT regulates NSCLC A549 cells. These results highlight the potential use of autophagy inhibitors in combination with traditional chemotherapy drugs for the treatment of lung cancer. [ABSTRACT FROM AUTHOR]

Abstract: Stable binder network of anode is essential for the enhanced cycling performance of lithium ion battery electrode especially for SnO2 anode, which has a big volume expansion/shrink during cycling. Here, we designed a cross‐linked polymer adhesive networks for SnO2 anode of sodium‐ion battery by utilizing esterification reaction of PAA (Polyacrylic acid) and SS (soluble starch). The cross‐linked adhesive networks can effectively enhance the contact between conductive agent and SnO2 particles, meanwhile serve as a buffer matrix of the expansion of SnO2 during cycling and a protecting layer to reduce the contact of SnO2 anode and electrolyte. As a result, a stable specific capacity of 370 mA h g−1 of the PAA‐SS@SnO2 anode was obtained at a current density of 0.1 A g−1 after 150 cycles, while the specific capacity of PVDF@SnO2 anode is only 200 mA h g−1 after 50 cycles. This cross‐linked adhesive networks present an effective and universal approach for application of pure SnO2 as high performance anode of sodium ion batteries. [ABSTRACT FROM AUTHOR]

Abstract: Granule sequencing batch reactors (GSBR) were established for landfill leachate treatment, and the COD removal was analyzed kinetically using a modified model. Results showed that COD removal rate decreased as influent ammonium concentration increasing. Characteristics of nitrogen removal at different influent ammonium levels were also studied. When the ammonium concentration in the landfill leachate was 366mgL−1, the dominant nitrogen removal process in the GSBR was simultaneous nitrification and denitrification (SND). Under the ammonium concentration of 788mgL−1, nitrite accumulation occurred and the accumulated nitrite was reduced to nitrogen gas by the shortcut denitrification process. When the influent ammonium increased to a higher level of 1105mgL−1, accumulation of nitrite and nitrate lasted in the whole cycle, and the removal efficiencies of total nitrogen and ammonium decreased to only 35.0% and 39.3%, respectively. Results also showed that DO was a useful process controlling parameter for the organics and nitrogen removal at low ammonium input. [Copyright &y& Elsevier]

• Thermography and shearography are simultaneously applied to NDE with a fusion scheme. • The fusion scheme is proposed based on the intrinsic mechanisms of both technologies. • Increasing visibility for all defects in a rubber-to-metal bonded structure is illustrated. Thermography and shearography are optical techniques for detecting and characterizing subsurface defects in structures. Both techniques possess unique advantages and abilities, as well as limitations, based on their distinct mechanisms. In this study, the feasibility in fusion of thermography and shearography is explored. Thermal excitation is selected to induce temperature variation and out-of-plane deformation on the object surface, acquiring the shearography phase maps and thermography image sequence simultaneously. Infrared images are dealt with by the Fourier phase analysis incorporate with an edge-aware filter. Fusion of the detecting images is obtained by performing a projective transform between each pair of the thermography and shearography images. Subsequently, both thermography and shearography images are separated into detail and background images. The fused image is reconstructed from the detail and background images by applying a weighting function. Experiments have indicated that the proposed method is effective in identifying defects by combining two image-based NDT methods. [ABSTRACT FROM AUTHOR]

• Thermal conduction considering lateral diffusion and sample thickness using pulse thermography is investigated. • Prediction of defect depth is influenced by the defect size and sample thickness in particular situations. • An accurate depth determination method based on the peak slope time is proposed. • The experimental results validate the accuracy of the proposed method in defect depth estimation, especially for small, deep defects. The determination of defect depth is one of the important advantages of pulsed thermography. The existing methods for depth estimation in pulsed thermography are generally established based on the one-dimension heat conduction along the thickness of the sample. However, most of the methods neglect the effects of defect size and sample thickness, which induces additional systematic errors in depth estimation. In this study, simulations are conducted to demonstrate that the accuracy of depth determination of defects is greatly influenced by the defect size and sample thickness in some simulations. By establishing the relationship between the characteristic time and these factors, an accurate depth determination method based on the peak slope time is proposed. The proposed method has been applied to characterize the defect depths of a composite panel with flat-bottomed holes. The experimental results show that the use of the proposed method is able to identify the depths of defects with high accuracy. [ABSTRACT FROM AUTHOR]

Organic semiconductors have significant potential for wearable optoelectronics due to their designable molecular structures, tunable photoelectronic properties, and flexibility. For their integration into optoelectronic devices, organic semiconductors need to be assembled into single‐crystalline arrays with both precise alignment of micro‐/nanostructures and long‐range order of molecules. In this study, an interface‐confined lithography technique is developed for fabricating organic single‐crystalline microwire arrays with layered molecular stacking and ordered crystallographic orientation toward wearable UV radiation monitor. This interface‐confined strategy introduces parallel gas–liquid–solid three‐phase contact line (TPCL) arrays to suppress the lateral growth of single crystals. Meanwhile, TPCLs with controlled dewetting direction promote longitudinal growth, thus universally yielding 1D arrays based on various organic semiconducting small molecules. These 1D single‐crystalline arrays exhibit high average mobility of 12.5 cm2 V−1 s−1 with uniform electronic properties of 9.3% variation, and good mechanical stability after 1000 bending times. Based on organic 1D arrays, wearable devices with robust photoresponsivity of 8.26 × 103 A W−1 and high mechanical flexibility achieve real‐time monitoring of UV radiation to prevent skin disease. This confined‐assembly strategy opens a new avenue to construct wearable optoelectronic devices for health monitoring. [ABSTRACT FROM AUTHOR]

Protein–ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein–ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel's generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein–ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein–ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel. [ABSTRACT FROM AUTHOR]

• AGS-SBR worked effectively in the treatment of landfill leachate. • The microbial community was evaluated by PCR-DGGE method while removing ammonium. • The diversity index of AOB population presented an uptrend during sludge granulation. • The microbial activity of NOB was inhibited during the high nitrogen loading. • Compared to AOB and NOB, the richness of denitrifying bacteria was lower. In this study, a laboratory-scale sequencing batch reactor (SBR) equipped with aerobic granular sludge (AGS) technology was continuously operated for 220 days to remove ammonium from an existing landfill leachate. The ammonium removal was characterized by polymerase chain reaction (PCR)-denaturing gradient gel electrophoresis (DGGE) technology. This method helped to analyze the long-term community structural stability of ammonia-oxidizing bacteria (AOB), nitrite-oxidizing bacteria (NOB) and denitrifying bacteria (DB) throughout the experiment. Simultaneously, 16S rRNA gene cloning and sequencing analysis identified the dominant species of different microbial species. Experimental results confirmed that ammonium removal was inhibited at the high nitrogen loading rate (NLR) stage while the low NLR stage achieved satisfactory ammonium removal. Moreover, the findings demonstrated that functionally stable wastewater treatment bioreactors facilitated the occurrence of stable microbial community structures. [ABSTRACT FROM AUTHOR]

A magnesia-pullulan composite (MgOP) was prepared by chemical sol-gel method and utilized for the simultaneous removal of fluoride and phosphate in a continuous fixed-bed column. A series of dynamic adsorption experiments were conducted to explore the impacts of different factors on the dynamic adsorption of fluoride and phosphate in the single- and binary-adsorbate systems, including bed mass, flow rate, temperature, inlet adsorbate concentration, influent pH and coexisting anions. The results demonstrated that the adsorption capacities of fluoride and phosphate ions were 6.21 and 7.76 mg/g, respectively, at the optimal conditions (i.e., 6 g of bed mass and 0.48 L/h of flow rate), when influent fluoride and phosphate concentrations were fixed at 10 mg/L. An increased bed mass had a negative effect on the simultaneous adsorption of fluoride and phosphate on MgOP. In contrast, increasing the volumetric flow rate facilitated the removal of fluoride and phosphate from an aqueous solution in the binary-adsorbate system. Besides, the MgOP can be effectively regenerated by calcination with high stability. The column adsorption of fluoride and phosphate in the binary-adsorbate system were well described by the Thomas model. In conclusion, the present study provides insight into the simultaneous removal of fluoride and phosphate through adsorption. Image 1 • Fixed-bed of MgOP was feasible for simultaneous fluoride and phosphate removal. • Thomas and Yan models described the simultaneous adsorption of fluoride and phosphate. • Bed mass, flow rate, concentration, pH and temperature effects were invesitgated. • Competitive effects were observed between fluoride and phosphate in MgOP fixed bed. [ABSTRACT FROM AUTHOR]

Infrared thermography (IRT) is a full-field, contactless technique that has been widely used for nondestructive evaluation of structural materials due to many advantages. One of the major limitations of IRT is the fuzzy edge and low contrast in the inspected images—as well as the cost of the system. An efficient image post-processing with an affordable and portable device is of great interest to the engineering society. In this study, a convenient and economical inspection system using common halogen lamps was constructed. The corresponding image-processing scheme, which includes Fourier phase analysis and specific image enhancement was developed to identify defects with sharp and clear edges and good contrast. This system was applied to localized of defects in glass-fiber-reinforced composite panels. The results showed that defects with an effective diameter as small as 5 mm can be detected with excellent image quality. As a conclusion, the developed system provides an economic alternative to traditional infrared thermography which is able to identify defects with good qualities. [ABSTRACT FROM AUTHOR]

• Five bivalent Smac mimetics are designed and synthesised. • Mimetics are evaluated in silico with the TwistDock workflow. • Compound 3 among the five displays the highest binding potency to BIR2 and BIR3. • Experiments with SK-OV-3 and MDA-MB-231 cancer cell lines confirm the prediction. Bivalent Smac mimetics have been shown to possess binding affinity and pro-apoptotic activity similar to or more potent than that of native Smac, a protein dimer able to neutralize the anti-apoptotic activity of an inhibitor of caspase enzymes, XIAP, which endows cancer cells with resistance to anticancer drugs. We design five new bivalent Smac mimetics, which are formed by various linkers tethering two diazabicyclic cores being the IAP binding motifs. We built in silico models of the five mimetics by the TwistDock workflow and evaluated their conformational tendency, which suggests that compound 3 , whose linker is n -hexylene, possess the highest binding potency among the five. After synthesis of these compounds, their ability in tumour cell growth inhibition and apoptosis induction displayed in experiments with SK-OV-3 and MDA-MB-231 cancer cell lines confirms our prediction. Among the five mimetics, compound 3 displays promising pro-apoptotic activity and deserves further optimization. [ABSTRACT FROM AUTHOR]

The automatic detection of cells in microscopy image sequences is a significant task in biomedical research. However, routine microscopy images with cells, which are taken during the process whereby constant division and differentiation occur, are notoriously difficult to detect due to changes in their appearance and number. Recently, convolutional neural network (CNN)-based methods have made significant progress in cell detection and tracking. However, these approaches require many manually annotated data for fully supervised training, which is time-consuming and often requires professional researchers. To alleviate such tiresome and labor-intensive costs, we propose a novel weakly supervised learning cell detection and tracking framework that trains the deep neural network using incomplete initial labels. Our approach uses incomplete cell markers obtained from fluorescent images for initial training on the Induced Pluripotent Stem (iPS) cell dataset, which is rarely studied for cell detection and tracking. During training, the incomplete initial labels were updated iteratively by combining detection and tracking results to obtain a model with better robustness. Our method was evaluated using two fields of the iPS cell dataset, along with the cell detection accuracy (DET) evaluation metric from the Cell Tracking Challenge (CTC) initiative, and it achieved 0.862 and 0.924 DET, respectively. The transferability of the developed model was tested using the public dataset FluoN2DH-GOWT1, which was taken from CTC; this contains two datasets with reference annotations. We randomly removed parts of the annotations in each labeled data to simulate the initial annotations on the public dataset. After training the model on the two datasets, with labels that comprise 10% cell markers, the DET improved from 0.130 to 0.903 and 0.116 to 0.877. When trained with labels that comprise 60% cell markers, the performance was better than the model trained using the supervised learning method. This outcome indicates that the model's performance improved as the quality of the labels used for training increased. [ABSTRACT FROM AUTHOR]

Recent studies have shed light on the potential of circular RNA (circRNA) as a biomarker for disease diagnosis and as a nucleic acid vaccine. The exploration of these functionalities requires correct circRNA full-length sequences; however, existing assembly tools can only correctly assemble some circRNAs, and their performance can be further improved. Here, we introduce a novel feature known as the junction contig (JC), which is an extension of the back-splice junction (BSJ). Leveraging the strengths of both BSJ and JC, we present a novel method called JCcirc (https://github.com/cbbzhang/JCcirc). It enables efficient reconstruction of all types of circRNA full-length sequences and their alternative isoforms using splice graphs and fragment coverage. Our findings demonstrate the superiority of JCcirc over existing methods on human simulation datasets, and its average F1 score surpasses CircAST by 0.40 and outperforms both CIRI-full and circRNAfull by 0.13. For circRNAs below 400 bp, 400–800 bp, 800 bp–1200 bp and above 1200 bp, the correct assembly rates are 0.13, 0.09, 0.04 and 0.03 higher, respectively, than those achieved by existing methods. Moreover, JCcirc also outperforms existing assembly tools on other five model species datasets and real sequencing datasets. These results show that JCcirc is a robust tool for accurately assembling circRNA full-length sequences, laying the foundation for the functional analysis of circRNAs. [ABSTRACT FROM AUTHOR]

Spiking neural network (SNN) simulators play an important role in neural system modeling and brain function research. They can help scientists reproduce and explore neuronal activities in brain regions, neuroscience, brain-like computing, and other fields and can also be applied to artificial intelligence, machine learning, and other fields. At present, many simulators using central processing unit (CPU) or graphics processing unit (GPU) have been developed. However, due to the randomness of connections between neurons and spiking events in SNN simulation, this causes a lot of memory access time. To alleviate this problem, we developed an SNN simulator SWsnn based on the new Sunway SW26010pro processor. The SW26010pro processor consists of six core groups, each with 16 MB of local data memory (LDM). LDM has the characteristics of high-speed read and write, which is suitable for performing simulation tasks similar to SNNs. Experimental results show that SWsnn runs faster than other mainstream GPU-based simulators when simulating a certain scale of neural network, showing a strong performance advantage. To conduct larger scale simulations, SWsnn designed a simulation computation based on a large shared model of Sunway processor and developed a multiprocessor version of SWsnn based on this mode, achieving larger scale SNN simulations. [ABSTRACT FROM AUTHOR]

Assessing the quality of sequencing data plays a crucial role in downstream data analysis. However, existing tools often achieve sub-optimal efficiency, especially when dealing with compressed files or performing complicated quality control operations such as over-representation analysis and error correction. We present RabbitQCPlus, an ultra-efficient quality control tool for modern multi-core systems. RabbitQCPlus uses vectorization, memory copy reduction, parallel (de)compression, and optimized data structures to achieve substantial performance gains. It is 1.1 to 5.4 times faster when performing basic quality control operations compared to state-of-the-art applications yet requires fewer compute resources. Moreover, RabbitQCPlus is at least 4 times faster than other applications when processing gzip-compressed FASTQ files and 1.3 times faster with the error correction module turned on. Furthermore, it takes less than 4 minutes to process 280 GB of plain FASTQ sequencing data, while other applications take at least 22 minutes on a 48-core server when enabling the per-read over-representation analysis. C++ sources are available at https://github.com/RabbitBio/RabbitQCPlus. • Basic quality control: 1.1-5.4x faster and uses fewer resources than rivals. • Processing gzip files: 4-15x faster by integrating parallel (de)compression engine. • Over-representation and error correction modules: integrated and heavily optimized. [ABSTRACT FROM AUTHOR]

• An out-of-plane force sensor is developed via a facile manufacturing method. • The sensor can detect press-in forces, pull-out forces, and shear stimuli. • The sensor is able to perceive sound field, flow field and magnetic field. • The sensor can perceive the applied direction and loading path of the excitation. This work presents an ultrasensitive three-dimensional (3D) structured sensor with out-of-plane tactility and non-contact perception capabilities. It is composed of flexible silver nanowire-patterned magnetic film and the printed heat-responsive polymer skeleton by means of spin-coating, masking, and printing technologies. Besides the capabilities in detecting tension, compression, shear, and even air vibration/flow, the 3D-structured sensor also exhibits the advantages of prompt response, super flexibility, outstanding sensitivity, high reversibility and stability. The responsive relative resistance variations of the 3D-structured sensor exhibit an excellent linear relationship with the pull-out/press-in forces, and the sensitivities are estimated as 21.8 % N−1 and −19.6 % N−1, respectively. Furthermore, it is demonstrated that the applied direction and strength of shear excitation can be well predicted via applying machine learning methods. Moreover, the sensor is able to clearly detect the loading paths of external excitations, including the load waveform and even the initial phase. Notably, the normalized resistance of the sensor changes with the magnetic flux density which is promising in tuning the sensitivity of the sensor within a magnetic field. The exceptional multi-mode sensing characteristics demonstrate that the 3D-structured sensor possess great potentials for implementation in next-generation smart devices or systems. [ABSTRACT FROM AUTHOR]

The recently reported machine learning- or deep learning-based scoring functions (SFs) have shown exciting performance in predicting protein–ligand binding affinities with fruitful application prospects. However, the differentiation between highly similar ligand conformations, including the native binding pose (the global energy minimum state), remains challenging that could greatly enhance the docking. In this work, we propose a fully differentiable, end-to-end framework for ligand pose optimization based on a hybrid SF called DeepRMSD+Vina combined with a multi-layer perceptron (DeepRMSD) and the traditional AutoDock Vina SF. The DeepRMSD+Vina, which combines (1) the root mean square deviation (RMSD) of the docking pose with respect to the native pose and (2) the AutoDock Vina score, is fully differentiable; thus is capable of optimizing the ligand binding pose to the energy-lowest conformation. Evaluated by the CASF-2016 docking power dataset, the DeepRMSD+Vina reaches a success rate of 94.4%, which outperforms most reported SFs to date. We evaluated the ligand conformation optimization framework in practical molecular docking scenarios (redocking and cross-docking tasks), revealing the high potentialities of this framework in drug design and discovery. Structural analysis shows that this framework has the ability to identify key physical interactions in protein–ligand binding, such as hydrogen-bonding. Our work provides a paradigm for optimizing ligand conformations based on deep learning algorithms. The DeepRMSD+Vina model and the optimization framework are available at GitHub repository https://github.com/zchwang/DeepRMSD-Vina_Optimization. [ABSTRACT FROM AUTHOR]

We present FastConv, a template-based code auto-generation open-source library that can automatically generate high-performance deep learning convolution kernels of arbitrary matrices/tensors shapes. FastConv is based on the Winograd algorithm, which is reportedly the highest performing algorithm for the time-consuming layers of convolutional neural networks. ARM CPUs cover a wide range of designs and specifications, from embedded devices to HPC-grade CPUs. The leads to the dilemma of how to consistently optimize Winograd-based convolution solvers for convolution layers of different shapes. FastConv addresses this problem by using templates to auto-generate multiple shapes of tuned kernels variants suitable for skinny tall matrices. As a performance portable library, FastConv transparently searches for the best combination of kernel shapes, cache tiles, scheduling of loop orders, packing strategies, access patterns, and online/offline computations. Auto-tuning is used to search the parameter configuration space for the best performance for a given target architecture and problem size. Results show 1.02x to 1.40x, 1.14x to 2.17x, and 1.22x and 2.48x speedup is achieved over NNPACK, ARM NN, and FeatherCNN on Kunpeng 920. Furthermore, performance portability experiments with various convolution shapes show that FastConv achieves 1.2x to 1.7x speedup and 2x to 22x speedup over NNPACK and ARM NN inference engine using Winograd on Kunpeng 920. CPU performance portability evaluation on VGG–16 show an average speedup over NNPACK of 1.42x, 1.21x, 1.26x, 1.37x, 2.26x, and 11.02x on Kunpeng 920, Snapdragon 835, 855, 888, Apple M1, and AWS Graviton2, respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • DeepBindBC can be a core component of a hybrid pipeline in drug screening. • The model, scripts and the online server were released. • We emphasize the importance of incorporating unbinding data. • We have taken pancreatic alpha-amylase as a test example. • The model can apply to drug screening for predicted structures. Identifying native-like protein–ligand complexes (PLCs) from an abundance of docking decoys is critical for large-scale virtual drug screening in early-stage drug discovery lead searching efforts. Providing reliable prediction is still a challenge for most current affinity predicting models because of a lack of non-binding data during model training, lost critical physical–chemical features, and difficulties in learning abstract information with limited neural layers. In this work, we proposed a deep learning model, DeepBindBC, for classifying putative ligands as binding or non-binding. Our model incorporates information on non-binding interactions, making it more suitable for real applications. ResNet model architecture and more detailed atom type representation guarantee implicit features can be learned more accurately. Here, we show that DeepBindBC outperforms Autodock Vina, Pafnucy, and DLSCORE for three DUD.E testing sets. Moreover, DeepBindBC identified a novel human pancreatic α-amylase binder validated by a fluorescence spectral experiment (K a = 1.0 × 105 M). Furthermore, DeepBindBC can be used as a core component of a hybrid virtual screening pipeline that incorporating many other complementary methods, such as DFCNN, Autodock Vina docking, and pocket molecular dynamics simulation. Additionally, an online web server based on the model is available at http://cbblab.siat.ac.cn/DeepBindBC/index.php for the user's convenience. Our model and the web server provide alternative tools in the early steps of drug discovery by providing accurate identification of native-like PLCs. [ABSTRACT FROM AUTHOR]

Deep learning is an artificial intelligence technique in which models express geometric transformations over multiple levels. This method has shown great promise in various fields, including drug development. The availability of public structure databases prompted the researchers to use generative artificial intelligence models to narrow down their search of the chemical space, a novel approach to chemogenomics and de novo drug development. In this study, we developed a strategy that combined an accelerated LSTM_Chem (long short-term memory for de novo compounds generation), dense fully convolutional neural network (DFCNN), and docking to generate a large number of de novo small molecular chemical compounds for given targets. To demonstrate its efficacy and applicability, six important targets that account for various human disorders were used as test examples. Moreover, using the M protease as a proof-of-concept example, we find that iteratively training with previously selected candidates can significantly increase the chance of obtaining novel compounds with higher and higher predicted binding affinities. In addition, we also check the potential benefit of obtaining reliable final de novo compounds with the help of MD simulation and metadynamics simulation. The generation of de novo compounds and the discovery of binders against various targets proposed here would be a practical and effective approach. Assessing the efficacy of these top de novo compounds with biochemical studies is promising to promote related drug development. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNAs) are RNA molecules formed by joining a downstream 3 splice donor site and an upstream 5 splice acceptor site. Several recent studies have identified circRNAs as potential biomarker for different diseases. A number of methods are available for the identification of circRNAs. The circRNA identification methods cannot provide full-length sequences. Reconstruction of the full-length sequences is crucial for the downstream analyses of circRNA research including differential expression analysis, circRNA-miRNA interaction analysis and other functional studies of the circRNAs. However, a limited number of methods are available in the literature for the reconstruction of full-length circRNA sequences. We developed a new method, circRNA-full, for full-length circRNA sequence reconstruction utilizing chimeric alignment information from the STAR aligner. To evaluate our method, we used full-length circRNA sequences produced by isocirc and ciri-long using long-reads RNA-seq data. Our method achieved better reconstruction rate, precision, sensitivity and F1 score than the existing full-length circRNA sequence reconstruction tool ciri-full for both human and mouse data. [ABSTRACT FROM AUTHOR]

Accurate forecast for water quality is of great importance because it can support water resource management with the future information. In this research, we propose a novel hybrid model by using data decomposition, error correction, and machine learning. In our method, first, the initial forecast is obtained by a prediction model that uses improved complete ensemble empirical mode decomposition with adaptive noise and bidirectional long short-term memory (BLSTM) neural network. Next, a novel error correction framework, which is built by variational mode decomposition and BLSTM neural network, is used to improve forecast accuracy by correcting the initial forecast error. Water quality data of Poyang Lake, China is used to evaluate our model. Results indicate that our model shows highly accurate forecast performance for all of the 9 water quality datasets (the average of mean absolute percentage error (MAPE) of 7 day-ahead forecast is 2.12%; 30 day-ahead forecast is 4.06%). In addition, our model outperforms the competitor models, particularly, compared to the prediction model without error correction framework, the average of MAPE is reduced by 33.33% for 7 day-ahead forecast; 30.48% for 30 day-ahead forecast. This research demonstrates that the proposed error correction framework is an effective tool to improve forecast accuracy for water quality. [Display omitted] [ABSTRACT FROM AUTHOR]

Scoring functions are important components in molecular docking for structure-based drug discovery. Traditional scoring functions, generally empirical- or force field-based, are robust and have proven to be useful for identifying hits and lead optimizations. Although multiple highly accurate deep learning- or machine learning-based scoring functions have been developed, their direct applications for docking and screening are limited. We describe a novel strategy to develop a reliable protein–ligand scoring function by augmenting the traditional scoring function Vina score using a correction term (OnionNet-SFCT). The correction term is developed based on an AdaBoost random forest model, utilizing multiple layers of contacts formed between protein residues and ligand atoms. In addition to the Vina score, the model considerably enhances the AutoDock Vina prediction abilities for docking and screening tasks based on different benchmarks (such as cross-docking dataset, CASF-2016, DUD-E and DUD-AD). Furthermore, our model could be combined with multiple docking applications to increase pose selection accuracies and screening abilities, indicating its wide usage for structure-based drug discoveries. Furthermore, in a reverse practice, the combined scoring strategy successfully identified multiple known receptors of a plant hormone. To summarize, the results show that the combination of data-driven model (OnionNet-SFCT) and empirical scoring function (Vina score) is a good scoring strategy that could be useful for structure-based drug discoveries and potentially target fishing in future. [ABSTRACT FROM AUTHOR]

Aims: This study aimed to investigate the expression of EphA7 in human laryngeal squamous cell carcinoma (LSCC) tissues and disclose the potential roles and molecular mechanisms of EphA7 in LSCC. Methods: In the present study, we examined EphA7 expression and its function and mechanism in LSCC. EphA7 expression levels were investigated by quantitative real-time PCR (qRT-PCR), western blotting, and immunohistochemistry in a panel of 35 LSCC patient cases. To investigate the potential mechanism of EphA7 in human laryngeal cancer, we employed EphA7 siRNA to knockdown EphA7 expression in LSCC cell line Hep-2 and AMCHN- 8. Subsequently, MTT, TUNEL, qRT-PCR, and western blotting were performed to disclose the roles of EphA7 on proliferation, invasion and migration, and apoptosis in LSCC cell line Hep-2 and AMC-HN-8. Results: Depletion of EphA7 remarkably inhibited the proliferation and invasion of Hep-2 and AMC-HN-8 cells in comparison to control and EphA7 siRNA negative control (NC)-transfected cells. TUNEL staining assay demonstrated that, compared with the control group, the rate of apoptosis in the EphA7 siRNA group was significantly increased. In addition, knockdown of EphA7 in Hep-2 or AMC-HN-8 cells markedly decreased the expression of EphA7 and PTEN, which could contribute to apoptosis. However, the bpV(phen), a PTEN inhibitor, could attenuate anti-proliferation and pro-apoptotic effects of EphA7 siRNA in Hep-2 and AMC-HN-8 cells. Conclusion: Up-regulation of EphA7 was observed in human LSCC samples and down-regulation of EphA7 effectively suppressed laryngeal carcinoma cell growth and promoted its apoptosis. Thus, EphA7 has a critical role in modulating cell growth and apoptosis, which serves as a potential therapeutic target in human LSCC. [ABSTRACT FROM AUTHOR]

A novel flower-like 3-dimensional ZnO hierarchical structure with high uniformity has been fabricated on a large scale through a facile aqueous solution route at room temperature. The as-prepared ZnO hierarchical structures with a size of about 1 μm were assembled by large amounts of interleaving nanosheets, which have a uniform thickness of about 15 nm and a well-crystalline structure with {110} planes as basal facets. The morphology and crystal structure of the products changed with the increase of Na 2 O 2 concentration. Finally, the hierarchical structures ZnO formed via a two-stage growth were also discussed. [ABSTRACT FROM AUTHOR]

Bioinformatics analysis has been playing a vital role in identifying potential genomic biomarkers more accurately from an enormous number of candidates by reducing time and cost compared to the wet-lab-based experimental procedures for disease diagnosis, prognosis, and therapies. Cervical cancer (CC) is one of the most malignant diseases seen in women worldwide. This study aimed at identifying potential key genes (KGs), highlighting their functions, signaling pathways, and candidate drugs for CC diagnosis and targeting therapies. Four publicly available microarray datasets of CC were analyzed for identifying differentially expressed genes (DEGs) by the LIMMA approach through GEO2R online tool. We identified 116 common DEGs (cDEGs) that were utilized to identify seven KGs (AURKA, BRCA1, CCNB1, CDK1, MCM2, NCAPG2, and TOP2A) by the protein–protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of KGs revealed some important functions and signaling pathways that were significantly associated with CC infections. The interaction network analysis identified four TFs proteins and two miRNAs as the key transcriptional and post-transcriptional regulators of KGs. Considering seven KGs-based proteins, four key TFs proteins, and already published top-ranked seven KGs-based proteins (where five KGs were common with our proposed seven KGs) as drug target receptors, we performed their docking analysis with the 80 meta-drug agents that were already published by different reputed journals as CC drugs. We found Paclitaxel, Vinorelbine, Vincristine, Docetaxel, Everolimus, Temsirolimus, and Cabazitaxel as the top-ranked seven candidate drugs. Finally, we investigated the binding stability of the top-ranked three drugs (Paclitaxel, Vincristine, Vinorelbine) by using 100 ns MD-based MM-PBSA simulations with the three top-ranked proposed receptors (AURKA, CDK1, TOP2A) and observed their stable performance. Therefore, the proposed drugs might play a vital role in the treatment against CC. [ABSTRACT FROM AUTHOR]

Robust encapsulation and controllable release of biomolecules have wide biomedical applications ranging from biosensing, drug delivery to information storage. However, conventional biomolecule encapsulation strategies have limitations in complicated operations, optical instability, and difficulty in decapsulation. Here, we report a simple, robust, and solvent-free biomolecule encapsulation strategy based on gallium liquid metal featuring low-temperature phase transition, self-healing, high hermetic sealing, and intrinsic resistance to optical damage. We sandwiched the biomolecules with the solid gallium films followed by low-temperature welding of the films for direct sealing. The gallium can not only protect DNA and enzymes from various physical and chemical damages but also allow the on-demand release of biomolecules by applying vibration to break the liquid gallium. We demonstrated that a DNA-coded image file can be recovered with up to 99.9% sequence retention after an accelerated aging test. We also showed the practical applications of the controllable release of bioreagents in a one-pot RPA-CRISPR/Cas12a reaction for SARS-COV-2 screening with a low detection limit of 10 copies within 40 min. This work may facilitate the development of robust and stimuli-responsive biomolecule capsules by using low-melting metals for biotechnology. [ABSTRACT FROM AUTHOR]

Background: Circular RNA (circRNA), a subclass of non-coding RNA, plays a critical role in cancer tumorigenesis and metastasis. It has been suggested that circRNA acts as a microRNA sponge or a scaffold to interact with protein complexes; however, its full range of functions remains elusive. Recently, some circRNAs have been found to have coding potential. Methods: To investigate the role of circRNAs in gastric cancer (GC), parallel sequencing was performed using five paired GC samples. Differentially expressed circAXIN1 was proposed to encode a novel protein. FLAG-tagged circRNA overexpression plasmid construction, immunoblotting, mass spectrometry, and luciferase reporter analyses were applied to confirm the coding potential of circAXIN1. Gain- and loss-of-function studies were conducted to study the oncogenic role of circAXIN1 and AXIN1-295aa on the proliferation, migration, invasion, and metastasis of GC cells in vitro and in vivo. The competitive interaction between AXIN1-295aa and adenomatous polyposis coli (APC) was investigated by immunoprecipitation analyses. Wnt signaling activity was observed using a Top/Fopflash assay, real-time quantitative RT-PCR, immunoblotting, immunofluorescence staining, and chromatin immunoprecipitation. Results: CircAXIN1 is highly expressed in GC tissues compared with its expression in paired adjacent normal gastric tissues. CircAXIN1 encodes a 295 amino acid (aa) novel protein, which was named AXIN1-295aa. CircAXIN1 overexpression enhances the cell proliferation, migration, and invasion of GC cells, while the knockdown of circAXIN1 inhibits the malignant behaviors of GC cells in vitro and in vivo. Mechanistically, AXIN1-295aa competitively interacts with APC, leading to dysfunction of the "destruction complex" of the Wnt pathway. Released β-catenin translocates to the nucleus and binds to the TCF consensus site on the promoter, inducing downstream gene expression. Conclusion: CircAXIN1 encodes a novel protein, AXIN1-295aa. AXIN1-295aa functions as an oncogenic protein, activating the Wnt signaling pathway to promote GC tumorigenesis and progression, suggesting a potential therapeutic target for GC. [ABSTRACT FROM AUTHOR]

Abstract: The performance of a sequencing batch reactor (SBR) seeded with aerobic granular sludge was studied. The lab-scale SBR treating domestic wastewater operated at a volumetric loading rate (VLR) of 0.75–3.41kg COD/(m3 d). The granule stability was related to the organic loading, and high loading would be favorable for granule stability. Analysis of typical cycle showed that granular sludge had good ability to simultaneously remove nitrogen and phosphorus. Most organic substances were removed at the anaerobic stage. At the aerobic stage, simultaneous nitrification and denitrification (SND) happened with phosphorus absorption. The SBR had good removal performance for organic matter and phosphate. However, the total nitrogen (TN) removal performance was ordinary, with average removal efficiency of about 52%. Batch experiments indicated that increases of influent C/N ratio and a large percentage of granule in the sludge were conducive for SND in SBR. [Copyright &y& Elsevier]

Sequence-based residue contact prediction plays a crucial role in protein structure reconstruction. In recent years, the combination of evolutionary coupling analysis (ECA) and deep learning (DL) techniques has made tremendous progress for residue contact prediction, thus a comprehensive assessment of current methods based on a large-scale benchmark data set is very needed. In this study, we evaluate 18 contact predictors on 610 non-redundant proteins and 32 CASP13 targets according to a wide range of perspectives. The results show that different methods have different application scenarios: (1) DL methods based on multi-categories of inputs and large training sets are the best choices for low-contact-density proteins such as the intrinsically disordered ones and proteins with shallow multi-sequence alignments (MSAs). (2) With at least 5L (L is sequence length) effective sequences in the MSA, all the methods show the best performance, and methods that rely only on MSA as input can reach comparable achievements as methods that adopt multi-source inputs. (3) For top L/5 and L/2 predictions, DL methods can predict more hydrophobic interactions while ECA methods predict more salt bridges and disulfide bonds. (4) ECA methods can detect more secondary structure interactions, while DL methods can accurately excavate more contact patterns and prune isolated false positives. In general, multi-input DL methods with large training sets dominate current approaches with the best overall performance. Despite the great success of current DL methods must be stated the fact that there is still much room left for further improvement: (1) With shallow MSAs, the performance will be greatly affected. (2) Current methods show lower precisions for inter-domain compared with intra-domain contact predictions, as well as very high imbalances in precisions for intra-domains. (3) Strong prediction similarities between DL methods indicating more feature types and diversified models need to be developed. (4) The runtime of most methods can be further optimized. Author summary: The amino acid sequence of a protein ultimately determines its tertiary structure, and the tertiary structure determines its function(s) and plays a key role in understanding biological processes and disease pathogenesis. Protein tertiary structure can be determined using experimental techniques such as cryo-electron microscopy, nuclear magnetic resonance and X-ray crystallography, which are very expensive and time-consuming. As an alternative, researchers are trying to use in silico methods to predict the 3D structures. Residue contact-assisted protein folding paves an avenue for sequence-based protein structure prediction and therefore has become one of the most challenging and promising problems in structural bioinformatics. Over the past years, contact prediction has undergone continuous evolution in techniques. Through a retrospective analysis of traditional machine learning /evolutionary coupling analysis methods/ consensus machine learning methods and a multi-perspective study on recently developed deep learning methods, we explore the most advanced contact predictors, pursue application scenarios for different methods, and seek prospective directions for further improvement. We anticipate that our study will serve as a practical and useful guide for the development of future approaches to contact prediction. [ABSTRACT FROM AUTHOR]

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus poses serious threats to the global public health and leads to worldwide crisis. No effective drug or vaccine is readily available. The viral RNA-dependent RNA polymerase (RdRp) is a promising therapeutic target. A hybrid drug screening procedure was proposed and applied to identify potential drug candidates targeting RdRp from 1906 approved drugs. Among the four selected market available drug candidates, Pralatrexate and Azithromycin were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC50 values of 0.008μM and 9.453 μM, respectively. For the first time, our study discovered that Pralatrexate is able to potently inhibit SARS-CoV-2 replication with a stronger inhibitory activity than Remdesivir within the same experimental conditions. The paper demonstrates the feasibility of fast and accurate anti-viral drug screening for inhibitors of SARS-CoV-2 and provides potential therapeutic agents against COVID-19. Author summary: Currently, a novel coronavirus called SARS-COV-2 is spreading across many parts of the world. Unfortunately, there is still a lack of effective drugs against the virus. Additionally, it usually takes much longer time to develop a new drug using traditional methods. Thus, it is now better to rely on some alternative methods to develop drugs that can treat such a disease effectively. In this paper, we have proposed a deep learning and molecular dynamics simulation based hybrid drug screening procedure for identifying potential drug candidates targeting RdRp from 1906 market available drugs. Our screening have successfully identified a FDA-approved drug called Pralatrexate that strongly inhibits the replication of 2019-nCoV in vitro with EC50 values of 0.008μM. This work demonstrated the feasibility of accurate virtual drug screening for inhibitors of SARS-CoV-2 and provides potential therapeutic agents against COVID-19. [ABSTRACT FROM AUTHOR]

Although great progress has been made in surgical techniques, traditional radiotherapy, and chemotherapy, gastric cancer (GC) is still the most common malignant tumor and has a high mortality, which highlights the importance of novel diagnostic markers. Emerging studies suggest that different microRNAs (miRNAs) are involved in tumorigenesis of GC. In this study, we found that miRNA‐192 and ‐215 are significantly upregulated in GC and promote cell proliferation and migration. Adenomatous polyposis coli (APC), a well‐known negative regulator in Wnt signaling, has been proved to be a target of miRNA‐192 and ‐215. Inhibition of miRNA‐192 or ‐215 reduced the Topflash activities and repressed the expression of Wnt signaling pathway proteins, while APC small interfering RNAs reversed the inhibitory effects, suggesting that miRNA‐192 and ‐215 activate Wnt signaling via APC. In addition, APC mediates the cell proliferation and migration regulated by miRNA‐192 and ‐215. Furthermore, APC is downregulated in GC tissues and negatively correlated with the expression of miRNA‐192 and ‐215. In summary, miRNA‐192 and ‐215 target APC and function as oncogenic miRNAs by activating Wnt signaling in GC, revealing to be potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNAs) are formed by joining the 3 ′ and 5 ′ ends of RNA molecules. Identification of circRNAs is an important part of circRNA research. The circRNA prediction methods can predict the circRNAs with start and end positions in the chromosome but cannot identify the full-length circRNA sequences. We present an R package FcircSEC (Full Length circRNA Sequence Extraction and Classification) to extract the full-length circRNA sequences based on gene annotation and the output of any circRNA prediction tools whose output has a chromosome, start and end positions, and a strand for each circRNA. To validate FcircSEC, we have used three databases, circbase, circRNAdb, and plantcircbase. With information such as the chromosome and strand of each circRNA as the input, the identified sequences by FcircSEC are consistent with the databases. The novelty of FcircSEC is that it can take the output of state-of-the-art circRNA prediction tools as input and is applicable for human and other species. We also classify the circRNAs as exonic, intronic, and others. The R package FcircSEC is freely available. [ABSTRACT FROM AUTHOR]

In situ transmission electron microscopy has achieved remarkable advances for atomic-scale dynamic analysis in low-dimensional materials and become an indispensable tool in view of linking a material's microstructure to its properties and performance. Here, accompanied with some cutting-edge researches worldwide, we briefly review our recent progress in dynamic atomistic characterization of low-dimensional materials under external mechanical stress, thermal excitations and electrical field. The electron beam irradiation effects in metals and metal oxides are also discussed. We conclude by discussing the likely future developments in this area. [ABSTRACT FROM AUTHOR]

Motivation Mash is a popular hash-based genome analysis toolkit with applications to important downstream analyses tasks such as clustering and assembly. However, Mash is currently not able to fully exploit the capabilities of modern multi-core architectures, which in turn leads to high runtimes for large-scale genomic datasets. Results We present RabbitMash, an efficient highly optimized implementation of Mash which can take full advantage of modern hardware including multi-threading, vectorization and fast I/O. We show that our approach achieves speedups of at least 1.3, 9.8, 8.5 and 4.4 compared to Mash for the operations sketch , dist , triangle and screen , respectively. Furthermore, RabbitMash is able to compute the all-versus-all distances of 100 321 genomes in <5 min on a 40-core workstation while Mash requires over 40 min. Availability and implementation RabbitMash is available at https://github.com/ZekunYin/RabbitMash. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

An 80-day feeding trial was conducted to assess the dietary protein requirement of sub-adult triploid rainbow trout (initial average weight: 233 g). Six isolipidic and isoenergetic diets were formulated to contain increasing levels of protein (31.6, 35.4, 40.7, 43.3, 45.8 and 52.4%). Each diet was fed to quadruplicate groups of fish (100 fish per group) in freshwater cages. Results showed that weight gain rate (WGR), protein gain, whole-body protein content, trypsin and chymotrypsin specific activities in intestine significantly (P < 0.05) increased with increasing dietary protein level and then plateaued (P > 0.05). Protein retention, protein efficiency ratio (PER), lipase in pyloric caeca and amylase specific activities significantly (P < 0.05) decreased with increasing dietary protein level. Fish fed the diet with 45.8% protein had higher feed efficiency (FE) and higher enteric peptide transporter 1 (Pep T1) gene expression level. Generally, fish fed diets with low dietary protein levels had lower feed intake (FI), higher hepatosomatic index (HSI), higher plasma alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activities, higher hepatic glutamate dehydrogenase (GDH) activity, as well as higher malondialdehyde (MDA) contents in plasma, liver and intestine and lower total anti-oxidative capacity (T-AOC) contents in plasma and intestine (P < 0.05). Based on WGR, the optimum dietary protein requirement of sub-adult triploid rainbow trout was 45.8% dry matter with a diet containing 20% lipid and 22 kJ gross energy/g. This optimal dietary protein level improved the digestive and absorptive abilities of rainbow trout. Diets with lower protein levels (31.6–35.4%) led to increase lipid peroxidation level in whole fish body, while the diet with excess protein (52.4%) appeared to limit enteric Pep T1 gene expression level. • The optimum dietary protein requirement of sub-adult triploid rainbow trout was 45.8% dry matter with a diet containing 20% lipid. • Optimal dietary protein level improved the digestive and absorptive abilities of protein. • Diets with low protein levels (31.6-35.4%) led to increase lipid peroxidation level in whole fish body. • Diet with excess protein (52.4%) appeared to limit enteric peptide transporter 1 gene expression level. [ABSTRACT FROM AUTHOR]

Metal oxide has been the focus of research for decades. In this work, besides the trigonal-structure Al 2 O 3 nanotubes and orthorhombic-structure Al 2 O 3 nanosheets, novel Mg-Al-O/Al 2 O 3 heterostructure nanowires were successfully synthesized at high temperature. The structural characteristics of these oxides were investigated based on electron microscopy and group theory analysis. More importantly, it was found that incorporating the orthorhombic-structure Al 2 O 3 could effectively modulate the occurrence of monoclinic domains and thus the related properties of Mg-Al-O/Al 2 O 3 heterostructures. This work provides a reference for understanding and modulating the complex structures with multiple domain variants in Mg-Al-O oxides. [ABSTRACT FROM AUTHOR]

In genetic data modeling, the use of a limited number of samples for modeling and predicting, especially well below the attribute number, is difficult due to the enormous number of genes detected by a sequencing platform. In addition, many studies commonly use machine learning methods to evaluate genetic datasets to identify potential disease-related genes and drug targets, but to the best of our knowledge, the information associated with the selected gene set was not thoroughly elucidated in previous studies. To identify a relatively stable scheme for modeling limited samples in the gene datasets and reveal the information that they contain, the present study first evaluated the performance of a series of modeling approaches for predicting clinical endpoints of cancer and later integrated the results using various voting protocols. As a result, we proposed a relatively stable scheme that used a set of methods with an ensemble algorithm. Our findings indicated that the ensemble methodologies are more reliable for predicting cancer prognoses than single machine learning algorithms as well as for gene function evaluating. The ensemble methodologies provide a more complete coverage of relevant genes, which can facilitate the exploration of cancer mechanisms and the identification of potential drug targets. [ABSTRACT FROM AUTHOR]

Motivation Detection and identification of viruses and microorganisms in sequencing data plays an important role in pathogen diagnosis and research. However, existing tools for this problem often suffer from high runtimes and memory consumption. Results We present RabbitV, a tool for rapid detection of viruses and microorganisms in Illumina sequencing datasets based on fast identification of unique k -mers. It can exploit the power of modern multi-core CPUs by using multi-threading, vectorization and fast data parsing. Experiments show that RabbitV outperforms fastv by a factor of at least 42.5 and 14.4 in unique k -mer generation (RabbitUniq) and pathogen identification (RabbitV), respectively. Furthermore, RabbitV is able to detect COVID-19 from 40 samples of sequencing data (255 GB in FASTQ format) in only 320 s. Availability and implementation RabbitUniq and RabbitV are available at https://github.com/RabbitBio/RabbitUniq and https://github.com/RabbitBio/RabbitV. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Emerging evidence has shown that miRNA-194 is aberrantly upregulated in gastric cancer (GC); however, the biological mechanisms underlying its involvement are largely unknown. Wnt/β-catenin signaling has been implicated in gastric tumorigenesis; we therefore hypothesized that miRNA-194 promotes gastric carcinogenesis by activating Wnt/β-catenin signaling. MiRNA-194 was found to be overexpressed in GC cell lines and 43 paired GC tissues. Overexpression of miRNA-194 promoted cell proliferation and migration, while inhibition of miRNA-194 blocked these processes. Inhibition of miRNA-194 decreased tumor volumes in nude mice. Furthermore, miRNA-194 inhibitors promoted cytoplasmic localization of β-catenin, leading to repression of Wnt signaling. We also discovered that SUFU, a known negative regulator of Hedgehog and Wnt signaling, was a target of miRNA-194. Anti-SUFU siRNAs rescued the inhibitory effects of miRNA-194 antagonists on cell proliferation and migration and on colony formation. We also found that SUFU expression was downregulated in GC tissues and cell lines and negatively correlated with miRNA-194 expression in primary GC tissues. Moreover, SUFU expression was negatively correlated with tumor stage, supporting its potential as a diagnostic or prognostic marker in GC. Taken together, these findings suggest that miRNA-194 is oncogenic and promotes GC cell proliferation and migration by activating Wnt signaling, at least in part, via suppression of SUFU. [ABSTRACT FROM AUTHOR]