DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning

Aberrant miRNA expression has been associated with a large number of human diseases. Therefore, targeting miRNAs to regulate their expression levels has become an important therapy against diseases that stem from the dysfunction of pathways regulated by miRNAs. In recent years, small molecules have demonstrated enormous potential as drugs to regulate miRNA expression (i.e., SM-miR). A clear understanding of the mechanism of action of small molecules on the upregulation and downregulation of miRNA expression allows precise diagnosis and treatment of oncogenic pathways. However, outside of a slow and costly process of experimental determination, computational strategies to assist this on an ad hoc basis have yet to be formulated. In this work, we developed, to the best of our knowledge, the first cross-platform prediction tool, DeepsmirUD, to infer small-molecule-mediated regulatory effects on miRNA expression (i.e., upregulation or downregulation). This method is powered by 12 cutting-edge deep-learning frameworks and achieved AUC values of 0.843/0.984 and AUCPR values of 0.866/0.992 on two independent test datasets. With a complementarily constructed network inference approach based on similarity, we report a significantly improved accuracy of 0.813 in determining the regulatory effects of nearly 650 associated SM-miR relations, each formed with either novel small molecule or novel miRNA. By further integrating miRNA–cancer relationships, we established a database of potential pharmaceutical drugs from 1343 small molecules for 107 cancer diseases to understand the drug mechanisms of action and offer novel insight into drug repositioning. Furthermore, we have employed DeepsmirUD to predict the regulatory effects of a large number of high-confidence associated SM-miR relations. Taken together, our method shows promise to accelerate the development of potential miRNA targets and small molecule drugs

In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning

Cracks and keyhole pores are detrimental defects in alloys produced by laser directed energy deposition (LDED). Laser-material interaction sound may hold information about underlying complex physical events such as crack propagation and pores formation. However, due to the noisy environment and intricate signal content, acoustic-based monitoring in LDED has received little attention. This paper proposes a novel acoustic-based in-situ defect detection strategy in LDED. The key contribution of this study is to develop an in-situ acoustic signal denoising, feature extraction, and sound classification pipeline that incorporates convolutional neural networks (CNN) for online defect prediction. Microscope images are used to identify locations of the cracks and keyhole pores within a part. The defect locations are spatiotemporally registered with acoustic signal. Various acoustic features corresponding to defect-free regions, cracks, and keyhole pores are extracted and analysed in time-domain, frequency-domain, and time-frequency representations. The CNN model is trained to predict defect occurrences using the Mel-Frequency Cepstral Coefficients (MFCCs) of the lasermaterial interaction sound. The CNN model is compared to various classic machine learning models trained on the denoised acoustic dataset and raw acoustic dataset. The validation results shows that the CNN model trained on the denoised dataset outperforms others with the highest overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC score (98%). Furthermore, the trained CNN model can be deployed into an in-house developed software platform for online quality monitoring. The proposed strategy is the first study to use acoustic signals with deep learning for insitu defect detection in LDED process.Comment: 36 Pages, 16 Figures, accepted at journal Additive Manufacturin

Polyclonal antibody against the DPV UL46M protein can be a diagnostic candidate

<p>Abstract</p> <p>Background</p> <p>The duck plague virus (DPV) UL46 protein (VP11/12) is a 739-amino acid tegument protein encoded by the <it>UL46 </it>gene. We analyzed the amino acid sequence of UL46 using bioinformatics tools and defined the main antigenic domains to be between nucleotides 700-2,220 in the <it>UL46 </it>sequence. This region was designated UL46M. The DPV <it>UL46 </it>and <it>UL46M </it>genes were both expressed in <it>Escherichia coli </it>Rosetta (DE3) induced by isopropy1-β-<smcaps>D</smcaps>-thiogalactopyranoside (IPTG) following polymerase chain reaction (PCR) amplification and subcloning into the prokaryotic expression vector pET32a(+). The recombinant proteins were purified using a Ni-NTA spin column and used to generate the polyclonal antibody against UL46 and UL46M in New Zealand white rabbits. The titer was then tested using enzyme-linked immunosorbent assay (ELISA) and agar diffusion reaction, and the specificity was tested by western blot analysis. Subsequently, we established Dot-ELISA using the polyclonal antibody and applied it to DPV detection.</p> <p>Results</p> <p>In our study, the DPV UL46M fusion protein, with a relative molecular mass of 79 kDa, was expressed in <it>E. coli </it>Rosetta (DE3). Expression of the full <it>UL46 </it>gene failed, which was consistent with the results from the bioinformatic analysis. The expressed product was directly purified using Ni-NTA spin column to prepare the polyclonal antibody against UL46M. The titer of the anti-UL46M antisera was over 1:819,200 as determined by ELISA and 1:8 by agar diffusion reaction. Dot-ELISA was used to detect DPV using a 1:60 dilution of anti-UL46M IgG and a 1:5,000 dilution of horseradish peroxidase (HRP)-labeled goat anti-rabbit IgG.</p> <p>Conclusions</p> <p>The anti-UL46M polyclonal antibody reported here specifically identifies DPV, and therefore, it is a promising diagnostic tool for DPV detection in animals. UL46M and the anti-UL46M antibody can be used for further clinical examination and research of DPV.</p

Influence of the Arctic Oscillation on the Vertical Distribution of Wintertime Ozone in the Stratosphere and Upper Troposphere over Northern Hemisphere

The influence of the Arctic Oscillation (AO) on the vertical distribution of stratospheric ozone in the Northern Hemisphere in winter is analyzed using observations and an offline chemical transport model. Positive ozone anomalies are found at low latitudes (0–30°N) and there are three negative anomaly centers in the northern mid- and high latitudes during positive AO phases. The negative anomalies are located in the Arctic middle stratosphere (~30 hPa, 70–90°N), Arctic upper troposphere/lower stratosphere (UTLS, 150–300 hPa, 70–90°N), and mid-latitude UTLS (70–300 hPa, 30–60°N). Further analysis shows that anomalous dynamical transport related to AO variability primarily controls these ozone changes. During positive AO events, positive ozone anomalies between 0–30°N at 50–150 hPa are related to the weakened meridional transport of the Brewer–Dobson circulation (BDC) and enhanced eddy transport. The negative ozone anomalies in the Arctic middle stratosphere are also caused by the weakened BDC, while the negative ozone anomalies in the Arctic UTLS are caused by the increased tropopause height, weakened BDC vertical transport, weaker exchange between the mid-latitudes and the Arctic, and enhanced ozone depletion via heterogeneous chemistry. The negative ozone anomalies in the mid-latitude UTLS are due mainly to enhanced eddy transport from the mid-latitudes to the equatorward of 30°N, while the transport of ozone-poor air from the Arctic to the mid-latitudes makes a minor contribution. Interpreting AO-related variability of stratospheric ozone, especially in the UTLS, would be helpful for the prediction of tropospheric ozone variability caused by AO

Chromosome-level genome assemblies of four wild peach species provide insights into genome evolution and genetic basis of stress resistance

[Background] Peach (Prunus persica) is an economically important stone fruit crop in Rosaceae and widely cultivated in temperate and subtropical regions, emerging as an excellent material to study the interaction between plant and environment. During its genus, there are four wild species of peach, all living in harsh environments. For example, one of the wild species, P. mira, originates from the Qinghai-Tibet Plateau (QTP) and exhibits strong cold/ultraviolet ray environmental adaptations. Although remarkable progresses in the gene discovery of fruit quality-related traits in peach using previous assembled genome were obtained, genomic basis of the response of these wild species to different geographical environments remains unclear.[Results] To uncover key genes regulating adaptability in different species and analyze the role of genetic variations in resistance formation, we performed de novo genome assembling of four wild relatives of peach (P. persica), P. mira, P. davidiana, P. kansuensis, and P. ferganensis and resequenced 175 peach varieties. The phylogenetic tree showed that the divergence time of P. mira and other wild relatives of peach was 11.5 million years ago, which was consistent with the drastic crustal movement of QTP. Abundant genetic variations were identified in four wild species when compared to P. persica, and the results showed that plant-pathogen interaction pathways were enriched in genes containing small insertions and deletions and copy number variations in all four wild relatives of peach. Then, the data were used to identify new genes and variations regulating resistance. For example, presence/absence variations which result from a hybridization event that occurred between P. mira and P. dulcis enhanced the resistance of their putative hybrid, P. davidiana. Using bulked segregant analysis, we located the nematode resistance locus of P. kansuensis in chromosome 2. Within the mapping region, a deletion in the promoter of one NBS-LRR gene was found to involve the resistance by regulating gene expression. Furthermore, combined with RNA-seq and selective sweeps analysis, we proposed that a deletion in the promoter of one CBF gene was essential for high-altitude adaptation of P. mira through increasing its resistance to low temperature.[Conclusions] In general, the reference genomes assembled in the study facilitate our understanding of resistance mechanism of perennial fruit crops, and provide valuable resources for future breeding and improvement.This work was supported by the National Key Research and Development Program (2019YFD1000203), the Agricultural Science and Technology Innovation Program (CAAS-ASTIP-2019-ZFRI-01), and National Horticulture Germplasm Resources Center.Peer reviewe

Synthesis of Visible-Light Driven CrxOy-TiO2 Binary Photocatalyst System Based on Hierarchical Macro-Mesoporous Silica

Hierarchical macro–mesoporous silica materials co-incorporated with Cr and Ti were directly synthesized by adopting close-packed array of polystyrene microsphere as hard template for macropore through a simple soaking-calcination way, where Si/Ti ratio was fixed at 200 and Si/Cr ratio was set between 200 and 10. Ti specie is highly dispersed in porous matrix and Cr specie mainly exists as tetra-coordinated CrO3 at higher Si/Cr ratio (Si/Cr ≥ 50), which transforms to a mixture of CrO3 and crystallized hexa-coordinated Cr2O3when Si/Cr ratio is below 50. This highly interconnected porous material co-incorporated with Cr and Ti presents highest visible-light driven photocatalytic activity at Si/Cr = 20 toward degradation of AO7. Moreover, macro–mesoporous photocatalyst presents higher activity than those of macroporous and mesoporous ones at the same Si/Cr ratio. The improved visible light driven catalytic activity is mainly attributed to effective metal to metal charge transfer from Cr(VI) to Ti(IV) benefitted from the uniform dispersion of these two species in hierarchical porous silica matrix

Hidden Service Website Response Fingerprinting Attacks Based on Response Time Feature

It has been shown that website fingerprinting attacks are capable of destroying the anonymity of the communicator at the traffic level. This enables local attackers to infer the website contents of the encrypted traffic by using packet statistics. Previous researches on hidden service attacks tend to focus on active attacks; therefore, the reliability of attack conditions and validity of test results cannot be fully verified. Hence, it is necessary to reexamine hidden service attacks from the perspective of fingerprinting attacks. In this paper, we propose a novel Website Response Fingerprinting (WRFP) Attack based on response time feature and extremely randomized tree algorithm to analyze the hidden information of the response fingerprint. The objective is to monitor hidden service website pages, service types, and mounted servers. WRFP relies on the hidden service response fingerprinting dataset. In addition to simulated website mirroring, two different mounting modes are taken into account, the same-source server and multisource server. A total of 300,000 page instances within 30,000 domain sites are collected, and we comprehensively evaluate the classification performance of the proposed WRFP. Our results show that the TPR of webpages and server classification remain greater than 93% in the small-scale closed-world performance test, and it is capable of tolerating up to 10% fluctuations in response time. WRFP also provides a higher accuracy and computational efficiency than traditional website fingerprinting classifiers in the challenging open-world performance test. This also indicates the importance of response time feature. Our results also suggest that monitoring website types improves the judgment effect of the classifier on subpages

Effect of Preheating Temperature on Microstructure and Properties of 42CrMo4/38MnVS6 Heterogeneous Laser Welded Joint

Laser-welded forged steel pistons can meet the needs of the new era of heavy truck engines. 42CrMo4 and 38MnVS6 are widely used as piston materials due to the good mechanical properties. This study investigates the influence of preheating on microstructure and mechanical properties of 42CrMo4/38MnVS6 laser welding joint. The experimental results show preheating increases the laser absorption capacity of the metal, which can lead to an increase in weld width. The microstructure of weld is the high-hardness and poor toughness twin martensite without preheating. As the temperature of preheating increases, the twin martensite in the weld begins to transform into lath martensite and regenerates ferrite and bainite. As the preheating temperature increases, the plane fracture toughness (K1C) of the weld increases and then decreases, reaching the highest value of 2322.94 MPa&middot;mm&minus;1/2 at 150 &deg;C. Compared with no preheating conditions, the tensile strength of the welded joint after preheating is improved. The fracture mode of welded joints changes from brittle fracture to ductile fracture. When the preheating temperature is 100&ndash;200 &deg;C, the tensile strength of the welded joint reaches 1018.1&ndash;1032.5 MPa; when the preheating temperature is 250 &deg;C&ndash;300 &deg;C, the tensile strength decreases