RFEM: A framework for essential microRNA identification in mice based on rotation forest and multiple feature fusion.

With the increasing number of microRNAs (miRNAs), identifying essential miRNAs has become an important task that needs to be solved urgently. However, there are few computational methods for essential miRNA identification. Here, we proposed a novel framework called Rotation Forest for Essential MicroRNA identification (RFEM) to predict the essentiality of miRNAs in mice. We first constructed 1,264 miRNA features of all miRNA samples by fusing 38 miRNA features obtained from the PESM paper and 1,226 miRNA functional features calculated based on miRNA-target gene interactions. Then, we employed 182 training samples with 1,264 features to train the rotation forest model, which was applied to compute the essentiality scores of the candidate samples. The main innovations of RFEM were as follows: 1) miRNA functional features were introduced to enrich the diversity of miRNA features; 2) the rotation forest model used decision tree as the base classifier and could increase the difference among base classifiers through feature transformation to achieve better ensemble results. Experimental results show that RFEM significantly outperformed two previous models with the AUC (AUPR) of 0.942 (0.944) in three comparison experiments under 5-fold cross validation, which proved the model's reliable performance. Moreover, ablation study was further conducted to demonstrate the effectiveness of the novel miRNA functional features. Additionally, in the case studies of assessing the essentiality of unlabeled miRNAs, experimental literature confirmed that 7 of the top 10 predicted miRNAs have crucial biological functions in mice. Therefore, RFEM would be a reliable tool for identifying essential miRNAs.

Preconstructing Asymmetric Interface in Air Cathodes for High-Performance Rechargeable Zn-Air Batteries.

Rechargeable zinc-air batteries afford great potential toward next-generation sustainable energy storage. Nevertheless, the oxygen redox reactions at the air cathode are highly sluggish in kinetics to induce poor energy efficiency and limited cycling lifespan. Air cathodes with asymmetric configurations significantly promote the electrocatalytic efficiency of the loaded electrocatalysts, whereas rational synthetic methodology to effectively fabricate asymmetric air cathodes remains insufficient. Herein, a strategy of asymmetric interface preconstruction is proposed to fabricate asymmetric air cathodes for high-performance rechargeable zinc-air batteries. Concretely, the asymmetric interface is preconstructed by introducing immiscible organic-water diphases within the air cathode, at which the electrocatalysts are in situ formed to achieve an asymmetric configuration. The as-fabricated asymmetric air cathodes realize high working rates of 50 mA cm-2 , long cycling stability of 3400 cycles at 10 mA cm-2 , and over 100 cycles under harsh conditions of 25 mA cm-2 and 25 mAh cm-2 . Moreover, the asymmetric interface preconstruction strategy is universal to many electrocatalytic systems and can be easily scaled up. This work provides an effective strategy toward advanced asymmetric air cathodes with high electrocatalytic efficiency and significantly promotes the performance of rechargeable zinc-air batteries.

Dual-Network Collaborative Matrix Factorization for predicting small molecule-miRNA associations.

MicroRNAs (miRNAs) play crucial roles in multiple biological processes and human diseases and can be considered as therapeutic targets of small molecules (SMs). Because biological experiments used to verify SM-miRNA associations are time-consuming and expensive, it is urgent to propose new computational models to predict new SM-miRNA associations. Here, we proposed a novel method called Dual-network Collaborative Matrix Factorization (DCMF) for predicting the potential SM-miRNA associations. Firstly, we utilized the Weighted K Nearest Known Neighbors (WKNKN) method to preprocess SM-miRNA association matrix. Then, we constructed matrix factorization model to obtain two feature matrices containing latent features of SM and miRNA, respectively. Finally, the predicted SM-miRNA association score matrix was obtained by calculating the inner product of two feature matrices. The main innovations of this method were that the use of WKNKN method can preprocess the missing values of association matrix and the introduction of dual network can integrate more diverse similarity information into DCMF. For evaluating the validity of DCMF, we implemented four different cross validations (CVs) based on two distinct datasets and two different case studies. Finally, based on dataset 1 (dataset 2), DCMF achieved Area Under receiver operating characteristic Curves (AUC) of 0.9868 (0.8770), 0.9833 (0.8836), 0.8377 (0.7591) and 0.9836 ± 0.0030 (0.8632 ± 0.0042) in global Leave-One-Out Cross Validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV and 5-fold CV, respectively. For case studies, plenty of predicted associations have been confirmed by published experimental literature. Therefore, DCMF is an effective tool to predict potential SM-miRNA associations.

Histopathological Diagnosis System for Gastritis Using Deep Learning Algorithm.

Objective To develope a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide images (WSIs). Methods We retrospectively collected 1,250 gastric biopsy specimens (1,128 gastritis, 122 normal mucosa) from PLA General Hospital. The deep learning algorithm based on DeepLab v3 (ResNet-50) architecture was trained and validated using 1,008 WSIs and 100 WSIs, respectively. The diagnostic performance of the algorithm was tested on an independent test set of 142 WSIs, with the pathologists' consensus diagnosis as the gold standard. Results The receiver operating characteristic (ROC) curves were generated for chronic superficial gastritis (CSuG), chronic active gastritis (CAcG), and chronic atrophic gastritis (CAtG) in the test set, respectively.The areas under the ROC curves (AUCs) of the algorithm for CSuG, CAcG, and CAtG were 0.882, 0.905 and 0.910, respectively. The sensitivity and specificity of the deep learning algorithm for the classification of CSuG, CAcG, and CAtG were 0.790 and 1.000 (accuracy 0.880), 0.985 and 0.829 (accuracy 0.901), 0.952 and 0.992 (accuracy 0.986), respectively. The overall predicted accuracy for three different types of gastritis was 0.867. By flagging the suspicious regions identified by the algorithm in WSI, a more transparent and interpretable diagnosis can be generated. Conclusion The deep learning algorithm achieved high accuracy for chronic gastritis classification using WSIs. By pre-highlighting the different gastritis regions, it might be used as an auxiliary diagnostic tool to improve the work efficiency of pathologists.

[Preparation and characterization of upconversion phosphor based on AlF3-YbF3 : Er3+].

In the present paper, AlF3-YbF3 : Er3+ was prepared by high temperature solid phase reaction, and the concentration effect of Er3+ on luminous intensity of phosphors was studied. The crystal structures of the phosphors were characterized by means of X-ray diffraction (XRD), and the upconversion luminescence properties of phosphor were studied by fluorescence emission spectra. Upon 980 nm excitation, when the Er3+ concentration was fixed to be 0.7 mol%, the maximum red emission intensities can be obtained in the sample. Furthermore, the research results showed that the fitted slope for red transition emission was 2.24, indicating that red emission is due to a two-photon excitation process.

[Investigation of synthesis and luminescent properties of the Sr2CeO4: Dy3+].

In the present article, Sr2CeO4:Dy3+ was synthesized with N,N-methylene bisacrylamide (MBAA) as the net agent. The structure, morphology and luminescent properties were also characterized. It is indicated that Sr2CeO4:Dy3+ was single-phased without other phase existing and also had good dispersion The UV-visible absorption spectra suggested that the absorption bands were almost at 480 nm. The excitation spectrum for 270 nm emission has several excitation bands; The emission spectrum of Sr2 CeO4:Dy3+ shows two broad bands at 292 and 338 nm under the 370 nm excitation. The effects of Dy3+ doping concentration on the emission spectrum intensity of Sr2 CeO4:Dy3+ were also studied, the results showed that the ratio of yellow emission to blue emission increases with increasing the D/3+ doping concentration, but with increasing the Dy3+ doping concentration, the emission intensity firstly increased, then decreased, and the maximal emission intensity was at 0.4 mol% Dy3+ concentration.

[PTD-hEGF fusion protein--gene expression and function analysis].

To produce membrane-permeable human epidermal growth factor (hEGF), a pPTD-hEGF prokaryocyte expression vector was constructed and transformed into E. coli BL 21 (DE3). The PTD-hEGF fusion protein was induced by 0.3 mmol/L of IPTG expressed in the form of inclusion body with an yield of 40% of the total protein in the cells, and then purified by Ni2+ -NTA affinity chromatography. The SDS-PAGE analysis showed a single fusion protein band with a molecular weight of 16 kD. The amino acid sequence was checked by MALDI-TOF-MS analysis. The genetic engineering PTD-hEGF fusion protein obviously promoted the proliferation and growth of the HEK-293 cells in vitro.

[Determination of dopamine hydrochloride by a reverse flow injection chemiluminescence method].

A novel chemiluminescence system coupled with a reverse flow injection analysis for the determination of dopamine hydrochloride was presented. It is based on th e strong quench effect of dopamine hydrochloride on the chemiluminescence reaction between luminol and hexacyanoferrate(III) under alkaline condition. Various factors affecting the chemiluminescence intensity of the system were investigated. The possible mechanism of the proposed method was also studied. The decrease of chemiluminescence intensity was linear with the dopamine hydrochloride content in the range of 2.0 x 10(-9) -8.0 x 10(-7) g x mL(-1), the detection limit of the method was 1.14 x 10(-9) g x mL(-1), and the relative standard deviation was 0.99% (4.0 x 10(-7) g x mL(-1), n = 11). It was successfully used for the determination of the content of dopamine hydrochloride in dopamine hydrochloride injection.

Purification and characterization of recombinant human liver prolidase expressed in Saccharomyces cerevisiae.

The recombinant human liver prolidase (rh-prolidase, EC 3.4.13.9) from the lysate supernatant of engineering yeast Saccharomyces cerevisiae was purified in two steps employing anion-exchange gradient chromatography (DEAE-Sepharose fast flow) and gel filtration chromatography (Sephacryl S-200 high resolution). The purified recombinant protein furnished a single band with a molecular weight of 56 kD. Intensity scanning of the SDS-PAGE gel revealed that the prolidase accounted for more than 90% of total protein. The optimum pH of the catalytic reaction was 8.0. The enzyme was stimulated by Mn2+, but strongly inhibited by Cu2+ and Zn2+. The rh-prolidase expressed in S. cerevisiae had both dipeptidase and organophosphorus acid anhydrolase activity. It catalyzed the hydrolysis of soman and the dipeptide Gly -Pro. In a detoxification test in vitro, purified rh-prolidase was remarkably efficient at eliminating the toxicity of a lethal dose of soman, with the result that mice survived injection of such a dose.

[Flow injection chemiluminescence method for the determination of phenol with N-bromosuccinimide-luminol system].

A novel flow injection chemiluminescence method was presented for the determination of phenol. The method is based on the quenching effect of phenols on the chemiluminescence reaction between luminol and N-bromosuccinimide. The linear range for the determination of phenol is 1.0 x 10(-5) -9.0 x 10(-4) mg x mL(-1), and the detection limit is 1.81 x 10(-7) mg x mL(-1). This method was used for the determination of phenol in water samples with satisfactory results. The mechanism of the reaction was also expounded.

Production of human liver prolidase by Saccharomyces cerevisiae as host cells.

AIM: To clone and express the recombinant human liver prolidase in yeast and explore the activities of both dipeptidase and organophosphoric acid anhydrolase (OPAA). METHODS: The cDNA encoding human liver prolidase derived from healthy adult liver was cloned into the pYES2, an expression vector of S cerevisiae, and then transformed into S cerevisiae INVSc1 by electroporation. The transformant with the highest enzymatic activity was induced by galactose for expression. The optimal induction conditions (temperature, induction time, and the initial amount of inoculation cells) were estimated by orthogonal experimental design. The recombinant prolidase and OPAA activities were assayed by spectrocolorimetric methods. RESULTS: The recombinant enzyme catalyzed the hydrolysis of organophosphorous compound soman as well as the hydrolysis of dipeptide Gly-Pro. Under the optimal induction conditions (20 h, 25 degree, initial OD(600)=0.4), the maximum activities of prolidase and OPAA came to 226.5 and 578 micromol.min(-1).g(-1) protein in cell lysate supernatants, respectively. SDS-PAGE of the recombinant enzyme in disrupted cell supernatants showed a molecular weight of 56 kDa. Intensity scanning of the SDS-PAGE gel revealed that the enzyme accounted for 3.16 % of the total protein in the supernatant. One liter incubation medium produced 7 g of wet yeast cell containing 4.56 mg of the recombination protein. CONCLUSION: The recombinant human liver prolidase produced by yeast cell (S cerevisiae) exhibited both dipeptidase and OPAA activities.