Celastrol-Induced Nur77 Interaction with TRAF2 Alleviates Inflammation by Promoting Mitochondrial Ubiquitination and Autophagy.

Mitochondria play an integral role in cell death, autophagy, immunity, and inflammation. We previously showed that Nur77, an orphan nuclear receptor, induces apoptosis by targeting mitochondria. Here, we report that celastrol, a potent anti-inflammatory pentacyclic triterpene, binds Nur77 to inhibit inflammation and induce autophagy in a Nur77-dependent manner. Celastrol promotes Nur77 translocation from the nucleus to mitochondria, where it interacts with tumor necrosis factor receptor-associated factor 2 (TRAF2), a scaffold protein and E3 ubiquitin ligase important for inflammatory signaling. The interaction is mediated by an LxxLL motif in TRAF2 and results not only in the inhibition of TRAF2 ubiquitination but also in Lys63-linked Nur77 ubiquitination. Under inflammatory conditions, ubiquitinated Nur77 resides at mitochondria, rendering them sensitive to autophagy, an event involving Nur77 interaction with p62/SQSTM1. Together, our results identify Nur77 as a critical intracellular target for celastrol and unravel a mechanism of Nur77-dependent clearance of inflamed mitochondria to alleviate inflammation.

The AalNix3&4 isoform is required and sufficient to convert Aedes albopictus females into males.

Aedes albopictus is one of the most invasive insect species in the world and an effective vector for many important arboviruses. We reported previously that Ae. albopictus Nix (AalNix) is the male-determining factor of this species. However, whether AalNix alone is sufficient to initiate male development is unknown. Transgenic lines that express each of the three AalNix isoforms from the native promoter were obtained using piggyBac transformation. We verified the stable expression of AalNix isoforms in the transgenic lines and confirm that one isoform, AalNix3&4, is sufficient to convert females into fertile males (pseudo-males) that are indistinguishable from wild-type males. We also established a stable sex-converted female mosquito strain, AalNix3&4-♂4-pseudo-male. The pseudo-male mosquitoes can fly and mate normally with wild-type female, although their mating competitiveness is lower than wild-type. This work further clarifies the role of AalNix in the sex determination pathway and will facilitate the development of Ae. albopictus control strategies that rely on male-only releases such as SIT and sex-ratio distortion.

Dual auxotrophy coupled red labeling strategy for efficient genome editing in Saccharomyces cerevisiae.

The homologous recombination strategy has a long history of editing Saccharomyces cerevisiae target genes. The application of CRISPR/Cas9 strategy to editing target genes in S. cerevisiae has also received a lot of attention in recent years. All findings seem to indicate that editing relevant target genes in S. cerevisiae is an extremely easy event. In this study, we systematically analyzed the advantages and disadvantages of homologous recombination (HR) strategy, CRISPR/Cas9 strategy, and CRISPR/Cas9 combined homology-mediated repair (CRISPR/Case9-HDR) strategy in knocking out BY4742 ade2. Our data showed that when the ade2 was knocked out by HR strategy, a large number of clones appeared to be off-target, and 10 %-80 % of the so-called knockout clones obtained were heteroclones. When the CRISPR/Cas9 strategy was applied, 60% of clones were off-target and the rest were all heteroclones. Interestingly, most of the cells were edited successfully, but at least 60 % of the clones were heteroclones, when the CRISPR/Cas9-HDR strategy was employed. Our results clearly showed that the emergence of heteroclone seems inevitable regardless of the strategies used for editing BY4742 ade2. Given the characteristics of BY4742 defective in ade2 showing red on the YPD plate, we attempted to build an efficient yeast gene editing strategy, in which the CRISPR/Cas9 combines homology-mediated repair template carrying an ade2 expression cassette, BY4742(ade2Δ0) as the start strain. We used this strategy to successfully achieve 100 % knockout efficiency of trp1, indicating that technical challenges of how to easily screen out pure knockout clones without color phenotype have been solved. Our data showed in this study not only establishes an efficient yeast gene knockout strategy with dual auxotrophy coupled red labeling but also provides new ideas and references for the knockout of target genes in the monokaryotic mycelium of macrofungi.

Subacromial osteolysis following hook plate fixation for acromioclavicular dislocation: a systematic review and meta-analysis.

BACKGROUND: Clavicular hook plates are extensively used in the treatment of acromioclavicular (AC) dislocation. Subacromial osteolysis is a typical complication following hook plate fixation. We performed a systematic review and meta-analysis to determine the incidence of subacromial osteolysis and analyzed the associated characteristics of subacromial osteolysis to guide surgeons. METHODS: PubMed, EMBASE, and Cochrane Library databases were comprehensively searched for relevant literature. We screened the literature based on the eligibility criteria, extracted relevant data and assessed the quality of the included studies. Pooled odds ratios (ORs) or mean differences (MDs) with 95% confidence intervals were calculated by a fixed-effects or random-effects model. Heterogeneity was evaluated by the chi-squared test and I2 statistics. A meta-regression analysis was performed to explore the potential source of heterogeneity. RESULTS: Thirty-two studies met the inclusion criteria. The total pooled incidence of subacromial osteolysis was 29%, and the only covariate that could influence the incidence of subacromial osteolysis was the radiological measurement method (P=0.017). Patients in the hook plate fixation with coracoclavicular (CC) ligament reconstruction group had lower odds of subacromial osteolysis (OR, 2.54, 95% CI 1.54 to 4.18; P < 0.001). There were no significant differences in the Constant-Murley scores at the final follow-up between patients with and without subacromial osteolysis (SMD, -0.17; 95% CI, -0.50 to 0.15; P= 0.294). CONCLUSIONS: Subacromial osteolysis has a relatively high and variable incidence, and the primary factor influencing the reported incidence is the radiological assessment method. The current analysis suggests CC ligament reconstruction as an effective surgical approach for decreasing the incidence of subacromial osteolysis. The presence or absence of subacromial osteolysis did not significantly impact the functional outcomes observed during the final follow-up period.

Effects of a Modified Long-Acting Cocktail on Analgesia and Enhanced Recovery After Total Hip Arthroplasty: A Double-Blinded Randomized Clinical Trial.

BACKGROUND: We compared the efficacy and safety of a modified cocktail for postoperative analgesia and early functional rehabilitation in patients undergoing total hip arthroplasty (THA). METHODS: Magnesium sulfate and sodium bicarbonate were added to a cocktail of ropivacaine, epinephrine, and dexamethasone. Primary outcome measures were visual analog scale (VAS) pain scores at various intervals after surgery, morphine consumption for rescue analgesia after surgery, and time to first rescue analgesia. Secondary outcomes were hip function after surgery, daily walking distance, quadriceps muscle strength, and the incidence of postoperative adverse reactions. RESULTS: Morphine consumption was significantly lower in the modified cocktail group than in the control group in the first 24 hours after surgery (6.2 ± 6.0 versus 14.2 ± 6.4 mg, P < .001), as was total morphine consumption (10.0 ± 8.6 versus 19.2 ± 10.1 mg, P < .001). The duration of the first rescue analgesia was significantly prolonged (23.7 ± 10.3 versus 11.9 ± 5.8 mg, P < .001). Morphine consumption was also reduced in the magnesium sulfate and sodium bicarbonate groups over a 24-hour period compared to the control group (P < .001). The modified cocktail group had significantly lower resting VAS pain scores than the control group within 24 hours after surgery (P < .050). The VAS pain scores during movement within 12 hours after surgery were also lower (P < .050). The experimental groups showed better hip range of motion (P < .050) and longer walking distance (P < .050) on the first postoperative day, and levels of inflammatory markers were significantly reduced. The incidence of postoperative adverse reactions was similar among the 4 groups. CONCLUSIONS: The modified cocktail with a new adjuvant can prolong the duration of postoperative analgesia, reduce the dosage of rescue analgesics, and accelerate early postoperative functional recovery in patients undergoing THA.

Hydrogel Encapsulated Core-Shell Photonic Barcodes for Multiplex Biomarker Quantification.

Acute myocardial infarction (AMI) is one of the most fatal diseases in the world in recent decades. Because rapid and accurate determination of AMI has the potential to save millions of lives globally, the development of new diagnostic method is of great significance. Here, we designed a magnetic responsive structural color core-shell hydrogel microcarrier as a novel platform for a high-throughput detection of a variety of cardiovascular biomarkers. The composite hydrogel shell was formed from methacrylated gelatin, acrylic acid, and poly(ethylene glycol diacrylate), and the core silica photonic crystals acted as a detector. Fe3O4 nanoparticles were infused into the void of the core-shell structure to impart magnetic response properties to the encoded carrier. The findings indicated that our method possessed high sensitivity and reliable specificity in the high-throughput detection of AMI-related biomarkers Myo, cTnI, and BNP. In addition, the developed method not only showed good specificity and high sensitivity in clinical samples but also was comparable to the clinical gold standard method. Therefore, the magnetic response structural color core-shell hydrogel carriers were regarded as a potential approach to detect some AMI disease-related biomarkers.

Lithographic Microneedle-Motors from Multimodal Microfluidics for Cargo Delivery.

Micromotors have led to an unprecedented revolution in the field of cargo delivery. Attempts in this area trend toward enriching their structures and improving their functions to promote their further applications. Herein, novel microneedle-motors (MNMs) for active drug delivery through a flexible multimodal microfluidic lithographic approach are presented. The multimodal microfluidics is composed of a co-flow geometry-derived droplet fluid and an active cargo mixed laminar flow in a triangular microchannel. The MNMs with sharp tips and spherical fuel-loading cavities are obtained continuously from microfluidics with the assistance of flow lithography. The structural parameters of the MNMs could be precisely tailored by simply choosing the flow speed or the shape of the photomask. As the actives are mixed into the phase solution during the generation, the resultant MNMs are loaded with cargoes for direct applications without any extra complex operation. Based on these features, it is demonstrated that with sharp tips and autonomous movement, the MNMs can efficiently penetrate the tissue-like substrates, indicating the potential in overcoming physiological barriers for cargo release. These results indicate that the proposed multimodal microfluidic lithographic MNMs are valuable for practical active cargo delivery in biomedical and other relative areas.

ITP-Pred: an interpretable method for predicting, therapeutic peptides with fused features low-dimension representation.

The peptide therapeutics market is providing new opportunities for the biotechnology and pharmaceutical industries. Therefore, identifying therapeutic peptides and exploring their properties are important. Although several studies have proposed different machine learning methods to predict peptides as being therapeutic peptides, most do not explain the decision factors of model in detail. In this work, an Interpretable Therapeutic Peptide Prediction (ITP-Pred) model based on efficient feature fusion was developed. First, we proposed three kinds of feature descriptors based on sequence and physicochemical property encoded, namely amino acid composition (AAC), group AAC and coding autocorrelation, and concatenated them to obtain the feature representation of therapeutic peptide. Then, we input it into the CNN-Bi-directional Long Short-Term Memory (BiLSTM) model to automatically learn recognition of therapeutic peptides. The cross-validation and independent verification experiments results indicated that ITP-Pred has a higher prediction performance on the benchmark dataset than other comparison methods. Finally, we analyzed the output of the model from two aspects: sequence order and physical and chemical properties, mining important features as guidance for the design of better models that can complement existing methods.

Drug repositioning based on the heterogeneous information fusion graph convolutional network.

In silico reuse of old drugs (also known as drug repositioning) to treat common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked drugs, with potentially lower overall development costs and shorter development timelines. Therefore, there is a pressing need for computational drug repurposing methodologies to facilitate drug discovery. In this study, we propose a new method, called DRHGCN (Drug Repositioning based on the Heterogeneous information fusion Graph Convolutional Network), to discover potential drugs for a certain disease. To make full use of different topology information in different domains (i.e. drug-drug similarity, disease-disease similarity and drug-disease association networks), we first design inter- and intra-domain feature extraction modules by applying graph convolution operations to the networks to learn the embedding of drugs and diseases, instead of simply integrating the three networks into a heterogeneous network. Afterwards, we parallelly fuse the inter- and intra-domain embeddings to obtain the more representative embeddings of drug and disease. Lastly, we introduce a layer attention mechanism to combine embeddings from multiple graph convolution layers for further improving the prediction performance. We find that DRHGCN achieves high performance (the average AUROC is 0.934 and the average AUPR is 0.539) in four benchmark datasets, outperforming the current approaches. Importantly, we conducted molecular docking experiments on DRHGCN-predicted candidate drugs, providing several novel approved drugs for Alzheimer's disease (e.g. benzatropine) and Parkinson's disease (e.g. trihexyphenidyl and haloperidol).

Bioinspired MXene-integrated colloidal crystal arrays for multichannel bioinformation coding.

Information coding strategies are becoming increasingly crucial due to the storage demand brought by the information explosion. In particular, bioinformation coding has attracted great attention for its advantages of excellent storage capacity and long lifetime. Herein, we present an innovative bioinspired MXene-integrated photonic crystal (PhC) array for multichannel bioinformation coding. PhC arrays with similar structure to Stenocara beetle's back are utilized as the substrate, exhibiting properties of high throughput and stability. MXene nanosheets are further integrated on the PhC array's substrate with the assistance of the adhesion capacity of mussel-inspired dopamine (DA). Benefitting from their fluorescence resonance energy transfer effect, MXene nanosheets can quench the fluorescence signals of quantum dot (QD) modified DNA probes unless the corresponding targets exist. Additionally, these black MXene nanosheets can enhance the contrast of structural color. In this case, the encrypted information can be easily read out by simply observing the fluorescence signal of DNA probes. It is demonstrated that this strategy based on bioinspired MXene-integrated PhC arrays can realize high-throughput information encoding and encryption, which opens a chapter of bioinformation coding.

Influencing factors of hidden blood loss after primary total hip arthroplasty through the posterior approach: a retrospective study.

BACKGROUND: Total hip arthroplasty (THA) is an excellent treatment for the end-stage hip disease, and perioperative blood management strategies have been effectively applied to this procedure. However, many patients still experience anemia after the operation, which is usually overlooked by orthopedic surgeons due to the hidden blood loss (HBL) in the perioperative period. Therefore, the objective of this study was to evaluate HBL in patients undergoing primary THA using the posterior approach and to explore its influencing factors. METHODS: A retrospective analysis of 707 patients who underwent primary THA through the posterior approach was conducted in our hospital from January 2020 to January 2022. By applying Gross's and Nadler's formula, the HBL was calculated. Six quantitative variables (age, body mass index, surgical duration, albumin loss, preoperative hemoglobin, and hemoglobin loss) as well as four qualitative variables (gender, American Society of Anesthesiologists class, major preoperative diagnosis, and hypertension) of patients were analyzed using multivariate linear regression. RESULTS: The HBL was recorded at 700.39 ± 368.59 mL. As a result of multivariate linear regression analysis, it was determined that body mass index, surgical duration, and hemoglobin loss were all significant risk factors for HBL, whereas preoperative hemoglobin was considered a protective factor. It has been demonstrated that HBL is not significantly correlated with age, albumin loss, gender, ASA class, or major preoperative diagnosis, but it also did not differ from HBL by hypertension. CONCLUSIONS: Hidden blood Loss (HBL) in patients after primary total hip arthroplasty (THA) using the posterior approach is large and significant. When optimizing the perioperative management of THA, orthopedic surgeons should keep in mind HBL and its influencing factors, especially for patients with high body mass indexes, long surgical durations, and low preoperative hemoglobin levels. TRIAL REGISTRATION: This study was registered in the Chinese Clinical Trial Registry (ChiCTR2100053888) in 02/12/2021, http://www.chictr.org.cn .

Complicated Bosworth fracture-dislocation: A case report and review of the literature.

Bosworth fracture and dislocation is relatively rare, accounting for about 1% of ankle fractures. It is characterized by the proximal fibula fracture embedded in the posterolateral distal tibia. Due to an insufficient understanding of this fracture, it is easy to cause missed diagnosis and misdiagnosis in clinical practice. Due to the insertion of the fracture, it is challenging to perform closed reduction, and improper treatment is easy to cause complications. Surgical treatment is recommended for this type of fracture. In order to improve the understanding of orthopedic surgeons about Bosworth fracture and dislocation, this paper reports the diagnosis and treatment of 2 cases of Bosworth fracture and dislocation, and reviews the literature on Bosworth fracture's mechanism, diagnosis, classification, complications, and treatment options in recent years.

Computational drug repositioning using similarity constrained weight regularization matrix factorization: A case of COVID-19.

Amid the COVID-19 crisis, we put sizeable efforts to collect a high number of experimentally validated drug-virus association entries from literature by text mining and built a human drug-virus association database. To the best of our knowledge, it is the largest publicly available drug-virus database so far. Next, we develop a novel weight regularization matrix factorization approach, termed WRMF, for in silico drug repurposing by integrating three networks: the known drug-virus association network, the drug-drug chemical structure similarity network, and the virus-virus genomic sequencing similarity network. Specifically, WRMF adds a weight to each training sample for reducing the influence of negative samples (i.e. the drug-virus association is unassociated). A comparison on the curated drug-virus database shows that WRMF performs better than a few state-of-the-art methods. In addition, we selected the other two different public datasets (i.e. Cdataset and HMDD V2.0) to assess WRMF's performance. The case study also demonstrated the accuracy and reliability of WRMF to infer potential drugs for the novel virus. In summary, we offer a useful tool including a novel drug-virus association database and a powerful method WRMF to repurpose potential drugs for new viruses.

Noninvasive Multiplexed Analysis of Bladder Cancer-Derived Urine Exosomes via Janus Magnetic Microspheres.

Bladder cancer greatly endangers human health, and its early diagnosis is of vital importance. Exosomes, which contain proteins and nucleic acids related to their source cells, are expected to be an emerging biomarker for bladder cancer detection. Here, we propose a novel system for multiplexed analysis of bladder cancer-derived urine exosomes based on Janus magnetic microspheres as barcoded microcarriers. The microcarriers are constructed by droplet-templated coassembly of colloidal silica nanoparticles and magnetic nanoparticles under a magnetic field. The microcarriers possess one hemisphere with structural color and the other hemisphere with magneto-responsiveness. Benefiting from the unique structure, these Janus microcarriers could serve as barcodes and could move controllably in a sample solution, thus realizing the multiplex detection of exosomes with high sensitivity. Notably, the present platform is noninvasive since a urine specimen, as an ideal source of bladder cancer-derived exosomes, is employed as the sample solution. This feature, together with the good sensitivity, specificity, low sample consumption, and easy operation, indicates the great potential of the platform for bladder cancer diagnosis in clinical applications.

Comparison of anther transcriptomes in response to cold stress at the reproductive stage between susceptible and resistant Japonica rice varieties.

BACKGROUND: Rice is one of the most important cereal crops in the world but is susceptible to cold stress (CS). In this study, we carried out parallel transcriptomic analysis at the reproductive stage on the anthers of two Japonica rice varieties with contrasting CS resistance: cold susceptible Longjing11 (LJ11) and cold resistant Longjing25 (LJ25). RESULTS: According to the obtained results, a total of 16,762 differentially expressed genes (DEGs) were identified under CS, including 7,050 and 14,531 DEGs in LJ25 and LJ11, respectively. Examining gene ontology (GO) enrichment identified 35 up- and 39 down-regulated biological process BP GO terms were significantly enriched in the two varieties, with 'response to heat' and 'response to cold' being the most enriched. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis identified 33 significantly enriched pathways. Only the carbon metabolism and amino acid biosynthesis pathways with down-regulated DEGs were enriched considerably in LJ11, while the plant hormone signal transduction pathway (containing 153 DEGs) was dramatically improved. Eight kinds of plant hormones were detected in the pathway, while auxin, abscisic acid (ABA), salicylic acid (SA), and ethylene (ETH) signaling pathways were found to be the top four pathways with the most DEGs. Furthermore, the protein-protein interaction (PPI) network analysis identified ten hub genes (co-expressed gene number ≥ 30), including six ABA-related genes. Various DEGs (such as OsDREB1A, OsICE1, OsMYB2, OsABF1, OsbZIP23, OsCATC, and so on) revealed distinct expression patterns among rice types when the DEGs between LJ11 and LJ25 were compared, indicating that they are likely responsible for CS resistance of rice in cold region. CONCLUSION: Collectively, our findings provide comprehensive insights into complex molecular mechanisms of CS response and can aid in CS resistant molecular breeding of rice in cold regions.

Self-Propelled Structural Color Cylindrical Micromotors for Heavy Metal Ions Adsorption and Screening.

Water contamination resulting from heavy metal ions (HMIs) poses a severe threat to public health and the ecosystem. Attempts are tending to develop functional materials to realize efficient and intelligent adsorption of HMIs. Herein, self-propelled structural color cylindrical micromotors (SCCMs) with reversible HMIs adsorption capacity and self-reporting property are presented. The SCCMs are fabricated by using platinum nanoparticles (Pt NPs) tagged responsive hydrogel of carboxymethyl chitosan (CMC) and polyacrylamide (PAM) to asymmetrically replicate tubular colloidal crystal templates (TCCTs). Owing to the self-propelled motion of the SCCMs, the enhancive ion-motor interactions bring significantly improved decontamination efficiency. Moreover, it is demonstrated that the SCCMs can realize quick and naked-eye-visible self-reporting during the adsorption/desorption process based on their responsive structure color variation and superior adsorption capacity. Thus, it is anticipated that such intelligent SCCMs can significantly facilitate the evolution of sensing assays and diverse environmental fields.

iEnhancer-XG: interpretable sequence-based enhancers and their strength predictor.

MOTIVATION: Enhancers are non-coding DNA fragments with high position variability and free scattering. They play an important role in controlling gene expression. As machine learning has become more widely used in identifying enhancers, a number of bioinformatic tools have been developed. Although several models for identifying enhancers and their strengths have been proposed, their accuracy and efficiency have yet to be improved. RESULTS: We propose a two-layer predictor called 'iEnhancer-XG.' It comprises a one-layer predictor (for identifying enhancers) and a second classifier (for their strength) and uses 'XGBoost' as a base classifier and five feature extraction methods, namely, k-Spectrum Profile, Mismatch k-tuple, Subsequence Profile, Position-specific scoring matrix (PSSM) and Pseudo dinucleotide composition (PseDNC). Each method has an independent output. We place the feature vector matrix into the ensemble learning for fusion. This experiment involves the method of 'SHapley Additive explanations' to provide interpretability for the previous black box machine learning methods and improve their credibility. The accuracies of the ensemble learning method are 0.811 (first layer) and 0.657 (second layer). The rigorous 10-fold cross-validation confirms that the proposed method is significantly better than existing technologies. AVAILABILITY AND IMPLEMENTATION: The source code and dataset for the enhancer predictions have been uploaded to https://github.com/jimmyrate/ienhancer-xg. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of Biomarker Volatile Organic Compounds Released by Three Stored-Grain Insect Pests in Wheat.

Monitoring and early detection of stored-grain insect infestation is essential to implement timely and effective pest management decisions to protect stored grains. We report a reliable analytical procedure based on headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) to assess stored-grain infestation through the detection of volatile compounds emitted by insects. Four different fibre coatings were assessed; 85 µm CAR/PDMS had optimal efficiency in the extraction of analytes from wheat. The headspace profiles of volatile compounds produced by Tribolium castaneum (Herbst), Rhyzopertha dominica (Fabricius), and Sitophilus granarius (Linnaeus), either alone or with wheat, were compared with those of non-infested wheat grains. Qualitative analysis of chromatograms showed the presence of different volatile compound profiles in wheat with pest infestation compared with the wheat controls. Wheat-specific and insect-specific volatile compounds were identified, including the aggregation pheromones, dominicalure-1 and dominicalure-2, from R. dominica, and benzoquinones homologs from T. castaneum. For the first time, the presence of 3-hydroxy-2-butanone was reported from S. granarius, which might function as an alarm pheromone. These identified candidate biomarker compounds can be utilized in insect surveillance and monitoring in stored grain to safeguard our grain products in future.

StackCPPred: a stacking and pairwise energy content-based prediction of cell-penetrating peptides and their uptake efficiency.

MOTIVATION: Cell-penetrating peptides (CPPs) are a vehicle for transporting into living cells pharmacologically active molecules, such as short interfering RNAs, nanoparticles, plasmid DNAs and small peptides, thus offering great potential as future therapeutics. Existing experimental techniques for identifying CPPs are time-consuming and expensive. Thus, the prediction of CPPs from peptide sequences by using computational methods can be useful to annotate and guide the experimental process quickly. Many machine learning-based methods have recently emerged for identifying CPPs. Although considerable progress has been made, existing methods still have low feature representation capabilities, thereby limiting further performance improvements. RESULTS: We propose a method called StackCPPred, which proposes three feature methods on the basis of the pairwise energy content of the residue as follows: RECM-composition, PseRECM and RECM-DWT. These features are used to train stacking-based machine learning methods to effectively predict CPPs. On the basis of the CPP924 and CPPsite3 datasets with jackknife validation, StackDPPred achieved 94.5% and 78.3% accuracy, which was 2.9% and 5.8% higher than the state-of-the-art CPP predictors, respectively. StackCPPred can be a powerful tool for predicting CPPs and their uptake efficiency, facilitating hypothesis-driven experimental design and accelerating their applications in clinical therapy. AVAILABILITY AND IMPLEMENTATION: Source code and data can be downloaded from https://github.com/Excelsior511/StackCPPred. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CMF-Impute: an accurate imputation tool for single-cell RNA-seq data.

MOTIVATION: Single-cell RNA-sequencing (scRNA-seq) technology provides a powerful tool for investigating cell heterogeneity and cell subpopulations by allowing the quantification of gene expression at single-cell level. However, scRNA-seq data analysis remains challenging because of various technical noises such as dropout events (i.e. excessive zero counts in the expression matrix). RESULTS: By taking consideration of the association among cells and genes, we propose a novel collaborative matrix factorization-based method called CMF-Impute to impute the dropout entries of a given scRNA-seq expression matrix. We test CMF-Impute and compare it with the other five state-of-the-art methods on six popular real scRNA-seq datasets of various sizes and three simulated datasets. For simulated datasets, CMF-Impute outperforms other methods in imputing the closest dropouts to the original expression values as evaluated by both the sum of squared error and Pearson correlation coefficient. For real datasets, CMF-Impute achieves the most accurate cell classification results in spite of the choice of different clustering methods like SC3 or T-SNE followed by K-means as evaluated by both adjusted rand index and normalized mutual information. Finally, we demonstrate that CMF-Impute is powerful in reconstructing cell-to-cell and gene-to-gene correlation, and in inferring cell lineage trajectories. AVAILABILITY AND IMPLEMENTATION: CMF-Impute is written as a Matlab package which is available at https://github.com/xujunlin123/CMFImpute.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.