No abstract

Antifreeze proteins (AFPs) are indispensable for living organisms to survive in an extremely cold environment and have a variety of potential biotechnological applications. The accurate prediction of antifreeze proteins has become an important issue and is urgently needed. Although considerable progress has been made, AFP prediction is still a challenging problem due to the diversity of species. In this study, we proposed a new sequence-based AFP predictor, called TargetFreeze. TargetFreeze utilizes an enhanced feature representation method that weightedly combines multiple protein features and takes the powerful support vector machine as the prediction engine. Computer experiments on benchmark datasets demonstrate the superiority of the proposed TargetFreeze over most recently released AFP predictors. We also implemented a user-friendly web server, which is openly accessible for academic use and is available at http://csbio.njust.edu.cn/bioinf/TargetFreeze. TargetFreeze supplements existing AFP predictors and will have potential applications in AFP-related biotechnology fields.

An intense luminescence flash can be induced during the collapse phase of bubbles generated by pulsed discharge in water. To gain insight into this special phenomenon, we experimentally investigated the optical characteristics and luminescence temperature inside collapsing bubbles. The duration of the luminescence flash generated by pulsed discharge was around tens of microseconds, which was confirmed by high-speed recording and the photodiode output, and the inception time of the luminescence flash was approximately 32.5 μs before the bubble collapsed to its minimum size. The temperatures of the luminescence flash at discharge energies of 25 and 30 J/pulse calculated according to the two-line radiance ratio method were 6673 and 6728 K, respectively.

Background Metagenomic next-generation sequencing (mNGS) has the potential to become a complementary, if not essential, test in some clinical settings. However, the clinical application of mNGS in a large population of children with various types of infectious diseases (IDs) has not been previously evaluated. Methods From April 2019 to April 2021, 640 samples were collected at a single pediatric hospital and classified as ID [479 (74.8%)], non-ID [NID; 156 (24.4%)], and unknown cases [5 (0.8%)], according to the final clinical diagnosis. We compared the diagnostic performance in pathogen detection between mNGS and standard reference tests. Results According to final clinical diagnosis, the sensitivity and specificity of mNGS were 75.0% (95% CI: 70.8%–79.2%) and 59.0% (95% CI: 51.3%–66.7%), respectively. For distinguishing ID from NID, the sensitivity of mNGS was approximately 45.0% higher than that of standard tests (75.0% vs 30.0%; P < 0.001). For fungal detection, mNGS showed positive results in 93.0% of cases, compared to 43.7% for standard tests (P < 0.001). Diagnostic information was increased in respiratory system samples through the addition of meta-transcriptomic sequencing. Further analysis also showed that the read counts in sequencing data were highly correlated with clinical diagnosis, regardless of whether infection was by single or multiple pathogens (Kendall’s tau b = 0.484, P < 0.001). Conclusions For pediatric patients in critical condition with suspected infection, mNGS tests can provide valuable diagnostic information to resolve negative or inconclusive routine test results, differentiate ID from NID cases, and facilitate accurate and effective clinical therapeutic decision-making.

BackgroundVitamins are typical ligands that play critical roles in various metabolic processes. The accurate identification of the vitamin-binding residues solely based on a protein sequence is of significant importance for the functional annotation of proteins, especially in the post-genomic era, when large volumes of protein sequences are accumulating quickly without being functionally annotated.ResultsIn this paper, a new predictor called TargetVita is designed and implemented for predicting protein-vitamin binding residues using protein sequences. In TargetVita, features derived from the position-specific scoring matrix (PSSM), predicted protein secondary structure, and vitamin binding propensity are combined to form the original feature space; then, several feature subspaces are selected by performing different feature selection methods. Finally, based on the selected feature subspaces, heterogeneous SVMs are trained and then ensembled for performing prediction.ConclusionsThe experimental results obtained with four separate vitamin-binding benchmark datasets demonstrate that the proposed TargetVita is superior to the state-of-the-art vitamin-specific predictor, and an average improvement of 10% in terms of the Matthews correlation coefficient (MCC) was achieved over independent validation tests. The TargetVita web server and the datasets used are freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetVita or http://www.csbio.sjtu.edu.cn/bioinf/TargetVita.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-297) contains supplementary material, which is available to authorized users.