Non-homology-based prediction of gene functions in maize (\u3ci\u3eZea mays\u3c/i\u3e ssp. \u3ci\u3emays\u3c/i\u3e)

Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions.As a result, homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random forest-based prediction consistently provided the most accurate gene function prediction. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. GO prediction models trained with homology-based annotations were able to successfully predict annotations from a manually curated “gold standard” GO annotation set. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations

A Review of Software Reliability Testing Techniques

In the era of intelligent systems, the safety and reliability of software have received more attention. Software reliability testing is a significant method to ensure reliability, safety and quality of software. The intelligent software technology has not only offered new opportunities but also posed challenges to software reliability technology. The focus of this paper is to explore the software reliability testing technology under the impact of intelligent software technology. In this study, the basic theories of traditional software and intelligent software reliability testing were investigated via related previous works, and a general software reliability testing framework was established. Then, the technologies of software reliability testing were analyzed, including reliability modeling, test case generation, reliability evaluation, testing criteria and testing methods. Finally, the challenges and opportunities of software reliability testing technology were discussed at the end of this paper

Synergic effect within n-type inorganic–p-type organic nano-hybrids in gas sensors

This paper describes the exploration of a synergic effect within n-type inorganic–p-type organic nanohybrids in gas sensors. One-dimensional (1D) n-type SnO2–p-type PPy composite nanofibers were prepared by combining the electrospinning and polymerization techniques, and taken as models to explore the synergic effect during the sensing measurement. Outstanding sensing performances, such as large responses and low detection limits (20 ppb for ammonia) were obtained. A plausible mechanism for the synergic effect was established by introducing p–n junction theory to the systems. Moreover, interfacial metal (Ag) nanoparticles were introduced into the n-type SnO2–p-type PPy nano-hybrids to further supplement and verify our theory. The generality of this mechanism was further verified using TiO2–PPy and TiO2–Au–PPy nano-hybrids. We believe that our results can construct a powerful platform to better understand the relationship between the microstructures and their gas sensing performances

A fast humidity sensor based on Li⁺-doped SnO₂ one-dimensional porous nanofibers

One-dimensional SnO₂- and Li⁺-doped SnO₂ porous nanofibers were easily fabricated via electrospinning and a subsequent calcination procedure for ultrafast humidity sensing. Different Li dopant concentrations were introduced to investigate the dopant\u27s role in sensing performance. The response properties were studied under different relative humidity levels by both statistic and dynamic tests. The best response was obtained with respect to the optimal doping of Li⁺ into SnO₂ porous nanofibers with a maximum 15 times higher response than that of pristine SnO₂ porous nanofibers, at a relative humidity level of 85%. Most importantly, the ultrafast response and recovery time within 1 s was also obtained with the 1.0 wt % doping of Li⁺ into SnO₂ porous nanofibers at 5 V and at room temperature, benefiting from the co-contributions of Li-doping and the one-dimensional porous structure. This work provides an effective method of developing ultrafast sensors for practical applications-especially fast breathing sensors

Heparan sulfate proteoglycans as attachment factor for SARS-CoV-2

Severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) is causing an unprecedented global pandemic demanding the urgent development of therapeutic strategies. Microarray binding experiments using an extensive heparan sulfate (HS) oligosaccharide library showed the spike of SARS-CoV-2 can bind HS in a length- and sequence-dependent manner. Hexa- and octasaccharides composed of IdoA2S-GlcNS6S repeating units were identified as optimal ligands. Surface plasma resonance (SPR) showed the SARS-CoV-2 spike protein binds with higher affinity to heparin (K D 55 nM) compared to the receptor binding domain (RBD, K D 1 µM) alone. An octasaccharide composed of IdoA2S-GlcNS6S could inhibit spike-heparin interaction with an IC 50 of 38 nM. Our data supports a model in which the RBD of the spike of SARS-CoV-2 confers sequence specificity for HS expressed by target cells whereas an additional HS binding site in the S1/S2 proteolytic cleavage site enhances the avidity of binding. Collectively, our results highlight the potential of using HS oligosaccharides as a therapeutic agent by inhibiting SARS-CoV-2 binding to target cells

TNFRSF10C methylation is a new epigenetic biomarker for colorectal cancer

Background Abnormal methylation of TNFRSF10C was found to be associated with different types of cancers, excluding colorectal cancer (CRC). In this paper, the performance of TNFRSF10C methylation in CRC was studied in two stages. Method The discovery stage was involved with 38 pairs of CRC tumor and paired adjacent non-tumor tissues, and 69 pairs of CRC tumor and paired adjacent non-tumor tissues were used for the validation stage. Quantitative methylation specific PCR (qMSP) method and percentage of methylated reference (PMR) were used to test and represent the methylation level of TNFRSF10C, respectively. A dual-luciferase reporter gene experiment was conducted to evaluate the promoter activity of TNFRSF10C fragment. Results A significant association of TNFRSF10C promoter hypermethylation with CRC was found and validated (discovery stage: 24.67 ± 7.52 vs. 3.36 ± 0.89; P = 0.003; validation stage: 31.21 ± 12.48 vs. 4.52 ± 1.47; P = 0.0005). Subsequent analyses of TCGA data among 46 pairs of CRC samples further confirmed our findings (cg23965061: P = 4E − 6; cg14015044: P = 1E − 7). Dual-luciferase reporter gene assay revealed that TNFRSF10C fragment was able to significantly promote gene expression (Fold change = 2.375, P = 0.013). Our data confirmed that TNFRSF10C promoter hypermethylation can predict shorter overall survival of CRC patients (P = 0.032). Additionally, bioinformatics analyses indicated that TNFRSF10C hypermethylation was significantly associated with lower TNFRSF10C expression. Conclusion Our work suggested that TNFRSF10C hypermethylation was significantly associated with the risk of CRC

The Extracytoplasmic Domain of the Mycobacterium tuberculosis Ser/Thr Kinase PknB Binds Specific Muropeptides and Is Required for PknB Localization

The Mycobacterium tuberculosis Ser/Thr kinase PknB has been implicated in the regulation of cell growth and morphology in this organism. The extracytoplasmic domain of this membrane protein comprises four penicillin binding protein and Ser/Thr kinase associated (PASTA) domains, which are predicted to bind stem peptides of peptidoglycan. Using a comprehensive library of synthetic muropeptides, we demonstrate that the extracytoplasmic domain of PknB binds muropeptides in a manner dependent on the presence of specific amino acids at the second and third positions of the stem peptide, and on the presence of the sugar moiety N-acetylmuramic acid linked to the peptide. We further show that PknB localizes strongly to the mid-cell and also to the cell poles, and that the extracytoplasmic domain is required for PknB localization. In contrast to strong growth stimulation by conditioned medium, we observe no growth stimulation of M. tuberculosis by a synthetic muropeptide with high affinity for the PknB PASTAs. We do find a moderate effect of a high affinity peptide on resuscitation of dormant cells. While the PASTA domains of PknB may play a role in stimulating growth by binding exogenous peptidoglycan fragments, our data indicate that a major function of these domains is for proper PknB localization, likely through binding of peptidoglycan fragments produced locally at the mid-cell and the cell poles. These data suggest a model in which PknB is targeted to the sites of peptidoglycan turnover to regulate cell growth and cell division

Non-homology-based prediction of gene functions in maize (Zea mays ssp. mays)

Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result, homology is widely used for gene function pre- diction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random- forest-based prediction consistently provided the most accurate gene function predic- tion. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. GO prediction models trained with homology-based annotations were able to successfully predict annotations from a manually curated “gold standard” GO annotation set. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations