Comprehensive evaluation of methods for differential expression analysis of metatranscriptomics data.

Understanding the function of the human microbiome is important but the development of statistical methods specifically for the microbial gene expression (i.e. metatranscriptomics) is in its infancy. Many currently employed differential expression analysis methods have been designed for different data types and have not been evaluated in metatranscriptomics settings. To address this gap, we undertook a comprehensive evaluation and benchmarking of 10 differential analysis methods for metatranscriptomics data. We used a combination of real and simulated data to evaluate performance (i.e. type I error, false discovery rate and sensitivity) of the following methods: log-normal (LN), logistic-beta (LB), MAST, DESeq2, metagenomeSeq, ANCOM-BC, LEfSe, ALDEx2, Kruskal-Wallis and two-part Kruskal-Wallis. The simulation was informed by supragingival biofilm microbiome data from 300 preschool-age children enrolled in a study of childhood dental disease (early childhood caries, ECC), whereas validations were sought in two additional datasets from the ECC study and an inflammatory bowel disease study. The LB test showed the highest sensitivity in both small and large samples and reasonably controlled type I error. Contrarily, MAST was hampered by inflated type I error. Upon application of the LN and LB tests in the ECC study, we found that genes C8PHV7 and C8PEV7, harbored by the lactate-producing Campylobacter gracilis, had the strongest association with childhood dental disease. This comprehensive model evaluation offers practical guidance for selection of appropriate methods for rigorous analyses of differential expression in metatranscriptomics. Selection of an optimal method increases the possibility of detecting true signals while minimizing the chance of claiming false ones.

GESLM algorithm for detecting causal SNPs in GWAS with multiple phenotypes.

With the development of genome-wide association studies, how to gain information from a large scale of data has become an issue of common concern, since traditional methods are not fully developed to solve problems such as identifying loci-to-loci interactions (also known as epistasis). Previous epistatic studies mainly focused on local information with a single outcome (phenotype), while in this paper, we developed a two-stage global search algorithm, Greedy Equivalence Search with Local Modification (GESLM), to implement a global search of directed acyclic graph in order to identify genome-wide epistatic interactions with multiple outcome variables (phenotypes) in a case-control design. GESLM integrates the advantages of score-based methods and constraint-based methods to learn the phenotype-related Bayesian network and is powerful and robust to find the interaction structures that display both genetic associations with phenotypes and gene interactions. We compared GESLM with some common phenotype-related loci detecting methods in simulation studies. The results showed that our method improved the accuracy and efficiency compared with others, especially in an unbalanced case-control study. Besides, its application on the UK Biobank dataset suggested that our algorithm has great performance when handling genome-wide association data with more than one phenotype.

SMetABF: A rapid algorithm for Bayesian GWAS meta-analysis with a large number of studies included.

Bayesian methods are widely used in the GWAS meta-analysis. But the considerable consumption in both computing time and memory space poses great challenges for large-scale meta-analyses. In this research, we propose an algorithm named SMetABF to rapidly obtain the optimal ABF in the GWAS meta-analysis, where shotgun stochastic search (SSS) is introduced to improve the Bayesian GWAS meta-analysis framework, MetABF. Simulation studies confirm that SMetABF performs well in both speed and accuracy, compared to exhaustive methods and MCMC. SMetABF is applied to real GWAS datasets to find several essential loci related to Parkinson's disease (PD) and the results support the underlying relationship between PD and other autoimmune disorders. Developed as an R package and a web tool, SMetABF will become a useful tool to integrate different studies and identify more variants associated with complex traits.

Electrochemical Reflective Absorption Microscopy for Probing the Local Diffusion Behavior in the Electrochemical Interface.

Electrochemical interfaces determine the performance of electrochemical devices, including energy-related systems. An in-depth understanding of the heterogeneous interfaces requires in situ techniques with high sensitivity and high temporal and spatial resolution. We develop here an electrochemical reflective absorption microscope (EC-RAM) by using the absorption signals of reacting species with a reasonably good spatial resolution and high sensitivity. We systematically study the response of absorbance ( A) and its derivative, i.e. d A/d t, at different positions of the electrode surface and at electrodes with different sizes (50 µm, 500 µm, and 2 mm) both experimentally and theoretically. We find that the derivative cyclic voltabsorptometry (DCVA) frequently used to obtain the local current response in conventional electrochemical optical microscopy techniques is only applicable to reactions of surface species or solution species under linear diffusion control. For processes when the radial diffusion cannot be ignored, as in the case of a microelectrode or the edge of a large electrode, the DCVA curves show distinct diffusion behaviors for the electroactive species in different regions of the electrode, which cannot be directly related to the CV curves. When the radial diffusion dominates the reaction, CVA curves follow the same shape as the CV curves. The developed EC-RAM technique can be applied to extract in situ the local response of an electrochemical system during the dynamic reaction processes.

Scalable identification of lineage-specific gene regulatory networks from metacells with NetID.

The identification of gene regulatory networks (GRNs) is crucial for understanding cellular differentiation. Single-cell RNA sequencing data encode gene-level covariations at high resolution, yet data sparsity and high dimensionality hamper accurate and scalable GRN reconstruction. To overcome these challenges, we introduce NetID leveraging homogenous metacells while avoiding spurious gene-gene correlations. Benchmarking demonstrates superior performance of NetID compared to imputation-based methods. By incorporating cell fate probability information, NetID facilitates the prediction of lineage-specific GRNs and recovers known network motifs governing bone marrow hematopoiesis, making it a powerful toolkit for deciphering gene regulatory control of cellular differentiation from large-scale single-cell transcriptome data.

Methodological Considerations in Longitudinal Analyses of Microbiome Data: A Comprehensive Review.

Biological processes underlying health and disease are inherently dynamic and are best understood when characterized in a time-informed manner. In this comprehensive review, we discuss challenges inherent in time-series microbiome data analyses and compare available approaches and methods to overcome them. Appropriate handling of longitudinal microbiome data can shed light on important roles, functions, patterns, and potential interactions between large numbers of microbial taxa or genes in the context of health, disease, or interventions. We present a comprehensive review and comparison of existing microbiome time-series analysis methods, for both preprocessing and downstream analyses, including differential analysis, clustering, network inference, and trait classification. We posit that the careful selection and appropriate utilization of computational tools for longitudinal microbiome analyses can help advance our understanding of the dynamic host-microbiome relationships that underlie health-maintaining homeostases, progressions to disease-promoting dysbioses, as well as phases of physiologic development like those encountered in childhood.

Exploration for the Optical Properties and Fluorescent Prediction of Nitrotriazole and Nitrofurazan: First-Principles and TD-DFT Calculations.

High-energy materials containing azole and furazan have revealed numerous properties; however, the underlying optical properties need to be solved. Meanwhile, the uncertainty for the choice of fluorescent matrix materials and the flexible situational conditions prompted us to estimate the optical and fluorescent properties of 5,5'-dinitro-2H,2H'-3,3'-bi-1,2,4-triazole (DNBT), 4,4'-dinitroazolefurazan (DNAF), and 4,4'-dinitro-3,3'-4,3'-ter-1,2,5-oxadiazole (DNTO). The first-principles calculation with improved dispersion correction terms and time-dependent density functional theory were utilized to calculate the absorbance and excitation energy of DNBT, DNAF, and DNTO, as well as characterization for their crystal structure, electronic structure, molecular orbitals, and so forth, synchronously. In this work, the absorbance anisotropy of DNBT and DNTO is stronger than that of DNAF. The absorbance for each of the (0,0,1) crystal planes in the three compounds is greater than that of the other two crystal planes. Moreover, DNBT has the maximum absorbance on the (0,0,1) crystal plane. The N-N-H from DNBT and N-O-N from DNTO and DNAF are responsible for these results, while N=N in DNAF weakens the performance of N-O-N. UV-vis spectra show that the maximum absorption wavelengths λmax for DNBT, DNAF, and DNTO are 225, 228, and 201 nm, respectively. The number of five-membered rings and the coplanarity of groups in the intermolecular non-conjugation interaction potentially improve this ability due to the results from the crystal diffraction analysis. In addition, the polarization rate DNBT > DNTO > DNAF based on the molecular orbital analysis and the electrostatic potential calculation implies that the excitation energy of DNBT is less than DNTO, and the excitation energy of DNTO is less than DNAF. This work is beneficial to the expansion of energetic materials into the optical field and the accelerated application process of the related industry.

Anatomies for the thermal decomposition behavior and product rule of 5,5'-dinitro-2H,2H'-3,3'-bi-1,2,4-triazole.

High-performance energetic materials are mainly used in the military, aerospace industry and chemical fields. The ordinary technology of producing energetic materials cannot avoid the domination of its unique needs. At present, revealing the underlying mechanism of the formation of high-energy materials is of great significance for improving their quality characteristics. We pay special attention to the decomposition and reactive molecular dynamics (RMD) simulation of 5,5'-dinitro-2H,2H'-3,3'-bi-1,2,4-triazole (DNBT). Various forms were captured in the simulation, and the form is determined by the temperature of the initial reactant. By observing the heating pattern and morphological changes under the initial thermal equilibrium, interesting temperature jumps were found in 325 K and 350 K. Observation of continuous heating (simulated temperatures are 2600 K, 2900 K, 3200 K and 3500 K) shows that DNBT has the maximum heating rate at 3500 K. In addition, N2 occupies this dominant position in the product, moreover, N2 and NO2 respectively dominate the gas phase products during the initial heating process. According to the transition state analysis results of the intermediates, we found 4 interesting intermediate products, which were determined by high frequency reaction under the 4 simulated temperatures and performed with transition state calculations. It shows that the selection of reactant temperature and its activity is the key to orderly decomposition of DNBT. It is expected that these findings will be widely used in comprehensive decomposition devices and to improve the concept of learning military and industrial technology.

Synergistic Flame Retardant Effect of Barium Phytate and Intumescent Flame Retardant for Epoxy Resin.

Recently, widespread concern has been aroused on environmentally friendly materials. In this article, barium phytate (Pa-Ba) was prepared by the reaction of phytic acid with barium carbonate in deionized water, which was used to blend with intumescent flame retardant (IFR) as a flame retardant and was added to epoxy resin (EP). Afterward, the chemical structure and thermal stability of Pa-Ba were characterized by Fourier transform infrared (FTIR) spectroscopy and thermogravimetric analysis (TGA), respectively. On this basis, the flammability and flame retardancy of EP composites were researched. It is shown that EP/14IFR/2Ba composite has the highest limiting oxygen index (LOI) value of 30.7%. Moreover, the peak heat release rate (PHRR) of EP/14IFR/2Ba decreases by 69.13% compared with pure EP. SEM and Raman spectra reveal the carbonization quality of EP/14IFR/2Ba is better than that of other composites. The results prove that Pa-Ba can cooperate with IFR to improve the flame retardancy of EP, reducing the addition amount of IFR in EP, thus expanding the application range of EP. In conclusion, adding Pa-Ba to IFR is a more environmentally friendly and efficient method compared with others.