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InN holds great promise for a wide range of applications, including broadband optical devices, high-frequency electronic devices, and serving as a substrate for highly sensitive gas detectors and efficient catalysts. Nevertheless, production of high-quality InN thin films through epitaxy has remained a significant challenge. This is primarily due to complexities arising from the low dissociation temperature and the high N2 equilibrium pressure of InN, which lead to formation of numerous nitrogen vacancies and a propensity to generate indium metal nanoparticles. Efficacy of the indium bilayer pre-deposition method for InN film growth has been established in prior studies. In this work, we introduce a specialized hydrogen insertion method to further enhance nitridation of the indium bilayer. The corresponding kinetic mechanism has been demonstrated through theoretical simulations and practical epitaxy experiments, leading to the development of an optimized hydrogen insertion process. This research represents a substantial improvement over existing InN epitaxial methods that involve control of the indium bilayer and introduces a novel mechanism for enhancing InN heteroepitaxy. [ABSTRACT FROM AUTHOR]

Aluminum hydride (AlH3), as a potential new metal fuel, has attracted extensive attention in the field of hydrogen storage materials, propellants and energetic materials due to its excellent properties. In this paper, a new organic liquid phase reduction method was used to prepare AlH3, and the microstructure and composition of the prepared samples were characterized by SEM–EDS, XRD, FT-IR, XPS, etc. The main crystal phase of the prepared AlH3 was α-AlH3, with high quality and no other impurities. The thermal decomposition behavior and non-isothermal reaction kinetics of AlH3 were investigated by TG-DSC. The results show that there are three exothermic stages in the heating process of AlH3: dehydrogenation of AlH3, first oxidation of Al, and second oxidation of Al. The activation energy of the dehydrogenation of AlH3 is 77.8675 kJ/mol (Kissinger method) and 81.4862 kJ/mol (Ozawa method), respectively. The morphology evolution of AlH3 particles during the heating process (300–3000 K) was simulated using a reaction kinetics method based on the ReaxFF force field. The response behavior of AlH3 in the instantaneous high-temperature detonation environment was investigated using the high-energy laser-induced shock wave technique. Under the impact of a high-energy laser (1006 mJ), the AlH3 sample undergoes an ultra-fast reaction and generates a large amount of plasma. The expansion of the plasma pushes the surrounding air to form a supersonic shock wave and propagates forward. The shock wave propagation velocity of AlH3 is 690.99 m/s in the range of 2.1–12.35 μs, and the higher the laser energy, the faster the shock wave propagation velocity. This study provides the basis for the application of AlH3. [ABSTRACT FROM AUTHOR]

In this study, the crystal appearance of industrial grade 2,6-diamino-3,5-dinitropyridine (PYX) was mostly needle-shaped or rod-shaped with an average aspect ratio of 3.47 and roundness of 0.47. According to national military standards, the explosion percentage of impact sensitivity s about 40% and friction sensitivity is about 60%. To improve loading density and pressing safety, the solvent–antisolvent method was used to optimize the crystal morphology, i.e., to reduce the aspect ratio and increase the roundness value. Firstly, the solubility of PYX in DMSO, DMF, and NMP was measured by the static differential weight method, and the solubility model was established. The results showed that the Apelblat equation and Van't Hoff equation could be used to clarify the temperature dependence of PYX solubility in a single solvent. Scanning electron microscopy (SEM) was used to characterize the morphology of the recrystallized samples. After recrystallization, the aspect ratio of the samples decreased from 3.47 to 1.19, and roundness increased from 0.47 to 0.86. The morphology was greatly improved, and the particle size decreased. The structures before and after recrystallization were characterized by infrared spectroscopy (IR). The results showed that no chemical structure changes occurred during recrystallization, and the chemical purity was improved by 0.7%. According to the GJB-772A-97 explosion probability method, the mechanical sensitivity of explosives was characterized. After recrystallization, the impact sensitivity of explosives was significantly reduced from 40% to 12%. A differential scanning calorimeter (DSC) was used to study the thermal decomposition. The thermal decomposition temperature peak of the sample after recrystallization was 5 °C higher than that of the raw PYX. The thermal decomposition kinetic parameters of the samples were calculated by AKTS software, and the thermal decomposition process under isothermal conditions was predicted. The results showed that the activation energy (E) of the samples after recrystallization was higher by 37.9~527.6 kJ/mol than raw PYX, so the thermal stability and safety of the recrystallized samples were improved. [ABSTRACT FROM AUTHOR]

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research. [ABSTRACT FROM AUTHOR]

The label-free quantification (LFQ) has emerged as an exceptional technique in proteomics owing to its broad proteome coverage, great dynamic ranges and enhanced analytical reproducibility. Due to the extreme difficulty lying in an in-depth quantification, the LFQ chains incorporating a variety of transformation, pretreatment and imputation methods are required and constructed. However, it remains challenging to determine the well-performing chain, owing to its strong dependence on the studied data and the diverse possibility of integrated chains. In this study, an R package EVALFQ was therefore constructed to enable a performance evaluation on >3000 LFQ chains. This package is unique in (a) automatically evaluating the performance using multiple criteria, (b) exploring the quantification accuracy based on spiking proteins and (c) discovering the well-performing chains by comprehensive assessment. All in all, because of its superiority in assessing from multiple perspectives and scanning among over 3000 chains, this package is expected to attract broad interests from the fields of proteomic quantification. The package is available at https://github.com/idrblab/EVALFQ. [ABSTRACT FROM AUTHOR]

The type IV bacterial secretion system (SS) is reported to be one of the most ubiquitous SSs in nature and can induce serious conditions by secreting type IV SS effectors (T4SEs) into the host cells. Recent studies mainly focus on annotating new T4SE from the huge amount of sequencing data, and various computational tools are therefore developed to accelerate T4SE annotation. However, these tools are reported as heavily dependent on the selected methods and their annotation performance need to be further enhanced. Herein, a convolution neural network (CNN) technique was used to annotate T4SEs by integrating multiple protein encoding strategies. First, the annotation accuracies of nine encoding strategies integrated with CNN were assessed and compared with that of the popular T4SE annotation tools based on independent benchmark. Second, false discovery rates of various models were systematically evaluated by (1) scanning the genome of Legionella pneumophila subsp. ATCC 33152 and (2) predicting the real-world non-T4SEs validated using published experiments. Based on the above analyses, the encoding strategies, (a) position-specific scoring matrix (PSSM), (b) protein secondary structure & solvent accessibility (PSSSA) and (c) one-hot encoding scheme (Onehot), were identified as well-performing when integrated with CNN. Finally, a novel strategy that collectively considers the three well-performing models (CNN-PSSM, CNN-PSSSA and CNN-Onehot) was proposed, and a new tool (CNN-T4SE, https://idrblab.org/cnnt4se/) was constructed to facilitate T4SE annotation. All in all, this study conducted a comprehensive analysis on the performance of a collection of encoding strategies when integrated with CNN, which could facilitate the suppression of T4SS in infection and limit the spread of antimicrobial resistance. [ABSTRACT FROM AUTHOR]

Microbial community (MC) has great impact on mediating complex disease indications, biogeochemical cycling and agricultural productivities, which makes metaproteomics powerful technique for quantifying diverse and dynamic composition of proteins or peptides. The key role of biostatistical strategies in MC study is reported to be underestimated, especially the appropriate application of feature selection method (FSM) is largely ignored. Although extensive efforts have been devoted to assessing the performance of FSMs, previous studies focused only on their classification accuracy without considering their ability to correctly and comprehensively identify the spiked proteins. In this study, the performances of 14 FSMs were comprehensively assessed based on two key criteria (both sample classification and spiked protein discovery) using a variety of metaproteomics benchmarks. First, the classification accuracies of those 14 FSMs were evaluated. Then, their abilities in identifying the proteins of different spiked concentrations were assessed. Finally, seven FSMs (FC, LMEB, OPLS-DA, PLS-DA, SAM, SVM-RFE and T-Test) were identified as performing consistently superior or good under both criteria with the PLS-DA performing consistently superior. In summary, this study served as comprehensive analysis on the performances of current FSMs and could provide a valuable guideline for researchers in metaproteomics. [ABSTRACT FROM AUTHOR]

Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first tool enabling performance assessment of whole LFQ workflow (collective assessment by five well-established criteria with distinct underlying theories), and it enabled the identification of the optimal LFQ workflow(s) by a comprehensive performance ranking. ANPELA not only automatically detects the diverse formats of data generated by all quantification tools but also provides the most complete set of processing methods among the available web servers and stand-alone tools. Systematic validation using metaproteomic benchmarks revealed ANPELA's capabilities in 1 discovering well-performing workflow(s), (2) enabling assessment from multiple perspectives and (3) validating LFQ accuracy using spiked proteins. ANPELA has a unique ability to evaluate the performance of whole LFQ workflow and enables the discovery of the optimal LFQs by the comprehensive performance ranking of all 560 workflows. Therefore, it has great potential for applications in metaproteomic and other studies requiring LFQ techniques, as many features are shared among proteomic studies. [ABSTRACT FROM AUTHOR]

Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) has emerged as one of the most popular techniques for label-free proteome quantification in current pharmacoproteomic research. It provides more comprehensive detection and more accurate quantitation of proteins comparing with the traditional techniques. The performance of SWATH-MS is highly susceptible to the selection of processing method. Till now, ≥27 methods (transformation, normalization, and missing-value imputation) are sequentially applied to construct numerous analysis chains for SWATH-MS, but it is still not clear which analysis chain gives the optimal quantification performance. Herein, the performances of 560 analysis chains for quantifying pharmacoproteomic data were comprehensively assessed. Firstly, the most complete set of the publicly available SWATH-MS based pharmacoproteomic data were collected by comprehensive literature review. Secondly, substantial variations among the performances of various analysis chains were observed, and the consistently well-performed analysis chains (CWPACs) across various datasets were for the first time generalized. Finally, the log and power transformations sequentially followed by the total ion current normalization were discovered as one of the best performed analysis chains for the quantification of SWATH-MS based pharmacoproteomic data. In sum, the CWPACs identified here provided important guidance to the quantification of proteomic data and could therefore facilitate the cutting-edge research in any pharmacoproteomic studies requiring SWATH-MS technique. [ABSTRACT FROM AUTHOR]

Bacillus Calmette-Guérin (BCG) vaccination is a prototype model for the study of trained immunity (TI) in humans, and results in a more effective response of innate immune cells upon stimulation with heterologous stimuli. Here, we investigate the heterogeneity of TI induction by single-cell RNA sequencing of immune cells collected from 156 samples. We observe that both monocytes and CD8+ T cells show heterologous transcriptional responses to lipopolysaccharide, with an active crosstalk between these two cell types. Furthermore, the interferon-γ pathway is crucial in BCG-induced TI, and it is upregulated in functional high responders. Data-driven analyses and functional experiments reveal STAT1 to be one of the important transcription factors for TI shared in all identified monocyte subpopulations. Finally, we report the role of type I interferon-related and neutrophil-related TI transcriptional programs in patients with sepsis. These findings provide comprehensive insights into the importance of monocyte heterogeneity during TI in humans. [Display omitted] • IFN-γ plays an important role in amplifying trained immunity response • Monocytes show heterogeneous trained immunity capacity after in vivo BCG vaccination • Trained monocytes are regulated by different transcription factors including STAT1 • A developed tool for user to test trained immunity signatures in transcriptome data Li et al. show that BCG vaccination induces an enhanced antimicrobial response upon secondary stimulation, and this effect is heterogeneous at single-cell level. These findings provide comprehensive insights into the molecular mechanism of trained immunity and its role in immune-mediated diseases. [ABSTRACT FROM AUTHOR]

Increasing studies have shown that mature spermatozoa contain many transcripts including mRNAs and miRNAs. However, the expression profile of long non-coding RNAs (lncRNAs) in mammalian sperm has not been systematically investigated. Here, we used highly purified RNA to investigate lncRNA expression profiles in mouse mature sperm by stranded-specific RNA-seq. We identified 20,907 known and 4,088 novel lncRNAs transcripts, and the existence of intact lncRNAs was confirmed by RT-PCR and fluorescence in situ hybridization on two representative lncRNAs. Compared to round spermatids, 1,794 upregulated and 165 downregulated lncRNAs and 4,435 upregulated and 3,920 downregulated mRNAs were identified in sperm. Based on the “Cis and Trans” RNA-RNA interaction principle, we found 14,259 targeted coding genes of differently expressed lncRNAs. In terms of Gene ontology (GO) analysis, differentially expressed lncRNAs targeted genes mainly related to nucleic acid metabolic, protein modification, chromatin and histone modification, heterocycle compound metabolic, sperm function, spermatogenesis and other processes. In contrast, differentially expressed transcripts of mRNAs were highly enriched for protein metabolic process and RNA metabolic, spermatogenesis, sperm motility, cell cycle, chromatin organization, heterocycle and aromatic compound metabolic processes. Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis showed that the differentially expressed lncRNAs were involved in RNA transport, mRNA surveillance pathway, PI3K-Akt signaling pathway, AMPK signaling pathway, protein processing in endoplasmic reticulum. Metabolic pathways, mRNA surveillance pathway, AMPK signaling pathway, cell cycle, RNA transport splicesome and endocytosis incorporated with the differentially expressed mRNA. Furthermore, many lncRNAs were specifically expressed in testis/sperm, and 880 lncRNAs were conserved between human and mouse. In summary, this study provides a preliminary database valuable for identifying lncRNAs critical in the late stage of spermatogenesis or important for sperm function regulation, fertilization and early embryo development. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel framework to simulate the crowd behavior under emergency situations in a confined space with multiple exits. In our work, we take the information asymmetry into consideration, which is used to model the different behaviors presented by pedestrians because of their different knowledge about the environment. We categorize the factors influencing the preferred velocity into two groups, the intrinsic and extrinsic factors, which are unified into a single space called influence space. At the same time, a finite state machine is employed to control the individual behavior. Different strategies are used to compute the preferred velocity in different states, so that our framework can produce the phenomena of decision change. Our experimental results prove that our framework can be employed to analyze the factors influencing the escape time, such as the number and location of exits, the density distribution of the crowd and so on. Thus it can be used to design and evaluate the evacuation plans. [ABSTRACT FROM AUTHOR]

Taking the vessel's RAO (response amplitude operator), wave and lowering velocity into consideration, a 3D mechanical analysis of a manifold installation by a drill pipe in deep water is presented in this paper. On the basis of the small-deformation bending theory, the theoretical formulas of displacements, axial tension, bending moment and inclination along the drill pipe are derived by finite-element discretisation. The key results obtained from the proposed method are compared with the ones achieved from the finite-element model using OrcaFlex and the results are in good consistency. A series of sensitivity analyses are also presented to highlight the most influencing parameters in the installation. The proposed method is useful for the feasibility study of the manifold installation. [ABSTRACT FROM AUTHOR]

High-quality GaN micro/nano columns were prepared with self-organized catalytic-free method. Young's modulus of GaN nanocolumns were measured under both compressive stress and tensile stress. It was found that the Young's modulus decreases with the increasing of nanocolumn diameter due to the increase of face defect density. Furthermore, we measured the piezoelectric properties and found that there was a 1000-fold current increase under a strain of 1% with a fixed bias voltage of 10 mV. Based on the Schottky Barrier Diode model, we modified it with the effect of polarization charge, image charge and interface state to analyze the experiment results which reveals that the strong piezopolarization effect plays an important role in this phenomenon. Therefore, the GaN nanocolumns has a great prospect to be applied in high-efficiency nanogenerators and high-sensitivity nanosensors. [ABSTRACT FROM AUTHOR]

Pituitary adenoma (PA) is prevalent in the general population. Due to its severe complications and aggressive infiltration into the surrounding brain structure, the effective management of PA is required. Till now, no drug has been approved for treating non-functional PA, and the removal of cancerous cells from the pituitary is still under experimental investigation. Due to its superior specificity and safety profile, immunotherapy stands as one of the most promising strategies for dealing with PA refractory to the standard treatment, and various studies have been carried out to discover immune-related gene markers as target candidates. However, the lists of gene markers identified among different studies are reported to be highly inconsistent because of the greatly limited number of samples analyzed in each study. It is thus essential to substantially enlarge the sample size and comprehensively assess the robustness of the identified immune-related gene markers. Herein, a novel strategy of direct data integration (DDI) was proposed to combine available PA microarray datasets, which significantly enlarged the sample size. First, the robustness of the gene markers identified by DDI strategy was found to be substantially enhanced compared with that of previous studies. Then, the DDI of all reported PA-related microarray datasets were conducted to achieve a comprehensive identification of PA gene markers, and 66 immune-related genes were discovered as target candidates for PA immunotherapy. Finally, based on the analysis of human protein–protein interaction network, some promising target candidates (GAL, LMO4, STAT3, PD-L1, TGFB and TGFBR3) were proposed for PA immunotherapy. The strategy proposed together with the immune-related markers identified in this study provided a useful guidance for the development of novel immunotherapy for PA. [ABSTRACT FROM AUTHOR]