Label-free proteome quantification and evaluation.

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.

Metabolomics meets systems immunology.

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.

Pharmacometabonomics: data processing and statistical analysis.

Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.

INTEDE: interactome of drug-metabolizing enzymes.

Drug-metabolizing enzymes (DMEs) are critical determinant of drug safety and efficacy, and the interactome of DMEs has attracted extensive attention. There are 3 major interaction types in an interactome: microbiome-DME interaction (MICBIO), xenobiotics-DME interaction (XEOTIC) and host protein-DME interaction (HOSPPI). The interaction data of each type are essential for drug metabolism, and the collective consideration of multiple types has implication for the future practice of precision medicine. However, no database was designed to systematically provide the data of all types of DME interactions. Here, a database of the Interactome of Drug-Metabolizing Enzymes (INTEDE) was therefore constructed to offer these interaction data. First, 1047 unique DMEs (448 host and 599 microbial) were confirmed, for the first time, using their metabolizing drugs. Second, for these newly confirmed DMEs, all types of their interactions (3359 MICBIOs between 225 microbial species and 185 DMEs; 47 778 XEOTICs between 4150 xenobiotics and 501 DMEs; 7849 HOSPPIs between 565 human proteins and 566 DMEs) were comprehensively collected and then provided, which enabled the crosstalk analysis among multiple types. Because of the huge amount of accumulated data, the INTEDE made it possible to generalize key features for revealing disease etiology and optimizing clinical treatment. INTEDE is freely accessible at: https://idrblab.org/intede/.

Facile Recrystallization Process for Tuning the Crystal Morphology and Thermal Safety of Industrial Grade PYX.

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.

Convolutional neural network-based annotation of bacterial type IV secretion system effectors with enhanced accuracy and reduced false discovery.

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.

ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies.

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.

A critical assessment of the feature selection methods used for biomarker discovery in current metaproteomics studies.

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.

Evaluation of NOx emissions before, during, and after the COVID-19 lockdowns in China: A comparison of meteorological normalization methods.

Meteorological normalization refers to the removal of meteorological effects on air pollutant concentrations for evaluating emission changes. There currently exist various meteorological normalization methods, yielding inconsistent results. This study aims to identify the state-of-the-art method of meteorological normalization for characterizing the spatiotemporal variation of NOx emissions caused by the COVID-19 pandemic in China. We obtained the hourly data of NO2 concentrations and meteorological conditions for 337 cities in China from January 1, 2019, to December 31, 2020. Three random-forest based meteorological normalization methods were compared, including (1) the method that only resamples meteorological variables, (2) the method that resamples meteorological and temporal variables, and (3) the method that does not need resampling, denoted as Resample-M, Resample-M&T, and Resample-None, respectively. The comparison results show that Resample-M&T considerably underestimated the emission reduction of NOx during the lockdowns, Resample-None generates widely fluctuating estimates that blur the emission recovery trend during work resumption, and Resample-M clearly delineates the emission changes over the entire period. Based on the Resample-M results, the maximum emission reduction occurred during January to February 2020, for most cities, with an average decrease of 19.1 ± 9.4% compared to 2019. During April of 2020 when work resumption initiated to the end of 2020, the emissions rapidly bounced back for most cities, with an average increase of 12.6 ± 15.8% relative to those during the strict lockdowns. Consequently, we recommend using Resample-M for meteorological normalization, and the normalized NO2 concentration dynamics for each city provide important implications for future emission reduction.

Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains.

The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.

Expression profiles and characteristics of human lncRNA in normal and asthenozoospermia sperm†.

Increasing studies have shown that specific mRNAs and miRNAs expressed in mature sperm may be related to sperm motility. However, the expression profiles and roles of lncRNAs in sperm remain unknown. In the present study, numerous lncRNAs were identified in human sperm, and some lncRNAs were expressed exclusively in sperm and testis. Compared with those in normal sperm, the lncRNA expression profiles in asthenozoospermia (AZS) sperm showed significant differences. Gene ontology and pathway analyses showed that function of differentially expressed lncRNA targets and mRNAs between AZS and normal sperm were closely linked with many processes involved in spermatogenesis and sperm function. Furthermore, among the upregulated lncRNAs in AZS sperm, lnc32058, lnc09522, and lnc98487, which exhibited specific/enriched sperm and testicular expression, increased simultaneously in the same AZS sperm samples, and their expression levels were correlated with sperm progressive motility. This is the first systematic study of lncRNA expression profiles in human mature sperm indicating an association between lncRNA expression and sperm motility. The study provides a preliminary database for identifying lncRNAs crucial for human spermatogenesis and sperm function, and new insights into our understanding of the regulation of sperm motility and causes of male infertility.

Biomarker Discovery for Immunotherapy of Pituitary Adenomas: Enhanced Robustness and Prediction Ability by Modern Computational Tools.

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.

Molecular dynamics simulation of DNAN/DNB cocrystal PBXs.

CONTEXT: The DNAN/DNB eutectic is a high-energy explosive eutectic with superior safety and thermal stability compared to traditional melt-cast explosives. However, the addition of polymer binders can effectively enhance its mechanical properties, allowing for continued production demands without the need for changes to existing factory equipment. In this paper, a model of the DNAN/DNB eutectic explosive was established, and five different types of polymers-cis-1,4-polybutadiene (BR), ethylene-vinyl acetate copolymer (EVA), polyethylene glycol (PEG), fluorinated polymer (F2603), and polyvinylidene fluoride (PVDF)-were added to the (1 0 - 1), (1 0 1), and (0 1 1) cleavage planes, respectively, to form polymer-bonded explosives (PBXs). The stability, trigger bond length, mechanical properties, and detonation performance of the various polymer-bound PBXs were predicted retrogressively. Among the five PBX models, the DNAN/DNB/PEG model exhibited the highest binding energy and the shortest trigger bond length, indicating a significant improvement in stability, compatibility, and sensitivity compared to the original eutectic. Additionally, although the detonation performance of DNAN/DNB decreased after the addition of binders, the final results were still satisfactory. Overall, the DNAN/DNB/PEG model demonstrated excellent comprehensive performance, proving that among the many polymer binders, PEG is the optimal choice for DNAN/DNB. METHODS: Within the Materials Studio software, molecular dynamics (MD) simulations were employed to predict the properties of the DNAN/DNB eutectic PBX. The MD simulation timestep was set to 1 fs, with a cumulative simulation duration of 2 ns. A 2 ns MD simulation was conducted using the isothermal-isobaric ensemble (NPT). The COMPASS force field was applied, and the temperature was fixed at 295 K.

Investigation of the decomposition mechanism of MTNP melt-cast explosive at different temperatures and pressures through ReaxFF/lg molecular dynamics simulations.

CONTEXT: Thermal decomposition of 1-methyl-3,4,5-trinitropyrazole (MTNP), a melt-cast explosive, was investigated at different temperatures (2500, 2750, 3000, 3250, and 3500 K) and pressures (3000 K/0.5 GPa, 3000 K/1 GPa) using the ReaxFF/lg force field. The study aimed to analyze the changes in reactant quantities, initial reaction pathways, and final product yields. The results demonstrated that complete decomposition of MTNP molecules occurred within a timeframe of 200 ps, with shorter decomposition times observed as the temperature increased. The high-temperature thermal decomposition of MTNP was found to follow two primary reaction pathways. Reaction 1 involved denitration, while reaction 2 proceeded with nitro group isomerization. DFT calculations indicated that nitro group isomerization was the most favorable reaction. During the initial stages, higher quantities of NO2, NO, and N2 were observed compared to other species. This can be attributed to the relatively higher nitrogen and oxygen content in the MTNP structure. Among the five reaction temperatures, it was observed that the quantities of small molecules followed the order of NO2 > NO > N2 > CO. Moreover, with increasing temperature, the quantities of all four small molecules increased, indicating that higher temperatures promoted the progression of the reactions. However, as the pressure increased, there was a trend of initially increasing and then decreasing to zero for the quantities of NO2 and NO. This suggests that high temperature accelerated the high-temperature thermal decomposition of NO2 and NO, leading to a significant increase in the content of N2. METHODS: A 3 × 5 × 5 supercell model of MTNP was constructed in Materials Studio, consisting of 75 unit cells and 300 MTNP molecules. The model was then subjected to a 20 ps geometric optimization using the Polak-Ribiere version of the conjugate gradient (CG) algorithm in the large-scale atomic/molecular massively parallel simulator (LAMMPS) under the isothermal-isobaric (NPT) ensemble at 1 atm pressure and 300 K temperature. Following the optimization, molecular dynamics simulations were performed on the model at five temperatures (2500, 2750, 3000, 3200, and 3500 K) under 1 atm using the NPT ensemble for a total duration of 1 ns. During the simulations, atomic trajectories, as well as information on atomic and molecular species, were output every 500 steps. Subsequently, a custom script was utilized to analyze the thermal decomposition pathways and products. A time step of 0.1 fs was employed for the calculations, and periodic boundary conditions were applied to eliminate boundary effects.

Molecular dynamics simulation of CL20/DNDAP cocrystal-based PBXs.

CONTEXT: CL-20/DNDAP cocrystal is a promising new type of explosive with exceptional energy density and detonation parameters. However, compared to TATB, FOX-7 and other insensitive explosives, it still has higher sensitivity. In order to decrease the sensitivity of CL20/DNDAP cocrystal explosive, in this article, a CL20/DNDAP cocrystal model was established, and six different types of polymers, including butadiene rubber (BR), ethylene-vinyl acetate copolymer (EVA), polyethylene glycol (PEG), hydroxyl-terminated polybutadiene (HTPB), fluoropolymer (F2603), and polyvinylidene difluoride (PVDF), were added to the three cleaved surfaces of (1 0 0), (0 1 0) and (0 0 1) to obtain polymer-bonded explosives (PBXs). Predict the effects of different polymers on the stability, trigger bond length, mechanical properties, and detonation performance of PBXs. Among the six PBX models, CL-20/DNDAP/PEG model exhibited the highest binding energy and the lowest trigger bond length, indicating that CL-20/DNDAP/PEG model had the best stability, compatibility, and the least sensitivity. Furthermore, although the CL-20/DNDAP/F2603 model demonstrated superior detonation capabilities, it should be noted that this model displayed low levels of compatibility. Overall, CL-20/DNDAP/PEG model exhibited the superior comprehensive properties, thereby demonstrating that PEG is a more suitable binder option for PBXs based on the CL20/DNDAP cocrystal. METHODS: The properties of CL-20/DNDAP cocrystal-based PBXs were predicted by molecular dynamics (MD) method under Materials Studio software. The MD simulation time step was set at 1fs and the total MD simulation time was 2ns. The Isothermal-isobaric (NPT) ensemble was used for the 2ns of MD simulation. The COMPASS force field was used, and the temperature was set at 295K.

A single-cell view on host immune transcriptional response to in vivo BCG-induced trained immunity.

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.

Optimization of metabolomic data processing using NOREVA.

A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in metabolomics, is characterized by its study dependency and combinatorial diversity. While various methods and tools have been developed to facilitate metabolomic data processing, it is challenging to determine which processing workflow will give good performance for a specific metabolomic study. NOREVA, an out-of-the-box protocol, was therefore developed to meet this challenge. First, the peak table is subjected to many processing workflows that consist of three to five defined calculations in combinatorially determined sequences. Second, the results of each workflow are judged against objective performance criteria. Third, various benchmarks are analyzed to highlight the uniqueness of this newly developed protocol in (1) evaluating the processing performance based on multiple criteria, (2) optimizing data processing by scanning thousands of workflows, and (3) allowing data processing for time-course and multiclass metabolomics. This protocol is implemented in an R package for convenient accessibility and to protect users' data privacy. Preliminary experience in R language would facilitate the usage of this protocol, and the execution time may vary from several minutes to a couple of hours depending on the size of the analyzed data.

Computational analysis the relationships of energy and mechanical properties with sensitivity for FOX-7 based PBXs via MD simulation.

Molecular dynamics (MD) simulations have been applied to investigate 1, 1-diamino-2, 2-dinitroethene (FOX-7) crystal and FOX-7 (011)-based polymer-bonded explosives (PBXs) with four typical polymers, polyethylene glycol (PEG), fluorine-polymer (F2603), ethylene-vinyl acetate copolymer (EVA) and ester urethane (ESTANE5703) under COMPASS force field. Binding energy (E bind), cohesive energy density (CED), initiation bond length distribution, RDG analysis and isotropic mechanical properties of FOX-7 and its PBXs at different temperatures were reported for the first time, and the relationship between them and sensitivity. Using quantum chemistry, FOX-7 was optimized with the four polymers at the B3LYP/6-311++G(d,p) level, and the structure and RDG of the optimized composite system were analysed. The results indicated that the binding energy presented irregular changes with the increase in temperature. The order of binding ability of different binders to the FOX-7 (011) crystal surface is PEG > ESTANE5703 > EVA > F2603. When the temperature increases, the maximum bond length (L max) of the induced bond increases and the CED decreases. This result is achieved in agreement with the known experimental fact that the sensitivity of explosives increases with temperature, and they can be used as the criterion to predict the sensitivity of explosives. The descending order of L max is FOX-7 > F2603 > ESTANE5703≈EVA > PEG. The intermolecular interactions between FOX-7 and the four polymers were mainly weak hydrogen bonding and van der Waals interactions, and these interactions helped to reduce the bond length of C-NO2, leading to a decrease in the sensitivity of FOX-7. The addition of polymers can effectively improve the mechanical properties of explosives. Among the four polymers, EVA has the best effect on improving the mechanical properties of FOX-7 (011). At the same temperature, the modulus can be used to predict the sensitivity of high-energy materials. Cauchy pressure can predict the sensitivity of non-brittle energetic materials. The nature of the interaction between FOX-7 and the four polymers is hydrogen bonding and van der Waals force, of which hydrogen bonding is the main one. These studies are meaningful for the formulation design and sensitivity prediction of FOX-7 and its PBXs.

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection.

BACKGROUND: Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets. OBJECTIVE: The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics. METHODS: Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics. RESULTS: In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed. CONCLUSION: In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Computational advances of tumor marker selection and sample classification in cancer proteomics.

Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.