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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 crystal appearance of industrial grade 2,6-bis(picrylamino)-3,5-dinitropyridine (PYX) was mostly needle-shaped or rod-shaped with an average aspect ratio of 3.47 and roundness of 0.47. To improve the loading density and pressing safety, the crystal shape of PYX was optimized by cooling crystallization method. After recrystallization, the aspect ratio decreased from 3.47 to 1.28, and the roundness value increased from 0.47 to 0.78. XRD data showed that there is no crystal pattern change before and after recrystallization. The friction sensitivity of the explosive decreases from 88% to 56%, and the impact sensitivity decreases from 76% to 12%. Differential scanning calorimeter (DSC) was used to study the thermal decomposition of PYX. The thermal decomposition peak temperature of the samples after recrystallization was 4~7℃ higher than that of raw PYX. The adiabatic induction time (TMRad) and self-accelerating decomposition temperature (SADT) were calculated with AKTS software. In the same adiabatic induction time, the initial temperature of recrystallization PYX is about 30℃~40℃ higher than that of the raw PYX. The SADT is increased by about 50℃~60℃. The thermal safety of recrystallized PYX is improved. [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]

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]

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]

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]

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., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

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 (F 2603 ), 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 > F 2603 . 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 > F 2603 > 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-NO 2 , 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., (© 2021 The Authors.)

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/., (© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

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., (© 2019 Tang et al.)

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., (© The Author(s) 2018. Published by Oxford University Press on behalf of Society for the Study of Reproduction.)

Background: Due to its ability to provide quantitative and dynamic information on tumor genesis and development by directly profiling protein expression, the proteomics has become intensely popular for characterizing the functional proteins driving the transformation of malignancy, tracing the large-scale protein alterations induced by anticancer drug, and discovering the innovative targets and first-in-class drugs for oncologic disorders., Objective: To quantify cancer proteomics data, the label-free quantification (LFQ) is frequently employed. However, low precision, poor reproducibility and inaccuracy of the LFQ of proteomics data have been recognized as the key "technical challenge" in the discovery of anticancer targets and drugs. In this paper, the recent advances and development in the computational perspective of LFQ in cancer proteomics were therefore systematically reviewed and analyzed., Methods: PubMed and Web of Science database were searched for label-free quantification approaches, cancer proteomics and computational advances., Results: First, a variety of popular acquisition techniques and state-of-the-art quantification tools are systematically discussed and critically assessed. Then, many processing approaches including transformation, normalization, filtering and imputation are subsequently discussed, and their impacts on improving LFQ performance of cancer proteomics are evaluated. Finally, the future direction for enhancing the computation-based quantification technique for cancer proteomics are also proposed., Conclusion: There is a dramatic increase in LFQ approaches in recent year, which significantly enhance the diversity of the possible quantification strategies for studying cancer proteomics., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Stability is an important issue for the application of resistive switching (RS) devices. In this work, the endurance and retention properties of Ag/CoO x /Ag interface-type RS device were investigated. This device exhibits rectifying I-V curve, multilevel storage states and retention decay behavior, which are all related to the Schottky barrier at the interface. The device can switch for thousands of cycles without endurance failure and shows narrow resistance distributions with relatively low fluctuation. However, both the high and low resistance states spontaneously decay to an intermediate resistance state during the retention test. This retention decay phenomenon is due to the short lifetime τ (τ = 0.5 s) of the metastable pinning effect caused by the interface states. The data analysis indicated that the pinning effect is dependent on the depth and density of the interface state energy levels, which determine the retention stability and the switching ratio, respectively. This suggests that an appropriate interface structure can improve the stability of the interface-type RS device.

2,4-Dichlorophenol (2,4-DCP) is an environmental pollutant exhibiting a wide spectrum of toxic effects. We investigated the toxic effects and potential mechanisms underlying 2,4-DCP-induced hepatotoxicity. In vitro, 2,4-DCP caused hepatotoxicity manifested by a decrease in cell viability and inhibition of colony formation. Bip and CHOP expression was up-regulated at the mRNA and protein levels. Moreover, 2,4-DCP induced eIF2α phosphorylation and Xbp1 mRNA splicing, indicating that endoplasmic reticulum (ER) stress was activated after exposure of HL7702 cells to 2,4-DCP for 12 hr. Furthermore, the mitochondrial membrane potential collapsed and apoptosis was triggered after exposure to 2,4-DCP for 24 hr. In vivo, 2,4-DCP caused histological changes in the liver, and dramatically elevated the serum alanine transaminase (ALT) and aspartate aminotransferase (AST) levels of mice. ER stress was also triggered in the liver of mice on days 1 and 3. The ER stress inhibitor TUDCA could partly relieve the liver damage, as indicated by the restoration of serum ALT and AST levels. Taken together, our results demonstrated that ER stress may serve as an early warning mechanism against 2,4-DCP-induced hepatotoxicity, and severe ER stress may lead to apoptosis.