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This paper presents a newly developed high-performance mobile single-photon ionization time-of-flight mass spectrometry (M-SPI-TOFMS) system for on-line analysis and stereoscopic monitoring of complex gas mixtures. The system is designed for stereoscopic imaging to map the distribution of volatile organic compounds (VOCs) and trace their emission sources in urban areas and industrial parks. It mainly consists of a SPI-TOFMS instrument, a customized commercial vehicle, a meteorological five-parameter monitor with GPS, a high-power generator, and an uninterruptible power supply. The SPI technique, using a 118 nm VUV lamp, can ionize compounds with an ionization potential below 10.78 eV. Mass spectra obtained using this technique show the profiles of various VOCs and some inorganic compounds. The VOCs composition information and mobile location data are simultaneously sent to the GIS software. In GIS software, this data is used for real-time stereoscopic imaging of VOC distribution and precise tracking of VOC movement. The system can achieve a spatial data resolution of 0.69 mm at 25 km/h due to the microsecond detection speed of the M-SPI-TOFMS instrument. The laboratory test provides a rapid overview characterization of benzene, toluene, and xylene. The M-SPI-TOFMS has limits of detection and mass resolution of 33.7 pptv and 1060, respectively. Several field applications were carried out using M-SPI-TOFMS at various locations to identify VOC sources near different factories. The M-SPI-TOFMS system has a navigation monitoring speed of 25 km/h with a time resolution of 1 s. The widespread use of this system will provide accurate data to support environmental management departments in formulating VOCs pollution control policies and improving control efficiency.

Flexible nanocomposite dielectrics with inorganic nanofillers exhibit great potential for energy storage devices in advanced microelectronics applications. However, high loading of inorganic nanofillers in the matrix results in an inhomogeneous electric field distribution, thereby hindering the improvement of the energy storage density (Ue) of the dielectrics. Herein, we proposed a strategy that utilized (00l)-oriented barium titanate (BT) single-crystal platelets to fabricate trilayered nanocomposite dielectrics for energy storage applications. The trilayered nanocomposites consisted of two high-permittivity layers of (Ta2O5, Al2O3) codoped TiO2nanoparticles (Ta-Al@TiO2nps) dispersed in a poly(vinylidene fluoride) (PVDF) matrix to facilitate large electric displacement and a middle layer of (00l)-oriented BT single-crystal platelets to provide high breakdown strength. Hence, the trilayered PVDF/Ta-Al@TiO2nps/BT single-crystal platelet nanocomposite film attains an outstanding Ueof 16.9 J cm–3at 370 kV mm–1, which is ∼625% higher than that of the single-layer PVDF/Ta-Al@TiO2nps film. Finite element simulation further clarified that the successive inner layer of highly (00l)-oriented BT single-crystal platelets could effectively restrain the propagation of electrical treeing in trilayered nanocomposites. This research offers an effective approach for developing flexible dielectric capacitors with an excellent energy storage performance.

Brain metastasis, with the highest incidence in patients with lung cancer, significantly worsens prognosis and poses challenges to clinical management. To date, how brain metastasis evades immune elimination remains unknown.

Lubrication failure accompanying with blackening phenomenon significantly reduces the long-running operational reliability of porous polymide (PPI) lubricated with poly-α-olefin (PAO) oil. Here, the effects of lubrication condition and counter-surface chemistry on the blackening failure of PAO impregnated PPI were studied through the comparison of the tribological tests against GCr15 steel ball and Al2O3ceramic ball with and without PAO oil lubrication. Black products were found to be formed on the PAO impregnated PPI surface slid against steel ball or Al2O3ball added with iron nano-particles, but be absent under the conditions without iron or PAO oil. Further analysis indicated that the iron-catalyzed splitting of PAO oil into small molecule alkanes and following the formation of black organic matter should be mainly responsible for the blackening phenomenon. Molecular dynamic (MD) simulations demonstrated that the iron facilitated the separation of hydrogen atom and the following broken of C-C bonds in PAO molecules, final resulting in the splitting of PAO oil.

Graphene is a promising material as a lubricant additive for reducing friction and wear. Here, a dispersing method which combines chemical modification of graphene by octadecylamine and dicyclohexylcarbodiimide with a kind of effective dispersant has been successfully developed to achieve the remarkable dispersion stability of graphene in base oil. The stable dispersion time of modified graphene (0.5 wt%) with dispersant (1 wt%) in PAO-6 could be up to about 120 days, which was the longest time reported so far. At the same time, the lubricant exhibits a significant improvement of tribological performance for a steel ball to plate tribo-system with a normal load of 2 N. The coefficient of friction between sliding surfaces was ~0.10 and the depth of wear track on plate was ~21 nm, which decreased by about 44% and 90% when compared to pure PAO-6, respectively. Furthermore, the analysis of the lubricating mechanisms in regard to the sliding-induced formation of nanostructured tribo-film has been contacted by using Raman spectra and TEM.

The Bessel beam, owing to its unique properties (keeping the intensity distribution unchanged during transmission, focusing energy on the central spot, self-reconstruction, etc.), has received extensive attention. In this study, a new type of combined axicon (CA) is designed that can generate a high-quality zero-order terahertz quasi-Bessel beam. The simulation and experimental results show that the quasi-Bessel beam generated by the CA has an obvious projection distance and sidelobe suppression effect. And the projection distance, length of the non-diffracting region, and sidelobe-suppression ratio of the beam can be modulated as required, which is not possible with ordinary axicon. The CA consists of a local negative axicon tightly bonded to an ordinary axicon. The quasi-Bessel beam generated by the CA exhibits enhanced intensity on the central axis. The influence of different parameters has been investigated, and the results have been explained using geometric optical. We believe that this element has broad application prospects in non-destructive testing, laser cutting, and microscopic imaging.

Optical delay line (ODL) plays a crucial role in various detection techniques such as terahertz time-domain spectroscopy (THz-TDS), and optical pump detection. This letter proposes a novel multi-mirror array optical delay line (MMAODL) structure suitable for THz-TDS systems, which consists of a laser collimator, a rotating disk, a multi mirror array, a pyramid prism, and a plane mirror. Based on the principle of ray tracing, a theoretical mathematical model of MMAODL was constructed. Combined with simulation, the working angle of MMAODL was obtained to be 13 deg, and the theoretical delay time was 228.71 ps. Using the THz TDS system, the actual time length of the 8-cycle MMAODL structure, calibrated by the FP standard, was tested and demonstrated to be 228.47 ps, and the time-domain error of the collected signal was found to be approximately 0.014 ps. When an 8-cycle MMAODL is combined with a 4000-rpm motor, the theoretical waveform reproduction rate can reach 533 Hz, which simultaneously meets the requirements of a THz-TDS system for long delay time and high scanning frequency.

There remains a lack of effective and noninvasive methods for the diagnosis and prognosis prediction of epithelial ovarian carcinoma (EOC). Here, we investigated the possibility of serum-derived small extracellular vesicle (sEV) microRNAs (miRNAs) as potential biomarkers for distinguishing between benign and malignant adnexal masses and predicting the prognosis of EOC patients. A serum sEV miRNA model for identifying the EOC (sEVmiR-EOC) was successfully established in the training cohort. Furthermore, the sEVmiR-EOC model was confirmed in the testing cohort and validation cohort, demonstrating robust diagnostic accuracy. The sEVmiR-EOC model showed better performance than carbohydrate antigen 125 (CA125) in discriminating patients with stage I EOC from benign patients. Using EOC samples and follow-up data, we identified miR-141-3p and miR-200c-3p as potential prognostic predictors. Finally, we confirmed the change of the sEVmiR-EOC RiskScore between the preoperative and postoperative samples and found that the sEVmiR-EOC model could predict the prognosis of EOC patients.

Achievement of steady and reliable super-low friction at the steel/steel contact interface, one of the most tribological systems applied for mechanical moving parts, is of importance for prolonging machine lifetime and reducing energy consumption. Here we reported that the superlubricity performance of the steel/steel sliding interface lubricated with tiny amounts of diketone solution strongly depends on the oxygen content in surrounding environment. The increase of oxygen not only significantly shortens the initial running-in time but also further reduces the stable coefficient of friction in superlubricity stage due to the enhancement of tribochemical reactions. On the one hand, more severe oxidation wear occurring at higher oxygen content facilitates material removal of the contact interface, lowering the contact pressure and the corresponding initial friction. On the other hand, the growth of iron ions during the shear process in high oxygen environment promotes the formation of chelate which acted as an effective lubricated film chemisorbed at the steel/steel friction interface to further lower the interfacial friction. The results provide a new opportunity to further optimize the tribological performance of diketone superlubricity system, especially towards the lubrication of mechanical engineering materials.

Ovulation is an integral part of women’s menstrual cycle and fertility. Understanding the mechanisms of ovulation has broad implications for the treatment of anovulatory diseases and the development of novel contraceptives. Now, few studies have developed effective models that both faithfully recapitulate the hallmarks of ovulation and possess scalability. We established a three-dimensional encapsulated in vitrofollicle growth (eIVFG) system that recapitulates folliculogenesis and produces follicles that undergo ovulation in a controlled manner. Here, we determined whether ex vivoovulation preserves molecular signatures of ovulation and demonstrated its use in discovering novel ovulatory pathways and nonhormonal contraceptive candidates through a high-throughput ovulation screening. Mature murine follicles from eIVFG were induced to ovulate ex vivousing human chorionic gonadotropin and collected at 0, 1, 4, and 8 hours post-induction. Phenotypic analyses confirmed key ovulatory events, including cumulus expansion, oocyte maturation, follicle rupture, and luteinization. Single-follicle RNA-sequencing analysis revealed the preservation of ovulatory genes and dynamic transcriptomic profiles and signaling. Soft clustering identified distinct gene expression patterns and new pathways that may critically regulate ovulation. We further used this ex vivoovulation system to screen 21 compounds targeting established and newly identified ovulatory pathways. We discovered that proprotein convertases activate gelatinases to sustain follicle rupture and do not regulate luteinization and progesterone secretion. Together, our ex vivoovulation system preserves molecular signatures of ovulation, presenting a new powerful tool for studying ovulation and anovulatory diseases as well as for establishing a high-throughput ovulation screening to identify novel nonhormonal contraceptives for women.Ex vivoovulation enables novel contraception discovery for women.Graphical Abstract

Terahertz (THz) imaging has broad application prospects in nondestructive testing (NDT). However, compared to visible light, it has a low resolution and limited depth of field (DOF) due to its long wavelength. To address these limitations, a 3-D point spread function (PSF) based on a simulated THz time-domain spectroscopy (THz-TDS) system combined with the Lucy–Richardson method is proposed for image defocus restoration. The experimental results show that the contrast of THz images at different defocus positions is 2–5 times higher than that of the original image after restoration using the proposed method, and the resolution is improved by approximately 60%–80%. Moreover, the maximum contrast enhancement at the boundaries of tiny features can reach 1.81 times. For a hole-defect sample, the relative error of the defect area at different depths is < 5%. The proposed method solves the problem of defocus in THz images, enhances the resolution of these images, and facilitates NDT.

Transfer learning has been applied to deal with the insufficient labeled target dataset problem in data-driven fault diagnosis. However, most existing solutions cannot work well when real data are completely unusable in the training process, which often occurs in engineering practice. Considering this challenge, a fully simulated-data-driven transfer-learning method is proposed for rolling-bearing-fault diagnosis. The proposed method’s key feature is using a domain-invariant data-transform method to convert domain-variant datasets to domain-invariant datasets so that common features from simulated and real datasets can be shared. The transform process relies on the physics knowledge of bearing faults and is implemented using the hidden Markov model (HMM). The proposed method is fully driven by simulated data without using real datasets under the fault-diagnosis-model construction process. This approach is a new way to implement transfer learning for rolling-bearing-fault diagnosis. The experimental results demonstrate the proposed method’s effectiveness with real data.

Currently, deep learning-based methods are widely used in the fault diagnosis of time-series data for their high precision. However, the application of traditional deep learning fault diagnosis models is limited by their calculational efficiency and poor interpretation ability. To address the problems, a fault diagnosis model named the deep parallel time-series relation network (DPTRN) is proposed in this article. There are three main advantages of the DPTRN. First, our proposed time relationship unit can perform feature extraction on each time node of a time-series sample simultaneously; therefore, the DPTRN performs fault diagnosis in a parallel way and improves the computing efficiency significantly. Second, by improving the absolute position embedding, our novel decoupling position embedding unit can be directly applied for fault diagnosis and can learn contextual information. Third, our proposed DPTRN has an obvious advantage in feature interpretability compared with traditional deep learning-based models. Applying the DPTRN model on four datasets, we achieved higher diagnosis performance with much lower cost, which indicates the effectiveness, efficiency, and interpretability of the proposed DPTRN model.

Background: Anlotinib is a targeted therapy indicated for some malignancies, including advanced non-small cell lung cancer (NSCLC). However, noninvasive biomarkers for identifying patients who will benefit from this disease remain lacking. Methods: Here, we investigated the potential of small extracellular vesicle (sEV) microRNAs (miRNAs) as predictive biomarkers for anlotinib efficacy. A total of 20 advanced NSCLC patients were enrolled. Patients were classified as having stable disease (SD) or progressive disease (PD) after the initial efficacy assessment. Results: Seven differentially expressed miRNAs (DEMs) were identified. Among them, miR-941 was significantly upregulated in the PD group, while the others were downregulated. Furthermore, these six downregulated miRNAs (miR-30a-3p, miR-150-5p, miR-122-5p, miR-10b-5p, miR-92a-3p, and miR-150-3p) were more pronounced in nonsmoking patients. Conclusions: It was found that sEV miRNAs have the potential to predict the benefit of anlotinib.

Follicle-stimulating hormone (FSH) binds to its membrane receptor (FSHR) in granulosa cells to activate various signal transduction pathways and drive the gonadotropin-dependent phase of folliculogenesis. Both FSH insufficiency (due to genetic or nongenetic factors) and FSH excess (as encountered with ovarian stimulation in assisted reproductive technology [ART]) can cause poor female reproductive outcomes, but the underlying molecular mechanisms remain elusive. Herein, we conducted single-follicle and single-oocyte RNA sequencing analysis along with other approaches in an ex vivo mouse folliculogenesis and oogenesis system to investigate the effects of different concentrations of FSH on key follicular events. Our study revealed that a minimum FSH threshold is required for follicle maturation into the high estradiol-secreting preovulatory stage, and such threshold is moderately variable among individual follicles between 5 and 10 mIU/mL. FSH at 5, 10, 20, and 30 mIU/mL induced distinct expression patterns of follicle maturation–related genes, follicular transcriptomics, and follicular cAMP levels. RNA sequencing analysis identified FSH-stimulated activation of G proteins and downstream canonical and novel signaling pathways that may critically regulate follicle maturation, including the cAMP/PKA/CREB, PI3K/AKT/FOXO1, and glycolysis pathways. High FSH at 20 and 30 mIU/mL resulted in noncanonical FSH responses, including premature luteinization, high production of androgen and proinflammatory factors, and reduced expression of energy metabolism-related genes in oocytes. Together, this study improves our understanding of gonadotropin-dependent folliculogenesis and provides crucial insights into how high doses of FSH used in ART may impact follicular health, oocyte quality, pregnancy outcome, and systemic health.

Porous cotton/phenolic resin has been widely made into bearing retainer because of its excellent good dimensional stability and self-lubricating functions. In this paper, the frictional anisotropy of porous cotton/phenolic bearing retainer on different surfaces were studied using a friction and wear testing machine. Results showed that the friction coefficient on the guiding surface of the retainer was higher than that on the ending surface. However, wear volumes on the guiding surface was lower than that on the ending surface. For the ending surface, the wear volumes in the direction perpendicular to cotton fiber were higher than that parallel to cotton fiber. Based on the experiments, a wear model was proposed to illustrate the frictional anisotropy of the retainer. Analysis indicated that the wear rate was inversely proportional to the square of elastic modulus. The ratio of wear rate obtained from the model was in good agreement with the experimental results. The findings can be used to explain the wear difference of the retainer on different surfaces during operation. Besides, the model has potential applications in the design of bearing composite materials to improve its anti-wear behaviors.

Fault diagnosis is essential to ensure the operation security and economic efficiency of the chemical system. Many fault diagnosis methods have been designed for the chemical process, but most of them ignore the temporal correlation in the sequential observation signals of the chemical process. A novel deep learning method based on Stacked Long Short-Term Memory (LSTM) neural network is proposed, which can effectively model sequential data and detect the abnormal values. The proposed method is also able to fully exploit the long-term dependencies information in raw data and adaptively extract the representative features. The dataset of Tennessee Eastman (TE) process is utilized to verify the practicability and superiority of the proposed method. Extensive experimental results show that the fault detection and diagnosis model we proposed has an excellent performance when compared with several state-of-the-art baseline methods.

This study achieved water-based superlubricity with the lubrication of H3PO4solution in vacuum (highest vacuum degree <10–4torr) for the first time by performing a pre-running process in air before running in vacuum. The stable water-based superlubricity was sustainable in vacuum (0.02 torr) for 14 h until the test was stopped by the user for non-experimental factor. A further analysis suggested that the superlubricity may be attributed to the phosphoric acid–water network formed in air, which can efficiently lock water molecules in the liquid lubricating film even in vacuum owing to the strong hydrogen bond interaction. Such capability to lock water is strongly affected by the strength of hydrogen bond and environmental conditions. The realization of water-based superlubricity with H3PO4solution in vacuum can lead to its application in space environment.

Crop water production functions require precise knowledge of crop productivity across a wide range of soil water availability (WA). Field studies have demonstrated the yield response of maize (Zea maysL.) to WA, but the relationship has not been quantified across various precipitation years in the North China Plain (NCP). This study was conducted to investigate the effects of the simulated interannual variability in precipitation on maize yield in a winter wheat (Triticum aestivumL.) –summer maize double‐cropping system in the NCP, in 2014 and 2015. A rainfall simulator system was used to simulate contrasting precipitation years, including wet, normal, normal‐dry, and dry years. A fixed amount of 70 mm irrigation was applied at different growth stages of maize, including at planting (I70), at planting and tasseling (VT) stages (I140), and at planting, VT, and milk stages (I210). The objective was to quantify the relationships among crop production, water use, and WA for maize. We found that I210improved soil water storage (SWS, mm) by 19 to 36% in the midseason of normal‐dry and dry years but had no effect in normal and wet years, compared with I70. Crop evapotranspiration (ETc, mm d−1) was least in the dry year with the I70treatment and differed with maize growth phases, with the peak occurring midseason for maize in both years. Regression analyses showed that ETcwas negatively related to SWS but positively related to WA. A quadratic function described grain yield responses to WA and ETc, with total WA of 478 mm and daily mean ETcof 5.1 mm d−1producing greatest grain yield. With yield response function acquired, we conclude that target maximum yield of 11.6 Mg ha−1can be achieved after probability of exceedance of precipitation equals 18 with irrigation water requirement applied.

Human leukocyte antigen–antigen D related (HLA-DR) molecules are highly expressed in synovial tissue (ST), the target of the immune response in chronic inflammatory forms of arthritis. Here, we used LC-MS/MS to identify HLA-DR-presented self-peptides in cells taken directly from clinical samples: ST, synovial fluid mononuclear cells (SFMC), or peripheral blood mononuclear cells (PBMC) from five patients with rheumatoid arthritis (RA) and eight with Lyme arthritis (LA). We identified 1593 non-redundant HLA-DR-presented peptides, derived from 870 source proteins. A total of 67% of the peptides identified in SFMC and 55% of those found in PBMC were found in ST, but analysis of SFMC/PBMC also revealed new antigen-presented peptides. Peptides were synthesized and examined for reactivity with the patients’ PBMC. To date, three autoantigens in RA and four novel autoantigens in LA, presented in ST and/or PBMC, were shown to be targets of T- and B-cell responses in these diseases; ongoing analyses may add to this list. Thus, immunoprecipitation and LC-MS/MS can now identify hundreds of HLA-DR-presented self-peptides from individual patients’ tissues or fluids with mixed cell populations. Importantly, identification of HLA-DR-presented peptides from SFMC or PBMC allows testing of more patients, including those early in the disease. Direct analysis of clinical samples facilitates identification of novel immunogenic T-cell epitopes.

The ubiquitin-like protein FAT10 (HLA-F adjacent transcript 10) is uniquely expressed in mammals. The fat10gene is encoded in the MHC class I locus in the human genome and is related to some specific processes, such as apoptosis, immune response, and cancer. However, biological knowledge of FAT10 is limited, owing to the lack of identification of its conjugates. FAT10 covalently modifies proteins in eukaryotes, but only a few substrates of FAT10 have been reported until now, and no FATylated sites have been identified. Here, we report the proteome-scale identification of FATylated proteins by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We identified 175 proteins with high confidence as FATylated candidates. A total of 13 modified sites were identified for the first time by a modified search of the raw MS data. The modified sites were highly enriched with hydrophilic amino acids. Furthermore, the FATylation processes of hnRNP C2, PCNA, and PDIA3 were verified by a coimmunoprecipitation assay. We confirmed that most of the substrates were covalently attached to a FAT10 monomer. The functional distribution of the FAT10 targets suggests that FAT10 participates in various biological processes, such as translation, protein folding, RNA processing, and macromolecular complex assembly. These results should be very useful for investigating the biological functions of FAT10.

Advances in proteomics research involve the use of high-precision and high-resolution mass spectrometry instruments. Although hardware improvements are the main impetus for the acquisition of high-quality data, enhancements in software tools are also needed. In this study, recalibration was verified as an important way to improve data accuracy. A new version tool, known as FTDR 2.0, was developed to recalibrate the mass-to-charge ratio error of most observed parent ions to the sub part per million level in routine experiments. First, many new parameters were introduced and screened as features online to reduce systematic error and to adapt to various data sets. Second, a support vector regression model was trained to characterize the complex nonlinear maps from features to mass-to-charge ratio measurement errors. Third, a specific mass-to-charge ratio error tolerance for each parent ion was estimated by considering the impact of signal intensity. FTDR 2.0 is a user-friendly tool that supports most commonly used data standards and formats. A C++ library and the source code are provided to support the redevelopment and integration into other mass spectrometry data processing tools. The performance of FTDR 2.0 was verified using several experimental data sets from different research programs. Recalibration with FTDR 2.0 has been proved to improve the peptide identification in qualitative, quantitative, and post-translational modification analyses.

Self-interacting proteins, whose two or more copies can interact with each other, play important roles in cellular functions and the evolution of protein interaction networks (PINs). Knowing whether a protein can self-interact can contribute to and sometimes is crucial for the elucidation of its functions. Previous related research has mainly focused on the structures and functions of specific self-interacting proteins, whereas knowledge on their overall properties is limited. Meanwhile, the two current most common high throughput protein interaction assays have limited ability to detect self-interactions because of biological artifacts and design limitations, whereas the bioinformatic prediction method of self-interacting proteins is lacking. This study aims to systematically study and predict self-interacting proteins from an overall perspective. We find that compared with other proteins the self-interacting proteins in the structural aspect contain more domains; in the evolutionary aspect they tend to be conserved and ancient; in the functional aspect they are significantly enriched with enzyme genes, housekeeping genes, and drug targets, and in the topological aspect tend to occupy important positions in PINs. Furthermore, based on these features, after feature selection, we use logistic regression to integrate six representative features, including Gene Ontology term, domain, paralogous interactor, enzyme, model organism self-interacting protein, and betweenness centrality in the PIN, to develop a proteome-wide prediction model of self-interacting proteins. Using 5-fold cross-validation and an independent test, this model shows good performance. Finally, the prediction model is developed into a user-friendly web service SLIPPER(SeLf-Interacting Protein PrEdictoR). Users may submit a list of proteins, and then SLIPPERwill return the probability_scoresmeasuring their possibility to be self-interacting proteins and various related annotation information. This work helps us understand the role self-interacting proteins play in cellular functions from an overall perspective, and the constructed prediction model may contribute to the high throughput finding of self-interacting proteins and provide clues for elucidating their functions.

A non-invasive diagnostic approach is crucial for the evaluation of severity of liver disease, treatment decisions, and assessing drug efficacy. This study evaluated plasma proteomic profiling via an N-terminal isotope tagging strategy coupled with liquid chromatography/Fourier transform ion cyclotron resonance mass spectrometry measurement to detect liver fibrosis staging. Pooled plasma from different liver fibrosis stages, which were assessed in advance by the current gold-standard of liver biopsy, was quantitatively analyzed. A total of 72 plasma proteins were found to be dysregulated during the fibrogenesis process, and this finding constituted a valuable candidate plasma biomarker bank for follow-up analysis. Validation results of fibronectin by Western blotting reconfirmed the mass-based data. Ingenuity Pathways Analysis showed four types of metabolic networks for the functional effect of liver fibrosis disease in chronic hepatitis B patients. Consequently, quantitative proteomics via the N-terminal acetyl isotope labeling technique provides an effective and useful tool for screening plasma candidate biomarkers for liver fibrosis. We quantitatively monitored the fibrogenesis process in CHB patients. We discovered many new valuable candidate biomarkers for the diagnosis of liver fibrosis and also partly identified the mechanism involved in liver fibrosis disease. These results provide a clearer understanding of liver fibrosis pathophysiology and will also hopefully lead to improvement of clinical diagnosis and treatment.

Tandem mass spectrometry combined with database searching allows high throughput identification of peptides in shotgun proteomics. However, validating database search results, a problem with a lot of solutions proposed, is still advancing in some aspects, such as the sensitivity, specificity, and generalizability of the validation algorithms. Here a Bayesian nonparametric (BNP) model for the validation of database search results was developed that incorporates several popular techniques in statistical learning, including the compression of feature space with a linear discriminant function, the flexible nonparametric probability density function estimation for the variable probability structure in complex problem, and the Bayesian method to calculate the posterior probability. Importantly the BNP model is compatible with the popular target-decoy database search strategy naturally. We tested the BNP model on standard proteins and real, complex sample data sets from multiple MS platforms and compared it with PeptideProphet, the cutoff-based method, and a simple nonparametric method (proposed by us previously). The performance of the BNP model was shown to be superior for all data sets searched on sensitivity and generalizability. Some high quality matches that had been filtered out by other methods were detected and assigned with high probability by the BNP model. Thus, the BNP model could be able to validate the database search results effectively and extract more information from MS/MS data.

We integrated five sets of proteomics data profiling the constituents of cerebrospinal fluid (CSF) derived from Huntington disease (HD)-affected and -unaffected individuals with genomics data profiling various human and mouse tissues, including the human HD brain. Based on an integrated analysis, we found that brain-specific proteins are 1.8 times more likely to be observed in CSF than in plasma, that brain-specific proteins tend to decrease in HD CSF compared with unaffected CSF, and that 81% of brain-specific proteins have quantitative changes concordant with transcriptional changes identified in different regions of HD brain. The proteins found to increase in HD CSF tend to be liver-associated. These protein changes are consistent with neurodegeneration, microgliosis, and astrocytosis known to occur in HD. We also discuss concordance between laboratories and find that ratios of individual proteins can vary greatly, but the overall trends with respect to brain or liver specificity were consistent. Concordance is highest between the two laboratories observing the largest numbers of proteins.

Tandem mass spectrometry combined with database searching allows high throughput identification of peptides in shotgun proteomics. However, validating database search results, a problem with a lot of solutions proposed, is still advancing in some aspects, such as the sensitivity, specificity, and generalizability of the validation algorithms. Here a Bayesian nonparametric (BNP) model for the validation of database search results was developed that incorporates several popular techniques in statistical learning, including the compression of feature space with a linear discriminant function, the flexible nonparametric probability density function estimation for the variable probability structure in complex problem, and the Bayesian method to calculate the posterior probability. Importantly the BNP model is compatible with the popular target-decoy database search strategy naturally. We tested the BNP model on standard proteins and real, complex sample data sets from multiple MS platforms and compared it with PeptideProphet, the cutoff-based method, and a simple nonparametric method (proposed by us previously). The performance of the BNP model was shown to be superior for all data sets searched on sensitivity and generalizability. Some high quality matches that had been filtered out by other methods were detected and assigned with high probability by the BNP model. Thus, the BNP model could be able to validate the database search results effectively and extract more information from MS/MS data.

We integrated five sets of proteomics data profiling the constituents of cerebrospinal fluid (CSF) derived from Huntington disease (HD)-affected and -unaffected individuals with genomics data profiling various human and mouse tissues, including the human HD brain. Based on an integrated analysis, we found that brain-specific proteins are 1.8 times more likely to be observed in CSF than in plasma, that brain-specific proteins tend to decrease in HD CSF compared with unaffected CSF, and that 81% of brain-specific proteins have quantitative changes concordant with transcriptional changes identified in different regions of HD brain. The proteins found to increase in HD CSF tend to be liver-associated. These protein changes are consistent with neurodegeneration, microgliosis, and astrocytosis known to occur in HD. We also discuss concordance between laboratories and find that ratios of individual proteins can vary greatly, but the overall trends with respect to brain or liver specificity were consistent. Concordance is highest between the two laboratories observing the largest numbers of proteins.

Background: Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. Results: In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5–100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. Conclusion: These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

The tilt-rotor unmanned aerial vehicle (TRUAV) has vertical take-off and landing and high-speed flight capabilities through conversion and reconversion modes, thereby presenting wide application prospects. This article presents a compact tilt tri-rotor UAV. For flying in the conversion mode, the TRUAV needs to realize the transition of two control logic and two kinds of actuators in both of the rotor and fixed-wing modes, achieving the control of the multi-input multioutput, input nonaffine, and nonlinear multichannel cross-coupling UAV system. Furthermore, the lateral dynamics of a tilt tri-rotor UAV is more unstable for unpaired rotors. These system characteristics present a great challenge to conversion control. To solve these problems, the nonlinear dynamic model of the vehicle in the conversion mode is provided. Furthermore, a cascade control system consisting of position control, velocity control, angle control, angular velocity control, and control mixer is proposed. The simulation result of the control system shows steady flight and a fast transition in the conversion mode with modeling uncertainty in case of no wind.

Tilt-rotor unmanned aerial vehicles have attracted increasing attention due to their ability to perform vertical take-off and landing and their high-speed cruising abilities, thereby presenting broad application prospects. Considering portability and applications in tasks characterized by constrained or small scope areas, this article presents a compact tricopter configuration tilt-rotor unmanned aerial vehicle with full modes of flight from the rotor mode to the fixed-wing mode and vice versa. The unique multiple modes make the tilt-rotor unmanned aerial vehicle a multi-input multi-output, non-affine, multi-channel cross coupling, and nonlinear system. Considering these characteristics, a control allocation method is designed to make the controller adaptive to the full modes of flight. To reduce the cost, the accurate dynamic model of the tilt-rotor unmanned aerial vehicle is not obtained, so a full-mode flight strategy is designed in view of this situation. An autonomous flight test was conducted, and the results indicate the satisfactory performance of the control allocation method and flight strategy.