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Peptides play important roles in regulating biological processes and form the basis of a multiplicity of therapeutic drugs. To date, only about 300 peptides in human have confirmed bioactivity, although tens of thousands have been reported in the literature. The majority of these are inactive degradation products of endogenous proteins and peptides, presenting a needle-in-a-haystack problem of identifying the most promising candidate peptides from large-scale peptidomics experiments to test for bioactivity. To address this challenge, we conducted a comprehensive analysis of the mammalian peptidome across seven tissues in four different mouse strains and used the data to train a machine learning model that predicts hundreds of peptide candidates based on patterns in the mass spectrometry data. We provide in silico validation examples and experimental confirmation of bioactivity for two peptides, demonstrating the utility of this resource for discovering lead peptides for further characterization and therapeutic development. Bioactive peptides regulate many physiological functions but progress in discovering them has been slow. Here, the authors use a machine learning framework to predict mammalian peptide candidates from the global and local structure of large-scale tissue-specific mass spectrometry data.

Background: Currently, no guidelines specifically recommend scoring systems and biomarkers for early evaluation of the severity and prognosis of acute pancreatitis in pregnancy (APIP). Objectives: This study aimed to explore the early predictive value of scoring systems and routine laboratory tests on APIP severity and maternofetal prognosis. Design: This study retrospectively analyzed 62 APIP cases in a 6-year period. Methods: The predictive value of scoring systems and routine laboratory tests that were collected 24 h and 48 h after admission, for APIP severity and fetal loss, were analyzed. Results: To detect severe acute pancreatitis (SAP), a 24-h Bedside Index for severity in acute pancreatitis (BISAP) achieved a higher area under the curve (AUC) value of 0.910 than the Acute Physiology and Chronic Health Evaluation II (AUC = 0.898) and Ranson score (AUC = 0.880). The combination of BISAP, glucose, neutrophil-to-lymphocyte ratio (NLR), hematocrit (Hct), and serum creatinine (Scr) provided an AUC value of 0.984, which had greater predictive power than BISAP (p = 0.015). 24-h BISAP and Hct were independent risk factors for predicting SAP of APIP. The cutoff values of Hct and blood urea nitrogen (BUN) to predict SAP were 35.60% and 3.75 mmol/l in the APIP. Furthermore, 24-h BISAP had the highest predictive power (AUC = 0.958) for fetal loss. Conclusion: BISAP is a convenient and reliable indicator for the early prediction of SAP and fetal loss in APIP. The combination of BISAP, glucose, NLR, Hct and Scr proved to be the optimal early markers for the prediction of SAP in APIP within 24 h after admission. In addition, Hct > 35.60% and BUN > 3.75 mmol/l may be suitable thresholds for predicting SAP in APIP.

CCUS (carbon capture, utilization, and storage) technology is regarded as a bottom method to achieve carbon neutrality globally. CO 2 storage in deep coal reservoirs serves as a feasible selection for CCUS, and its storage potential can be attributed to the CO 2 adsorption capacity of the coal. In this paper, a series of CO 2 adsorption isotherm experiments were performed at different pressures and temperatures in sub-bituminous coal from the southern Junggar Basin (reservoir temperature ∼25.9°C and pressure ∼3.91 MPa). In addition, the high-pressure CO 2 adsorption characteristics of the southern Junggar Basin coal were characterized using a supercritical D-R adsorption model. Finally, the CO 2 storage capacities in sub-bituminous coal under the in situ reservoir temperature and pressure were analyzed. Results indicated that the excess adsorption capacities increase gradually with increasing injection pressure before reaching an asymptotic maximum magnitude of ∼34.55 cm 3 /g. The supercritical D-R adsorption model is suitable for characterizing the excess/absolute CO 2 adsorption capacity, as shown by the high correlation coefficients > 0.99. The CO 2 adsorption capacity increases with declining temperature, indicating a negative effect of temperature on CO 2 geological sequestration. By analyzing the statistical relationships of the D-R adsorption fitting parameters with the reservoir temperature, a CO 2 adsorption capacity evolution model was established, which can be further used for predicting CO 2 sequestration potential at in situ reservoir conditions. CO 2 adsorption capacity slowly increases before reaching the critical CO 2 density, following a rapid decrease at depths greater than ∼800 m in the southern Junngar Basin. The research results presented in this paper can provide guidance for evaluating CO 2 storage potential in deep coal seams.

Reports the activities of the library and information system of the Chinese Academy of Sciences as an important component of the library and information development in the Chinese People's Republic during the period 1950 to 1994. It currently has over 140 institutions, a staff of 2,260 and a stock of about 30 million items. Reports briefly on the work of selected information centres and concludes that, with the application of modern information technology, the system will be better able to serve national economic development and scientific and technological advancement. Original abstract-amended.

Recently, biomass-derived three-dimensional (3D) porous carbon materials have been gaining more interest as promising microwave absorbers due to their low cost, vast availability, and sustainability. Here, a novel 3D interconnected porous magnetic carbon foams are in-situ synthesized via a combination of sol-gel and carbonization process with wheat straw as the carbon source and FeCl 3 ·6H 2 O as the magnetic regulating agent. During the process of foams formation, the lignocelluloses from the steam-exploded wheat straw are converted into interconnected carbon sheet networks with hierarchical porous structures, and the precursor FeCl 3 ·6H 2 O is converted into magnetic nanoparticles uniformly embedded in the porous carbon foams. The generated magnetic nanoparticles are benefit to enhance the interface polarization and magnetic loss ability to improve the efficient complementarities between the dielectric and magnetic loss, thus increasing the impedance matching. The obtained sample treated at 600 °C displays the best microwave absorption (MA) performance. It presents a minimal reflection loss (RL) of −43.6 dB at 7.1 GHz and the effective bandwidth (RL < −10 dB) is 3.3 GHz with the thickness of 4.7 mm. The 3D porous structure, multi-interfaces and the synergy of dielectric loss and magnetic loss make great contribution to the outstanding MA performance.

The normalized difference vegetation index (NDVI) can depict the status of vegetation growth and coverage in grasslands, whereas coarse spatial resolution, cloud cover, and vegetation phenology limit its applicability in fine-scale research, especially in areas covering various vegetation or in fragmented landscapes. In this study, a methodology was developed for obtaining the 30 m annual maximum NDVI to overcome these shortcomings. First, the Landsat NDVI was simulated by fusing Landsat and MODIS NDVI by using the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and then a single-peaked symmetric logistic model was employed to fit the Landsat NDVI data and derive the maximum NDVI in a year. The annual maximum NDVI was then used as a season-independent substitute to monitor grassland variation from 2001 to 2022 in a typical area covering the major vegetation types in the Qinghai Lake Basin. The major conclusions are as follows: (1) Our method for reconstructing the NDVI time series yielded higher accuracy than the existing dataset. The root mean square error (RMSE) for 91.8% of the pixels was less than 0.1. (2) The annual maximum NDVI from 2001 to 2022 exhibited spatial distribution characteristics, with higher values in the northern and southern regions and lower values in the central area. In addition, the earlier vegetation growth maximum dates were related to the vegetation type and accompanied by higher NDVI maxima in the study area. (3) The overall interannual variation showed a slight increasing trend from 2001 to 2022, and the degraded area was characterized as patches and was dominated by Alpine kobresia spp., Forb Meadow, whose change resulted from a combination of permafrost degradation, overgrazing, and rodent infestation and should be given more attention in the Qinghai Lake Basin.

Pore structure and methane adsorption of coal reservoir are closely correlated to the coalbed gas occurrence and outburst. Full-scale pore structure and its fractal heterogeneity of coal samples were quantitatively characterized using low-pressure N2 gas adsorption (LP-N2GA) and high-pressure mercury intrusion porosimetry (HP-MIP). Fractal pore structure and adsorption capacities between outburst and nonoutburst coals were compared, and their geological significance to gas occurrence and outburst was discussed. The results show that pore volume (PV) is mainly contributed by macropores (>1000 nm) and mesopores (100–1000 nm), while specific surface area (SSA) is dominated by micropores (<10 nm) and transition pores (10–100 nm). On average, the PV and SSA of outburst coal samples are 4.56 times and 5.77 times those of nonoutburst coal samples, respectively, which provide sufficient place for gas adsorption and storage. The pore shape is dominated by semiclosed pores in the nonoutburst coal, whereas open pores and inkbottle pores are prevailing in the outburst coal. The pore size is widely distributed in the outburst coal, in which not only micropores are dominant, but also, transition pores and mesopores are developed to a certain extent. Based on the data from HP-MIP and LP-N2GA, pore spatial structure and surface are of fractal characteristics with fractal dimensions Dm1 (2.81–2.97) and Dn (2.50–2.73) calculated by Menger model and Frenkel–Halsey–Hill (FHH) model, respectively. The pore structure in the outburst coal is more heterogeneous as its Dn and Dm1 are generally larger than those of the nonoutburst coal. The maximum methane adsorption capacities (VL: 15.34–20.86 cm3/g) of the outburst coal are larger than those of the nonoutburst coal (VL: 9.97–13.51cm3/g). The adsorptivity of coal samples is governed by the micropores, transition pores, and Dn because they are positively correlated with the SSA. The outburst coal belongs to tectonically deformed coal (TDC) characterized by weak strength, rich microporosity, complex pore structure, strong adsorption capacity, but poor pore connectivity because of inkbottle pores. Therefore, the area of TDC is at high risk for gas outburst as there is a high-pressure gas sealing zone with abundant gas enrichment but limited gas migration and extraction.

Silicon is one of the essential trace elements in the human body; the deficiency of which may lead to bone diseases. Numerous animal experiments have shown that an appropriate increase in the intake of silicon is beneficial to enhancing bone density and toughness to prevent osteoporosis. However, the molecular mechanisms of the silicon-mediated osteogenesis process have not been sufficiently clarified. In this study, we determined the possible osteogenesis-related mechanisms of orthosilicic acid at a molecular level. We detected the relevant pathway and osteogenic indicators by immunofluorescence (IF), Western blot, alkaline phosphatase (ALP) staining (using 5-bromo-4-chloro-3-indolyl phosphate/nitro blue tetrazolium [BCIP/NBT]), ALP enzyme labeling method, osteocalcin (OCN), and N -terminal propeptide of type 1 procollagen (P1NP) enzyme-linked immunosorbent assay (ELISA). We found that orthosilicic acid is capable of enhancing the expression of phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K), phospho-protein kinase B (P-Akt), phospho-mammalian target of rapamycin (P-mTOR), and related osteogenic markers (runt-related transcription factor 2 [RUNX2], type I collagen [COL1], ALP, OCN, and P1NP). However, with the addition of PI3K–Akt–mTOR pathway-specific inhibitor LY294002, the expression of PI3K, P-Akt, P-mTOR, RUNX2, COL1, ALP, OCN, and P1NP decreased. The results indicated that the PI3K–Akt–mTOR pathway played a positive regulatory role in the process of orthosilicic acid–mediated osteogenesis in vitro.

This paper proposes a new numerical method for the tribo-dynamic analysis of the CRB by coupling the roller–raceway mixed EHL model and the motion equations of the CRB multibody system. The mixed EHL model is established by an implicit solution of the empirical EHL film thickness expression and Greenwood–Tripp asperity contact formula. The CRB motion equations are derived using Lagrange multibody dynamics methodology. The proposed model improves the previous CRB dynamics models by considering the roller–raceway mixed EHL contact. Based on this model, the tribo-dynamic characteristics of CRB system and the effects of radial load, rotation speed and lubricant viscosity are analyzed. The results show that the asperity interaction accounts for 90% of the friction but a small fraction of the total contact force. The roller-inner raceway friction causes the inner ring to move horizontally, leading to a saw-like fluctuation of the contact forces within the contact zone. Moreover, a higher radial load leads to a smaller vertical motion amplitude of the inner ring. The asperity contact force shows a nonlinear decrease as the rotation speed and lubricant viscosity increase. In addition, the high rotation speed tends more to cause skidding (more than 4%). This model provides a new option for the tribo-dynamic simulations of the CRB system.