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The pavements are subjected to the moving vehicle, pore pressure and temperature loadings, the analytical solutions of thermo-hydro-mechanical response for the pavements are paid more attentions by the road engineers while the pavements are maintained. The analytical solutions of the multi-layered pavement model are derived by the novel DVP (duel variable and position) method in this paper. Starting with the basic equations of the pavement system, obeying Biot's dynamic poroelastic theory and generalized thermoelastic theory, the original problem is divided into two sub-systems and solved twice to avoid tangling two different loading frequencies. With the aid of moving coordinate and vector functions, the decomposition expressions of stress, displacement, temperature and pore pressure variations are deduced and propagated by using DVP method, and the transformed solutions are numerically inverted. In the numerical examples, the obtained solutions are firstly verified. Then the difference among the results of several pavement models are compared and analyzed. Finally, the effects of the surface temperature, moving load speed and interface conditions on the displacement, stress, and strain distributions are discussed by the solutions of thermo-hydro-mechanical pavement models in the numerical results.

In the Lixiahe region of China, co-culture has been rapidly promoted in flooded paddy fields owing to its ecological and economic benefits. Rice-prawn co-culture can reduce the damage of crab and shrimp to rice growth and paddy field and substantially change the soil microbial community and soil fertility. In this study, we compared changes in the soil microbial community and soil fertility in waterlogged paddies under conventional rice culture (CR), rice-prawn (Macrobrachium nipponense) co-culture (RP), and pond culture (PC). The microbial abundance in RP was significantly higher than that in CR. RP soil microbial diversity was significantly higher than PC soil microbial diversity. The dominant bacteria in RP soil were Proteobacteria, Chloroflexi, and Bacteroidetes. Compared with those in CR, total organic matter (TOM) and total nitrogen in RP were relatively stable, available potassium and available phosphorus (AP) decreased, and other indicators increased significantly. Soil fertility significantly benefited from co-culture, with total organic carbon (TOC) increasing. Interactive relationship analysis showed that TOM, TOC, AP, and NH

The understanding of pore characteristics is the basis for studying the macroscopic physical properties of wood. Herein, mercury intrusion porosimetry (MIP) was analyzed in detail as a function of pressure to analyze wood samples. Additionally, fractal analysis was performed from the MIP derived data to establish the relationship between the fractal dimension and the structural parameters. The results demonstrate that in spite of the large variability of the MIP data from different wood species, the overall trend can be approximated. The aperture size ranged from 2.0 nm to 350 µm with a corresponding porosity ranging from 44.26 to 73.18%. The threshold pressure, average pore radius and pore-to-throat ratio extracted from the MIP data can be used as a valid parameter to evaluate the structural characteristics. The fractal dimension values in the corresponding macropore, mesopore and micropore aperture range intervals are 2.978, 2.827 and 2.438, respectively, which indicate a higher degree of pore complexity for larger pores. Negative correlations are observed between the fractal dimension and porosity with R2 values of 0.85, and in addition, positive correlations are observed between the fractal dimension and the average pore radius and pore-to-throat ratio with corresponding R2 values of 0.66 and 0.52, respectively.

The elastic strain of wood reflects the nature (stretching or compression) and the magnitude of the drying stress at that time during the conventional drying process. The accurate prediction of strain is important to optimize the drying process and to improve drying speed and quality. In this work, the elastic strain was measured in real time, and moisture content was measured by periodic weighing during the drying process. Using these data, the GM (1,1) grey prediction model was used to predict moisture content in adjacent periods in the future. Based on the moisture content predicted by GM (1,1), a BP neural network was constructed to predict the development trend of elastic strain in the surface layer and core layer. The prediction results of the GM-BP combination model showed that the fitting error range of the prediction of the surface layer elastic strain was [-5×10-3~5×10-3], with a mean square error (MSE) of 2.31×10-7. The elastic strain of the core layer was [-2×10-3~2×10-3], and the MSE was 3.86×10-8. Thus, the GM-BP model achieved high accuracy for predicting the development trend of elastic strain. It can provide a new method and innovative thinking for the optimization and control of wood drying process.

Let $$1< c < \frac{59}{44},\, c\ne \frac{4}{3}$$ . In this paper it is proved that for any sufficiently large real N, for almost all real $$T\in (N,2N]$$ (in the sense of Lebesgue measure), the inequality $$|p_1^{c} + p_2^{c} - T|< T^{-\frac{9}{10c}(\frac{59}{44}-c)}$$ is solvable in primes $$p_1, p_2$$ . This result constitutes an improvement upon that of Kumchev and Laporta.

Large-section boxed-heart square timber is prone to surface and radial cracks during the drying process. A high-temperature setting pretreatment could prevent cracking in boxed-heart timber. The pretreatment process is primarily determined by the preheating time. To determine the preheating time of the test material, a mathematical model for the internal temperature distribution of Mongolian pine (Pinus sylvestris var. mongolica Litv.) boxed-heart square timber in the preheating stage was established by the finite element method. The model viability was verified by tests that showed that the model simulation results were stable and non-fluctuating. For the prediction error of the temperature change, the early-period error was large, while the later predicted values and measured values tended to be consistent. For the prediction error of the temperature distribution, the core-layer error was smallest, while that of the surface layer was greatest. For the prediction error of the preheating time, greater preheating temperatures exhibited greater error, and the minimum error was 10 min, at 90 °C. Overall, the predicted values were close to the measured values and can be referenced for subsequent pretreatment process research.

This study was based on the finite element method to construct a high-frequency heating temperature field simulation model. In the model, the bending length and angle of top plate were changed upwards. The heating uniformity of test material was then analyzed under different conditions (bending length and angle) to identify the best improvement scheme and carry out the experimental verification. The results showed that when the bending length and angle of top plate were 200mm and 45°, the heating uniformity was the best. Comparison of temperature distribution along the width direction of test material before and after improvement, it was concluded that the temperature distribution was more uniform after improvement. Comparison the temperature difference between the center of test material and its length, width and thickness direction measurement point before and after improvement, the temperature difference was reduced by 7.6 ℃ , 1.7 ℃ , 3.4 ℃ , respectively, which effectively improved the heating uniformity. Comparison of the changes in the electromagnetic field distributions between plates before and after improvement revealed an increase in distribution uniformity from 0.631 to 0.811. This indicated that the electromagnetic field distribution after improvement was more uniform with ideal heating effect. Overall, the improvement in bending length and angle of top plate could change the distribution of electromagnetic fields between the plates and enhance temperature distribution uniformity of wood during high-frequency heating.

The pretreatment of Mongolian pine (Pinus sylvestris var. mongholica Litv.) was conducted with saturated wet air (80 °C) and atmospheric saturated steam (100 °C) before high-frequency vacuum drying. Effects of the pretreatment on moisture content (MC), drying rate, drying crack, and drying shrinkage strain were investigated. The results showed that through pretreatment, the initial MC of the test material decreased by 2.6% to 6%, and the MC distribution was uniform. The maximum difference of the untreated wood MC distribution after drying was 2%, while that within the pretreated wood was less than 1%. The drying rate also increased, as the drying rates of the untreated and pretreated wood were 0.268%/h, 0.333%/h, and 0.398%/h, respectively. The drying shrinkage strain of the sample was reduced. The drying shrinkage strain of untreated wood was between 0.044 and 0.063, while the drying shrinkage strain of pretreatment wood was between 0.013 and 0.049. The crack of the test material was also reduced, and the drying quality was improved. The pretreatment reduced the high-frequency vacuum drying stress of wood and improved the drying speed and quality.

Liver is the central place for drug metabolism. Drug-induced liver injury (DILI) is hence inevitable, and has become one of the leading causes for drug failure in development and drug withdrawal from the market. Due to lack of reliable preclinical and in vivo toxicology test conditions, it is time-consuming, laborious and costly to interpret the mechanisms of DILI through bioassays. In this paper, we developed a computational systems toxicology approach to investigate the molecular mechanisms of DILI. Totally 1478 DILI compounds were collected, together with 1067 known targets for 896 DILI compounds. Then, 173 new potential targets of these compounds were predicted by our bSDTNBI (balanced substructure-drug-target network-based inference) method. After network analysis, 145 primary genes were found to relate with hepatotoxicity and have higher expression in liver, among which 26 genes were predicted by our method, such as CYP2E1, GSTA1, EPHX1, ADH1B, ADH1C, ALDH2, F7, and IL2. A scoring function, DILI-Score, was further proposed to assess the hepatotoxic severity of a given compound. Finally, as case studies, we analyzed the mechanisms of DILI from the perspective of off-targets, and found out the pivotal genes for liver injuries induced by tyrosine kinase inhibitors and TAK-875. This work would be helpful for better understanding mechanisms of DILI and provide clues for reducing risk of DILI.

Skin sensitisation, one of the most frequent forms of human immune toxicity, is authenticated to be a significant endpoint in the field of drug discovery and cosmetics. Due to the drawbacks of traditional animal testing methods, in silico methods have advanced to study skin sensitisation. In this study, mechanism-based binary and ternary classification models were constructed with a comprehensive data set. 1007 compounds were collected to develop five series of local and global models based on mechanisms. In each series, compounds were classified into five groups according to EC3 values, and applied as training sets, test sets and external validation sets. For each of the five series, 81 binary classification models and 81 ternary classification models were acquired via 9 molecular fingerprints and 9 machine learning methods using a novel KNIME workflow. Meanwhile, the applicability domains for the best 10 models were figured out to certify the rationality of prediction effect. In addition, 8 toxic substructures probably causing skin sensitisation were identified to speculate whether a compound is a skin sensitiser. The mechanism-based prediction models and the toxic substructures can be applied to predict the skin sensitising potential and potency of compounds.

Two runs of commercial-scale radio-frequency/vacuum (RF/V) drying for maple hardwood were performed to explore the practical technology and its drying characteristics. The results revealed that the power density was a prerequisite for the drying schedule development. The drying time and in-process moisture content (MC) were evaluated by the calculated amount of dehydration at 1% MC removal. The drying defects, such as checks, bowing, and twist, and MC variation met the requirements of GB/T 6491 (2012). The drying rate of run 2 increased 22% after the drying schedule modification. The dehydration capacity was affected by the temperature, which first increased fast as the wood temperature increased to the boiling point and then increased more slowly after that point. The dehydration capacity was also associated with the initial lumber MC. Approximately 30% of the total energy maintained the chamber vacuum and approximately 70% was used for RF heating for both runs. The overall specific energy for water removal during RF/V drying had a competitive advantage compared with conventional kiln drying. The energy conversion efficiency of both runs was low at 50% during the warming stage, and increased to 80% and 90% for Run 1 and Run 2, respectively, during the drying stage.

Let 1 0 such that for each real numberN>N(c) the inequality\(|p_1^c + p_2^c + p_3^c - N|< N^{ - \tfrac{1}{c}(\tfrac{{11}}{{10}} - c)} \log ^{c_1 } N\) is solvable in prime numbersp 1,p 2,p 3, wherec 1 is some absolute positive constant.

A series of tests studies have been carried out to evaluate the potential impact of basalt fibers on mechanical performance (compressive strength, flexural strength), fracture energy and anti-shrinkage properties of cement-stabilized macadam, which is a kind of popular semi-rigid base and subbase composite material. Four series of cement-stabilized macadam mixtures (the cement contents are 85 kg/m3, 110 kg/m3, 130 kg/m3 and 160 kg/m3) were prepared by incorporating four various dosages (1 kg/m3, 3 kg/m3, 6 kg/m3, 10 kg/m3) of basalt fiber. The optimum basalt fibers length was determined based on the mechanical properties of cement-stabilized macadam mixtures. Then compressive strength test, flexural strength test, fracture energy test, cracking resistance test and scanning electron microscopy (SEM) test were carried out in this study. The results show that addition of basalt fibers can improve the flexural strength, while its effect on compressive strength of cement-stabilized macadam was not remarkable. The maximum deflection in midspan of the specimen was also improved due to the reinforcement effect of basalt fibers. The fracture energy and gain ratio of basalt fibers reinforced cement-stabilized macadam vary nonlinearly with increasing basalt fibers dosage, especially when the basalt fibers dosage was more than 6 kg/m3 where the effect of added fibers on the fracture energy was significant. When the basalt fibers dosage was more than 6 kg/m3, the anti-dry shrinkage properties and cracking ability under temperature cycle of cement-stabilized macadam were both improved effectively. Moreover, the scanning electron microscopy images revealed that the tight bond between basalt fibers and mortar matrix could help the matrix to transfer and share the tensile stress, and consequently, the flexural strength, fracture energy and cracking resistance capacity of cement-stabilized macadam could be improved by the addition of basalt fibers.

The accurate measurement of wood dielectric properties and their relationship with many influencing factors are not only necessary for the study of other relevant wood properties, but also improve the wood dielectric moisture content detection method. In this study, the relationships between dielectric properties and frequency, as well as between moisture content and texture direction were analyzed. The results showed that the effect of the moisture content on the dielectric constant is significant. With increasing moisture content, the dielectric constant increased exponentially below the fiber saturation point. The effect of texture direction slightly exceeded that of the radial direction, and the change rate of the tangential direction above the fiber saturation point was higher than that of the radial direction. The relationship between the dielectric constant and frequency was related to the wood moisture content. The dielectric loss was approximately linear with the frequency below the fiber saturation point.

Estrogen-related receptor alpha (ERRα) has attracted increasing concerns. ERRα, orphan nuclear receptor, plays important roles in energy metabolism. Therefore, small molecule agonists of ERRα could be a potential therapeutic strategy in the treatment of metabolic diseases such as diabetes. Recently, Wei et al. identified cholesterol as the endogenous agonist of ERRα. However, the detailed molecular mechanism of cholesterol bound with ERRα remains ambiguous. Thus, in this study molecular docking and molecular dynamics (MD) simulations were performed to characterize how cholesterol affects the behavior of ERRα. Based on the results, we found that a proven residue Phe232 and others including Leu228, Glu235, Arg276, and Phe399 were key residues to ligand binding. A hydrogen-bonding interaction between cholesterol and Glu235 ensured the orientation of the ligand in the binding pocket, while hydrophobic interactions between cholesterol and the above-mentioned residues promoted the stability of ERRα-cholesterol complex. In the presence of the proliferator-activated receptor γ coactivator 1α (PGC-1α), the cholesterol-ERRα interaction became more stable. Interestingly, we observed that cholesterol facilitated the binding of ERRα with its coactivator PGC-1α via stabilizing the conformation of helix 12 and the interaction surface of ERRα/PGC-1α. Overall, these findings would be valuable for the future rational design of novel ERRα agonists.

In this paper it is proved that every sufficiently large even integer N satisfying one of the congruence conditions N ≡ 10, 58, 130, or 178 (mod 240) may be represented as the sum of one square and nine fourth powers of prime numbers.

Collapse is the severest defect for collapse-prone species used as solid wood product materials. Previous study focused on the morphological structures by scanning electron microscopy (SEM)...

Long noncoding RNAs (LncRNAs) may serve as miRNA sponges to regulate the expressions of miRNA target genes. LncRNA LINC00969 has been indicated to be upregulated in intervertebral disk degeneration. However, the regulatory mechanism of LINC00969 in intervertebral disk degeneration progression remains unclear. Differently expressed LINC00969, miR-335-3p, and thioredoxin-interacting protein (TXNIP) were determined in nucleus pulposus (NP) tissues and cells isolated from patients with intervertebral disk degeneration. The interaction between LINC00969, miR-335-3p, and TXNIP was also assessed. In this study, we demonstrated that LINC00969 was highly expressed, whereas miR-335-3p was aberrantly downregulated in NP tissues and cells of intervertebral disk degeneration patients. In addition, our results suggested that LINC00969 enhanced NP cell apoptosis. More importantly, LINC00969 was identified to function as a competitive endogenous RNA (ceRNA) for miR-335-3p to positively regulate TXNIP expression in vitro. Our study provided evidence for the cross-talk between LINC00969, miR-335-3p, and TXNIP, shedding light on the therapy for intervertebral disk degeneration. © 2018 IUBMB Life, 71(5):611-618, 2019.

Backgroud Few studies compared radiographic and clinical outcomes between oblique lumbar interbody fusion and posterior lumbar interbody fusion in degenerative lumbar scoliosis. Methods This study retrospectively analyzed the case data of 40 patients with degenerative lumbar scoliosis in our hospital from July 2016 to October 2018. Among which,19 cases underwent oblique lumbar Interbody fusion(OLIF group) and 21 cases underwent posterior lumbar interbody fusion(PLIF group). The duration of the operation, volume of intraoperative hemorrhage, incision length, bed rest time, length of hospital stay, and complications were recorded for all patients. The clinical effects of 40 patients were evaluated by VAS for back pain and Oswestry Disability Index (ODI) and The radiographic parameters were evaluated using the lumbar scoliosis Cobb angle, sagittal vertical axis(SVA), coronal vertical axis(CVA), lumbar lordosis(LL), pelvic tilt(PT), sacral slope(SS), and Disc height(DH). Results The duration of the operation, the volume of intraoperative hemorrhage, incision length, bed rest time, length of hospital stay of the OLIF group were shorter than the PLIF group( P ＜ 0.05 ). The VAS scores for back pain, the ODI of the two groups were significantly decreased, which compared with the preoperative( P ＜ 0.05 )，which in OLIF group was significantly more decreased than in PLIF( P ＜ 0.05 ) at 7 days and 3 months postoperatively, but at the last follow-up there were no significant difference between the two groups( P ＞ 0.05 ); The lumbar scoliosis Cobb angle, SVA, CVA, PT, LL, SS were significantly improved postoperatively( P ＜ 0.05 ). The OLIF group showed higher DH, smaller Cobb angle, and greater LL than the PLIF group at any time point( P ＜ 0.05 ). but there were no significant difference in SVA, CVA, PT and SS between the two groups at any follow-up points( P ＞ 0.05 ). The overall complication rate was slightly higher in the PLIF group（47.62%）than in the OLIF group（26.32%）without significant difference(x 2 =1.931, P =0.165). But the incidence of major complications in the PLIF group was significantly higher than that in the OLIF group (Fisher, P =0.026). Conclusion OLIF provides an alternative minimally invasive treatment for DLS, which compared with PLIF. It has the characteristics of a small incision, rapid recovery, fewer complications related to the surgical approach, and satisfactory orthopedics. Keywords : Minimally invasive, Oblique lumbar interbody fusion, Degenerative adult lumbar scoliosis , Posterior lumbar interbody fusion

Radio frequency heating combined with convection (RF/C) drying of larch boxed-heart square timber and its influence on drying kinetics such as rate, moisture content distribution, and stresses was ...

Epoxidation is one of the reactions in drug metabolism. Since epoxide metabolites would bind with proteins or DNA covalently, drugs should avoid epoxidation metabolism in the body. Due to the instability of epoxide, it is difficult to determine epoxidation experimentally. In silico models based on big data and machine learning methods are hence valuable approaches to predict whether a compound would undergo epoxidation. In this study, we collected 884 epoxidation data manually from various sources, and finally got 829 unique sites of epoxidation. Three types of molecular fingerprints with different lengths (1024, 2048 or 4096 bits) were used to describe the reaction sites. Six machine learning methods were used to build the classification models. The training set and test set were randomly divided into 8 : 2, and 54 models were constructed and evaluated. Four best models were selected for feature selection. The features were then chosen and verified by external validation set. The resulted optimal model had the accuracy and AUC (area under the curve) values at 0.873 and 0.944 for the test set, 0.838 and 0.987 for the external validation set, respectively. The models built in this study could accurately predict whether a compound will undergo epoxidation and which part is most susceptible to epoxidation, which is of great significance for drug design.

Let [Formula: see text]. In this paper, it is proved that for every sufficiently large real number [Formula: see text], the Diophantine inequality [Formula: see text] is solvable in primes [Formula: see text]. This result constitutes an improvement upon that of Baker and Weingartner.

Let $$[\theta ]$$ denote the integral part of the real number $$\theta $$ . In this paper it is proved that for $$1

Drug metabolism is a complex procedure in the human body, including a series of enzymatically catalyzed reactions. However, it is costly and time consuming to investigate drug metabolism experimentally; computational methods are hence developed to predict drug metabolism and have shown great advantages. As the first step, classification of metabolic reactions and enzymes is highly desirable for drug metabolism prediction. In this study, we developed multiclassification models for prediction of reaction types catalyzed by oxidoreductases and hydrolases, in which three reaction fingerprints were used to describe the reactions and seven machine learnings algorithms were employed for model building. Data retrieved from KEGG containing 1055 hydrolysis and 2510 redox reactions were used to build the models, respectively. The external validation data consisted of 213 hydrolysis and 512 redox reactions extracted from the Rhea database. The best models were built by neural network or logistic regression with a 2048-bit transformation reaction fingerprint. The predictive accuracies of the main class, subclass, and superclass classification models on external validation sets were all above 90%. This study will be very helpful for enzymatic reaction annotation and further study on metabolism prediction.

In this paper, we present an efficient and comprehensive approach to study the response of layered flexible pavements under general surface loading. Each layer can be transversely isotropic and the interface between the layers can be imperfect. Multiple surface loadings in both vertical and horizontal directions over circular regions can be applied. To handle efficiently multilayered pavements under very general loading and interface conditions, a new method called DVP (dual variable and position method) is proposed. The new method is computationally stable and can handle multiple imperfect interfaces very well in the multilayered pavement. The solution is further expressed in terms of the cylindrical system of vector functions so that the normal (dilatational) and shear (torsional) deformations can be separated and solved in terms of the LM- and N-types of this vector-function system. Various equivalent imperfect interface models are also investigated and their equivalence to the thin interface layer is further discussed.

Despite the rapid development of risk analysis, there is a relative absence of risk criteria for dams in developing countries recently, which restrained the practical application of the research results. This paper proposes guidelines for establishing risk criteria of dams in developing countries in considering the coordination of social, economic and engineering factors, then establishes a method of targeted analysis and demonstrates relevant parameters selected according to the ALARP principle and F-N curves, using China as an example. Different individual life risk criteria are established based on different safety levels for existing dams and newly built dams. Social life risk criteria and economic risk criteria are established on the basis of the different safety levels of all size reservoirs, the seriousness of the consequences of accidents, and the acceptability of social risks. The process of establishing risk criteria of dams and the analysis of the parameters demonstrated in this paper are meaningful to provide a reference and promote the required level of management for developing countries.

Environmental chemicals may affect endocrine systems through multiple mechanisms, one of which is via effects on aromatase (also known as CYP19A1), an enzyme critical for maintaining the normal balance of estrogens and androgens in the body. Therefore, rapid and efficient identification of aromatase-related endocrine disrupting chemicals (EDCs) is important for toxicology and environment risk assessment. In this study, on the basis of the Tox21 10K compound library, in silico classification models for predicting aromatase binders/nonbinders were constructed by machine learning methods. To improve the prediction ability of the models, a combined classifier (CC) strategy that combines different independent machine learning methods was adopted. Performances of the models were measured by test and external validation sets containing 1336 and 216 chemicals, respectively. The best model was obtained with the MACCS (Molecular Access System) fingerprint and CC method, which exhibited an accuracy of 0.84 for the test set and 0.91 for the external validation set. Additionally, several representative substructures for characterizing aromatase binders, such as ketone, lactone, and nitrogen-containing derivatives, were identified using information gain and substructure frequency analysis. Our study provided a systematic assessment of chemicals binding to aromatase. The built models can be helpful to rapidly identify potential EDCs targeting aromatase.

Elastic strain is one of the most important parameters associated with drying stresses. The research presented in this paper attempts to develop an artificial neural network based model for predicting elastic strain in white birch (Betula platyphylla Suk) disks during drying as a function of temperature, moisture content, relative humidity and distance from the pith. The data set was obtained by using image analysis method under two drying schedules and divided into three subsets for training (60%), validation (20%) and test (20%). According to the results, the values of determination coefficient (R2) obtained were greater than 0.97, 0.96 and 0.95 for the training, validation and test sets, respectively.

Human cytochrome P450 3A4 (CYP3A4) is a major drug-metabolizing enzyme responsible for the metabolism of ∼50% of clinically used drugs and is often involved in drug-drug interactions. It exhibits atypical binding and kinetic behavior toward many ligands. Binding of ligands to CYP3A4 is a complex process. Recent studies from both crystallography and biochemistry suggested the existence of a peripheral ligand-binding site at the enzyme surface. However, the stability of the ligand bound at this peripheral site and the possibility of discovering new CYP3A4 ligands based on this site remain unclear. In this study, we employed a combination of molecular docking, multiparalleled molecular dynamics (MD) simulations, virtual screening, and experimental bioassay to investigate these issues. Our results revealed that the binding mode of progesterone (PGS), a substrate of CYP3A4, in the crystal structure was not stable and underwent a significant conformational change. Through Glide docking and MD refinement, it was found that PGS was able to stably bind at the peripheral site via contacts with Phe215, Phe219, Phe220, and Asp214. On the basis of the refined peripheral site, virtual screening was then performed against the Enamine database. A total of three compounds were finally found to have inhibitory activity against CYP3A4 in both human liver microsome and recombinant human CYP3A4 enzyme assays, one of which showed potent inhibitory activity with IC

Chemical subcellular localization is closely related to drug distribution in the body and hence important in drug discovery and design. Although many in vivo and in vitro methods have been developed, in silico methods play key roles in the prediction of chemical subcellular localization due to their low costs and high performance. For that purpose, machine learning-based methods were developed here. At first, 614 unique compounds localized in the lysosome, mitochondria, nucleus and plasma membrane were collected from the literature. 80% of the compounds were used to build the models and the rest as the external validation set. Both fingerprints and molecular descriptors were used to describe the molecules, and six machine learning methods were applied to build the multi-classification models. The performance of the models was measured by 5-fold cross-validation and external validation. We further detected key substructures for each localization and analyzed potential structure–localization relationships, which could be very helpful for molecular design and modification. The key substructures can also be used as features complementary to fingerprints to improve the performance of the models.

Rapidly and correctly identifying eye irritants or corrosive chemicals is an important issue in health hazard assessment. The purpose of this study is to describe the development of in silico methods for the classification of chemicals into irritants/corrosives or non-irritants/non-corrosives. A total of 5220 chemicals for a serious eye irritation (EI) dataset and 2299 chemicals as an eye corrosion (EC) dataset were collected from available databases and literature. Structure–activity relationship (SAR) models were developed to separately predict serious EI or EC via machine learning methods. According to the overall prediction accuracy, the Pub-SVM model gave the best results for both serious EI (overall classification accuracy CA = 0.946) and EC (CA = 0.959). The sensitivity and specificity of serious EI were 97.3% and 86.7% for the training set, and 96.9% and 82.7% for the external validation set, respectively. Similarly, the sensitivity and specificity of EC were 95.5% and 96.2% for the training set, and 94.9% and 96.2% for the external validation set, respectively. The high specificity and sensitivity indicated that our models were reliable and robust, which can be used to predict the potential seriousness of EI/EC of compounds. Moreover, several structural alerts for characterizing serious EI/EC were identified using the combination of information gain and substructure frequency analysis.

Summary admetSAR was developed as a comprehensive source and free tool for the prediction of chemical ADMET properties. Since its first release in 2012 containing 27 predictive models, admetSAR has been widely used in chemical and pharmaceutical fields. This update, admetSAR 2.0, focuses on extension and optimization of existing models with significant quantity and quality improvement on training data. Now 47 models are available for either drug discovery or environmental risk assessment. In addition, we added a new module named ADMETopt for lead optimization based on predicted ADMET properties. Availability and implementation Free available on the web at http://lmmd.ecust.edu.cn/admetsar2/ Supplementary information Supplementary data are available at Bioinformatics online.

Objective To explore the effect of ginsenoside Rg1 on intervertebral disc degeneration (IVDD) in vivo and in vitro and its mechanism. Methods 60 rats were underwent surgery to construct rat models of IVDD and divided in the sham group, model group and gradient G-Rg1 groups (10 mg/kg/d, 20 mg/kg/d and 40 mg/kg/d).The change of histology was observed by HE staining, the water content and the expression of β-catenin in IVD were detected. Rat nucleus pulposus cells(NPCs) were isolated from IVDD rats and divided in D-NPCs group, and gradient G-Rg1 groups(20 μg/ml, 50 μg/ml and 100 μg/ml).The cell proliferation activity, cell apoptosis rate,the expression of proteins related to ECM and Wnt/β-catenin were detected respectively, Finally the agonist of Wnt/β-catenin pathway LiCl was used for reversed experiments. Results In vivo, G-Rg1 treatment could improve the structural disorganization, low water content, NPCs number and aggrecan and collagenⅡ expression in IVD and down-regulate the expression of β-catenin. In vitro NPCs, G-Rg1 treatment could improve the low cell proliferation, high apoptosis rate and low expression of aggrecan and collagenⅡ in degenerative NPCs in a dose-dependent manner.G-Rg1 treatment could down-regulate the expression of proteins related to β-catenin signal and LiCl could reverse the increase of cell proliferation and ECM synthesis, decrease of apoptosis of degenerative NPCs induced by G-Rg1. Conclusion G-Rg1 could promote ECM synthesis of degenerative NPCs and inhibiting its apoptosis, improve the IVDD via inhibiting the Wnt/β-catenin pathway.

Estrogen-related receptor alpha (ERRα), a member of nuclear receptors (NRs), participates in energy metabolism. Recent experiments identified that several agonists to increase the activity of ERRα, which have a therapeutic effect in improving insulin sensitivity and lowering blood glucose levels. However, the detailed molecular mechanism about how the ligands affect the structure of ERRα remains elusive. To better understand the conformational change of ERRα complexed with agonists and inverse agonists, unbiased molecular dynamics (MD) simulations were performed on the ligand binding domain of ERRα (ERRα-LBD) bound with different ligands. According to the results, the ERRα-agonist interactions were more stable in the presence of the peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α). More importantly, we observed that the binding of inverse agonists would decrease the stability of helix 12 (H12) of ERRα. Moreover, we suggested that Phe232 and Phe414 should be key residues in the interaction pathway from ligands to H12, which provided a possible explanation about how ligands impact the structure of ERRα. These results would provide insights into the design of novel and efficient agonists of ERRα to treat metabolic diabetes.Communicated by Ramaswamy H. Sarma.

Endocrine disruption (ED) has become a serious public health issue and also poses a significant threat to the ecosystem. Due to complex mechanisms of ED, traditional in silico models focusing on only one mechanism are insufficient for detection of endocrine disrupting chemicals (EDCs), let alone offering an overview of possible action mechanisms for a known EDC. To remove these limitations, in this study both single-label and multilabel models were constructed across six ED targets, namely, AR (androgen receptor), ER (estrogen receptor alpha), TR (thyroid receptor), GR (glucocorticoid receptor), PPARg (peroxisome proliferator-activated receptor gamma), and aromatase. Two machine learning methods were used to build the single-label models, with multiple random under-sampling combining voting classification to overcome the challenge of data imbalance. Four methods were explored to construct the multilabel models that can predict the interaction of one EDC against multiple targets simultaneously. The single-label models of all the six targets have achieved reasonable performance with balanced accuracy (BA) values from 0.742 to 0.816. Each top single-label model was then joined to predict the multilabel test set with BA values from 0.586 to 0.711. The multilabel models could offer a significant boost over the single-label baselines with BA values for the multilabel test set from 0.659 to 0.832. Therefore, we concluded that single-label models could be employed for identification of potential EDCs, while multilabel ones are preferable for prediction of possible mechanisms of known EDCs.

Overexpression of P-glycoprotein (P-gp) is one of the major causes for multidrug resistance (MDR), which has become a major obstacle in cancer therapy. One hopeful approach to reverse the MDR is to develop inhibitors of P-gp in expression and/or function. Here, we designed and synthesized a series of chalcone derivatives as P-gp inhibitors and evaluated their potential reversal activities against MDR. Among them, the most active compound MY3 had little intrinsic cytotoxicity and showed the highest activity (RF = 50.19) in reversing DOX resistance in MCF-7/DOX cells. Further studies demonstrated that MY3 could increase intracellular accumulation of DOX and inhibit expression of P-gp at mRNA and protein levels. More importantly, MY3 significantly enhanced the efficacy of DOX against the tumor xenografts bearing MCF-7/DOX cells with the precondition of unchanged body weight. Therefore, MY3 might represent a promising lead to develop MDR reversal agents for cancer chemotherapy.

Microglial activation and inflammatory response played an important role in the secondary injury of spinal cord injury (SCI). Several microRNAs were associated with this procedure, but the underlying molecular mechanism was poorly understood. Sprague-Dawley (SD) rats were divided into four groups: SCI group (n = 7), agomiR-325-3p group (n = 7), and their control groups. Expression of miR-325-3p and proteins in epidermal growth factor receptor (EGFR)/mitogen-activated protein kinase (MAPK) signaling pathway was evaluated in microglia from SCI rats and primary microglia/BV2 cells activated by lipopolysaccharide (LPS). Concentrations of interleukin-1β (IL-1β) and tumor necrosis factor α (TNF-α) in supernatants were measured by ELISA. Low expression of miR-325-3p and activation of EGFR/MAPK was observed in microglia of SCI and LPS-induced primary microglia. Overexpression of miR-325-3p in LPS-induced BV2 cells inhibited microglial activation and release of TNF-α and IL-1β. Luciferase reporter assay confirmed that miR-325-3p negatively regulated EGFR by targeting its 3′-untranslated regions. Additionally, agomiR-325-3p inhibited the activation of microglia and EGFR/MAPK, alleviating the inflammatory response. These results indicated that miR-325-3p attenuated secondary injury after SCI through inhibition of EGFR/MAPK signaling pathway, the microglial activation, and the release of inflammatory cytokines, suggesting that miR-325-3p may be employed as a therapeutic target for SCI.

The investigation of metabolically liable sites of xenobiotics mediated by UDP-glucuronosyltransferases (UGTs) is important for lead optimization in early drug discovery. However, it is time-consuming and costly to identify potentially susceptible sites experimentally. Hence, in silico approaches have been developed to predict the site of metabolism (SOM) of UGT-catalyzed substrates. In the present work, four major types of reactions catalyzed by UGTs were collected from the Handbook of Metabolic Pathways of Xenobiotics along with their corresponding glucuronidation products. These observed and nonobserved SOMs were identified and encoded by atom environment fingerprints. Four resampling methods were performed to treat data imbalance, while four feature selection methods were utilized to choose appropriate features. Three tree-form machine learning algorithms were conducted to build discriminating models, and optimal models were then obtained for the different types of reaction. The results indicated that all of the chosen best models showed area under the curve values ranging from 0.713 to 0.869 for two independent external validation sets. Our study could supply valuable information for optimization of pharmacokinetic profiles and contribute to metabolism prediction.

Let P r denote an almost-prime with at most r prime factors, counted according to multiplicity. In this paper it is proved that for every large odd integer N , and 5 ≤ a ≤ 8 , a ≤ b , 13 60 1 a + 1 b ≤ 1 3 , the equation N = x 2 + p 2 + p 1 3 + p 2 4 + p 3 5 + p 4 a + p 5 b is solvable with x being an almost-prime P r ( a , b ) and the other variables primes, where r ( a , b ) is defined in the Theorem, in particular, r ( 6 , 7 ) = 5 . This result constitutes an refinement upon that of J. Brűdern.

Estrogen related receptor alpha (ERRα), the first identified orphan nuclear receptor, participates in the metabolism of body and adjusts some diseases such as breast cancer, osteoporosis and diabetes. Recent experiments have identified several ligands such as 7a, 7b, DK1 and DK3 toward ERRα as agonists, to have a therapeutic ability in reducing blood glucose, and impact the activity of ERRα receptor. Nevertheless, the detailed interaction modes between the agonists and the ERRα have not been fully understood, which will hinder the design and development of more potent agonists targeting ERRα for the treatment of diabetes. To address this issue, molecular docking and molecular dynamic simulation (MD) were performed to study how the four agonists regulate the behavior of ERRα. It was found that a proved residue Phe232 and some others such as Arg276, Phe286, His398 and Phe399 were the key residues in the ligand binding pocket. Hydrogen bonds between agonists and residues Arg276 and His398 and the pi–pi interactions between agonists and Phe232/Phe286/Phe399 both play important roles for agonist–ERRα binding. Interestingly, we observed that the agonists kept the binding of ERRα and its coactivator PGC-1α by stabilizing the site of helix12 (H12). All these findings are not only beneficial to understand the interaction mechanism between ERRα and its agonists, but also provide clues for designing novel and potent agonists of ERRα for the treatment of diabetes.

To accurately and effectively simulate the cracking and crushing process of concrete pavement under multi-head impact loading, a large-scale road 3D model that considers concrete pavement, subgrade and soil foundation is established. Then, the discontinuum element method is applied in the concrete pavement's potential fracture area, and the continuum element method is applied in the hammer body and the non-fracture area of concrete pavement. A cohesive model is applied to the element interface of the concrete fracture area, and a contact element is used to deal with the interaction between the hammer body and the pavement. Mesoscopic simulations on the impact cracking process of concrete pavement are conducted by considering the element shapes, three-dimensional aggregates, impact locations and impact velocities. Finally, impact testing and core drilling are conducted in the field to evaluate the simulation results. The results demonstrate that the cracking and crushing process of concrete pavement can be quickly simulated via the continuum-discontinuum element method (CDEM), and the simulation results agree with the results of the in-site tests. The concrete pavement model that is meshed by tetrahedral elements and includes aggregates can result in a higher calculation efficiency and superior impact cracking performance. Concrete pavement is severely crushed in the impact contact area, closed quadrangle crack surfaces form along the inside of four hammerheads, and tension crack surfaces mainly propagate along the long axis of the rectangular hammerheads. The impact locations mainly affect the crack distribution of the concrete pavement, and the impact velocities determine the fracture degree. As the impact velocity increases, the impact force linearly changes, whereas the fracture degree varies according to a power function. Overall, the influence of the impact velocities on concrete cracking is greater than the impact locations.

Chemical absorption, distribution, metabolism, excretion, and toxicity (ADMET), play key roles in drug discovery and development. A high-quality drug candidate should not only have sufficient efficacy against the therapeutic target, but also show appropriate ADMET properties at a therapeutic dose. A lot of in silico models are hence developed for prediction of chemical ADMET properties. However, it is still not easy to evaluate the drug-likeness of compounds in terms of so many ADMET properties. In this study, we proposed a scoring function named the ADMET-score to evaluate drug-likeness of a compound. The scoring function was defined on the basis of 18 ADMET properties predicted via our web server admetSAR. The weight of each property in the ADMET-score was determined by three parameters: the accuracy rate of the model, the importance of the endpoint in the process of pharmacokinetics, and the usefulness index. The FDA-approved drugs from DrugBank, the small molecules from ChEMBL and the old drugs withdrawn from the market due to safety concerns were used to evaluate the performance of the ADMET-score. The indices of the arithmetic mean and p-value showed that the ADMET-score among the three data sets differed significantly. Furthermore, we learned that there was no obvious linear correlation between the ADMET-score and QED (quantitative estimate of drug-likeness). These results suggested that the ADMET-score would be a comprehensive index to evaluate chemical drug-likeness, and might be helpful for users to select appropriate drug candidates for further development.