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Bullous pemphigoid (BP) is a complex inflammatory process with elevated levels of autoantibodies, eosinophils, neutrophils, and various cytokines. Hematological inflammatory biomarkers can reflect inflammatory state in various diseases. Up to now, the correlations of hematological inflammatory biomarkers and disease activity of BP remain unknown. The purpose of this study was to clarify the associations between hematological inflammatory biomarkers and disease activity of BP. The levels of neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), platelet-to-neutrophil ratio (PNR) and mean platelet volume (MPV) of 36 untreated BP patients were detected by routine blood tests, and their correlations with clinical characteristics of BP were statistically analyzed. The Bullous Pemphigoid Disease Area Index (BPDAI) was used to measure disease activity of BP. The mean levels of NLR, PLR, PNR and MPV in 36 untreated BP patients were 3.9, 157.9, 45.7 and 9.4 fl, respectively. The levels of NLR were positively correlated to BPDAI Erosion/Blister Scores (p < 0.01). The levels of NLR and PLR were both positively correlated to BPDAI without Damage Score (both p < 0.05) and BPDAI Total Score (both p

Unilateral temporal lobe epilepsy (TLE) is the most common type of focal epilepsy characterized by foci in the unilateral temporal lobe grey matters of regions such as the hippocampus. However, it remains unclear how the functional features of white matter are altered in TLE. In the current study, resting-state functional magnetic resonance imaging (fMRI) was performed on 71 left TLE (LTLE) patients, 79 right TLE (RTLE) patients and 47 healthy controls (HC). Clustering analysis was used to identify fourteen white matter networks (WMN). The functional connectivity (FC) was calculated among WMNs and between WMNs and grey matter. Furthermore, the FC laterality of hemispheric WMNs was assessed. First, both patient groups showed decreased FCs among WMNs. Specifically, cerebellar white matter illustrated decreased FCs with the cerebral superficial WMNs, implying a dysfunctional interaction between the cerebellum and the cerebral cortex in TLE. Second, the FCs between WMNs and the ipsilateral hippocampus (grey matter foci) were also reduced in patient groups, which may suggest insufficient functional integration in unilateral TLE. Interestingly, RTLE showed more severe abnormalities of white matter FCs, including links to the bilateral hippocampi and temporal white matter, than LTLE. Taken together, these findings provide functional evidence of white matter abnormalities, extending the understanding of the pathological mechanism of white matter impairments in unilateral TLE.

Background Being physically active is important for cardiovascular health. This study aimed to examine the trend in adherence to the physical activity guidelines (PAG) for aerobic activity among US adults with a history of cardiovascular disease (CVD) and evaluated its association with cardiovascular risk factors. Methods We studied participants from the national health and nutrition examination survey 2007–08 to 2017–18. Regression models were used to evaluate the significance of the trend and the association between adherence to the PAG with cardiovascular risk factors. Results A total of 3638 participants were reported to have a history of CVD. The proportion of adherence to PAG significantly increased from 41.5% in 2007–08 to 54.3% in 2017–18. Males had a higher proportion of adherence compared to the females, while the trend in adherence was only significant in females. Adherence to the PAG was significantly associated with decreased levels of waist circumference, body mass index, hemoglobin A1c, and triglycerides. Conclusions There is a significant increase in the proportion of adherence to the PAG among US adults with a history of CVD from 2007–08 to 2017–18, and adherence to the PAG was associated with improvement in cardiovascular risk factors.

Rhododendron genus is famous with important ecology impacts, ornamental values, and high medicine values. To obtain a comprehensive overview of anthocyanin regulatory networks, RNA-seq and de novo assembly of five Rhododendron species flower tissues were performed, generating 159,408 unigenes with an average length of 490 bp and an N50 of 552 bp. In particular, 106,766 unigenes could be annotated. Flavonoid biosynthesis was the most abundant KEGG pathway. Genes controlling flower color varied in different species: transcripts involving in carotenoid biosynthesis and isoflavonoid biosynthesis highly expressed in Rhododendron molle G. Don possessing yellow color flower; genes involved in flavone and flavonol biosynthesis showed higher abundance in Rhododendron fortune Lindl. with light pink flowers; transcripts of unigenes participated in flavonoid biosynthesis exhibited higher levels in Rhododendron mariesii Hemsl. with pink color; unigenes involved in anthocyanin biosynthesis showed higher mRNA levels in Rhododendron simsii Planch. with red color; genes involved in carotenoid biosynthesis and flavonoid biosynthesis showed higher expression levels in Rhododendron pulchrum Sweet with purplish red flowers. The five species were clustered into two main groups: group 1 (R. fortune and R. mariesii) and group 2 (R. simsii, R. molle and R. pulchrum) based on expression levels of differentially expressed genes (DEGs). In particular, R. molle showed a closer relationship with R. simsii. This study will provide rich genetic information for further mechanism analysis of flower color variation and genetic improvement of flower color in Rhododendron species.

Background This study was performed to develop and validate machine learning models for early detection of ventilator-associated pneumonia (VAP) 24 h before diagnosis, so that VAP patients can receive early intervention and reduce the occurrence of complications. Patients and methods This study was based on the MIMIC-III dataset, which was a retrospective cohort. The random forest algorithm was applied to construct a base classifier, and the area under the receiver operating characteristic curve (AUC), sensitivity and specificity of the prediction model were evaluated. Furthermore, We also compare the performance of Clinical Pulmonary Infection Score (CPIS)-based model (threshold value ≥ 3) using the same training and test data sets. Results In total, 38,515 ventilation sessions occurred in 61,532 ICU admissions. VAP occurred in 212 of these sessions. We incorporated 42 VAP risk factors at admission and routinely measured the vital characteristics and laboratory results. Five-fold cross-validation was performed to evaluate the model performance, and the model achieved an AUC of 84% in the validation, 74% sensitivity and 71% specificity 24 h after intubation. The AUC of our VAP machine learning model is nearly 25% higher than the CPIS model, and the sensitivity and specificity were also improved by almost 14% and 15%, respectively. Conclusions We developed and internally validated an automated model for VAP prediction using the MIMIC-III cohort. The VAP prediction model achieved high performance based on its AUC, sensitivity and specificity, and its performance was superior to that of the CPIS model. External validation and prospective interventional or outcome studies using this prediction model are envisioned as future work.

Basal ganglia, which include the striatum and thalamus, have key roles in motivation, emotion, motor function, also contribute to higher-order cognitive function. Previous researches have documented structural and functional alterations in basal ganglia in schizophrenia. While few studies have assessed asymmetries of these characters in basal ganglia of schizophrenia. The current study investigated this issue by using diffusion tensor imaging, anatomic T1-weight image and resting-state functional data from 88 chronic schizophrenic subjects and 92 healthy controls. The structural characteristic, including fractional anisotropy, mean diffusivity (MD) and volume, were extracted and quantified from the subregions of basal ganglia, including caudate, putamen, pallidum and thalamus, through automated atlas-based method. The resting-state functional maps of these regions were also calculated through seed-based functional connectivity. Then, the laterality indexes of structural and functional features were calculated. Compared with healthy controls, schizophrenic subjects showed increased left laterality of volume in striatum and reduced left laterality of volume in thalamus. Furthermore, the difference of laterality of subregions in thalamus is compensatory in schizophrenic subjects. Importantly, the severity of patients' positive symptom was negative corelated with reduced left laterality of volume in thalamus. Our findings provide preliminary evidence demonstrating that the possibility of aberrant laterality in neural pathways and connectivity patterns related to the basal ganglia in schizophrenia.

Momordica charantia L., a typical vegetable with multiple medicinal properties, is widely culti-vated around the world. Anti-diabetic, anti-obesity, anti-cancer, anti-microbial, hypotensive, antioxidant, anti-hyperlipidemic, anti-inflammatory, immunomodulatory, anthelmintic, and protective properties have been well clarified both in vitro and in vivo, which might be due to various phytochemicals, such as carbohydrates, protein, fatty acids, amino acids, phenolic acids, minerals, essential oils, alkaloids, vitamins, flavonoids, and triterpenoids. This review summarizes the active components and medicinal properties of different parts of M. charantia, especially the anti-diabetic and anti-cancer properties, aiming to highlight the medicinal usage of M. charantia and provide valuable references for the development of M. charantia as multifunctional food and medicine.

The present study aims to investigate structural and functional connectivity (SC and FC) in cerebello-cerebral circuit in idiopathic generalized epilepsy (IGE). Diffusion tensor imaging and resting-state imaging data were collected from 57 patients with IGE and 66 controls in the present study. First, we performed bidirectional probabilistic fiber tracking between cerebellum and cerebral cortex, consisting of cerebellar efferent and afferent fibers. Then, strength of structural connectivity (SCS), fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) were extracted and compared between groups. Finally, cerebellar FC with cerebral cortex was evaluated with seeding at dentate nucleus. Between-group comparisons were performed using t tests with a significant level setting at p

Flower color is the major characteristics and critical breeding program for most Rhododendron species. However, little is known about their coloration mechanism and color inheritance. In this study, petal pigment constituents of three Rhododendron pulchrum Sweet cultivars with different colors were clarified based on LCESI- MS/MS method. Using a broad-targeted metabolomic approach, a total of 149 flavonoids and their glycosylated or methylated derivatives were identified, including 18 anthocyanins (Pg, Cy, Dp, Pn, Pt, and Mv) and 32 flavonols (mainly kaempferol 3-O-glycosides and quercetin 3-O-glycosides). Moreover, anthocyanins were mainly represented by anthocyanidin-3-O-glycosides (glucoside, rutinoside, galactoside, and di-glycosides). Flavone and C-glycosylated flavone were major second metabolites responsible for the difference among three different R. pulchrum cultivars. The accumulation of total flavonoids displayed a clear phenotypic variation: cultivars 'zihe' and 'fenhe' were clustered together, while 'baihe' was clustered alone in the HCA analysis. The composition and content of anthocyanins were more complex in colored flowers ('zehe' and 'fenhe') than in white flower ('baihe'). This study further enhanced our understanding on the flavonoids profile of flower coloration and will provide biochemical basis for further genetic breeding in Rhododendron species.

The aim of this study was to investigate whether RIPK1 mediated mitochondrial dysfunction and oxidative stress contributed to compression-induced nucleus pulposus (NP) cells necroptosis and apoptosis, together with the interplay relationship between necroptosis and apoptosis in vitro. Rat NP cells underwent various periods of 1.0 MPa compression. To determine whether compression affected mitochondrial function, we evaluated the mitochondrial membrane potential, mitochondrial permeability transition pore (mPTP), mitochondrial ultrastructure and ATP content. Oxidative stress-related indicators reactive oxygen species, superoxide dismutase and malondialdehyde were also assessed. To verify the relevance between oxidative stress and necroptosis together with apoptosis, RIPK1 inhibitor necrostatin-1(Nec-1), mPTP inhibitor cyclosporine A (CsA), antioxidants and small interfering RNA technology were utilized. The results established that compression elicited a time-dependent mitochondrial dysfunction and elevated oxidative stress. Nec-1 and CsA restored mitochondrial function and reduced oxidative stress, which corresponded to decreased necroptosis and apoptosis. CsA down-regulated mitochondrial cyclophilin D expression, but had little effects on RIPK1 expression and pRIPK1 activation. Additionally, we found that Nec-1 largely blocked apoptosis; whereas, the apoptosis inhibitor Z-VAD-FMK increased RIPK1 expression and pRIPK1 activation, and coordinated regulation of necroptosis and apoptosis enabled NP cells survival more efficiently. In contrast to Nec-1, SiRIPK1 exacerbated mitochondrial dysfunction and oxidative stress. In summary, RIPK1-mediated mitochondrial dysfunction and oxidative stress play a crucial role in NP cells necroptosis and apoptosis during compression injury. The synergistic regulation of necroptosis and apoptosis may exert more beneficial effects on NP cells survival, and ultimately delaying or even retarding intervertebral disc degeneration.

This paper presents a novel design code for double anode magnetron injection guns (MIGs) in gyro-devices based on boundary element method (BEM). The physical and mathematical models were constructed, and then the code using BEM for MIG’s calculation was developed. Using the code, a double anode MIG for a Q-band gyrotron traveling-wave tube (gyro-TWT) amplifier operating in the circular TE01 mode at the fundamental cyclotron harmonic was designed. In order to verify the reliability of this code, velocity spread and guiding center radius of the MIG simulated by the BEM code were compared with these from the commonly used EGUN code, showing a reasonable agreement. Then, a Q-band gyro-TWT was fabricated and tested. The testing results show that the device has achieved an average power of 5kW and peak power ≥ 150 kW at a 3% duty cycle within bandwidth of 2 GHz, and maximum output peak power of 220 kW, with a corresponding saturated gain of 50.9 dB and efficiency of 39.8%. This paper demonstrates that the BEM code can be used as an effective approach for analysis of electron optics system in gyro-devices.

Ribosome-inactivating proteins (RIPs), a type of defensive chemicals, play key roles in host defense mechanisms against foreign pathogenic invaders and plant stress resistance. Alpha-momorcharin, a member of typical type I RIPs isolated from Momordica charantia, showed strongly antifungal, antitumor, and antibacterial activities both in vitro and in vivo. In this study, the expression level of alpha-momorcharin gene in response to environmental stress and phytohormones were examined through quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting analyses. Results showed that α-momorcharin gene expression was up-regulated in response to salinity and osmotic stresses. Phytohormones, including abscisic acid and gibberellic acid, also enhanced α-momorcharin expression, while the expression level was not affected by salicylic acid. It’s speculated that the α-momorcharin gene might be functional in plant basal or defense resistance against environmental stresses, and it also involved in hormonal signaling pathways. This research might be exploited in further genetic engineering programs and plant protection.

Te/Bi2Te3 heterostructure nanostrings composed of multiple hexagon Bi2Te3 nanosheets, which were strung together by Te nanowires, were rationally designed and synthesized via an in situ growth process. High-yield heterostructure products were obtained via the two-step solution phase method. The rhombohedral Bi2Te3 nanosheets and hexagonal phase Te nanowires were characterized using x-ray diffractometer patterns and high-resolution transmission electron microscopy images. Detailed scanning electron microscopy and transmission electron microscopy images revealed that the lengths of the nanostrings were approximately 3.6 μm, and the diameters of the Bi2Te3 nanosheets were approximately 290 nm. The results indicated that the dimensions and morphologies of the Te/Bi2Te3 heterostructure nanostrings can be controlled by regulating the dosage of Bi(NO3)3·5H2O from 0 mmol to 3 mmol, and the number of the Bi2Te3 nanosheets can be adjusted by controlling the concentration of N2H4·H2O. Exceedingly low thermal conductivities from 0.45 W m−1 K−1 to 0.49 W m−1 K−1 were demonstrated by the measurements of the thermoelectric properties at near-room temperature from 350 K to 500 K. The phonon scattering mechanisms were systematically analyzed using three-dimensional schematic structure models.

P–type SnS compound and SnS1−xSex solid solutions were prepared by mechanical alloying followed by spark plasma sintering (SPS) and their thermoelectric properties were then studied in different compositions (x = 0.0, 0.2, 0.5, 0.8) along the directions parallel (//) and perpendicular (⊥) to the SPS–pressurizing direction in the temperature range 323–823 Κ. SnS compound and SnS1−xSex solid solutions exhibited anisotropic thermoelectric performance and showed higher power factor and thermal conductivity along the direction ⊥ than the // one. The thermal conductivity decreased with increasing contents of Se and fell to 0.36 W m−1 K−1 at 823 K for the composition SnS0.5Se0.5. With increasing selenium content (x) the formation of solid solutions substantially improved the electrical conductivity due to the increased carrier concentration. Hence, the optimized power factor and reduced thermal conductivity resulted in a maximum ZT value of 0.64 at 823 K for SnS0.2Se0.8 along the parallel direction.

Studies suggested that the activation of the coagulation system and inflammation mutually may reinforce each other in some cases. In this study, we test the plasma tissue factor (TF) and interleukin 18 in patients with coronary heart disease (CHD) before and after treatment and aim to determine whether this relationship exists in patients with CHD. The levels of plasma TF and IL-18 from 118 patients with CHD were determined by enzyme-linked immunosorbent assay. One hundred and eighteen patients were divided into acute coronary syndrome (ACS) group (61 cases) and stable angina pectoris group (51 cases). Upon admission, patients with ACS showed significantly higher levels of plasma TF and interleukin 18 compared with those having stable angina (P0.01), and the levels decreased after treatment; levels of plasma TF and interleukin 18 of patients with ACS were closely and positively correlated (r = 0.74, P0.01); after 7 days of treatment, the correlation decreased (r = 0.53, P0.05); and after 1 month of treatment, no significant correlation can be detected between the two (r = 0.18, P0.05). The results suggest that levels of plasma TF and interleukin 18 are significantly increased in patients with ACS, and there is significant correlation between the two, indicating an interaction between inflammation and coagulation in patients with ACS and an important role of these two in the occurrence and development of such disease.

This article describes experimental investigation of thermal and combustion phenomena during the self-propagating combustion synthesis of Ni-Ti intermetallic materials for structural application. Ni-Ti mixture is prepared from elemental powders of Ni and Ti. The mixture is pressed into solid cylindrical samples of 1.1-cm diameter and 2-3-cm length, with initial porosity ranging from 30 to 42%. The samples are preheated to various initial temperatures and ignited from the top surface such that the flame propagates axially downward. The flame speed images are recorded with a motion camera, and the temperature profile is recorded. The flame front propagation velocity is deduced as a function of the preheat temperature and initial porosity as well as the effective thermal conductivity of the reactants.

A mathematical model is developed to investigate the effect of various processing parameters on pressure-assisted combustion synthesis of NiTi intermetallics. Specifically, preheat and ambient temperature, particle size, initial porosity, and pressure differential are studied to determine their influence on propagation behavior and final porosity. The governing equations are solved using a high-order-implicit numerical scheme capable of accommodating the steep spatial and temporal gradients of properties. The predicted results appear plausible and consistent with the trends presented in the available literature.

The design and experimental study of a Ka-band gyrotron-travelling-wave tube (gyro-TWT) amplifier operating in the circular TE01 mode at fundamental cyclotron harmonic are presented. A mode-selective technique was adopted in the interaction circuit of the gyro-TWT to suppress spurious modes TE11 and TE21. A saturated peak power of 86 kW was obtained with saturated gain of 33 dB , an efficiency of 21.3%, in operating voltage 62 kV, electron current 6.5 A and -3 dB bandwidth of 2 GHz (~6%) .

Natural-language information is often mathematically expressed by fuzzy sets. With the random set theory as a bridge, this kind of information can be transformed into fuzzy evidence in Dempster-Shafer (DS) theory. Then Dempster’s combination rule or other combination rules of evidence can be used perfectly for fusing natural-language and other information. However, this traditional transformation involves the use of α-cut sets to construct the focal elements which have to be represented as consonant sets. This construction is very inflexible and unreasonable in some practical applications. In this paper, with the desire to overcome this limitation, a method for constructing more general non-consonant focal elements is proposed based on the random set theory. Some examples are given to show the generality and the efficiency of this new method. Finally, we validate that non-consonant constructions provide less degrees of total uncertainty than that of the consonant case in these examples by using the evaluation criterion of total uncertainty

Simultaneous faults often occur in running equipments, in order to solve the problems of the simultaneous faults, a new approach based on random sets and Dezert-Smarandache Theory (DSmT) is proposed in this paper. Firstly, the simultaneous faults’ model is built based on the generalized frame of discernment in DSmT. Secondly, according to the unified description of combination rules in evidence reasoning based on random sets, a new combination rule for simultaneous faults diagnosis is proposed. Thirdly, according to the working characteristics and environment of the sensors used to acquire fault characteristic information, a new method to construct basic probability assignment function is proposed based on membership. Finally, diagnosis result is obtained by use of the new combination rule combined with decision rules. A case pertaining to the fault diagnosis for a multi-function rotor test-bed is given, and the result shows that the proposed diagnosis approach is feasible and efficient.

A new descriptor, called vector of topological and structural information for coded and noncoded amino acids (VTSA), was derived by principal component analysis (PCA) from a matrix of 66 topological and structural variables of 134 amino acids. The VTSA vector was then applied into two sets of peptide quantitative structure-activity relationships or quantitative sequence-activity modelings (QSARs/QSAMs). Molded by genetic partial least squares (GPLS), support vector machine (SVM), and immune neural network (INN), good results were obtained. For the datasets of 58 angiotensin converting enzyme inhibitors (ACEI) and 89 elastase substrate catalyzed kinetics (ESCK), the R2, cross-validation R2, and root mean square error of estimation (RMSEE) were as follows: ACEI, Rcu2⩾0.82, Qcu2⩾0.77, Ermse⩽0.44 (GPLS+SVM); ESCK, Rcu2⩾0.84, Qcu2⩾0.82, Ermse⩽0.20 (GPLS+INN), respectively.

A new set of descriptors, namely score vectors of the zero dimension, one dimension, two dimensions and three dimensions (SZOTT), was derived from principle component analysis of a matrix of 1369 structural variables including 0D, 1D, 2D and 3D information for the 20 coded amino acids. SZOTT scales were then used in cleavage site prediction of human immunodeficiency virus type 1 protease. Linear discriminant analysis (LDA) and support vector machines (SVM) were applied to developing models to predict the cleavage sites. The results obtained by linear discriminant analysis (LDA) and support vector machines (SVM) are as follows. The Matthews correlation coefficients (MCC) by the resubstitution test, leave-one-out cross validation (LOOCV) and external validation are 0.879 and 0.911, 0.849 and 0.901, 0.822 and 0.846, respectively. The receiver operating characteristic (ROC) analysis showed that the SVM model possesses better simulative and predictive ability in comparison with the LDA model. Satisfactory results show that SZOTT descriptors can be further used to predict cleavage sites of human immunodeficiency virus type 1 protease.

Both the concept and the model of snug quantitative structure-activity relationship (QSAR) were proposed and developed for molecular design through constructing QSAR based on some known mode of receptor/ligand interactions. Many disadvantages of traditional models can be avoided by using the proposed method because the traditional models only determined upon molecular structural features in sample sets themselves. A genetic virtual screening of peptide/protein combinations (GVSPPC) is proposed for the first time by utilizing this idea to examine peptide/protein affinity activities. A genetic algorithm (GA) was developed for screening combinative targets with an interaction mode for virtual receptors. GVSPPC succeeds in disposing difficulties in rational QSAR, in order to search for the ligand/receptor interactions on conditions of unknown structures. Some bioactive oligo-/poly-peptide systems covering 58 angiotensin converting enzyme (ACE) inhibitors and 18 double site mutation residues in camel antibody protein cAb-Lys3 were investigated by GVSPPC with satisfactory results (Rcu2 > 0.91, Qcv2 > 0.86, ERMS = 0.19−0.95), respectively, which demonstrates that GVSPPC is more interpretable in the ligand-receptor interaction than the traditional QSAR method.

Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples. Some recognition models for HA proteins of avian influenza virus (AIV) were developed using support vector machine (SVM) and linear discriminant analysis (LDA). The results obtained from LDA are as follows: the identification accuracy (R ia ) for training samples is 99.8% and R ia by leave one out cross validation is 99.5%. Both R ia of 99.8% for training samples and R ia of 99.3% by leave one out cross validation are obtained using SVM model, respectively. External 200 HA proteins of influenza virus were used to validate the external predictive power of the resulting model. The external R ia for them is 95.5% by LDA and 96.5% by SVM, respectively, which shows that HA proteins of AIVs are preferably recognized by SVM and LDA, and the performances by SVM are superior to those by LDA.

Interaction between proteins and stationary phase in hydrophobic interaction chromatography (HIC) is differentiated into two thermodynamic processes involving direct nonbonding/conformation interaction and surface hydrophobic effect of proteins, hence quantitatively giving rise to a binary linear relation between HIC retention time (RT) at concentrated salting liquid and ligand-protein binding free energy. Then, possible binding manners for 27 proteins of known crystal structures with hydrophobic ligands are simulated and analyzed via ICM flexible molecular docking and genetic algorithm, with results greatly consistent with experimental values. By investigation, it is confirmed local hydrophobic effects of proteins and nonbinding/conformation interaction between ligand and protein both notably influence HIC chromatogram retention behaviors, mainly focusing on exposed portions on the protein surface.

Canceling grids accommodating probes in comparative molecular field analysis (CoMFA), the idea of flexibleness is introduced into the CoMFA, and in combination with swarm intelligent algorithm which attempts to optimize distributions of diverse probes around drug molecules, a new 3D-QSAR method is proposed in this context as flexible comparative molecular field analysis (FCoMFA). In preliminary attempts to performing QSAR studies on 47 glycogen phosphorylase inhibitors, FCoMFA is employed and confirmed to be potent to exploring ligand-receptor interaction manners at active positions and thus to generating stable and predictable models. Simultaneously by an intuitive graphics regarding probe distribution patterns, impacts of different substituted groups on activities is also given an insight into.

Based on two-dimensional topological characters, a novel method called molecular electronegativity-interaction vector (MEIV) is proposed to parameterize molecular structures. Applying MEIV into quantitative structure-spectrometry relationship studies on ion mobility spectrometry collision cross-sections of 113 singly protonated peptides, three models were strictly obtained, with correlative coefficient r and leave-one-out cross-validation q of 0.983, 0.979, 0.981, 0.979 and 0.980, 0.978, respectively. Thus, the MEIV is confirmed to be potent to structural characterizations and property predictions for organic and biologic molecules.

A molecular electronegativity-distance vector (MEDV) is developed to encode the information about molecular structures of polychlorinated naphthalenes (PCNs) and relate to their relative retention indices (RRIs). A six-parameter equation of 62 PCNs is developed using the multiple linear regression (MLR) and cross validation (CV) of leave-one-out (LOO) technique and the statistical results indicate that the correlation coefficient, standard deviation and the F statistic are 0.9879, 0.9698; 39.03, 40.06 and 371.97, 352.47, respectively. The descriptor variables in the MED vector are analyzed using the stepwise multiple regression (SMR) technique, and only one variable, m 13 descriptor, is selected to establish another reduced QSRR model. Results of both the estimation for the RRIs of 62 PCNs and the prediction for that of other 13 PCNs plus the naphthalene show that the MEDV descriptors not only serve as a good molecular structural descriptor for PCNs, but also have powerful prediction abilities in estimating and predicting their chromatographic retention.

A newly developed descriptor, three-dimensional holographic vector of atomic interaction field (3D-HoVAIF), was used to describe the chemical structures of purine bases. After variable screening by stepwise multiple regression (SMR) technique, a partial least square (PLS) regression model was built with 3D-HoVAIF. The model was satisfactory comparing to reference since correlation coefficients of molecular modeling (R cum 2 ), cross-validation (Q cum 2 ) and standard deviation of estimation (SD) were 0.966, 0.860 and 0.112, respectively, showing that the model had favorable estimation and prediction capabilities. It was illustrated that information related to retention data of purine bases could preferably be expressed by 3D-HoVAIF with definite physico-chemical meanings and easy structural interpretation for purine bases. It was illustrated that 3D-HoVAIF was to preferably express retention data of purine bases and had definite physicochemical significance. So 3D-HoVAIF was a useful structural expression technique for quantitative structure activity (or property or retention) relationships (QSAR/QSPR/QSRR) study, such as structural characterization and chromatographic retention prediction.

A noval molecular structural expression method, three-dimensional vector of atomic interaction field (3D-VAIF), has been newly developed based on electrostatic and steric interaction between different types of atoms. Feature descriptors of single amino acid, i.e. principal component scores of structural information for amino acids (SSIA), are obtained through calculation of structural information of 20 coded amino acids using principal component analysis (PCA) method, and the strict tests are performed on the property of SSIA by three quantitative structure-activity relationships (QSARs)/quantitative sequence-activity models (QSAMs) models of 58 ngiotensin-converting enzymes (ACE), 48 bitter tasting thresholds (BTT) and 31 bradykinin potentiating pentapeptides (BPP). Cumulative multiple correlation coefficients (R cum 2 ) are 0.789, 0.856 and 0.838; and corresponding cross-validated correlation coefficients (Q LOO 2 ) are 0.773, 0.837 and 0.815, respectively. Good results indicate that SSIA are better than traditional descriptors of amino acid in quantitative sequence-activity relationships of peptide analogues.

Support vector machine (SVM), partial least squares (PLS), and Back-Propagation artificial neural network (ANN) were employed to establish QSAR models of 2 dipeptide datasets. In order to validate predictive capabilities on external dataset of the resulting models, both internal and external validations were performed. The division of dataset into both training and test sets was carried out by D-optimal design. The results showed that support vector machine (SVM) behaved well in both calibration and prediction. For the dataset of 48 bitter tasting dipeptides (BTD), the results obtained by support vector regression (SVR) were superior to that by PLS in both calibration and prediction. When compared with BP artificial neural network, SVR showed less calibration power but more predictive capability. For the dataset of angiotensin-converting enzyme (ACE) inhibitors, the results obtained by support vector machine (SVM) regression were equivalent to those by PLS and BP artificial neural network. In both datasets, SVR using linear kernel function behaved well as that using radial basis kernel function. The results showed that there is wide prospect for the application of support vector machine (SVM) into QSAR modeling.