A Network-enhanced Prediction Method for Automobile Purchase Classification using Deep Learning

Automobile purchase intentions of customers relate to car dealers’ costs and affect the car dealers’ marketing strategy and manufacturing process in the long term. Automobile purchase intention classification has become critically important for car dealers. In our paper, we innovatively constructed a hobby based network and a working based network of customers, and used customers’ profile of same group as inputs to the deep learning model to predict customers’ purchase intention based on community detection by social network analysis. Based on the real-world dataset, our experimental results verify that the framework with both hobby-based network and working-based network using deep learning method has best performance, which is 14% better than the baseline model. And the hobby-based network outperforms working-based network. Because of the advantage of consumer’s personality, hobbies can be used for better predicting the purchase intention. Therefore, our proposed framework is a potential tool for automobile purchase intention classification

Next-to-leading order QCD corrections to the form factors of BB to scalar meson decays

We calculate the next-to-leading order QCD corrections to $B$ to scalar meson form factors from QCD light-cone sum rules with $B$ meson light-cone distribution amplitudes. We demonstrate that the $B$ meson-to-vacuum correlation functions can be factorized into the convolution of short-distance coefficients and light-cone distribution amplitudes at the one-loop level and find that only $\phi_B^+(\omega,\mu)$ contributes to the form factors. We then employ the $z$-parameterization combined with constraints from strong coupling constants to reconstruct the $q^2$ dependence of the form factors in the whole kinematic allowed regions. Due to the large cancellations between the hard functions and the jet functions, the next-to-leading order results show a modest increase of approximately 5\% compared to the leading order results. Based on the results of form factors, we predict the branching ratios of semi-leptonic $B\to S\ell\bar{\nu}_\ell$ and $B\to S\nu_\ell\bar{\nu}_\ell$ processes, as well as several angular observables, such as forward-backward asymmetries, "flat terms" and lepton polarization asymmetries. We compare these results with calculations from other methods. Experimental verification of these results is required in future experiments.Comment: 46 pages, 13 figure

A δ2H offset correction method for quantifying root water uptake of riparian trees

Root water uptake plays an important role in water cycle in Groundwater-Soil-Plant-Atmosphere-Continuum. Stable isotopes (δ2H and δ18O) are effective tools to quantify the use of different water sources by plant roots. However, the widespread δ2H offsets of stem water from its potential sources due to δ2H fractionation during root water uptake result in conflicting interpretations of water utilization using stable isotopes. In this study, a potential water source line (PWL), i.e., a linear regression line between δ18O and δ2H data of both soil water at different depths and groundwater, was proposed to correct δ2H offsets of stem water. The PWL-corrected δ2H was determined by subtracting the deviation between δ2H in stem water and PWL from the original value. The MixSIAR model coupled with seven types of input data (i.e. various combinations of single or dual isotopes with uncorrected or corrected δ2H data by PWL or soil water line (SWL)) were used to determine seasonal variations in water uptake patterns of riparian tree of Salix babylonica (L.) along the Jian and Chaobai River in Beijing, China. These methods were evaluated via three criteria including Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and root mean square error (RMSE). Results showed that different types of input data led to considerable differences in the contributions of soil water at upper 30 cm (9.9–57.6%) and below 80 cm depths (29.0–76.4%). Seasonal water uptake patterns were significantly different especially when δ2H offset was pronounced (p < 0.05). The dual-isotopes method with uncorrected δ2H underestimated the contributions of soil water in the 0–30 cm layer (by 30.4%) and groundwater (by 56.3%) compared to that with PWL-corrected δ2H. The PWL correction method obtained a higher groundwater contribution (mean of 29.5%) than that estimated by the SWL correction method (mean of 24.5%). The MixSIAR model using dual-isotopes with PWL-corrected δ2H produced the smallest AIC (94.1), BIC (91.9) and RMSE values (4.8%) than other methods (94.9–101.7, 92.6–99.5 and 5.3–12.4%, respectively), which underlined the best performance of PWL correction method. The present study provides crucial insights into quantifying accurate root water uptake sources even if δ2H offset exists

Saikosaponin A Alleviates Symptoms of Attention Deficit Hyperactivity Disorder through Downregulation of DAT and Enhancing BDNF Expression in Spontaneous Hypertensive Rats

The disturbed dopamine availability and brain-derived neurotrophic factor (BDNF) expression are due in part to be associated with attention deficit hyperactivity disorder (ADHD). In this study, we investigated the therapeutical effect of saikosaponin a (SSa) isolated from Bupleurum Chinese DC, against spontaneously hypertensive rat (SHR) model of ADHD. Methylphenidate and SSa were orally administered for 3 weeks. Activity was assessed by open-field test and Morris water maze test. Dopamine (DA) and BDNF were determined in specific brain regions. The mRNA or protein expression of tyrosine hydroxylase (TH), dopamine transporter (DAT), and vesicles monoamine transporter (VMAT) was also studied. Both MPH and SSa reduced hyperactivity and improved the spatial learning memory deficit in SHRs. An increased DA concentration in the prefrontal cortex (PFC) and striatum was also observed after treating with the SSa. The increased DA concentration may partially be attributed to the decreased mRNA and protein expression of DAT in PFC while SSa exhibited no significant effects on the mRNA expression of TH and VMAT in PFC of SHRs. In addition, BDNF expression in SHRs was also increased after treating with SSa or MPH. The obtained result suggested that SSa may be a potential drug for treating ADHD

Computational Prediction of Protein-Protein Interactions of Human Tyrosinase

The various studies on tyrosinase have recently gained the attention of researchers due to their potential application values and the biological functions. In this study, we predicted the 3D structure of human tyrosinase and simulated the protein-protein interactions between tyrosinase and three binding partners, four and half LIM domains 2 (FHL2), cytochrome b-245 alpha polypeptide (CYBA), and RNA-binding motif protein 9 (RBM9). Our interaction simulations showed significant binding energy scores of −595.3 kcal/mol for FHL2, −859.1 kcal/mol for CYBA, and −821.3 kcal/mol for RBM9. We also investigated the residues of each protein facing toward the predicted site of interaction with tyrosinase. Our computational predictions will be useful for elucidating the protein-protein interactions of tyrosinase and studying its binding mechanisms

Soil Abiotic Properties and Plant Functional Traits Mediate Associations Between Soil Microbial and Plant Communities During a Secondary Forest Succession on the Loess Plateau

In the context of secondary forest succession, aboveground-belowground interactions are known to affect the dynamics and functional structure of plant communities. However, the links between soil microbial communities, soil abiotic properties, plant functional traits in the case of semi-arid and arid ecosystems, are unclear. In this study, we investigated the changes in soil microbial species diversity and community composition, and the corresponding effects of soil abiotic properties and plant functional traits, during a ≥150-year secondary forest succession on the Loess Plateau, which represents a typical semi-arid ecosystem in China. Plant community fragments were assigned to six successional stages: 1–4, 4–8, 8–15, 15–50, 50–100, and 100–150 years after abandonment. Bacterial and fungal communities were analyzed by high-throughput sequencing of the V4 hypervariable region of the 16S rRNA gene and the internal transcribed spacer (ITS2) region of the rRNA operon, respectively. A multivariate variation-partitioning approach was used to estimate the contributions of soil properties and plant traits to the observed microbial community composition. We found considerable differences in bacterial and fungal community compositions between the early (S1–S3) and later (S4–S6) successional stages. In total, 18 and 12 unique families were, respectively, obtained for bacteria and fungi, as indicators of microbial community succession across the six stages. Bacterial alpha diversity was positively correlated with plant species alpha diversity, while fungal diversity was negatively correlated with plant species diversity. Certain fungal and bacterial taxa appeared to be associated with the occurrence of dominant plant species at different successional stages. Soil properties (pH, total N, total C, NH4-N, NO3-N, and PO4-P concentrations) and plant traits explained 63.80% and 56.68% of total variance in bacterial and fungal community compositions, respectively. These results indicate that soil microbial communities are coupled with plant communities via the mediation of microbial species diversity and community composition over a long-term secondary forest succession in the semi-arid ecosystem. The bacterial and fungal communities show distinct patterns in response to plant community succession, according to both soil abiotic properties and plant functional traits