Variational inference of the drift function for stochastic differential equations driven by Lévy processes.

In this work, we consider the nonparametric estimation problem of the drift function of stochastic differential equations driven by the α-stable Lévy process. We first optimize the Kullback-Leibler divergence between the path probabilities of two stochastic differential equations with different drift functions. We then construct the variational formula based on the stationary Fokker-Planck equation using the Lagrangian multiplier. Moreover, we apply the empirical distribution to replace the stationary density, combining it with the data information, and we present the estimator of the drift function from the perspective of the process. In the numerical experiment, we investigate the effect of the different amounts of data and different α values. The experimental results demonstrate that the estimation result of the drift function is related to both and that the exact drift function agrees well with the estimated result. The estimation result will be better when the amount of data increases, and the estimation result is also better when the α value increases.

The transcriptomes from two adipocyte progenitor cell types provide insight into the differential functions of MSTN.

Myostatin (MSTN) was previously shown to differentially regulate the adipogenesis of adipose-derived stem cells (ADSCs) and muscle satellite cells (MSCs), both of which can serve as progenitor cells for intramuscular adipocytes. We previously showed that MSTN mediates the differential regulation of MyoD and PPARγ in ADSCs and MSCs. Here, we analyzed the effects of MSTN on whole-transcriptome expression profiles of ADSCs and MSCs, revealing that MSTN differentially regulates ADSCs and MSCs, with MSCs being more responsive to MSTN treatment. More genes and pathways were altered in MSCs than in ADSCs. These changes may be responsible for the differences in the adipogenesis potential of ADSCs and MSCs after MSTN treatment. Analysis of the functions of genes that are differentially expressed in ADSCs and MSCs showed that KLF6 is a positive regulator of adipogenesis. In conclusion, the results provide important molecular insights into the regulatory mechanisms of MSTN in ADSCs and MSCs.

A classification model for lncRNA and mRNA based on k-mers and a convolutional neural network.

BACKGROUND: Long-chain non-coding RNA (lncRNA) is closely related to many biological activities. Since its sequence structure is similar to that of messenger RNA (mRNA), it is difficult to distinguish between the two based only on sequence biometrics. Therefore, it is particularly important to construct a model that can effectively identify lncRNA and mRNA. RESULTS: First, the difference in the k-mer frequency distribution between lncRNA and mRNA sequences is considered in this paper, and they are transformed into the k-mer frequency matrix. Moreover, k-mers with more species are screened by relative entropy. The classification model of the lncRNA and mRNA sequences is then proposed by inputting the k-mer frequency matrix and training the convolutional neural network. Finally, the optimal k-mer combination of the classification model is determined and compared with other machine learning methods in humans, mice and chickens. The results indicate that the proposed model has the highest classification accuracy. Furthermore, the recognition ability of this model is verified to a single sequence. CONCLUSION: We established a classification model for lncRNA and mRNA based on k-mers and the convolutional neural network. The classification accuracy of the model with 1-mers, 2-mers and 3-mers was the highest, with an accuracy of 0.9872 in humans, 0.8797 in mice and 0.9963 in chickens, which is better than those of the random forest, logistic regression, decision tree and support vector machine.

MyoD promotes porcine PPARγ gene expression through an E-box and a MyoD-binding site in the PPARγ promoter region.

Peroxisome proliferator-activated receptor γ (PPARγ) is a key transcription factor in adipogenesis and can be regulated by adipogenesis-related factors. However, little information is available regarding its regulation by myogenic factors. In this study, we found that over-expression of MyoD enhanced porcine adipocyte differentiation and up-regulated PPARγ expression, whereas small interfering RNA against MyoD significantly attenuated porcine adipocyte differentiation and inhibited PPARγ expression. The MyoD-binding sites in the PPARγ promoter region at -412 to -396 and -155 to -150 were identified by promoter deletion analysis and site-directed mutagenesis. Electrophoretic mobility shift assays and chromatin immunoprecipitation further showed that these two regions are MyoD-binding sites, both in vitro and in vivo, indicating that MyoD directly interacts with the porcine PPARγ promoter. Thus, our results demonstrate that an Enhancer box and a binding site for a cooperative co-activator of MyoD are present in the promoter region of porcine PPARγ; furthermore, MyoD up-regulates PPARγ expression and promotes porcine adipocyte differentiation.

PPARγ and MyoD are differentially regulated by myostatin in adipose-derived stem cells and muscle satellite cells.

Myostatin (MSTN) is a secreted protein belonging to the transforming growth factor-ß (TGF-ß) family that is primarily expressed in skeletal muscle and also functions in adipocyte maturation. Studies have shown that MSTN can inhibit adipogenesis in muscle satellite cells (MSCs) but not in adipose-derived stem cells (ADSCs). However, the mechanism by which MSTN differently regulates adipogenesis in these two cell types remains unknown. Peroxisome proliferator-activated receptor-γ (PPARγ) and myogenic differentiation factor (MyoD) are two key transcription factors in fat and muscle cell development that influence adipogenesis. To investigate whether MSTN differentially regulates PPARγ and MyoD, we analyzed PPARγ and MyoD expression by assessing mRNA, protein and methylation levels in ADSCs and MSCs after treatment with 100 ng/mL MSTN for 0, 24, and 48 h. PPARγ mRNA levels were downregulated after 24 h and upregulated after 48 h of treatment in ADSCs, whereas in MSCs, PPARγ levels were downregulated at both time points. MyoD expression was significantly increased in ADSCs and decreased in MSCs. PPARγ and MyoD protein levels were upregulated in ADSCs and downregulated in MSCs. The CpG methylation levels of the PPARγ and MyoD promoters were decreased in ADSCs and increased in MSCs. Therefore, this study demonstrated that the different regulatory adipogenic roles of MSTN in ADSCs and MSCs act by differentially regulating PPARγ and MyoD expression.

Fractional grey unequal-interval time-varying Lotka-Volterra model and its application for microbial communities in compost.

Aerobic compost is an effective method for the treatment of livestock manure, which is usually accompanied by complex interspecific competition. Describing these competitive relationships through mathematical models can help understand the interaction of microorganisms and analyze the effect of exogenous additive to regulate the composting process. The common model for analyzing competition problem is the Lotka-Volterra model. However, the fixed parameters of the Lotka-Volterra model are not suitable to reflect the dynamic variations of the competitive relationship when the environmental conditions change during composting process. Therefore, this paper establishes a novel fractional grey unequal-interval time-varying Lotka-Volterra model. Firstly, a fractional grey derivate operator is proposed on the basis of the unequal interval of composting data and historical dependence of microbial growth. Secondly, considering the influence of temperature, a time-varying parameter matrix is defined to reflect the variation of competitive relationship at different composting phases, and it is estimated by forgetting factor recursive least squares. Thirdly, the optimal coefficients are optimized by grey prediction evolution algorithm. Finally, the proposed model is employed to analyze the chicken manure composting experiment. The results show that the proposed model has lower error criteria and more accurate trend of fitting curve than the other five existing models. The parameter matrix describes the dynamical variation of microbial competitive relationship in two taxonomic levels and reveals that effect of the exogenous additive is principally reacted in the thermophilic phase and the competitive advantage is shifted from Bacteroidota to Firmicutes after treatment with the exogenous additive.

Functional analysis of pig myostatin gene promoter with some adipogenesis- and myogenesis-related factors.

Myostatin (MSTN) is primarily expressed in muscle and plays an important role in muscle and fat development in pigs. However, there is little information about the regulation of pig MSTN. In order to elucidate whether pig MSTN could be regulated by muscle- and fat-related factors, the porcine MSTN promoter was amplified and cloned into pGL3-basic vector, and transfected into cells to analyze the transcriptional activity of promoter with muscle- and fat-related factors through dual-luciferase reporter assays. 5'-deletion expression showed that there was a negative-regulatory region located between nucleotides -1519 and -1236 bp, and there were some positive-regulatory regions located between -1236 and -568 bp. The longest fragment (1.7 kb) was cotransfected with muscle-related transcription factor myogenic differentiation 1 (MyoD), resulting in promoter transcriptional activity upregulation. The fragment was treated by the adipogenic agents (DIM) including dexamethasone, insulin, and isobutyl-1-methylxanthine (IBMX). We found that MSTN promoter transcriptional activity can be regulated by IBMX, but not by DIM. CCAAT/enhancer binding protein (C/EBP) α and C/EBPß, two proteins which are induced by DIM during adipogenesis were cotransfected with the 1.7-kb fragment, respectively, resulting in promoter transcriptional activity downregulation. Treating the fragment with rosiglitazone which induce the expression of peroxisome proliferator-activated receptor γ (PPARγ), resulting in promoter transcriptional activity upregulation. Cotransfection experiments confirmed this result. Taken together, we showed that porcine MSTN could be upregulated by IBMX, MyoD, and PPARγ but downregulated by C/EBPα and C/EBPß.

Different regulation role of myostatin in differentiating pig ADSCs and MSCs into adipocytes.

Myostation (MSTN), which is primarily expressed in muscle, plays an important role in myogenic and adipogenic cells. However, there is little information about whether MSTN displays different roles between adipose-derived stem cells (ADSCs) and muscle satellite cells (MSCs). The two kinds of cells can both exist in the muscle and differentiate into adiposities. In this research, we isolated ADSCs and MSCs from porcine fat tissues and semitendinosus muscle, respectively, to investigate the effect of MSTN on the adipogenesis of those cells. ADSCs and MSCs were treated with recombinant human MSTN during the induction of adipogenesis or before the induction of differentiation. Then, we evaluated adipogenesis by Oil Red O staining and assessed the expression patterns of adipocyte-specific fatty acid binding protein (aP2) and peroxisome proliferator-activated receptor (PPAR) γ using real-time polymerase chain reaction methods. Our results indicated that the treatment with MSTN before or during the induction of differentiation in MSCs could both inhibit the adipogenesis. However, the treatment with MSTN only during the induction of differentiation in ADSCs could suppress the adipogenesis. Those results showed that MSTN had different roles in the adipogenesis of ADSCs and MSCs. It can shed new light on the origin of adipocyte located in muscle.

[Temporal and spatial variation of ecosystem service value and its response to landscape pattern change in Guizhou Province, China.] / è´µå·žçœç”Ÿæ€ç³»ç»ŸæœåŠ¡ä»·å€¼æ—¶ç©ºæ¼”å˜åŠå ¶å¯¹æ™¯è§‚æ ¼å±€å˜åŒ–çš„å“åº”.

Guizhou Province is a test area of ecological civilization in China, and one of the most typical areas of karst landform development in the world. Exploring the change of ecosystem service value and its spatial correlation distribution characteristics with landscape pattern is conducive to formulating ecological environment improvement and protection measures according to local conditions. We analyzed the spatial and temporal variations of landscape pattern and ecosystem services in Guizhou Province from 2005 to 2018 and constructed a geographically weighted regression model to explore the spatial changes of their correlation coefficients. The results showed that the transfer of landscape types in Guizhou Province from 2005 to 2018 mainly occurred in the five landscapes of construction land, forest, water, grassland, and cropland. The landscape fragmentation and diversity increased. The value of ecosystem services was increasing year by year. Forests were the main contributors to the value of ecosystem services in Guizhou Province, with regulation services as the main ecosystem services. For high-value landscape types, the increase of diversity and the reduction of fragmentation were conducive to the increases of ecosystem service value. In the area with concentrated low-value landscape types, it showed the opposite effect. The direction of landscape transfer and the spatial distribution of diversity and fragmentation of different landscape types should be adjusted according to the characteristics of ecological environment in different regions of Guizhou Province, to realize the increases of ecosystem service value.

An improved method used for evaluating potential environmental pollution risk based on spatial distribution and density of farms.

The livestock manure nitrogen load on farmland (LMNLF) is often used to assess the potential environmental pollution risk of livestock manure nitrogen nutrient (LMN) in a target region. First, the LMNLF of Wuhan city is calculated, and the potential environmental pollution risk of LMN is mainly concentrated in Jiangxia District, Xinzhou District, and Huangpi District, but does not exceed the European Union (EU) standards. Heat map results also certificate this conclusion. Therefore, these three districts are the research emphases. Second, considering spatial distribution of farms, an improved LMNLF method is proposed combining the previous LMNLF and density-based spatial clustering of applications with noise (DBSCAN) methods. Several regions with pollution risks including seven regions in Jiangxia District, one in Xinzhou District, and one in Huangpi District are found out using the improved LMNLF method. Third, to evaluate the carrying capacity of the intensive breeding areas more reasonably, 2 km is taken as the farthest transportation distance of manure; there is still one region in Huangpi District which has serious pollution risk on the environment. These above results can help evaluate the pollution degree of livestock manure to the surrounding environment more precisely.

Optimized Mahalanobis-Taguchi System for High-Dimensional Small Sample Data Classification.

The Mahalanobis-Taguchi system (MTS) is a multivariate data diagnosis and prediction technology, which is widely used to optimize large sample data or unbalanced data, but it is rarely used for high-dimensional small sample data. In this paper, the optimized MTS for the classification of high-dimensional small sample data is discussed from two aspects, namely, the inverse matrix instability of the covariance matrix and the instability of feature selection. Firstly, based on regularization and smoothing techniques, this paper proposes a modified Mahalanobis metric to calculate the Mahalanobis distance, which is aimed at reducing the influence of the inverse matrix instability under small sample conditions. Secondly, the minimum redundancy-maximum relevance (mRMR) algorithm is introduced into the MTS for the instability problem of feature selection. By using the mRMR algorithm and signal-to-noise ratio (SNR), a two-stage feature selection method is proposed: the mRMR algorithm is first used to remove noise and redundant variables; the orthogonal table and SNR are then used to screen the combination of variables that make great contribution to classification. Then, the feasibility and simplicity of the optimized MTS are shown in five datasets from the UCI database. The Mahalanobis distance based on regularization and smoothing techniques (RS-MD) is more robust than the traditional Mahalanobis distance. The two-stage feature selection method improves the effectiveness of feature selection for MTS. Finally, the optimized MTS is applied to email classification of the Spambase dataset. The results show that the optimized MTS outperforms the classical MTS and the other 3 machine learning algorithms.

Rear-end collision warning of connected automated vehicles based on a novel stochastic local multivehicle optimal velocity model.

Studying the rear-end early warning methods of connected automated vehicles (CAVs) is useful for issuing early warnings and reducing traffic accidents. Establishing a corresponding driving model according to CAV characteristics is necessary when designing intelligent decision and control systems, especially for the safety speed threshold. However, since traffic systems are stochastic, there are random factors that influence car-following behavior. Therefore, this study proposes a rear-end collision warning method for CAVs based on a stochastic local multivehicle optimal speed (SLMOV) car-following model. First, the SLMOV model is proposed to characterize the car-following behavior of CAVs. Simultaneously, a stability analysis and parameter estimation method are discussed. Second, the safety distance between the CAVs changes with time because the speed of the rear vehicles satisfies the SLMOV model, which is used to calculate the safety probability of rear-end CAV collisions through an analysis of the driving process. The speed threshold is assessed by controlling the rear-end collision probability. Third, next-generation simulation (NGSIM) data are used in an empirical analysis of a rear-end collision warning method on the basis of a parameter estimation of the SLMOV model. The results present the speed thresholds of vehicles under different braking deceleration levels. Finally, the merits and demerits of fixed-speed and variable-speed adjustment time intervals are compared by considering driving safety and comfort as evaluation indexes. A reasonable CAV adjustment time interval of 0.4 s is determined. This result can be used to help develop a vehicle loading rear-end collision warning system.

An Approach of Integrating Domain Knowledge into Data-Driven Diagnostic Model.

A diagnostic model of general diseases could help general practitioners to decrease misdiagnoses and reduce workload. In this paper, we developed a neural network model that can classify potential diagnoses among 100 selected common diseases based on ambulatory health care data. We propose a novel approach to integrate domain knowledge into neural network training. The evaluation results show our model outperforming the baseline model in terms of knowledge consistency and model generalization.

The function of myostatin in the regulation of fat mass in mammals.

Myostatin (MSTN), also referred to as growth and differentiation factor-8, is a protein secreted in muscle tissues. Researchers believe that its primary function is in negatively regulating muscle because a mutation in its coding region can lead to the famous double muscle trait in cattle. Muscle and adipose tissue develop from the same mesenchymal stem cells, and researchers have found that MSTN is expressed in fat tissues and plays a key role in adipogenesis. Interestingly, MSTN can exert a dual function, either inhibiting or promoting adipogenesis, according to the situation. Due to its potential function in controlling body fat mass, MSTN has attracted the interest of researchers. In this review, we explore its function in regulating adipogenesis in mammals, including preadipocytes, multipotent stem cells and fat mass.