MicroPC (μPC): A comprehensive resource for predicting and comparing plant microRNAs

<p>Abstract</p> <p>Background</p> <p>Plant microRNA (miRNA) has an important role in controlling gene regulation in various biological processes such as cell development, signal transduction, and environmental responses. While information on plant miRNAs and their targets is widely available, accessible online plant miRNA resources are limited; most of them are intended for economically important crops or plant model organisms. With abundant sequence data of numerous plants in public databases such as NCBI and PlantGDB, the identification of their miRNAs and targets would benefit researchers as a central resource for the comparative studies of plant miRNAs.</p> <p>Results</p> <p>MicroPC (μPC) is an online plant miRNA resource resulted from large-scale Expressed Sequence Tag (EST) analysis. It consists of 4,006 potential miRNA candidates in 128 families of 125 plant species and 2,995 proteins (4,953 EST sequences) potentially targeted by 78 families of miRNA candidates. In addition, it is incorporated with 1,727 previously reported plant mature miRNA sequences from miRBase. The μPC enables users to compare stored mature or precursor miRNAs and user-supplied sequences among plant species. The search utility allows users to investigate the predicted miRNAs and miRNA targets in detail via various search options such as miRNA family and plant species. To enhance the database usage, the prediction utility provides interactive steps for determining a miRNA or miRNA targets from an input nucleotide sequence and links the prediction results to their homologs in the μPC.</p> <p>Conclusion</p> <p>The μPC constitutes the first online resource that enables users to comprehensively compare and predict plant miRNAs and their targets. It imparts a basis for further research on revealing miRNA conservation, function, and evolution across plant species and classification. The μPC is available at <url>http://www.biotec.or.th/isl/micropc</url>.</p

Structural component composition of Pacific Northwest grass-derived biomass : A survey

The structural component compositions of thirty nine non-commercial Pacific Northwest grasses were analyzed in order to develop a database of the grasses that may have application in bioconversion processes. The samples were chosen based on near infrared reflectance (NIR) data that suggested this group of grasses, collectively, was representative of the broad range of compositions that are likely to be encountered due to genotypic and phenotypic variability. Solvent-extracted samples were prepared by extracting the native grass sequentially with water and then 95% ethanol. Each of the grasses residues was analyzed for glycans (glucans, xylan, and arabinan), acidinsoluble lignin, acid soluble lignin, and ash. Total glycans ranged from a low of 32% to a high of 50%. Glucan was the major glycan component, typically in the range of 60%. Xylan represented about onethird of the total glycans which is 9.5% to 18.3% while arabinan represented 1.0% to 3.3%. Total glycans seemed to increase from the younger stage to the more mature stage. The mean total glycans of the vegetative stage was 32%, while that of the seed mature stage was 45.9%. While the amount of glycans varied between species, the ratio of glucan, xylan, and arabinan (12: 7: 1) remained relatively constant. Acid-insoluble lignin ranged from 6.38% to 14.58%, while the acid-soluble lignin ranged from 1.57% to 4.35%. The acid-insoluble lignin of seed mature, flower, boot, and vegetative stages were 12.38%, 11.65%, 9.39%, and 8.10%, respectively. The acid-soluble lignin of seed mature, flower, boot, and vegetative stages were 2.03%, 2.6 1%, 3.02%, and 3.46%, respectively. Extractives represented 20,29% to 41.55% of the oven-dry grasses. The ash values of the unextracted grasses ranged from 5% to 10% while the ash values for the extracted samples were typically in the range of 0.5% to 2.5%, showing that the sequential water and ethanol extractions tended to remove 60 80% of the quantifiable inorganic matter from the grasses

การศึกษาปัจจัยที่มีผลต่อยอดจำหน่ายน้ำมันเชื้อเพลิงของสถานีบริการน้ำมันกรณีศึกษาจังหวัดนครราชสีมา

The objective of this research to develop models for fuel sale of a gas station and proportion of fuel sale to various vehicle types in order to estimate vehicle kilometers of travel. This study randomly selected 31 gas stations in Nakhon Ratchasima province. The fuel sale models were developed using multiple linear regression to find a relationship between fuel sale of a gas station with physical conditions, the location vehicles passing station. The final models have coefficient of determinant (R2) of 0.512 and 0.280 for gasoline and diesel groups, respectively. Factors influencing gasoline sale of a gas station include the number of gasoline nozzles, the distance from the gas station to CBD and road density. For diesel group, factors include the distance from the gas station to CBD and the percentage of heavy vehicle passing gas stations. For the proportion of fuel sale to various vehicle types, multinomial logit models were developed. The final models have likelihood ratio index (ρ2) of 0.290 and 0.405 for gasoline and diesel, respectively. The benefit of the study is to estimate vehicle kilometer of travel (VKT) for various vehicle types in Nakhon Ratchasima which will be useful for transport-related energy planning in the future

Influence of occupational exposure to pigs or chickens on human gut microbiota composition in Thailand.

Pig farming's influence on human gut microbiota has been observed previously, but its pervasiveness is unclear. We therefore aimed at studying whether pig farming influenced human gut microbiota composition in Thailand and whether poultry farming did too. We collected human stool samples (71 pig farmers, 131 chicken farmers, 55 non-farmers) for 16S rRNA sequencing and performed subsequent DADA2 analyses of amplicon sequence variants. We found that Alpha diversity values were highest among chicken farmers. Relative abundances of Prevotellaceae were significantly higher among pig farmers than among chicken farmers and non-farmers (p < 0.001). Beta diversity plots revealed different clustering according to occupation. The presence or absence of antimicrobial-resistant Escherichia coli was not associated with changes in gut microbiota composition. In conclusion, occupation was the strongest factor influencing gut microbiota composition in Thailand. We hypothesize that Prevotellaceae amplicon sequence variants are transmitted from pigs to pig farmers

Red Blood Cell Segmentation with Overlapping Cell Separation and Classification on Imbalanced Dataset

Automated red blood cell (RBC) classification on blood smear images helps hematologists to analyze RBC lab results in a reduced time and cost. However, overlapping cells can cause incorrect predicted results, and so they have to be separated into multiple single RBCs before classifying. To classify multiple classes with deep learning, imbalance problems are common in medical imaging because normal samples are always higher than rare disease samples. This paper presents a new method to segment and classify RBCs from blood smear images, specifically to tackle cell overlapping and data imbalance problems. Focusing on overlapping cell separation, our segmentation process first estimates ellipses to represent RBCs. The method detects the concave points and then finds the ellipses using directed ellipse fitting. The accuracy from 20 blood smear images was 0.889. Classification requires balanced training datasets. However, some RBC types are rare. The imbalance ratio of this dataset was 34.538 for 12 RBC classes from 20,875 individual RBC samples. The use of machine learning for RBC classification with an imbalanced dataset is hence more challenging than many other applications. We analyzed techniques to deal with this problem. The best accuracy and F1-score were 0.921 and 0.8679, respectively, using EfficientNet-B1 with augmentation. Experimental results showed that the weight balancing technique with augmentation had the potential to deal with imbalance problems by improving the F1-score on minority classes, while data augmentation significantly improved the overall classification performance.Comment: This work has been submitted to the Heliyon for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Adsorption and Photocatalytic Processes of Mesoporous SiO2-Coated Monoclinic BiVO4

The silicon dioxide (SiO2)–coated bismuth vanadate (BiVO4) composites as visible–driven–photocatalysts were successfully synthesized by the co–precipitation method. The effects of SiO2 coating on the structure, optical property, morphology and surface properties of the composites were investigated by X–ray diffraction (XRD), UV–visible diffuse reflectance spectroscopy (DRS), transmission electron microscopy (TEM) and Brunauer–Emmette–Teller (BET) measurements. The photocatalytic activity of monoclinic BiVO4 and BiVO4/SiO2 composites were evaluated according to the degradation of methylene blue (MB) dye aqueous solution under visible light irradiation. The SiO2−coated BiVO4 composites showed the enhancing photocatalytic activity approximately threefold in comparison with the single phase BiVO4

การศึกษาปัจจัยที่มีผลต่อความตั้งใจในการสวมหมวกนิรภัยโดยทฤษฎีแบบแผนความเชื่อด้านสุขภาพ

This research aimed to study the factors affecting the intention in helmet use for motorcycles in the context of Thailand. The factors to be considered were applied from Health Belief Model by questioning 801 nationwide motorcycle riders divided into 401 of urban society and 400 of rural society. For data analysis, Stepwise Multiple Regression analysis was used. Regarding urban society, it was found that the four factors affecting the intention in helmet use for motorcycles were motivation (β = 0.411), Cue to Action (β = 0.173), Perceived Severity (β = 0.177), and Perceived Barriers (β = 0.053) at statistical significance .000, .001, .004 and .047 respectively. These four factors predicted that the intention in helmet use would be 26.40 percent. Regarding rural society, the four factors including Perceived Benefits (β = 0.249), Perceived Severity (β = 0.244), Cue to Action (β = 0.237) were at statistical significance .000 while motivation (β = 0.126) was at statistical .040. These factors predicted that the intention in helmet use would be 40.40 percent. The results from this study acknowledged the factors affecting the intention in helmet use between urban society and rural society. Thus, the organizations in government sectors potentially take the data to develop the suitable policies for each area