Methods for Increasing Renewable Oil Productions

Methods of increasing renewable oil production are provided and include transforming a plant cell with an isolated nucleic acid encoding a Vernonia galamensis diacylglycerol acyltransferase (V gDGAT) polypeptide, where the expression of the V gDGAT polypeptide increases an amount of renewable oil in the plant. Transgenic plant cells comprising an isolated nucleic acid encoding a Vernonia galamensis diacylglycerol acyl transferase 1 (V gDGATl) polypeptide are further provided

Diacylglycerol Acyltransferase Sequences and Related Methods

Isolated nucleic acid and amino acid sequences encoding a diacylglycerol acyltransferase 2 (DGAT2) polypeptide are provided. Vectors and transgenic cells that include a nucleic acid sequence encoding a DGAT2 polypeptide are also described. Further provided are methods of producing an epoxy fatty acid by transforming a cell with a first isolated nucleic acid that encodes a diacylglycerol acyltransferase polypeptide and a second isolated nucleic acid that encodes an epoxygenase polypeptide, such that expression of the diacylglycerol acyltransferase polypeptide and the epoxygenase polypeptide increases an amount of epoxy fatty acid in the cell

An Ensemble Multilabel Classification for Disease Risk Prediction

It is important to identify and prevent disease risk as early as possible through regular physical examinations. We formulate the disease risk prediction into a multilabel classification problem. A novel Ensemble Label Power-set Pruned datasets Joint Decomposition (ELPPJD) method is proposed in this work. First, we transform the multilabel classification into a multiclass classification. Then, we propose the pruned datasets and joint decomposition methods to deal with the imbalance learning problem. Two strategies size balanced (SB) and label similarity (LS) are designed to decompose the training dataset. In the experiments, the dataset is from the real physical examination records. We contrast the performance of the ELPPJD method with two different decomposition strategies. Moreover, the comparison between ELPPJD and the classic multilabel classification methods RAkEL and HOMER is carried out. The experimental results show that the ELPPJD method with label similarity strategy has outstanding performance

Evaluation of anti-smoking television advertising on tobacco control among urban community population in Chongqing, China

Background China is the largest producer and consumer of tobacco in the world. Considering the constantly growing urban proportion, persuasive tobacco control measures are important in urban communities. Television, as one of the most pervasive mass media, can be used for this purpose. Methods The anti-smoking advertisement was carried out in five different time slots per day from 15 May to 15 June in 2011 across 12 channels of Chongqing TV. A cross-sectional study was conducted in the main municipal areas of Chongqing. A questionnaire was administered in late June to 1,342 native residents aged 18–45, who were selected via street intercept survey. Results Respondents who recognized the advertisement (32.77 %) were more likely to know or believe that smoking cigarettes caused impotence than those who did not recognize the advertisement (26.11 %). According to 25.5 % of smokers, the anti-smoking TV advertising made them consider quitting smoking. However, females (51.7 %) were less likely to be affected by the advertisement to stop and think about quitting smoking compared to males (65.6 %) (OR = 0.517, 95 % CI [0.281–0.950]). In addition, respondents aged 26–35 years (67.4 %) were more likely to try to persuade others to quit smoking than those aged 18–25 years (36.3 %) (OR = 0.457, 95 % CI [0.215–0.974]). Furthermore, non-smokers (87.4 %) were more likely to find the advertisement relevant than smokers (74.8 %) (OR = 2.34, 95 % CI [1.19–4.61]). Conclusions This study showed that this advertisement did not show significant differences on smoking-related knowledge and attitude between non-smokers who had seen the ad and those who had not. Thus, this form may not be the right tool to facilitate change in non-smokers. The ad should instead be focused on the smoking population. Gender, smoking status, and age influenced the effect of anti-smoking TV advertising on the general population in China

Functional Analysis and Marker Development of TaCRT-D Gene in Common Wheat (Triticum aestivum L.)

Calreticulin (CRT), an endoplasmic reticulum (ER)-localized Ca2+-binding/buffering protein, is highly conserved and extensively expressed in animal and plant cells. To understand the function of CRTs in wheat (Triticum aestivum L.), particularly their roles in stress tolerance, we cloned the full-length genomic sequence of the TaCRT-D isoform from D genome of common hexaploid wheat, and characterized its function by transgenic Arabidopsis system. TaCRT-D exhibited different expression patterns in wheat seedling under different abiotic stresses. Transgenic Arabidopsis plants overexpressing ORF of TaCRT-D displayed more tolerance to drought, cold, salt, mannitol, and other abiotic stresses at both seed germination and seedling stages, compared with the wild-type controls. Furthermore, DNA polymorphism analysis and gene mapping were employed to develop the functional markers of this gene for marker-assistant selection in wheat breeding program. One SNP, S440 (T→C) was detected at the TaCRT-D locus by genotyping a wheat recombinant inbred line (RIL) population (114 lines) developed from Opata 85 × W7984. The TaCRT-D was then fine mapped between markers Xgwm645 and Xgwm664 on chromosome 3DL, corresponding to genetic distances of 3.5 and 4.4 cM, respectively, using the RIL population and Chinese Spring nulli-tetrasomic lines. Finally, the genome-specific and allele-specific markers were developed for the TaCRT-D gene. These findings indicate that TaCRT-D function importantly in plant stress responses, providing a gene target for genetic engineering to increase plant stress tolerance and the functional markers of TaCRT-D for marker-assistant selection in wheat breeding

A Novel Deep Neural Network Model for Multi-Label Chronic Disease Prediction

Chronic diseases are one of the biggest threats to human life. It is clinically significant to predict the chronic disease prior to diagnosis time and take effective therapy as early as possible. In this work, we use problem transform methods to convert the chronic diseases prediction into a multi-label classification problem and propose a novel convolutional neural network (CNN) architecture named GroupNet to solve the multi-label chronic disease classification problem. Binary Relevance (BR) and Label Powerset (LP) methods are adopted to transform multiple chronic disease labels. We present the correlated loss as the loss function used in the GroupNet, which integrates the correlation coefficient between different diseases. The experiments are conducted on the physical examination datasets collected from a local medical center. In the experiments, we compare GroupNet with other methods and models. GroupNet outperforms others and achieves the best accuracy of 81.13%

Immune infiltration analysis reveals immune cell signatures in salivary gland tissue of primary Sjögren’s syndrome

IntroductionMouse models are the basis for primary Sjögren’s syndrome (pSS) research. However, the depth of comparisons between mice and humans in salivary gland (SG) immune cells remains limited.MethodsThe gene expression profiles of SGs from normal subjects and pSS patients were downloaded from the Gene Expression Comprehensive Database. The proportion of infiltrating immune cell subsets was then assessed by cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT). An experimental Sjögren’s syndrome (ESS) mouse model was successfully constructed using SG protein. Based on mouse SG tissue RNA-Seq data, the seq-ImmuCC model was used to quantitatively analyze the compositional ratios of 10 immune cells in pSS patients and mouse model SG tissues.ResultsComputed and obtained 31 human data samples using the CIBERSORT deconvolution method. The immune cell infiltration results showed that, compared to normal human SG tissue, the content of gamma delta T cells was significantly different from naive CD4+ T cells and significantly increased, while the plasma cell content decreased. Principal component analysis indicated differences in immune cell infiltration between pSS patients and normal subjects. Meanwhile, for ESS model mouse data analysis, we found that the proportion of macrophages increased, while the proportion of CD4+ T cells, B cells, and monocytes decreased. Furthermore, we found that the proportion of monocytes was decreased, while the proportion of macrophages was increased in the SG tissues of pSS patients and model mice. The infiltration of CD4+ T, CD8+ T, and B cells also showed some differences.DiscussionWe comprehensively analyzed SG immune infiltration in pSS patients and model mice. We demonstrated conserved and nonconserved aspects of the immune system in mice and humans at the level of immune cells to help explain the primary regulation of immune mechanisms during the development of Sjögren’s syndrome

An Ensemble Multilabel Classification for Disease Risk Prediction

It is important to identify and prevent disease risk as early as possible through regular physical examinations. We formulate the disease risk prediction into a multilabel classification problem. A novel Ensemble Label Power-set Pruned datasets Joint Decomposition (ELPPJD) method is proposed in this work. First, we transform the multilabel classification into a multiclass classification. Then, we propose the pruned datasets and joint decomposition methods to deal with the imbalance learning problem. Two strategies size balanced (SB) and label similarity (LS) are designed to decompose the training dataset. In the experiments, the dataset is from the real physical examination records. We contrast the performance of the ELPPJD method with two different decomposition strategies. Moreover, the comparison between ELPPJD and the classic multilabel classification methods RAkEL and HOMER is carried out. The experimental results show that the ELPPJD method with label similarity strategy has outstanding performance