Albrechtsenhodges2940

currently in phase II clinical trials, may be useful as migraine preventives in both sexes, while dopamine agonists and prolactin/ prolactin receptor antibodies may improve therapy for migraine, and other stress-related neurological disorders, in females.There are a large number of unannotated proteins with unknown functions in rice, which are difficult to be verified by biological experiments. Therefore, computational method is one of the mainstream methods for rice proteins function prediction. Two representative rice proteins, indica protein and japonica protein, are selected as the experimental dataset. In this paper, two feature extraction methods (the residue couple model method and the pseudo amino acid composition method) and the Principal Component Analysis method are combined to design protein descriptive features. Moreover, based on the state-of-the-art MIML algorithm EnMIMLNN, a novel MIML learning framework MK-EnMIMLNN is proposed. And the MK-EnMIMLNN algorithm is designed by learning multiple kernel fusion function neural network. The experimental results show that the hybrid feature extraction method is better than the single feature extraction method. More importantly, the MK-EnMIMLNN algorithm is superior to most classic MIML learning algorithms, which proves the effectiveness of the MK-EnMIMLNN algorithm in rice proteins function prediction.

The timing of seed dispersal determines the environmental conditions that plants face during early life stages. In seasonal environments, selection is expected to favour dispersal timing that is matched to environmental conditions suitable for successful recruitment. Our aim here was to test whether the timing of seed dispersal influences seedling establishment success in two populations of Euterpe edulis that are located at contrasting altitudes, have different seed-dispersal phenologies and are subjected to distinct climatic conditions.

We sowed E. edulis seeds in contrasting altitudes on different dates, and monitored seed germination, emergence and seedling establishment at each altitude over 4 years. At the high-altitude site, five seed-dispersal cohorts were established during the natural dispersal period. At the low-altitude site, three seed-dispersal cohorts were established during natural dispersal, and two were established either before or after natural dispersal.

At the high-altitude site, sere restrictive window of opportunity for successful seedling establishment.

The greater effect of seed-dispersal timing on seedling establishment at the low-altitude site is probably related to a more seasonal and drought-prone environment that favours a restricted period of seed dispersal. The magnitude of the effect of dispersal timing on seedling establishment success was modulated by environmental conditions that vary across altitude. Furthermore, reproductive phenology appears to be subject to more intense selection at the lower limit of the altitudinal range, due to a more restrictive window of opportunity for successful seedling establishment.Nitrogen is essential for life and its transformations are an important part of the global biogeochemical cycle. Being an essential nutrient, nitrogen exists in a range of oxidation states from +5 (nitrate) to -3 (ammonium and amino-nitrogen), and its oxidation and reduction reactions catalyzed by microbial enzymes determine its environmental fate. The functional annotation of the genes encoding the core nitrogen network enzymes has a broad range of applications in metagenomics, agriculture, wastewater treatment and industrial biotechnology. This study developed an alignment-free computational approach to determine the predicted nitrogen biochemical network-related enzymes from the sequence itself. We propose deepNEC, a novel end-to-end feature selection and classification model training approach for nitrogen biochemical network-related enzyme prediction. The algorithm was developed using Deep Learning, a class of machine learning algorithms that uses multiple layers to extract higher-level features from the phase, it predicts the enzyme commission number out of 20 classes for nitrogen metabolism. Among all, the DPC + NMBroto hybrid feature gave the best prediction performance (accuracy of 96.15% in k-fold training and 93.43% in independent testing) with an Matthews correlation coefficient (0.92 training and 0.87 independent testing) in phase I; phase II (accuracy of 99.71% in k-fold training and 98.30% in independent testing); phase III (overall accuracy of 99.03% in k-fold training and 98.98% in independent testing); phase IV (overall accuracy of 99.05% in k-fold training and 98.18% in independent testing), the DPC feature gave the best prediction performance. We have also implemented a homology-based method to remove false negatives. All the models have been implemented on a web server (prediction tool), which is freely available at http//bioinfo.usu.edu/deepNEC/.In many non-Asian countries, soy is consumed via soy-based meat and dairy alternatives, in addition to the traditional Asian soyfoods, such as tofu and miso. Meat alternatives are typically made using concentrated sources of soy protein, such as soy protein isolate (SPI) and soy protein concentrate (SPC). Therefore, these products are classified as ultra-processed foods (UPFs; group 4) according to NOVA, an increasingly widely used food-classification system that classifies all foods into 1 of 4 groups according to the processing they undergo. Furthermore, most soymilks, even those made from whole soybeans, are also classified as UPFs because of the addition of sugars and emulsifiers. Increasingly, recommendations are being made to restrict the consumption of UPFs because their intake is associated with a variety of adverse health outcomes. Critics of UPFs argue these foods are unhealthful for a wide assortment of reasons. Explanations for the proposed adverse effects of UPFs include their high energy density, high glycemic index (GI), hyper-palatability, and low satiety potential. Claims have also been made that UPFs are not sustainably produced. However, this perspective argues that none of the criticisms of UPFs apply to soy-based meat and dairy alternatives when compared with their animal-based counterparts, beef and cow milk, which are classified as unprocessed or minimally processed foods (group 1). Classifying soy-based meat and dairy alternatives as UPFs may hinder their public acceptance, which could detrimentally affect personal and planetary health. In conclusion, the NOVA classification system is simplistic and does not adequately evaluate the nutritional attributes of meat and dairy alternatives based on soy.

In quantitative bottom-up mass spectrometry (MS)-based proteomics the reliable estimation of protein concentration changes from peptide quantifications between different biological samples is essential. This estimation is not a single task but comprises the two processes of protein inference and protein abundance summarization. Furthermore, due to the high complexity of proteomics data and associated uncertainty about the performance of these processes, there is a demand for comprehensive visualization methods able to integrate protein with peptide quantitative data including their post-translational modifications. Hence, there is a lack of a suitable tool that provides post-identification quantitative analysis of proteins with simultaneous interactive visualization.

In this article, we present VIQoR, a user-friendly web service that accepts peptide quantitative data of both labeled and label-free experiments and accomplishes the crucial components protein inference and summarization and interactive visualization modules, including the novel VIQoR plot. We implemented two different parsimonious algorithms to solve the protein inference problem, while protein summarization is facilitated by a well established factor analysis algorithm called fast-FARMS followed by a weighted average summarization function that minimizes the effect of missing values. In addition, summarization is optimized by the so-called Global Correlation Indicator (GCI). We test the tool on three publicly available ground truth datasets and demonstrate the ability of the protein inference algorithms to handle shared peptides. We furthermore show that GCI increases the accuracy of the quantitative analysis in data sets with replicated design.

VIQoR is accessible at http//computproteomics.bmb.sdu.dk8192/app_direct/VIQoR/ The source code is available at https//bitbucket.org/veitveit/viqor/.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.Royal jelly (RJ) intake has been reported to be effective for reducing serum lipids; however, the mechanism is not fully understood. Angiopoietin-like protein 8 (ANGPTL8), a secreted protein, plays a key role in lipid metabolism. In this study, we investigated the effects of specific fatty acids included in RJ (RJ fatty acids), such as 10-hydroxy-2-decenoic acid, 10-hydroxydecanoic acid, and sebacic acid (SA), on expression of ANGPTL8 in human hepatoma HepG2 cells. SA markedly reduced the expression of ANGPTL8. Reporter assay revealed that SA suppressed ANGPTL8 promoter activity. In addition, we identified a functional binding site of hepatocyte nuclear factor-4α (HNF4α), a liver-enriched transcription factor, in the ANGPTL8 promoter. Recilisib SA reduced the levels of HNF4α protein and the binding of HNF4α to the ANGPTL8 promoter. Moreover, siRNA knockdown of HNF4α suppressed the expression of ANGTPL8 mRNA. Taken together, we conclude that SA downregulates ANGPTL8 expression via the decrease in HNF4α protein.In recent years, with the rapid development of techniques in bioinformatics and life science, a considerable quantity of biomedical data has been accumulated, based on which researchers have developed various computational approaches to discover potential associations between human microbes, drugs and diseases. This paper provides a comprehensive overview of recent advances in prediction of potential correlations between microbes, drugs and diseases from biological data to computational models. Firstly, we introduced the widely used datasets relevant to the identification of potential relationships between microbes, drugs and diseases in detail. And then, we divided a series of a lot of representative computing models into five major categories including network, matrix factorization, matrix completion, regularization and artificial neural network for in-depth discussion and comparison. Finally, we analysed possible challenges and opportunities in this research area, and at the same time we outlined some suggestions for further improvement of predictive performances as well.

The ecohydrological significance of leaf wetting due to atmospheric water in arid and semiarid ecosystems is not well understood. In these environments, the inputs of precipitation or dew formation resulting in leaf wetting have positive effects on plant functioning. However, its impact on plant water relations may depend on the degree of leaf surface wettability. In this study we evaluated leaf wettability and other leaf traits and its effects on foliar water uptake and canopy interception in plant species of a Patagonian steppe. We also studied how leaf traits affecting wettability vary seasonally from growing to dry season.

Contact angle of a water droplet with the leaf surface, water adhesion, droplet retention angle, stomatal density, cuticular conductance, canopy interception and maximum foliar water uptake were determined in six dominant shrub species.

All species increased leaf wettability during the dry season and most species were considered highly wettable. The leaf surface had very high capacity to store and retain water.