Using chained machine learning models for scientific articles recommendation

Recommender systems are commonly used when it comes to online multimedia service providers or worldwide retail companies. Although, regarding educational resources, scientific papers and books, or other items with extensive textual content and description, recommendation systems are only in early development. In this paper, we propose a new approach entirely based on chained machine learning model store present and rank scientific papers. The first model a word embeddings model supported on a shallow neural network - is trained using a synthesized paper unit - a composition of the title, the abstract, the publishing conference or journal, and the year - that accurately captures paper’s semantic information. Later we train pairwise learning to a rank model based on a support vector machine (SVM) using relevant and irrelevant papers. We show that our approach achieves state-of-art results and does not rely on any language dependent or domain knowledge. It only uses available on-line data and proves to be efficient in either user-dependent and user independent modeling.info:eu-repo/semantics/acceptedVersio

Dispersal in the Desert: Genetic Diversity and Population Structure of the Guinea Baboon (Papio papio) in Mauritania

info:eu-repo/semantics/publishedVersio

QCAR models to predict wild mushrooms radical scavenging activity, reducing power and lipid peroxidation inhibition

Wild mushrooms have become attractive as a source of physiologically beneficial compounds including antioxidants such as phenolic compounds and tocopherols. The concentrations of antioxidant compounds (phenolics and α-tocopherol) and EC50 values of antioxidant activity (concentration required to achieve 50% of radical scavenging activity and lipid peroxidation inhibition, or 0.5 of absorbance in reducing power) were analyzed by partial least square (PLS) regression analysis. Three QCAR (Quantitative Composition-Activity Relationship) models were constructed and their robustness and predictability were verified by internal and external cross-validation methods. Antioxidant activity correlated well with phenolics and -tocopherol contents, the major antioxidants in wild mushrooms. The models proved to be useful tools in the prediction of mushrooms radical scavenging activity, reducing power and lipid peroxidation inhibition

Virtual ligand screening studies between mushroom compounds and proteins involved in breast cancer

Mushrooms are a vast and yet largely untapped source of powerful new pharmaceutical products. In particular, and most importantly for modern medicine, they represent an unlimited source of compounds with antitumor and immunostimulating properties [1]. Particularly, the intake of some wild mushrooms has shown to reduce the risk of breast cancer in Chinese women [2]. A large number of LMW (low molecular weight) compounds have been "identified in wild mushrooms including phenolic acids, flavonoids, tocopherols, carotenoids, sugars and fatty acids [3]. In this study we used AutoDock 4 [4] to perform virtual ligand screening in order to evaluate which LMW compounds may be involved in the inhibition of the activity of proteins related to human breast cancer: aromatase (EC: 1.14.14.1), estrone sulfatase (EC: 3.1.6.2) and 17- hydroxysteroid dehydrogenase type 1 activity (17~-HSD-1; EC:. 1.1.1.62) [5]. A representative dataset of 43 LMW compounds was selected and molecular docking was performed against the three protein targets. 4-0-caffeoylquinic, naringin and lycopene stand out as the top ranked potential inhibitors for aromatase, estrone sulfatase and 1 7~HSD1, respectively. The information provided shows several interesting starting points for further development of inhibitors of the studied proteins, as also for the development of nutraceuticals or functional foods

Using molecular docking to investigate the anti-breast cancer activity of low molecular weight compounds present on wild mushrooms.

Mushrooms represent an unlimited source of compounds with antitumor and immunostimulating properties and mushroom intake as been shown to reduce the risk of breast cancer. A large number of LMW (low molecular weight) compounds present in mushrooms have been identified including: phenolic acids, flavonoids, tocopherols, carotenoids, sugars and fatty acids. In order to evaluate which wild mushroom LMW compounds may be involved in anti-breast cancer activity we selected a representative dataset of 43 LMW compounds and performed molecular docking against 3 known protein targets involved in breast cancer (Aromatase, Estrone Sulfatase and 17β-HSD-1) using AutoDock4 as docking software. The estimated inhibition constants for all LMW compounds were determined and the potential structure-activity relationships for the compounds with the best estimated inhibition constants are discussed for each compound family. 4-O-caffeoylquinic, naringin and lycopene stand out as the top ranked potential inhibitors for Aromatase, Estrone Sulfatase and 17β-HSD1, respectively, and the 3-D docked conformation for these compounds are discussed in detail. This information provides several interesting starting points for further development of Aromatase, Estrone Sulfatase and 17β-HSD1 inhibitors

Valorização de cogumelos silvestres como alimentos funcionais: estudos de química computacional

As interacções intermoleculares desempenham um papel essencial nos diversos processos biológicos, sendo fundamental a compreensão destas interacções nos Sectores das Indústrias Farmacêuticas e de Alimentos Funcionais. Os cogumelos representam uma fonte ilimitada de compostos com propriedades antitumorais e imunoestimulantes, e o seu consumo foi já relacionado com a redução do risco de cancro da mama. No presente trabalho, foram desenvolvidos dois estudos in silico com o intuito de compreender algumas das interacções moleculares presentes em cogumelos e responsáveis pela sua bioactividade. A técnica dos Mínimos Quadrados Parciais foi utilizada para avaliar a relação entre o potencial antioxidante (efeitos bloqueadores de radicais livres e poder redutor) e a composição química de vinte e três amostras de dezassete espécies de cogumelos silvestres Portugueses. Estudaram-se vários parâmetros analíticos tais como cinzas, hidratos de carbono, proteínas, gorduras, ácidos gordos monoinsaturados, ácidos gordos polinsaturados, ácidos gordos saturados, fenóis, flavonóides, ácido ascórbico e β-caroteno, e os seus resultados foram analisados pela técnica anteriormente mencionada de forma a estabelecer correlações entre todos os parâmetros. A actividade antioxidante mostrou estar correlacionada com o teor em fenóis e flavonóides. Foi construído um modelo QCAR (Relações Quantitativas Composição – Actividade), cuja robustez e previsibilidade foram verificadas por métodos de validação cruzada internos e externos. Finalmente, este modelo provou ser uma ferramenta útil na previsão do poder redutor de cogumelos

An interleukin-33/ST2 signaling deficiency reduces overt pain-like behaviors in mice

Interleukin (IL)-33, the most recent member of the IL family of cytokines, signals through the ST2 receptor. IL-33/ST2 signaling mediates antigen challenge-induced mechanical hyperalgesia in the joints and cutaneous tissues of immunized mice. The present study asked whether IL-33/ST2 signaling is relevant to overt pain-like behaviors in mice. Acetic acid and phenyl-p-benzoquinone induced significant writhing responses in wild-type (WT) mice; this overt nociceptive behavior was reduced in ST2-deficient mice. In an antigen-challenge model, ST2-deficient immunized mice had reduced induced flinch and licking overt pain-like behaviors. In the formalin test, ST2-deficient mice also presented reduced flinch and licking responses, compared with WT mice. Naive WT and ST2-deficient mice presented similar responses in the rota-rod, hot plate, and electronic von Frey tests, indicating no impairment of motor function or alteration in basal nociceptive responses. The results demonstrate that IL-33/ST2 signaling is important in the development of overt pain-like behaviors

The Bronchiectasis Severity Index and FACED score for assessment of the severity of bronchiectasis

Bronchiectasis (BC) is a multidimensional and etiologically diverse disease and, therefore, no single parameter can be used to determine its overall severity and prognosis. In this regard, two different validated scores are currently used to assess the severity of non-cystic fibrosis bronchiectasis (NCFB): the FACED score and the Bronchiectasis Severity Index (BSI).info:eu-repo/semantics/publishedVersio

Docking studies to evaluate mushrooms low molecular weight compounds as inhibitors of the anti-apoptotic protein BCL-2

Several reports indicate that mushrooms have the ability to promote apoptosis in tumour cell lines, but the mechanism of action is not quite well understood. Inhibition of the interaction between Bcl-2 (anti-apoptotic protein) and pro-apoptotic proteins could be an important step that leads to apoptosis. Therefore, the discovery of compounds with the capacity to inhibit Bcl-2 is an ongoing research topic on cancer therapy. Herein, Autodock4 virtual screening was applied to a dataset of 40 low molecular weight compounds present in mushrooms, using 3D Bcl-2 protein structure (PDB:2XA0) as target. Results suggested that steroids mainly ergosta-4,6,8(14),22-tetraen-3-one, lucidenic lactone, cerevisterol, ganoderic acid w and ganoderic acid x, with a binding energy lower than -10 kcal/mol, had the ability to interact with Bcl-2