New trends in peptide-based anti-biofilm strategies : a review of recent achievements and bioinformatics approaches

Antimicrobial peptides (AMPs) have a broad spectrum of activity and unspecific mechanisms of action. Therefore, they are seen as valid alternatives to overcome clinically relevant biofilms and reduce the chance of acquired resistance. This paper reviews AMPs and anti-biofilm AMP-based strategies and discusses ongoing and future work. Recent studies report successful AMP-based prophylactic and therapeutic strategies, several databases catalogue AMP information and analysis tools, and novel bioinformatics tools are supporting AMP discovery and design. However, most AMP studies are performed with planktonic cultures, and most studies on sessile cells test AMPs on growing rather than mature biofilms. Promising preliminary synergistic studies have to be consubstantiated and the study of functionalized coatings with AMPs must be further explored. Standardized operating protocols, to enforce the repeatability and reproducibility of AMP anti-biofilm tests, and automated means of screening and processing the ever-expanding literature are still missing.Financial support from IBB-CEB and Fundacao para a Ciencia e Tecnologia (FCT) and European Community fund FEDER, through Program COMPETE, in the ambit of the FCT project 'PTDC/SAU-SAP/113196/2009/ FCOMP-01-0124-FEDER-016012' is gratefully acknowledged

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression

We applied several regression and deep learning methods to predict fluid intelligence scores from T1-weighted MRI scans as part of the ABCD Neurocognitive Prediction Challenge (ABCD-NP-Challenge) 2019. We used voxel intensities and probabilistic tissue-type labels derived from these as features to train the models. The best predictive performance (lowest mean-squared error) came from Kernel Ridge Regression (KRR; $\lambda=10$), which produced a mean-squared error of 69.7204 on the validation set and 92.1298 on the test set. This placed our group in the fifth position on the validation leader board and first place on the final (test) leader board.Comment: Winning entry in the ABCD Neurocognitive Prediction Challenge at MICCAI 2019. 7 pages plus references, 3 figures, 1 tabl

Predicting Stag and Hare Hunting Behaviors Using Hidden Markov Model

In this paper, we used Hidden Markov Model (HMM) to describe the gaming behaviors of students and whether they will exhibit “stag” or “hare” hunting behavior in a mobile game for mathematics learning. We found that there is a 99% probability that the students will stay either as stag or hare hunters. Our results also suggest that they would choose arithmetic problems involving addition. These game behaviors are not beneficial to learning because they are only exhibiting mathematical skills they already know. The results of the study show that stag and hare hunters have unique traits that separate the one from the other

Chronic hypothermia and energy expenditure in a neurodevelopmentally disabled patient: a case study

Hypothermia is defined as a core body temperature of \u3c35°C and results in a decrease in measured resting energy expenditure. A 51-year-old mentally disabled patient experienced chronic hypothermia from neurologic sequelae. Because of her continued weight gain and increased body fat in the presence of presumed hypocaloric nutrition, indirect calorimetry measurements were performed twice in a 3-month period. The resting energy expenditure measurements prompted a reduction of her daily caloric intake to prevent further overfeeding. Hypothermia reduces oxygen consumption and, as a consequence, decreases resting energy expenditure. In patients for whom chronic hypothermia is a problem, nutritional intake must be adjusted to prevent overfeeding, excessive weight gain, and the long-term complications of an excess of total calories

Field template-based design and biological evaluation of new sphingosine kinase 1 inhibitors

Purpose: Sphingosine kinase 1 (SK1) is a protooncogenic enzyme expressed in many human tumours and is associated with chemoresistance and poor prognosis. It is a potent therapy target and its inhibition chemosensitises solid tumours. Despite recent advances in SK1 inhibitors synthesis and validation, their clinical safety and chemosensitising options are not well described. In this study, we have designed, synthesised and tested a new specific SK1 inhibitor with a low toxicity profile. Methods: Field template molecular modelling was used for compound design. Lead compounds were tested in cell and mouse cancer models. Results: Field template analysis of three known SK1 inhibitors, SKI-178, 12aa and SK1-I, was performed and compound screening identified six potential new SK1 inhibitors. SK1 activity assays in both cell-free and in vitro settings showed that two compounds were effective SK1 inhibitors. Compound SK-F has potently decreased cancer cell viability in vitro and sensitised mouse breast tumours to docetaxel (DTX) in vivo, without significant whole-body toxicity. Conclusion: Through field template screening, we have identified a new SK1 inhibitor, SK-F, which demonstrated antitumour activity in vitro and in vivo without overt toxicity when combined with DTX

MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional Networks

Graph embedding is an important approach for graph analysis tasks such as node classification and link prediction. The goal of graph embedding is to find a low dimensional representation of graph nodes that preserves the graph information. Recent methods like Graph Convolutional Network (GCN) try to consider node attributes (if available) besides node relations and learn node embeddings for unsupervised and semi-supervised tasks on graphs. On the other hand, multi-layer graph analysis has been received attention recently. However, the existing methods for multi-layer graph embedding cannot incorporate all available information (like node attributes). Moreover, most of them consider either type of nodes or type of edges, and they do not treat within and between layer edges differently. In this paper, we propose a method called MGCN that utilizes the GCN for multi-layer graphs. MGCN embeds nodes of multi-layer graphs using both within and between layers relations and nodes attributes. We evaluate our method on the semi-supervised node classification task. Experimental results demonstrate the superiority of the proposed method to other multi-layer and single-layer competitors and also show the positive effect of using cross-layer edges

SoyTEdb: a comprehensive database of transposable elements in the soybean genome

<p>Abstract</p> <p>Background</p> <p>Transposable elements are the most abundant components of all characterized genomes of higher eukaryotes. It has been documented that these elements not only contribute to the shaping and reshaping of their host genomes, but also play significant roles in regulating gene expression, altering gene function, and creating new genes. Thus, complete identification of transposable elements in sequenced genomes and construction of comprehensive transposable element databases are essential for accurate annotation of genes and other genomic components, for investigation of potential functional interaction between transposable elements and genes, and for study of genome evolution. The recent availability of the soybean genome sequence has provided an unprecedented opportunity for discovery, and structural and functional characterization of transposable elements in this economically important legume crop.</p> <p>Description</p> <p>Using a combination of structure-based and homology-based approaches, a total of 32,552 retrotransposons (Class I) and 6,029 DNA transposons (Class II) with clear boundaries and insertion sites were structurally annotated and clearly categorized, and a soybean transposable element database, SoyTEdb, was established. These transposable elements have been anchored in and integrated with the soybean physical map and genetic map, and are browsable and visualizable at any scale along the 20 soybean chromosomes, along with predicted genes and other sequence annotations. BLAST search and other infrastracture tools were implemented to facilitate annotation of transposable elements or fragments from soybean and other related legume species. The majority (> 95%) of these elements (particularly a few hundred low-copy-number families) are first described in this study.</p> <p>Conclusion</p> <p>SoyTEdb provides resources and information related to transposable elements in the soybean genome, representing the most comprehensive and the largest manually curated transposable element database for any individual plant genome completely sequenced to date. Transposable elements previously identified in legumes, the third largest family of flowering plants, are relatively scarce. Thus this database will facilitate structural, evolutionary, functional, and epigenetic analyses of transposable elements in soybean and other legume species.</p

Is the meiofauna a good indicator for climate change and anthropogenic impacts?

Our planet is changing, and one of the most pressing challenges facing the scientific community revolves around understanding how ecological communities respond to global changes. From coastal to deep-sea ecosystems, ecologists are exploring new areas of research to find model organisms that help predict the future of life on our planet. Among the different categories of organisms, meiofauna offer several advantages for the study of marine benthic ecosystems. This paper reviews the advances in the study of meiofauna with regard to climate change and anthropogenic impacts. Four taxonomic groups are valuable for predicting global changes: foraminifers (especially calcareous forms), nematodes, copepods and ostracods. Environmental variables are fundamental in the interpretation of meiofaunal patterns and multistressor experiments are more informative than single stressor ones, revealing complex ecological and biological interactions. Global change has a general negative effect on meiofauna, with important consequences on benthic food webs. However, some meiofaunal species can be favoured by the extreme conditions induced by global change, as they can exhibit remarkable physiological adaptations. This review highlights the need to incorporate studies on taxonomy, genetics and function of meiofaunal taxa into global change impact research