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

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

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

Foreign body ingestion mimicking irritable bowel syndrome: a case report

<p>Abstract</p> <p>Introduction</p> <p>Foreign body ingestion is associated with a variety of symptoms and complications, often mimicking various diseases. This case report describes an unusual presentation following foreign body ingestion.</p> <p>Case presentation</p> <p>A 56-year-old Greek Caucasian woman presented to a primary care setting, in rural Crete, Greece, with complaints of abdominal pain, cramping and bloating, for the last four months. Alternating constipation and diarrhea was reported. The patient had unknowingly ingested a foreign body that resulted in an irritable bowel syndrome-like presentation.</p> <p>Conclusions</p> <p>This case report emphasizes the need for a high index of suspicion from physicians for a wide differential in their approach to abdominal complaints, as well as the importance of an individualized approach to patients in the setting of clinical medicine.</p

Campus Mental Health: Implications for Instructors Supporting Students

The recent escalation in student suicides due to mental health problems has encouraged higher education institutions to not only modify their overall support structures, but to also (re)define the role of faculty and staff. Despite the increased attention given to student mental health in Canadian higher education institutions, little is known and understood about how instructors view their role as supporters or promoters of student mental health. The purpose of this study was to explore the role of college instructors in supporting students with mental health problems or illnesses. Participants were 42 instructors between the ages of 25 to 64 from Molize College in Toronto, Ontario. Qualitative ethnography was employed to gather data from participants, specifically through a survey questionnaire and interviews. A constructivist framework was adopted to analyze and understand the values, perceptions, meanings, and practices post-secondary instructors carry around notions of student mental health and intervention. Findings revealed that instructors were generally aware of student mental health concerns in post-secondary institutions, but that greater awareness was still warranted, namely in the areas of instructor mental health and location of support services. Findings also demonstrated that most instructors evaluated their knowledge and confidence in relation to student mental health as poor, which was often credited to limited relevant professional development and training. Additionally, data indicated that instructors carried skepticism towards the role of some student support services departments, as well as towards their own role when supporting the mental health and well-being of students. On a final note, findings revealed that instructors commonly employed four practices to support the mental health and well-being of students: conversation, referral, accommodations, and curricular inclusion and instruction. Future studies are encouraged to acknowledge the narratives of instructors through ethnographic inquiry, to allow for greater insights into their awareness, knowledge/confidence, responsibilities, and practices when it comes to supporting the mental health and well-being of students in higher education settings. Incorporating the instructor may not be a panacea for the shortcomings of current mental health policies and practices in higher education settings, but it can certainly represent a colossal step in that direction. KEYWORDS: student mental health, higher education, instructor

Development and evaluation of a new fully automatic motion detection and correction technique in cardiac SPECT imaging

In cardiac SPECT perfusion imaging, motion correction of the data is critical to the minimization of motion introduced artifacts in the reconstructed images. Software-based (data-driven) motion correction techniques are the most convenient and economical approaches to fulfill this purpose. However, the accuracy is significantly affected by how the data complexities, such as activity overlap, non-uniform tissue attenuation, and noise are handled. We developed STASYS, a new, fully automatic technique, for motion detection and correction in cardiac SPECT. We evaluated the performance of STASYS by comparing its effectiveness of motion correcting patient studies with the current industry standard software (Cedars-Sinai MoCo) through blind readings by two readers independently. For 204 patient studies from multiple clinical sites, the first reader identified (1) 69 studies with medium to large axial motion, of which STASYS perfectly or significantly corrected 86.9% and MoCo 72.5%; and (2) 20 studies with medium to large lateral motion, of which STASYS perfectly or significantly corrected 80.0% and MoCo 60.0%. The second reader identified (1) 84 studies with medium to large axial motion, of which STASYS perfectly or significantly corrected 82.2% and MoCo 76.2%; and (2) 34 studies with medium to large lateral motion, of which STASYS perfectly or significantly corrected 58.9% and MoCo 50.0%. We developed a fully automatic software-based motion correction technique, STASYS, for cardiac SPECT. Clinical studies showed that STASYS was effective and corrected a larger percent of cardiac SPECT studies than the current industrial standard software