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https://www.readbyqxmd.com/read/29156419/segmentation-of-the-hippocampus-by-transferring-algorithmic-knowledge-for-large-cohort-processing
#1
Benjamin Thyreau, Kazunori Sato, Hiroshi Fukuda, Yasuyuki Taki
The hippocampus is a particularly interesting target for neuroscience research studies due to its essential role within the human brain. In large human cohort studies, bilateral hippocampal structures are frequently identified and measured to gain insight into human behaviour or genomic variability in neuropsychiatric disorders of interest. Automatic segmentation is performed using various algorithms, with FreeSurfer being a popular option. In this manuscript, we present a method to segment the bilateral hippocampus using a deep-learned appearance model...
November 10, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/29155996/hierarchical-attention-networks-for-information-extraction-from-cancer-pathology-reports
#2
Shang Gao, Michael T Young, John X Qiu, Hong-Jun Yoon, James B Christian, Paul A Fearn, Georgia D Tourassi, Arvind Ramanthan
Objective: We explored how a deep learning (DL) approach based on hierarchical attention networks (HANs) can improve model performance for multiple information extraction tasks from unstructured cancer pathology reports compared to conventional methods that do not sufficiently capture syntactic and semantic contexts from free-text documents. Materials and Methods: Data for our analyses were obtained from 942 deidentified pathology reports collected by the National Cancer Institute Surveillance, Epidemiology, and End Results program...
November 16, 2017: Journal of the American Medical Informatics Association: JAMIA
https://www.readbyqxmd.com/read/29155928/modeling-positional-effects-of-regulatory-sequences-with-spline-transformations-increases-prediction-accuracy-of-deep-neural-networks
#3
Žiga Avsec, Mohammadamin Barekatain, Jun Cheng, Julien Gagneur
Motivation: Regulatory sequences are not solely defined by their nucleic acid sequence but also by their relative distances to genomic landmarks such as transcription start site, exon boundaries, or polyadenylation site. Deep learning has become the approach of choice for modeling regulatory sequences because of its strength to learn complex sequence features. However, modeling relative distances to genomic landmarks in deep neural networks has not been addressed. Results: Here we developed spline transformation, a neural network module based on splines to flexibly and robustly model distances...
November 16, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29154858/visuospatial-function-predicts-one-week-motor-skill-retention-in-cognitively-intact-older-adults
#4
Jennapher Lingo VanGilder, Caitlin Rose Hengge, Kevin Duff, Sydney Yoshie Schaefer
Motor learning declines with aging, such that older adults retain less motor skill after practice compared to younger adults. However, it remains unclear if these motor learning declines are related to normal cognitive changes associated with aging. The purpose of this study was to examine which cognitive domains would best predict the amount of retention on a motor task one week after training in cognitively intact older adults. Twenty-one adults ages 65 to 84 years old were assessed with Repeatable Battery for the Assessment of Neuropsychological Status, which assesses five cognitive domains (immediate and delayed memory, visuospatial/constructional, language, and attention)...
November 14, 2017: Neuroscience Letters
https://www.readbyqxmd.com/read/29154409/sensitive-topics-missing-data-and-refusal-in-social-network-studies-an-ethical-examination
#5
Erin Rose Ellison, Regina Day Langhout
We describe our ethics-driven process of addressing missing data within a social network study about accountability for racism, classism, sexism, heterosexism, cis-sexism, ableism, and other forms of oppression among social justice union organizers. During data collection, some would-be participants did not return emails and others explicitly refused to engage in the research. All refusals came from women of color. We faced an ethical dilemma: Should we continue to seek participation from those who had not yet responded, with the hopes of recruiting more women of color from within the network so their perspectives would not be tokenized? Or, should we stop asking those who had been contacted multiple times, which would compromise the social network data and analysis? We delineate ways in which current discussions of the ethics of social network studies fell short, given our framework and our community psychology (CP) values...
November 20, 2017: American Journal of Community Psychology
https://www.readbyqxmd.com/read/29153957/adaptive-neuro-heuristic-hybrid-model-for-fruit-peel-defects-detection
#6
Marcin Woźniak, Dawid Połap
Fusion of machine learning methods benefits in decision support systems. A composition of approaches gives a possibility to use the most efficient features composed into one solution. In this article we would like to present an approach to the development of adaptive method based on fusion of proposed novel neural architecture and heuristic search into one co-working solution. We propose a developed neural network architecture that adapts to processed input co-working with heuristic method used to precisely detect areas of interest...
November 15, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29151990/robot-embodied-neuronal-networks-as-an-interactive-model-of-learning
#7
Abraham M Shultz, Sangmook Lee, Mary Guaraldi, Thomas B Shea, Holly C Yanco
Background and Objective: The reductionist approach of neuronal cell culture has been useful for analyses of synaptic signaling. Murine cortical neurons in culture spontaneously form an ex vivo network capable of transmitting complex signals, and have been useful for analyses of several fundamental aspects of neuronal development hitherto difficult to clarify in situ. However, these networks lack the ability to receive and respond to sensory input from the environment as do neurons in vivo...
2017: Open Neurology Journal
https://www.readbyqxmd.com/read/29151843/acquiring-and-preprocessing-leaf-images-for-automated-plant-identification-understanding-the-tradeoff-between-effort-and-information-gain
#8
Michael Rzanny, Marco Seeland, Jana Wäldchen, Patrick Mäder
Background: Automated species identification is a long term research subject. Contrary to flowers and fruits, leaves are available throughout most of the year. Offering margin and texture to characterize a species, they are the most studied organ for automated identification. Substantially matured machine learning techniques generate the need for more training data (aka leaf images). Researchers as well as enthusiasts miss guidance on how to acquire suitable training images in an efficient way...
2017: Plant Methods
https://www.readbyqxmd.com/read/29151574/machine-learning-meets-complex-networks-via-coalescent-embedding-in-the-hyperbolic-space
#9
Alessandro Muscoloni, Josephine Maria Thomas, Sara Ciucci, Ginestra Bianconi, Carlo Vittorio Cannistraci
Physicists recently observed that realistic complex networks emerge as discrete samples from a continuous hyperbolic geometry enclosed in a circle: the radius represents the node centrality and the angular displacement between two nodes resembles their topological proximity. The hyperbolic circle aims to become a universal space of representation and analysis of many real networks. Yet, inferring the angular coordinates to map a real network back to its latent geometry remains a challenging inverse problem...
November 20, 2017: Nature Communications
https://www.readbyqxmd.com/read/29151135/classification-of-g-protein-coupled-receptors-based-on-a-rich-generation-of-convolutional-neural-network-n-gram-transformation-and-multiple-sequence-alignments
#10
Man Li, Cheng Ling, Qi Xu, Jingyang Gao
Sequence classification is crucial in predicting the function of newly discovered sequences. In recent years, the prediction of the incremental large-scale and diversity of sequences has heavily relied on the involvement of machine-learning algorithms. To improve prediction accuracy, these algorithms must confront the key challenge of extracting valuable features. In this work, we propose a feature-enhanced protein classification approach, considering the rich generation of multiple sequence alignment algorithms, N-gram probabilistic language model and the deep learning technique...
November 18, 2017: Amino Acids
https://www.readbyqxmd.com/read/29150748/an-evaluation-of-a-rural-community-based-breast-education-and-navigation-program-highlights-and-lessons-learned
#11
Essie Torres, Alice R Richman, Ann M Schreier, Nasreen Vohra, Kathryn Verbanac
Cancer has become the leading cause of death in North Carolina (NC) (North Carolina DHHS, State Center for Health Statistics 2015) and the eastern region of North Carolina (ENC) has experienced greater cancer mortality than the remainder of the state. The Pitt County Breast Wellness Initiative-Education (PCBWI-E) provides culturally tailored breast cancer education and navigation to screening services for uninsured/underinsured women in Pitt and Edgecombe Counties in ENC. PCBWI-E created a network of 23 lay breast health educators, and has educated 735 women on breast health and breast cancer screening guidelines...
November 17, 2017: Journal of Cancer Education: the Official Journal of the American Association for Cancer Education
https://www.readbyqxmd.com/read/29150626/constructing-genetic-networks-using-biomedical-literature-and-rare-event-classification
#12
Amira Al-Aamri, Kamal Taha, Yousof Al-Hammadi, Maher Maalouf, Dirar Homouz
Text mining has become an important tool in bioinformatics research with the massive growth in the biomedical literature over the past decade. Mining the biomedical literature has resulted in an incredible number of computational algorithms that assist many bioinformatics researchers. In this paper, we present a text mining system called Gene Interaction Rare Event Miner (GIREM) that constructs gene-gene-interaction networks for human genome using information extracted from biomedical literature. GIREM identifies functionally related genes based on their co-occurrences in the abstracts of biomedical literature...
November 17, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29150140/visual-pathways-from-the-perspective-of-cost-functions-and-multi-task-deep-neural-networks
#13
REVIEW
H Steven Scholte, Max M Losch, Kandan Ramakrishnan, Edward H F de Haan, Sander M Bohte
Vision research has been shaped by the seminal insight that we can understand the higher-tier visual cortex from the perspective of multiple functional pathways with different goals. In this paper, we try to give a computational account of the functional organization of this system by reasoning from the perspective of multi-task deep neural networks. Machine learning has shown that tasks become easier to solve when they are decomposed into subtasks with their own cost function. We hypothesize that the visual system optimizes multiple cost functions of unrelated tasks and this causes the emergence of a ventral pathway dedicated to vision for perception, and a dorsal pathway dedicated to vision for action...
October 7, 2017: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
https://www.readbyqxmd.com/read/29149859/medicinal-plants-used-by-women-in-mecca-urban-muslim-and-gendered-knowledge
#14
Afnan Alqethami, Julie A Hawkins, Irene Teixidor-Toneu
BACKGROUND: This study explores medicinal plant knowledge and use among Muslim women in the city of Mecca, Saudi Arabia. Ethnobotanical research in the region has focused on rural populations and male herbal healers in cities, and based on these few studies, it is suggested that medicinal plant knowledge may be eroding. Here, we document lay, female knowledge of medicinal plants in an urban centre, interpreting findings in the light of the growing field of urban ethnobotany and gendered knowledge and in an Islamic context...
November 17, 2017: Journal of Ethnobiology and Ethnomedicine
https://www.readbyqxmd.com/read/29149684/combining-benford-s-law-and-machine-learning-to-detect-money-laundering-an-actual-spanish-court-case
#15
Elena Badal-Valero, José A Alvarez-Jareño, Jose M Pavía
OBJECTIVES: This paper is based on the analysis of the database of operations from a macro-case on money laundering orchestrated between a core company and a group of its suppliers, 26 of which had already been identified by the police as fraudulent companies. In the face of a well-founded suspicion that more companies have perpetrated criminal acts and in order to make better use of what are very limited police resources, we aim to construct a tool to detect money laundering criminals...
November 11, 2017: Forensic Science International
https://www.readbyqxmd.com/read/29149087/taxonomic-classification-for-living-organisms-using-convolutional-neural-networks
#16
Saed Khawaldeh, Usama Pervaiz, Mohammed Elsharnoby, Alaa Eddin Alchalabi, Nayel Al-Zubi
Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algorithm to increase performance and avoid misclassifications...
November 17, 2017: Genes
https://www.readbyqxmd.com/read/29148082/insights-into-flood-coping-appraisals-of-protection-motivation-theory-empirical-evidence-from-germany-and-france
#17
Philip Bubeck, W J Wouter Botzen, Jonas Laudan, Jeroen C J H Aerts, Annegret H Thieken
Protection motivation theory (PMT) has become a popular theory to explain the risk-reducing behavior of residents against natural hazards. PMT captures the two main cognitive processes that individuals undergo when faced with a threat, namely, threat appraisal and coping appraisal. The latter describes the evaluation of possible response measures that may reduce or avert the perceived threat. Although the coping appraisal component of PMT was found to be a better predictor of protective intentions and behavior, little is known about the factors that influence individuals' coping appraisals of natural hazards...
November 17, 2017: Risk Analysis: An Official Publication of the Society for Risk Analysis
https://www.readbyqxmd.com/read/29147563/a-novel-microaneurysms-detection-approach-based-on-convolutional-neural-networks-with-reinforcement-sample-learning-algorithm
#18
Umit Budak, Abdulkadir Şengür, Yanhui Guo, Yaman Akbulut
Microaneurysms (MAs) are known as early signs of diabetic-retinopathy which are called red lesions in color fundus images. Detection of MAs in fundus images needs highly skilled physicians or eye angiography. Eye angiography is an invasive and expensive procedure. Therefore, an automatic detection system to identify the MAs locations in fundus images is in demand. In this paper, we proposed a system to detect the MAs in colored fundus images. The proposed method composed of three stages. In the first stage, a series of pre-processing steps are used to make the input images more convenient for MAs detection...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29147518/machine-learning-molecular-dynamics-for-the-simulation-of-infrared-spectra
#19
Michael Gastegger, Jörg Behler, Philipp Marquetand
Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects - typically neglected by conventional quantum chemistry approaches - we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated...
October 1, 2017: Chemical Science
https://www.readbyqxmd.com/read/29146561/recurrent-neural-networks-with-specialized-word-embeddings-for-health-domain-named-entity-recognition
#20
Iñigo Jauregi Unanue, Ehsan Zare Borzeshi, Massimo Piccardi
BACKGROUND: Previous state-of-the-art systems on Drug Name Recognition (DNR) and Clinical Concept Extraction (CCE) have focused on a combination of text "feature engineering" and conventional machine learning algorithms such as conditional random fields and support vector machines. However, developing good features is inherently heavily time-consuming. Conversely, more modern machine learning approaches such as recurrent neural networks (RNNs) have proved capable of automatically learning effective features from either random assignments or automated word "embeddings"...
November 13, 2017: Journal of Biomedical Informatics
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