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https://www.readbyqxmd.com/read/27921372/a-fuzzy-neural-network-sliding-mode-controller-for-vibration-suppression-in-robotically-assisted-minimally-invasive-surgery
#1
Hongqiang Sang, Chenghao Yang, Fen Liu, Jintian Yun, Guoguang Jin
BACKGROUND: It is very important for robotically assisted minimally invasive surgery to achieve a high-precision and smooth motion control. However, the surgical instrument tip will exhibit vibration caused by nonlinear friction and unmodeled dynamics, especially when the surgical robot system is attempting low-speed, fine motion. METHODS: A fuzzy neural network sliding mode controller (FNNSMC) is proposed to suppress vibration of the surgical robotic system. Nonlinear friction and modeling uncertainties are compensated by a Stribeck model, a radial basis function (RBF) neural network and a fuzzy system, respectively...
December 2016: International Journal of Medical Robotics + Computer Assisted Surgery: MRCAS
https://www.readbyqxmd.com/read/27921216/neural-correlates-of-rumination-in-adolescents-with-remitted-major-depressive-disorder-and-healthy-controls
#2
Katie L Burkhouse, Rachel H Jacobs, Amy T Peters, Olu Ajilore, Edward R Watkins, Scott A Langenecker
The aim of the present study was to use fMRI to examine the neural correlates of engaging in rumination among a sample of remitted depressed adolescents, a population at high risk for future depressive relapse. A rumination induction task was used to assess differences in the patterns of neural activation during rumination versus a distraction condition among 26 adolescents in remission from major depressive disorder (rMDD) and in 15 healthy control adolescents. Self-report depression and rumination, as well as clinician-rated depression, were also assessed among all participants...
December 5, 2016: Cognitive, Affective & Behavioral Neuroscience
https://www.readbyqxmd.com/read/27920978/network-interactions-underlying-mirror-feedback-in-stroke-a-dynamic-causal-modeling-study
#3
Soha Saleh, Mathew Yarossi, Thushini Manuweera, Sergei Adamovich, Eugene Tunik
Mirror visual feedback (MVF) is potentially a powerful tool to facilitate recovery of disordered movement and stimulate activation of under-active brain areas due to stroke. The neural mechanisms underlying MVF have therefore been a focus of recent inquiry. Although it is known that sensorimotor areas can be activated via mirror feedback, the network interactions driving this effect remain unknown. The aim of the current study was to fill this gap by using dynamic causal modeling to test the interactions between regions in the frontal and parietal lobes that may be important for modulating the activation of the ipsilesional motor cortex during mirror visual feedback of unaffected hand movement in stroke patients...
2017: NeuroImage: Clinical
https://www.readbyqxmd.com/read/27920762/artificial-intelligence-vs-statistical-modeling-and-optimization-of-continuous-bead-milling-process-for-bacterial-cell-lysis
#4
Shafiul Haque, Saif Khan, Mohd Wahid, Sajad A Dar, Nipunjot Soni, Raju K Mandal, Vineeta Singh, Dileep Tiwari, Mohtashim Lohani, Mohammed Y Areeshi, Thavendran Govender, Hendrik G Kruger, Arshad Jawed
For a commercially viable recombinant intracellular protein production process, efficient cell lysis and protein release is a major bottleneck. The recovery of recombinant protein, cholesterol oxidase (COD) was studied in a continuous bead milling process. A full factorial response surface methodology (RSM) design was employed and compared to artificial neural networks coupled with genetic algorithm (ANN-GA). Significant process variables, cell slurry feed rate (A), bead load (B), cell load (C), and run time (D), were investigated and optimized for maximizing COD recovery...
2016: Frontiers in Microbiology
https://www.readbyqxmd.com/read/27919795/current-and-novel-insights-into-the-neurophysiology-of-migraine-and-its-implications-for-therapeutics
#5
REVIEW
Simon Akerman, Marcela Romero-Reyes, Philip R Holland
Migraine headache and its associated symptoms have plagued humans for two millennia. It is manifest throughout the world, and affects more than 1/6 of the global population. It is the most common brain disorder, and is characterized by moderate to severe unilateral headache that is accompanied by vomiting, nausea, photophobia, phonophobia, and other hypersensitive symptoms of the senses. While there is still a clear lack of understanding of its neurophysiology, it is beginning to be understood, and it seems to suggest migraine is a disorder of brain sensory processing, characterized by a generalized neuronal hyperexcitability...
December 2, 2016: Pharmacology & Therapeutics
https://www.readbyqxmd.com/read/27919554/quantitative-structure-property-relationships-for-predicting-sorption-of-pharmaceuticals-to-sewage-sludge-during-waste-water-treatment-processes
#6
L Berthod, D C Whitley, G Roberts, A Sharpe, R Greenwood, G A Mills
Understanding the sorption of pharmaceuticals to sewage sludge during waste water treatment processes is important for understanding their environmental fate and in risk assessments. The degree of sorption is defined by the sludge/water partition coefficient (Kd). Experimental Kd values (n=297) for active pharmaceutical ingredients (n=148) in primary and activated sludge were collected from literature. The compounds were classified by their charge at pH7.4 (44 uncharged, 60 positively and 28 negatively charged, and 16 zwitterions)...
December 2, 2016: Science of the Total Environment
https://www.readbyqxmd.com/read/27919382/a-clinical-decision-support-system-for-prediction-of-pregnancy-outcome-in-pregnant-women-with-systemic-lupus-erythematosus
#7
Khadijeh Paydar, Sharareh R Niakan Kalhori, Mahmoud Akbarian, Abbas Sheikhtaheri
OBJECTIVE: Pregnancy among systemic lupus erythematosus (SLE)-affected women is highly associated with poor obstetric outcomes. Predicting the risk of foetal outcome is essential for maximizing the success of pregnancy. This study aimed to develop a clinical decision support system (CDSS) to predict pregnancy outcomes among SLE-affected pregnant women. METHODS: We performed a retrospective analysis of 149 pregnant women with SLE, who were followed at Shariati Hospital (104 pregnancies) and a specialized clinic (45 pregnancies) from 1982 to 2014...
January 2017: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/27919375/software-intelligent-system-for-effective-solutions-for-hearing-impaired-subjects
#8
Rajkumar S, Muttan S, Sapthagirivasan V, Jaya V, Vignesh S S
PURPOSE: The anatomy and physiology of the ear is complex in nature, which makes it a challenge for audiologists to prescribe solutions for varied hearing-impaired subjects. There is a need to increase the satisfaction level of hearing-aid users by adopting better strategies that involve modern technological advancements. AIM: To design and develop a decision support Software Intelligent System (SIS) that performs audiological investigations to assess the degree of hearing loss and to suggest appropriate hearing-aid gain values...
January 2017: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/27919370/knowledge-discovery-in-cardiology-a-systematic-literature-review
#9
REVIEW
I Kadi, A Idri, J L Fernandez-Aleman
CONTEXT: Data mining (DM) provides the methodology and technology needed to transform huge amounts of data into useful information for decision making. It is a powerful process employed to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, DM applications can greatly benefit all those involved in cardiology, such as patients, cardiologists and nurses. OBJECTIVE: The purpose of this paper is to review papers concerning the application of DM techniques in cardiology so as to summarize and analyze evidence regarding: (1) the DM techniques most frequently used in cardiology; (2) the performance of DM models in cardiology; (3) comparisons of the performance of different DM models in cardiology...
January 2017: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/27919220/deepqa-improving-the-estimation-of-single-protein-model-quality-with-deep-belief-networks
#10
Renzhi Cao, Debswapna Bhattacharya, Jie Hou, Jianlin Cheng
BACKGROUND: Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. RESULTS: We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information...
December 5, 2016: BMC Bioinformatics
https://www.readbyqxmd.com/read/27918886/computational-principles-and-models-of-multisensory-integration
#11
REVIEW
Chandramouli Chandrasekaran
Combining information from multiple senses creates robust percepts, speeds up responses, enhances learning, and improves detection, discrimination, and recognition. In this review, I discuss computational models and principles that provide insight into how this process of multisensory integration occurs at the behavioral and neural level. My initial focus is on drift-diffusion and Bayesian models that can predict behavior in multisensory contexts. I then highlight how recent neurophysiological and perturbation experiments provide evidence for a distributed redundant network for multisensory integration...
December 2, 2016: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/27918835/the-ubiquitous-self-what-the-properties-of-self-bias-tell-us-about-the-self
#12
Jie Sui, Glyn W Humphreys
People show systematic biases in perception, memory, and attention to favor information related to themselves over information related to other people. Researchers have examined these biases in order to throw light on the nature of the self. We review this evidence in memory, face recognition, and simple perceptual matching tasks through objective measures of self-biases. We argue that the self serves as a stable anchor across different forms of judgment and that referring a stimulus to ourselves enhances the binding of stimulus features at different stages of processing (e...
December 5, 2016: Annals of the New York Academy of Sciences
https://www.readbyqxmd.com/read/27918531/shared-memories-reveal-shared-structure-in-neural-activity-across-individuals
#13
Janice Chen, Yuan Chang Leong, Christopher J Honey, Chung H Yong, Kenneth A Norman, Uri Hasson
Our lives revolve around sharing experiences and memories with others. When different people recount the same events, how similar are their underlying neural representations? Participants viewed a 50-min movie, then verbally described the events during functional MRI, producing unguided detailed descriptions lasting up to 40 min. As each person spoke, event-specific spatial patterns were reinstated in default-network, medial-temporal, and high-level visual areas. Individual event patterns were both highly discriminable from one another and similar among people, suggesting consistent spatial organization...
December 5, 2016: Nature Neuroscience
https://www.readbyqxmd.com/read/27918346/electrical-stimulation-for-seizure-induction-and-functional-mapping-in-stereoelectroencephalography
#14
Agnès Trébuchon, Patrick Chauvel
The challenge in epilepsy surgery is to remove the epileptogenic zone without compromising postoperative function. Electrical stimulation (ES) was developed to identify with greater anatomical and physiological precision cortical structures essential to language and motor function. Because the language network is widely distributed in the left (dominant) hemisphere, and often varies among patients, brain mapping should delineate eloquent areas at the individual level. Although ES for language is commonly performed during a presurgical investigation, the procedure remains nonstandardized among centers, and differences between grids and intracerebral depth electrodes (stereoelectroencephalography) should be taken into account when comparing results from different series...
December 2016: Journal of Clinical Neurophysiology: Official Publication of the American Electroencephalographic Society
https://www.readbyqxmd.com/read/27918206/children-and-adolescents-show-altered-visual-working-memory-related-brain-activity-more-than-one-decade-after-arterial-switch-operation-for-d-transposition-of-the-great-arteries
#15
Peter Klaver, Walter Knirsch, Karoline Wurmitzer, David Yoh von Allmen
This pilot study investigated neural correlates of visual working memory using functional magnetic resonance imaging (fMRI) in seven patients more than one decade after neonatal arterial switch operation for surgical correction of d-transposition of the great arteries (d-TGA, aged 10-18 years, 1 female). Compared with age and sex matched healthy controls patients showed similar visual working memory performance and a smaller increase in brain activity in the posterior parietal cortex with higher visual working memory load...
May 2016: Developmental Neuropsychology
https://www.readbyqxmd.com/read/27917958/functional-complexity-emerging-from-anatomical-constraints-in-the-brain-the-significance-of-network-modularity-and-rich-clubs
#16
Gorka Zamora-López, Yuhan Chen, Gustavo Deco, Morten L Kringelbach, Changsong Zhou
The large-scale structural ingredients of the brain and neural connectomes have been identified in recent years. These are, similar to the features found in many other real networks: the arrangement of brain regions into modules and the presence of highly connected regions (hubs) forming rich-clubs. Here, we examine how modules and hubs shape the collective dynamics on networks and we find that both ingredients lead to the emergence of complex dynamics. Comparing the connectomes of C. elegans, cats, macaques and humans to surrogate networks in which either modules or hubs are destroyed, we find that functional complexity always decreases in the perturbed networks...
December 5, 2016: Scientific Reports
https://www.readbyqxmd.com/read/27917895/small-molecules-increase-direct-neural-conversion-of-human-fibroblasts
#17
Ulrich Pfisterer, Fredrik Ek, Stefan Lang, Shamit Soneji, Roger Olsson, Malin Parmar
The generation of human induced neurons (hiNs) via exogenous delivery of neural transcription factors represents a novel technique to obtain disease and patient specific neurons. These cells have the potential to be used for disease modeling, diagnostics and drug screening, and also to be further developed for brain repair. In the present study, we utilized hiNs to develop an unbiased screening assay for small molecules that increase the conversion efficiency. Using this assay, we screened 307 compounds from five annotated libraries and identified six compounds that were very potent in potentiating the reprogramming process...
December 5, 2016: Scientific Reports
https://www.readbyqxmd.com/read/27917450/the-short-term-effect-of-liver-transplantation-on-the-low-frequency-fluctuation-of-brain-activity-in-cirrhotic-patients-with-and-without-overt-hepatic-encephalopathy
#18
Gaoyan Zhang, Yue Cheng, Wen Shen, Baolin Liu, Lixiang Huang, Shuangshuang Xie
Previous neuropsychological studies have demonstrated that liver transplantation (LT) is an effective method for improving the cognitive function of cirrhotic patients. However, the neural basis underlying the effects of LT is still unclear. Neuroimaging studies investigating changes in brain structures or functional networks mainly focus on patients without overt hepatic encephalopathy (HE). In this study, we recruited patients with and without overt HE and studied alterations in resting-state brain activity by quantizing the amplitude of low-frequency fluctuation (ALFF) before and 1 month after LT to study the short-term effect of LT in each group...
December 5, 2016: Brain Imaging and Behavior
https://www.readbyqxmd.com/read/27917394/a-deep-ensemble-learning-method-for-monaural-speech-separation
#19
Xiao-Lei Zhang, DeLiang Wang
Monaural speech separation is a fundamental problem in robust speech processing. Recently, deep neural network (DNN)-based speech separation methods, which predict either clean speech or an ideal time-frequency mask, have demonstrated remarkable performance improvement. However, a single DNN with a given window length does not leverage contextual information sufficiently, and the differences between the two optimization objectives are not well understood. In this paper, we propose a deep ensemble method, named multicontext networks, to address monaural speech separation...
March 2016: IEEE/ACM Transactions on Audio, Speech, and Language Processing
https://www.readbyqxmd.com/read/27917360/load-balancing-prediction-method-of-cloud-storage-based-on-analytic-hierarchy-process-and-hybrid-hierarchical-genetic-algorithm
#20
Xiuze Zhou, Fan Lin, Lvqing Yang, Jing Nie, Qian Tan, Wenhua Zeng, Nian Zhang
With the continuous expansion of the cloud computing platform scale and rapid growth of users and applications, how to efficiently use system resources to improve the overall performance of cloud computing has become a crucial issue. To address this issue, this paper proposes a method that uses an analytic hierarchy process group decision (AHPGD) to evaluate the load state of server nodes. Training was carried out by using a hybrid hierarchical genetic algorithm (HHGA) for optimizing a radial basis function neural network (RBFNN)...
2016: SpringerPlus
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