Página 1 dos resultados de 1094 itens digitais encontrados em 0.074 segundos
Resultados filtrados por Publicador: Hindawi Publishing Corporation

‣ Feature Detection Techniques for Preprocessing Proteomic Data

Sellers, Kimberly F.; Miecznikowski, Jeffrey C.
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Português
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461.03094%
Numerous gel-based and nongel-based technologies are used to detect protein changes potentially associated with disease. The raw data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. Low-level analysis issues (including normalization, background correction, gel and/or spectral alignment, feature detection, and image registration) are substantial problems that need to be addressed, because any large-level data analyses are contingent on appropriate and statistically sound low-level procedures. Feature detection approaches are particularly interesting due to the increased computational speed associated with subsequent calculations. Such summary data corresponding to image features provide a significant reduction in overall data size and structure while retaining key information. In this paper, we focus on recent advances in feature detection as a tool for preprocessing proteomic data. This work highlights existing and newly developed feature detection algorithms for proteomic datasets, particularly relating to time-of-flight mass spectrometry, and two-dimensional gel electrophoresis. Note, however, that the associated data structures (i.e., spectral data, and images containing spots) used as input for these methods are obtained via all gel-based and nongel-based methods discussed in this manuscript...

‣ Multivoxel Pattern Analysis for fMRI Data: A Review

Mahmoudi, Abdelhak; Takerkart, Sylvain; Regragui, Fakhita; Boussaoud, Driss; Brovelli, Andrea
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Português
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451.7804%
Functional magnetic resonance imaging (fMRI) exploits blood-oxygen-level-dependent (BOLD) contrasts to map neural activity associated with a variety of brain functions including sensory processing, motor control, and cognitive and emotional functions. The general linear model (GLM) approach is used to reveal task-related brain areas by searching for linear correlations between the fMRI time course and a reference model. One of the limitations of the GLM approach is the assumption that the covariance across neighbouring voxels is not informative about the cognitive function under examination. Multivoxel pattern analysis (MVPA) represents a promising technique that is currently exploited to investigate the information contained in distributed patterns of neural activity to infer the functional role of brain areas and networks. MVPA is considered as a supervised classification problem where a classifier attempts to capture the relationships between spatial pattern of fMRI activity and experimental conditions. In this paper , we review MVPA and describe the mathematical basis of the classification algorithms used for decoding fMRI signals, such as support vector machines (SVMs). In addition, we describe the workflow of processing steps required for MVPA such as feature selection...

‣ Optic Neuritis as Isolated Manifestation of Leptomeningeal Carcinomatosis: A Case Report and Systematic Review of Ocular Manifestations of Neoplastic Meningitis

Lanfranconi, Silvia; Basilico, Paola; Trezzi, Ilaria; Borellini, Linda; Franco, Giulia; Civelli, Vittorio; Pallotti, Francesco; Bresolin, Nereo; Baron, Pierluigi
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Português
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461.87766%
Introduction. Leptomeningeal carcinomatosis occurs in about 5% of cancer patients. Ocular involvement is a common clinical manifestation and often the presenting clinical feature. Materials and Methods. We report the case of a 52-year old lady with optic neuritis as isolated manifestation of neoplastic meningitis and a review of ocular involvement in neoplastic meningitis. Ocular symptoms were the presenting clinical feature in 34 patients (83%) out of 41 included in our review, the unique manifestation of meningeal carcinomatosis in 3 patients (7%). Visual loss was the presenting clinical manifestation in 17 patients (50%) and was the most common ocular symptom (70%). Other ocular signs were diplopia, ptosis, papilledema, anisocoria, exophthalmos, orbital pain, scotomas, hemianopsia, and nystagmus. Associated clinical symptoms were headache, altered consciousness, meningism, limb weakness, ataxia, dizziness, seizures, and other cranial nerves involvement. All patients except five underwent CSF examination which was normal in 1 patient, pleocytosis was found in 11 patients, increased protein levels were observed in 16 patients, and decreased glucose levels were found in 8 patients. Cytology was positive in 29 patients (76%). Conclusion. Meningeal carcinomatosis should be considered in patients with ocular symptoms even in the absence of other suggestive clinical symptoms.

‣ Psychomotor Retardation in Depression: A Systematic Review of Diagnostic, Pathophysiologic, and Therapeutic Implications

Bennabi, Djamila; Vandel, Pierre; Papaxanthis, Charalambos; Pozzo, Thierry; Haffen, Emmanuel
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
451.7804%
Psychomotor retardation is a central feature of depression which includes motor and cognitive impairments. Effective management may be useful to improve the classification of depressive subtypes and treatment selection, as well as prediction of outcome in patients with depression. The aim of this paper was to review the current status of knowledge regarding psychomotor retardation in depression, in order to clarify its role in the diagnostic management of mood disorders. Retardation modifies all the actions of the individual, including motility, mental activity, and speech. Objective assessments can highlight the diagnostic importance of psychomotor retardation, especially in melancholic and bipolar depression. Psychomotor retardation is also related to depression severity and therapeutic change and could be considered a good criterion for the prediction of therapeutic effect. The neurobiological process underlying the inhibition of activity includes functional deficits in the prefrontal cortex and abnormalities in dopamine neurotransmission. Future investigations of psychomotor retardation should help improve the understanding of the pathophysiological mechanisms underlying mood disorders and contribute to improving their therapeutic management.

‣ Modeling the Formation Process of Grouping Stimuli Sets through Cortical Columns and Microcircuits to Feature Neurons

Klefenz, Frank; Williamson, Adam
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
459.1989%
A computational model of a self-structuring neuronal net is presented in which repetitively applied pattern sets induce the formation of cortical columns and microcircuits which decode distinct patterns after a learning phase. In a case study, it is demonstrated how specific neurons in a feature classifier layer become orientation selective if they receive bar patterns of different slopes from an input layer. The input layer is mapped and intertwined by self-evolving neuronal microcircuits to the feature classifier layer. In this topical overview, several models are discussed which indicate that the net formation converges in its functionality to a mathematical transform which maps the input pattern space to a feature representing output space. The self-learning of the mathematical transform is discussed and its implications are interpreted. Model assumptions are deduced which serve as a guide to apply model derived repetitive stimuli pattern sets to in vitro cultures of neuron ensembles to condition them to learn and execute a mathematical transform.

‣ A Review of Feature Extraction Software for Microarray Gene Expression Data

Tan, Ching Siang; Ting, Wai Soon; Mohamad, Mohd Saberi; Chan, Weng Howe; Deris, Safaai; Ali Shah, Zuraini
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
467.2696%
When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data, allowing further analysis by using this reduced representation instead of the full size data. In this paper, we review numerous software applications that can be used for feature extraction. The software reviewed is mainly for Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE). A summary and sources of the software are provided in the last section for each feature extraction method.

‣ Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A Technical Review of Current Research

Wang, Shijun; Burtt, Karen; Turkbey, Baris; Choyke, Peter; Summers, Ronald M.
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
451.7804%
Prostate cancer (PCa) is the most commonly diagnosed cancer among men in the United States. In this paper, we survey computer aided-diagnosis (CADx) systems that use multiparametric magnetic resonance imaging (MP-MRI) for detection and diagnosis of prostate cancer. We review and list mainstream techniques that are commonly utilized in image segmentation, registration, feature extraction, and classification. The performances of 15 state-of-the-art prostate CADx systems are compared through the area under their receiver operating characteristic curves (AUC). Challenges and potential directions to further the research of prostate CADx are discussed in this paper. Further improvements should be investigated to make prostate CADx systems useful in clinical practice.

‣ Severe/Extreme Hypertriglyceridemia and LDL Apheretic Treatment: Review of the Literature, Original Findings

Diakoumakou, Olga; Hatzigeorgiou, Georgios; Gontoras, Nikos; Boutsikou, Maria; Kolovou, Vana; Mavrogeni, Sophie; Giannakopoulou, Vassiliki; Kolovou, Genovefa D.
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
451.7804%
Hypertriglyceridemia (HTG) is a feature of numerous metabolic disorders including dyslipidemias, metabolic syndrome, and diabetes mellitus type 2 and can increase the risk of premature coronary artery disease. HTG may also be due to genetic factors (called primary HTG) and particularly the severe/extreme HTG (SEHTG), which is a usually rare genetic disorder. Even rarer are secondary cases of SEHTG caused by autoimmune disease. This review considers the causes of SEHTG, and their management including treatment with low density lipoprotein apheresis and analyzes the original findings.

‣ A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data

Hira, Zena M.; Gillies, Duncan F.
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
464.4953%
We summarise various ways of performing dimensionality reduction on high-dimensional microarray data. Many different feature selection and feature extraction methods exist and they are being widely used. All these methods aim to remove redundant and irrelevant features so that classification of new instances will be more accurate. A popular source of data is microarrays, a biological platform for gathering gene expressions. Analysing microarrays can be difficult due to the size of the data they provide. In addition the complicated relations among the different genes make analysis more difficult and removing excess features can improve the quality of the results. We present some of the most popular methods for selecting significant features and provide a comparison between them. Their advantages and disadvantages are outlined in order to provide a clearer idea of when to use each one of them for saving computational time and resources.

‣ Nigral Iron Elevation Is an Invariable Feature of Parkinson's Disease and Is a Sufficient Cause of Neurodegeneration

Ayton, Scott; Lei, Peng
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Português
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457.39605%
Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor deficits accompanying degeneration of substantia nigra pars compactor (SNc) neurons. Although familial forms of the disease exist, the cause of sporadic PD is unknown. Symptomatic treatments are available for PD, but there are no disease modifying therapies. While the neurodegenerative processes in PD may be multifactorial, this paper will review the evidence that prooxidant iron elevation in the SNc is an invariable feature of sporadic and familial PD forms, participates in the disease mechanism, and presents as a tractable target for a disease modifying therapy.

‣ Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Masood, Ammara; Al-Jumaily, Adel Ali
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
453.48766%
Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided.