Diabetes mellitus has become a serious and chronic metabolic disorder that results from a complex interaction of genetic and environmental factors, principally characterized by hyperglycemia, polyuria, and polyphagia. Uncontrolled high blood sugar can result in a host of diabetic complications. Prolonged diabetes leads to serious complications some of which are life-threatening. The prevalence of diabetes patients is rising at epidemic proportions throughout the world. Every year, a major portion of the annual health budget is spent on diabetes and related illnesses. Multiple risk factors are involved in the etiopathogenesis of the disease and turning the disease into an epidemic. Diabetes, for which there is no cure, apparently can be kept under control by maintaining self-care in daily living, effective diabetes education, with comprehensive improvements in knowledge, attitudes, skills, and management. In this review, we focused on the biochemical aspects of diabetes, risk factors including both environmental and genetic, disease complications, diagnosis, management, and currently available medications for the treatment of diabetes.
Thirty yellow inbred lines of normal maize were evaluated for thirteen parameters at the experimental field of Hajee Mohammad Danesh Science and Technology University during 2010-11 to study the genetic divergence using multivariate analysis. The thirty inbreds fell into six distinct clusters. The intra-cluster distances in all the six clusters were more or less low, indicating that the genotypes within the same cluster were closely related. The highest inter-cluster distance was observed between cluster I and cluster IV and the lowest between the cluster II and III. The cluster V and cluster IV contained the highest (9) and lowest (1) number of genotypes, respectively. Cluster VI showed the highest mean values for kernel yield and all the yield contributing traits except days to 50% tasseling and 50% silking. Cluster II had the lowest mean values for ear height and ear length. Days to maturity and ear diameter showed maximum contribution towards total divergence among different characters. Based on medium to high inter-cluster distances, six inbred lines viz. ML06, ML10, ML14, MK19, ML25 and ML26 were selected for hybrid program. Development of hybrids utilizing these genotypes has the chance to obtain higher heterosis with high performing crosses.
Alzheimer's disease (AD) is a leading cause of dementia, which causes serious health and socioeconomic problems. A progressive neurodegenerative disorder, Alzheimer's causes the structural change in the brain, thereby affecting behavior, cognition, emotions, and memory. Numerous multivariate analysis algorithms have been used for classifying AD, distinguishing it from healthy controls (HC). Efficient early classification of AD and mild cognitive impairment (MCI) from HC is imperative as early preventive care could help to mitigate risk factors. Magnetic resonance imaging (MRI), a noninvasive biomarker, displays morphometric differences and cerebral structural changes. A novel approach for distinguishing AD from HC using dual-tree complex wavelet transforms (DTCWT), principal coefficients from the transaxial slices of MRI images, linear discriminant analysis, and twin support vector machine is proposed here. The prediction accuracy of the proposed method yielded up to 92.65 ± 1.18 over the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, with a specificity of 92.19 ± 1.56 and sensitivity of 93.11 ± 1.29, and 96.68 ± 1.44 over the Open Access Series of Imaging Studies (OASIS) dataset, with a sensitivity of 97.72 ± 2.34 and specificity of 95.61 ± 1.67. The accuracy, sensitivity, and specificity achieved using the proposed method are comparable or superior to those obtained by various conventional AD prediction methods.
Early diagnosis of Alzheimer disease (AD) and mild cognitive impairment (MCI) is always useful. Preventive measures might have an impact on reducing AD risk factors. Structural magnetic resonance (MR) imaging, one of the vital sensitive biomarkers for cerebral atrophy in the brain, is used to extract volumetric feature by FreeSurfer and the CIVET toolbox. All of the structural magnetic resonance imaging (s-MRI) data that we used were downloaded from the Alzheimer's disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu) of imaging data. This novel approach is applied for the diagnosis of AD and MCI from healthy controls (HCs) combining extracted features with the MMSE (mini-mental state examination) scores, applying a two sample t-test to select a subset of features. The subset of features is fed to kernel principal component analysis (KPCA) module to project data onto the reduced principal component coefficients at higher dimensional kernel space to increase the linear separability. Then, the kernel PCA coefficients are projected into the more efficient linear discriminant space using linear discriminant analysis. A multi-kernel learning support vector machine (SVM) is used on newly projected data for stratification of AD and MCI from HCs. Using this approach, we obtain 93.85% classification accuracy when detecting AD from HCs for segmented volumetric features (using FreeSurfer) with high sensitivity and specificity. When distinguishing MCI from HCs and AD using volumetric features after subcortical segmentation, the detection rate reaches 86.54% and 75.12%, respectively. K E Y W O R D S FreeSurfer, CIVET, KPCA, PCA, LDA, MK-SVM | I N T R O D U C T I O NAlzheimer's disease (AD) is the most frequent type of dementia, which is suffered primarily by the elders. AD, which is a progressive neurodegenerative disorder, damages brain cells, and then, induces cognitive assessments, behavioral dilemmas, and memory disarray. According to a statistical report, over 135 million people worldwide will suffer from dementia by 2030, which is triple the current number of affected patients. The diagnosis cost of all AD patients is estimated to be $220 billion in The United States and $605 billion per year globally.Previous non-invasive diagnosis methods relied primarily on patient history, clinical observation, and cognitive assessment. Recently, researchers identified the sensitivity of different biomarkers for early detection of AD, and mild cognitive impairment (MCI). 1 Biomarkers from structural magnetic resonance imaging (sMRI) can be used for brain atrophy measurement so as to detect the abnormal volumetric changes related to AD. Functional imaging (e.g., FDG-PET) provides hypo metabolism quantification, whereas cerebrospinal fluid (CSF) provides information about changes in proteins. 2 Brain *Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni. usc.edu). As such, the investigators within the ADNI contributed to the design and impl...
Human interaction with computer is recent trend in computer technology. In order to obtain age information, image-based age estimation systems have been developed using information from the human facial images. We develop a new technology which identify the characteristic of human being like age. Facial information study will lead us to identify age. While generic growth patterns that are characteristics of different age groups can be identified. In order to create an accurate algorithm for age classification, we build an appropriate da-tasets for training is build using SVM classification method. We build an application base on MATLAB software to estimate age based on the trained data. Feature of face is extracted using PCA method and stored the data in array matrix. The accuracy of the trained data is 95.65%. We have an average matching percentage of 92%. We have Euclidean distance calculation method to verify the matched data and we found 100% verified.
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