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The traditional negative selection algorithm with a randomly generated hypersphere detector is unable to satisfy the development needs of continuous learning due to the inherent defects of the detector. This paper proposes a novel negative selection algorithm for hyper-rectangle detectors that overcomes the shortcomings of randomly generated hyper-sphere detectors and lays the foundation for a negative selection algorithm with continuous learning capability. It uses self-sample clusters of equal-sized hypercubes instead of self-samples for training. The hyper-rectangle detectors are generated by cutting the nonself-space along the boundary of the self-sample clusters. The state space is covered without overlapping each other by self-sample clusters and detectors. The anomaly detection performance of the proposed method was demonstrated using Iris data, vowel recognition data (Vowel), and Wisconsin Breast Cancer (BCW) data. The experimental results show that the proposed method outperforms other artificial immune algorithms and clustering algorithms under the same parameter conditions. [ABSTRACT FROM AUTHOR]

Multimodal analysis focuses on the internal and external manifestations of cancer cells to provide physicians, oncologists and surgeons with timely information on personalized diagnosis and treatment for patients. Decision fusion in multimodal analysis reduces manual intervention, and improves classification accuracy facilitating doctors to make quick decisions. Genetic characteristics extracted on biopsies do not, however, provide details on adjacent cells. Images can only provide external observable details of cancer cells. While mammograms can detect breast cancer, region wise details can be obtained from ultrasound images. Hence, different types of imaging techniques are used. Features are extracted using the SelectKbest method in the Wisconsin Breast Cancer, Clinical and gene expression datasets. The features are extracted using Gray Level Co-occurrence Matrix from Histology, Mammogram and Sonogram images. For image datasets, the Convolution Neural Network (CNN) is used as a classifier. The combined features from clinical, gene expression and image datasets are used to train an Integrated Stacking Classifier. The integrated multimodal system's effectiveness is shown by experimental findings. [ABSTRACT FROM AUTHOR]

The hybrid of artificial neural network (ANN) and fuzzy logic system (FLS) can expend itself dynamically in a strong discovery of explicit knowledge to solve classification and regression problems with new input patterns. In this paper, a hybrid of Generalized Adaptive Resonance Theory (GART) and interval type-2 fuzzy logic system (IT2FLS) algorithm is proposed, and named as Generalized Adaptive Resonance Theory and interval type-2 fuzzy logic system (GART-IT2FLS). The GART is a combination of adaptive resonance theory network (ART) and Generalized Regression Neural Network (GRNN). GART is capable to deal with classification task effectively. However, type-2 fuzzy sets (T2 FS) is used to represent and model the uncertainties on inputs. The performance evaluation of GART-IT2FLS algorithm in three medical datasets has proven that GART-IT2FLS is capable to learn incrementally without plasticity-stability dilemma, and model uncertainties in medical datasets. The inferences of GAR-IT2FLS in these applications are discussed. The performance results show that GART-IT2FLS has obtained a comparable number of rules. The Wisconsin Breast Cancer and Heart Disease experiments demonstrated GART-IT2FLS has improved the testing accuracies. [ABSTRACT FROM AUTHOR]

BACKGROUND: The evidence base supports effectiveness of dental sealants for prevention of childhood caries in school-aged children. OBJECTIVE: This study describes planning, development, usability testing and outcomes following implementation of DentaSeal, a web-based application designed to accurately track unique student data and generate reports for all Wisconsin school-based sealant placement (SP) programs. METHODS: Application software development was informed by a steering committee of representative stakeholders who were interviewed to inform design and provide feedback for design of DentaSeal during development and evaluation. Software development proceeded based on wireframes developed to build architectural design. Usability testing followed and informed any required adjustments to the application. The DentaSeal prototype was beta tested and fully implemented subsequently in the public health sector. RESULTS: The DentaSeal application demonstrated capacity to: 1) track unique student SP data and longitudinal encounter history, 2) generate reports and 3) support administrative tracking. In 2019, DentaSeal captured SP data of 47 school-based programs in Wisconsin that sponsored > 7,000 program visits for 184,000 children from 62 counties. Delivery of > 548,000 SP services were catalogued. CONCLUSIONS: For public health initiatives targeting reduction in caries incidence, web-based applications such as DentaSeal represent useful longitudinal tracking tools for cataloguing SP in school-based program participants. [ABSTRACT FROM AUTHOR]

In data mining, deep learning and machine learning models face class imbalance problems, which result in a lower detection rate for minority class samples. An improved Synthetic Minority Over-sampling Technique (SMOTE) is introduced for effective imbalanced data classification. After collecting the raw data from PIMA, Yeast, E.coli, and Breast cancer Wisconsin databases, the pre-processing is performed using min-max normalization, cleaning, integration, and data transformation techniques to achieve data with better uniqueness, consistency, completeness and validity. An improved SMOTE algorithm is applied to the pre-processed data for proper data distribution, and then the properly distributed data is fed to the machine learning classifiers: Support Vector Machine (SVM), Random Forest, and Decision Tree for data classification. Experimental examination confirmed that the improved SMOTE algorithm with random forest attained significant classification results with Area under Curve (AUC) of 94.30%, 91%, 96.40%, and 99.40% on the PIMA, Yeast, E.coli, and Breast cancer Wisconsin databases. [ABSTRACT FROM AUTHOR]

An editorial is presented on the research paper om Alzheimer's disease written by Shi-Jiang Li who selected as the recipient of the Alzheimer Award 2017. It talks about the research being on the risks of Alzheimer's disease through sequencing of cerebrospinal fluid, brain function and cognition biomarkers.

Background: There is a robust consensus, most recently articulated in the 2020 Lancet Commission, that the roots of dementia can be traced to early life, and that the path to prevention may start there as well. Indeed, a growing body of research demonstrates that early life disadvantage may influence the risk for later life dementia and cognitive decline. A still understudied risk, however, is early life rural residence, a plausible pathway given related economic and educational disadvantages, as well as associations between later life rural living and lower levels of cognitive functioning.Objective: We aim to examine whether living in rural environments during early life has long term implications for cognitive health in later life.Methods: We employed the Wisconsin Longitudinal Study, which tracked 1 in every 3 high school graduates from the class of 1957, from infancy to ∼age 72. The data include a rich array of prospectively collected early life data, unique among existing studies, as well as later life measures of cognitive functioning.Results: We found a robust relationship between early life rural residence, especially living on a farm, and long-term risk for reduced cognitive performance on recall and fluency tasks. Controls for adolescent cognitive functioning, APOEɛ2 and APOEɛ4, as well as childhood and adult factors, ranging from early life socioeconomic conditions to later life health and rural and farm residency, did not alter the findings.Conclusion: Rural living in early life is an independent risk for lower levels of cognitive functioning in later life. [ABSTRACT FROM AUTHOR]

DKIFCM (Density Based Kernelized Intuitionistic Fuzzy C Means) is the new proposed clustering algorithm that is based on outlier identification, kernel functions, and intuitionist fuzzy approach. DKIFCM is an inspiration from Kernelized Intuitionistic Fuzzy C Means (KIFCM) algorithm and it addresses the performance issue in the presence of outliers. It first identifies outliers based on density of data and then clusters are computed accurately by mapping the data to high dimensional feature space. Performance and effectiveness of various algorithms are evaluated on synthetic 2D data sets such as Diamond data set (D10, D12, and D15), and noisy Dunn data set as well as on high dimension real-world data set such as Fisher-Iris, Wine, and Wisconsin Breast Cancer Data-set. Results of DKIFCM are compared with results of other algorithms such as Fuzzy-C-Means (FCM), Intuitionistic FCM (IFCM), Kernel-Intuitionistic FCM (KIFCM), and density-oriented FCM (DOFCM), and the performance of proposed algorithm is found to be superior even in the presence of outliers and noise. Key advantages of DKIFCM are outlier identification, robustness to noise, and accurate centroid computation. [ABSTRACT FROM AUTHOR]

Background: It is well-documented that African Americans have elevated risk for cognitive impairment and dementia in late life, but reasons for the racial disparities remain unknown. Stress processes have been linked to premature age-related morbidity, including Alzheimer's and related dementias (ADRD), and plausibly contribute to social disparities in cognitive aging.Objective: We examined the relationship between stressful life events and cognitive decline among African American and White participants enrolled in the Wisconsin Registry for Alzheimer's Prevention (WRAP).Methods: Linear mixed models including demographic, literacy, and health-related covariates were used to estimate (1) relationships between a life event index score and decline in cognitive test performance in two domains of executive function (Speed & Flexibility, Working Memory) and one domain of episodic memory (Verbal Learning & Memory) among 1,241 WRAP enrollees, stratified by race, and (2) contributions of stressful life events to racial differences in cognition within the full sample.Results: African Americans (N = 50) reported more stressful life events than Whites (N = 1,191). Higher stress scores associated with poorer Speed & Flexibility performance in both groups, though not with declines across time, and partially explained racial differentials in this domain. Among African Americans only, stressor exposure also associated with age-related decline in Verbal Learning & Memory. Stressor-cognition relationships were independent of literacy and health-related variables.Conclusions: Greater lifetime stress predicted poorer later-life cognition, and, in a small sample of African Americans, faster declines in a key domain of episodic memory. These preliminary findings suggest that future work in large minority aging cohorts should explore stress as an important source of modifiable, socially-rooted risk for impairment and ADRD in African Americans, who are disproportionately exposed to adverse experiences across the life course. [ABSTRACT FROM AUTHOR]

PURPOSE: Measuring self-management behaviors in adolescents and young adults with chronic health conditions has become a priority in health care, yet there is a paucity of instruments that capture these behaviors. The purpose of this psychometric study was to evaluate the reliability and validity of the 17-item generic Adolescent/Young Adult Self-Management and Independence Scale II (AMIS II). METHOD: Data were collected from 201 adolescents/young adults (AYA) with spina bifida and 129 of their parents. Exploratory factor analysis, confirmatory factor analysis, Cronbach alpha, frequencies, Pearson correlations, and intraclass correlations were used to evaluate the data. RESULTS: The exploratory factor analysis of parent data supported two related self-management factors (Condition Self-Management and Independent Living Self-Management). Confirmatory factor analysis of AYA data confirmed these two factors and an overall scale with good fit statistics (GFI and CFI = 0.86–0.95; RMSEA = 0.057). Internal reliabilities ranged from α = 0.72–0.89. Intraclass correlation analysis supported the stability of the instrument (ICC parent report = 0.82, AYA report = 0.84). Concurrent validity was supported with low to moderate correlations to six related but distinct variables. CONCLUSION: Psychometric analysis supports this expanded measure of self-management for AYA with spina bifida. Evaluation of this instrument in AYA with other chronic health conditions is underway. [ABSTRACT FROM AUTHOR]

The rapid migration of health care from the institution to the home presents a plethora of consumer health technology options.. The fit of these technologies to the users' actual task performance and environment remains to be explored. In the vizHOME study, we set out to conduct in-depth analyses of health information management tasks conducted by individuals residing in 20 homes in the Midwestern United States who self-reported with diabetes. This paper will explore early results from five of the 13 assessments we have performed to-date. Early observations are described and implications for informatics are posited.