Cumulative Risks of Developing Extrapyramidal Signs, Psychosis, or Myoclonus in the Course of Alzheimer's Disease

Cumulative risks of developing extrapyramidal signs, psychosis, and myoclonus in the course of Alzheimer's disease (AD) were estimated in 72 patients with probable AD by the Kaplan-Meier survival method. The cumulative risk functions were found to increase at different rates for different signs as AD progressed. Comparisons of the cumulative risk functions revealed that in the early stages of AD, extrapyramidal signs and psychosis were more likely to develop than myoclonus. As AD progressed, the risk of developing myoclonus became as great as that of developing the other two signs. This study suggests that extrapyramidal signs, psychosis, and myoclonus represent developmental features that mark the progression of AD, rather than indicators of disease subtypes. The estimated cumulative risk functions set a reasonable expectation for the timing and likelihood of the emergence of the clinical signs. This, in turn, might aid in disease prognosis because the biological bases of these signs have been established and they have been shown to be predictive of other markers of disease course

Comparison of feature selection and classification for MALDI-MS data

INTRODUCTION: In the classification of Mass Spectrometry (MS) proteomics data, peak detection, feature selection, and learning classifiers are critical to classification accuracy. To better understand which methods are more accurate when classifying data, some publicly available peak detection algorithms for Matrix assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS) data were recently compared; however, the issue of different feature selection methods and different classification models as they relate to classification performance has not been addressed. With the application of intelligent computing, much progress has been made in the development of feature selection methods and learning classifiers for the analysis of high-throughput biological data. The main objective of this paper is to compare the methods of feature selection and different learning classifiers when applied to MALDI-MS data and to provide a subsequent reference for the analysis of MS proteomics data. RESULTS: We compared a well-known method of feature selection, Support Vector Machine Recursive Feature Elimination (SVMRFE), and a recently developed method, Gradient based Leave-one-out Gene Selection (GLGS) that effectively performs microarray data analysis. We also compared several learning classifiers including K-Nearest Neighbor Classifier (KNNC), Naïve Bayes Classifier (NBC), Nearest Mean Scaled Classifier (NMSC), uncorrelated normal based quadratic Bayes Classifier recorded as UDC, Support Vector Machines, and a distance metric learning for Large Margin Nearest Neighbor classifier (LMNN) based on Mahanalobis distance. To compare, we conducted a comprehensive experimental study using three types of MALDI-MS data. CONCLUSION: Regarding feature selection, SVMRFE outperformed GLGS in classification. As for the learning classifiers, when classification models derived from the best training were compared, SVMs performed the best with respect to the expected testing accuracy. However, the distance metric learning LMNN outperformed SVMs and other classifiers on evaluating the best testing. In such cases, the optimum classification model based on LMNN is worth investigating for future study

Comparison of Feature Selection and Classification for MALDI-MS Data

Introduction: In the classification of Mass Spectrometry (MS) proteomics data, peak detection, feature selection, and learning classifiers are critical to classification accuracy. To better understand which methods are more accurate when classifying data, some publicly available peak detection algorithms for Matrix assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS) data were recently compared; however, the issue of different feature selection methods and different classification models as they relate to classification performance has not been addressed. With the application of intelligent computing, much progress has been made in the development of feature selection methods and learning classifiers for the analysis of high-throughput biological data. The main objective of this paper is to compare the methods of feature selection and different learning classifiers when applied to MALDI-MS data and to provide a subsequent reference for the analysis of MS proteomics data. Results: We compared a well-known method of feature selection, Support Vector Machine Recursive Feature Elimination (SVMRFE), and a recently developed method, Gradient based Leave-one-out Gene Selection (GLGS) that effectively performs microarray data analysis. We also compared several learning classifiers including K-Nearest Neighbor Classifier (KNNC), Naïve Bayes Classifier (NBC), Nearest Mean Scaled Classifier (NMSC), uncorrelated normal based quadratic Bayes Classifier recorded as UDC, Support Vector Machines, and a distance metric learning for Large Margin Nearest Neighbor classifier (LMNN) based on Mahanalobis distance. To compare, we conducted a comprehensive experimental study using three types of MALDI-MS data. Conclusion: Regarding feature selection, SVMRFE outperformed GLGS in classification. As for the learning classifiers, when classification models derived from the best training were compared, SVMs performed the best with respect to the expected testing accuracy. However, the distance metric learning LMNN outperformed SVMs and other classifiers on evaluating the best testing. In such cases, the optimum classification model based on LMNN is worth investigating for future study

Haemophilus influenzae type b reemergence after combination immunization

An increase in Haemophilus influenzae type b (Hib) in British children has been linked to the widespread use of a diphtheria/tetanus/acellular pertussis combination vaccine (DTaP-Hib). We measured anti-polyribosyl-ribitol phos- phate antibody concentration and avidity before and after a Hib booster in 176 children 2–4 years of age who had received 3 doses of DTP-Hib (either DT whole cell pertus- sis-Hib or DTaP-Hib) combination vaccine in infancy. We also measured pharyngeal carriage of Hib. Antibody con- centrations before and avidity indices after vaccination were low (geometric mean concentration 0.46μg/mL, 95% confidence interval [CI] 0.36–0.58; geometric mean avidity index 0.16, 95% CI 0.14–0.18) and inversely related to the number of previous doses of DTaP-Hib (p = 0.02 and p<0.001, respectively). Hib was found in 2.1% (95% CI 0.7%–6.0%) of study participants. Our data support an association between DTaP-Hib vaccine combinations and clinical Hib disease through an effect on antibody concen- tration and avidit

A new statistical approach to combining p-values using gamma distribution and its application to genome-wide association study

Background: Combining information from different studies is an important and useful practice in bioinformatics, including genome-wide association study, rare variant data analysis and other set-based analyses. Many statistical methods have been proposed to combine p-values from independent studies. However, it is known that there is no uniformly most powerful test under all conditions; therefore, finding a powerful test in specific situation is important and desirable. Results: In this paper, we propose a new statistical approach to combining p-values based on gamma distribution, which uses the inverse of the p-value as the shape parameter in the gamma distribution. Conclusions: Simulation study and real data application demonstrate that the proposed method has good performance under some situations

Uptake of gases in bundles of carbon nanotubes

Model calculations are presented which predict whether or not an arbitrary gas experiences significant absorption within carbon nanotubes and/or bundles of nanotubes. The potentials used in these calculations assume a conventional form, based on a sum of two-body interactions with individual carbon atoms; the latter employ energy and distance parameters which are derived from empirical combining rules. The results confirm intuitive expectation that small atoms and molecules are absorbed within both the interstitial channels and the tubes, while large atoms and molecules are absorbed almost exclusively within the tubes.Comment: 9 pages, 12 figures, submitted to PRB Newer version (8MAR2K). There was an error in the old one (23JAN2K). Please download thi

Association between financial links to indoor tanning industry and conclusions of published studies on indoor tanning: Systematic review

Objective To assess whether an association exists between financial links to the indoor tanning industry and conclusions of indoor tanning literature. Design Systematic review. Data sources PubMed, Embase, and Web of Science, up to 15 February 2019. Study selection criteria Articles discussing indoor tanning and health were eligible for inclusion, with no article type restrictions (original research, systematic reviews, review articles, case reports, editorials, commentaries, and letters were all eligible). Basic science studies, articles describing only indoor tanning prevalence, non-English articles, and articles without full text available were excluded. Results 691 articles were included in analysis, including empiric articles (eg, original articles or systematic reviews) (357/691; 51.7%) and non-empiric articles letters (eg, commentaries, letters, or editorials) (334/691; 48.3%). Overall, 7.2% (50/691) of articles had financial links to the indoor tanning industry; 10.7% (74/691) articles favored indoor tanning, 3.9% (27/691) were neutral, and 85.4% (590/691) were critical of indoor tanning. Among the articles without industry funding, 4.4% (27/620) favored indoor tanning, 3.5% (22/620) were neutral, and 92.1% (571/620) were critical of indoor tanning. Among the articles with financial links to the indoor tanning industry, 78% (39/50) favored indoor tanning, 10% (5/50) were neutral, and 12% (6/50) were critical of indoor tanning. Support from the indoor tanning industry was significantly associated with favoring indoor tanning (risk ratio 14.3, 95% confidence interval 10.0 to 20.4). Conclusions Although most articles in the indoor tanning literature are independent of industry funding, articles with financial links to the indoor tanning industry are more likely to favor indoor tanning. Public health practitioners and researchers need to be aware of and account for industry funding when interpreting the evidence related to indoor tanning. Systematic review registration PROSPERO CRD42019123617

Systems Analysis for a Venus Aerocapture Mission

Previous high level analysis has indicated that significant mass savings may be possible for planetary science missions if aerocapture is employed to place a spacecraft in orbit. In 2001 the In-Space Propulsion program identified aerocapture as one of the top three propulsion technologies for planetary exploration but that higher fidelity analysis was required to verify the favorable results and to determine if any supporting technology gaps exist that would enable or enhance aerocapture missions. A series of three studies has been conducted to assess, from an overall system point of view, the merit of using aerocapture at Titan, Neptune and Venus. These were chosen as representative of a moon with an atmosphere, an outer giant gas planet and an inner planet. The Venus mission, based on desirable science from plans for Solar System Exploration and Principal Investigator proposals, to place a spacecraft in a 300km polar orbit was examined and the details of the study are presented in this paper

Persistent Lipophilic Environmental Chemicals and Endometriosis: The ENDO Study

Background: An equivocal literature exists regarding the relation between persistent organochlorine pollutants (POPs) and endometriosis in women, with differences attributed to methodologies