A Symbiotic View Of Life: We Have Never Been Individuals

The notion of the biological individual is crucial to studies of genetics, immunology, evolution, development, anatomy, and physiology. Each of these biological subdisciplines has a specific conception of individuality, which has historically provided conceptual contexts for integrating newly acquired data. During the past decade, nucleic acid analysis, especially genomic sequencing and high-throughput RNA techniques, has challenged each of these disciplinary definitions by finding significant interactions of animals and plants with symbiotic microorganisms that disrupt the boundaries that heretofore had characterized the biological individual. Animals cannot be considered individuals by anatomical or physiological criteria because a diversity of symbionts are both present and functional in completing metabolic pathways and serving other physiological functions. Similarly, these new studies have shown that animal development is incomplete without symbionts. Symbionts also constitute a second mode of genetic inheritance, providing selectable genetic variation for natural selection. The immune system also develops, in part, in dialogue with symbionts and thereby functions as a mechanism for integrating microbes into the animal-cell community. Recognizing the holobiont -the multicellular eukaryote plus its colonies of persistent symbionts-as a critically important unit of anatomy, development, physiology, immunology, and evolution opens up new investigative avenues and conceptually challenges the ways in which the biological subdisciplines have heretofore characterized living entities

A Scalable Supervised Subsemble Prediction Algorithm

Subsemble is a flexible ensemble method that partitions a full data set into subsets of observations, fits the same algorithm on each subset, and uses a tailored form of V-fold cross-validation to construct a prediction function that combines the subset-specific fits with a second metalearner algorithm. Previous work studied the performance of Subsemble with subsets created randomly, and showed that these types of Subsembles often result in better prediction performance than the underlying algorithm fit just once on the full dataset. Since the final Subsemble estimator varies depending on the data used to create the subset-specific fits, different strategies for creating the subsets used in Subsemble result in different Subsembles. We propose supervised partitioning of the covariate space to create the subsets used in Subsemble, and using a form of histogram regression as the metalearner used to combine the subset-specific fits. We discuss applications to large-scale data sets, and develop a practical Supervised Subsemble method using regression trees to both create the covariate space partitioning, and select the number of subsets used in Subsemble. Through simulations and real data analysis, we show that this subset creation method can have better prediction performance than the random subset version

Subsemble: An Ensemble Method for Combining Subset-Specific Algorithm Fits

Ensemble methods using the same underlying algorithm trained on different subsets of observations have recently received increased attention as practical prediction tools for massive datasets. We propose Subsemble: a general subset ensemble prediction method, which can be used for small, moderate, or large datasets. Subsemble partitions the full dataset into subsets of observations, fits a specified underlying algorithm on each subset, and uses a clever form of V-fold cross-validation to output a prediction function that combines the subset-specific fits. We give an oracle result that provides a theoretical performance guarantee for Subsemble. Through simulations, we demonstrate that Subsemble can be a beneficial tool for small to moderate sized datasets, and often has better prediction performance than the underlying algorithm fit just once on the full dataset. We also describe how to include Subsemble as a candidate in a SuperLearner library, providing a practical way to evaluate the performance of Subsemble relative to the underlying algorithm fit just once on the full dataset

Targeted Estimation of Variable Importance Measures with Interval-Censored Outcomes

In most experimental and observational studies, participants are not followed in continuous time. Instead, data is collected about participants only at certain monitoring times. These monitoring times are random, and often participant specific. As a result, outcomes are only known up to random time intervals, resulting in interval-censored data. In contrast, when estimating variable importance measures on interval-censored outcomes, practitioners often ignore the presence of interval-censoring, and instead treat the data as continuous or right-censored, applying ad-hoc approaches to mask the true interval-censoring. In this paper, we describe Targeted Minimum Loss-based Estimation methods tailored for estimation of variable importance measures with interval-censored outcomes. We demonstrate the performance of the interval-censored TMLE procedure through simulation studies, and apply the method to analyze the effects of a variety of variables on spontaneous hepatitis C virus clearance among injection drug users, using data from the “International Collaboration of Incident HIV and HCV in Injecting Cohorts” project

Experiment K-6-16. Morphological examination of rat testes. The effect of Cosmos 1887 flight on spermatogonial population and testosterone level in rat testes

Testes from rats flown on Cosmos 1887 for twelve and a half days were compared to basal control, synchronous control and vivarium maintained rats. When the mean weights of flight testes, normalized for weight/100 gms, were compared to the vivarium controls they were 6.7 percent lighter. Although the flight testes were lighter than the synchronous, the difference is not significant. Counts of spermatogonial cells from 5 animals in each group revealed a 4 percent decrease in flight compared to vivarium controls. In both cases the t-Test significance was less than 0.02. The serum testosterone levels of all animals (flight, synchronous and vivarium) were significantly below the basal controls

Fitness and Metabolic Syndrome Components Affect Serum-Induced Endothelial Migration and MicroRNAs in Postmenopausal African-American Women

Please download pdf version here

Molecular Analysis Expands the Spectrum of Phenotypes Associated with GLI3 Mutations

A range of phenotypes including Greig cephalopolysyndactyly and Pallister-Hall syndromes (GCPS, PHS) are caused by pathogenic mutation of the GLI3 gene. To characterize the clinical variability of GLI3 mutations, we present a subset of a cohort of 174 probands referred for GLI3 analysis. Eighty-one probands with typical GCPS or PHS were previously reported, and we report the remaining 93 probands here. This includes 19 probands (12 mutations) who fulfilled clinical criteria for GCPS or PHS, 48 probands (16 mutations) with features of GCPS or PHS but who did not meet the clinical criteria (sub-GCPS and sub-PHS), 21 probands (6 mutations) with features of PHS or GCPS and oral-facial-digital syndrome, and 5 probands (1 mutation) with nonsyndromic polydactyly. These data support previously identified genotype-phenotype correlations and demonstrate a more variable degree of severity than previously recognized. The finding of GLI3 mutations in patients with features of oral-facial-digital syndrome supports the observation that GLI3 interacts with cilia. We conclude that the phenotypic spectrum of GLI3 mutations is broader than that encompassed by the clinical diagnostic criteria, but the genotype-phenotype correlation persists. Individuals with features of either GCPS or PHS should be screened for mutations in GLI3 even if they do not fulfill clinical criteria