Values,Errors, and Precautions

In the environmental health literature, errors in interpreting studies or data are not infrequent. Many are of the Type II variety. Common solecisms of this type are: treating the criterion of p < 0.05 as a sacrament; demanding complete confounder control; arguing for the existence of phantom confounders; arguing that the effect size is trivial; building nonveridical models; arguing for no effect from inadequate sample size; demanding causal proof; arguing that causality is reversed; conducting a ballot of published studies. These are examined in this paper.     

Experimental study of the influence of errors of genetic correlation estimates on unrestricted,optimum, and desired gains selection indices

Summary Effects of errors in estimates of the genetic correlation on the accuracy of unrestricted, optimum, and desired gains selection indices were examined experimentally in Tribolium castaneum. Three lines were selected for three generations for pupal weight at 21 days and adult weight at 31 days, using unrestricted (I9), optimum (O9), and desired gains (G9) index selection methods. The genetic correlation between pupal and adult weights in the base population was 0.95. The optimum index was designed to set the response of pupal weight by a fixed amount, while in the desired gains index the responses of pupal and adult weights were specified as being equal to 31. Three other indices were constructed using a deliberately incorrect genetic correlation (0.25), i.e., unrestricted (I2), optimum (O2), and desired gains (G2). Responses observed in unrestricted index lines (I9 versus I2) and optimum index lines (O9 versus O2) did not differ significantly, even though lines I9 and I2 differed in a practical sense. Responses in desired gains index lines (G9 versus G2) differed significantly. Responses obtained for aggregate genotype (pupal weight + adult weight) and for the component traits were greater in line I9 than those obtained in line I2. Responses obtained in the O9 and O2 lines for pupal and adult weights were similar, while those obtained in the G9 and G2 lines were similar for pupal weight but not (P<0.05) for adult weight. Therefore, underestimation of the genetic correlation seems to affect the efficiency of a desired gains index more than that of unrestricted or optimum indices.     

Identification of minor tightly bound H1 histone subfractions which fail to cleave their initiator methionine

Groups of CBA mice were administered [³⁵S] methionine (1 mCi/mouse). Non-histone proteins, H1 and H1⁰ histones and nucleosomal core histones were isolated from different issues by selective extractions. The measurements of radioactivity of individual bands and autoradiography of dry gels were used to identify methionine-containing and methionine-free histone variants. H1A and H1B histone variants extracted with 5% perchloric acid were methionine-free. However, minor sub-fractions of these histones which are more tightly bound to DNA (and which can be extracted only with 0.25 N HC1) contained [³⁵S] methionine and did show a higher specific activity than methionine-containing nucleosomal hitones. Cyanogen Bromide reaction which destroys non-histone proteins and methionine-containing nucleosomal histones removes radioactivity but does not alter the position of methionine-containing H1 minor bands. This indicates that the radioactive methionine occupies only the N-terminus of the H1 molecules. It is suggested that this methionine is an uncleaved initiator methionine. The presence of these methionine-containing minor H1 subfractions varies in different tissues.     

Strand targeting signal(s) for in vivo mutation avoidance by post-replication mismatch repair in Escherichia coli

Summary The involvement of GATC sites in directing mismatch correction for the elimination of replication errors in Escherichia coli was investigated in vivo by analyzing mutation rates for a gene carried on a series of related plasmids that contain 2, 1 and 0 such sites. This gene encoding chloramphenicol acetyl transferase (Cat protein) was inactivated by a point mutation. In vivo mutations restoring resistance to chloramphenicol were scored in mismatch repair proficient (mut ⁺) and deficient (mutHLS^-) strains. In mut ⁺ cells, reduction of GATC sites from 2 to 0 increased mutation rates approximately 10-fold. Removal of the GATC site distal to the cat ^- mutation increased the rate of mutation less than 2-fold, indicating that mismatch repair can proceed normally with a single site. The mutation rate increased 3-fold after removal of the GATC site proximal to the mutation. In the absence of a GATC site, mutL^- and mutS^- strains exhibited a 2- to 3-fold increased mutation rate as compared to isogenic mutH^- and mut ⁺ strains. This indicates that 50%–70% of replication errors can be corrected in a mutLS-dependent way in the absence of any GATC site to target mismatch correction to newly synthesized DNA strands. Other strand targeting signals, possibly single strand discontinuities, might be used in mutLS-dependent repair     

Effects of recent experience on foraging decisions by bumble bees

The temporal and spatial scales employed by foraging bees in sampling their environment and making foraging decisions should depend both on the limits of bumble bee memory and on the spatial and temporal pattern of rewards in the habitat. We analyzed data from previous experiments to determine how recent foraging experience by bumble bees affects their flight distances to subsequent flowers. A single visit to a flower as sufficient to affect the flight distance to the next flower. However, longer sequences of two or three visits had an additional effect on the subsequent flight distance of individual foragers. This suggests that bumble bees can integrate information from at least three flowers for making a subsequent foraging decision. The existence of memory for floral characteristics at least at this scale may have significance for floral selection in natural environments.     

Bayesian tests for random mating in polyploids

Hardy–Weinberg proportions (HWP) are often explored to evaluate the assumption of random mating. However, in autopolyploids, organisms with more than two sets of homologous chromosomes, HWP and random mating are different hypotheses that require different statistical testing approaches. Currently, the only available methods to test for random mating in autopolyploids (i) heavily rely on asymptotic approximations and (ii) assume genotypes are known, ignoring genotype uncertainty. Furthermore, these approaches are all frequentist, and so do not carry the benefits of Bayesian analysis, including ease of interpretability, incorporation of prior information, and consistency under the null. Here, we present Bayesian approaches to test for random mating, bringing the benefits of Bayesian analysis to this problem. Our Bayesian methods also (i) do not rely on asymptotic approximations, being appropriate for small sample sizes, and (ii) optionally account for genotype uncertainty via genotype likelihoods. We validate our methods in simulations and demonstrate on two real datasets how testing for random mating is more useful for detecting genotyping errors than testing for HWP (in a natural population) and testing for Mendelian segregation (in an experimental S1 population). Our methods are implemented in Version 2.0.2 of the hwep R package on the Comprehensive R Archive Network https://cran.r-project.org/package=hwep .     

Possible role for protein kinase C in the pathogenesis of inborn errors of metabolism

Protein kinase C (PKC) is a ubiquitous enzyme family implicated in the regulation of a large number of short- and long-term intracellular processes. It is hypothesized that modulation of PKC activity may represent, at least in part, a functional link between mutations (genotype) that lead to the pathological accumulation of naturally occurring compounds that affect PKC activity and perturbation of PKC-mediated substrate phosphorylation and cellular function in the corresponding diseases (phenotype). This model provides a unifying putative mechanism by which the phenotypic expression of some inborn errors of metabolism may be explained. Recent studies in a cell-free system of human skin fibroblasts support the hypothesis that alteration of PKC activity may represent the functional link between accumulation of sphingolipids and fatty acyl-CoA esters, and perturbation of cell function in sphingolipidoses and fatty acid oxidation defects, respectively. Further studies will elucidate the effects of these alterations on PKC-mediated short- and long-term cellular functions in these diseases, as well as the possible role of PKC in the pathogensis of other diseases. © 1995 Wiley-Liss, Inc.     

Identification of marker genes in Alzheimer's disease using a machine-learning model

Alzheimer''s Disease (AD) is one of the most common causes of dementia, mostly affecting the elderly population. Currently, there is no proper diagnostic tool or method available for the detection of AD. The present study used two distinct data sets of AD genes, which could be potential biomarkers in the diagnosis. The differentially expressed genes (DEGs) curated from both datasets were used for machine learning classification, tissue expression annotation and co-expression analysis. Further, CNPY3, GPR84, HIST1H2AB, HIST1H2AE, IFNAR1, LMO3, MYO18A, N4BP2L1, PML, SLC4A4, ST8SIA4, TLE1 and N4BP2L1 were identified as highly significant DEGs and exhibited co-expression with other query genes. Moreover, a tissue expression study found that these genes are also expressed in the brain tissue. In addition to the earlier studies for marker gene identification, we have considered a different set of machine learning classifiers to improve the accuracy rate from the analysis. Amongst all the six classification algorithms, J48 emerged as the best classifier, which could be used for differentiating healthy and diseased samples. SMO/SVM and Logit Boost further followed J48 to achieve the classification accuracy.