Senescence vs. sustenance: evolutionary-demographic models of aging

Humans, and many other species, suffer senescence: mortality increases and fertility declines with adult age. Some species, however, enjoy sustenance: mortality and fertility remain constant. Here we develop simple but general evolutionary-demographic models to explain the conditions that favor senescence vs. sustenance. The models illustrate how mathematical demography can deepen understanding of the evolution of aging.

Senescence vs. sustenance: Evolutionary-demographic models of aging

Humans, and many other species, suffer senescence: mortality increases and fertility declines with adult age. Some species, however, enjoy sustenance: mortality and fertility remain constant. Here we develop simple but general evolutionary-demographic models to explain the conditions that favor senescence vs. sustenance. The models illustrate how mathematical demography can deepen understanding of the evolution of aging.aging, eusociality, evolution, fertility, hydra, mortality, senescence, sustenance

Translucent windows: How uncertainty in competitive interactions impacts detection of community pattern

Trait variation and similarity among coexisting species can provide a window into the mechanisms that maintain their coexistence. Recent theoretical explorations suggest that competitive interactions will lead to groups, or clusters, of species with similar traits. However, theoretical predictions typically assume complete knowledge of the map between competition and measured traits. These assumptions limit the plausible application of these patterns for inferring competitive interactions in nature. Here we relax these restrictions and find that the clustering pattern is robust to contributions of unknown or unobserved niche axes. However, it may not be visible unless measured traits are close proxies for niche strategies. We conclude that patterns along single niche axes may reveal properties of interspecific competition in nature, but detecting these patterns requires natural history expertise firmly tying traits to niches.Comment: Main text: 18 pages, 6 figures. Appendices: A-G, 6 supplementary figures. This is the peer reviewed version of the article of the same title which has been accepted for publication at Ecology Letters. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archivin

The Bipartite Structure of the tRNA m\u3csup\u3e1\u3c/sup\u3eA58 Methyltransferase from \u3cem\u3eS. cerevisiae\u3c/em\u3e is Conserved in Humans

Among all types of RNA, tRNA is unique given that it possesses the largest assortment and abundance of modified nucleosides. The methylation at N1 of adenosine 58 is a conserved modification, occurring in bacterial, archaeal, and eukaryotic tRNAs. In the yeast Saccharomyces cerevisiae, the tRNA 1-methyladenosine 58 (m1A58) methyltransferase (Mtase) is a two-subunit enzyme encoded by the essential genes TRM6 (GCD10) and TRM61 (GCD14). While the significance of many tRNA modifications is poorly understood, methylation of A58 is known to be critical for maintaining the stability of initiator tRNAMet in yeast. Furthermore, all retroviruses utilize m1A58-containing tRNAs to prime reverse transcription, and it has been shown that the presence of m1A58 in human tRNA3 Lys is needed for accurate termination of plus-strand strong-stop DNA synthesis during HIV-1 replication. In this study we have identified the human homologs of the yeast m1A Mtase through amino acid sequence identity and complementation of trm6 and trm61 mutant phenotypes. When coexpressed in yeast, human Trm6p and Trm61p restored the formation of m1A in tRNA, modifying both yeast initiator tRNAMet and human tRNA3 Lys. Stable hTrm6p/hTrm61p complexes purified from yeast maintained tRNA m1A Mtase activity in vitro. The human m1A Mtase complex also exhibited substrate specificity—modifying wild-type yeast tRNAi Met but not an A58U mutant. Therefore, the human tRNA m1A Mtase shares both functional and structural homology with the yeast tRNA m1A Mtase, possessing similar enzymatic activity as well as a conserved binary composition

An evidence based ranking system for multiple studies designs for informing public policy. An example using interventions associated with Salmonella in swine.

Using the association between feed characteristics and Salmonella prevalence we will present an approach to combining data with multiple outcomes from multiple studies designs. The approach may be a method of informing policy makers in the area of food safety when a large amount of heterogeneous literature is available about a topic. The procedure for a systematic review of the literature was followed until the synthesis component. However, to combine the evidence we modified of the FDA Interim Evidence Ranking System for Scientific Information. Each study was characterized as one of 5 study design types based on evidentiary value. After classification by evidentiary value, the studies were considered collectively to rate the strength of the body of evidence based on quantity and consistency. The quantity ranking considered the number of studies, the number of individuals studied and generalizability to the target population. The consistency ranking considered whether studies with different designs reported similar findings. After ranking the body of evidence, an overall ranking was assigned for the strength of the evidence. The final ranking system had four levels. For example, the highest rank of scientific evidence, reflects a high level of comfort among qualified scientists that the association/relationship is scientifically valid. This level ranked relationship would be considered to have a very low probability of significant new data overturning the conclusion that the relationship is valid or significantly changing the nature of the relationship

A focus on cross-purpose tools, automated recognition of study design in multiple disciplines, and evaluation of automation tools: a summary of significant discussions at the fourth meeting of the International Collaboration for Automation of Systematic Reviews (ICASR)

The fourth meeting of the International Collaboration for Automation of Systematic Reviews (ICASR) was held 5–6 November 2019 in The Hague, the Netherlands. ICASR is an interdisciplinary group whose goal is to maximize the use of technology for conducting rapid, accurate, and efficient systematic reviews of scientific evidence. The group seeks to facilitate the development and acceptance of automated techniques for systematic reviews. In 2018, the major themes discussed were the transferability of automation tools (i.e., tools developed for other purposes that might be used by systematic reviewers), the automated recognition of study design in multiple disciplines and applications, and approaches for the evaluation of automation tools

The case for negative senescence

Negative senescence is characterized by a decline in mortality with age after reproductive maturity, generally accompanied by an increase in fecundity. Hamilton (1966) ruled out negative senescence: we adumbrate the deficiencies of his model. We review empirical studies of various plants and some kinds of animals that may experience negative senescence and conclude that negative senescence may be widespread, especially in indeterminate-growth species for which size and fertility increase with age. We develop optimization models of life-history strategies that demonstrate that negative senescence is theoretically possible. More generally, our models contribute to understanding of the evolutionary and demographic forces that mold the agetrajectories of mortality, fertility and growth.

A question of trust: can we build an evidence base to gain trust in systematic review automation technologies?

Background Although many aspects of systematic reviews use computational tools, systematic reviewers have been reluctant to adopt machine learning tools. Discussion We discuss that the potential reason for the slow adoption of machine learning tools into systematic reviews is multifactorial. We focus on the current absence of trust in automation and set-up challenges as major barriers to adoption. It is important that reviews produced using automation tools are considered non-inferior or superior to current practice. However, this standard will likely not be sufficient to lead to widespread adoption. As with many technologies, it is important that reviewers see “others” in the review community using automation tools. Adoption will also be slow if the automation tools are not compatible with workflows and tasks currently used to produce reviews. Many automation tools being developed for systematic reviews mimic classification problems. Therefore, the evidence that these automation tools are non-inferior or superior can be presented using methods similar to diagnostic test evaluations, i.e., precision and recall compared to a human reviewer. However, the assessment of automation tools does present unique challenges for investigators and systematic reviewers, including the need to clarify which metrics are of interest to the systematic review community and the unique documentation challenges for reproducible software experiments. Conclusion We discuss adoption barriers with the goal of providing tool developers with guidance as to how to design and report such evaluations and for end users to assess their validity. Further, we discuss approaches to formatting and announcing publicly available datasets suitable for assessment of automation technologies and tools. Making these resources available will increase trust that tools are non-inferior or superior to current practice. Finally, we identify that, even with evidence that automation tools are non-inferior or superior to current practice, substantial set-up challenges remain for main stream integration of automation into the systematic review process