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The Gompertz model is well known and widely used in many aspects of biology. It has been frequently used to describe the growth of animals and plants, as well as the number or volume of bacteria and cancer cells. Numerous parametrisations and re-parametrisations of varying usefulness are found in the literature, whereof the Gompertz-Laird is one of the more commonly used. Here, we review, present, and discuss the many re-parametrisations and some parameterisations of the Gompertz model, which we divide into Ti (type I)- and W0 (type II)-forms. In the W0-form a starting-point parameter, meaning birth or hatching value (W0), replaces the inflection-time parameter (Ti). We also propose new \"unified\" versions (U-versions) of both the traditional Ti -form and a simplified W0-form. In these, the growth-rate constant represents the relative growth rate instead of merely an unspecified growth coefficient. We also present U-versions where the growth-rate parameters return absolute growth rate (instead of relative). The new U-Gompertz models are special cases of the Unified-Richards (U-Richards) model and thus belong to the Richards family of U-models. As U-models, they have a set of parameters, which are comparable across models in the family, without conversion equations. The improvements are simple, and may seem trivial, but are of great importance to those who study organismal growth, as the two new U-Gompertz forms give easy and fast access to all shape parameters needed for describing most types of growth following the shape of the Gompertz model.

Statistics about the primary research papers on reproductive biology published between 2005 and 2007 that have the highest number of citations in the literature are presented.

Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small “many analyst” study found similar variability in ecology. We expanded the scope of this prior work by implementing a large-scale empirical exploration of the variation in effect sizes and model predictions generated by the analytical decisions of different researchers in ecology and evolutionary biology. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus , to compare sibling number and nestling growth) and one from conservation ecology ( Eucalyptus , to compare grass cover and tree seedling recruitment). The project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects (compatible with our meta-analyses and with all necessary information provided) for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future.

A variety of microscopy techniques are used by researchers in the life and biomedical sciences. As these techniques become more powerful and more complex, it is vital that scientific articles containing images obtained with advanced microscopes include full details about how each image was obtained. To explore the reporting of such details we examined 240 original research articles published in eight journals. We found that the quality of reporting was poor, with some articles containing no information about how images were obtained, and many articles lacking important basic details. Efforts by researchers, funding agencies, journals, equipment manufacturers and staff at shared imaging facilities are required to improve the reporting of experiments that rely on microscopy techniques.

Morphogenesis emerges from complex multiscale interactions between genetic and mechanical processes. To understand these processes, the evolution of cell shape, proliferation and gene expression must be quantified. This quantification is usually performed either in full 3D, which is computationally expensive and technically challenging, or on 2D planar projections, which introduces geometrical artifacts on highly curved organs. Here we present MorphoGraphX (www.MorphoGraphX.org), a software that bridges this gap by working directly with curved surface images extracted from 3D data. In addition to traditional 3D image analysis, we have developed algorithms to operate on curved surfaces, such as cell segmentation, lineage tracking and fluorescence signal quantification. The software's modular design makes it easy to include existing libraries, or to implement new algorithms. Cell geometries extracted with MorphoGraphX can be exported and used as templates for simulation models, providing a powerful platform to investigate the interactions between shape, genes and growth.

Single-cell RNA sequencing studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells by treating cell fate as a probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudo-time ordering of cells and, for each cell state, assigns a probability of differentiating into each terminal state. We apply our algorithm to human bone marrow single-cell RNA sequencing data and detect important landmarks of hematopoietic differentiation. Palantir’s resolution enables the identification of key transcription factors that drive lineage fate choice and closely track when cells lose plasticity. We show that Palantir outperforms existing algorithms in identifying cell lineages and recapitulating gene expression trends during differentiation, is generalizable to diverse tissue types, and is well-suited to resolving less-studied differentiating systems. Palantir uses single-cell RNA-seq data to generate continuous models of differentiation, infer developmental trajectories, and calculate the probabilities of cell fate choices.

Uncovering the genetic and evolutionary basis of local adaptation is a major focus of evolutionary biology. The recent development of cost-effective methods for obtaining high-quality genome-scale data makes it possible to identify some of the loci responsible for adaptive differences among populations. Two basic approaches for identifying putatively locally adaptive loci have been developed and are broadly used: one that identifies loci with unusually high genetic differentiation among populations (differentiation outlier methods) and one that searches for correlations between local population allele frequencies and local environments (genetic-environment association methods). Here, we review the promises and challenges of these genome scan methods, including correcting for the confounding influence of a species’ demographic history, biases caused by missing aspects of the genome, matching scales of environmental data with population structure, and other statistical considerations. In each case, we make suggestions for best practices for maximizing the accuracy and efficiency of genome scans to detect the underlying genetic basis of local adaptation. With attention to their current limitations, genome scan methods can be an important tool in finding the genetic basis of adaptive evolutionary change.

Numerous drug treatments that have recently entered the clinic or clinical trials have their genesis in zebrafish. Zebrafish are well established for their contribution to developmental biology and have now emerged as a powerful preclinical model for human disease, as their disease characteristics, aetiology and progression, and molecular mechanisms are clinically relevant and highly conserved. Zebrafish respond to small molecules and drug treatments at physiologically relevant dose ranges and, when combined with cell-specific or tissue-specific reporters and gene editing technologies, drug activity can be studied at single-cell resolution within the complexity of a whole animal, across tissues and over an extended timescale. These features enable high-throughput and high-content phenotypic drug screening, repurposing of available drugs for personalized and compassionate use, and even the development of new drug classes. Often, drugs and drug leads explored in zebrafish have an inter-organ mechanism of action and would otherwise not be identified through targeted screening approaches. Here, we discuss how zebrafish is an important model for drug discovery, the process of how these discoveries emerge and future opportunities for maximizing zebrafish potential in medical discoveries.Zebrafish are useful model organisms for drug discovery, particularly in screening, disease modelling and toxicity assays. Here, Patton, Zon and Langenau discuss key recent successes in which zebrafish models have had key roles. Advances in gene modification technologies have enabled individual mutations to be recapitulated in zebrafish, leading to individualized treatments. Zebrafish xenografts as cancer models could also lead to rapid, personalized platforms for drug discovery.

Survivors of infant group B streptococcal (GBS) disease are at risk of neurodevelopmental impairment (NDI), a burden not previously systematically quantified. This is the 10th of 11 articles estimating the burden of GBS disease. Here we aimed to estimate NDI in survivors of infant GBS disease. We conducted systematic literature reviews (PubMed/Medline, Embase, Latin American and Caribbean Health Sciences Literature [LILACS], World Health Organization Library Information System [WHOLIS], and Scopus) and sought unpublished data on the risk of NDI after invasive GBS disease in infants <90 days of age. We did meta-analyses to derive pooled estimates of the percentage of infants with NDI following GBS meningitis. We identified 6127 studies, of which 18 met eligibility criteria, all from middle- or high-income contexts. All 18 studies followed up survivors of GBS meningitis; only 5 of these studies also followed up survivors of GBS sepsis and were too few to pool in a meta-analysis. Of meningitis survivors, 32% (95% CI, 25%-38%) had NDI at 18 months of follow-up, including 18% (95% CI, 13%-22%) with moderate to severe NDI. GBS meningitis is an important risk factor for moderate to severe NDI, affecting around 1 in 5 survivors. However, data are limited, and we were unable to estimate NDI after GBS sepsis. Comparability of studies is difficult due to methodological differences including variability in timing of clinical reviews and assessment tools. Follow-up of clinical cases and standardization of methods are essential to fully quantify the total burden of NDI associated with GBS disease, and inform program priorities.