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Methods in human growth research
This volume is a review of up-to-date methods used in human growth research.
Book
Methods in Human Growth Research
In order to gain an understanding of the dynamics of human individual and average growth patterns it is essential that the right methods are selected. There are a variety of methods available to analyse individual growth patterns, to estimate variation in different growth measures in populations and to relate genetic and environmental factors to individual and average growth. This 2004 volume provides an overview of modern techniques for the assessment and collection of growth data and methods of analysis for individual and population growth data. The book contains the basic mathematical and statistical tools required to understand the concepts of the methods under discussion and worked examples of analyses, but it is neither a mathematical treatise, nor a recipe book for growth data analysis. Aimed at junior and senior researchers involved in the analysis of human growth data, this book will be an essential reference for anthropologists, auxologists and paediatricians.
eBook
Exploring the potential benefits of growth hormone co-treatment on embryo quality in IVF: a randomized controlled open-label trial
Research questionWhile growth hormone (GH) is hypothesized to potentially enhance embryo quality, results of current basic and clinical researches remain inconclusive. This study assesses the effect of GH supplementation on embryo quality and explores the relationship between baseline insulin-like growth factor-1 (IGF-1) levels and the efficacy of GH supplementation among Chinese patients undergoing in vitro fertilization (IVF).DesignA randomized controlled Open-label trial was performed with 128 women experiencing poor embryonic development in IVF. Participants were allocated to the GH group (GH + Gonadotropin-Releasing Hormone [GnRH] antagonist protocol) and the Control group (GnRH antagonist protocol). The primary outcome was the number of high-quality embryos on Day 3.ResultsPatients in the GH group required significantly lower total doses of gonadotropin (2213 ± 667 IU vs. 2573 ± 630 IU, p = 0.0058) and shorter duration of controlled ovarian stimulation (10.1 ± 1.60 days vs. 10.6 ± 1.30 days, p = 0.0488). While there was no statistically significant overall increase in the number of high-quality embryos, subgroup analysis indicated that patients with lower baseline IGF-1 levels, especially those below the lowest quartile, might show a higher rate of high-quality embryos with GH supplementation (p = 0.0488). Additionally, the fresh embryo transfer clinical pregnancy rate was numerically higher in the GH supplementation group (46.2%) compared to the control group (38.5%), although not statistically significant.ConclusionsThis study suggests that GH co-treatment may enhance ovarian responsiveness in IVF patients with poor embryo quality, thereby reducing the dosage and duration of Gonadotropin (Gn) administration. Among individuals with lower IGF-1 levels, adding GH may improve the rate of high-quality embryos, highlighting the potential benefits of personalized treatment strategies in IVF.Clinical trial registrationClinicalTrials.gov ID: NCT03966339 (Registration time: 2019-05-24).
Journal Article
Fast and simple tool for the quantification of biofilm-embedded cells sub-populations from fluorescent microscopic images
Fluorescent staining is a common tool for both quantitative and qualitative assessment of pro- and eukaryotic cells sub-population fractions by using microscopy and flow cytometry. However, direct cell counting by flow cytometry is often limited, for example when working with cells rigidly adhered either to each other or to external surfaces like bacterial biofilms or adherent cell lines and tissue samples. An alternative approach is provided by using fluorescent microscopy and confocal laser scanning microscopy (CLSM), which enables the evaluation of fractions of cells subpopulations in a given sample. For the quantitative assessment of cell fractions in microphotographs, we suggest a simple two-step algorithm that combines single cells selection and the statistical analysis. To facilitate the first step, we suggest a simple procedure that supports finding the balance between the detection threshold and the typical size of single cells based on objective cell size distribution analysis. Based on a series of experimental measurements performed on bacterial and eukaryotic cells under various conditions, we show explicitly that the suggested approach effectively accounts for the fractions of different cell sub-populations (like the live/dead staining in our samples) in all studied cases that are in good agreement with manual cell counting on microphotographs and flow cytometry data. This algorithm is implemented as a simple software tool that includes an intuitive and user-friendly graphical interface for the initial adjustment of algorithm parameters to the microphotographs analysis as well as for the sequential analysis of homogeneous series of similar microscopic images without further user intervention. The software tool entitled BioFilmAnalyzer is freely available online at https://bitbucket.org/rogex/biofilmanalyzer/downloads/.
Journal Article
PyClone-VI: scalable inference of clonal population structures using whole genome data
Background
At diagnosis tumours are typically composed of a mixture of genomically distinct malignant cell populations. Bulk sequencing of tumour samples coupled with computational deconvolution can be used to identify these populations and study cancer evolution. Existing computational methods for populations deconvolution are slow and/or potentially inaccurate when applied to large datasets generated by whole genome sequencing data.
Results
We describe PyClone-VI, a computationally efficient Bayesian statistical method for inferring the clonal population structure of cancers. We demonstrate the utility of the method by analyzing data from 1717 patients from PCAWG study and 100 patients from the TRACERx study.
Conclusions
Our proposed method is 10–100× times faster than existing methods, while providing results which are as accurate. Software implementing our method is freely available
https://github.com/Roth-Lab/pyclone-vi
.
Journal Article
Identifying typical trajectories in longitudinal data: modelling strategies and interpretations
Individual-level longitudinal data on biological, behavioural, and social dimensions are becoming increasingly available. Typically, these data are analysed using mixed effects models, with the result summarised in terms of an average trajectory plus measures of the individual variations around this average. However, public health investigations would benefit from finer modelling of these individual variations which identify not just one average trajectory, but several typical trajectories. If evidence of heterogeneity in the development of these variables is found, the role played by temporally preceding (explanatory) variables as well as the potential impact of differential trajectories may have on later outcomes is often of interest. A wide choice of methods for uncovering typical trajectories and relating them to precursors and later outcomes exists. However, despite their increasing use, no practical overview of these methods targeted at epidemiological applications exists. Hence we provide: (a) a review of the three most commonly used methods for the identification of latent trajectories (growth mixture models, latent class growth analysis, and longitudinal latent class analysis); and (b) recommendations for the identification and interpretation of these trajectories and of their relationship with other variables. For illustration, we use longitudinal data on childhood body mass index and parental reports of fussy eating, collected in the Avon Longitudinal Study of Parents and Children.
Journal Article
Statistically based splicing detection reveals neural enrichment and tissue-specific induction of circular RNA during human fetal development
Background
The pervasive expression of circular RNA is a recently discovered feature of gene expression in highly diverged eukaryotes, but the functions of most circular RNAs are still unknown. Computational methods to discover and quantify circular RNA are essential. Moreover, discovering biological contexts where circular RNAs are regulated will shed light on potential functional roles they may play.
Results
We present a new algorithm that increases the sensitivity and specificity of circular RNA detection by discovering and quantifying circular and linear RNA splicing events at both annotated and un-annotated exon boundaries, including intergenic regions of the genome, with high statistical confidence. Unlike approaches that rely on read count and exon homology to determine confidence in prediction of circular RNA expression, our algorithm uses a statistical approach. Using our algorithm, we unveiled striking induction of general and tissue-specific circular RNAs, including in the heart and lung, during human fetal development. We discover regions of the human fetal brain, such as the frontal cortex, with marked enrichment for genes where circular RNA isoforms are dominant.
Conclusions
The vast majority of circular RNA production occurs at major spliceosome splice sites; however, we find the first examples of developmentally induced circular RNAs processed by the minor spliceosome, and an enriched propensity of minor spliceosome donors to splice into circular RNA at un-annotated, rather than annotated, exons. Together, these results suggest a potentially significant role for circular RNA in human development.
Journal Article
Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model
Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian time series model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the log transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectories. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for cases and deaths in U.S. states. We evaluated the model by training it on progressively longer periods of the pandemic and computing its predictive accuracy over 21-day forecasts. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Colorado, and West Virginia. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course.
Journal Article
The early spread and epidemic ignition of HIV-1 in human populations
Thirty years after the discovery of HIV-1, the early transmission, dissemination, and establishment of the virus in human populations remain unclear. Using statistical approaches applied to HIV-1 sequence data from central Africa, we show that from the 1920s Kinshasa (in what is now the Democratic Republic of Congo) was the focus of early transmission and the source of pre-1960 pandemic viruses elsewhere. Location and dating estimates were validated using the earliest HIV-1 archival sample, also from Kinshasa. The epidemic histories of HIV-1 group M and nonpandemic group O were similar until ~1960, after which group M underwent an epidemiological transition and outpaced regional population growth. Our results reconstruct the early dynamics of HIV-1 and emphasize the role of social changes and transport networks in the establishment of this virus in human populations.
Journal Article