Cell Death and Growth Recovery of Barley after Transient Salt Stress

3 H-thymidine) in old roots were partially recycled for new tissue formation. This result indicates that cell death may have some physiological roles under transient salt stress. Received 11 January 2000/ Accepted in revised form 29 June 2000     

Incorporating data received after a sequential trial has stopped into the final analysis: implementation and comparison of methods

In a sequential clinical trial, accrual of data on patients often continues after the stopping criterion for the study has been met. This is termed "overrunning." Overrunning occurs mainly when the primary response from each patient is measured after some extended observation period. The objective of this article is to compare two methods of allowing for overrunning. In particular, simulation studies are reported that assess the two procedures in terms of how well they maintain the intended type I error rate. The effect on power resulting from the incorporation of "overrunning data" using the two procedures is evaluated.     

Analyzing Variety by Environment Data Using Multiplicative Mixed Models and Adjustments for Spatial Field Trend

The recommendation of new plant varieties for commercial use requires reliable and accurate predictions of the average yield of each variety across a range of target environments and knowledge of important interactions with the environment. This information is obtained from series of plant variety trials, also known as multi-environment trials (MET). Cullis, Gogel, Verbyla, and Thompson (1998) presented a spatial mixed model approach for the analysis of MET data. In this paper we extend the analysis to include multiplicative models for the variety effects in each environment. The multiplicative model corresponds to that used in the multivariate technique of factor analysis. It allows a separate genetic variance for each environment and provides a parsimonious and interpretable model for the genetic covariances between environments. The model can be regarded as a random effects analogue of AMMI (additive main effects and multiplicative interactions). We illustrate the method using a large set of MET data from a South Australian barley breeding program.     

Southern Hemisphere biodiversity and global change: Data gaps and strategies

Long‐term datasets needed to detect the impacts of global change on southern biodiversity are still scarce and often incomplete, challenging adaptation planning and conservation management. Biological data are probably most limited in arid countries and from the oceans, where natural environmental variability (‘noise’) means that long time series are required to detect the ‘signal’ of directional change. Significant national and international investment and collaboration are needed for most southern nations to reliably track biodiversity trends and improve conservation adaptation to rapid climate change. Emerging early warning systems for biodiversity, incorporating regional environmental change drivers, citizen science and regional partnerships, can all help to compensate for existing information gaps and contribute to adaptation planning.     

Protein Phosphotyrosine in Mouse Brain: Developmental Changes and Regulation by Epidermal Growth Factor, Type I Insulin-Like Growth Factor, and Insulin

Using antiphosphotyrosine antibodies, we have investigated protein phosphorylation in mouse brain during development in intact animals and in reaggregated cerebral cultures. Under basal conditions, in vivo and in vitro, the levels of two main phosphoproteins, of Mr 120,000 and 180,000 (pp180), increased with development, reaching a maximum in the early postnatal period and decreasing thereafter. In adult forebrain, pp180 was still highly phosphorylated, but it was not detected in cerebellum or in peripheral tissues. In reaggregated cortical cultures, epidermal growth factor (EGF), type I insulin-like growth factor (IGF-I), and insulin enhanced protein tyrosine phosphorylation of several proteins, which were specific for EGF or IGF-I/insulin. In highly enriched neuronal or astrocytic monolayer cultures, some proteins phosphorylated in basal conditions, or in response to EGF and IGF-I, were found in both types of culture, whereas others appeared cell type specific. In addition, in each cell type, some proteins were phosphorylated under the action of both growth factors. These results indicate that tyrosine protein phosphorylation is maximal in mouse brain during development and is regulated by growth factors in neurons as well as in astrocytes.     

Evaluation of self-similar features in time series of serum growth hormone and prolactin levels by fractal analysis: Effect of delayed sleep and complexity of diurnal variation

We assayed the diurnal concentrations of growth hormone (GH) and prolactin (PRL) in 6 healthy male volunteers to evaluate the self-similar features in the time series of each hormone on the basis of fractal theory and to determine the fractal dimension as an index of the complexity of the diurnal variation. In addition, we assessed the effects of a 6-hour delay in the sleep period on the complexity of the diurnal variaton of these hormones. There was a statistically significant fractal feature in the serum levels of GH both under the nocturnal-sleep and delayed-sleep conditions in all subjects. The time series of the serum PRL concentrations also showed a statistically significant fractal feature under the nocturnal-sleep and delayed-sleep conditions in all subjects. The fractal dimensions of the patterns of the GH or PRL levels were 1.879 and 1.929 or 1.754 and 1.785 under the nocturnal-sleep and delayed-sleep conditions, respectively. Two-way ANOVA revealed no significant difference in the fractal dimension between the two sleep conditions but did reveal a significant difference between the fractal dimensions of the GH and PRL levels. These results showed (1) that delayed sleep had no significant effect on the complexity of the diurnal pattern of these hormones, and (2) that the diurnal pattern of the GH levels was more complex than that of the PRL levels.     

Ecology of Mangrove Growth and Recovery in the Lesser Antilles: State of Knowledge and Basis for Restoration Projects

Whereas the increasing knowledge on tropical coastal wetlands highlights the ecological and economical importance of such ecosystems, anthropogenic activities within the coastal zone have caused substantial, irreversible losses of mangrove areas in the Lesser Antilles during the last decades. Such a paradox gives strength to compensatory policy efforts toward mangrove restoration. We review the available knowledge on the ecology of mangrove growth and recovery in the Lesser Antilles as a contribution to possible restoration projects in such islands. Distribution of species follows a general pattern of seaward/landward zonation according to their respective tolerance to flooding and to pore‐water salinity. An experimental study of seedling growth following simulated oil spill has documented the tolerance of Rhizophora mangle and Avicennia germinans seedlings to oil concentration in soils and the effects of natural biotic and abiotic factors on seedlings growth and survival. Monitoring mangrove recovery following hurricane Hugo has given information on growth patterns, from seedling to sapling stages, according to species and site conditions. Forest recovery was mostly due to pre‐established seedlings. For the large Rhizophora propagules, buoyancy appears to be a quite inefficient way of dispersal far inland from the sea shore or riversides. Causes of recovery failure are discussed. From these results we attempt to answer the questions when, where, how to plant mangroves, and what species to use.     

Growth and mortality of bigeye tuna <Emphasis Type="Italic">Thunnus obesus</Emphasis> (Scombridae) in the eastern and central tropical Pacific Ocean

Biological parameters such as age, growth and age (or size) at maturity are vital for stock assessment and management. Aging is essential in yielding such information. However, limited aging studies have been conducted for large tropical pelagic species in the eastern and central tropical Pacific Ocean. The objective of this study is to conduct a length frequency analysis for estimating growth and mortality of bigeye tuna in the eastern and central tropical Pacific Ocean using samples from the Chinese longline fishery during February to November 2006. The von Bertalanffy growth parameters of asymptotic fork length L _∞ and growth coefficient k were estimated at L _∞ = 207.4 cm fork length, k = 0.23 year^-1, and theoretical age at zero length t ₀ = −0.40 year. The total mortality rate (Z) was estimated to be 0.60; the fishing mortality rate (F) and the natural mortality rate (M) were 0.25 year^-1 and 0.35 year^-1, respectively. The exploitation rate (E) was 0.16. This study provides the estimates of growth and mortality rate for bigeye tuna in the eastern and central tropical Pacific Ocean, which can be used as biological input parameters in further stock evaluations in this region. However, age analysis, further validation of the age composition and stock structure are needed for future studies.     

Growth in epiphytic bromeliads: response to the relative supply of phosphorus and nitrogen

Insufficient nitrogen (N) and phosphorus (P) frequently limit primary production. Although most nutrient studies on vascular epiphytes have focused on N uptake, circumstantial evidence suggests that P rather than N is the most limiting element for growth in this plant group. We directly tested this by subjecting a total of 162 small individuals of three bromeliad species ( Guzmania monostachia , Tillandsia elongata , Werauhia sanguinolenta ) to three N and three P levels using a full-factorial experimental design, and determined relative growth rates (RGR) and nutrient acquisition over a period of 11 weeks. Both N and P supply had a significant effect on RGR, but only tissue P concentrations were correlated with growth. Uptake rates of N and P, in contrast, were not correlated with RGR. Increased nutrient supply led to an up to sevenfold increase in tissue P concentration compared to natural conditions, while concentrations of N hardly changed or even decreased. All treatment combinations, even at the lowest experimental P supply, led to decreased N:P ratios. We conclude that P is at least as limiting as N for vegetative function under natural conditions in these epiphytic bromeliads. This conclusion is in line with the general notion of the prevalence of P limitation for the functioning of terrestrial vegetation in the tropics.     

Sampling Plan Optimization: A Data Review and Sampling Frequency Evaluation Process

Lawrence Livermore National Laboratory (LLNL) uses a cost-effective sampling (CES) methodology to evaluate and review ground water contaminant data and optimize the site's ground water monitoring plan. The CES methodology is part of LLNL's regulatory approved compliance monitoring plan (Lamarre et al., 1996). It allows LLNL to adjust the ground water sampling plan every quarter in response to changing conditions at the site. Since the use of the CES methodology has been approved by the appropriate regulatory agencies, such adjustments do not need additional regulatory approval. This permits LLNL to respond more quickly to changing conditions. The CES methodology bases the sampling frequency for each location on trend, variability, and magnitude statistics describing the contaminants at that location, and on the input of the technical staff (hydrologists, chemists, statisticians, and project leaders). After initial setup is complete, each application of CES takes only a few days for as many as 400 wells. Effective use of the CES methodology requires sufficient data, an understanding of contaminant transport at the site, and an adequate number of monitoring wells downgradient of the contamination. The initial implementation of CES at LLNL in 1992 produced a 40% reduction in the required number of annual routine ground water samples at LLNL. This has saved LLNL $390,000 annually in sampling, analysis, and data management costs.     

A Bayesian Two‐Part Latent Class Model for Longitudinal Medical Expenditure Data: Assessing the Impact of Mental Health and Substance Abuse Parity

Summary In 2001, the U.S. Office of Personnel Management required all health plans participating in the Federal Employees Health Benefits Program to offer mental health and substance abuse benefits on par with general medical benefits. The initial evaluation found that, on average, parity did not result in either large spending increases or increased service use over the four‐year observational period. However, some groups of enrollees may have benefited from parity more than others. To address this question, we propose a Bayesian two‐part latent class model to characterize the effect of parity on mental health use and expenditures. Within each class, we fit a two‐part random effects model to separately model the probability of mental health or substance abuse use and mean spending trajectories among those having used services. The regression coefficients and random effect covariances vary across classes, thus permitting class‐varying correlation structures between the two components of the model. Our analysis identified three classes of subjects: a group of low spenders that tended to be male, had relatively rare use of services, and decreased their spending pattern over time; a group of moderate spenders, primarily female, that had an increase in both use and mean spending after the introduction of parity; and a group of high spenders that tended to have chronic service use and constant spending patterns. By examining the joint 95% highest probability density regions of expected changes in use and spending for each class, we confirmed that parity had an impact only on the moderate spender class.     

Convolution-based one and two component FRAP analysis: theory and application

The method of fluorescence redistribution after photobleaching (FRAP) is increasingly receiving interest in biological applications as it is nowadays used not only to determine mobility parameters per se, but to investigate dynamic changes in the concentration or distribution of diffusing molecules. Here, we develop a new simple convolution-based approach to analyze FRAP data using the whole image information. This method does not require information about the timing and localization of the bleaching event but uses the first image acquired directly after photobleaching to calculate the intensity distributions, instead. Changes in pools of molecules with different velocities, which are monitored by applying repetitive FRAP experiments within a single cell, can be analyzed by means of a global model by assuming two global diffusion coefficients with changing portions. We validate the approach by simulation and show that translocation of the YFP-fused PH-domain of phospholipase Cδ1 can be quantitatively monitored by FRAP analysis in a time-resolved manner. The new FRAP data analysis procedure may be applied to investigate signal transduction pathways using biosensors that change their mobility. An altered mobility in response to the activation of signaling cascades may result either from an altered size of the biosensor, e.g. due to multimerization processes or from translocation of the sensor to an environment with different viscosity. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Comparative Analyses of Data Independent Acquisition Mass Spectrometric Approaches: DIA,WiSIM‐DIA,and Untargeted DIA

Data‐independent acquisition (DIA) is an emerging technology for quantitative proteomics. Current DIA focusses on the identification and quantitation of fragment ions that are generated from multiple peptides contained in the same selection window of several to tens of m/z. An alternative approach is WiSIM‐DIA, which combines conventional DIA with wide‐SIM (wide selected‐ion monitoring) windows to partition the precursor m/z space to produce high‐quality precursor ion chromatograms. However, WiSIM‐DIA has been underexplored; it remains unclear if it is a viable alternative to DIA. We demonstrate that WiSIM‐DIA quantified more than 24 000 unique peptides over five orders of magnitude in a single 2 h analysis of a neuronal synapse‐enriched fraction, compared to 31 000 in DIA. There is a strong correlation between abundance values of peptides quantified in both the DIA and WiSIM‐DIA datasets. Interestingly, the S/N ratio of these peptides is not correlated. We further show that peptide identification directly from DIA spectra identified >2000 proteins, which included unique peptides not found in spectral libraries generated by DDA.     

Towards a Circular Economy in Australian Agri‐food Industry: An Application of Input‐Output Oriented Approaches for Analyzing Resource Efficiency and Competitiveness Potential

The food industry in Australia (agriculture and manufacturing) plays a fundamental role in contributing to socioeconomic sectors nationally. However, alongside the benefits, the industry also produces environmental burdens associated with the production of food. Sectorally, agriculture is the largest consumer of water. Additionally, land degradation, greenhouse gas emissions, energy consumption, and waste generation are considered the main environmental impacts caused by the industry. The research project aims to evaluate the eco‐efficiency performance of various subsectors in the Australian agri‐food systems through the use of input‐output–oriented approaches of data envelopment analysis and material flow analysis. This helps in establishing environmental and economic indicators for the industry. The results have shown inefficiencies during the life cycle of food production in Australia. Following the principles of industrial ecology, the study recommends the implementation of sustainable processes to increase efficiency, diminish undesirable outputs, and decrease the use of nonrenewable inputs within the production cycle. Broadly, the research outcomes are useful to inform decision makers about the advantages of moving from a traditional linear system to a circular production system, where a sustainable and efficient circular economy could be created in the Australian food industry.     

Unified Materials Information System (UMIS): An Integrated Material Stocks and Flows Data Structure

Modern society depends on the use of many diverse materials. Effectively managing these materials is becoming increasingly important and complex, from the analysis of supply chains, to quantifying their environmental impacts, to understanding future resource availability. Material stocks and flows data enable such analyses, but currently exist mainly as discrete packages, with highly varied type, scope, and structure. These factors constitute a powerful barrier to holistic integration and thus universal analysis of existing and yet to be published material stocks and flows data. We present the Unified Materials Information System (UMIS) to overcome this barrier by enabling material stocks and flows data to be comprehensively integrated across space, time, materials, and data type independent of their disaggregation, without loss of information, and avoiding double counting. UMIS can therefore be applied to structure diverse material stocks and flows data and their metadata across material systems analysis methods such as material flow analysis (MFA), input‐output analysis, and life cycle assessment. UMIS uniquely labels and visualizes processes and flows in UMIS diagrams; therefore, material stocks and flows data visualized in UMIS diagrams can be individually referenced in databases and computational models. Applications of UMIS to restructure existing material stocks and flows data represented by block flow diagrams, system dynamics diagrams, Sankey diagrams, matrices, and derived using the economy‐wide MFA classification system are presented to exemplify use. UMIS advances the capabilities with which complex quantitative material systems analysis, archiving, and computation of material stocks and flows data can be performed.     

Structured exploratory data analysis (SEDA) of finger ridge-count inheritance: I. Major gene index, midparental correlation, and offspring-between-parents function in 125 south Indian families

Fourteen dermatoglyphic traits measured on 125 Velanadu Brahmin families were analyzed for mode of inheritance using three Structured Exploratory Data Analysis (SEDA) statistics: the major gene index, the offspring between parents function, and the traditional midparental correlation coefficient. Since the traits are integer valued with restricted ranges of variation, we simulated various transmission models with discrete expression to better understand the nature of the SEDA statistics for such variables. In addition, permutation procedures were employed to aid the interpretation of the SEDA results. These analyses suggest that corresponding homologous fingers on the left and right hands exhibit similar transmission characteristics. The relationship of the parent and child total ridge-counts of the two hands separately, as well as their combined total, virtually simulate complete Galtonian blending inheritance. Results for the individual digital ridge-counts as well as the pattern-intensity-index variable also suggest a multifactorial mode of transmission or possibly one involving several genes.     

popsicleR: A R Package for Pre-processing and Quality Control Analysis of Single Cell RNA-seq Data

The advent of single-cell sequencing is providing unprecedented opportunities to disentangle tissue complexity and investigate cell identities and functions. However, the analysis of single cell data is a challenging, multi-step process that requires both advanced computational skills and biological sensibility. When dealing with single cell RNA-seq (scRNA-seq) data, the presence of technical artifacts, noise, and biological biases imposes to first identify, and eventually remove, unreliable signals from low-quality cells and unwanted sources of variation that might affect the efficacy of subsequent downstream modules. Pre-processing and quality control (QC) of scRNA-seq data is a laborious process consisting in the manual combination of different computational strategies to quantify QC-metrics and define optimal sets of pre-processing parameters.Here we present popsicleR, a R package to interactively guide skilled and unskilled command line-users in the pre-processing and QC analysis of scRNA-seq data. The package integrates, into several main wrapper functions, methods derived from widely used pipelines for the estimation of quality-control metrics, filtering of low-quality cells, data normalization, removal of technical and biological biases, and for cell clustering and annotation. popsicleR starts from either the output files of the Cell Ranger pipeline from 10X Genomics or from a feature-barcode matrix of raw counts generated from any scRNA-seq technology. Open-source code, installation instructions, and a case study tutorial are freely available at https://github.com/bicciatolab/popsicleR.