Primer-directed enzymatic amplification of DNA with a thermostable DNA polymerase

A thermostable DNA polymerase was used in an in vitro DNA amplification procedure, the polymerase chain reaction. The enzyme, isolated from Thermus aquaticus, greatly simplifies the procedure and, by enabling the amplification reaction to be performed at higher temperatures, significantly improves the specificity, yield, sensitivity, and length of products that can be amplified. Single-copy genomic sequences were amplified by a factor of more than 10 million with very high specificity, and DNA segments up to 2000 base pairs were readily amplified. In addition, the method was used to amplify and detect a target DNA molecule present only once in a sample of 10(5) cells.     

Phosphorus removal in a marine prototype, recirculating aquaculture system

Phosphorus dynamics were examined in a prototype, zero-discharge, marine-recirculating system. Operation of the system without discharge of water and sludge was enabled by recirculation of effluent water through two separate treatment loops. Surface water from the fish basin was pumped over a trickling filter in one loop, while bottom-water was recirculated through a sedimentation basin followed by a fluidized bed reactor in the other treatment loop. Ammonia oxidation to nitrate in the trickling filter and organic matter digestion together with nitrate reduction in the sedimentation basin and fluidized bed reactor were the main biological features of this treatment system. Orthophosphate concentrations did not exceed 15 mg PO₄–P/l in the culture water during more than 1 year of system operation. Much of the phosphorus was retained within the sedimentation basin and fluidized bed reactor. In these treatment stages, the phosphorus content of organic matter was as high as 17.5% and 19%, respectively. High concentrations of total phosphorus and low concentrations of soluble orthophosphate were measured in the initial stages of sedimentation under oxic and anoxic conditions, suggesting that most of the phosphorus was associated with organic matter. Depletion of oxygen and nitrate in the sludge layers of the sedimentation basin coincided with sulfate reduction to sulfide and a release of soluble orthophosphate. The observed phosphorus dynamics in this marine system supported findings from previous studies in which it was demonstrated that denitrifiers underlie phosphorus immobilization under these conditions.     

Differences between tight and loose cultures: a 33-nation study

With data from 33 nations, we illustrate the differences between cultures that are tight (have many strong norms and a low tolerance of deviant behavior) versus loose (have weak social norms and a high tolerance of deviant behavior). Tightness-looseness is part of a complex, loosely integrated multilevel system that comprises distal ecological and historical threats (e.g., high population density, resource scarcity, a history of territorial conflict, and disease and environmental threats), broad versus narrow socialization in societal institutions (e.g., autocracy, media regulations), the strength of everyday recurring situations, and micro-level psychological affordances (e.g., prevention self-guides, high regulatory strength, need for structure). This research advances knowledge that can foster cross-cultural understanding in a world of increasing global interdependence and has implications for modeling cultural change.     

Afforestation of semi-arid shrubland reduces biogenic NO emission from soil

Nitric oxide (NO) plays a central role in the formation of tropospheric ozone, hydroxyl radicals, as well as nitrous and nitric acids. There are, however, large uncertainties around estimates of global NO emissions due to the paucity of data. In particular, there is little information on the rate of NO emission and its sensitivity to processes such as land use changes in dry environments. Here we report on a two-year study on the influence of afforestation on soil NO fluxes in the semi-arid afforestation system in Southern Israel (Yatir forest, mean annual precipitation ∼280 mm). Laboratory incubations were carried out under seasonally defined conditions of soil moisture and temperature using soils sampled in different seasons from the native shrubland (taken both under shrub canopy and in the inter-shrub areas), and from the adjacent ∼2800 ha, 40-year-old pine afforestation site. Combining laboratory results with field measurements of soil moisture and temperature, we up-scaled soil-atmosphere NO fluxes to the ecosystem level. The different microsites differed in their annual mean NO release rates (0.04, 0.14 and 0.03 mg m⁻² d⁻¹ for the shrubland under and between shrubs and for the forest, respectively), and exhibited high inter-seasonal variability in NO emission rates (ranging from zero up to 0.25 mg m⁻² d⁻¹ in the wet and dry-rewetting seasons, respectively), as well as in temperature responses. Up-scaling results to annual and ecosystem scales indicated that afforestation of the semi-arid shrubland could reduce soil NO emission by up to 65%.     

A Spatio-Temporal Downscaler for Output From Numerical Models

Often, in environmental data collection, data arise from two sources: numerical models and monitoring networks. The first source provides predictions at the level of grid cells, while the second source gives measurements at points. The first is characterized by full spatial coverage of the region of interest, high temporal resolution, no missing data but consequential calibration concerns. The second tends to be sparsely collected in space with coarser temporal resolution, often with missing data but, where recorded, provides, essentially, the true value. Accommodating the spatial misalignment between the two types of data is of fundamental importance for both improved predictions of exposure as well as for evaluation and calibration of the numerical model. In this article we propose a simple, fully model-based strategy to downscale the output from numerical models to point level. The static spatial model, specified within a Bayesian framework, regresses the observed data on the numerical model output using spatially-varying coefficients which are specified through a correlated spatial Gaussian process.     

Screening for heat tolerance in common bean (Phaseolus vulgaris L.) lines and cultivars using JIP-test

The changes in chlorophyll a fluorescence, caused by high temperature (HT), have been used to study heat-tolerance in nine common bean (Phaseolus vulgaris L.) accessions (3 cultivars and 6 lines) available in the gene banks of Maritsa Vegetable Crops Research Institute and Institute for Plant Genetic Resources. The plants were grown in controlled greenhouse conditions until the blossoming stage, treated with HT (45° C) for 2 h and returned for recovery for 4 h at 23° C. The obtained results allowed us to differentiate four groups, among the studied bean plants, by their response to HT. The first group, includes a single cultivar – ‘Secuntsa’. The accession was considered heat sensitive, because it showed a decrease of its total performance index (PI_total), calculated by JIP-test, during HT stress and did not recover to the initial values. The second group, comprised of lines RH13, BBSR17, BBSR28, and ‘Starozagorski cher’, expressed an increase in PI_total during heat stress, which however was followed by a decline in PI_total values during recovery after HT treatment. The third group – RRR46 displayed a decrease in various JIP-test parameters during HT treatment followed by full recovery after returning the plants to 23° C. The accessions from the fourth group RH26D, ‘Ranit’, similarly to the control heat tolerant line 83201007 did not show significant alteration in PI_total. We assumed that the accessions from the fourth group are heat-tolerant. The line RRR46 is also promising in terms of heat tolerance because of its flexible response to HT treatment. These genotypes will be used in our further breeding program.     

Joint Modeling of Climate Niches for Adult and Juvenile Trees

Typical ecological gradient analysis for plant species considers variation in the response along a gradient of covariate values, for example, temperature or precipitation. Response is customarily modeled through the presence/absence or a suitable measure of abundance or both. Such analysis enables the creation of a climate niche or range limits for the species using this covariate. Interest often extends to two climate covariates, thus seeking a climate niche in two-dimensional space. It also seeks to learn whether the niche changes over life stages of the species. For instance, is the niche for juveniles different from that for adults? Across the climate domain, where are seedlings relatively more or less abundant than adults? Adult abundance is measured through basal area, juvenile abundance through seedling counts. Our contribution is to describe a coherent modeling approach to address the foregoing objectives. We construct a hierarchical stochastic specification that jointly models juveniles and adults with regard to their two-dimensional climate niches. Joint modeling of the abundance response surfaces is proposed because seedlings and adults are living jointly, competitively and is justified through exploratory analysis. Joint modeling can be challenging when one response is counts and the other is area. We model adult abundance and then juvenile abundance driven by adult abundance. Due to excess zeroes over our study plots, we employ zero-inflated models for both adult and seedling abundance. We demonstrate the benefits of the joint modeling through out-of-sample predictive performance. Our abundance data come from the USDA Forest Service’s Forest Inventory and Analysis dataset. Our climate data come from the 800 m resolution Parameter-elevation Regressions on Independent Slopes Model dataset. In order to extract a response to climate, we aggregate FIA plots to ecological subsections. At plot scale, micro-scale covariates explain variation in abundance; at a larger spatial scale, climate covariates can explain variation in abundance.     

Bivariate spatial process modeling for constructing indicator or intensity weighted spatial CDFs

A spatial cumulative distribution function (SCDF) gives the proportion of a spatial domain D having the value of some response variable less than a particular level w. This article provides a fully hierarchical approach to SCDF modeling, using a Bayesian framework implemented via Markov chain Monte Carlo (MCMC) methods. The approach generalizes the customary SCDF to accommodate density or indicator weighting. Bivariate spatial processes emerge as a natural approach for framing such a generalization. Indicator weighting leads to conditional SCDFs, useful in studying, for example, adjusted exposure to one pollutant given a specified level of exposure to another. Intensity weighted (or population density weighted) SCDFs are particularly natural in assessments of environmental justice, where it is important to determine if a particular sociodemographic group is being excessively exposed to harmful levels of certain pollutants. MCMC methods (combined with a convenient Kronecker structure) enable straightforward estimates or approximate estimates of bivariate, conditional, and weighted SCDFs. We illustrate our methods with two air pollution datasets, one recording both nitric oxide (NO) and nitrogen dioxide (NO₂) ambient levels at 67 monitoring sites in central and southern California, and the other concerning ozone exposure and race in Atlanta, GA.     

Spatial Regression Modeling for Compositional Data With Many Zeros

Compositional data analysis considers vectors of nonnegative-valued variables subject to a unit-sum constraint. Our interest lies in spatial compositional data, in particular, land use/land cover (LULC) data in the northeastern United States. Here, the observations are vectors providing the proportions of LULC types observed in each 3 km×3 km grid cell, yielding order 10⁴ cells. On the same grid cells, we have an additional compositional dataset supplying forest fragmentation proportions. Potentially useful and available covariates include elevation range, road length, population, median household income, and housing levels. We propose a spatial regression model that is also able to capture flexible dependence among the components of the observation vectors at each location as well as spatial dependence across the locations of the simplex-restricted measurements. A key issue is the high incidence of observed zero proportions for the LULC dataset, requiring incorporation of local point masses at 0. We build a hierarchical model prescribing a power scaling first stage and using latent variables at the second stage with spatial structure for these variables supplied through a multivariate CAR specification. Analyses for the LULC and forest fragmentation data illustrate the interpretation of the regression coefficients and the benefit of incorporating spatial smoothing.