A secondary and tertiary structure editor for nucleic acids

A major difficulty in the evaluation of secondary and tertiarystructures of nucleic acids is the lack of convenient methodsfor their construction and representation. As a first step ina study of the symbolic representation of biopolymers, we reportthe development of a structure editor written in Pascal, permittingmodel construction on the screen of a personal computer. Theprogram calculates energies for helical regions, allows user-definedhelices and displays the secondary structure of a nucleic acidbased on a user-selected set of helices. Screen and printeroutputs can be in the form of a backbone or the letters of theprimary sequence. The molecule can then be displayed in a formatwhich simulates its three-dimensional structure. Using appropriateglasses, the molecule can be viewed on the screen in three dimensions.Other options include the manipulation of helices and single-strandedregions which results in changes in the spatial relationshipbetween different regions of the molecule. The editor requiresan IBM or compatible PC, 640 kbyte memory and a medium or highresolution graphics card. Received on September 19, 1987; accepted on November 15, 1987     

Modification of DNA ends and detection of restriction enzyme recognition sequences in their ligated junctions

A program has been developed for the modelling of modificationsin DNA ends, for the construction of ligated junctions, andfor the analysis in these junctions of new restriction enzymerecognition sequences. This program allows the analysis of restrictionenzyme specifities in ligated junctions of cohesive or bluntDNA ends. Cohesive ends are considered in their natural configurationor after modification by possible blunt-ending procedures. Theprogram also allows the modelling of partial filling-in for5'-single-stranded ends. This program has proven useful forthe design of sequences with new restriction sites or to predictor confirm the sequence of junctions created by the ligationof modified ends. Received on October 28, 1987; accepted on November 23, 1987     

An independent method of deriving the carbon dioxide fertilization effect in dry conditions using historical yield data from wet and dry years

Accurate estimates of the fertilization effect that elevated carbon dioxide [CO₂] has on crop yields are valuable for estimation of future crop production, yet there is still some controversy over these estimates due to possible CO₂‐by‐water‐status interactions in chamber studies and the difficulty of conducting field experiments with elevated [CO₂]. This study presents a new method to estimate the CO₂ fertilization effect (CFE) in dry conditions (CFE_dry), based on a combination of historical yield and climatic data and field experiments that do not require elevated [CO₂]. It was estimated that approximately 50 years of increasing [CO₂] (i.e., a 73 ppm increase) resulted in a 9% and 14% improvement of yield in dry conditions for maize and soybean, respectively, which are similar to estimates derived from free air CO₂ enrichment (FACE) studies. The main source of uncertainty in this approach relates to differential effects of technology trends such as new cultivars in wet vs. dry years. Estimates of this technology–water interaction can be refined by further experimentation under ambient [CO₂], offering a cost‐effective path for improving CFE estimates. The results should prove useful for modeling future yield impacts of climate change, and the approach could be used to derive estimates for other species using relatively simple yield trials.     

Accuracy and selection success in yield trial analyses

Summary Yield trials serve research purposes of estimation and selection. Order statistics are used here to quantify the successes or problems to be expected in selection tasks commonly encountered in breeding and agronomy. Greater accuracy of yield estimates implies greater selection success. A New York soybean yield trial serves as a specific example. The Additive Main effects and Multiplicative Interaction (AMMI) statistical model is used to increase the accuracy of these soybean yield estimates, thereby increasing the probability of successfully selecting, on the basis of the empirical yield data, that genotype which has the maximum true mean. The statistical strategy for increasing accuracy is extremely cost effective relative to the alternative strategy of increasing the number of replications. Better selections increase the speed and effectiveness of breeding programs, and increase the reliability of variety recommendations. Selection tasks are frequently more difficult than may be suspected.This research was supported by the Rhizobotany Project of the USDA-ARS     

An evaluation of techniques for measuring yield turgor in excised Salix leaves

Abstract. The use of three techniques for determining yield turgor in excised Salix leaves was investigated. These were the osmotic-solutions technique, the psychrometer technique, and the pressure-chamber technique. The application of the osmotic-solutions technique to a range of leaf types was discussed and the appropriate corrections for volume changes and the contribution of apoplastic water were detailed. It was concluded that the osmotic-solutions technique is not satisfactory for use with slowly growing and/or very elastic leaves. The psychrometer and pressure-chamber techniques were both simple compared with the osmotic-solutions technique, and gave values for yield turgor in the range of 0·3–0·5 MPa. A disadvantage of the psychrometer technique for field applications is that it requires one psychrometer chamber per sample. The pressure-chamber technique was modified for use as a field technique where multiple sampling could be easily and inexpensively achieved. Particular care was required with this technique to prevent water loss from the leaf during stress relaxation, but simple and effective procedures for doing so were found. The modified pressure-chamber technique described here, is recommended as the preferred technique for measuring the yield turgor of leaves in experiments where many simultaneous estimates of yield turgor are to be made.     

Algorithm and computer program Pro--Anal for analysis of relationship between structure and activity in a family of proteins or peptides

In this paper we introduce a computer algorithm and programPro—Anal for analysis of the structure-activity relationshipin a family of evolutionarily related (and/or artificially mutated)proteins/peptides. The program uses aligned amino acid sequenceswith data of their activity (pK, K_m, ED₅₀ or any other) andsearches for correlations between data on activity and variousphysico-chemical characteristics of different regions in primarystructures. In automatic mode, the program generates and verifieshypotheses on the disposition of a sequential modulating regionin a protein, and key characteristics of the region. In manualmode, users can generate and analyze their own hypotheses. Theprogram is implemented on IBM PC or compatible computers. Itis designed to be easily handled by the occasional computeruser and yet it is powerful enough for experienced professionals.Pro—Anal operation is demonstrated on the example of findingmodulating centers in a family of disintegrins—proteinsfrom snake venoms which inhibit fibrinogen interaction withplatelet receptors. In another example it is shown that theimmunogenicity of peptides is connected with their positivecharge.     

PROMOT: a FORTRAN program to scan protein sequences against a library of known motifs

Information about the three-dimensional structure or functionof a newly determined protein sequence can be obtained if theprotein is found to contain a characterized motif or patternof residues. Recently a database (PROSITE) has been establishedthat contains 337 known motifs encoded as a list of allowedresidue types at specific positions along the sequence. PROMOTis a FORTRAN computer program that takes a protein sequenceand examines if it contains any of the motifs in PROSITE. Theprogram also extends the definitions of patterns beyond thoseused in PROSITE to provide a simple, yet flexible, method toscan either a PROSITE or a user-defined pattern against a proteinsequence database. Received on October 17, 1990; accepted on November 15, 1990     

PHYLOGENETIC ANALYSES OF THE CORRELATED EVOLUTION OF CONTINUOUS CHARACTERS: A SIMULATION STUDY

We use computer simulation to compare the statistical properties of several methods that have been proposed for estimating the evolutionary correlation between two continuous traits, and define alternative evolutionary correlations that may be of interest. We focus on Felsenstein's (1985) method and some variations of it and on several “minimum evolution” methods (of which the procedure of Huey and Bennett [1987] is a special case), as compared with a nonphylogenetic correlation. The last, a simple correlation of trait values across the tips of a phylogeny, virtually always yields inflated Type I error rates, relatively low power, and relatively poor estimates of evolutionary correlations. We therefore cannot recommend its use. In contrast, Felsenstein's (1985) method yields acceptable significance tests, high power, and good estimates of what we term the input correlation and the standardized realized evolutionary correlation, given complete phylogenetic information and knowledge of the rate and mode of character change (e.g., gradual and proportional to time [“Brownian motion”] or punctuational, with change only at speciation events). Inaccurate branch length information may affect any method adversely, but only rarely does it cause Felsenstein's (1985) method to perform worse than do the others tested. Other proposed methods generally yield inflated Type I error rates and have lower power. However, certain minimum evolution methods (although not the specific procedure used by Huey and Bennett [1987]) often provide more accurate estimates of what we term the unstandardized realized evolutionary correlation, and their use is recommended when estimation of this correlation is desired. We also demonstrate how correct Type I error rates can be obtained for any method by reference to an empirical null distribution derived from computer simulations, and provide practical suggestions on choosing an analytical method, based both on the evolutionary correlation of interest and on the availability of branch lengths and knowledge of the model of evolutionary change appropriate for the characters being analyzed. Computer programs that implement the various methods and that will simulate (correlated) character evolution along a known phylogeny are available from the authors on request. These programs can be used to test the effectiveness of any new methods that might be proposed, and to check the generality of our conclusions with regard to other phylogenies.     

Effect of lactation length adjustment procedures on genetic parameter estimates for buffalo milk yield

The objectives of this study were to estimate the genetic parameters for milk yield unadjusted and adjusted for days in milk and, subsequently, to assess the influence of adjusting for days in milk on sire rank. Complete lactations from 90 or 150 days of lactation to 270 or 350 days in milk were considered in these analyses. Milk yield was adjusted for days in milk by multiplicative correction factors, or by including lactation length as a covariable in the model. Milk yields adjusted by different procedures were considered as different traits. Heritability estimates varied from 0.17 to 0.28. Genetic correlation estimates between milk yields unadjusted and adjusted for days in milk were greater than 0.82. Adjusting for days in milk affected the parameter estimates. Multiplicative correction factors produced the highest heritability estimates. More reliable breeding value estimates can be expected by including short length lactation records in the analyses and adjusting the milk yields for days in milk, regardless of the method used for the adjustment. High selection intensity coupled to the inclusion of short length lactations and adjustment with multiplicative factors can change the sire rank..     

Estimating two-stage models for genetic influences on alcohol, tobacco or drug use initiation and dependence vulnerability in twin and family data.

Genetic research on risk of alcohol, tobacco or drug dependence must make allowance for the partial overlap of risk-factors for initiation of use, and risk-factors for dependence or other outcomes in users. Except in the extreme cases where genetic and environmental risk-factors for initiation and dependence overlap completely or are uncorrelated, there is no consensus about how best to estimate the magnitude of genetic or environmental correlations between Initiation and Dependence in twin and family data. We explore by computer simulation the biases to estimates of genetic and environmental parameters caused by model misspecification when Initiation can only be defined as a binary variable. For plausible simulated parameter values, the two-stage genetic models that we consider yield estimates of genetic and environmental variances for Dependence that, although biased, are not very discrepant from the true values. However, estimates of genetic (or environmental) correlations between Initiation and Dependence may be seriously biased, and may differ markedly under different two-stage models. Such estimates may have little credibility unless external data favor selection of one particular model. These problems can be avoided if Initiation can be assessed as a multiple-category variable (e.g. never versus early-onset versus later onset user), with at least two categories measurable in users at risk for dependence. Under these conditions, under certain distributional assumptions, recovery of simulated genetic and environmental correlations becomes possible. Illustrative application of the model to Australian twin data on smoking confirmed substantial heritability of smoking persistence (42%) with minimal overlap with genetic influences on initiation.     

Nitrogen and yield potential of irrigated rice

Yield potential of modern rice varieties and implications for N management were evaluated in a series of field studies that provided data for validation of an eco-physiological simulation model for rice. We tested the hypothesis that N was the major factor limiting yield potential of irrigated rice. The simulation model ORYZA1 was used to evaluate the observed yield differences between varieties grown with different N management and in different environments. The model explained differences in yield of the treatments resonably well on the basis of differences in radiation, temperature, leaf N content and variety coefficients for phenological development. It was demonstrated by the model and experimental data that yield levels of 6 t ha^-1 in the wet season and 10 t ha^-1 in the dry season can be obtained in the tropics with the current short duration varieties only when the N supply from soil and fertilizer is adequately maintained at key growth stages. Yield probabilities for rice crops were simulated for different environments using long term weather data at two Philippine sites.     

Global evaluation of a semiempirical model for yield anomalies and application to within‐season yield forecasting

Quantifying the influence of weather on yield variability is decisive for agricultural management under current and future climate anomalies. We extended an existing semiempirical modeling scheme that allows for such quantification. Yield anomalies, measured as interannual differences, were modeled for maize, soybeans, and wheat in the United States and 32 other main producer countries. We used two yield data sets, one derived from reported yields and the other from a global yield data set deduced from remote sensing. We assessed the capacity of the model to forecast yields within the growing season. In the United States, our model can explain at least two‐thirds (63%–81%) of observed yield anomalies. Its out‐of‐sample performance (34%–55%) suggests a robust yield projection capacity when applied to unknown weather. Out‐of‐sample performance is lower when using remote sensing‐derived yield data. The share of weather‐driven yield fluctuation varies spatially, and estimated coefficients agree with expectations. Globally, the explained variance in yield anomalies based on the remote sensing data set is similar to the United States (71%–84%). But the out‐of‐sample performance is lower (15%–42%). The performance discrepancy is likely due to shortcomings of the remote sensing yield data as it diminishes when using reported yield anomalies instead. Our model allows for robust forecasting of yields up to 2 months before harvest for several main producer countries. An additional experiment suggests moderate yield losses under mean warming, assuming no major changes in temperature extremes. We conclude that our model can detect weather influences on yield anomalies and project yields with unknown weather. It requires only monthly input data and has a low computational demand. Its within‐season yield forecasting capacity provides a basis for practical applications like local adaptation planning. Our study underlines high‐quality yield monitoring and statistics as critical prerequisites to guide adaptation under climate change.     

Analyzing population growth curves

Assessing animal population growth curves is an essential feature of field studies in ecology and wildlife management. We used five models to assess population growth rates with a number of sets of population growth rate data. A 'generalized' logistic curve provides a better model than do four other popular models. Use of difference equations for fitting was checked by a comparison of that method and direct fitting of the analytical (integrated) solution for three of the models. Fits to field data indicate that estimates of the asymptote, K, from the 'generalized logistic' and the ordinary logistic agree well enough to support use of estimates of K from the ordinary logistic on data that cannot be satisfactorily fitted with the generalized logistic. Akaike's information criterion is widely used, often with a small sample version AIC_c. Our study of five models indicated a bias in the AIC_c criterion, so we recommend checking results with estimates of variance about regression for fitted models. Fitting growth curves provides a valuable supplement to, and check on computer models of populations.     

Estimated yield of double-strand breaks from internal exposure to tritium

Internal exposure to tritium may result in DNA lesions. Of those, DNA double-strand breaks (DSBs) are believed to be important. However, experimental and computational data of DSBs induction by tritium are very limited. In this study, microdosimetric characteristics of uniformly distributed tritium were determined in dimensions of critical significance in DNA DSBs. Those characteristics were used to identify other particles comparable to tritium in terms of microscopic energy deposition. The yield of DSBs could be strongly dependent on biological systems and cellular environments. After reviewing theoretically predicted and experimentally determined DSB yields available in the literature for low-energy electrons and high-energy protons of comparable microdosimetric characteristics to tritium in the dimensions relevant to DSBs, it is estimated that the average DSB yields of 2.7 × 10(-11), 0.93 × 10(-11), 2.4 × 10(-11) and 1.6 × 10(-11) DSBs Gy(-1) Da(-1) could be reasonable estimates for tritium in plasmid DNAs, yeast cells, Chinese hamster V79 cells and human fibroblasts, respectively. If a biological system is not specified, the DSB yield from tritium exposure can be estimated as (2.3 ± 0.7) × 10(-11) DSBs Gy(-1) Da(-1), which is a simple average over experimentally determined yields of DSBs for low-energy electrons in various biological systems without considerations of variations caused by different techniques used and obvious differences among different biological systems where the DSB yield was measured.     

Low cost multi user multi platform accessible HPLC data acquisition system

Summary A low cost multi user multi platform accessible HPLC data acquisition system has been designed for use in a laboratory environment. This system uses available HPLC measurement systems that lack modern network communication tools and a low cost computer with reliable software. HPLC data are portable to any other computer by means of File Transport Protocol (FTP) communication and can then be used for data analysis. Off line analysis of ethanol data showed a substantial improvement over the old system in terms of data accuracy and skewness. Furthermore, off line data analysis could resolve hidden acetaldehyde peaks which revealed to be oscillatory.     

Relationship between leaf area index and yield in double-crop and full-season soybean systems

Previous research indicates a correlation between leaf area index (LAI) and yield of full-season soybean [Glycine max (L.) Merrill], which is a single crop planted early in the season. Leaf area index values of at least 3.5-4.0 in the reproductive stages are required for maximum potential yield. It is unknown how yields of double-crop soybean, which is planted late into harvested small grain fields, respond to changes in leaf area index. We hypothesized that double-crop soybean would be more sensitive to defoliation than full-season soybean. This study used linear and linear plateau models to describe the yield response of full-season and double-crop soybean to reductions in leaf area index through manual defoliation, and evaluated the yield response of double-crop soybean to reductions in leaf area index through natural insect defoliation. From 15 manual defoliation experiments over 3 yr, significant linear decreases in yield occurred in both full-season and double-crop soybean when leaf area index values were below 3.5-4.0 by developmental stages R4 to R5, whereas yields usually reached a plateau at higher leaf area index levels. Average yield loss was 769 +/- 319 kg ha(-1) for each unit decrease in leaf area index below the plateau; average maximum yield was 3,484 +/- 735 kg ha(-1). From eight field experiments over 2 yr, insect defoliators had no effect on double-crop soybean yield; leaf area index levels were above 4.0 by the developmental stage when leaf area index estimates were taken (R3 to R6). Therefore, double-crop soybean that maintains leaf area index values above the 3.5-4.0 critical level by mid-reproductive developmental stages can tolerate defoliating pests.     

Roles of type II thioesterases and their application for secondary metabolite yield improvement

A large number of antibiotics and other industrially important microbial secondary metabolites are synthesized by polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs). These multienzymatic complexes provide an enormous flexibility in formation of diverse chemical structures from simple substrates, such as carboxylic acids and amino acids. Modular PKSs and NRPSs, often referred to as megasynthases, have brought about a special interest due to the colinearity between enzymatic domains in the proteins working as an “assembly line” and the chain elongation and modification steps. Extensive efforts toward modified compound biosynthesis by changing organization of PKS and NRPS domains in a combinatorial manner laid good grounds for rational design of new structures and their controllable biosynthesis as proposed by the synthetic biology approach. Despite undeniable progress made in this field, the yield of such “unnatural” natural products is often not satisfactory. Here, we focus on type II thioesterases (TEIIs)—discrete hydrolytic enzymes often encoded within PKS and NRPS gene clusters which can be used to enhance product yield. We review diverse roles of TEIIs (removal of aberrant residues blocking the megasynthase, participation in substrate selection, intermediate, and product release) and discuss their application in new biosynthetic systems utilizing PKS and NRPS parts.     

The use of SSRs for predicting the hybrid yield and yield heterosis in 15 key inbred lines of Chinese maize

A challenge to maize breeders is to predict and identify inbred lines that can produce highly heterotic hybrids precisely. In the present study we surveyed the genetic diversity among 15 elite inbred lines of maize in China with SSR markers and assessed the relationship between SSR marker and hybrid yield/yield heterosis in a diallel set of 105 crosses. Forty-three SSR primers selected from all sixty-three primers gave stable profiles amplified in the sample of 15 inbred lines, which could clearly resolve on 4% metaphor agarose gel. The average number of alleles per SSR locus was 4.44 with a range from 2 to 9. The polymorphism information content (PIC) for the SSR loci varied from 0.28 to 0.81 with a mean of 0.6281. Genetic similarity (GS) among 15 lines was estimated with 191 alleles identified as raw data, the Nei's coefficient of GS ranged from 0.492 for 478 vs HZ4 up to 0.745 for E28 to ZH64 with a mean of 0.619. The cluster diagram based upon the SSR data grouped the 15 lines into families consistent with the yield heterotic response of these. Genetic distance (GD) based on SSR data was significantly correlated with hybrid yield/yield heterosis, the correlation coefficient (r) being 0.5432 and 0.4271 in 1999 and 0.4305 and 0.3614 in 1998 field test, respectively, whereas the determination coefficient (r2) was lower. The correlation between GD based on SSR data and hybrid yield/yield heterosis changed alone with the difference of number and pedigree relationship among parents that were used in this study. SSR makers showed high polymorphism and could be used to assess the relationship between inbred lines of maize, but it was difficult to predict the yield heterosis of maize.     

Effects of ascertainment bias on recovering human demographic history.

In recent years multilocus data sets have been used to study the demographic history of human populations. In this paper (1) analyses previously done on 60 short tandem repeat (STR) loci are repeated on 30 restriction site polymorphism (RSP) markers; (2) relative population weights are estimated from the RSP data set and compared to previously published estimates from STR and craniometric data sets; and (3) computer simulations are performed to show the effects of ascertainment bias on relative population weight estimates. Not surprisingly, given that the RSP markers were originally identified in a small panel of Caucasians, estimates of relative population weights are biased and the European population weight is artificially inflated. However, the effects of ascertainment bias are not apparent in a principal components plot or estimates of FST. Ascertainment bias can have a large effect in other genetic systems with inherently low heterozygosity such as Alus or single nucleotide polymorphisms (SNPs), and care must be taken to have prior knowledge of how polymorphic markers in a given data set were originally identified. Otherwise, results can be skewed and interpretations faulty.     

Diversification practices reduce organic to conventional yield gap

Agriculture today places great strains on biodiversity, soils, water and the atmosphere, and these strains will be exacerbated if current trends in population growth, meat and energy consumption, and food waste continue. Thus, farming systems that are both highly productive and minimize environmental harms are critically needed. How organic agriculture may contribute to world food production has been subject to vigorous debate over the past decade. Here, we revisit this topic comparing organic and conventional yields with a new meta-dataset three times larger than previously used (115 studies containing more than 1000 observations) and a new hierarchical analytical framework that can better account for the heterogeneity and structure in the data. We find organic yields are only 19.2% (±3.7%) lower than conventional yields, a smaller yield gap than previous estimates. More importantly, we find entirely different effects of crop types and management practices on the yield gap compared with previous studies. For example, we found no significant differences in yields for leguminous versus non-leguminous crops, perennials versus annuals or developed versus developing countries. Instead, we found the novel result that two agricultural diversification practices, multi-cropping and crop rotations, substantially reduce the yield gap (to 9 ± 4% and 8 ± 5%, respectively) when the methods were applied in only organic systems. These promising results, based on robust analysis of a larger meta-dataset, suggest that appropriate investment in agroecological research to improve organic management systems could greatly reduce or eliminate the yield gap for some crops or regions.