Structural sustainability of cambisol under different land use system

Incongruous management techniques have been associated with some significant loss of agricultural land to degradation in many parts of the world. Land degradation results in the alteration of physical, chemical and biological properties of the soil, thereby posing a serious threat to sustainable agricultural development. In this study, our objective is to evaluate the changes in a Cambisol structure under six land use systems using the load bearing capacity model. Sampling was conducted in Amazonas Region, Brazil, in the following land use: a) young secondary forest; b) old secondary forest; c) forest; d) pasture; e) cropping, and f) agroforestry. To obtain the load bearing capacity models the undisturbed soil samples were collected in those land use systems and subjected to the uniaxial compression test. These models were used to evaluate which land use system preserved or degraded the Cambisol structure. The results of the bulk density and total porosity of the soil samples were not adequate to quantify structural degradation in Cambisol. Using the forest topsoil level (0-0.03 m) as a reference, it was observed that pasture land use system was most severe in the degradation of the soil structure while the structure were most preserved under old secondary forest, cropping system and forest. At the subsoil level (0.10-0.13 m depth), the soil structure was most degraded in the cropping land use system while it was most preserved in young secondary forest and pasture. At the 0.20-0.23 m depth, soil structure degradation was most severe in the old secondary forest system and well preserved in young secondary forest, cropping and agroforestry.

Cost-effective experimental design to support modeling of concentration-response functions

Modeling concentration-response function became extremely popular in ecotoxicology during the last decade. Indeed, modeling allows determining the total response pattern of a given substance. However, reliable modeling is consuming in term of data, which is in contradiction with the current trend in ecotoxicology, which aims to reduce, for cost and ethical reasons, the number of data produced during an experiment. It is therefore crucial to determine experimental design in a cost-effective manner. In this paper, we propose to use the theory of locally D-optimal designs to determine the set of concentrations to be tested so that the parameters of the concentration-response function can be estimated with high precision. We illustrated this approach by determining the locally D-optimal designs to estimate the toxicity of the herbicide dinoseb on daphnids and algae. The results show that the number of concentrations to be tested is often equal to the number of parameters and often related to the their meaning, i.e. they are located close to the parameters. Furthermore, the results show that the locally D-optimal design often has the minimal number of support points and is not much sensitive to small changes in nominal values of the parameters. In order to reduce the experimental cost and the use of test organisms, especially in case of long-term studies, reliable nominal values may therefore be fixed based on prior knowledge and literature research instead of on preliminary experiments

Estimating dormant and active hematopoietic stem cell kinetics through extensive modeling of bromodeoxyuridine label-retaining cell dynamics.

Bone marrow hematopoietic stem cells (HSCs) are responsible for both lifelong daily maintenance of all blood cells and for repair after cell loss. Until recently the cellular mechanisms by which HSCs accomplish these two very different tasks remained an open question. Biological evidence has now been found for the existence of two related mouse HSC populations. First, a dormant HSC (d-HSC) population which harbors the highest self-renewal potential of all blood cells but is only induced into active self-renewal in response to hematopoietic stress. And second, an active HSC (a-HSC) subset that by and large produces the progenitors and mature cells required for maintenance of day-to-day hematopoiesis. Here we present computational analyses further supporting the d-HSC concept through extensive modeling of experimental DNA label-retaining cell (LRC) data. Our conclusion that the presence of a slowly dividing subpopulation of HSCs is the most likely explanation (amongst the various possible causes including stochastic cellular variation) of the observed long term Bromodeoxyuridine (BrdU) retention, is confirmed by the deterministic and stochastic models presented here. Moreover, modeling both HSC BrdU uptake and dilution in three stages and careful treatment of the BrdU detection sensitivity permitted improved estimates of HSC turnover rates. This analysis predicts that d-HSCs cycle about once every 149-193 days and a-HSCs about once every 28-36 days. We further predict that, using LRC assays, a 75%-92.5% purification of d-HSCs can be achieved after 59-130 days of chase. Interestingly, the d-HSC proportion is now estimated to be around 30-45% of total HSCs - more than twice that of our previous estimate.

A natural structural variant of the mouse TCR beta-chain displays intrinsic receptor function and antigen specificity.

The Cbeta0 alternate cassette exon is located between the Jbeta1 and Cbeta1 genes in the mouse TCR beta-locus. In T cells with a VDJbeta1 rearrangement, the Cbeta0 exon may be included in TCRbeta transcripts (herein called TCRbeta-Cbeta0 transcripts), potentially inserting an additional 24 aa between the V and C domains of the TCR beta-chain. These TCRbeta splice isoforms may be differentially regulated after Ag activation, because we detected TCRbeta-Cbeta0 transcripts in a high proportion (>60%) of immature and mature T cells having VDJbeta1 rearrangements but found a substantially reduced frequency (<35%) of TCRbeta-Cbeta0 expression among CD8 T cells selected by Ag in vivo. To study the potential activity of the TCRbeta-Cbeta0 splice variant, we cloned full-length TCR cDNAs by single-cell RT-PCR into retroviral expression vectors. We found that the TCRbeta-Cbeta0 splice isoform can function during an early stage of T cell development normally dependent on TCR beta-chain expression. We also demonstrate that T hybridoma-derived cells expressing a TCRbeta-Cbeta0 isoform together with the clonally associated TCR alpha-chain recognize the same cognate peptide-MHC ligand as the corresponding normal alphabetaTCR. This maintenance of receptor function and specificity upon insertion of the Cbeta0 peptide cassette signifies a remarkable adaptability for the TCR beta-chain, and our findings open the possibility that this splice isoform may function in vivo.

Structural characterization of laser-treated Cr3C2-NiCr coatings

The interconnected porosity of the Cr3C2-NiCr coatings obtained by high-velocity oxy fuel spraying is detrimental in corrosion and wear resistance applications. Laser treatments allow sealing of their surfaces through melting and resolidification of a thin superficial layer. A Nd:YAG laser beam was used to irradiate Cr3C2-NiCr coatings either in the continuous wave mode or at different repetition rates in the pulsed one. Results indicated that high peak and low mean laser irradiances are not good, since samples presented deep grooves and an extensive crack network. At low peak and higher mean laser irradiances the surface was molten, and only a few shallow cracks were observed. The interconnected porosity was completely eliminated in a layer up to 80 m thick, formed by large Cr7C3 grains imbedded in a NiCr matrix.

Influence of pressure and radio frequency power on deposition rate and structural properties of hydrogenated amorphous silicon thin films prepared by plasma deposition

The influence of radio frequency (rf) power and pressure on deposition rate and structural properties of hydrogenated amorphous silicon (a-Si:H) thin films, prepared by rf glow discharge decomposition of silane, have been studied by phase modulated ellipsometry and Fourier transform infrared spectroscopy. It has been found two pressure regions separated by a threshold value around 20 Pa where the deposition rate increases suddenly. This behavior is more marked as rf power rises and reflects the transition between two rf discharges regimes. The best quality films have been obtained at low pressure and at low rf power but with deposition rates below 0.2 nm/s. In the high pressure region, the enhancement of deposition rate as rf power increases first gives rise to a reduction of film density and an increase of content of hydrogen bonded in polyhydride form because of plasma polymerization reactions. Further rise of rf power leads to a decrease of polyhydride bonding and the material density remains unchanged, thus allowing the growth of a-Si:H films at deposition rates above 1 nm/s without any important detriment of material quality. This overcoming of deposition rate limitation has been ascribed to the beneficial effects of ion bombardment on the a-Si:H growing surface by enhancing the surface mobility of adsorbed reactive species and by eliminating hydrogen bonded in polyhydride configurations.

Fuzzy logic applied to the modeling of water dynamics in an Oxisol in northeastern Brazil

Modeling of water movement in non-saturated soil usually requires a large number of parameters and variables, such as initial soil water content, saturated water content and saturated hydraulic conductivity, which can be assessed relatively easily. Dimensional flow of water in the soil is usually modeled by a nonlinear partial differential equation, known as the Richards equation. Since this equation cannot be solved analytically in certain cases, one way to approach its solution is by numerical algorithms. The success of numerical models in describing the dynamics of water in the soil is closely related to the accuracy with which the water-physical parameters are determined. That has been a big challenge in the use of numerical models because these parameters are generally difficult to determine since they present great spatial variability in the soil. Therefore, it is necessary to develop and use methods that properly incorporate the uncertainties inherent to water displacement in soils. In this paper, a model based on fuzzy logic is used as an alternative to describe water flow in the vadose zone. This fuzzy model was developed to simulate the displacement of water in a non-vegetated crop soil during the period called the emergency phase. The principle of this model consists of a Mamdani fuzzy rule-based system in which the rules are based on the moisture content of adjacent soil layers. The performances of the results modeled by the fuzzy system were evaluated by the evolution of moisture profiles over time as compared to those obtained in the field. The results obtained through use of the fuzzy model provided satisfactory reproduction of soil moisture profiles.

Structural quality of polyacrylamide-treated cohesive soils in the coastal tablelands of Pernambuco

Water-soluble polymers are characterized as effective flocculating agents due to their molecular features. Their application to soils with horizons with structural problems, e.g, a cohesive character, contributes to improvements in the physical quality and thus to the agricultural suitability of such soils. The purpose of this study was to evaluate the structural quality of soils with cohesive horizons of coastal tablelands in the State of Pernambuco treated with polyacrylamide (PAM) as chemical soil conditioner. To this end, three horizons (one cohesive and two non-cohesive) of a Yellow Argisol (Ultisol) were evaluated and to compare cohesive horizons, the horizon of a Yellow Latosol (Oxisol) was selected. The treatments consisted of aqueous PAM solutions (12.5; 50.0; 100.0 mg kg-1) and distilled water (control). The structural aspects of the horizons were evaluated by the stability (soil mass retained in five diameter classes), aggregate distribution per size class (mean weight diameter- MWD, geometric mean diameter - GMD) and the magnitude of the changes introduced by PAM by measuring the sensitivity index (Si). Aqueous PAM solutions increased aggregate stability in the largest evaluated diameter class of the cohesive and non-cohesive horizons, resulting in higher MWD and GMD, with highest efficiency of the 100 mg kg-1 solution. The cohesive horizon Bt1 in the Ultisol was most sensitive to the action of PAM, where highest Si values were found, but the structural quality of the BA horizon of the Oxisol was better in terms of stability and aggregate size distribution.

Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions

Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR) and geostatistical (ordinary kriging and co-kriging). The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap). Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI), soil wetness index (SWI), normalized difference vegetation index (NDVI), and b3/b2 band ratio were significantly correlated with soil properties. The predictive models had a mean coefficient of determination of 0.21. Best results were obtained with the geostatistical predictive models, where the highest coefficient of determination 0.43 was associated with sand properties between 60 to 100 cm deep. The use of a sparse data set of soil properties for digital mapping can explain only part of the spatial variation of these properties. The results may be related to the sampling density and the quantity and quality of the environmental covariates and predictive models used.

MODELING OF SOIL LOAD-BEARING CAPACITY AS A FUNCTION OF SOIL MECHANICAL RESISTANCE TO PENETRATION

Estimation of soil load-bearing capacity from mathematical models that relate preconsolidation pressure (σp) to mechanical resistance to penetration (PR) and gravimetric soil water content (U) is important for defining strategies to prevent compaction of agricultural soils. Our objective was therefore to model the σp and compression index (CI) according to the PR (with an impact penetrometer in the field and a static penetrometer inserted at a constant rate in the laboratory) and U in a Rhodic Eutrudox. The experiment consisted of six treatments: no-tillage system (NT); NT with chiseling; and NT with additional compaction by combine traffic (passing 4, 8, 10, and 20 times). Soil bulk density, total porosity, PR (in field and laboratory measurements), U, σp, and CI values were determined in the 5.5-10.5 cm and 13.5-18.5 cm layers. Preconsolidation pressure (σp) and CI were modeled according to PR in different U. The σp increased and the CI decreased linearly with increases in the PR values. The correlations between σp and PR and PR and CI are influenced by U. From these correlations, the soil load-bearing capacity and compaction susceptibility can be estimated by PR readings evaluated in different U.

High-throughput SELEX SAGE method for quantitative modeling of transcription-factor binding sites.

The ability to determine the location and relative strength of all transcription-factor binding sites in a genome is important both for a comprehensive understanding of gene regulation and for effective promoter engineering in biotechnological applications. Here we present a bioinformatically driven experimental method to accurately define the DNA-binding sequence specificity of transcription factors. A generalized profile was used as a predictive quantitative model for binding sites, and its parameters were estimated from in vitro-selected ligands using standard hidden Markov model training algorithms. Computer simulations showed that several thousand low- to medium-affinity sequences are required to generate a profile of desired accuracy. To produce data on this scale, we applied high-throughput genomics methods to the biochemical problem addressed here. A method combining systematic evolution of ligands by exponential enrichment (SELEX) and serial analysis of gene expression (SAGE) protocols was coupled to an automated quality-controlled sequence extraction procedure based on Phred quality scores. This allowed the sequencing of a database of more than 10,000 potential DNA ligands for the CTF/NFI transcription factor. The resulting binding-site model defines the sequence specificity of this protein with a high degree of accuracy not achieved earlier and thereby makes it possible to identify previously unknown regulatory sequences in genomic DNA. A covariance analysis of the selected sites revealed non-independent base preferences at different nucleotide positions, providing insight into the binding mechanism.

Statistical Modeling of the Eye for Multimodal Treatment Planning for External Beam Radiation Therapy of Intraocular Tumors.

PURPOSE: Ocular anatomy and radiation-associated toxicities provide unique challenges for external beam radiation therapy. For treatment planning, precise modeling of organs at risk and tumor volume are crucial. Development of a precise eye model and automatic adaptation of this model to patients' anatomy remain problematic because of organ shape variability. This work introduces the application of a 3-dimensional (3D) statistical shape model as a novel method for precise eye modeling for external beam radiation therapy of intraocular tumors. METHODS AND MATERIALS: Manual and automatic segmentations were compared for 17 patients, based on head computed tomography (CT) volume scans. A 3D statistical shape model of the cornea, lens, and sclera as well as of the optic disc position was developed. Furthermore, an active shape model was built to enable automatic fitting of the eye model to CT slice stacks. Cross-validation was performed based on leave-one-out tests for all training shapes by measuring dice coefficients and mean segmentation errors between automatic segmentation and manual segmentation by an expert. RESULTS: Cross-validation revealed a dice similarity of 95% ± 2% for the sclera and cornea and 91% ± 2% for the lens. Overall, mean segmentation error was found to be 0.3 ± 0.1 mm. Average segmentation time was 14 ± 2 s on a standard personal computer. CONCLUSIONS: Our results show that the solution presented outperforms state-of-the-art methods in terms of accuracy, reliability, and robustness. Moreover, the eye model shape as well as its variability is learned from a training set rather than by making shape assumptions (eg, as with the spherical or elliptical model). Therefore, the model appears to be capable of modeling nonspherically and nonelliptically shaped eyes.

Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.