Selecting statistical models to study the relationship between soybean yield and soil physical properties

Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plant-atmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial transect with 84 points spaced 3 m apart. At each sampling point, soybean samples were collected for yield quantification. At the same site, soil penetration resistance was also measured and soil samples were collected to measure soil bulk density in the 0-0.10 m and 0.10-0.20 m layers. Results showed autocorrelation and a cross correlation structure of soybean yield and soil penetration resistance data. Soil bulk density data, however, were only autocorrelated in the 0-0.10 m layer and not cross correlated with soybean yield. The results showed the higher efficiency of the autoregressive space-state models in relation to the equivalent simple and multiple linear regression models using Akaike's Information Criterion. The resulting values were comparatively lower than the values obtained by the regression models, for all combinations of explanatory variables.

State-of-the-art of 3D cultures (organs-on-a-chip) in safety testing and pathophysiology.

Integrated approaches using different in vitro methods in combination with bioinformatics can (i) increase the success rate and speed of drug development; (ii) improve the accuracy of toxicological risk assessment; and (iii) increase our understanding of disease. Three-dimensional (3D) cell culture models are important building blocks of this strategy which has emerged during the last years. The majority of these models are organotypic, i.e., they aim to reproduce major functions of an organ or organ system. This implies in many cases that more than one cell type forms the 3D structure, and often matrix elements play an important role. This review summarizes the state of the art concerning commonalities of the different models. For instance, the theory of mass transport/metabolite exchange in 3D systems and the special analytical requirements for test endpoints in organotypic cultures are discussed in detail. In the next part, 3D model systems for selected organs--liver, lung, skin, brain--are presented and characterized in dedicated chapters. Also, 3D approaches to the modeling of tumors are presented and discussed. All chapters give a historical background, illustrate the large variety of approaches, and highlight up- and downsides as well as specific requirements. Moreover, they refer to the application in disease modeling, drug discovery and safety assessment. Finally, consensus recommendations indicate a roadmap for the successful implementation of 3D models in routine screening. It is expected that the use of such models will accelerate progress by reducing error rates and wrong predictions from compound testing.

Phosphorus accumulation and pollution potential in a hapludult fertilized with pig manure

Successive applications of pig litter to the soil surface can increase the phosphorus (P) content and alter its adsorption, promoting P transfer to surface or subsurface waters. The purpose of this study was to evaluate P accumulation and the pollution potential of a soil after application of pig litter. In March 2010, eight years after the installation of an experiment in Braço do Norte, Santa Catarina, SC, Brazil, on a Typic Hapludult, soil was sampled (layers 0-2.5, 2.5-5, 5-10, 10-15, 15-20 and 20-30 cm) after the following fertilization treatments: no pig litter fertilization, pig slurry application and pig manure application. In this period, 694 and 1,890 kg P2O5 ha-1 were applied in the treatments with pig slurry and pig manure, respectively. The P content was determined, based on Mehlich-1, anion exchange resin (AER), 0.01 mol L-1 CaCl2 and total P in the samples. The adsorption isotherm parameters were also determined by the Langmuir and Koski-Vähälä & Hartikainem models in the layers 0-2.5 and 20-30 cm. The application of 1,890 kg P2O5 ha-1 in the form of pig manure led to P accumulation, as evidenced by Mehlich-1, down to a depth of 15 cm, by AER and 0.01 mol L-1 CaCl2 down to 20 cm and by total P to 30 cm. After application of 1,890 kg P2O5 ha-1 in the form of pig manure, the values of maximum P adsorption capacity were lowest in the deepest layer (20-30 cm), indicating the occupation of part of the adsorption sites of the particles. The application of swine manure to the soil over eight years increased the P quantity in the soil solution of the surface layer, indicating environmental contamination risk for surface and subsurface waters.

An appropriate data set size for digital soil mapping in Erechim, Rio Grande do Sul, Brazil

Digital information generates the possibility of a high degree of redundancy in the data available for fitting predictive models used for Digital Soil Mapping (DSM). Among these models, the Decision Tree (DT) technique has been increasingly applied due to its capacity of dealing with large datasets. The purpose of this study was to evaluate the impact of the data volume used to generate the DT models on the quality of soil maps. An area of 889.33 km² was chosen in the Northern region of the State of Rio Grande do Sul. The soil-landscape relationship was obtained from reambulation of the studied area and the alignment of the units in the 1:50,000 scale topographic mapping. Six predictive covariates linked to the factors soil formation, relief and organisms, together with data sets of 1, 3, 5, 10, 15, 20 and 25 % of the total data volume, were used to generate the predictive DT models in the data mining program Waikato Environment for Knowledge Analysis (WEKA). In this study, sample densities below 5 % resulted in models with lower power of capturing the complexity of the spatial distribution of the soil in the study area. The relation between the data volume to be handled and the predictive capacity of the models was best for samples between 5 and 15 %. For the models based on these sample densities, the collected field data indicated an accuracy of predictive mapping close to 70 %.

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.

Adsorption of colloidal particles in the presence of external fields

We present a new class of sequential adsorption models in which the adsorbing particles reach the surface following an inclined direction (shadow models). Capillary electrophoresis, adsorption in the presence of a shear, and adsorption on an inclined substrate are physical manifestations of these models. Numerical simulations are carried out to show how the new adsorption mechanisms are responsible for the formation of more ordered adsorbed layers and have important implications in the kinetics, in particular, modifying the jamming limit.

Cilengitide: an RGD pentapeptide ανβ3 and ανβ5 integrin inhibitor in development for glioblastoma and other malignancies.

Cilengitide, a cyclicized arginine-glycine-aspartic acid-containing pentapeptide, potently blocks ανβ3 and ανβ5 integrin activation. Integrins are upregulated in many malignancies and mediate a wide variety of tumor-stroma interactions. Cilengitide and other integrin-targeting therapeutics have preclinical activity against many cancer subtypes including glioblastoma (GBM), the most common and deadliest CNS tumor. Cilengitide is active against orthotopic GBM xenografts and can augment radiotherapy and chemotherapy in these models. In Phase I and II GBM trials, cilengitide and the combination of cilengitide with standard temozolomide and radiation demonstrate consistent antitumor activity and a favorable safety profile. Cilengitide is currently under evaluation in a pivotal, randomized Phase III study (Cilengitide in Combination With Temozolomide and Radiotherapy in Newly Diagnosed Glioblastoma Phase III Randomized Clinical Trial [CENTRIC]) for newly diagnosed GBM. In addition, randomized controlled Phase II studies with cilengitide are ongoing for non-small-cell lung cancer and squamous cell carcinoma of the head and neck. Cilengitide is the first integrin inhibitor in clinical Phase III development for oncology.

Modelling Bone Mineral Density in Swiss Breast Cancer Patients Treated with Letrozole, Tamoxifen and Sequences of Letrozole and Tamoxifen in the BIG 1-98 Study (SAKK 21/07).

Background: Bone health is a concern when treating early stage breast cancer patients with adjuvant aromatase inhibitors. Early detection of patients (pts) at risk of osteoporosis and fractures may be helpful for starting preventive therapies and selecting the most appropriate endocrine therapy schedule. We present statistical models describing the evolution of lumbar and hip bone mineral density (BMD) in pts treated with tamoxifen (T), letrozole (L) and sequences of T and L. Methods: Available dual-energy x-ray absorptiometry exams (DXA) of pts treated in trial BIG 1-98 were retrospectively collected from Swiss centers. Treatment arms: A) T for 5 years, B) L for 5 years, C) 2 years of T followed by 3 years of L and, D) 2 years of L followed by 3 years of T. Pts without DXA were used as a control for detecting selection biases. Patients randomized to arm A were subsequently allowed an unplanned switch from T to L. Allowing for variations between DXA machines and centres, two repeated measures models, using a covariance structure that allow for different times between DXA, were used to estimate changes in hip and lumbar BMD (g/cm2) from trial randomization. Prospectively defined covariates, considered as fixed effects in the multivariable models in an intention to treat analysis, at the time of trial randomization were: age, height, weight, hysterectomy, race, known osteoporosis, tobacco use, prior bone fracture, prior hormone replacement therapy (HRT), bisphosphonate use and previous neo-/adjuvant chemotherapy (ChT). Similarly, the T-scores for lumbar and hip BMD measurements were modeled using a per-protocol approach (allowing for treatment switch in arm A), specifically studying the effect of each therapy upon T-score percentage. Results: A total of 247 out of 546 pts had between 1 and 5 DXA; a total of 576 DXA were collected. Number of DXA measurements per arm were; arm A 133, B 137, C 141 and D 135. The median follow-up time was 5.8 years. Significant factors positively correlated with lumbar and hip BMD in the multivariate analysis were weight, previous HRT use, neo-/adjuvant ChT, hysterectomy and height. Significant negatively correlated factors in the models were osteoporosis, treatment arm (B/C/D vs. A), time since endocrine therapy start, age and smoking (current vs. never).Modeling the T-score percentage, differences from T to L were -4.199% (p = 0.036) and -4.907% (p = 0.025) for the hip and lumbar measurements respectively, before any treatment switch occurred. Conclusions: Our statistical models describe the lumbar and hip BMD evolution for pts treated with L and/or T. The results of both localisations confirm that, contrary to expectation, the sequential schedules do not seem less detrimental for the BMD than L monotherapy. The estimated difference in BMD T-score percent is at least 4% from T to L.

Particulate matter exposure in cars is associated with cardiovascular effects in healthy young men

Exposure to fine airborne particulate matter (PM(2.5)) is associated with cardiovascular events and mortality in older and cardiac patients. Potential physiologic effects of in-vehicle, roadside, and ambient PM(2.5) were investigated in young, healthy, nonsmoking, male North Carolina Highway Patrol troopers. Nine troopers (age 23 to 30) were monitored on 4 successive days while working a 3 P.M. to midnight shift. Each patrol car was equipped with air-quality monitors. Blood was drawn 14 hours after each shift, and ambulatory monitors recorded the electrocardiogram throughout the shift and until the next morning. Data were analyzed using mixed models. In-vehicle PM(2.5) (average of 24 microg/m(3)) was associated with decreased lymphocytes (-11% per 10 microg/m(3)) and increased red blood cell indices (1% mean corpuscular volume), neutrophils (6%), C-reactive protein (32%), von Willebrand factor (12%), next-morning heart beat cycle length (6%), next-morning heart rate variability parameters, and ectopic beats throughout the recording (20%). Controlling for potential confounders had little impact on the effect estimates. The associations of these health endpoints with ambient and roadside PM(2.5) were smaller and less significant. The observations in these healthy young men suggest that in-vehicle exposure to PM(2.5) may cause pathophysiologic changes that involve inflammation, coagulation, and cardiac rhythm.

T cell receptor delta gene rearrangement and T early alpha (TEA) expression in immature alpha beta lineage thymocytes: implications for alpha beta/gamma delta lineage commitment.

Mature T cells comprise two mutually exclusive lineages expressing heterodimeric alpha beta or gamma delta antigen receptors. During development, beta, gamma, and delta genes rearrange before alpha, and mature gamma delta cells arise in the thymus prior to alpha beta cells. The mechanism underlying commitment of immature T cells to the alpha beta or gamma delta lineage is controversial. Since the delta locus is located within the alpha locus, rearrangement of alpha genes leads to deletion of delta. We have examined the rearrangement status of the delta locus immediately prior to alpha rearrangement. We find that many thymic precursors of alpha beta cells undergo VDJ delta rearrangements. Furthermore, the same cells frequently coexpress sterile T early alpha (TEA) transcripts originating 3' of C delta and 5' of the most upstream J alpha, thus implying that individual alpha beta lineage cells undergo sequential VDJ delta and VJ alpha rearrangements. Finally, VDJ delta rearrangements in immature alpha beta cells appear to be random, supporting models in which alpha beta lineage commitment is determined independently of the rearrangement status at the TCR delta locus.

Bump time-frequency toolbox: a toolbox for time-frequency oscillatory bursts extraction in electrophysiological signals

Background: oscillatory activity, which can be separated in background and oscillatory burst pattern activities, is supposed to be representative of local synchronies of neural assemblies. Oscillatory burst events should consequently play a specific functional role, distinct from background EEG activity – especially for cognitive tasks (e.g. working memory tasks), binding mechanisms and perceptual dynamics (e.g. visual binding), or in clinical contexts (e.g. effects of brain disorders). However extracting oscillatory events in single trials, with a reliable and consistent method, is not a simple task. Results: in this work we propose a user-friendly stand-alone toolbox, which models in a reasonable time a bump time-frequency model from the wavelet representations of a set of signals. The software is provided with a Matlab toolbox which can compute wavelet representations before calling automatically the stand-alone application. Conclusion: The tool is publicly available as a freeware at the address: http:// www.bsp.brain.riken.jp/bumptoolbox/toolbox_home.html

Muscle moment-arms: a key element in muscle-force estimation.

A clear and rigorous definition of muscle moment-arms in the context of musculoskeletal systems modelling is presented, using classical mechanics and screw theory. The definition provides an alternative to the tendon excursion method, which can lead to incorrect moment-arms if used inappropriately due to its dependency on the choice of joint coordinates. The definition of moment-arms, and the presented construction method, apply to musculoskeletal models in which the bones are modelled as rigid bodies, the joints are modelled as ideal mechanical joints and the muscles are modelled as massless, frictionless cables wrapping over the bony protrusions, approximated using geometric surfaces. In this context, the definition is independent of any coordinate choice. It is then used to solve a muscle-force estimation problem for a simple 2D conceptual model and compared with an incorrect application of the tendon excursion method. The relative errors between the two solutions vary between 0% and 100%.

Neuropeptide Y expression and regulation in a differentiated rat insulin-secreting cell line.

Neuropeptide-Y (NPY) is a 36-amino acid peptide known to inhibit glucose-stimulated insulin secretion in various animal models in vitro and in vivo. NPY is thought to be one of the mediators of sympathetic action in the pancreas through nerve endings surrounding the islets, and it has recently been shown to be synthesized within the islets of Langerhans. To elucidate the potential role of NPY in the endocrine pancreas, we studied the expression and regulation of NPY secretion in a rat insulinoma cell line (INS-1). NPY mRNA and peptide are highly expressed and secreted by INS-1 cells. NPY levels were determined by a sensitive and specific two-site amplified enzyme-linked immunosorbent assay. Incubation of INS-1 cells with various glucose concentrations did not modify NPY secretion; however, stimulation of adenylate cyclase by forskolin induced a dose- and time-dependent increase in NPY release in the medium. The glucagon-like peptide-I-(7-36) amide (GLP-1), a known gluco-incretin in humans, induced at low concentration (10(-9) M) a similar expression of NPY mRNA and peptide secretion in INS-1 cells. On the other hand, the inhibition of cAMP accumulation by the alpha 2-adrenergic agonist clonidine decreased NPY secretion. In conclusion, 1) high levels of gene expression and secretion of NPY are found in a rat insulinoma cell line (INS-1). 2) Accumulation of cAMP induced by forskolin or a gluco-incretin (GLP-1) induces a further increase in NPY gene expression and release. 3) NPY secretion is not modulated by low or high glucose concentrations in the medium. 4) Induction of NPY, a known inhibitor of insulin secretion, may represent a novel counterregulatory mechanism of insulin secretion, limiting the stimulatory effect of GLP-1 on insulin secretion.

Implementation Manual — 3D Engineered Models for Highway Construction: The Iowa Experience; RB33-014, 2015

3D engineered modeling is a relatively new and developing technology that can provide numerous benefits to owners, engineers, contractors, and the general public. This manual is for highway agencies that are considering or are in the process of switching from 2D plan sets to 3D engineered models in their highway construction projects. It will discuss some of the benefits, applications, limitations, and implementation considerations for 3D engineered models used for survey, design, and construction. Note that is not intended to cover all eventualities in all states regarding the deployment of 3D engineered models for highway construction. Rather, it describes how one state—Iowa—uses 3D engineered models for construction of highway projects, from planning and surveying through design and construction.

Do stacked species distribution models reflect altitudinal diversity patterns?

The objective of this study was to evaluate the performance of stacked species distribution models in predicting the alpha and gamma species diversity patterns of two important plant clades along elevation in the Andes. We modelled the distribution of the species in the Anthurium genus (53 species) and the Bromeliaceae family (89 species) using six modelling techniques. We combined all of the predictions for the same species in ensemble models based on two different criteria: the average of the rescaled predictions by all techniques and the average of the best techniques. The rescaled predictions were then reclassified into binary predictions (presence/absence). By stacking either the original predictions or binary predictions for both ensemble procedures, we obtained four different species richness models per taxa. The gamma and alpha diversity per elevation band (500 m) was also computed. To evaluate the prediction abilities for the four predictions of species richness and gamma diversity, the models were compared with the real data along an elevation gradient that was independently compiled by specialists. Finally, we also tested whether our richness models performed better than a null model of altitudinal changes of diversity based on the literature. Stacking of the ensemble prediction of the individual species models generated richness models that proved to be well correlated with the observed alpha diversity richness patterns along elevation and with the gamma diversity derived from the literature. Overall, these models tend to overpredict species richness. The use of the ensemble predictions from the species models built with different techniques seems very promising for modelling of species assemblages. Stacking of the binary models reduced the over-prediction, although more research is needed. The randomisation test proved to be a promising method for testing the performance of the stacked models, but other implementations may still be developed.