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This study presents a novel formulation for incorporating anisotropy into the generalized plasticity constitutive model. Generalized plasticity is a hierarchical framework allowing for extensibility, in order to encompass new phenomena and improve its predictive capabilities. Anisotropy formulation is based experimentally on the phase transformation state and considers explicitly the direction of the maximum principal stress and the magnitude of the intermediate principal stress, through an anisotropy state variable that contributes to the state parameter. Additionally, the model incorporates the fabric using an evolving fabric variable that reflects initial fabric due to sample preparation method for granular soils. The formulation is simple and introduces three constitutive parameters, allowing for straightforward implementation into the constitutive model and direct application in finite element analysis. The model is validated with undrained triaxial tests conducted on Toyoura sand, covering a wide range of initial conditions with a unique set of constitutive parameters, and yielding overall satisfactory results despite some limitations. [ABSTRACT FROM AUTHOR]

In silico methods can be used for an early assessment of arrhythmogenic properties of drug candidates. However, their use for decision-making is conditioned by the possibility to estimate the predictions' uncertainty. This work describes our efforts to develop uncertainty quantification methods for the predictions produced by multi-level proarrhythmia models. In silico models used in this field usually start with experimental or predicted IC50 values that describe drug-induced ion channel blockade. Using such inputs, an electrophysiological model computes how the ion channel inhibition, exerted by a drug in a certain concentration, translates to an altered shape and duration of the action potential in cardiac cells, which can be represented as arrhythmogenic risk biomarkers such as the APD90. Using this framework, we identify the main sources of aleatory and epistemic uncertainties and propose a method based on probabilistic simulations that replaces single-point estimates predicted using multiple input values, including the IC50s and the electrophysiological parameters, by distributions of values. Two selected variability types associated with these inputs are then propagated through the multi-level model to estimate their impact on the uncertainty levels in the output, expressed by means of intervals. The proposed approach yields single predictions of arrhythmogenic risk biomarkers together with value intervals, providing a more comprehensive and realistic description of drug effects on a human population. The methodology was tested by predicting arrhythmogenic biomarkers on a series of twelve well-characterised marketed drugs, belonging to different arrhythmogenic risk classes. [ABSTRACT FROM AUTHOR]

Landslides cause severe economic damage and a large number of casualties every year around the world. In many cases, it is not possible to avoid them, and the task of engineers and geologists is to mitigate their effects using measures such as building diverting structures or preparing escape roads to be used when an alarm is triggered. It is necessary, therefore, to predict the path of the landslide, its depth and velocity, and the runout. These objectives are usually are attained by using mathematical, numerical and rheological models. An important limitation of the analysis is the lack of data, specially when few laboratory tests are available, and in cases where their present important variations. This leads to performing sensitivity analyses in which analysts study the influence of several magnitudes of interest, such as friction angle, porosity, basal pore pressure and geometry of the sliding mass, just to mention a few, leading in turn to perform a large number of simulations. We propose in this paper a methodology to speed up the process, which is based on: (i) using depth integrated models, which provide a good combination of accuracy and computer effort and (ii) using artificial intelligence tools to reduce the number of simulations. Let us consider a case where we have N mag main variables to explore; for each of them we select a number of cases, which can differ from one magnitude to another. The number of cases will be where N cases (i) is the number of cases we have selected for magnitude i. We can consider these variables as nodes belonging to a hypercube of dimension N mag . We will refer from now on as "hypercube" to the set of all cases generated in this way. The paper presents two cases where these techniques will be applied: (i) a 1D dam break problem and (ii) a case of a real debris flow which happened in Hong Kong, for which there is available information. [ABSTRACT FROM AUTHOR]

There is a widely recognized need to reduce human activity's impact on the environment. Many industries of the leather and textile sector (LTI), being aware of producing a significant amount of residues (Keßler et al. 2021; Liu et al. 2021), are adopting measures to reduce the impact of their processes on the environment, starting with a more comprehensive characterization of the chemical risk associated with the substances commonly used in LTI. The present work contributes to these efforts by compiling and toxicologically annotating the substances used in LTI, supporting a continuous learning strategy for characterizing their chemical safety. This strategy combines data collection from public sources, experimental methods and in silico predictions for characterizing four different endpoints: CMR, ED, PBT, and vPvB. We present the results of a prospective validation exercise in which we confirm that in silico methods can produce reasonably good hazard estimations and fill knowledge gaps in the LTI chemical space. The proposed protocol can speed the process and optimize the use of resources including the lives of experimental animals, contributing to identifying potentially harmful substances and their possible replacement by safer alternatives, thus reducing the environmental footprint and impact on human health. [ABSTRACT FROM AUTHOR]

Traditionally, Biot's formulation is employed to model the behavior of saturated soils. The u - p w (solid displacement–pore water pressure) formulation can be considered as the standard one, since involves a good computational performance together with excellent accuracy for slow and moderate speed phenomena. Dynamic processes can be studied even if the acceleration of the water is neglected, what occurs in the undrained limit. It is well-known that u - p w formulation might display instabilities in the undrained-incompressible limit. Several techniques have been proposed to overcome this issue, principally within an implicit time integration scheme for small strains. In this paper, a robust implementation of the divergence of the momentum equation technique is presented for an explicit u - p w approach within the framework of optimal transportation meshfree scheme at finite strain. Several examples are provided in order to assess the good performance of the proposed methodology. [ABSTRACT FROM AUTHOR]

Standard finite element formulation and implementation in solid dynamics at large strains usually relies upon and indicial-tensor Voigt notation to factorized the weighting functions and take advantage of the symmetric structure of the algebraic objects involved. In the present work, a novel component-free approach, where no reference to a basis, axes or components is made, implied or required, is adopted for the finite element formulation. Under this approach, the factorisation of the weighting function and also of the increment of the displacement field, can be performed by means of component-free operations avoiding both the use of any index notation and the subsequent reorganisation in matrix Voigt form. This new approach leads to a straightforward implementation of the formulation where only vectors and second order tensors in R 3 are required. The proposed formulation is as accurate as the standard Voigt based finite element method however is more efficient, concise, transparent and easy to implement. [ABSTRACT FROM AUTHOR]

Rapid flow-like landslides, particularly debris flows and debris avalanches, cause significant economic damage and many victims worldwide every year. They are usually extremely fast with the capability of travelling long distances in short times, sweeping away everything in their path. The principal objective of this paper is to test the ability of the 'GeoFlow-SPH' two-phase model developed by the authors, to reproduce the complex behaviour of natural debris avalanches where pore-water pressure evolution plays a key role. To reach this goal, the model is applied to reproduce the complex dynamic behaviour observed in Johnsons Landing debris avalanche including the observed bifurcation caused by the flowing out of part of the moving mass from the mid-channel. Initial thickness deposit trim-line, distribution of deposit volume, and the average velocities were provided for this real case, making it an appropriate case to validate the developed model. The paper also contributes to evaluate the SPH-FD model's potentialities to simulate the structural countermeasure, like bottom drainage screens, used to reduce the impact of debris flows. The analysis of the results shows the adequacy of the proposed model to solve this complicated geophysical problem. [ABSTRACT FROM AUTHOR]

Read-across approaches are considered key in moving away from in vivo animal testing towards addressing data-gaps using new approach methods (NAMs). Ample successful examples are still required to substantiate this strategy. Here we present and discuss the learnings from two OECD IATA endorsed read-across case studies. They involve two classes of pesticides - rotenoids and strobilurins - each having a defined mode-of-action that is assessed for its neurological hazard by means of an AOP-based testing strategy coupled to toxicokinetic simulations of human tissue concentrations. The endpoint in question is potential mitochondrial respiratory chain mediated neurotoxicity, specifically through inhibition of complex I or III. An AOP linking inhibition of mitochondrial respiratory chain complex I to the degeneration of dopaminergic neurons formed the basis for both cases but was deployed in two different regulatory contexts. The two cases also exemplify several different read-across concepts: analogue versus category approach, consolidated versus putative AOP, positive versus negative prediction (i.e., neurotoxicity versus low potential for neurotoxicity), and structural versus biological similarity. We applied a range of NAMs to explore the toxicodynamic properties of the compounds, e.g., in silico docking as well as in vitro assays and readouts - including transcriptomics - in various cell systems, all anchored to the relevant AOPs. Interestingly, although some of the data addressing certain elements of the read-across were associated with high uncertainty, their impact on the overall read-across conclusion remained limited. Coupled to the elaborate regulatory review that the two cases underwent, we propose some generic learnings of AOP-based testing strategies supporting read-across. [ABSTRACT FROM AUTHOR]

Inflammatory bowel disease is a multifactorial etiology, associated with environmental factors that can trigger both debut and relapses. A high level of tumor necrosis factor-α in the gut is the main consequence of immune system imbalance. The aim of treatment is to restore gut homeostasis. In this study, fresh blood and serum samples were used to identify biomarkers and to discriminate between Crohn's disease and ulcerative colitis patients under remission treated with anti-TNF. Metabolomics based on Nuclear Magnetic Resonance spectroscopy (NMR) was used to detect unique biomarkers for each class of patients. Blood T lymphocyte repertories were characterized, as well as cytokine and transcription factor profiling, to complement the metabolomics data. Higher levels of homoserine-methionine and isobutyrate were identified as biomarkers of Crohn's disease with ileocolic localization. For ulcerative colitis, lower levels of creatine-creatinine, proline, and tryptophan were found that reflect a deficit in the absorption of essential amino acids in the gut. T lymphocyte phenotyping and its functional profiling revealed that the overall inflammation was lower in Crohn's disease patients than in those with ulcerative colitis. These results demonstrated that NMR metabolomics could be introduced as a high-throughput evaluation method in routine clinical practice to stratify both types of patients related to their pathology. Key messages: NMR metabolomics is a non-invasive tool that could be implemented in the normal clinical practice for IBD to assess beneficial effect of the treatment. NMR metabolomics is a useful tool for precision medicine, in order to sew a specific treatment to a specific group of patients. Finding predictors of response to IFX would be desirable to select patients affected by IBD. Immunological status of inflammations correlates with NMR metabolomics biomarkers. [ABSTRACT FROM AUTHOR]

Debris flows are a type of fast landslides where a mixture of soil and water propagates along narrow channels. The main characteristics are (1) important relative displacements between the solid and fluid phases, and (2) development of pore-water pressures in excess to hydrostatic. The ratios between vertical and horizontal displacements of the flow, from the triggering point to the deposition, indicate that friction angles are much smaller than those measured in laboratories. Debris flows are modeled as two phases flow, but implementing pore-water pressure is an important issue. The purpose of this paper is to improve the existing two phases debris flow models by implementing pore-water pressures in excess to hydrostatic. It is found that pore pressure evolution depends on consolidation, changes in the flow depth, and changes and gradients of porosity. The proposed depth integrated mathematical model is discretized using two sets of SPH nodes (solid and fluid), with a set of finite difference meshes associated with each solid material SPH point. The paper presents two examples from where it is possible to gain insight into the differences between the models (with and without excess pore water pressure). [ABSTRACT FROM AUTHOR]

This article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application with a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures since the software supports structural normalization, molecular descriptor calculation, and machine learning model generation using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively. Moreover, Flame implements a mechanism allowing to extend its source code, adding unlimited model customization. Models generated with Flame can be easily exported, facilitating collaborative model development. All models are stored in a model repository supporting model versioning. Models are identified by unique model IDs and include detailed documentation formatted using widely accepted standards. The current version is the result of nearly 3 years of development in collaboration with users from the pharmaceutical industry within the IMI eTRANSAFE project, which aims, among other objectives, to develop high-quality predictive models based on shared legacy data for assessing the safety of drug candidates. [ABSTRACT FROM AUTHOR]

Pre-competitive data sharing can offer the pharmaceutical industry significant benefits in terms of reducing the time and costs involved in getting a new drug to market through more informed testing strategies and knowledge gained by pooling data. If sufficient data is shared and can be co-analyzed, then it can also offer the potential for reduced animal usage and improvements in the in silico prediction of toxicological effects. Data sharing benefits can be further enhanced by applying the FAIR Guiding Principles, reducing time spent curating, transforming and aggregating datasets and allowing more time for data mining and analysis. We hope to facilitate data sharing by other organizations and initiatives by describing lessons learned as part of the Enhancing TRANslational SAFEty Assessment through Integrative Knowledge Management (eTRANSAFE) project, an Innovative Medicines Initiative (IMI) partnership which aims to integrate publicly available data sources with proprietary preclinical and clinical data donated by pharmaceutical organizations. Methods to foster trust and overcome non-technical barriers to data sharing such as legal and IPR (intellectual property rights) are described, including the security requirements that pharmaceutical organizations generally expect to be met. We share the consensus achieved among pharmaceutical partners on decision criteria to be included in internal clearance procedures used to decide if data can be shared. We also report on the consensus achieved on specific data fields to be excluded from sharing for sensitive preclinical safety and pharmacology data that could otherwise not be shared. [ABSTRACT FROM AUTHOR]

Experimental multinuclear determination of the chemical shifts, especially 15N and 109Ag, of five silver(I) trinuclear pyrazolate complexes, (PzAg)3, coupled with ZORA (zeroth-order regular approximation) and GIAO (gauge-independent atomic orbital) calculations, has shed new light on these compounds. An almost complete collection of spin-spin coupling constants has been measured for the first time for several of these compounds. The aromaticity of (PzAg)3 was examined using NICS (nucleus-independent chemical shift) and ACID (anisotropy of the induced current density) approaches reaching the conclusion of its absence. [ABSTRACT FROM AUTHOR]

Many toxicological test methods, including assays of cell viability and function, require an evaluation of concentration-response data. This often involves curve fitting, and the resulting mathematical functions are then used to determine the concentration at which a certain deviation from the control value occurs (e.g., a decrease of cell viability by 15%). Such a threshold is called the benchmark response (BMR). For a toxicological test, it is often of interest to determine the concentration of test compound at which a pre-defined BMR of, e.g., 10, 25 or 50% is reached. The concentration at which the modelled curve crosses the BMR is called the benchmark concentration (BMC). We present a user-friendly, web-based tool (BMCeasy), designed for operators without programming skills and profound statistical background, to determine BMCs and their confidence intervals. BMCeasy allows simultaneous analysis of viability plus a functional test endpoint, and it yields absolute BMCs with confidence intervals for any BMR. Besides an explanation of the algorithm underlying BMCeasy, this article also gives multiple examples of data outputs. BMCeasy was used within the EU-ToxRisk project for preparing data packages that were submitted to regulatory authorities, demonstrating the real-life applicability of the tool. [ABSTRACT FROM AUTHOR]

Read-across is one of the most frequently used alternative tools for hazard assessment, in particular for complex endpoints such as repeated dose or developmental and reproductive toxicity. Read-across extrapolates the outcome of a specific toxicological in vivo endpoint from tested (source) compounds to "similar" (target) compound(s). If appropriately applied, a read-across approach can be used instead of de novo animal testing. The read-across approach starts with structural/physicochemical similarity between target and source compounds, assuming that similar structural characteristics lead to similar human hazards. In addition, similarity also has to be shown for the toxicokinetic and toxicodynamic properties of the grouped compounds. To date, many read-across cases fail to demonstrate toxicokinetic and toxicodynamic similarities. New concepts, in vitro and in silico tools are needed to better characterise these properties, collectively called new approach methodologies (NAMs). This white paper outlines a general read-across assessment concept using NAMs to support hazard characterization of the grouped compounds by generating data on their dynamic and kinetic properties. Based on the overarching read-across hypothesis, the read-across workflow suggests targeted or untargeted NAM testing also outlining how mechanistic knowledge such as adverse outcome pathways (AOPs) can be utilized. Toxicokinetic models (biokinetic and PBPK), enriched by in vitro parameters such as plasma protein binding and hepatocellular clearance, are proposed to show (dis)similarity of target and source compound toxicokinetics. Furthermore, in vitro to in vivo extrapolation is proposed to predict a human equivalent dose, as potential point of departure for risk assessment. Finally, the generated NAM data are anchored to the existing in vivo data of source compounds to predict the hazard of the target compound in a qualitative and/or quantitative manner. To build this EU-ToxRisk read-across concept, case studies have been conducted and discussed with the regulatory community. These case studies are briefly outlined. [ABSTRACT FROM AUTHOR]

In this work, a two-layer depth-integrated smoothed particle hydrodynamics (SPH) model is applied to investigate the effects of landslide propagation on the impulsive waves generated when entering a water body. In order to deal with the open boundary in practical engineering problems, an absorbing boundary method, based on Riemann invariants which can be applied to arbitrary geometries, is implemented. In order to examine the accuracy of the proposed formulation, the model is tested against both available laboratory tests and numerical examples from the literature. Then, it is adopted to model the characteristics of the impulse waves generated by the Halaowo landslide in the Jinsha River, China. The results provide a technical basis for the emergency plan to the Halaowo landslide and benefit the disaster prevention policy, which helps mitigating future hazards in similar reservoir areas. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses topics within the issue including hazard evaluation and risk mitigation; landslides research; and flood.

The (developmental) neurotoxicity hazard is still unknown for most chemicals. Establishing a test battery covering most of the relevant adverse outcome pathways may close this gap without requiring a huge animal experimentation program. Ideally, each of the assays would cover multiple mechanisms of toxicity. One candidate test is the human LUHMES cell-based NeuriTox test. To evaluate its readiness for larger-scale testing, a proof of concept library, assembled by the U.S. National Toxicology Program (NTP), was screened. Out of the 75 unique compounds, seven were defined as specifically neurotoxic after the hit-confirmation phase and ten further compounds were generally cytotoxic within the concentration range of up to 20 µM. As complementary approach, the library was screened in the PeriTox test, which identifies toxicants affecting the human peripheral nervous system. Of the eight PeriTox hits, five were similar to the NeuriTox hits: rotenone, colchicine, diethylstilbestrol, berberine chloride, and valinomycin. The unique NeuriTox hit, methyl-phenylpyridinium (MPP+), is known from in vivo studies to affect only dopaminergic neurons (which LUHMES cells are). Conversely, the known peripheral neurotoxicant acrylamide was picked up in the PeriTox, but not in the NeuriTox assay. All of the five common hits had also been identified in the published neural crest migration (cMINC) assay, while none of them emerged as a cardiotoxicant in a previous screen using the same library. These comparative data suggest that complementary in vitro tests can pick up a broad range of toxicants, and that multiple test results might help to predict organ specificity patterns. [ABSTRACT FROM AUTHOR]

Many in vitro tests have been developed to screen for potential neurotoxicity. However, only few cell function-based tests have been used for comparative screening, and thus experience is scarce on how to confirm and evaluate screening hits. We addressed these questions for the neural crest cell migration test (cMINC). After an initial screen, a hit follow-up strategy was devised. A library of 75 compounds plus internal controls (NTP80-list), assembled by the National Toxicology Program of the USA (NTP) was used. It contained some known classes of (developmental) neurotoxic compounds. The primary screen yielded 23 confirmed hits, which comprised ten flame retardants, seven pesticides and six drug-like compounds. Comparison of concentration-response curves for migration and viability showed that all hits were specific. The extent to which migration was inhibited was 25-90%, and two organochlorine pesticides (DDT, heptachlor) were most efficient. In the second part of this study, (1) the cMINC assay was repeated under conditions that prevent proliferation; (2) a transwell migration assay was used as a different type of migration assay; (3) cells were traced to assess cell speed. Some toxicants had largely varying effects between assays, but each hit was confirmed in at least one additional test. This comparative study allows an estimate on how confidently the primary hits from a cell function-based screen can be considered as toxicants disturbing a key neurodevelopmental process. Testing of the NTP80-list in more assays will be highly interesting to assemble a test battery and to build prediction models for developmental toxicity. [ABSTRACT FROM AUTHOR]

The present study applies a systems biology approach for the in silico predictive modeling of drug toxicity on the basis of high-quality preclinical drug toxicity data with the aim of increasing the mechanistic understanding of toxic effects of compounds at different levels (pathway, cell, tissue, organ). The model development was carried out using 77 compounds for which gene expression data for treated primary human hepatocytes is available in the LINCS database and for which rodent in vivo hepatotoxicity information is available in the eTOX database. The data from LINCS were used to determine the type and number of pathways disturbed by each compound and to estimate the extent of disturbance (network perturbation elasticity), and were used to analyze the correspondence with the in vivo information from eTOX. Predictive models were developed through this integrative analysis, and their specificity and sensitivity were assessed. The quality of the predictions was determined on the basis of the area under the curve (AUC) of plots of true positive vs. false positive rates (ROC curves). The ROC AUC reached values of up to 0.9 (out of 1.0) for some hepatotoxicity endpoints. Moreover, the most frequently disturbed metabolic pathways were determined across the studied toxicants. They included, e.g., mitochondrial beta-oxidation of fatty acids and amino acid metabolism. The process was exemplified by successful predictions on various statins. In conclusion, an entirely new approach linking gene expression alterations to the prediction of complex organ toxicity was developed and evaluated. [ABSTRACT FROM AUTHOR]

Most computational methods used for the prediction of toxicity endpoints are based on the assumption that similar compounds have similar biological properties. This principle can be exploited using computational methods like read across or quantitative structure-activity relationships. However, there is no general agreement about which method is the most appropriate for quantifying compound similarity neither for exploiting the similarity principle in order to obtain reliable estimations of the compound properties. Moreover, optimal similarity metrics and modeling methods might depend on the characteristics of the endpoints and training series used in each case. This study describes a comparative analysis of the predictive performance of diverse similarity metrics and modeling methods in toxicological applications. A collection of two quantitative ( n = 660, n = 1114) and three qualitative ( n = 447, n = 905, n = 1220) datasets representing very different endpoints of interest in drug safety evaluation and rigorous methods were used to estimate the external predictive ability in each case. The results confirm that no single approach produces the best results in all instances, and the best predictions were obtained using different tools in different situations. The trends observed in this study were exploited to propose a unifying strategy allowing the use of the most suitable method for every compound. A comparison of the quality of the predictions obtained by the unifying strategy with those obtained by standard prediction methods confirmed the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

The detection of taxol and related taxanes in Corylus avellana has generated considerable interest, particularly for in vitro cell cultures. Cell suspensions are a sustainable and rational option for obtaining a continuous and reliable source of secondary metabolites in large-scale processes. We therefore focused our study on the main factors that affect the growth of C. avellana cell suspensions as a key approach to improving culture productivity. In this work, calli were successfully induced from C. avellana seeds, leaves, and stems, and the efficiency of different sterilization methods was analyzed. The effects of the basal medium, carbon source, and the type and quantity of plant growth regulators on culture growth were studied. A fractional factorial design allowed us to reduce the number of experiments and analyze all the combinations in one run, thereby reducing time, variability, and costs. Statistical analysis (ANOVA) revealed that 1-naphthaleneacetic acid (NAA) and sucrose are mandatory for the growth of C. avellana cell suspension cultures, with no interactions detected between the parameters analyzed, while growth did not depend on the addition of cytokinins. The secondary metabolism was not inhibited, detecting 1175.45 ng/L of baccatin III and traces of taxol, deacetyltaxol, and cephalomannine. Additionally, prompted by the high growth rate of the C. avellana calli, we assayed a new cold-temperature-based method to maintain a stock of calli using half-strength Murashige and Skoog solid medium, concluding that up to 5 mo at 4°C is optimal to ensure white friable calli upon regrowth at 25°C. [ABSTRACT FROM AUTHOR]

Background: Computational models based in Quantitative-Structure Activity Relationship (QSAR) methodologies are widely used tools for predicting the biological properties of new compounds. In many instances, such models are used as a routine in the industry (e.g. food, cosmetic or pharmaceutical industry) for the early assessment of the biological properties of new compounds. However, most of the tools currently available for developing QSAR models are not well suited for supporting the whole QSAR model life cycle in production environments. Results: We have developed eTOXlab; an open source modeling framework designed to be used at the core of a self-contained virtual machine that can be easily deployed in production environments, providing predictions as web services. eTOXlab consists on a collection of object-oriented Python modules with methods mapping common tasks of standard modeling workflows. This framework allows building and validating QSAR models as well as predicting the properties of new compounds using either a command line interface or a graphic user interface (GUI). Simple models can be easily generated by setting a few parameters, while more complex models can be implemented by overriding pieces of the original source code. eTOXlab benefits from the object-oriented capabilities of Python for providing high flexibility: any model implemented using eTOXlab inherits the features implemented in the parent model, like common tools and services or the automatic exposure of the models as prediction web services. The particular eTOXlab architecture as a self-contained, portable prediction engine allows building models with confidential information within corporate facilities, which can be safely exported and used for prediction without disclosing the structures of the training series. Conclusions: The software presented here provides full support to the specific needs of users that want to develop, use and maintain predictive models in corporate environments. The technologies used by eTOXlab (web services, VM, object-oriented programming) provide an elegant solution to common practical issues; the system can be installed easily in heterogeneous environments and integrates well with other software. Moreover, the system provides a simple and safe solution for building models with confidential structures that can be shared without disclosing sensitive information. [ABSTRACT FROM AUTHOR]

Aims: Afforestation causes important alterations in SOM content and composition that affect the soil functions and C balance. The aim of this study was to identify the mechanisms that determine the changes in SOM composition following afforestation of grasslands. Methods: The study included 4 chronosequences and 5 paired plots comprising pastures and land afforested with Pinus radiata. The SOM was characterized by C CP-MAS NMR spectroscopy and differential scanning calorimetry. Results: During the first 10-20 year after afforestation, the changes in SOM content varied from slight gains to large losses (>40 %). The analyses revealed that even SOM compounds considered resistant to decomposition were degraded during this time. The SOM gains, observed 20 year after stand establishment, were favoured by the higher recalcitrance of pine litter and possibly by soil acidification. The concentrations of most SOM compounds, particularly the stable compounds, were higher at the end of the rotation. The low degree of protection, along with the favourable climatic conditions, may also explain the rapid decomposition of SOM, including resistant compounds, in these soils. DSC analysis complemented the information about SOM composition provided by other techniques. Conclusions: The accumulation of stable SOM compounds at the end of the rotation suggests a longer soil C turnover in these afforested soils, which may alleviate the gradual loss of SOC in intensively managed forest soils. [ABSTRACT FROM AUTHOR]

In addition to lower carbon dioxide emissions, policies to reduce fossil fuel combustion can yield substantial air quality co-benefits via reduced emissions of co-pollutants such as particulate matter and air toxics. If co-pollutant intensity (the ratio of co-pollutant impacts to carbon dioxide emissions) varies across pollution sources, efficient policy design would seek greater emissions reductions where co-benefits are higher. The distribution of co-benefits also raises issues of environmental equity. This paper presents evidence on intersectoral, intrasectoral and spatial variations in co-pollutant intensity of industrial point sources in the United States, and discusses options for integrating co-benefits into climate policy design to advance efficiency and equity. [ABSTRACT FROM AUTHOR]

Survivin, the smallest inhibitor of apoptosis protein (IAP), is a valid target for cancer research. It mediates both the apoptosis pathway and the cell cycle and has been proposed to form a complex with the cyclin-dependent kinase protein CDK4. The resulting complex transports CDK4 from the cytosol to the nucleus, where CDK4 participates in cell division. Survivin has been recognized as a node protein that interacts with several partners; disruption of the formed complexes can lead to new anticancer compounds. We propose a rational model of the survivin/CDK4 complex that fulfills the experimental evidence and that can be used for structure-based design of inhibitors modifying its interface recognition. In particular, the suggested complex involves the alpha helical domain of survivin and resembles the mode of binding of survivin in the survivin/borealin X-ray structure. The proposed model has been obtained by combining protein-protein docking, fractal-based shape complementarity, electrostatics studies and extensive molecular dynamics simulations. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

The ATP-binding cassette efflux transporter P-glycoprotein (P-gp) is notorious for contributing to multidrug resistance in antitumor therapy. Due to its expression in many blood-organ barriers, it also influences the pharmacokinetics of drugs and drug candidates and is involved in drug/drug- and drug/nutrient interactions. However, due to lack of structural information the molecular basis of ligand/transporter interaction still needs to be elucidated. Towards this goal, a series of Benzopyranes and Benzopyrano[3,4b][1,4]oxazines have been synthesized and pharmacologically tested for their ability to inhibit P-gp mediated daunomycin efflux. Both quantitative structure-activity relationship (QSAR) models using simple physicochemical and novel GRID-independent molecular descriptors (GRIND) were established to shed light on the structural requirements for high P-gp inhibitory activity. The results from 2D-QSAR showed a linear correlation of vdW surface area (Å) of hydrophobic atoms with the pharmacological activity. GRIND (3D-QSAR) studies allowed to identify important mutual distances between pharmacophoric features, which include one H-bond donor, two H-bond acceptors and two hydrophobic groups as well as their distances from different steric hot spots of the molecules. Activity of the compounds particularly increases with increase of the distance of an H-bond donor or a hydrophobic feature from a particular steric hot spot of the benzopyrane analogs. [ABSTRACT FROM AUTHOR]

Protein surface roughness is a structural property associated with ligand-protein and protein-protein binding interfaces. In this work we apply for the first time the concept of surface roughness, expressed as the fractal dimension, to address structure and function of G protein-coupled receptors (GPCRs) which are an important group of drug targets. We calculate the exposure ratio and the fractal dimension for helix-forming residues of the β adrenergic receptor (βAR), a model system in GPCR studies, in different conformational states: in complex with agonist, antagonist and partial inverse agonists. We show that both exposure ratio and roughness exhibit periodicity which results from the helical structure of GPCRs. The pattern of roughness and exposure ratio of a protein patch depends on its environment: the residues most exposed to membrane are in general most rough whereas parts of receptors mediating interhelical contacts in a monomer or protein complex are much smoother. We also find that intracellular ends (TM3, TM5, TM6 and TM7) which are relevant for G protein binding and thus receptor signaling, are exposed but smooth. Mapping the values of residual fractal dimension onto receptor 3D structures makes it possible to conclude that the binding sites of orthosteric ligands as well as of cholesterol are characterized with significantly higher roughness than the average for the whole protein. In summary, our study suggests that identification of specific patterns of roughness could be a novel approach to spot possible binding sites which could serve as original drug targets for GPCRs modulation. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Climate change is an issue of great importance for human rights, public health, and socioeconomic equity because of its diverse consequences overall as well as its disproportionate impact on vulnerable and socially marginalized populations. Vulnerability to climate change is determined by a community's ability to anticipate, cope with, resist, and recover from the impact of major weather events. Climate change will affect industrial and agricultural sectors, as well as transportation, health, and energy infrastructure. These shifts will have significant health and economic consequences for diverse communities throughout California. Without proactive policies to address these equity concerns, climate change will likely reinforce and amplify current as well as future socioeconomic disparities, leaving low-income, minority, and politically marginalized groups with fewer economic opportunities and more environmental and health burdens. This review explores the disproportionate impacts of climate change on vulnerable groups in California and investigates the costs and benefits of the climate change mitigation strategies specified for implementation in the California Global Warming Solutions Act of 2006 (AB 32). Lastly, knowledge gaps, future research priorities, and policy implications are identified. [ABSTRACT FROM AUTHOR]

Any work toward racial equity in America will need to include strengthening the black–Latino coalition. While there are political and social tensions between these groups, much of the underlying issue involves real and perceived perceptions of economic competition, particularly the notion that immigrant Latinos undercut African American wages. We note that there is some evidence that immigrants pull down incomes of US-born unskilled workers—both black and Latino. We argue, however, that highly restrictive immigration policy will have minimal effects and erode collective political power; a superior alternative is working together to reduce high school dropout rates, raise the minimum wage, reintegrate ex-offenders, rigorously enforce antidiscrimination law, promote comprehensive immigration reform, and pursue community development. Such an analysis is gaining ground in grassroots efforts to build trust and forge policy coalitions between Latinos and African Americans. [ABSTRACT FROM AUTHOR]

The saturation transfer difference (STD) experiment is a rich source of information on topological aspects of ligand binding to a receptor. The epitope mapping is based on a magnetization transfer after signal saturation from the receptor to the ligand, where interproton distances permit this process. Signal overlap in the STD spectrum can cause difficulties to correctly assign and/or quantitate the measured enhancements. To address this issue we report here a modified version of the routine experiment and a processing scheme that provides a 1D-STD homodecoupled spectrum (i.e. an experiment in which all STD signals appear as singlets) with line widths similar to those in original STD spectrum. These refinements contribute to alleviate problems of signal overlap. The experiment is based on 2D-J-resolved spectroscopy, one of the fastest 2D experiments under conventional data sampling in the indirect dimension, and provides excellent sensitivity, a key factor for the difference experiments. [ABSTRACT FROM AUTHOR]

The binding site and backbone dynamics of a bioactive complex formed by the acidic fibroblast growth factor (FGF-1) and a specifically designed heparin hexasaccharide has been investigated by HSQC and relaxation NMR methods. The comparison of the relaxation data for the free and bound states has allowed showing that the complex is monomeric, and still induces mutagenesis, and that the protein backbone presents reduced motion in different timescale in its bound state, except in certain points that are involved in the interaction with the fibroblast growth factor receptor (FGFR). [ABSTRACT FROM AUTHOR]

A 2D-HSQC-carbon selective/proton selective-constant time COSY, 2D-HSQC-(sel C, sel H)-CT COSY experiment, which is applicable to uniformly 13C isotopically enriched samples (U-13C) of oligosaccharides or oligonucleotides is proposed for the measurement of proton–proton RDC in crowded regions of 2D-spectra. In addition, a heteronuclear constant time-COSY experiment, 13C-13C CT-COSY, is proposed for the measurement of one bond carbon–carbon RDC in these molecules. These two methods provide an extension, to U-13C molecules, of the original homonuclear constant time-COSY experiment proposed by Tian et al. (1999) for saccharides. The combination of a number of these RDC with NOE data may provide the method of choice to study oligosaccharide conformation in the free and receptor-bound state. [ABSTRACT FROM AUTHOR]

The selection of a sample of diverse compounds is a common strategy for exploring large molecular libraries. However, the success of such approach depends on the selection of relevant molecular descriptors and the use of appropriate sampling methods. In the context of pharmaceutical research, the molecular descriptors should be based on physicochemical properties related with the pharmacological behaviour of the compounds. In this sense, the alignment-free GRIND and VolSurf molecular descriptors are promising candidates since they have been successfully used in the modelling of both pharmacodynamic and pharmacokinetic properties of drugs. This work describes the use of such descriptors in the diversity sampling of a library of primary amines and compares the results with those obtained in a previous study that used quantum-mechanical descriptors. As in the previous work, principal component (PC) analysis was applied to reduce the dimensionality and remove redundant information of the original descriptors, and the compounds were sampled on the basis of k-means clustering on the space of the selected PCs. The results of the present study show that VolSurf and GRIND provide similar quality sampling regarding global features of the molecules such as hydrophilicity, however the topology of the compounds is considered differently. The similarity between particular compounds strongly depends on the original descriptors used. However all the sample selections done in the PC space after k-means clustering provide the same apparent diversity in comparison to the whole dataset. The results indicate that there is no best set of descriptors on a diversity basis. The selection of descriptors must be based on the drug features to be investigated. [ABSTRACT FROM AUTHOR]

The work describes the development of novel software supporting synchronous distant collaboration between scientists involved in drug discovery and development projects. The program allows to visualize and share data as well as to interact in real time using standard intranets and Internet resources. Direct visualization of 2D and 3D molecular structures is supported and original tools for facilitating remote discussion have been integrated. The software is multiplatform (MS-Windows, SGI-IRIX, Linux), allowing for a seamless integration of heterogeneous working environments. The project aims to support collaboration both within and between academic and industrial institutions. Since confidentiality is very important in some scenarios, special attention has been paid to security aspects. The article presents the research carried out to gather the requirements of collaborative software in the field of drug discovery and development and describes the features of the first fully functional prototype obtained. Real-world testing activities carried out on this prototype in order to guarantee its adequacy in diverse environments are also described and discussed. [ABSTRACT FROM AUTHOR]

The heptasaccharide isolated from the cell wall polysaccharide of Streptococcus mitis J22 serves as an important model for the dynamics and conformation of complex polysaccharides, illustrating the nature of flexibility with rigid epitopes joined by flexible hinges. One-bond C-H residual dipolar couplings (1DCH) and long-range H-H residual dipolar couplings (nDHH) were measured for the heptasaccharide in a cetylpyridinium chloride/hexanol/brine lamellar liquid crystal medium. A method is proposed to determine the nDHH in natural abundance based on a 13C resolved 1H TOCSY pulse sequence previously published to determine the homonuclear scalar couplings. Different methods for interpretation of the 1DCH and the nDHH residual dipolar coupling data obtained were compared and combined with the NOE and long-range H,C and C,C scalar couplings available for this heptasaccharide. A flexible model of the heptasaccharide was determined in which two structurally well-defined regions involving four and two sugar residues, respectively are joined by a flexible hinge which involves two 1→6 glycosidic linkages. [ABSTRACT FROM AUTHOR]

Many heterocyclic amines (HCA) present in cooked food exert a genotoxic activity when they are metabolised (N-oxidated) by the human cytochrome P450 1A2 (CYP1A2h). In order to rationalize the observed differences in activity of this enzyme on a series of 12 HCA, 3D-QSAR methods were applied on the basis of models of HCA–CYP1A2h complexes. The CYP1A2h enzyme model has been previously reported and was built by homology modeling based on cytochrome P450 BM3. The complexes were automatically generated applying the AUTODOCK software and refined using AMBER. A COMBINE analysis on the complexes identified the most important enzyme–ligand interactions that account for the differences in activity within the series. A GRID/GOLPE analysis was then performed on just the ligands, in the conformations and orientations found in the modeled complexes. The results from both methods were concordant and confirmed the advantages of incorporating structural information from series of ligand–receptor complexes into 3D-QSAR methodologies. [ABSTRACT FROM AUTHOR]

The conformational and dynamical features of a branched mannan isolated from a fungal cell wall have been analysed by homo and heteronuclear NMR methods, employing different magnetic fields. 1HNMR cross relaxation times have been obtained for this polysaccharide and have been interpreted qualitatively using different motional models. 13C NMR relaxation parameters (T1, T2, NOE) have also been measured and interpreted using different approximations based on the Lipari and Szabo model free approach. The analysis of the data indicate the existence of important flexibility for the different linkages of the polysaccharide. Motions in the range of 4–6 ns contribute to the relaxation of the macromolecule, although faster internal motions in the 500 ps and 100 ps timescales are also present. These time scales indicate that segmental motions as well as internal motions around the glycosidic linkages are the major sources of relaxation for this molecule at 318 K. Molecular dynamics simulations have also been performed. The obtained results also indicate that the polysaccharide possess a substantial amount of conformational freedom. [ABSTRACT FROM AUTHOR]

1H-NMR cross-relaxation rates and nonselectivelongitudinal relaxation times have been obtained at two magnetic fields (7.0and 11.8 T) and at a variety of temperatures for the branchedtetrasaccharide methyl3-O-α-N-acetyl-galactosaminyl-β-galactopyranosyl-(1→4)[3-O-α-fucosyl]-glucopyranoside (1), an inhibitor of astrocyte growth. Inaddition, 13C-NMR relaxation data have also been recorded atboth fields. The 1H-NMR relaxation data have been interpretedusing different motional models to obtain proton–proton correlationtimes. The results indicate that the GalNAc and Fuc rings display moreextensive local motion than the two inner Glc and Gal moieties, since thosepresent significantly shorter local correlation times. The13C-NMR relaxation parameters have been interpreted in termsof the Lipari–Szabo model-free approach. Thus, order parameters andinternal motion correlation times have been deduced. As obtained for the1H-NMR relaxation data, the two outer residues possess smallerorder parameters than the two inner rings. Internal correlation times are inthe order of 100 ps. The hydroxymethyl groups have also different behaviour,with the exocyclic carbon on the glucopyranoside unit showing the highestS2. Molecular dynamics simulations using a solvated systemhave also been performed and internal motion correlation functions have beendeduced from these calculations. Order parameters and interproton distanceshave been compared to those inferred from the NMR measurements. The obtainedresults are in fair agreement with the experimental data. [ABSTRACT FROM AUTHOR]