Reviewing the potential for including habitat fragmentation to improve life cycle impact assessments for land use impacts on biodiversity

Purpose

The biosphere is progressively subjected to a variety of pressures resulting from anthropogenic activities. Habitat conversion, resulting from anthropogenic land use, is considered the dominant factor driving terrestrial biodiversity loss. Hence, adequate modelling of land use impacts on biodiversity in decision-support tools, like life cycle assessment (LCA), is a priority. State-of-the-art life cycle impact assessment (LCIA) characterisation models for land use impacts on biodiversity translate natural habitat transformation and occupation into biodiversity impacts. However, the currently available models predominantly focus on total habitat loss and ignore the spatial configuration of the landscape. That is, habitat fragmentation effects are ignored in current LCIAs with the exception of one recently developed method.

Methods

Here, we review how habitat fragmentation may affect biodiversity. In addition, we investigate how land use impacts on biodiversity are currently modelled in LCIA and how missing fragmentation impacts can influence the LCIA model results. Finally, we discuss fragmentation literature to evaluate possible methods to include habitat fragmentation into advanced characterisation models.

Results and discussion

We found support in available ecological literature for the notion that habitat fragmentation is a relevant factor when assessing biodiversity loss. Moreover, there are models that capture fragmentation effects on biodiversity that have the potential to be incorporated into current LCIA characterisation models.

Conclusions and recommendations

To enhance the credibility of LCA biodiversity assessments, we suggest that available fragmentation models are adapted, expanded and subsequently incorporated into advanced LCIA characterisation models and promote further efforts to capture the remaining fragmentation effects in LCIA characterisation models.

     

Multiple-source models for electron beams of a medical linear accelerator using BEAMDP computer code

AimThe aim of this work was to develop multiple-source models for electron beams of the NEPTUN 10PC medical linear accelerator using the BEAMDP computer code.BackgroundOne of the most accurate techniques of radiotherapy dose calculation is the Monte Carlo (MC) simulation of radiation transport, which requires detailed information of the beam in the form of a phase-space file. The computing time required to simulate the beam data and obtain phase-space files from a clinical accelerator is significant. Calculation of dose distributions using multiple-source models is an alternative method to phase-space data as direct input to the dose calculation system.Materials and methodsMonte Carlo simulation of accelerator head was done in which a record was kept of the particle phase-space regarding the details of the particle history. Multiple-source models were built from the phase-space files of Monte Carlo simulations. These simplified beam models were used to generate Monte Carlo dose calculations and to compare those calculations with phase-space data for electron beams.ResultsComparison of the measured and calculated dose distributions using the phase-space files and multiple-source models for three electron beam energies showed that the measured and calculated values match well each other throughout the curves.ConclusionIt was found that dose distributions calculated using both the multiple-source models and the phase-space data agree within 1.3%, demonstrating that the models can be used for dosimetry research purposes and dose calculations in radiotherapy.     

Theoretical Models of Chronotherapy: I. Periodic Perturbations in Oscillating Chemical Reactions

Dynamics resulting from periodic perturbations in oscillating chemical reaction, as theoretical models of chronotherapy techniques are studied. Replication reactions and fluctuations take place in these models giving rise to new species with autocatalitic properties. Oscillating chemical reaction models due to Hanusse (H) with three intermediaries and Bourceanu (E₁, E₂, E₃ ) with four, five and six intermediaries, respectively, are taken for this study and are manipulated numerically using a Gear algorithm. The bifurcation diagrams are obtained applying non-linear dynamic techniques, such as, power spectrum, stroboscopic method, Lyapunov exponents and correlation dimension D 2. The dynamics of Hanusse and Bourceanu perturbed models are very similar. The main dynamic behaviours observed after perturbation are quasiperiodicity, entrainment, some Arnold tongues and situations without chemical meaning. Chaos is not observed. This study contributes to understand theoretical and practical aspects of the chronotherapy techniques.     

Designing hybrid life cycle assessment models based on uncertainty and complexity

Purpose

Despite the wide use of LCA for environmental profiling, the approach for determining the system boundary within LCA models continues to be subjective and lacking in mathematical rigor. As a result, life cycle models are often developed in an ad hoc manner, and are difficult to compare. Significant environmental impacts may be inadvertently left out. Overcoming this shortcoming can help elicit greater confidence in life cycle models and their use for decision making.

Methods

This paper describes a framework for hybrid life cycle model generation by selecting activities based on their importance, parametric uncertainty, and contribution to network complexity. The importance of activities is determined by structural path analysis—which then guides the construction of life cycle models based on uncertainty and complexity indicators. Information about uncertainty is from the available life cycle inventory; complexity is quantified by cost or granularity. The life cycle model is developed in a hierarchical manner by adding the most important activities until error requirements are satisfied or network complexity exceeds user-specified constraints.

Results and Discussion

The framework is applied to an illustrative example for building a hybrid LCA model. Since this is a constructed example, the results can be compared with the actual impact, to validate the approach. This application demonstrates how the algorithm sequentially develops a life cycle model of acceptable uncertainty and network complexity. Challenges in applying this framework to practical problems are discussed.

Conclusion

The presented algorithm designs system boundaries between scales of hybrid LCA models, includes or omits activities from the system based on path analysis of environmental impact contribution at upstream network nodes, and provides model quality indicators that permit comparison between different LCA models.

     

Estimating dengue transmission intensity from serological data: A comparative analysis using mixture and catalytic models

BackgroundDengue virus (DENV) infection is a global health concern of increasing magnitude. To target intervention strategies, accurate estimates of the force of infection (FOI) are necessary. Catalytic models have been widely used to estimate DENV FOI and rely on a binary classification of serostatus as seropositive or seronegative, according to pre-defined antibody thresholds. Previous work has demonstrated the use of thresholds can cause serostatus misclassification and biased estimates. In contrast, mixture models do not rely on thresholds and use the full distribution of antibody titres. To date, there has been limited application of mixture models to estimate DENV FOI.MethodsWe compare the application of mixture models and time-constant and time-varying catalytic models to simulated data and to serological data collected in Vietnam from 2004 to 2009 (N ≥ 2178) and Indonesia in 2014 (N = 3194).ResultsThe simulation study showed larger mean FOI estimate bias from the time-constant and time-varying catalytic models (-0.007 (95% Confidence Interval (CI): -0.069, 0.029) and -0.006 (95% CI -0.095, 0.043)) than from the mixture model (0.001 (95% CI -0.036, 0.065)). Coverage of the true FOI was > 95% for estimates from both the time-varying catalytic and mixture model, however the latter had reduced uncertainty. When applied to real data from Vietnam, the mixture model frequently produced higher FOI and seroprevalence estimates than the catalytic models.ConclusionsOur results suggest mixture models represent valid, potentially less biased, alternatives to catalytic models, which could be particularly useful when estimating FOI from data with largely overlapping antibody titre distributions.     

Construction of analytically solvable models for interacting species

By observing that the n-tuple of rate functionsQ(c) is orthogonal to the c-space gradients of each of the (n - 1) constants of the motion Φ _v (c), a generic canonical expression for the rate functions is given in terms of the exterior product of the gradients of the (n - 1) Φ _v 's. For models withQ so prescribed from the outset, an analytical general solution is obtainable directly for the system of autonomous ordinary differential equations dc/dt =Q(c). Thus, the generic canonical expression for the rate functions can be utilized to construct analytically solvable models for interacting biological species, as ilIus~rated by examples here.     

Structure prediction and network analysis of chitinases from the Cape sundew,Drosera capensis

BackgroundCarnivorous plants possess diverse sets of enzymes with novel functionalities applicable to biotechnology, proteomics, and bioanalytical research. Chitinases constitute an important class of such enzymes, with future applications including human-safe antifungal agents and pesticides. Here, we compare chitinases from the genome of the carnivorous plant Drosera capensis to those from related carnivorous plants and model organisms.MethodsUsing comparative modeling, in silico maturation, and molecular dynamics simulation, we produce models of the mature enzymes in aqueous solution. We utilize network analytic techniques to identify similarities and differences in chitinase topology.ResultsHere, we report molecular models and functional predictions from protein structure networks for eleven new chitinases from D. capensis, including a novel class IV chitinase with two active domains. This architecture has previously been observed in microorganisms but not in plants. We use a combination of comparative and de novo structure prediction followed by molecular dynamics simulation to produce models of the mature forms of these proteins in aqueous solution. Protein structure network analysis of these and other plant chitinases reveal characteristic features of the two major chitinase families.General significanceThis work demonstrates how computational techniques can facilitate quickly moving from raw sequence data to refined structural models and comparative analysis, and to select promising candidates for subsequent biochemical characterization. This capability is increasingly important given the large and growing body of data from high-throughput genome sequencing, which makes experimental characterization of every target impractical.     

Performance and environmental accounting of nutrient cycling models to estimate nitrogen emissions in agriculture and their sensitivity in life cycle assessment

Purpose

Several models are available in the literature to estimate agricultural emissions. From life cycle assessment (LCA) perspective, there is no standardized procedure for estimating emissions of nitrogen or other nutrients. This article aims to compare four agricultural models (PEF, SALCA, Daisy and Animo) with different complexity levels and test their suitability and sensitivity in LCA.

Methods

Required input data, obtained outputs, and main characteristics of the models are presented. Then, the performance of the models was evaluated according to their potential feasibility to be used in estimating nitrogen emissions in LCA using an adapted version of the criteria proposed by the United Nations Framework Convention on Climate Change (UNFCCC), and other relevant studies, to judge their suitability in LCA. Finally, nitrogen emissions from a case study of irrigated maize in Spain were estimated using the selected models and were tested in a full LCA to characterize the impacts.

Results and discussion

According to the set of criteria, the models scored, from best to worst: Daisy (77%), SALCA (74%), Animo (72%) and PEF (70%), being Daisy the most suitable model to LCA framework. Regarding the case study, the estimated emissions agreed to literature data for the irrigated corn crop in Spain and the Mediterranean, except N₂O emissions. The impact characterization showed differences of up to 56% for the most relevant impact categories when considering nitrogen emissions. Additionally, an overview of the models used to estimate nitrogen emissions in LCA studies showed that many models have been used, but not always in a suitable or justified manner.

Conclusions

Although mechanistic models are more laborious, mainly due to the amount of input data required, this study shows that Daisy could be a suitable model to estimate emissions when fertilizer application is relevant for the environmental study. In addition, and due to LCA urgently needing a solid methodology to estimate nitrogen emissions, mechanistic models such as Daisy could be used to estimate default values for different archetype scenarios.

     

Normal tissue complication probability models for severe acute radiological lung injury after radiotherapy for lung cancer

PurposeTo derive Normal Tissue Complication Probability (NTCP) models for severe patterns of early radiological radiation-induced lung injury (RRLI) in patients treated with radiotherapy (RT) for lung tumors. Second, derive threshold doses and optimal doses for prediction of RRLI to be used in differential diagnosis of tumor recurrence from RRLI during follow-up.Methods and materialsLyman-EUD (LEUD), Logit-EUD (LogEUD), relative seriality (RS) and critical volume (CV) NTCP models, with DVH corrected for fraction size, were used to model the presence of severe early RRLI in follow-up CTs. The models parameters, including α/β, were determined by fitting data from forty-five patients treated with IMRT for lung cancer. Models were assessed using Akaike information criterion (AIC) and area under receiver operating characteristic curve (AUC). Threshold doses for risk of RRLI and doses corresponding to the optimal point of the receiver operating characteristic (ROC) curve were determined.ResultsThe α/βs obtained with different models were 2.7–3.2 Gy. The thresholds and optimal doses curves were EUDs of 3.2–7.8 Gy and 15.2–18.1 Gy with LEUD, LogEUD and RS models, and μ_d of 0.013 and 0.071 with the CV model. NTCP models had AUCs significantly higher than 0.5. Occurrence and severity of RRLI were correlated with patients’ values of EUD and μ_d.ConclusionsThe models and dose levels derived can be used in differential diagnosis of tumor recurrence from RRLI in patients treated with RT. Cross validation is needed to prove prediction performance of the model outside the dataset from which it was derived.     

Reconstruction of low-resolution molecular structures from simulated atomic force microscopy images

BackgroundAtomic Force Microscopy (AFM) is an experimental technique to study structure-function relationship of biomolecules. AFM provides images of biomolecules at nanometer resolution. High-speed AFM experiments produce a series of images following dynamics of biomolecules. To further understand biomolecular functions, information on three-dimensional (3D) structures is beneficial.MethodWe aim to recover 3D information from an AFM image by computational modeling. The AFM image includes only low-resolution representation of a molecule; therefore we represent the structures by a coarse grained model (Gaussian mixture model). Using Monte-Carlo sampling, candidate models are generated to increase similarity between AFM images simulated from the models and target AFM image.ResultsThe algorithm was tested on two proteins to model their conformational transitions. Using a simulated AFM image as reference, the algorithm can produce a low-resolution 3D model of the target molecule. Effect of molecular orientations captured in AFM images on the 3D modeling performance was also examined and it is shown that similar accuracy can be obtained for many orientations.ConclusionsThe proposed algorithm can generate 3D low-resolution protein models, from which conformational transitions observed in AFM images can be interpreted in more detail.General significanceHigh-speed AFM experiments allow us to directly observe biomolecules in action, which provides insights on biomolecular function through dynamics. However, as only partial structural information can be obtained from AFM data, this new AFM based hybrid modeling method would be useful to retrieve 3D information of the entire biomolecule.     

Are there infinitely many trucks in the technosphere,or exactly one? How independent sampling of instances of unit processes affects uncertainty analysis in LCA

Purpose

Product systems use the same unit process models to represent distinct but similar activities. This notably applies to activities in cyclic dependency relationships (or “feedback loops”) that are required an infinite number of times in a product system. The study aims to test the sensitivity of uncertainty results on the assumption made concerning these different instances of the same activities. The default assumption assumes homogeneous production, and the same parameter values are sampled for all instances (e.g., there is one truck). The alternative assumption is that every instance is distinct, and parameter values are independently sampled for different instances of unit processes (e.g., there are infinitely many trucks). Intuitively, sampling the same values for each instance of a unit process should result in more uncertain results.

Methods

The results of uncertainty analyses carried out under either assumption are compared. To simulate models where each instance of a unit process is independent, we convert network models to acyclic LCI models (tree models). This is done three times: (1) for a very simple product system, to explain the methodology; (2) for a sample product system from the ecoinvent database, for illustrative purposes; and (3) for thousands of product systems from ecoinvent databases.

Results and discussion

The uncertainty of network models is indeed greater than that of corresponding tree models. This is shown mathematically for the analytical approximation method to uncertainty propagation and is observed for Monte Carlo simulations with very large numbers of iterations. However, the magnitude of the difference in indicators of dispersion is, for the ecoinvent product systems, often less than a factor of 1.5. In few extreme cases, indicators of dispersion are different by a factor of 4. Monte Carlo simulations with smaller numbers of iterations sometimes give the opposite result.

Conclusions

Given the small magnitude of the difference, we believe that breaking away from the default approach is generally not warranted. Indeed, (1) the alternative approach is not more robust, (2) the current default approach is conservative, and (3) there are more pressing challenges for the LCA community to meet. This being said, the study focused on ecoinvent, which should normally be used as a background database. The difference in dispersion between the two approaches may be important in some contexts, and calculating the uncertainty of tree models as a sensitivity analysis could be useful.

     

Microarray analysis of miRNA expression profiles following whole body irradiation in a mouse model

Abstract

Context: Accidental exposure to life-threatening radiation in a nuclear event is a major concern; there is an enormous need for identifying biomarkers for radiation biodosimetry to triage populations and treat critically exposed individuals.

Objective: To identify dose-differentiating miRNA signatures from whole blood samples of whole body irradiated mice.

Methods: Mice were whole body irradiated with X-rays (2?Gy–15?Gy); blood was collected at various time-points post-exposure; total RNA was isolated; miRNA microarrays were performed; miRNAs differentially expressed in irradiated vs. unirradiated controls were identified; feature extraction and classification models were applied to predict dose-differentiating miRNA signature.

Results: We observed a time and dose responsive alteration in the expression levels of miRNAs. Maximum number of miRNAs were altered at 24-h and 48-h time-points post-irradiation. A 23-miRNA signature was identified using feature selection algorithms and classifier models. An inverse correlation in the expression level changes of miR-17 members, and their targets were observed in whole body irradiated mice and non-human primates.

Conclusion: Whole blood-based miRNA expression signatures might be used for predicting radiation exposures in a mass casualty nuclear incident.     

Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer

PurposeRadiomic models have been demonstrated to have acceptable discrimination capability for detecting lymph node metastasis (LNM). We aimed to develop a computed tomography–based radiomic model and validate its usefulness in the prediction of normal-sized LNM at node level in cervical cancer.MethodsA total of 273 LNs of 219 patients from 10 centers were evaluated in this study. We randomly divided the LNs from the 2 centers with the largest number of LNs into the training and internal validation cohorts, and the rest as the external validation cohort. Radiomic features were extracted from the arterial and venous phase images. We trained an artificial neural network (ANN) to develop two single-phase models. A radiomic model reflecting the features of two-phase images was also built for directly predicting LNM in cervical cancer. Moreover, four state-of-the-art methods were used for comparison. The performance of all models was assessed using the area under the receiver operating characteristic curve (AUC).ResultsAmong the models we built, the models combining the features of two phases surpassed the single-phase models, and the models generated by ANN had better performance than the others. We found that the radiomic model achieved the highest AUCs of 0.912 and 0.859 in the training and internal validation cohorts, respectively. In the external validation cohort, the AUC of the radiomic model was 0.800.ConclusionWe constructed a radiomic model that exhibited great ability in the prediction of LNM. The application of the model could optimize clinical staging and decision-making.     

Modelling Metal Complexes of Calixarene Esters and Phosphine Oxides Using Molecular Mechanics

This paper focuses on the molecular modelling of a number of calixarene ester and phosphine oxide metal ion complexes. Monte Carlo conformational searches, in conjunction with the Merck Molecular Force Field, were carried out using Spartan SGI Version 5.0.1. running on Silicon Graphics O2 workstations. In the case of the calix[4]arene tetraesters, the optimised models strongly suggest that the selectivity of these ligands is strongly related to the eight-fold nature of the coordination with the Na⁺ ion, while coordination with the Li⁺ ion, for example, is merely three-fold. This feature of eight-fold coordination is also observed in the models of the complexes formed by the calix[4]arene tetraphosphine oxides with calcium. However, whereas the eight-fold coordination is unique to the model of the TPOL:Ca²⁺ complex among the ions modelled, this mode of coordination occurs for TPOS with sodium and potassium, in addition to calcium. This concurs with the observation that calcium selectivity is obtained with ion selective electrodes based on TPOL but not TPOS. Though the cavity in the calix[5]arenes PPOL and PPOLx and the calix[6]arene HPOL, in their uncomplexed form, are much larger than that of the corresponding calix[4]arenes, the pattern of selectivity is the same – the ligands are selective for calcium. The models of the complexes of these larger calixarenes, such as PPOL:Ca²⁺, strongly suggest that the reason for this similarity is that four of the available phosphine oxide groups complex with the calcium ion, and the others are forced away from the cavity region for steric reasons. The resulting eight-fold coordination, is therefore, similar to that of the calix[4]arenes studied.Electronic Supplementary Material available.     

Occurrence of l-Saccharopine and γ-L-Glutamylglycine in the Mushroom,Agaricus bisporus

Microfolicoumarin (1), a prenylcoumarin from Cedrelopsis microfoliata, was synthesized from 2,4,5-trimethoxybenzaldehyde in five steps. 1 did not show significant antioxidative activity, but the key intermediates, esculetin (3) and 5-prenylesculetin (6), exhibited strong antioxidative activity in both the superoxide-radical and 1,1-diphenyl-2-picrylhydrazyl radical-scavenging models.     

Association between smoking status and homocysteine levels and possible effect modification by cholesterol and oestradiol

Abstract

Introduction: This study aimed to examine the association of smoking status with homocysteine levels and to determine whether the association is modified by oestradiol or cholesterol.

Methods: Data (N?=?4580) were obtained from National Health and Nutrition Examination Survey 2003–2004 with analysis done in 2018 on adults aged ≥20 years. The outcome was homocysteine; smoking status was the exposure variable and categorized as current, former or never smoker. Generalized linear models were used to examine the associations between smoking status and homocysteine levels, while assessing the impact of oestradiol and cholesterol.

Results: After adjusting for age, sex, ethnicity, education and income level, homocysteine levels did differ by smoking status ((current smokers versus never smokers: β: 0.18?CI: 0.00, 0.36), (former smokers: β: 0.10?CI: –0.09, 0.28)). The addition of oestradiol as an interaction term in adjusted models was associated with a 16.6% increase in homocysteine levels when compared to models without the interaction term. Oestradiol but not cholesterol did moderate the association between smoking status and homocysteine levels.

Discussion and conclusions: Homocysteine levels did differ across smoking status after adjusting for confounders. Oestradiol did moderate the relationship between homocysteine and smoking status.     

Micafungina en el tratamiento de la infección fúngica en modelos animales

BackgroundMicafungin is an echinocandin antifungal drug recently approved for the treatment of candidiasis. The possibility of its clinical use against other invasive mycoses, has aroused the interest of numerous investigators in evaluating its efficacy in different animal models.ObjectivesTo critically review the current data on the use of micafungin in the treatment of invasive mycoses in animal models.MethodsWe searched the PubMed/Medline data base (National Library of Medicine) from 2005 to 2008, both inclusive, on the use of micafungin in the experimental treatment of the fungal infection.Results and conclusionsSeven, of a total of 18 articles reviewed, were done in animal models of candidiasis and six in animal models of pulmonary or SNC aspergillosis. Similarly to the other echinocandins, caspofungin and anidulafungin, micafungin seems to exert a fungicidal activity against Candida albicans and Candida glabrata and a fungistatic activity against Aspergillus fumigatus. The paradoxical effect observed in lung tissue the experimental caspofungin treatment of aspergillosis has not been seen in the case of micafungin. The available data demonstrate a higher efficacy of micafungin versus fluconazole in the experimental treatment of C. albicans infections caused by strains susceptible in vitro to both drugs. To improve the efficacy of micafungin in the treatment of C. glabrata and A. fumigatus infections, several authors have tested different combined therapies, the combination of micafungin with amphotericin B being that showed the best results.     

Accurate Prediction of Transposon-Derived piRNAs by Integrating Various Sequential and Physicochemical Features

BackgroundPiwi-interacting RNA (piRNA) is the largest class of small non-coding RNA molecules. The transposon-derived piRNA prediction can enrich the research contents of small ncRNAs as well as help to further understand generation mechanism of gamete.MethodsIn this paper, we attempt to differentiate transposon-derived piRNAs from non-piRNAs based on their sequential and physicochemical features by using machine learning methods. We explore six sequence-derived features, i.e. spectrum profile, mismatch profile, subsequence profile, position-specific scoring matrix, pseudo dinucleotide composition and local structure-sequence triplet elements, and systematically evaluate their performances for transposon-derived piRNA prediction. Finally, we consider two approaches: direct combination and ensemble learning to integrate useful features and achieve high-accuracy prediction models.ResultsWe construct three datasets, covering three species: Human, Mouse and Drosophila, and evaluate the performances of prediction models by 10-fold cross validation. In the computational experiments, direct combination models achieve AUC of 0.917, 0.922 and 0.992 on Human, Mouse and Drosophila, respectively; ensemble learning models achieve AUC of 0.922, 0.926 and 0.994 on the three datasets.ConclusionsCompared with other state-of-the-art methods, our methods can lead to better performances. In conclusion, the proposed methods are promising for the transposon-derived piRNA prediction. The source codes and datasets are available in S1 File.     

Development of ligands at γ-aminobutyrric acid type A (GABAA) receptor subtype as new agents for pain relief

The identification of compounds with selective anxiolytic-like effects, exerted through the benzodiazepine site on γ-aminobutyric acid type A (GABA_A) receptors, and that show pronounced antihyperalgesia in several pain models, has oriented research towards the development of new agents for the relief of pain. Starting from our previously reported ligands at the benzodiazepine site on GABA_A receptors showing selective anxiolytic-like effects, we have designed new compounds with the aim of identifying those devoid of the typical side effects of the classical benzodiazepines. Our preliminary results indicate that compounds 4, 10(±) and 11 have a very promising antihyperalgesic profile in different animal pain models (peripheral mono-neuropathy, STZ-induced hyperalgesia). In particular 11 exhibits high potency since it exerted its protective effect starting from the dose of 3 mg/kg po, after single injection.