Sundarban, a Ramsar site of India, has been encountering an ecological threat due to the presence of microplastic (MP) wastes generated from different anthropogenic sources. Clibanarius longitarsus, ...an intertidal hermit crab of Sundarban Biosphere Reserve, resides within the abandoned shell of a gastropod mollusc, Telescopium telescopium. We characterized and estimated the MP in the gills and gut of hermit crab, as well as in the water present in its occupied gastropod shell. The average microplastic abundance in sea water, sand and sediment were 0.175 ± 0.145 MP L−1, 42 ± 15.03 MP kg−1 and 67.63 ± 24.13 MP kg−1 respectively. The average microplastic load in hermit crab was 1.94 ± 0.59 MP crab−1, with 33.89 % and 66.11 % in gills and gut respectively. Gastropod shell water exhibited accumulation of 1.69 ± 1.43 MP L−1. Transparent and fibrous microplastics were documented as the dominant polymers of water, sand and sediment. Shell water exhibited the prevalence of green microplastics followed by transparent ones. Microscopic examination revealed microplastics with 100–300 μm size categories were dominant across all abiotic compartments. ATR-FTIR and Raman spectroscopy confirmed polyethylene and polypropylene as the prevalent polymers among the five identified polymers of biotic and abiotic components. The target group index indicated green and black as the preferable microplastics of crab. The ecological risk analysis indicated a considerable level of environmental pollution risk in Sundarban and its inhabiting organisms. This important information base may facilitate in developing a strategy of mitigation to limit the MP induced ecological risk at Sundarban Biosphere Reserve.
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•Microplastic contamination in Sundarbans leads to its accumulation in hermit crab.•Polyethylene and polypropylene are the dominant polymers.•Target Group Index reveals that crabs prefer fragmented and green microplastics.•Risk assessment classifies crab's shell water as “dangerous to extremely dangerous”.•Hermit crabs are in “high” to “dangerous” risk category.
Resumo Este artigo aborda o método da prosopografia e oferece recomendações práticas para sua aplicação na pesquisa histórica. O autor baseia-se em suas próprias experiências e compartilha casos ...reais e desafios enfrentados por pesquisadores, em especial estudantes de pós-graduação, para enfatizar soluções práticas, sobretudo na definição do grupo-alvo da pesquisa. Ele destaca os desafios envolvidos nessa etapa crítica e os potenciais riscos de uma definição inadequada do grupo para as fases posteriores do processo de pesquisa, bem como o imenso potencial da prosopografia no campo da história social contemporânea e da micro-história. Além disso, o artigo destaca a necessidade e os benefícios do trabalho colaborativo em pesquisa, na reunião de recursos, no treinamento de jovens pesquisadores, no compartilhamento de experiências e na disseminação de técnicas, oferecendo uma alternativa às experiências de pesquisa solitárias. Em resumo, este artigo fornece insights sobre procedimentos práticos em prosopografia, com foco na definição do grupo-alvo.
Resumen Este artículo aborda el método de la prosopografía y plantea recomendaciones prácticas para su aplicación en la investigación histórica. El autor parte de su propia experiencia y comparte casos reales y retos a los que se enfrentan los investigadores, especialmente los estudiantes de posgrado, para hacer hincapié en las soluciones prácticas, en particular en la definición del grupo objetivo de la investigación. Destaca los retos que se encuentran en esta etapa crítica y los riesgos potenciales de una definición inadecuada del grupo para las etapas posteriores del proceso de investigación, así como el gran potencial de la prosopografía para el campo de la historia social contemporánea y de la microhistoria. Además, este artículo señala la necesidad y los beneficios del trabajo colaborativo en la investigación, en la reunión de recursos, en la formación de jóvenes investigadores, en el compartir experiencias y en la difusión de técnicas, ofreciendo una alternativa a las experiencias de investigación solitarias. De esta manera, este artículo ofrece una visión de los procedimientos prácticos de la prosopografía centrándose en la definición del grupo objetivo.
Abstract This article discusses the method of prosopography and offers practical recommendations for its application in historical research. The author draws on his own experiences and shares real cases and challenges faced by researchers, especially graduate students, to emphasize practical solutions, mainly in defining the research target group. It highlights the challenges involved in this critical stage and the potential risks of an inadequate definition of the group for the later stages of the research process, as well as the immense potential of prosopography in the field of contemporary social history and microhistory. Furthermore, the article highlights the need and benefits of collaborative work in research, in pooling resources, in training young researchers, in sharing experiences, and in disseminating techniques, offering an alternative to solitary research experiences. In summary, this article provides insights into practical procedures in prosopography, with a focus on defining the target group.
Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods ...have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments.
In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model.
Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.
Most methods for modeling species distributions from occurrence records require additional data representing the range of environmental conditions in the modeled region. These data, called background ...or pseudo-absence data, are usually drawn at random from the entire region, whereas occurrence collection is often spatially biased toward easily accessed areas. Since the spatial bias generally results in environmental bias, the difference between occurrence collection and background sampling may lead to inaccurate models. To correct the estimation, we propose choosing background data with the same bias as occurrence data. We investigate theoretical and practical implications of this approach. Accurate information about spatial bias is usually lacking, so explicit biased sampling of background sites may not be possible. However, it is likely that an entire target group of species observed by similar methods will share similar bias. We therefore explore the use of all occurrences within a target group as biased background data. We compare model performance using target-group background and randomly sampled background on a comprehensive collection of data for 226 species from diverse regions of the world. We find that target-group background improves average performance for all the modeling methods we consider, with the choice of background data having as large an effect on predictive performance as the choice of modeling method. The performance improvement due to target-group background is greatest when there is strong bias in the target-group presence records. Our approach applies to regression-based modeling methods that have been adapted for use with occurrence data, such as generalized linear or additive models and boosted regression trees, and to Maxent, a probability density estimation method. We argue that increased awareness of the implications of spatial bias in surveys, and possible modeling remedies, will substantially improve predictions of species distributions.
•A mixed method study explored emerging country consumer household food waste.•Recent food waste accounts indicate leftovers and fresh produce is wasted most.•Sub-optimality of food and prolonged ...storage or excess stock are major reasons for discard.•Waste occurs while conducting kitchen-related tasks rather than planning or shopping.•Improved planning and acceptance of sub-optimality should be tackled in anti-food waste campaigns.
A major share of food waste is caused in consumer households. Globally, this share is expected to increase with growing middle classes in emerging countries. Consumer behaviour factors causing food waste differ across the various food handling stages from purchase to use, as well as for the individual and the specific situation in question. In order to tackle food waste in the household, knowledge on the type of food wasted, the cause of waste, and the situation in which it occurs is needed for different target groups. Little research so far has studied household consumer related food waste in emerging countries. This study explored food waste in consumer´s own accounts of a recent food waste incident in 540 Uruguayan households. It used a mixed-method approach composed of open-ended questions, which were analysed using content analysis. Differences in the frequency of mention of the identified categories between socioeconomic and sociodemographic groups were analysed. Results showed that leftovers and fresh vegetable and fruit were the categories most consumers recall wasting, and indicated that sub-optimality and prolonged storage were major reasons for discarding food. The higher the socioeconomic group, the greater the likelihood of wasting fresh produce, and the more often due to sub-optimality. Findings imply that avoidable food waste might increase with affluence levels. Public policies or collaborative public-private information or intervention campaigns directed at consumer households can more effectively contribute to decreasing food waste if targeted at the most relevant categories and causes of food waste.
•Facebook usage was associated with body image concerns (BIC) in young women.•Facebook appearance comparisons mediated the Facebook usage–BIC relation.•Comparison frequency to peers on Facebook ...mediated the usage–BIC relation.•Comparison direction to peers and celebrities mediated the usage–BIC relation.
Use of social media, such as Facebook, is pervasive among young women. Body dissatisfaction is also highly prevalent in this demographic. The present study examined the relationship between Facebook usage and body image concerns among female university students (N=227), and tested whether appearance comparisons on Facebook in general, or comparisons to specific female target groups (family members, close friends, distant peers women one may know but do not regularly socialize with, celebrities) mediated this relationship. Results showed a positive relationship between Facebook usage and body image concerns, which was mediated by appearance comparisons in general, frequency of comparisons to close friends and distant peers, and by upward comparisons (judging one's own appearance to be worse) to distant peers and celebrities. Thus, young women who spend more time on Facebook may feel more concerned about their body because they compare their appearance to others (especially to peers) on Facebook.
Aim
Accounting for sampling bias is the greatest challenge facing presence‐only and presence‐background species distribution models; no matter what type of model is chosen, using biased data will ...mask the true relationship between occurrences and environmental predictors. To address this issue, we review four established bias correction techniques, using empirical occurrences with known sampling effort, and virtual species with known distributions.
Innovation
Occurrence data come from a national recording scheme of hoverflies (Syrphidae) in Great Britain, spanning 1983–2002. Target‐group backgrounds, distance‐restricted backgrounds, travel time to cities and human population density were used to account for sampling bias in 58 species of hoverfly. Distributions generated by bias correction techniques were compared in geographical space to the distribution produced accounting for known sampling effort, using Schoener's distance, centroid shifts and range size changes. To validate our results, we performed the same comparisons using 50 randomly generated virtual species. We used sampling effort from the hoverfly recording scheme to structure our biased sampling regime, emulating complex real‐life sampling bias.
Main conclusions
Models made without any correction typically produced distributions that mapped sampling effort rather than the underlying habitat suitability. Target‐group backgrounds performed the best at emulating sampling effort and unbiased virtual occurrences, but also showed signs of overcompensation in places. Other methods performed better than no‐correction, but often differences were difficult to visually detect. In line with previous studies, when sampling effort is unknown, target‐group backgrounds provide a useful tool for reducing the effect of sampling bias. Models should be visually inspected for biological realism to identify any areas of potential overcompensation. Given the disparity between corrected and un‐corrected models, sampling bias constitutes a major source of error in species distribution modelling, and more research is needed to confidently address the issue.
•Implementation context has to be taken seriously in policy mix analysis.•The moderating role of settings between policy instruments and target groups is crucial.•Coordination between implementation ...settings is crucial in increasing policy mix performance.
This article proposes the extension of a conceptual framework aimed at analysing policy mixes and their outcomes and demonstrates its value added for the study of sustainability transitions. The argument is that policy mixes research should not focus only on the form of policy instruments, but also on their implementation context. Policy mix form designates the specific policy instruments that are involved according to a policy strategy. Policy mix context includes the specific setting where each policy measure is implemented, such as enterprise or family. It also includes the specific target group of each measure, such as youth or smokers. We apply this conceptual framework to the policy concept and implementation of tobacco control policies in Switzerland, which are an exemplary case for analysing transitions as they are geared towards behavioural change. In a mixed method approach, we triangulate different sets of quantitative and qualitative indicators in order to assess the implementation of eleven subnational policy mixes. Our findings show that taking into account the moderating role of settings between policy instruments and target groups allows for a more in depth analysis of policy processes. Observing the interactions between the four elements policy instruments, policy strategy, the implementation settings and the target groups allows capturing the complexity of policy mixes, at the crossroads of policy design, policy implementation and policy outcomes. Taking implementation settings and target groups into account in the analysis of policy mixes allows for a refined understanding of policy compliance and thus, from a broader perspective, of sustainability transitions.
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