scholarly journals Indirect transfer of pyriproxyfen to European honeybees via an autodissemination approach

2021 ◽  
Vol 15 (10) ◽  
pp. e0009824
Author(s):  
Sri Jyosthsna Kancharlapalli ◽  
Cameron J. Crabtree ◽  
Kaz Surowiec ◽  
Scott D. Longing ◽  
Corey L. Brelsfoard
Keyword(s):  
Vector Control ◽  
Large Scale ◽  
Fluorescent Imaging ◽  
Insect Growth Regulators ◽  
Nontarget Organisms ◽  
Novel Approach ◽  
Nectar Sources ◽  
Nectar Source ◽  
Disease Epidemics ◽  
Arboviral Transmission

The frequency of arboviral disease epidemics is increasing and vector control remains the primary mechanism to limit arboviral transmission. Container inhabiting mosquitoes such as Aedes albopictus and Aedes aegypti are the primary vectors of dengue, chikungunya, and Zika viruses. Current vector control methods for these species are often ineffective, suggesting the need for novel control approaches. A proposed novel approach is autodissemination of insect growth regulators (IGRs). The advantage of autodissemination approaches is small amounts of active ingredients compared to traditional insecticide applications are used to impact mosquito populations. While the direct targeting of cryptic locations via autodissemination seems like a significant advantage over large scale applications of insecticides, this approach could actually affect nontarget organisms by delivering these highly potent long lasting growth inhibitors such as pyriproxyfen (PPF) to the exact locations that other beneficial insects visit, such as a nectar source. Here we tested the hypothesis that PPF treated male Ae. albopictus will contaminate nectar sources, which results in the indirect transfer of PPF to European honey bees (Apis mellifera). We performed bioassays, fluorescent imaging, and mass spectrometry on insect and artificial nectar source materials to examine for intra- and interspecific transfer of PPF. Data suggests there is direct transfer of PPF from Ae. albopictus PPF treated males and indirect transfer of PPF to A. mellifera from artificial nectar sources. In addition, we show a reduction in fecundity in Ae. albopictus and Drosophila melanogaster when exposed to sublethal doses of PPF. The observed transfer of PPF to A. mellifera suggests the need for further investigation of autodissemination approaches in a more field like setting to examine for risks to insect pollinators.

Green synthesis, characterization of silver sulfide nanoparticles and antibacterial activity evaluation

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Antibacterial Activity ◽  
Large Scale ◽  
Research Work ◽  
Silver Sulfide ◽  
Morphological Properties ◽  
Effective Diameter ◽  
Novel Approach ◽  
Transmission Electron ◽  
Green Route ◽  
Silver Sulfide Nanoparticles

This research work presents a facile and green route for synthesis silver sulfide (Ag2SNPs) nanoparticles from silver nitrate (AgNO3) and sodium sulfide nonahydrate (Na2S.9H2O) in the presence of rosemary leaves aqueous extract at ambient temperature (27 oC). Structural and morphological properties of Ag2SNPs nanoparticles were analyzed by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The surface Plasmon resonance for Ag2SNPs was obtained around 355 nm. Ag2SNPs was spherical in shape with an effective diameter size of 14 nm. Our novel approach represents a promising and effective method to large scale synthesis of eco-friendly antibacterial activity silver sulfide nanoparticles.

scholarly journals Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Ariel Rokem ◽  
Kendrick Kay
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Imaging Data ◽  
Regularization Technique ◽  
Large Scale Data ◽  
Novel Approach ◽  
Manual Exploration ◽  
L2 Norm ◽  
Software Implementations ◽  
Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

scholarly journals Novel approach for large‐scale monitoring of submerged aquatic vegetation – a nationwide example from Sweden

2021 ◽  
Author(s):  
Silvia Huber ◽  
Lars B. Hansen ◽  
Lisbeth T. Nielsen ◽  
Mikkel L. Rasmussen ◽  
Jonas Sølvsteen ◽  
...  
Keyword(s):  
Large Scale ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Novel Approach

scholarly journals Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

2021 ◽  
Author(s):  
Jin Zhou ◽  
Qing Zhang ◽  
Jian-Hao Fan ◽  
Wei Sun ◽  
Wei-Shi Zheng
Keyword(s):  
Large Scale ◽  
Assessment Model ◽  
Generic Model ◽  
Small Subset ◽  
Deep Convolutional Neural Networks ◽  
Personal Taste ◽  
Hinge Loss ◽  
Novel Approach ◽  
Large Scale Dataset ◽  
Image Pairs

AbstractRecent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks (CNNs). However, these methods focus primarily on predicting generally perceived preference of an image, making them usually have limited practicability, since each user may have completely different preferences for the same image. To address this problem, this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste. We achieve this in a coarse to fine manner, by joint regression and learning from pairwise rankings. Specifically, we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs. We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores, and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression. Next, we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss. Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences, clearly outperforming state-of-the-art methods. Moreover, we show that the learned personalized image aesthetic benefits a wide variety of applications.

scholarly journals Reservoir-Induced Land Deformation: Case Study from the Grand Ethiopian Renaissance Dam

2021 ◽  
Vol 13 (5) ◽  
pp. 874
Author(s):  
Yu Chen ◽  
Mohamed Ahmed ◽  
Natthachet Tangdamrongsub ◽  
Dorina Murgulet
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Surface Deformation ◽  
Vertical Displacement ◽  
Nile River ◽  
Reservoir Volume ◽  
Novel Approach ◽  
Land Deformation ◽  
Large Scale Land ◽  
The Impact

The Nile River stretches from south to north throughout the Nile River Basin (NRB) in Northeast Africa. Ethiopia, where the Blue Nile originates, has begun the construction of the Grand Ethiopian Renaissance Dam (GERD), which will be used to generate electricity. However, the impact of the GERD on land deformation caused by significant water relocation has not been rigorously considered in the scientific research. In this study, we develop a novel approach for predicting large-scale land deformation induced by the construction of the GERD reservoir. We also investigate the limitations of using the Gravity Recovery and Climate Experiment Follow On (GRACE-FO) mission to detect GERD-induced land deformation. We simulated three land deformation scenarios related to filling the expected reservoir volume, 70 km3, using 5-, 10-, and 15-year filling scenarios. The results indicated: (i) trends in downward vertical displacement estimated at −17.79 ± 0.02, −8.90 ± 0.09, and −5.94 ± 0.05 mm/year, for the 5-, 10-, and 15-year filling scenarios, respectively; (ii) the western (eastern) parts of the GERD reservoir are estimated to move toward the reservoir’s center by +0.98 ± 0.01 (−0.98 ± 0.01), +0.48 ± 0.00 (−0.48 ± 0.00), and +0.33 ± 0.00 (−0.33 ± 0.00) mm/year, under the 5-, 10- and 15-year filling strategies, respectively; (iii) the northern part of the GERD reservoir is moving southward by +1.28 ± 0.02, +0.64 ± 0.01, and +0.43 ± 0.00 mm/year, while the southern part is moving northward by −3.75 ± 0.04, −1.87 ± 0.02, and −1.25 ± 0.01 mm/year, during the three examined scenarios, respectively; and (iv) the GRACE-FO mission can only detect 15% of the large-scale land deformation produced by the GERD reservoir. Methods and results demonstrated in this study provide insights into possible impacts of reservoir impoundment on land surface deformation, which can be adopted into the GERD project or similar future dam construction plans.

scholarly journals A NOTE ON PHASING LONG GENOMIC REGIONS USING LOCAL HAPLOTYPE PREDICTIONS

2006 ◽  
Vol 04 (03) ◽  
pp. 639-647 ◽  
Author(s):  
ELEAZAR ESKIN ◽  
RODED SHARAN ◽  
ERAN HALPERIN
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Novel Approach ◽  
Maximum Likelihood Criterion ◽  
The Common ◽  
Genomic Regions ◽  
High Computational Cost ◽  
Combining Information

The common approaches for haplotype inference from genotype data are targeted toward phasing short genomic regions. Longer regions are often tackled in a heuristic manner, due to the high computational cost. Here, we describe a novel approach for phasing genotypes over long regions, which is based on combining information from local predictions on short, overlapping regions. The phasing is done in a way, which maximizes a natural maximum likelihood criterion. Among other things, this criterion takes into account the physical length between neighboring single nucleotide polymorphisms. The approach is very efficient and is applied to several large scale datasets and is shown to be successful in two recent benchmarking studies (Zaitlen et al., in press; Marchini et al., in preparation). Our method is publicly available via a webserver at .

scholarly journals The impact of transfluthrin on the spatial repellency of the primary malaria mosquito vectors in Vietnam: Anopheles dirus and Anopheles minimus

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Nicholas J. Martin ◽  
Vu S. Nam ◽  
Andrew A. Lover ◽  
Tran V. Phong ◽  
Tran C. Tu ◽  
...  
Keyword(s):  
Vector Control ◽  
Large Scale ◽  
Limit Of Detection ◽  
Malaria Vectors ◽  
Adult Mosquito ◽  
Anopheles Dirus ◽  
Anopheles Minimus ◽  
Control Programmes ◽  
The Impact ◽  
Spatial Repellency

Abstract Background The complexity of mosquito-borne diseases poses a major challenge to global health efforts to mitigate their impact on people residing in sub-tropical and tropical regions, to travellers and deployed military personnel. To supplement drug- and vaccine-based disease control programmes, other strategies are urgently needed, including the direct control of disease vectors. Modern vector control research generally focuses on identifying novel active ingredients and/or innovative methods to reduce human-mosquito interactions. These efforts include the evaluation of spatial repellents, which are compounds capable of altering mosquito feeding behaviour without direct contact with the chemical source. Methods This project examined the impact of airborne transfluthrin from impregnated textile materials on two important malaria vectors, Anopheles dirus and Anopheles minimus. Repellency was measured by movement within taxis cages within a semi-field environment at the National Institute of Hygiene and Epidemiology in Hanoi, Vietnam. Knockdown and mortality were measured in adult mosquito bioassay cages. Metered-volume air samples were collected at a sub-set of points in the mosquito exposure trial. Results Significant differences in knockdown/mortality were observed along a gradient from the exposure source with higher rates of knockdown/mortality at 2 m and 4 m when compared with the furthest distance (16 m). Knockdown/mortality was also greater at floor level and 1.5 m when compared to 3 m above the floor. Repellency was not significantly different except when comparing 2 m and 16 m taxis cages. Importantly, the two species reacted differently to transfluthrin, with An. minimus being more susceptible to knockdown and mortality. The measured concentrations of airborne transfluthrin ranged from below the limit of detection to 1.32 ng/L, however there were a limited number of evaluable samples complicating interpretation of these results. Conclusions This study, measuring repellency, knockdown and mortality in two malaria vectors in Vietnam demonstrates that both species are sensitive to airborne transfluthrin. The differences in magnitude of response between the two species requires further study before use in large-scale vector control programmes to delineate how spatial repellency would impact the development of insecticide resistance and the disruption of biting behaviour.

scholarly journals O Ensino da Antibioterapia: Estado da arte

2019 ◽  
Vol 20 ◽  
pp. 632-637
Author(s):  
Maria José Saavedraa ◽  
João Carlos Sousa
Keyword(s):  
Infectious Diseases ◽  
Large Scale ◽  
Action Plan ◽  
Antimicrobial Properties ◽  
Hospital Setting ◽  
Multidrug Resistant ◽  
World Health ◽  
Novel Approach ◽  
Paul Ehrlich ◽  
Alexander Fleming

Resumo A elevada mortalidade pelas doenças infecciosas, sobretudo epidémicas, mobilizou os cientistas na pesquisa de compostos naturais e produtos de síntese química dotados de propriedades antimicrobianas. Fazendo um pouco de história, referimos Paul Ehrlich, que utilizou o primeiro agente quimioterapêutico -Salvarsan, mais tarde Gerhard Domagk, que utilizou um pro-fármaco percursor de uma sulfamida. Em 1928, Alexander Fleming, descobriu de forma “casual” a penicilina, o primeiro antibiótico. Posteriormente em 1941 Howard Florey e Ernest Chain isolam e purificam a penicilina o que permitiu a sua utilização em larga escala -Era dos Antibióticos. A utilização dos antibióticos (AB) no tratamento das doenças infecciosas constituiu um dos maiores avanços da Medicina no séc. XX. No entanto a sua utilização em larga escala promoveu o aumento da incidência de estirpes multiresistentes aos AB, sobretudo em ambiente hospitalar. Adicionalmente verifica-se uma ocorrência cada vez mais elevada de estirpes resistentes na comunidade–humanos, animais e ambiente. O conhecimento dos mecanismos de ação e da ineficácia dos diferentes grupos farmacológicos de antibióticos é vital para o desenvolvimento de futuros microbianos, estando a ser estudados microrganismos do solo com a finalidade de encontrara novos fármacos. De realçar que a OMS preconiza que caminhamos rumo a uma "era pós-antibiótico”. Se não houver um plano de ação global para o "uso racional de antibióticos" a OMS prevê que em 2050 a resistência aos antibióticos, poderá matar mais de 10 milhões de pessoas.Palavras-chave: antibioterapia; resistência; antibióticos Abstract The current research on infectious diseases, especially with epidemic potential, has mobilized the scientific community to research on the natural substance and chemical probing products with antimicrobial properties. In a brief history of antibiotics, we refer to Paul Ehrlich, who used the first chemotherapeutic agent - Salvarsan, later Gerhard Domagk, who used a sulfamide precursor prodrug. In 1928 Alexander Fleming "casually" discovered penicillin, the first antibiotic. Later in 1941 Howard Florey and Ernest Chain isolate and purify penicillin that can be used on a large scale - Antibiotics Era. The use of antibiotics (AB) in the treatment of infectious diseases is one of the greatest advances of medicine in the 19th century. However, its large-scale use has increased the incidence of multidrug-resistant processes in AB, especially in a hospital setting. Besides, there is an increasing occurrence of resistant strains in different communities - humans, animals and in the environment. Understand the mechanisms of action and the ineffectiveness of the diverse pharmacological groups of antibiotics is crucial to provide further new antibiotic therapies in the near future. Recent studies have highlighted the soil-derived microorganisms as a novel approach to identify new drug substances. In this context, it is noteworthy that the World Health Organization (WHO) considers that we are moving towards a “post-antibiotic era”. If there is no global action plan for “rational use of antibiotics” WHO predicts that in 2050 the global impacts of antibiotic resistance on human heath will be catastrophic, killing more than 10 million people worldwide. Keywords: antibiotic therapy; resistence; antibiotics

scholarly journals Participatory Mapping for Strengthening Environmental Governance on Socio-Ecological Impacts of Infrastructure in the Amazon: Lessons to Improve Tools and Strategies

2021 ◽  
Vol 13 (24) ◽  
pp. 14048
Author(s):  
Carla Mere-Roncal ◽  
Gabriel Cardoso Carrero ◽  
Andrea Birgit Chavez ◽  
Angelica Maria Almeyda Zambrano ◽  
Bette Loiselle ◽  
...  
Keyword(s):  
Large Scale ◽  
Environmental Governance ◽  
Oil And Gas ◽  
Good Governance ◽  
Knowledge Exchange ◽  
Ecological Impacts ◽  
Participatory Mapping ◽  
Infrastructure Development ◽  
Development Agenda ◽  
Novel Approach

The Amazon region has been viewed as a source of economic growth based on extractive industry and large-scale infrastructure development endeavors, such as roads, dams, oil and gas pipelines and mining. International and national policies advocating for the development of the Amazon often conflict with the environmental sector tasked with conserving its unique ecosystems and peoples through a sustainable development agenda. New practices of environmental governance can help mitigate adverse socio-economic and ecological effects. For example, forming a “community of practice and learning” (CoP-L) is an approach for improving governance via collaboration and knowledge exchange. The Governance and Infrastructure in the Amazon (GIA) project, in which this study is embedded, has proposed that fostering a CoP-L on tools and strategies to improve infrastructure governance can serve as a mechanism to promote learning and action on factors related to governance effectiveness. A particular tool used by the GIA project for generating and sharing knowledge has been participatory mapping (Pmap). This study analyzes Pmap exercises conducted through workshops in four different Amazonian regions. The goal of Pmap was to capture different perspectives from stakeholders based on their experiences and interests to visualize and reflect on (1) areas of value, (2) areas of concern and (3) recommended actions related to reducing impacts of infrastructure development and improvement of governance processes. We used a mixed-methods approach to explore textual analysis, regional multi-iteration discussion with stakeholders, participatory mapping and integration with ancillary geospatial datasets. We believe that by sharing local-knowledge-driven data and strengthening multi-actor dialogue and collaboration, this novel approach can improve day to day practices of CoP-L members and, therefore, the transparency of infrastructure planning and good governance.

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