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Supplementary material 2 from: Evans T, Blackburn TM, Jeschke JM, Probert AF, Bacher S (2020) Application of the Socio-Economic Impact Classification for Alien Taxa (SEICAT) to a global assessment of alien bird impacts. In: Wilson JR, Bacher S, Daehler CC, Groom QJ, Kumschick S, Lockwood JL, Robinson TB, Zengeya TA, Richardson DM. NeoBiota 62: 123-142. https://doi.org/10.3897/neobiota.62.51150
Appendix B
Supplementary material 1 from: Evans T, Blackburn TM, Jeschke JM, Probert AF, Bacher S (2020) Application of the Socio-Economic Impact Classification for Alien Taxa (SEICAT) to a global assessment of alien bird impacts. In: Wilson JR, Bacher S, Daehler CC, Groom QJ, Kumschick S, Lockwood JL, Robinson TB, Zengeya TA, Richardson DM. NeoBiota 62: 123-142. https://doi.org/10.3897/neobiota.62.51150
Appendix A
Dataset associated to Tailored glycosylated anode surfaces: Addressing the exoelectrogen bacterial community via functional layers for microbial fuel cell applications
<p>This file contains the dataset associated to the published research article "<a href="https://www.sciencedirect.com/science/article/pii/S1567539420302887">Tailored glycosylated anode surfaces: Addressing the exoelectrogen bacterial community via functional layers for microbial fuel cell applications</a>". The dataset contains Atomic Force Microscopy, electrochemistry, Microbial Fuel Cells power output and water contact angle raw data from their relative instruments. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. <a href="https://www.sciencedirect.com/science/article/pii/S1567539420302887#gp005">799175</a> (HiBriCarbon). The results of this publication reflect only the authors' view and the Commission is not responsible for any use that may be made of the information it contains. This publication has also emanated from research conducted with the financial support of Science Foundation Ireland under Grant No. <a href="https://www.sciencedirect.com/science/article/pii/S1567539420302887#gp010">13/CDA/2213</a>. The authors also thank the France-Ireland PHC ULYSSES programme for support, project 36028UB. JAB acknowledges support from the Irish Research Council under Grant No. <a href="https://www.sciencedirect.com/science/article/pii/S1567539420302887#gp015">GOIPG/2014/399</a>. </p>
Figure 1 from: Zhelyazkova M, Kirilov B, Momekov G (2020) The pharmacological basis for application of cannabidiol in cancer chemotherapy. Pharmacia 67(4): 239-252. https://doi.org/10.3897/pharmacia.67.e51304
Figure 1 Chemical structures of Δ9-tetrahydrocannabinol (Δ9-THC), cannabidiol (CBD), anandamide (AEA) and synthetic cannabinoid agonist WIN55212-2. Chemical name of CBD: 2-[1R-3-methyl-6R-(1-methylethenyl)-2-cyclohexen-1-yl]-5-pentyl-1,3-benzenediol; IUPAC name: 2-[(6R)-3-methyl-6-prop-1-en-2-ylcyclohex-2-en-1- yl]-5-pentylbenzene-1,3-diol.
Figure 2 from: Zhelyazkova M, Kirilov B, Momekov G (2020) The pharmacological basis for application of cannabidiol in cancer chemotherapy. Pharmacia 67(4): 239-252. https://doi.org/10.3897/pharmacia.67.e51304
Figure 2 Some of the potential biological targets of Cannabidiol (CBD): cannabinoid receptors CB1, and CB2; ion channel receptors, incl. transient receptor potential vanilloid (TRPV) type 1 and 2; EGF-R, epidermal-growth factor receptor; 'orphan' G protein-coupled receptors, GPR55 and GPR3, GPR6; peroxisome proliferator-activated receptor, PPAR; vascular endothelial growth factor, VEGF-R; allosteric modulation and enhanced activity at αIγ glycine receptor; enhanced activity at the 5-HT1a, adenosine and N-acetylcholine receptors; inhibiting activity on mu and delta opioid receptors. Note: (+) stimulation; (-) inhibition.
Social (-ecological) network analysis in environmental governance - central publications, important concepts and areas of application data collected in January 2019
<p>Social and Social-Ecological Network Analysis (S(E)NA) have recently emerged as new methods in the environmental governance (EG) literature. By investigating networks of connections between actors, S(E)NA advances the understanding of who is involved in EG and how. We provide an overview of the EG literature applying S(E)NA and respective metrics of 1) a citation network analysis, 2) topic detection methods and 3) a similarity network analysis. 20 publications occupy central positions as sources, storers or bridges of knowledge in the citation network. While we see S(E)NA applied in diverse resource contexts, these are mainly discussed on the local spatial level, with a focus on “policy” or “collaboration”. We discover that “power structures” and “the production of knowledge” are themes influencing the whole field.</p>
Supplementary material 2 from: Bustamante RO, Alves L, Goncalves E, Duarte M, Herrera I (2020) A classification system for predicting invasiveness using climatic niche traits and global distribution models: application to alien plant species in Chile. NeoBiota 63: 127-146. https://doi.org/10.3897/neobiota.63.50049
Table S2. Basic information obtained for 49 exotic plants in Chile
Supplementary material 3 from: Bustamante RO, Alves L, Goncalves E, Duarte M, Herrera I (2020) A classification system for predicting invasiveness using climatic niche traits and global distribution models: application to alien plant species in Chile. NeoBiota 63: 127-146. https://doi.org/10.3897/neobiota.63.50049
Map of the species
Double-difference seismic attenuation tomography method and its application to The Geysers geothermal field, California
<p>Earthquake relocations and Vp and Qp models of The Geysers geothermal field developed by Guo and Thurber (2021).</p> <p> </p> <p>Reference:</p> <p>Hao Guo, Clifford Thurber, 2021. Double-difference seismic attenuation tomography method and its application to The Geysers geothermal field, California, Geophysical Journal International, ggab017, https://doi.org/10.1093/gji/ggab017</p>
The synthesis of SBA-Pr-3AP@Pd and its application as a highly dynamic, eco-friendly heterogeneous catalyst for Suzuki–Miyaura cross-coupling reaction
<p>The hexagonal mesoporous organic–inorganic hybrid as a new nanocatalyst was prepared by the treatment of SBA-15 with (3-chloropropyl)triethoxysilane, the 2,4,6-triamino pyrimidine ligand, and then PdCl2 to obtain the SBA-15-propyl-triamino pyrimidine@Pd called as SBA-Pr-3AP@Pd, which was examined through Suzuki–Miyaura cross-coupling reaction by several aryl halides and phenylboronic acid under mild conditions in high yield.</p>
Data from: A penalized likelihood framework for high- dimensional phylogenetic comparative methods and an application to new-world monkeys brain evolution
Working with high-dimensional phylogenetic comparative datasets is challenging because likelihood-based multivariate methods suffer from low statistical performances as the number of traits p approaches the number of species n and because some computational complications occur when p exceeds n. Alternative phylogenetic comparative methods have recently been proposed to deal with the large p small n scenario but their use and performances are limited. Here we develop a penalized likelihood framework to deal with high-dimensional comparative datasets. We propose various penalizations and methods for selecting the intensity of the penalties. We apply this general framework to the estimation of parameters (the evolutionary trait covariance matrix and parameters of the evolutionary model) and model comparison for the high-dimensional multivariate Brownian (BM), Early-burst (EB), Ornstein-Uhlenbeck (OU) and Pagel's lambda models. We show using simulations that our penalized likelihood approach dramatically improves the estimation of evolutionary trait covariance matrices and model parameters when p approaches n, and allows for their accurate estimation when p equals or exceeds n. In addition, we show that penalized likelihood models can be efficiently compared using Generalized Information Criterion (GIC). We implement these methods, as well as the related estimation of ancestral states and the computation of phylogenetic PCA in the R package RPANDA and mvMORPH. Finally, we illustrate the utility of the new proposed framework by evaluating evolutionary models fit, analyzing integration patterns, and reconstructing evolutionary trajectories for a high-dimensional 3-D dataset of brain shape in the New World monkeys. We find a clear support for an Early-burst model suggesting an early diversification of brain morphology during the ecological radiation of the clade. Penalized likelihood offers an efficient way to deal with high-dimensional multivariate comparative data.
Data from: Optimized modelling and application of exhaust gas recirculation performance evaluation of turbocharged diesel engine
Aimed at the problem of turbocharged diesel engine EGR (exhaust gas recirculation) performance evaluation and optimal EGR rate determination, especially in some cases with poor data because of the limit of conditions, an optimized multi-objective grey decision method based on grey theory and entropy weight is proposed. The internal combustion pressure ,fuel consumption rate, NOX ,CO and smoke degree were selected as the decision target, and the initial decision-making model was established based on traditional grey situation theory. According to the characteristics and optimization requirements of EGR, The optimal compromise between combustion and emission performance is proposed to transform into the decision-making target weight problem , then a subjective weight optimization method based on expert scoring and grey relational analysis is proposed. In addition, the entropy weight method was used to solve the subjective and objective weight vector and the optimized multi-objective grey situation decision making model was established, which can not weaken the human error of subjective empowerment, but also fully explore the intrinsic objective relationship of the evaluation index parameters. At last, an optimization simulation platform for EGR performance evaluation based MATLB/GUIDE is designed and established. The results show that the optimization simulation platform can effectively improve the efficiency of simulation calculation, which is more convenient for practical engineering applications; the optimized method can successfully solve the EGR performance evaluation and optimal EGR rate determination under different working conditions, the decision result is reasonable and consistent with the prevailing principle of the EGR determining, which provide a new research ideas for EGR performance optimization.
Data from: The application of optical coherence tomography to image subsurface tissue structure of Antarctic krill Euphausia superba
Many small open ocean animals, such as Antarctic krill, are an important part of marine ecosystems. To discover what will happen to animals such as krill in a changing ocean, experiments are run in aquaria where conditions can be controlled to simulate water characteristics predicted to occur in the future. The response of individual animals to changing water conditions can be hard to observe, and with current observation techniques it is very difficult to follow the progress of an individual animal through its life. Optical coherence tomography (OCT) is an optical imaging technique that allows images at high resolution to be obtained from depths up to a few millimeters inside biological specimens. It is compatible with in vivo imaging and can be used repeatedly on the same specimens. In this work, we show how OCT may be applied to post mortem krill samples and how important physiological data such as shell thickness and estimates of organ volume can be obtained. Using OCT we find an average value for the thickness of krill exoskeleton to be (30±4) µm along a 1 cm length of the animal body. We also show that the technique may be used to provide detailed imagery of the internal structure of a pleopod joint and provide an estimate for the heart volume of (0.73±0.03) mm3.
Data from: Risk-aware multi-armed bandit problem with application to portfolio selection
Sequential portfolio selection has attracted increasing interests in the machine learning and quantitative finance communities in recent years. As a mathematical framework for reinforcement learning policies, the stochastic multi-armed bandit problem addresses the primary difficulty in sequential decision making under uncertainty, namely the exploration versus exploitation dilemma, and therefore provides a natural connection to portfolio selection. In this paper, we incorporate risk-awareness into the classic multi-armed bandit setting and introduce an algorithm to construct portfolio. Through filtering assets based on the topological structure of financial market and combining the optimal multi-armed bandit policy with the minimization of a coherent risk measure, we achieve a balance between risk and return.
Data from: Application of wMelPop Wolbachia strain to crash local populations of Aedes aegypti
The endosymbiotic bacteria Wolbachia pipientis (wMel strain) has been successfully established in several populations of Aedes aegypti, the primary dengue vector. The virulent Wolbachia strain wMelPop is known to cause several pathological impacts (increased egg mortality, life shortening, etc.) reducing overall fitness in the mosquito Ae. aegypti. Increased egg mortality could substantially reduce egg banks in areas with a lengthy monsoonal dry season, and be employed to eliminate local populations. We tested this application under semi-field cage conditions. First, we determined that wMelPop infection significantly reduced the survival of desiccation-resistant eggs of the dengue vector Ae. aegypti, with shade and temperature having a significant impact; nearly all wMelPop-infected eggs failed to hatch after 6 and 10 weeks in summer and winter conditions, respectively. In laboratory selection experiments we found that egg desiccation resistance can be increased by selection, and that this effect of wMelPop infection is due to the nuclear background of the host rather than Wolbachia. We then conducted an invasion of wMelPop within a semi-field cage using sustained weekly releases of wMelPop infected mosquitoes, with fixation achieved after 9 weeks. The egg populations wMelPop infected and an uninfected control were then subjected to a simulated prolonged monsoonal dry season (2.5 months) before flooding to induce hatching. The wMelPop infected eggs suffered significantly greater mortality than the controls, with only 0.67% and 4.35% of respective infected and uninfected eggs held in 99% shade hatching after 80 days. These studies suggest that wMelPop could be used to locally eliminate populations of Ae. aegypti that are exposed to prolonged dry conditions, particularly if combined with vector control.
Data from: Predicting classifier performance with limited training data: applications to computer-aided diagnosis in breast and prostate cancer
Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeated sampling (RRS) in conjunction with a cross-validation sampling strategy. Extrapolated error rates are subsequently validated via comparison with leave-one-out cross-validation performed on a larger dataset. The ability to predict error rates as dataset size increases is demonstrated on both synthetic data as well as three different computational imaging tasks: detecting cancerous image regions in prostate histopathology, differentiating high and low grade cancer in breast histopathology, and detecting cancerous metavoxels in prostate magnetic resonance spectroscopy. For each task, the relationships between 3 distinct classifiers (k-nearest neighbor, naive Bayes, Support Vector Machine) are explored. Further quantitative evaluation in terms of interquartile range (IQR) suggests that our approach consistently yields error rates with lower variability (mean IQRs of 0.0070, 0.0127, and 0.0140) than a traditional RRS approach (mean IQRs of 0.0297, 0.0779, and 0.305) that does not employ cross-validation sampling for all three datasets.
Data from: Bioclimatic variables derived from remote sensing: assessment and application for species distribution modeling
Remote sensing techniques offer an opportunity to improve biodiversity modeling and prediction worldwide. Yet, to date, the weather-station based WorldClim dataset has been the primary source of temperature and precipitation information used in correlative species distribution models. WorldClim consists of grids interpolated from in situ station data recorded primarily from 1960 to 1990. Those datasets suffer from uneven geographic coverage, with many areas of Earth poorly represented. Here, we compare two remote sensing data sources for the purposes of biodiversity prediction: MERRA climate reanalysis data and AMSR-E, a pure remote sensing data source. We use these data to generate novel temperature-based bioclimatic information and to model the distributions of 20 species of vertebrates endemic to four regions of South America: Amazonia, the Atlantic Forest, the Cerrado, and Patagonia. We compare the bioclimatic datasets derived from MERRA and AMSR-E information with in situ station data, and contrast species distribution models based on these two products to models built with WorldClim. Surface temperature estimates provided by MERRA and AMSR-E showed warm temperature biases relative to the in situ data fields, but the reliability of these datasets varied in geographic space. Species distribution models derived from the MERRA data performed equally well (in Cerrado, Amazonia, and Patagonia) or better (Atlantic Forest) than models built with the WorldClim data. In contrast, the performance of models constructed with the AMSR-E data was similar to (Amazonia, Atlantic Forest, Cerrado) or worse than (Patagonia) that of models built with WorldClim data. Whereas this initial comparison assessed only temperature fields, efforts to estimate precipitation from remote sensing information hold great promise; furthermore, other environmental datasets with higher spatial and temporal fidelity may improve upon these results.
Data from: Generation of a monoclonal antibody recognizing the heavily glycosylated CD45 Protein and its application on identifying circulating tumor cells
Here, we provide direct evidence that using recombinant proteins expressed in eukaryotic cells as antigen is a practical way to generate monoclonal antibodies (mAbs) against heavily glycosylated proteins. Heavily glycosylated proteins are typically difficult targets for mAb generation, being limited by unsatisfactory affinity and low specificity. Using the heavily glycosylated CD45 protein as an example, we demonstrate the entire process of expressing the protein in eukaryotic cells and using it as an antigen to generate CD45-targeting mAbs in mice. The mAbs generated showed robust affinity and specificity, which are crucial factors for differentiate circulating tumor cells from white blood cells in human breast cancer patient samples. Only 1 cell fusion and 2 cyclic sub-cloning steps were necessary before mAbs with satisfactory performance were obtained.
Data from: Insecticide applications to soil contribute to development of Burkholderia mediating insecticide resistance in stinkbugs
Some soil Burkholderia strains are capable of degrading the organophosphorus insecticide, fenitrothion, and establish symbiosis with stinkbugs, making the host insects fenitrothion-resistant. However, the ecology of the symbiotic degrading Burkholderia adapting to fenitrothion in the free-living environment is unknown. We hypothesized that fenitrothion applications affect the dynamics of fenitrothion-degrading Burkholderia, thereby controlling the transmission of symbiotic degrading Burkholderia from the soil to stinkbugs. We investigated changes in the density and diversity of culturable Burkholderia (i.e. symbiotic and nonsymbiotic fenitrothion degraders and nondegraders) in fenitrothion-treated soil using microcosms. During the incubation with five applications of pesticide, the density of the degraders increased from less than the detection limit to around 106/g of soil. The number of dominant species among the degraders declined with the increasing density of degraders; eventually, one species predominated. This process can be explained according to the competitive exclusion principle using Vmax and Km values for fenitrothion metabolism by the degraders. We performed a phylogenetic analysis of representative strains isolated from the microcosms and evaluated their ability to establish symbiosis with the stinkbug Riptortus pedestris. The strains that established symbiosis with R. pedestris were assigned to a cluster including symbionts commonly isolated from stinkbugs. The strains outside the cluster could not necessarily associate with the host. The degraders in the cluster predominated during the initial phase of degrader dynamics in the soil. Therefore, only a few applications of fenitrothion could allow symbiotic degraders to associate with their hosts and may cause the emergence of symbiont-mediated insecticide resistance.
Data from: A highly sensitive LC-MS/MS method to determine novel Bruton's tyrosine kinase inhibitor spebrutinib: application to metabolic stability evaluation
Spebrutinib (SBT) is a Bruton's tyrosine kinase inhibitor. SBT is currently in phase II and phase I clinical trials for the management of rheumatoid arthritis and chronic lymphocytic leukaemia, respectively. We developed and validated a liquid chromatography tandem mass spectrometry analytical method to quantify SBT and investigate its metabolic stability. SBT and the naquotinib as internal standard were isocratically eluted on a C18 column. The linearity of the developed method is 5-500 ng/mL (r2≥ 0.9999) in the human liver microsomes (HLMs) matrix. Good sensitivity was approved by the very low limit of detection (0.39 ng/mL). Inter- and intra- assay accuracy values of -1.41 to 12.44 and precision values of 0.71 to 4.78%, were obtained. SBT was found to have an in vitro half-life (82.52 min) and intrinsic clearance (8.4 µL/min/mg) as computed following its incubation with HLMs. The latter finding, hypothesize that SBT could be deliberately expelled from the body unlike other related tyrosine kinase inhibitors. So, drug plasma level and kidney function should be monitored because of potential bioaccumulation. To the best of our knowledge, this is considered the first analytical method for SBT quantification using LC-MS/MS with application to metabolic stability evaluation.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.