Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

BMC Ecology image competition: the winning images.

ABSTRACT: BMC Ecology announces the winning entries in its inaugural Ecology Image Competition, open to anyone affiliated with a research institute. The competition, which received more than 200 entries from international researchers at all career levels and a wide variety of scientific disciplines, was looking for striking visual interpretations of ecological processes. In this Editorial, our academic Section Editors and guest judge Dr Yan Wong explain what they found most appealing about their chosen winning entries, and highlight a few of the outstanding images that didn't quite make it to the top prize.

SUBMITTER: Harold S

PROVIDER: S-EPMC3606310 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

BMC Ecology image competition: the winning images.

Harold Simon S Wong Yan Y Baguette Michel M Bonsall Michael B MB Clobert Jean J Royle Nick J NJ Settele Josef J

BMC ecology 20130322

BMC Ecology announces the winning entries in its inaugural Ecology Image Competition, open to anyone affiliated with a research institute. The competition, which received more than 200 entries from international researchers at all career levels and a wide variety of scientific disciplines, was looking for striking visual interpretations of ecological processes. In this Editorial, our academic Section Editors and guest judge Dr Yan Wong explain what they found most appealing about their chosen wi ...[more]

PMID: 23517630

Similar Datasets

BMC Ecology image competition 2014: the winning images.

Project description:BMC Ecology showcases the winning entries from its second Ecology Image Competition. More than 300 individual images were submitted from an international array of research scientists, depicting life on every continent on earth. The journal's Editorial Board and guest judge Caspar Henderson outline why their winning selections demonstrated high levels of technical skill and aesthetic sense in depicting the science of ecology, and we also highlight a small selection of highly commended images that we simply couldn't let you miss out on.

| S-EPMC4236560 | biostudies-literature

BMC ecology image competition 2017: the winning images.

Project description:For the fifth year, BMC Ecology is proud to present the winning images from our annual image competition. The 2017 edition received entries by talented shutterbug-ecologists from across the world, showcasing research that is increasing our understanding of ecosystems worldwide and the beauty and diversity of life on our planet. In this editorial we showcase the winning images, as chosen by our Editorial Board and guest judge Chris Darimont, as well as our selection of highly commended images. Enjoy!

| S-EPMC5561582 | biostudies-literature

BMC ecology image competition 2018: the winning images.

Project description:The sixth BMC Ecology Image Competition received more than 145 photographs from talented ecologists around the world, showcasing the amazing biodiversity, natural beauty and biological interactions found in nature. In this editorial, we showcase the winning images, as selected by our guest judge, Professor Zhigang Jiang from the Institute of Zoology of the Chinese Academy of Sciences, with help from the journal's editorial board. Enjoy!

| S-EPMC6407182 | biostudies-literature

Seventh BMC ecology image competition: the winning images.

Project description:The seventh BMC Ecology competition attracted entries from talented ecologists from around the world. Together, they showcase the beauty and diversity of life on our planet as well as providing an insight into the biological interactions found in nature. This editorial celebrates the winning images as selected by the Editor of BMC Ecology and senior members of the journal's editorial board. Enjoy!

| S-EPMC7410526 | biostudies-literature

Winning a competition predicts dishonest behavior.

Project description:Winning a competition engenders subsequent unrelated unethical behavior. Five studies reveal that after a competition has taken place winners behave more dishonestly than competition losers. Studies 1 and 2 demonstrate that winning a competition increases the likelihood of winners to steal money from their counterparts in a subsequent unrelated task. Studies 3a and 3b demonstrate that the effect holds only when winning means performing better than others (i.e., determined in reference to others) but not when success is determined by chance or in reference to a personal goal. Finally, study 4 demonstrates that a possible mechanism underlying the effect is an enhanced sense of entitlement among competition winners.

| S-EPMC4763788 | biostudies-other

Winning isn't everything: mood and testosterone regulate the cortisol response in competition.

Project description:Dominance contests are recurrent and widespread causes of stress among mammals. Studies of activation of the stress axis in social defeat - as reflected in levels of adrenal glucocorticoid, cortisol - have generated scattered and sometimes contradictory results, suggesting that biopsychological individual differences might play an important mediating role, at least in humans. In the context of a larger study of the regulation of endocrine responses to competition, we evaluated the notion that mood states, such as self-assurance and hostility, may influence cortisol reactivity to dominance cues via an interplay with baseline testosterone, considered as a potential marker of individual differences in dominance. Seventy healthy male university students (mean age 20.02, range 18-26) provided saliva samples before and after competing for fifteen minutes on a rigged computer task. After a winner was determined, all participants were assessed on their mood states through a standardized psychometric instrument (PANAS-X). Among winners of a rigged videogame competition, we found a significant interaction between testosterone and self-assurance in relation to post-competition cortisol. Specifically, self-assurance was associated with lower post-competition cortisol in subjects with high baseline testosterone levels, but no such relationship was observed in subjects with lower baseline testosterone levels. In losers of the competition no interaction effect between basal testosterone and hostility was observed. However, in this subgroup a significant negative relationship between basal testosterone and post-competition cortisol was evident. Overall, these findings provide initial support for the novel hypothesis that biological motivational predispositions (i.e. basal testosterone) and state (i.e. mood changes) may interact in regulating the hypothalamic-pituitary-adrenal axis activation after a social contest.

| S-EPMC3541278 | biostudies-literature

BMC Rheumatology: a home for all rheumatology research within the BMC Series.

Project description:This editorial accompanies the launch of BMC Rheumatology, a new open access, peer-reviewed journal within the BMC Series portfolio, which considers studies on all aspects of rheumatological diseases. The Journal will also place a special emphasis on manuscripts reporting systemic and inflammatory conditions and connective tissue diseases, along with related comorbidities, including cardiovascular disease, malignancy and infection. Through the publication of a variety of article types, including Research articles, Case reports, Study protocols and Debates, BMC Rheumatology will provide the rheumatology community with an open access avenue to disseminate research into rheumatological diseases, with the ultimate aim of improving patient care.

| S-EPMC6383591 | biostudies-literature

Learning With Fewer Images via Image Clustering: Application to Intravascular OCT Image Segmentation.

Project description:Deep learning based methods are routinely used to segment various structures of interest in varied medical imaging modalities. Acquiring annotations for a large number of images requires a skilled analyst, and the process is both time consuming and challenging. Our approach to reduce effort is to reduce the number of images needing detailed annotation. For intravascular optical coherence tomography (IVOCT) image pullbacks, we tested 10% to 100% of training images derived from two schemes: equally-spaced image subsampling and deep-learning- based image clustering. The first strategy involves selecting images at equally spaced intervals from the volume, accounting for the high spatial correlation between neighboring images. In clustering, we used an autoencoder to create a deep feature space representation, performed k-medoids clustering, and then used the cluster medians for training. For coronary calcifications, a baseline U-net model was trained on all images from volumes of interest (VOIs) and compared with fewer images from the sub-sampling strategies. For a given sampling ratio, the clustering based method performed better or similar as compared to the equally spaced sampling approach (e.g., with 10% of images, mean F1 score for calcific class increased from 0.52 to 0.63, with equal spacing and with clustering, respectively). Additionally, for a fixed number of training images, sampling images from more VOIs performed better than otherwise. In conclusion, we recommend the clustering based approach to annotate a small fraction of images, creating a baseline model, which potentially can be improved further by annotating images selected using methods described in active learning research.

| S-EPMC8023588 | biostudies-literature

Analysis of the Human Protein Atlas Image Classification competition.

Project description:Pinpointing subcellular protein localizations from microscopy images is easy to the trained eye, but challenging to automate. Based on the Human Protein Atlas image collection, we held a competition to identify deep learning solutions to solve this task. Challenges included training on highly imbalanced classes and predicting multiple labels per image. Over 3?months, 2,172 teams participated. Despite convergence on popular networks and training techniques, there was considerable variety among the solutions. Participants applied strategies for modifying neural networks and loss functions, augmenting data and using pretrained networks. The winning models far outperformed our previous effort at multi-label classification of protein localization patterns by ~20%. These models can be used as classifiers to annotate new images, feature extractors to measure pattern similarity or pretrained networks for a wide range of biological applications.

| S-EPMC6976526 | biostudies-literature

Robust image registration of biological microscopic images.

Project description:Image registration of biological data is challenging as complex deformation problems are common. Possible deformation effects can be caused in individual data preparation processes, involving morphological deformations, stain variations, stain artifacts, rotation, translation, and missing tissues. The combining deformation effects tend to make existing automatic registration methods perform poor. In our experiments on serial histopathological images, the six state of the art image registration techniques, including TrakEM2, SURF + affine transformation, UnwarpJ, bUnwarpJ, CLAHE + bUnwarpJ and BrainAligner, achieve no greater than 70% averaged accuracies, while the proposed method achieves 91.49% averaged accuracy. The proposed method has also been demonstrated to be significantly better in alignment of laser scanning microscope brain images and serial ssTEM images than the benchmark automatic approaches (p < 0.001). The contribution of this study is to introduce a fully automatic, robust and fast image registration method for 2D image registration.

| S-EPMC4131219 | biostudies-other

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data