THE WORKSHOP WAS CO-ORGANIZED BY CSH EXTERNAL FACULTY MEMBER MIRTA GALESIC (SFI) AND TOOK PLACE ON OCTOBER 19–21, 2022 AT THE COMPLEXITY SCIENCE HUB (VIENNA)
Collectives emerge, change, grow and dissolve across temporal and spatial scales. These processes have been studied and modeled in different species, but many of the underlying mechanisms and regularities still remain elusive. This is problematic as understanding these collective processes is essential for maintenance, curation, and development of our own, human societies facing a rapid succession of physical and social challenges.
This workshop brought together scientists (and TheElab member Daniel Oro) studying collectives from biological, evolutionary, neurological, psychological, and sociological perspectives, to discuss the following questions:
Are collectives bound by the same forces of selection as are multicellular species? Do collectives develop, reproduce (fission), age and die, analogous to multicellular individuals?
How do collectives adapt and evolve to maintain/increase function (obtain resources, protect against antagonists, expand in size) and to what extent are there trade-offs, or constraints that result in aging and failure through time?
How can collective responses to perturbations and challenges across scales inform theories, quantitative models, and design of resilient social systems including human societies?
From june 14th to june 18th 2021 Del 14 al 18 de juny del 2021 is held the INTERNATIONAL CONFERENCE ON DYNAMICS IN SYSTEMS AND SYNTHETIC BIOLOGY (DYNS ^ 3BIO), a virtual conference that is part of the Intensive Research Program: DYNAMICAL SYSTEMS IN SYSTEMS AND SYNTHETIC BIOLOGY (DYNS3BIO) organized by Centre de Recerca Matemàtica, ICREA-Catalan for Research and Advanced Studies i Institute for Integrative Systems Biology CSIC-UV.
This conference is a great opportunity to bring together scientists working in systems and synthetic biology and dynamicists. The combination of both plenary and short talks may allow senior and young researchers at their postdoctoral and predoctoral stages to expose recent developments and establish productive discussions.
The possibility to mix scientists working in experimental and field data in systems and synthetic biology with scientists specialised in dynamical systems provides a unique opportunity to foster multidisciplinary and rigorous biology-based research.
The generic topics to be discussed include:
Dynamical structures in biology
Bifurcations and transitions in systems and synthetic biology
Dynamical systems approaches to experimental and field data
Ecological dynamical systems
Virology and immunology
Cancer systems biology
The impact of dynamics in systems and synthetic biology is of paramount importance.
Typically, biological systems are highly nonlinear, and nontrivial dynamics can occur even in low-dimensional systems. Nonlinear dynamics can have a huge impact on complex ecosystems, tumor dynamics, or cell circuits, to cite a few examples. Dynamical systems theory offers a unique opportunity to model, simulate, and understand the dynamical outcome in systems and synthetic biology. These outcomes range from stationary states, to transient phenomena, or to transitions between states (bifurcations).
The current development of new technologies and data processing may allow to handle high-ressolution dynamical information for complex biological systems. This fact, due to the development of low- and high-dimensional dynamical systems, offers a unique opportunity to provide qualitative and quantitative information by means of mathematical and computational models. The usage of dynamical systems to describe real-experimental dynamics is bidirectional: models allow us to understand and predict the behaviour of biological systems; new biologically-inspired models can give place to the discovery of new mathematical phenomena. The importance of dynamics in nonlinear systems spans the fields of theoretical ecology, biomedicine, diseases, epidemiology, among others.
The ultimate goal of this IRP is to bring together leading experts in dynamical systems, theoretical and computational biology, and both experimental and field biology.
Researchers from Mosquito Alert (who belong to CEAB-CSIC,CREAF and UPF) together with researchers from the University of Budapest have shown that an artificial intelligence algorithm is capable of recognizing the tiger mosquito (Aedes albopictus) in the photos sent by Mosquito Alert users. The results of the study published in Scientific Reportshave been obtained by applying deep learning technology or deep learning, an aspect of artificial intelligence that seeks to emulate the way of learning of humans and that has previously been used in the health field to interpret medical images (X-rays of patients with COVID19 to detect pneumonia, or facial features to detect heart disease, among others). Deep learning needs a lot of training data for the machine to learn. In the case of the Mosquito Alert app, these images have been sent by the public and labeled by the project experts as “tiger mosquito” or “no tiger mosquito” for years. Specifically, the study used 7,168 classified photographs of mosquitoes that the project participants had sent between 2015 and 2019. After training, the algorithm has been able to correctly classify 96% of the photographs of this insect.
“The initial idea is to get the machine to classify the simplest photos, and leave the task of identifying the most problematic images that require consensus to the experts. As the artificial system learns from the classifications of the experts, we will be able to expand the range of automatically cataloged species”
John Palmer, UPF researcher and co-director of Mosquito Alert
This milestone can mark a before and after in the surveillance and monitoring of the tiger mosquito and other mosquitoes capable of transmitting diseases. “We are training a social immune system against these mosquitoes. The faster the threat is detected, the faster it can be acted upon”, comments Frederic Bartumeus, co-director of Mosquito Alert and ICREA researcher at CEAB-CSIC and CREAF. On the one hand, the citizen science of Mosquito Alert allows anyone to be part of this new social immune system and contribute a massive number of photos of mosquitoes, on the other, artificial intelligence allows, to accelerate the classification process of the received photos and thus help public health experts make better and faster decisions about mosquito management.
“In times of greatest need, such as in the months of greatest mosquito activity or in a context of epidemiological crisis, artificial intelligence can help us so that the system can absorb a greater amount of information, controlling its quality at all times, which it is key if the data is to be used for decision-making in public health”
Automating saves lives
The presence of the tiger mosquito in Spain poses a threat to public health. Millions of people are affected by its presence and are exposed to the risk of transmitting diseases such as dengue or chikungunya. In Europe, the tiger mosquito has been implicated every year since 2007 in small locally transmitted outbreaks of these viral diseases for which no vaccines are available. The only preventive measure is to control the mosquitoes that transmit them. Assessing the risk and the necessary action measures to mitigate it requires having accurate information on tiger mosquito populations, a costly and laborious task that requires manual placement and inspection of traps and their subsequent analysis in the laboratory where the insects are identified. A methodology that is not feasible to cover large geographic areas.
Mosquito Alert’s citizen science methods, which allow anyone to report the presence of a mosquito through a mobile application available on Android and iOS, is an alternative that makes it easy to cover large geographic areas throughout the mosquito season. Since 2015, the initiative receives thousands of photographs every year that help estimate the abundance of mosquitoes. However, this large volume of photographs continues to be classified by visual examination by expert entomologists, a task that requires time and years of experience. Integrating artificial intelligence into this process can speed up classification and thus develop near-real-time hazard maps that improve tiger mosquito management.
The international team of researchers made up of more than 70 collaborators, including the researcher Daniel Oro of the group of Theoretical and Computational Ecology of the Center for Advanced Studies of Blanes (CEAB-CSIC), tracked the movements of 5.775 individuals belonging to 39 species using small electronic devices.
They found that all species regularly cross into the waters of other countries, meaning that no single nation can adequately ensure their conservation. Furthermore, all species depended on the high seas, which are areas of international waters covering half of the world’s oceans and a third of the earth’s surface.
“Seabirds like albatrosses are the ultimate globetrotters, but this incredible lifestyle makes them vulnerable to threats in places where legal protection is inadequate”
Albatrosses and their close relatives, the large petrels, are among the world’s most-threatened animals, with over half of the species at risk of extinction. While at sea they face a number of threats, including injury or mortality from fishing gear, pollution and loss of their natural prey due to overfishing and climate change.
A comprehensive legal framework for biodiversity conservation is lacking
According to co-author Maria Dias, from BirdLife International, “Negative interactions with fisheries are particularly serious in international waters because there is less monitoring of industry practices and compliance with regulations. Also, beyond fish there is currently no global legal framework for addressing the conservation of biodiversity in the high seas.”
The study comes as the United Nations (UN) are discussing a global treaty for the conservation and sustainable use of biodiversity in international waters.
“Our study unequivocally shows that albatrosses and large petrels need reliable protection that extends beyond the borders of any single country”, said Martin Beal, and adds this treaty represents a massive opportunity for countries to commit to protecting species wherever they may roam.
Legal measures up for discussion under the treaty, such as instituting environmental impact assessments on industrial activities in the high seas, may help reduce impact on species found in the high seas
“Animals have no concept of human borders. What we’ve shown here with seabirds is certainly true for many other marine animals, like sea turtles, seals, whales, and fish. To ensure their survival, we must work together to protect and conserve the global ocean”
Cooperation between countries has been basic to the study
The study was possible thanks to the cooperation of dozens of researchers across sixteen countries, who agreed to share their data through the Seabird Tracking Database, a repository managed by BirdLife International to facilitate international collaborations between researchers working on the conservation of seabirds.
The Center for Advanced Studies of Blanes (CEAB-CSIC) has participated in the project by providing radiotracking monitoring data at large spatial scales of shearwaters migration. Shearwaters are seabirds that breed on islands in the Mediterranean during the summer and that in winter can cross the Atlantic up to four times, between the Caribbean, the Benguela Current and the argentine coast. The species in the present study occupy all the pelagic regions of the planet and given their state of conservation creates the need to protect these regions for the benefit of the rest of the ecosystem.
“This is a major challenge, as it always happens when we go beyond the jurisdictions of each country”
Daniel Oro, researcher in the group of Theoretical and Computational Ecology of the CEAB-CSIC
Beal et al. “Global political responsibility for the conservation of albatrosses and large petrels”. Science Advances 03 Mar 2021: Vol. 7, no. 10, eabd7225 DOI: 10.1126/sciadv.abd7225
This research group is coordinated by
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