Open Calls for Papers

EPJE Topical Issue : Microplastics pollution in environmental flows

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Published on 12 December 2024

Guest Editors: Marco De Paoli, Giuseppe Suaria and Fabrizio Croccolo

Submissions are invited for a Topical Issue of EPJ E on Microplastics pollution in environmental flows.

Microplastics are defined as fragments less than 5 mm in size and made from synthetic plastic materials. They originate from anthropogenic activities such as industrial or household processes and are dispersed in the environment via natural pathways like rivers, oceanic currents and air streams. Microplastic contamination has been going on for decades and has been found in remote aquatic and terrestrial places from Mount Everest down to the oceans and the poles. In the marine environment, microplastics are ingested by biota such as turtles, marine mammals, seabirds, and fish and make their way through trophic transfer to humans, where they may pose health risks. Realistic modelling and measuring the behavior of microplastic particles in the environment, is crucial to better understand microplastic pollution and designing effective remediation strategies.

EPJE participates in a larger effort to support the UN Sustainable Development Goals (SDG), together with other journals in the EPJ portfolio. Authors contributing to this collection will see their work highlighted as part of the EPJ SDG project on the portfolio website and channels. This EPJE Topical Collection aims to collect contributions focused on understanding the dynamics of microplastics in lakes, rivers, oceans, the atmosphere and beyond. Predicting their fate and movements through ecosystems is crucial to conserve and sustainably use the water resources (SDG14), and to drive sustainable consumption and production patterns (SDG12) with indirect positive effects on good health and well-being (SDG3).

The list of the topics covered includes (but is not limited to):

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EPJE Topical Issue : Physical modeling and experimental studies of cancer progression

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Published on 27 August 2024

Guest Editors: Corey O’Hern and Stefano Villa

Submissions are invited for a Topical Issue of EPJ E on Physical modeling and experimental studies of cancer progression.

A physical understanding of the progression of cancer, including cancer growth and metastasis, will greatly aid in the development of therapeutic treatments. Tumor growth creates large pressures on surrounding tissue and stroma, which can further stimulate tumor proliferation. During cancer metastasis, cancer cells migrate away from the primary tumor and form secondary tumor sites in other organs, which leads to much higher mortality rates for cancer patients. Previous experimental studies of many cancer types have revealed that mechanical forces play a crucial role in both cancer growth and metastasis. For example, pressure built up during tumor growth alters cell gene expression, cancer cell proliferation, and blood flow. Cancer cells can also readily change their shapes and have a heightened migratory response that facilitates tumor invasion and metastasis. This special issue seeks contributions from researchers who carry out experimental, theoretical, and computational studies to understand the physical mechanisms that promote tumor growth and give rise to cancer metastasis. We also welcome articles that seek to identify features of the tumor microenvironment, as well as physical properties of tumor cells that promote or inhibit cancer cell migration, growth, and metastasis.

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EPJ E Topical Issue: Endowing Active Particles with Artificial Intelligence

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Published on 30 August 2023

Guest Editors: Frank Cichos, On Shun Pak, Holger Stark and Lailai Zhu

Submissions are invited for a Topical Issue of EPJ E on Endowing Active Particles with Artificial Intelligence.

There has been substantial interest in synthetic and biological active particles capable of self-propelling in recent decades. These active particles not only overcome the challenges of swimming, but also represent a novel class of nonequilibrium systems, exhibiting intriguing collective behaviors. Recently, the remarkable advancements and growing interest in machine learning have opened exciting opportunities for the studies of both synthetic and biological active particles. The prowess of machine learning has been leveraged in a multitude of applications, from learning novel mechanisms for generating active motion, finding optimal strategies for navigating complex environments, to generating new insights into the functioning and evolution of biological active matter.

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