Ecological and evolutionary consequences of anticancer adaptations

Cellular cheating leading to cancers exists in all branches of multicellular life, favoring the evolution of adaptations to avoid or suppress malignant progression, and/or to alleviate its fitness consequences. Ecologists have until recently largely neglected the importance of cancer cells for animal ecology, presumably because they did not consider either the potential ecological or evolutionary consequences of anticancer adaptations. Here, we review the diverse ways in which the evolution of anticancer adaptations has significantly constrained several aspects of the evolutionary ecology of multicellular organisms at the cell, individual, population, species, and ecosystem levels and suggest some avenues for future research

Stream diatom biodiversity in islands and continents—A global perspective on effects of area, isolation and environment

Aim The species-area relationship (SAR) is one of the most distinctive biogeographic patterns, but global comparisons of the SARs between island and mainland are lacking for microbial taxa. Here, we explore whether the form of the SAR and the drivers of species richness, including area, environmental heterogeneity, climate and physico-chemistry, differ between islands and similarly sized areas on mainland, referred to as continental area equivalents (CAEs). Location Global. Taxon Stream benthic diatoms. Methods We generated CAEs on six continental datasets and examined the SARs of CAEs and islands (ISAR). Then, we compared CAEs and islands in terms of total richness and richness of different ecological guilds. We tested the factors contributing to richness in islands and CAEs with regressions. We used structural equation models to determine the effects of area versus environmental heterogeneity, climate and local conditions on species richness. Results We found a non-significant ISAR, but a significant positive SAR in CAEs. Richness in islands was related to productivity. Richness in CAEs was mainly dependent on area and climate, but not directly on environmental heterogeneity. Species richness within guilds exhibited inconsistent relationships with island isolation and area. Main conclusions Ecological and evolutionary processes shaping diatom island biogeography do not depend on area at the worldwide scale probably due to the presence of distinct species pool across islands. Conversely, area was an important driver of diatom richness in continents, and this effect could be attributed to dispersal. Continents had greater richness than islands, but this was a consequence of differences in environmental conditions such as specific island climatic conditions. We stress the need for more island data on benthic diatoms, particularly from archipelagos, to better understand the biogeography of this most speciose group of algae

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding technique

Conservation of a pH-sensitive structure in the C-terminal region of spider silk extends across the entire silk gene family

Spiders produce multiple silks with different physical properties that allow them to occupy a diverse range of ecological niches, including the underwater environment. Despite this functional diversity, past molecular analyses show a high degree of amino acid sequence similarity between C-terminal regions of silk genes that appear to be independent of the physical properties of the resulting silks; instead, this domain is crucial to the formation of silk fibres. Here we present an analysis of the C-terminal domain of all known types of spider silk and include silk sequences from the spider Argyroneta aquatica, which spins the majority of its silk underwater. Our work indicates that spiders have retained a highly conserved mechanism of silk assembly, despite the extraordinary diversification of species, silk types and applications of silk over 350 million years. Sequence analysis of the silk C-terminal domain across the entire gene family shows the conservation of two uncommon amino acids that are implicated in the formation of a salt bridge, a functional bond essential to protein assembly. This conservation extends to the novel sequences isolated from A. aquatica. This finding is relevant to research regarding the artificial synthesis of spider silk, suggesting that synthesis of all silk types will be possible using a single process

Metabolomic, proteomic and preclinical imaging of patient-derived tumor xenografts for improving treatment of liver metastases patients

peer reviewe

Disposable sensors in diagnostics, food and environmental monitoring

Disposable sensors are low‐cost and easy‐to‐use sensing devices intended for short‐term or rapid single‐point measurements. The growing demand for fast, accessible, and reliable information in a vastly connected world makes disposable sensors increasingly important. The areas of application for such devices are numerous, ranging from pharmaceutical, agricultural, environmental, forensic, and food sciences to wearables and clinical diagnostics, especially in resource‐limited settings. The capabilities of disposable sensors can extend beyond measuring traditional physical quantities (for example, temperature or pressure); they can provide critical chemical and biological information (chemo‐ and biosensors) that can be digitized and made available to users and centralized/decentralized facilities for data storage, remotely. These features could pave the way for new classes of low‐cost systems for health, food, and environmental monitoring that can democratize sensing across the globe. Here, a brief insight into the materials and basics of sensors (methods of transduction, molecular recognition, and amplification) is provided followed by a comprehensive and critical overview of the disposable sensors currently used for medical diagnostics, food, and environmental analysis. Finally, views on how the field of disposable sensing devices will continue its evolution are discussed, including the future trends, challenges, and opportunities

Computationally-Optimized Bone Mechanical Modeling from High-Resolution Structural Images

Image-based mechanical modeling of the complex micro-structure of human bone has shown promise as a non-invasive method for characterizing bone strength and fracture risk in vivo. In particular, elastic moduli obtained from image-derived micro-finite element (μFE) simulations have been shown to correlate well with results obtained by mechanical testing of cadaveric bone. However, most existing large-scale finite-element simulation programs require significant computing resources, which hamper their use in common laboratory and clinical environments. In this work, we theoretically derive and computationally evaluate the resources needed to perform such simulations (in terms of computer memory and computation time), which are dependent on the number of finite elements in the image-derived bone model. A detailed description of our approach is provided, which is specifically optimized for μFE modeling of the complex three-dimensional architecture of trabecular bone. Our implementation includes domain decomposition for parallel computing, a novel stopping criterion, and a system for speeding up convergence by pre-iterating on coarser grids. The performance of the system is demonstrated on a dual quad-core Xeon 3.16 GHz CPUs equipped with 40 GB of RAM. Models of distal tibia derived from 3D in-vivo MR images in a patient comprising 200,000 elements required less than 30 seconds to converge (and 40 MB RAM). To illustrate the system's potential for large-scale μFE simulations, axial stiffness was estimated from high-resolution micro-CT images of a voxel array of 90 million elements comprising the human proximal femur in seven hours CPU time. In conclusion, the system described should enable image-based finite-element bone simulations in practical computation times on high-end desktop computers with applications to laboratory studies and clinical imaging