Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer

With the long-term rapid increase in incidences of colorectal cancer (CRC), there is an urgent clinical need to improve risk stratification. The conventional pathology report is usually limited to only a few histopathological features. However, most of the tumor microenvironments used to describe patterns of aggressive tumor behavior are ignored. In this work, we aim to learn histopathological patterns within cancerous tissue regions that can be used to improve prognostic stratification for colorectal cancer. To do so, we propose a self-supervised learning method that jointly learns a representation of tissue regions as well as a metric of the clustering to obtain their underlying patterns. These histopathological patterns are then used to represent the interaction between complex tissues and predict clinical outcomes directly. We furthermore show that the proposed approach can benefit from linear predictors to avoid overfitting in patient outcomes predictions. To this end, we introduce a new well-characterized clinicopathological dataset, including a retrospective collective of 374 patients, with their survival time and treatment information. Histomorphological clusters obtained by our method are evaluated by training survival models. The experimental results demonstrate statistically significant patient stratification, and our approach outperformed the state-of-the-art deep clustering methods

Overview of marine fisheries in India during 2007

Fisheries sector in India plays an important role in the country’s economy and it supports the livelihood of millions of people. India is having 8,129 km of coastal length with 2.02 million sq. km of Exclusive Economic Zone (upto 200 m depth) and 0.452 million sq. km of continental shelf area

Deep learning for brains?: Different linear and nonlinear scaling in UK Biobank brain images vs. machine-learning datasets

AbstractIn recent years, deep learning has unlocked unprecedented success in various domains, especially in image, text, and speech processing. These breakthroughs may hold promise for neuroscience and especially for brain-imaging investigators who start to analyze thousands of participants. However, deep learning is only beneficial if the data have nonlinear relationships and if they are exploitable at currently available sample sizes.We systematically profiled the performance of deep models, kernel models, and linear models as a function of sample size on UK Biobank brain images against established machine learning references. On MNIST and Zalando Fashion, prediction accuracy consistently improved when escalating from linear models to shallow-nonlinear models, and further improved when switching to deep-nonlinear models. The more observations were available for model training, the greater the performance gain we saw. In contrast, using structural or functional brain scans, simple linear models performed on par with more complex, highly parameterized models in age/sex prediction across increasing samplesizes. In fact, linear models kept improving as the sample size approached ~10,000 participants. Our results indicate that the increase in performance of linear models with additional data does not saturate at the limit of current feasibility. Yet, nonlinearities of common brain scans remain largely inaccessible to both kernel and deep learning methods at any axemined scal

Are isomeric alkenes used in species recognition among neo-tropical stingless bees (Melipona spp)

The majority of our understanding of the role of cuticular hydrocarbons (CHC) in recognition is based largely on temperate ant species and honey bees. The stingless bees remain relatively poorly studied, despite being the largest group of eusocial bees, comprising more than 400 species in some 60 genera. The Meliponini and Apini diverged between 80-130 Myr B.P. so the evolutionary trajectories that shaped the chemical communication systems in ants, honeybees and stingless bees may be very different. Therefore, the main aim of this study was to study if a unique species CHC signal existed in Neotropical stingless bees, as shown for many temperate species, and if so what compounds are involved. This was achieved by collecting CHC data from 24 colonies belonging to six species of Melipona from North-eastern Brazil and comparing this new data with all previously published CHC studies on Melipona. We found that each of the eleven Melipona species studied so far each produced a unique species CHC signal based around their alkene isomer production. A remarkable number of alkene isomers, up to 25 in M. asilvai, indicated the diversification of alkene positional isomers among the stingless bees. The only other group to have really diversified in alkene isomer production are the primitively eusocial Bumblebees (Bombus spp), which are the sister group of the stingless bees. Furthermore, among the eleven Neotropical Melipona species we could detect no effect of the environment on the proportion of alkane production as has been suggested for some other species

Life history and chemical ecology of the Warrior wasp Synoeca septentrionalis (Hymenoptera : Vespidae, Epiponini)

Swarm-founding ‘Warrior wasps’ (Synoeca spp.) are found throughout the tropical regions of South America, are much feared due to their aggressive nest defence and painful sting. There are only five species of Synoeca, all construct distinctive nests that consist of a single sessile comb built onto the surface of a tree or rock face, which is covered by a ribbed envelope. Although locally common, research into this group is just starting. We studied eight colonies of Synoeca septentrionalis, a species recently been described from Brazil. A new colony is established by a swarm of 52 to 140 adults that constructs a colony containing around 200 brood cells. The largest colony collected containing 865 adults and over 1400 cells. The number of queen’s present among the eight colonies varied between 3 and 58 and no clear association between colony development and queen number was detected. Workers and queens were morphologically indistinguishable, but differences in their cuticular hydrocarbons were detected, particularly in their (Z)-9-alkenes. The simple cuticular profile, multiple queens, large size and small number of species makes the ‘Warrior wasps’ an excellent model group for further chemical ecology studies of swarm-founding wasps

“Just one animal among many?” Existential phenomenology, ethics, and stem cell research

Stem cell research and associated or derivative biotechnologies are proceeding at a pace that has left bioethics behind as a discipline that is more or less reactionary to their developments. Further, much of the available ethical deliberation remains determined by the conceptual framework of late modern metaphysics and the correlative ethical theories of utilitarianism and deontology. Lacking, to any meaningful extent, is a sustained engagement with ontological and epistemological critiques, such as with “postmodern” thinking like that of Heidegger’s existential phenomenology. Some basic “Heideggerian” conceptual strategies are reviewed here as a way of remedying this deficiency and adding to ethical deliberation about current stem cell research practices

Artificial intelligence for detection of microsatellite instability in colorectal cancer-a multicentric analysis of a pre-screening tool for clinical application.

BACKGROUND Microsatellite instability (MSI)/mismatch repair deficiency (dMMR) is a key genetic feature which should be tested in every patient with colorectal cancer (CRC) according to medical guidelines. Artificial intelligence (AI) methods can detect MSI/dMMR directly in routine pathology slides, but the test performance has not been systematically investigated with predefined test thresholds. METHOD We trained and validated AI-based MSI/dMMR detectors and evaluated predefined performance metrics using nine patient cohorts of 8343 patients across different countries and ethnicities. RESULTS Classifiers achieved clinical-grade performance, yielding an area under the receiver operating curve (AUROC) of up to 0.96 without using any manual annotations. Subsequently, we show that the AI system can be applied as a rule-out test: by using cohort-specific thresholds, on average 52.73% of tumors in each surgical cohort [total number of MSI/dMMR = 1020, microsatellite stable (MSS)/ proficient mismatch repair (pMMR) = 7323 patients] could be identified as MSS/pMMR with a fixed sensitivity at 95%. In an additional cohort of N = 1530 (MSI/dMMR = 211, MSS/pMMR = 1319) endoscopy biopsy samples, the system achieved an AUROC of 0.89, and the cohort-specific threshold ruled out 44.12% of tumors with a fixed sensitivity at 95%. As a more robust alternative to cohort-specific thresholds, we showed that with a fixed threshold of 0.25 for all the cohorts, we can rule-out 25.51% in surgical specimens and 6.10% in biopsies. INTERPRETATION When applied in a clinical setting, this means that the AI system can rule out MSI/dMMR in a quarter (with global thresholds) or half of all CRC patients (with local fine-tuning), thereby reducing cost and turnaround time for molecular profiling

Network Models of TEM β-Lactamase Mutations Coevolving under Antibiotic Selection Show Modular Structure and Anticipate Evolutionary Trajectories

Understanding how novel functions evolve (genetic adaptation) is a critical goal of evolutionary biology. Among asexual organisms, genetic adaptation involves multiple mutations that frequently interact in a non-linear fashion (epistasis). Non-linear interactions pose a formidable challenge for the computational prediction of mutation effects. Here we use the recent evolution of β-lactamase under antibiotic selection as a model for genetic adaptation. We build a network of coevolving residues (possible functional interactions), in which nodes are mutant residue positions and links represent two positions found mutated together in the same sequence. Most often these pairs occur in the setting of more complex mutants. Focusing on extended-spectrum resistant sequences, we use network-theoretical tools to identify triple mutant trajectories of likely special significance for adaptation. We extrapolate evolutionary paths (n = 3) that increase resistance and that are longer than the units used to build the network (n = 2). These paths consist of a limited number of residue positions and are enriched for known triple mutant combinations that increase cefotaxime resistance. We find that the pairs of residues used to build the network frequently decrease resistance compared to their corresponding singlets. This is a surprising result, given that their coevolution suggests a selective advantage. Thus, β-lactamase adaptation is highly epistatic. Our method can identify triplets that increase resistance despite the underlying rugged fitness landscape and has the unique ability to make predictions by placing each mutant residue position in its functional context. Our approach requires only sequence information, sufficient genetic diversity, and discrete selective pressures. Thus, it can be used to analyze recent evolutionary events, where coevolution analysis methods that use phylogeny or statistical coupling are not possible. Improving our ability to assess evolutionary trajectories will help predict the evolution of clinically relevant genes and aid in protein design

Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma.

Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical outcomes, genetic alterations and in situ spatial gene expression profiling. This type of approach could improve treatment decisions and ultimately clinical outcome for patients with rare and biphenotypic cancers such as cHCC-CCA