Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes COVID-19 and is responsible for the ongoing pandemic. Screening of potential antiviral drugs against SARS-CoV-2 depend on in vitro experiments, which are based on the quantification of the virus titer. Here, we used virus-induced cytopathic effects (CPE) in brightfield microscopy of SARS-CoV-2-infected monolayers to quantify the virus titer. Images were classified using deep transfer learning (DTL) that fine-tune the last layers of a pre-trained Resnet18 (ImageNet). To exclude toxic concentrations of potential drugs, the network was expanded to include a toxic score (TOX) that detected cell death (CPETOXnet). With this analytic tool, the inhibitory effects of chloroquine, hydroxychloroquine, remdesivir, and emetine were validated. Taken together we developed a simple method and provided open access implementation to quantify SARS-CoV-2 titers and drug toxicity in experimental settings, which may be adaptable to assays with other viruses. The quantification of virus titers from brightfield images could accelerate the experimental approach for antiviral testing.

Pancreatic cancer is a fatal malignancy with poor prognosis and limited treatment options. Early detection in primary and secondary locations is critical, but fraught with challenges. While digital pathology can assist with the classification of histopathological images, the training of such networks always relies on a ground truth, which is frequently compromised as tissue sections contain several types of tissue entities. Here we show that pancreatic cancer can be detected on hematoxylin and eosin (H&E) sections by convolutional neural networks using deep transfer learning. To improve the ground truth, we describe a preprocessing data clean-up process using two communicators that were generated through existing and new datasets. Specifically, the communicators moved image tiles containing adipose tissue and background to a new data class. Hence, the original dataset exhibited improved labeling and, consequently, a higher ground truth accuracy. Deep transfer learning of a ResNet18 network resulted in a five-class accuracy of about 94% on test data images. The network was validated with independent tissue sections composed of healthy pancreatic tissue, pancreatic ductal adenocarcinoma, and pancreatic cancer lymph node metastases. The screening of different models and hyperparameter fine tuning were performed to optimize the performance with the independent tissue sections. Taken together, we introduce a step of data preprocessing via communicators as a means of improving the ground truth during deep transfer learning and hyperparameter tuning to identify pancreatic ductal adenocarcinoma primary tumors and metastases in histological tissue sections.

The Arctic Ocean is undergoing a major transition as a result of global warming, with uncertain consequences for its ecosystems. Our study introduces an integrated analytical approach using co-occurrence networks, convergent cross-mapping, and energy landscape analysis. Applied to four years of amplicon data from Fram Strait, located at the boundary between Arctic and Atlantic waters, our method identifies keystone species in seasonal microbial communities, elucidates causal interactions, and predicts stable configurations across changing environments. We find strong evidence for a “winter reset”, implying that organisms representing the spring bloom are largely determined by prevailing environmental conditions during winter. In addition, our analysis suggests that winter communities may adapt more readily to expected Atlantification than summer communities. These results highlight the utility of innovative time-series analyses in disentangling ecosystem dynamics. This approach provides critical insights into Arctic ecological interactions, dynamics, and resilience and aids in understanding ecosystem responses to environmental change.

Ferroptosis is a recently described type of regulated necrotic cell death whose induction has anti-cancer therapeutic potential, especially in hematological malignancies. However, efforts to uncover novel ferroptosis-inducing therapeutics have been largely unsuccessful. In the current investigation, we classified brightfield microscopy images of tumor cells undergoing defined modes of cell death using deep transfer learning (DTL). The trained DTL network was subsequently combined with high-throughput pharmacological screening approaches using automated live cell imaging to identify novel ferroptosis-inducing functions of the polo-like kinase inhibitor volasertib. Secondary validation showed that subsets of B-cell acute lymphoblastic leukemia (B-ALL) cell lines, namely 697, NALM6, HAL01, REH and primary patient B-ALL samples were sensitive to ferroptosis induction by volasertib. This was accompanied by an upregulation of ferroptosis-related genes post-volasertib treatment in cell lines and patient samples. Importantly, using several leukemia models, we determined that volasertib delayed tumor growth and induced ferroptosis in vivo. Taken together, we have applied DTL to automated live-cell imaging in pharmacological screening to identify novel ferroptosis-inducing functions of a clinically relevant anti-cancer therapeutic.

A thorough understanding of ecosystem functioning in the Arctic Ocean, a region under severe threat by climate change, requires detailed studies on linkages between biodiversity and ecosystem stability. The identification of keystone species with special relevance for ecosystem stability is of great importance, yet difficult to achieve with established community assessments. In the case of microbes, metabarcoding and metagenomics offer fundamental insights into community structure and function, yet remain limited regarding the ecological relevance of individual taxa. To overcome this limitation, we have developed an analytical approach based on three different methods: Co-Occurrence Networks, Convergent Cross Mapping, and Energy Landscape Analysis. These methods enable the identification of seasonal communities in microbial ecosystems, elucidate their interactions, and predict potential stable community configurations under varying environmental conditions. Combining the outcomes of these three methods allowed us to define 38 keystone species in the Arctic Fram Strait that represent different trophic modes within the food web, and might signify indicator for ecosystem functionality under the impact of environmental change. Our research reveals a clear seasonal pattern in phytoplankton composition, with distinct assemblages characterizing the phases of carbon fixation (polar day) and consumption (polar night). Species interactions exhibited strong seasonality, with significant influence of summer communities on winter communities but not vice versa. Spring harbored two distinct groups: consumers (heterotrophs), strongly linked to polar night, and photoautotrophs (mainly Bacillariophyta). These groups are not causally related, suggesting a “winter reset” with selective effects that facilitates a new blooming period, allowing survivors of the dark phase to emerge. Energy Landscape Analysis showed that winter communities are more stable than summer communities. In summary, the ecological landscape of the Fram Strait can be categorized into two distinct phases: a production phase governed by specialized organisms that are highly responsive to environmental variability, and a heterotrophic phase dominated by generalist species with enhanced resilience.