Biology subjects

Kalinin, A.

Publications and source records attributed to Kalinin, A..

5 recordsLinked to original sources

bioRxiv2023

Do working forest easements work for conservation?

Conservation easements are voluntary legal agreements designed to achieve conservation goals on private land. Extensive public and private funding has been used to establish "working forest" conservation easements (WFCE) that aim to protect conservation values while maintaining commercial timber production. We use variation in the timing and location of easements to estimate the impacts of WFCEs in Maine from a 33-year time-series of forest loss and harvesting. Compared to matched controls, WFCEs reduced forest loss rates by just 0.0004% yr-1, the equivalent of 3ha yr-1, and increased the rate of harvesting by 0.37% yr-1, or 2935ha yr-1 within the 839,000 ha enrolled. More recent easements contained stricter restrictions on harvest practices and these easements reduced harvest by 0.62% yr-1. Overall, WFCEs supported continued harvests, but did not appear to provide substantial ecological benefits. Future easements could be more effective if they include additional provisions for harvest restrictions and monitoring.

ecology↗

bioRxiv2022

The simulation of functional heterogeneity in procedurally generated fibrotic atrial tissue

The occurrence of atrial fibrillation (AF), one of the most common and socially significant arrhythmias, is associated with the presence of fibrosis sites. Fibrosis is the presence of non-conductive fibroblast cells, separating cardiomyocytes and introducing heterogeneity into the conducting atrial tissue. Thus fibrosis may be a substrate of spiral-wave reentry, provoking the occurrence of AF and is often associated with its persistent form. In this work, we propose for the first time a fundamentally new approach to modeling the fibrotic heart that takes into account the cellular structure of the tissue: a realistic texture of atrial tissue remodeled by fibroblasts is generated by the Potts model, and the local membrane potential of individual cells is calculated by the Courtemanche model. We have shown the occurrence of conductive pathways in such a system with a low proportion of fibroblasts (up to 10%) and revealed the connection of the form of the action potential (AP) of cells with their location in the tissue and the direction of the propagating wave front. The combination of these effects creates dynamic heterogeneity of the conducting tissue and affects the migration and pinning of spiral waves, which makes the model a potential tool for prognostic modeling of AP and search for ablation targets. The computer prediction of ablation targets (reentry nodes) will help to increase the efficiency of treatment of patients with persistent form of AF.

biophysics↗

bioRxiv2018

Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning

Semantic segmentation of robotic instruments is an important problem for the robot-assisted surgery. One of the main challenges is to correctly detect an instruments position for the tracking and pose estimation in the vicinity of surgical scenes. Accurate pixel-wise instrument segmentation is needed to address this challenge. In this paper we describe our deep learning-based approach for robotic instrument segmentation. Our approach demonstrates an improvement over the state-of-the-art results using several novel deep neural network architectures. It addressed the binary segmentation problem, where every pixel in an image is labeled as an instrument or background from the surgery video feed. In addition, we solve a multi-class segmentation problem, in which we distinguish between different instruments or different parts of an instrument from the background. In this setting, our approach outperforms other methods for automatic instrument segmentation thereby providing state-of-the-art results for these problems. The source code for our solution is made publicly available.

bioengineering↗

bioRxiv2018

Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement between pathologists. Computer-aided diagnosis systems showed potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures and gradient boosted trees classifier. For 4-class classification task, we report 87.2% accuracy. For 2-class classification task to detect carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity 96.5/88.0% at the high-sensitivity operating point. To our knowledge, this approach outperforms other common methods in automated histopathological image classification. The source code for our approach is made publicly available at https://github.com/alexander-rakhlin/ICIAR2018

pathology↗

bioRxiv2017

Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks

Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a fully automated deep learning approach to the problem of bone age assessment using data from the 2017 Pediatric Bone Age Challenge organized by the Radiological Society of North America. The dataset for this competition consists of 12,600 radiological images. Each radiograph in this dataset is an image of a left hand labeled with bone age and sex of a patient. Our approach utilizes several deep neural network architectures trained end-to-end. We use images of whole hands as well as specific parts of a hand for both training and prediction. This approach allows us to measure the importance of specific hand bones for automated bone age analysis. We further evaluate the performance of the suggested method in the context of skeletal development stages. Our approach outperforms other common methods for bone age assessment.

pathology↗