Placental vessel segmentation and registration in fetoscopy: Literature review and MICCAI FetReg2021 challenge findings.

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. For the segmentation task, overall baseline performed was the top performing (aggregated mIoU of 0.6763) and was the best on the vessel class (mIoU of 0.5817) while team RREB was the best on the tool (mIoU of 0.6335) and fetus (mIoU of 0.5178) classes. For the registration task, overall the baseline performed better than team SANO with an overall mean 5-frame SSIM of 0.9348. Qualitatively, it was observed that team SANO performed better in planar scenarios, while baseline was better in non-planner scenarios. The detailed analysis showed that no single team outperformed on all 6 test fetoscopic videos. The challenge provided an opportunity to create generalized solutions for fetoscopic scene understanding and mosaicking. In this paper, we present the findings of the FetReg2021 challenge, alongside reporting a detailed literature review for CAI in TTTS fetoscopy. Through this challenge, its analysis and the release of multi-center fetoscopic data, we provide a benchmark for future research in this field.

Transformer neural network for protein-specific de novo drug generation as a machine translation problem.

Drug discovery for a protein target is a very laborious, long and costly process. Machine learning approaches and, in particular, deep generative networks can substantially reduce development time and costs. However, the majority of methods imply prior knowledge of protein binders, their physicochemical characteristics or the three-dimensional structure of the protein. The method proposed in this work generates novel molecules with predicted ability to bind a target protein by relying on its amino acid sequence only. We consider target-specific de novo drug design as a translational problem between the amino acid "language" and simplified molecular input line entry system representation of the molecule. To tackle this problem, we apply Transformer neural network architecture, a state-of-the-art approach in sequence transduction tasks. Transformer is based on a self-attention technique, which allows the capture of long-range dependencies between items in sequence. The model generates realistic diverse compounds with structural novelty. The computed physicochemical properties and common metrics used in drug discovery fall within the plausible drug-like range of values.

Conserved 3' UTR stem-loop structure in L1 and Alu transposons in human genome: possible role in retrotransposition.

BACKGROUND: In the process of retrotransposition LINEs use their own machinery for copying and inserting themselves into new genomic locations, while SINEs are parasitic and require the machinery of LINEs. The exact mechanism of how a LINE-encoded reverse transcriptase (RT) recognizes its own and SINE RNA remains unclear. However it was shown for the stringent-type LINEs that recognition of a stem-loop at the 3'UTR by RT is essential for retrotransposition. For the relaxed-type LINEs it is believed that the poly-A tail is a common recognition element between LINE and SINE RNA. However polyadenylation is a property of any messenger RNA, and how the LINE RT recognizes transposon and non-transposon RNAs remains an open question. It is likely that RNA secondary structures play an important role in RNA recognition by LINE encoded proteins. RESULTS: Here we selected a set of L1 and Alu elements from the human genome and investigated their sequences for the presence of position-specific stem-loop structures. We found highly conserved stem-loop positions at the 3'UTR. Comparative structural analyses of a human L1 3'UTR stem-loop showed a similarity to 3'UTR stem-loops of the stringent-type LINEs, which were experimentally shown to be recognized by LINE RT. The consensus stem-loop structure consists of 5-7 bp loop, 8-10 bp stem with a bulge at a distance of 4-6 bp from the loop. The results show that a stem loop with a bulge exists at the 3'-end of Alu. We also found conserved stem-loop positions at 5'UTR and at the end of ORF2 and discuss their possible role. CONCLUSIONS: Here we presented an evidence for the presence of a highly conserved 3'UTR stem-loop structure in L1 and Alu retrotransposons in the human genome. Both stem-loops show structural similarity to the stem-loops of the stringent-type LINEs experimentally confirmed as essential for retrotransposition. Here we hypothesize that both L1 and Alu RNA are recognized by L1 RT via the 3'-end RNA stem-loop structure. Other conserved stem-loop positions in L1 suggest their possible functions in protein-RNA interactions but to date no experimental evidence has been reported.