Highly efficient optogenetic cell ablation in C. elegans using membrane-targeted miniSOG.

The genetically encoded photosensitizer miniSOG (mini Singlet Oxygen Generator) can be used to kill cells in C. elegans. miniSOG generates the reactive oxygen species (ROS) singlet oxygen after illumination with blue light. Illumination of neurons expressing miniSOG targeted to the outer mitochondrial membrane (mito-miniSOG) causes neuronal death. To enhance miniSOG's efficiency as an ablation tool in multiple cell types we tested alternative targeting signals. We find that membrane targeted miniSOG allows highly efficient cell killing. When combined with a point mutation that increases miniSOG's ROS generation, membrane targeted miniSOG can ablate neurons in less than one tenth the time of mito-miniSOG. We extend the miniSOG ablation technique to non-neuronal tissues, revealing an essential role for the epidermis in locomotion. These improvements expand the utility and throughput of optogenetic cell ablation in C. elegans

Wounding triggers MIRO-1 dependent mitochondrial fragmentation that accelerates epidermal wound closure through oxidative signaling.

Organisms respond to tissue damage through the upregulation of protective responses which restore tissue structure and metabolic function. Mitochondria are key sources of intracellular oxidative metabolic signals that maintain cellular homeostasis. Here we report that tissue and cellular wounding triggers rapid and reversible mitochondrial fragmentation. Elevated mitochondrial fragmentation either in fzo-1 fusion-defective mutants or after acute drug treatment accelerates actin-based wound closure. Wounding triggered mitochondrial fragmentation is independent of the GTPase DRP-1 but acts via the mitochondrial Rho GTPase MIRO-1 and cytosolic Ca2+. The fragmented mitochondria and accelerated wound closure of fzo-1 mutants are dependent on MIRO-1 function. Genetic and transcriptomic analyzes show that enhanced mitochondrial fragmentation accelerates wound closure via the upregulation of mtROS and Cytochrome P450. Our results reveal how mitochondrial dynamics respond to cellular and tissue injury and promote tissue repair

Fine-Grained Extraction of Road Networks via Joint Learning of Connectivity and Segmentation

Road network extraction from satellite images is widely applicated in intelligent traffic management and autonomous driving fields. The high-resolution remote sensing images contain complex road areas and distracted background, which make it a challenge for road extraction. In this study, we present a stacked multitask network for end-to-end segmenting roads while preserving connectivity correctness. In the network, a global-aware module is introduced to enhance pixel-level road feature representation and eliminate background distraction from overhead images; a road-direction-related connectivity task is added to ensure that the network preserves the graph-level relationships of the road segments. We also develop a stacked multihead structure to jointly learn and effectively utilize the mutual information between connectivity learning and segmentation learning. We evaluate the performance of the proposed network on three public remote sensing datasets. The experimental results demonstrate that the network outperforms the state-of-the-art methods in terms of road segmentation accuracy and connectivity maintenance

Knowledge-based planning in robotic intracranial stereotactic radiosurgery treatments

PURPOSE: To develop a knowledge-based planning (KBP) model that predicts dosimetric indices and facilitates planning in CyberKnife intracranial stereotactic radiosurgery/radiotherapy (SRS/SRT). METHODS: Forty CyberKnife SRS/SRT plans were retrospectively used to build a linear KBP model which correlated the equivalent radius of the PTV (req_PTV ) and the equivalent radius of volume that receives a set of prescription dose (req_Vi , where Vi = V10% , V20% ... V120% ). To evaluate the model\u27s predictability, a fourfold cross-validation was performed for dosimetric indices such as gradient measure (GM) and brain V50% . The accuracy of the prediction was quantified by the mean and the standard deviation of the difference between planned and predicted values, (i.e., DeltaGM = GMpred - GMclin and fractional DeltaV50% = (V50%pred - V50%clin )/V50%clin ) and a coefficient of determination, R(2) . Then, the KBP model was incorporated into the planning for another 22 clinical cases. The training plans and the KBP test plans were compared in terms of the new conformity index (nCI) as well as the planning efficiency. RESULTS: Our KBP model showed desirable predictability. For the 40 training plans, the average prediction error from cross-validation was only 0.36 +/- 0.06 mm for DeltaGM, and 0.12 +/- 0.08 for DeltaV50% . The R(2) for the linear fit between req_PTV and req_vi was 0.985 +/- 0.019 for isodose volumes ranging from V10% to V120% ; particularly, R(2) = 0.995 for V50% and R(2) = 0.997 for V100% . Compared to the training plans, our KBP test plan nCI was improved from 1.31 +/- 0.15 to 1.15 +/- 0.08 (P \u3c 0.0001). The efficient automatic generation of the optimization constraints by using our model requested no or little planner\u27s intervention. CONCLUSION: We demonstrated a linear KBP based on PTV volumes that accurately predicts CyberKnife SRS/SRT planning dosimetric indices and greatly helps achieve superior plan quality and planning efficiency

Development and validation of a three-dimensional deep learning-based system for assessing bowel preparation on colonoscopy video

BackgroundThe performance of existing image-based training models in evaluating bowel preparation on colonoscopy videos was relatively low, and only a few models used external data to prove their generalization. Therefore, this study attempted to develop a more precise and stable AI system for assessing bowel preparation of colonoscopy video.MethodsWe proposed a system named ViENDO to assess the bowel preparation quality, including two CNNs. First, Information-Net was used to identify and filter out colonoscopy video frames unsuitable for Boston bowel preparation scale (BBPS) scoring. Second, BBPS-Net was trained and tested with 5,566 suitable short video clips through three-dimensional (3D) convolutional neural network (CNN) technology to detect BBPS-based insufficient bowel preparation. Then, ViENDO was applied to complete withdrawal colonoscopy videos from multiple centers to predict BBPS segment scores in clinical settings. We also conducted a human-machine contest to compare its performance with endoscopists.ResultsIn video clips, BBPS-Net for determining inadequate bowel preparation generated an area under the curve of up to 0.98 and accuracy of 95.2%. When applied to full-length withdrawal colonoscopy videos, ViENDO assessed bowel cleanliness with an accuracy of 93.8% in the internal test set and 91.7% in the external dataset. The human-machine contest demonstrated that the accuracy of ViENDO was slightly superior compared to most endoscopists, though no statistical significance was found.ConclusionThe 3D-CNN-based AI model showed good performance in evaluating full-length bowel preparation on colonoscopy video. It has the potential as a substitute for endoscopists to provide BBPS-based assessments during daily clinical practice

Maternal xNorrin, a Canonical Wnt Signaling Agonist and TGF-β Antagonist, Controls Early Neuroectoderm Specification in Xenopus

Xenopus maternal Norrin, which activates Wnt signaling but inhibits TGF-β family molecules, is essential for neuroectoderm formation. Loss of TGF-β inhibition in Norrin may contribute to the development of Norrie disease

MiniSOG-mediated Photoablation in Caenorhabdtis elegans

This protocol describes a method for light-inducible cell ablation in live worms. miniSOG (mini Singlet Oxygen Generator) generates singlet oxygen upon blue light illumination (Shu et al., 2011). Mitochondrially membrane targeted miniSOG (the first 55 a. a. of C. e. tomm-20 fused at the N’-terminus of miniSOG, termed as mito-miniSOG in the following) is transgenically expressed in specific cells/tissues (Qi et al., 2012). Groups of transgenic animals are illuminated under open field fluorescence light on a compound microscope or LED light setup for photo-ablation