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1. Mechano-inhibition of endocytosis sensitizes cancer cells to Fas-induced Apoptosis

   https://doi.org/10.1038/s41419-024-06822-3  

   Kural, Mehmet_H; Djakbarova, Umidahan; Cakir, Bilal; Tanaka, Yoshiaki; Chan, Emily_T; Arteaga_Muniz, Valeria_I; Madraki, Yasaman; Qian, Hong; Park, Jinkyu; Sewanan, Lorenzo_R; et al (June 2024, Cell Death & Disease)

   Abstract The transmembrane death receptor Fas transduces apoptotic signals upon binding its ligand, FasL. Although Fas is highly expressed in cancer cells, insufficient cell surface Fas expression desensitizes cancer cells to Fas-induced apoptosis. Here, we show that the increase in Fas microaggregate formation on the plasma membrane in response to the inhibition of endocytosis sensitizes cancer cells to Fas-induced apoptosis. We used a clinically accessible Rho-kinase inhibitor, fasudil, that reduces endocytosis dynamics by increasing plasma membrane tension. In combination with exogenous soluble FasL (sFasL), fasudil promoted cancer cell apoptosis, but this collaborative effect was substantially weaker in nonmalignant cells. The combination of sFasL and fasudil prevented glioblastoma cell growth in embryonic stem cell-derived brain organoids and induced tumor regression in a xenograft mouse model. Our results demonstrate that sFasL has strong potential for apoptosis-directed cancer therapy when Fas microaggregate formation is augmented by mechano-inhibition of endocytosis. 

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2. Data-Driven Flood Alert System (FAS) Using Extreme Gradient Boosting (XGBoost) to Forecast Flood Stages

   https://doi.org/10.3390/w14050747  

   Sanders, Will; Li, Dongfeng; Li, Wenzhao; Fang, Zheng N. (March 2022, Water)

   Heavy rainfall leads to severe flooding problems with catastrophic socio-economic impacts worldwide. Hydrologic forecasting models have been applied to provide alerts of extreme flood events and reduce damage, yet they are still subject to many uncertainties due to the complexity of hydrologic processes and errors in forecasted timing and intensity of the floods. This study demonstrates the efficacy of using eXtreme Gradient Boosting (XGBoost) as a state-of-the-art machine learning (ML) model to forecast gauge stage levels at a 5-min interval with various look-out time windows. A flood alert system (FAS) built upon the XGBoost models is evaluated by two historical flooding events for a flood-prone watershed in Houston, Texas. The predicted stage values from the FAS are compared with observed values with demonstrating good performance by statistical metrics (RMSE and KGE). This study further compares the performance from two scenarios with different input data settings of the FAS: (1) using the data from the gauges within the study area only and (2) including the data from additional gauges outside of the study area. The results suggest that models that use the gauge information within the study area only (Scenario 1) are sufficient and advantageous in terms of their accuracy in predicting the arrival times of the floods. One of the benefits of the FAS outlined in this study is that the XGBoost-based FAS can run in a continuous mode to automatically detect floods without requiring an external starting trigger to switch on as usually required by the conventional event-based FAS systems. This paper illustrates a data-driven FAS framework as a prototype that stakeholders can utilize solely based on their gauging information for local flood warning and mitigation practices. 

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3. DocParseNet: Advanced Semantic Segmentation and OCR Embeddings for Efficient Scanned Document Annotation

   Mohammadshirazi, Ahmad; AFiroozsalari, li Nosrati; Zhou, Mengxi; Kulshrestha, Dheeraj; Ramnath, Rajiv (July 2024, https://doi.org/10.48550/arXiv.2406.17591)

   Automating the annotation of scanned documents is challenging, requiring a balance between computational efficiency and accuracy. DocParseNet addresses this by combining deep learning and multi-modal learning to process both text and visual data. This model goes beyond traditional OCR and semantic segmentation, capturing the interplay between text and images to preserve contextual nuances in complex document structures. Our evaluations show that DocParseNet significantly outperforms conventional models, achieving mIoU scores of 49.12 on validation and 49.78 on the test set. This reflects a 58% accuracy improvement over state-of-the-art baseline models and an 18% gain compared to the UNext baseline. Remarkably, DocParseNet achieves these results with only 2.8 million parameters, reducing the model size by approximately 25 times and speeding up training by 5 times compared to other models. These metrics, coupled with a computational efficiency of 0.039 TFLOPs (BS=1), highlight DocParseNet's high performance in document annotation. The model's adaptability and scalability make it well-suited for real-world corporate document processing applications. 

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4. α–Catenin-dependent vinculin recruitment to adherens junctions is antagonistic to focal adhesions

   https://doi.org/10.1091/mbc.E22-02-0071  

   Bejar-Padilla, Vidal; Cabe, Jolene I.; Lopez, Santiago; Narayanan, Vani; Mezher, Mazen; Maruthamuthu, Venkat; Conway, Daniel E. (September 2022, Molecular Biology of the Cell)

   Yap, Alpha (Ed.)

   Vinculin is a protein found in both focal adhesions (FAs) and adherens junctions (AJs) which regulates actin connectivity to these structures. Many studies have demonstrated that mechanical perturbations of cells result in enhanced recruitment of vinculin to FAs and/or AJs. Likewise, many other studies have shown “cross-talk” between FAs and AJs. Vinculin itself has been suggested to be a probable regulator of this adhesion cross-talk. In this study we used MDCK as a model system of epithelia, developing cell lines in which vinculin recruitment was reduced or enhanced at AJs. Careful analysis of these cells revealed that perturbing vinculin recruitment to AJs resulted in a reduction of detectable FAs. Interestingly the cross-talk between these two structures was not due to a limited pool of vinculin, as increasing expression of vinculin did not rescue FA formation. Instead, we demonstrate that vinculin translocation between AJs and FAs is necessary for actin cytoskeleton rearrangements that occur during cell migration, which is necessary for large, well-formed FAs. Last, we show using a wound assay that collective cell migration is similarly hindered when vinculin recruitment is reduced or enhanced at AJs, highlighting that vinculin translocation between each compartment is necessary for efficient collective migration. 

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5. TAB-VCR: Tags and Attributes based VCR Baselines

   Jingxiang Lin, Unnat Jain (January 2019, Advances in Neural Information Processing Systems)

   Reasoning is an important ability that we learn from a very early age. Yet, reasoning is extremely hard for algorithms. Despite impressive recent progress that has been reported on tasks that necessitate reasoning, such as visual question answering and visual dialog, models often exploit biases in datasets. To develop models with better reasoning abilities, recently, the new visual commonsense reasoning (VCR) task has been introduced. Not only do models have to answer questions, but also do they have to provide a reason for the given answer. The proposed baseline achieved compelling results, leveraging a meticulously designed model composed of LSTM modules and attention nets. Here we show that a much simpler model obtained by ablating and pruning the existing intricate baseline can perform better with half the number of trainable parameters. By associating visual features with attribute information and better text to image grounding, we obtain further improvements for our simpler & effective baseline, TAB-VCR. We show that this approach results in a 5.3%, 4.4% and 6.5% absolute improvement over the previous state-of-the-art [103] on question answering, answer justification and holistic VCR. 

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