Spatially and Spectrally Consistent Deep Functional Maps

Cycle consistency has long been exploited as a powerful prior for jointly optimizing maps within a collection of shapes. In this paper, we investigate its utility in the approaches of Deep Functional Maps, which are considered state-of-the-art in non-rigid shape matching. We first justify that under certain conditions, the learned maps, when represented in the spectral domain, are already cycle consistent. Furthermore, we identify the discrepancy that spectrally consistent maps are not necessarily spatially, or point-wise, consistent. In light of this, we present a novel design of unsupervised Deep Functional Maps, which effectively enforces the harmony of learned maps under the spectral and the point-wise representation. By taking advantage of cycle consistency, our framework produces state-of-the-art results in mapping shapes even under significant distortions. Beyond that, by independently estimating maps in both spectral and spatial domains, our method naturally alleviates over-fitting in network training, yielding superior generalization performance and accuracy within an array of challenging tests for both near-isometric and non-isometric datasets. Codes are available at https://github.com/rqhuang88/Spatiallyand-Spectrally-Consistent-Deep-Functional-Maps.Comment: Accepted by ICCV202

TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems

Large Language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools that require a blend of task planning and the utilization of external tools, such as APIs. However, real-world complex systems present three prevalent challenges concerning task planning and tool usage: (1) The real system usually has a vast array of APIs, so it is impossible to feed the descriptions of all APIs to the prompt of LLMs as the token length is limited; (2) the real system is designed for handling complex tasks, and the base LLMs can hardly plan a correct sub-task order and API-calling order for such tasks; (3) Similar semantics and functionalities among APIs in real systems create challenges for both LLMs and even humans in distinguishing between them. In response, this paper introduces a comprehensive framework aimed at enhancing the Task Planning and Tool Usage (TPTU) abilities of LLM-based agents operating within real-world systems. Our framework comprises three key components designed to address these challenges: (1) the API Retriever selects the most pertinent APIs for the user task among the extensive array available; (2) LLM Finetuner tunes a base LLM so that the finetuned LLM can be more capable for task planning and API calling; (3) the Demo Selector adaptively retrieves different demonstrations related to hard-to-distinguish APIs, which is further used for in-context learning to boost the final performance. We validate our methods using a real-world commercial system as well as an open-sourced academic dataset, and the outcomes clearly showcase the efficacy of each individual component as well as the integrated framework

Enhanced plasticity of spontaneous coagulation cast oxide ceramic green bodies

In the preparation of large-sized ceramics, the use of a green body with relatively high plasticity is crucial to minimize the risk of cracking during processing. To achieve this goal, glycerol and polyethylene glycol (PEG) were utilized as plasticizers in the shaping of green bodies of oxide ceramics through spontaneous coagulation casting (SCC). This study investigated the effects of plasticizers and particle sizes ranging from the submicron to nanoscale on the slurry viscosity, drying shrinkage of wet gels, and mechanical properties of green bodies. The plasticity of the green bodies was assessed by measuring the impact toughness and flexural stress‒strain curves. By incorporating an appropriate plasticizer, the peak width of the flexural stress‒strain curve for dried green bodies from particles of different sizes was nearly twice that without plasticizers, and the impact toughness was enhanced by approximately 71%, 34%, and 41% when the particle size decreased from the submicron scale to the nanoscale (0.45 μm, 0.18 μm, and 50 nm, respectively). The drilling test revealed that there was nearly no cracking around the holes in the green bodies with plasticizers. The plasticity mechanism of the green bodies was examined based on ultraviolet–visible (UV–Vis) spectroscopy and scanning electron microscopy (SEM). It was discovered that plasticizers might mitigate the brittleness of green bodies by adjusting the interactions between molecules and modifying the gel network properly

Hippo dictates signaling for cellular homeostasis and immune defense in Crassostrea hongkongensis hemocytes

IntroductionThe Hippo signaling pathway is an evolutionarily conserved signaling cascade that plays a crucial role in regulating cell proliferation, differentiation, and apoptosis. It has been shown to be a key regulator of cell fate and cellular homeostasis in various immune processes. Despite its well-established functions in vertebrate immunity, its roles in marine invertebrate immunity remain poorly understood. Therefore, our present work provides fresh mechanistic insights into how the Hippo pathway orchestrates hemocytic functions in Crassostrea hongkongensis, with implications for studies on its major forms and modifications in animal evolution.MethodThe complete set of Hippo pathway genes, including SAV1, MOB1, LATS, YAP/TAZ, TEAD, and MST, were identified from the C. hongkongensis genome. Quantitative PCR assays were conducted to examine the mRNA expression levels of these genes in different tissues and the levels of these genes in hemocytes before and after bacterial challenges. The study also examined the crosstalk between the Hippo pathway and other immune pathways, such as the AP-1 and p53-dependent p21 signaling cascades. RNA interference was used to knock down MST and TEAD, and MST is a core orchestrator of non-canonical Hippo signaling, to investigate its impact on phagocytosis and bacterial clearance in hemocytes.ResultThe results demonstrated that members of the Hippo pathway were highly expressed in hemocytes, with their expression levels significantly increasing following bacterial challenges. Crosstalk between the Hippo pathway and other immune pathways triggered hemocytic apoptosis, which functioned similarly to the canonical Mst-Lats-Yap signaling pathway in Drosophila and mammals. Knocking down MST resulted in increased phagocytosis and boosted the efficiency of bacterial clearance in hemocytes, presumably due to mobilized antioxidant transcription by Nrf for maintaining immune homeostasis.DiscussionThis study provides novel insights into the regulatory mechanisms underlying the Hippo pathway in immune responses of C. hongkongensis hemocytes. The study highlights the importance of the Hippo pathway in maintaining immune homeostasis and orchestrating hemocytic functions in oysters. Moreover, this study demonstrates the divergence of the Hippo pathway's roles in marine invertebrate immunity from mammalian observations, indicating the need for further comparative studies across species. These findings have significant implications for future research aimed at elucidating the evolutionary trajectory and functional diversity of the Hippo signaling pathway in animal evolution

TNFRSF10C methylation is a new epigenetic biomarker for colorectal cancer

Background Abnormal methylation of TNFRSF10C was found to be associated with different types of cancers, excluding colorectal cancer (CRC). In this paper, the performance of TNFRSF10C methylation in CRC was studied in two stages. Method The discovery stage was involved with 38 pairs of CRC tumor and paired adjacent non-tumor tissues, and 69 pairs of CRC tumor and paired adjacent non-tumor tissues were used for the validation stage. Quantitative methylation specific PCR (qMSP) method and percentage of methylated reference (PMR) were used to test and represent the methylation level of TNFRSF10C, respectively. A dual-luciferase reporter gene experiment was conducted to evaluate the promoter activity of TNFRSF10C fragment. Results A significant association of TNFRSF10C promoter hypermethylation with CRC was found and validated (discovery stage: 24.67 ± 7.52 vs. 3.36 ± 0.89; P = 0.003; validation stage: 31.21 ± 12.48 vs. 4.52 ± 1.47; P = 0.0005). Subsequent analyses of TCGA data among 46 pairs of CRC samples further confirmed our findings (cg23965061: P = 4E − 6; cg14015044: P = 1E − 7). Dual-luciferase reporter gene assay revealed that TNFRSF10C fragment was able to significantly promote gene expression (Fold change = 2.375, P = 0.013). Our data confirmed that TNFRSF10C promoter hypermethylation can predict shorter overall survival of CRC patients (P = 0.032). Additionally, bioinformatics analyses indicated that TNFRSF10C hypermethylation was significantly associated with lower TNFRSF10C expression. Conclusion Our work suggested that TNFRSF10C hypermethylation was significantly associated with the risk of CRC

Single-Species Leaf Detection against Complex Backgrounds with YOLOv5s

Accurate and rapid localization and identification of tree leaves are of significant importance for urban forest planning and environmental protection. Existing object detection neural networks are complex and often large, which hinders their deployment on mobile devices and compromises their efficiency in detecting plant leaves, especially against complex backgrounds. To address this issue, we collected eight common types of tree leaves against complex urban backgrounds to create a single-species leaf dataset. Each image in this dataset contains only one type of tree but may include multiple leaves. These leaves share similar shapes and textures and resemble various real-world background colors, making them difficult to distinguish and accurately identify, thereby posing challenges to model precision in localization and recognition. We propose a lightweight single-species leaf detection model, SinL-YOLOv5, which is only 15.7 MB. First, we integrated an SE module into the backbone to adaptively adjust the channel weights of feature maps, enhancing the expression of critical features such as the contours and textures of the leaves. Then, we developed an adaptive weighted bi-directional feature pyramid network, SE-BiFPN, utilizing the SE module within the backbone. This approach enhances the information transfer capabilities between the deep semantic features and shallow contour texture features of the network, thereby accelerating detection speed and improving detection accuracy. Finally, to enhance model stability during learning, we introduced an angle cost-based bounding box regression loss function (SIoU), which integrates directional information between ground-truth boxes and predicted boxes. This allows for more effective learning of the positioning and size of leaf edges and enhances the model’s accuracy in detecting leaf locations. We validated the improved model on the single-species leaf dataset. The results showed that compared to YOLOv5s, SinL-YOLOv5 exhibited a notable performance improvement. Specifically, SinL-YOLOv5 achieved an increase of nearly 4.7 percentage points in the [email protected] and processed an additional 20 frames per second. These enhancements significantly enhanced both the accuracy and speed of localization and recognition. With this improved model, we achieved accurate and rapid detection of eight common types of single-species tree leaves against complex urban backgrounds, providing technical support for urban forest surveys, urban forestry planning, and urban environmental conservation

Spatially and Spectrally Consistent Deep Functional Maps

International audienceCycle consistency has long been exploited as a powerful prior for jointly optimizing maps within a collection of shapes. In this paper, we investigate its utility in the approaches of Deep Functional Maps, which are considered state-of-the-art in non-rigid shape matching. We first justify that under certain conditions, the learned maps, when represented in the spectral domain, are already cycle consistent. Furthermore, we identify the discrepancy that spectrally consistent maps are not necessarily spatially, or point-wise, consistent. In light of this, we present a novel design of unsupervised Deep Functional Maps, which effectively enforces the harmony of learned maps under the spectral and the point-wise representation. By taking advantage of cycle consistency, our framework produces state-of-the-art results in mapping shapes even under significant distortions. Beyond that, by independently estimating maps in both spectral and spatial domains, our method naturally alleviates over-fitting in network training, yielding superior generalization performance and accuracy within an array of challenging tests for both near-isometric and non-isometric datasets. Codes are available at https://github.com/rqhuang88/Spatiallyand-Spectrally-Consistent-Deep-Functional-Maps

Spatially and Spectrally Consistent Deep Functional Maps

International audienceCycle consistency has long been exploited as a powerful prior for jointly optimizing maps within a collection of shapes. In this paper, we investigate its utility in the approaches of Deep Functional Maps, which are considered state-of-the-art in non-rigid shape matching. We first justify that under certain conditions, the learned maps, when represented in the spectral domain, are already cycle consistent. Furthermore, we identify the discrepancy that spectrally consistent maps are not necessarily spatially, or point-wise, consistent. In light of this, we present a novel design of unsupervised Deep Functional Maps, which effectively enforces the harmony of learned maps under the spectral and the point-wise representation. By taking advantage of cycle consistency, our framework produces state-of-the-art results in mapping shapes even under significant distortions. Beyond that, by independently estimating maps in both spectral and spatial domains, our method naturally alleviates over-fitting in network training, yielding superior generalization performance and accuracy within an array of challenging tests for both near-isometric and non-isometric datasets. Codes are available at https://github.com/rqhuang88/Spatiallyand-Spectrally-Consistent-Deep-Functional-Maps

Targeting the p53 signaling pathway in cancers: Molecular mechanisms and clinical studies

Abstract Tumor suppressor p53 can transcriptionally activate downstream genes in response to stress, and then regulate the cell cycle, DNA repair, metabolism, angiogenesis, apoptosis, and other biological responses. p53 has seven functional domains and 12 splice isoforms, and different domains and subtypes play different roles. The activation and inactivation of p53 are finely regulated and are associated with phosphorylation/acetylation modification and ubiquitination modification, respectively. Abnormal activation of p53 is closely related to the occurrence and development of cancer. While targeted therapy of the p53 signaling pathway is still in its early stages and only a few drugs or treatments have entered clinical trials, the development of new drugs and ongoing clinical trials are expected to lead to the widespread use of p53 signaling‐targeted therapy in cancer treatment in the future. TRIAP1 is a novel p53 downstream inhibitor of apoptosis. TRIAP1 is the homolog of yeast mitochondrial intermembrane protein MDM35, which can play a tumor‐promoting role by blocking the mitochondria‐dependent apoptosis pathway. This work provides a systematic overview of recent basic research and clinical progress in the p53 signaling pathway and proposes that TRIAP1 is an important therapeutic target downstream of p53 signaling