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Eggplant is the fifth most economically important solanaceous crop after potato, tomato, pepper, and tobacco. Apart from the well-known brinjal eggplant ( L.), two other under-utilized eggplant species, the scarlet eggplant ( L.) and the gboma eggplant ( L.) are also cultivated. The taxonomy and identification of eggplant wild relatives is challenging for breeders due to the large number of related species, but recent phenotypic and genetic data and classification in primary, secondary, and tertiary genepools, as well as information on the domestication process and wild progenitors, facilitates their utilization in breeding. The World Vegetable Center (WorldVeg) holds a large public germplasm collection of eggplant, which includes the three cultivated species and more than 30 eggplant wild relatives, with more than 3,200 accessions collected from 90 countries. Over the last 15 years, more than 10,000 seed samples from the Center's eggplant collection have been shared with public and private sector entities, including other genebanks. An analysis of the global occurrences and genebank holdings of cultivated eggplants and their wild relatives reveals that the WorldVeg genebank holds the world's largest public collection of the three cultivated eggplant species. The composition, seed dissemination and utilization of germplasm from the Center's collection are highlighted. In recent years more than 1,300 accessions of eggplant have been characterized for yield and fruit quality parameters. Further screening for biotic and abiotic stresses in eggplant wild relatives is a priority, as is the need to amass more comprehensive knowledge regarding wild relatives' potential for use in breeding. However, as is the case for many other crops, wild relatives are highly under-represented in the global conservation system of eggplant genetic resources.

The major concept of \"oxidative stress\" is an excess elevated level of reactive oxygen species (ROS) which are generated from vigorous metabolism and consumption of oxygen. The precise harmonization of oxidative stresses between mitochondria and other organelles in the cell is absolutely vital to cell survival. Under oxidative stress, ROS produced from mitochondria and are the major mediator for tumorigenesis in different aspects, such as proliferation, migration/invasion, angiogenesis, inflammation, and immunoescape to allow cancer cells to adapt to the rigorous environment. Accordingly, the dynamic balance of oxidative stresses not only orchestrate complex cell signaling events in cancer cells but also affect other components in the tumor microenvironment (TME). Immune cells, such as M2 macrophages, dendritic cells, and T cells are the major components of the immunosuppressive TME from the ROS-induced inflammation. Based on this notion, numerous strategies to mitigate oxidative stresses in tumors have been tested for cancer prevention or therapies; however, these manipulations are devised from different sources and mechanisms without established effectiveness. Herein, we integrate current progress regarding the impact of mitochondrial ROS in the TME, not only in cancer cells but also in immune cells, and discuss the combination of emerging ROS-modulating strategies with immunotherapies to achieve antitumor effects.

Although sepsis and acute kidney injury (AKI) have a bidirectional interplay, the pathophysiological mechanisms between AKI and sepsis are not clarified and worthy of a comprehensive and updated review. The primary pathophysiology of sepsis-associated AKI (SA-AKI) includes inflammatory cascade, macrovascular and microvascular dysfunction, cell cycle arrest, and apoptosis. The pathophysiology of sepsis following AKI contains fluid overload, hyperinflammatory state, immunosuppression, and infection associated with kidney replacement therapy and catheter cannulation. The preventive strategies for SA-AKI are non-specific, mainly focusing on infection control and preventing further kidney insults. On the other hand, the preventive strategies for sepsis following AKI might focus on decreasing some metabolites, cytokines, or molecules harmful to our immunity, supplementing vitamin D3 for its immunomodulation effect, and avoiding fluid overload and unnecessary catheter cannulation. To date, several limitations persistently prohibit the understanding of the bidirectional pathophysiologies. Conducting studies, such as the Kidney Precision Medicine Project, to investigate human kidney tissue and establishing parameters or scores better to determine the occurrence timing of sepsis and AKI and the definition of SA-AKI might be the prospects to unveil the mystery and improve the prognoses of AKI patients.

The range of applications for additive manufacturing is expanding quickly, including mass production of athletic footwear parts 1 , dental ceramics 2 and aerospace components 3 as well as fabrication of microfluidics 4 , medical devices 5 , and artificial organs 6 . The light-induced additive manufacturing techniques 7 used are particularly successful owing to their high spatial and temporal control, but such techniques still share the common motifs of pointwise or layered generation, as do stereolithography 8 , laser powder bed fusion 9 , and continuous liquid interface production 10 and its successors 11 , 12 . Volumetric 3D printing 13 – 20 is the next step onward from sequential additive manufacturing methods. Here we introduce xolography, a dual colour technique using photoswitchable photoinitiators to induce local polymerization inside a confined monomer volume upon linear excitation by intersecting light beams of different wavelengths. We demonstrate this concept with a volumetric printer designed to generate three-dimensional objects with complex structural features as well as mechanical and optical functions. Compared to state-of-the-art volumetric printing methods, our technique has a resolution about ten times higher than computed axial lithography without feedback optimization, and a volume generation rate four to five orders of magnitude higher than two-photon photopolymerization. We expect this technology to transform rapid volumetric production for objects at the nanoscopic to macroscopic length scales. By combining the use of photoswitchable photoinitators and intersecting light beams, objects and complex systems can be produced rapidly with higher definition than is possible using state-of-the art macroscopic volumetric methods.

An essential aspect of medical research is the prediction for a health outcome and the scientific identification of important factors. As a result, numerous methods were developed for model selections in recent years. In the era of big data, machine learning has been broadly adopted for data analysis. In particular, the Support Vector Machine (SVM) has an excellent performance in classifications and predictions with the high-dimensional data. In this research, a novel model selection strategy is carried out, named as the Stepwise Support Vector Machine (StepSVM). The new strategy is based on the SVM to conduct a modified stepwise selection, where the tuning parameter could be determined by 10-fold cross-validation that minimizes the mean squared error. Two popular methods, the conventional stepwise logistic regression model and the SVM Recursive Feature Elimination (SVM-RFE), were compared to the StepSVM. The Stability and accuracy of the three strategies were evaluated by simulation studies with a complex hierarchical structure. Up to five variables were selected to predict the dichotomous cancer remission of a lung cancer patient. Regarding the stepwise logistic regression, the mean of the C-statistic was 69.19%. The overall accuracy of the SVM-RFE was estimated at 70.62%. In contrast, the StepSVM provided the highest prediction accuracy of 80.57%. Although the StepSVM is more time consuming, it is more consistent and outperforms the other two methods.

This study provides insights in patterns of distribution of abiotic and biotic stress resilience across Vigna gene pools to enhance the use and conservation of these genetic resources for legume breeding. Vigna is a pantropical genus with more than 88 taxa including important crops such as V. radiata (mung bean) and V. unguiculata (cowpea). Our results show that sources of pest and disease resistance occur in at least 75 percent of the Vigna taxa, which were part of screening assessments, while sources of abiotic stress resilience occur in less than 30 percent of screened taxa. This difference in levels of resilience suggests that Vigna taxa co-evolve with pests and diseases while taxa are more conservative to adapt to climatic changes and salinization. Twenty-two Vigna taxa are poorly conserved in genebanks or not at all. This germplasm is not available for legume breeding and requires urgent germplasm collecting before these taxa extirpate on farm and in the wild. Vigna taxa, which tolerate heat and drought stress are rare compared with taxa, which escape these stresses because of short growing seasons or with taxa, which tolerate salinity. We recommend prioritizing these rare Vigna taxa for conservation and screening for combined abiotic and biotic stress resilience resulting from stacked or multifunctional traits. The high presence of salinity tolerance compared with drought stress tolerance, suggests that Vigna taxa are good at developing salt-tolerant traits. Vigna taxa are therefore of high value for legume production in areas that will suffer from salinization under global climate change.

This paper introduces deep gradient network (DGNet), a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD). It decouples the task into two connected branches, i.e., a context and a texture encoder. The essential connection is the gradient-induced transition, representing a soft grouping between context and texture features. Benefiting from the simple but efficient framework, DGNet outperforms existing state-of-the-art COD models by a large margin. Notably, our efficient version, DGNet-S, runs in real-time (80 fps) and achieves comparable results to the cutting-edge model JCSOD-CVPR21 with only 6.82% parameters. The application results also show that the proposed DGNet performs well in the polyp segmentation, defect detection, and transparent object segmentation tasks. The code will be made available at https://github.com/GewelsJI/DGNet.

We present the first comprehensive video polyp segmentation (VPS) study in the deep learning era. Over the years, developments in VPS are not moving forward with ease due to the lack of a large-scale dataset with fine-grained segmentation annotations. To address this issue, we first introduce a high-quality frame-by-frame annotated VPS dataset, named SUN-SEG, which contains 158 690 colonoscopy video frames from the well-known SUN-database. We provide additional annotation covering diverse types, i.e., attribute, object mask, boundary, scribble, and polygon. Second, we design a simple but efficient baseline, named PNS+, which consists of a global encoder, a local encoder, and normalized self-attention (NS) blocks. The global and local encoders receive an anchor frame and multiple successive frames to extract long-term and short-term spatial-temporal representations, which are then progressively refined by two NS blocks. Extensive experiments show that PNS+ achieves the best performance and real-time inference speed (170 fps), making it a promising solution for the VPS task. Third, we extensively evaluate 13 representative polyp/object segmentation models on our SUN-SEG dataset and provide attribute-based comparisons. Finally, we discuss several open issues and suggest possible research directions for the VPS community. Our project and dataset are publicly available at https://github.com/GewelsJI/VPS.

A bstract Quantum cosmology aims at elucidating the beginning of our Universe. Back in early 80’s, Vilenkin and Hartle-Hawking put forward the “tunneling from nothing” and “no boundary” proposals. Recently there has been renewed interest in this subject from the viewpoint of defining the oscillating path integral for Lorentzian quantum gravity using the Picard-Lefschetz theory. Aiming at going beyond the mini-superspace and saddle-point approximations, we perform Monte Carlo calculations using the generalized Lefschetz thimble method to overcome the sign problem. In particular, we confirm that either the Vilenkin or the Hartle-Hawking saddle point becomes relevant if one uses the Robin boundary condition depending on its parameter. We also clarify some fundamental issues in quantum cosmology, such as an issue related to the integration domain of the lapse function.

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality, emphasizing the need for novel therapeutic strategies. Decorin (DCN), a chondroitin sulfate proteoglycan (CSPG), has been proposed as a tumor suppressor, yet its precise role in HCC and the tumor microenvironment (TME) remains underexplored. Through integrated analyses of bulk RNA and single-cell RNA sequencing datasets, we identified a distinct tumor stromal subset highly expressing DCN and associated chondroitin sulfate (CS) synthases. Our findings revealed that DCN expression is significantly downregulated in HCC tissue, but upregulated in peri-tumor stroma, where it correlates with better prognosis and reduced capsular invasion. Western blot analysis demonstrated that CS-DCN, the glycosylated form of DCN, plays a dominant role in this context. Single-cell clustering analysis identified a unique stromal subset in HCC characterized by elevated expression of DCN, CSPGs, and CS synthases, associated with extracellular matrix (ECM) remodeling and protective barrier functions. A six-gene DCN-associated signature derived from this subset, including DCN, BGN, SRPX, CHSY3, CHST3, and CHPF, was validated as a prognostic marker for HCC. Furthermore, functional assays demonstrated that CS-DCN significantly inhibited HCC cell proliferation and invasion. Our study highlights the critical role of DCN in HCC TME and provides insights into its therapeutic potential. Modulating CSPG pathways, particularly on CS-DCN-expressing stromal cells, may offer a promising approach for improving HCC treatment and patient outcomes. Trial registration number This study was approved by the Ethical Committees of Chung Shan Medical University Hospital, and all patients gave informed consent to have their tissues before collection (CSMUH No: CS218075).