Spatial variation of perceived equity and its determinants in a gateway community of Giant Panda National Park, China

Unidad de excelencia María de Maeztu CEX2019-000940-MSocial equity is essential in the governance of protected areas (PAs), as ignoring such consideration can lead to resistance and jeopardize conservation objectives. However, more research is required to understand the spatial heterogeneity of perceived social equity and its underlying spatial factors. Using a survey of 361 respondents, we presented spatial distribution patterns of perceived equity by kernel density estimation (KDE) in Giant Panda National Park, China. The regression analysis showed that local residents who live closer to the PA boundary are more likely to develop negative responses and those who with easy access to tourism spots have more positive procedural and distributional perceptions. Notably, the proximity to the PA authority decreases locals' perceptions of fairness in all aspects, which is potentially due to the opaque participative channels provided by the PA authority. We argue that those spatial differentials in fairness perceptions are driven by the intrinsic discrepancy of biodiversity protection requirements and the unevenly distributed consequences of management policies. Key steps to advance social equity considerations include multi-industry guidance, extending participative channels, and co-producing better compensation plans. Herein, this study appeals to a greater focus on the spatial aspect of social equity issues in PAs

Robust median reversion strategy for on-line portfolio selection

Ministry of Education, Singapore under its Academic Research Funding Tier

TGFβƒ1 Promotes Gemcitabine Resistance Through Regulating the LncRNA-LET/NF90/miR-145 Signaling Axis in Bladder Cancer

High tumor recurrence is frequently observed in patients with urinary bladder cancers (UBCs), with the need for biomarkers of prognosis and drug response. Chemoresistance and subsequent recurrence of cancers are driven by a subpopulation of tumor initiating cells, namely cancer stem-like cells (CSCs). However, the underlying molecular mechanism in chemotherapy-induced CSCs enrichment remains largely unclear. In this study, we found that during gemcitabine treatment lncRNA-Low Expression in Tumor (lncRNA-LET) was downregulated in chemoresistant UBC, accompanied with the enrichment of CSC population. Knockdown of lncRNA-LET increased UBC cell stemness, whereas forced expression of lncRNA-LET delayed gemcitabine-induced tumor recurrence. Furthermore, lncRNA-LET was directly repressed by gemcitabine treatment-induced overactivation of TGFβ/SMAD signaling through SMAD binding element (SBE) in the lncRNA-LET promoter. Consequently, reduced lncRNA-LET increased the NF90 protein stability, which in turn repressed biogenesis of miR-145 and subsequently resulted in accumulation of CSCs evidenced by the elevated levels of stemness markers HMGA2 and KLF4. Treatment of gemcitabine resistant xenografts with LY2157299, a clinically relevant specific inhibitor of TGFβRI, sensitized them to gemcitabine and significantly reduced tumorigenecity in vivo. Notably, overexpression of TGFβ1, combined with decreased levels of lncRNA-LET and miR-145 predicted poor prognosis in UBC patients. Collectively, we proved that the dysregulated lncRNA-LET/NF90/miR-145 axis by gemcitabine-induced TGFβ1 promotes UBC chemoresistance through enhancing cancer cell stemness. The combined changes in TGFβ1/lncRNA-LET/miR-145 provide novel molecular prognostic markers in UBC outcome. Therefore, targeting this axis could be a promising therapeutic approach in treating UBC patients

Robust median reversion strategy for online portfolio selection

Ministry of Education, Singapore under its Academic Research Funding Tier

A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation

Although deep learning have revolutionized abdominal multi-organ segmentation, models often struggle with generalization due to training on small, specific datasets. With the recent emergence of large-scale datasets, some important questions arise: \textbf{Can models trained on these datasets generalize well on different ones? If yes/no, how to further improve their generalizability?} To address these questions, we introduce A-Eval, a benchmark for the cross-dataset Evaluation ('Eval') of Abdominal ('A') multi-organ segmentation. We employ training sets from four large-scale public datasets: FLARE22, AMOS, WORD, and TotalSegmentator, each providing extensive labels for abdominal multi-organ segmentation. For evaluation, we incorporate the validation sets from these datasets along with the training set from the BTCV dataset, forming a robust benchmark comprising five distinct datasets. We evaluate the generalizability of various models using the A-Eval benchmark, with a focus on diverse data usage scenarios: training on individual datasets independently, utilizing unlabeled data via pseudo-labeling, mixing different modalities, and joint training across all available datasets. Additionally, we explore the impact of model sizes on cross-dataset generalizability. Through these analyses, we underline the importance of effective data usage in enhancing models' generalization capabilities, offering valuable insights for assembling large-scale datasets and improving training strategies. The code and pre-trained models are available at \href{https://github.com/uni-medical/A-Eval}{https://github.com/uni-medical/A-Eval}

SAM-Med3D

Although the Segment Anything Model (SAM) has demonstrated impressive performance in 2D natural image segmentation, its application to 3D volumetric medical images reveals significant shortcomings, namely suboptimal performance and unstable prediction, necessitating an excessive number of prompt points to attain the desired outcomes. These issues can hardly be addressed by fine-tuning SAM on medical data because the original 2D structure of SAM neglects 3D spatial information. In this paper, we introduce SAM-Med3D, the most comprehensive study to modify SAM for 3D medical images. Our approach is characterized by its comprehensiveness in two primary aspects: firstly, by comprehensively reformulating SAM to a thorough 3D architecture trained on a comprehensively processed large-scale volumetric medical dataset; and secondly, by providing a comprehensive evaluation of its performance. Specifically, we train SAM-Med3D with over 131K 3D masks and 247 categories. Our SAM-Med3D excels at capturing 3D spatial information, exhibiting competitive performance with significantly fewer prompt points than the top-performing fine-tuned SAM in the medical domain. We then evaluate its capabilities across 15 datasets and analyze it from multiple perspectives, including anatomical structures, modalities, targets, and generalization abilities. Our approach, compared with SAM, showcases pronouncedly enhanced efficiency and broad segmentation capabilities for 3D volumetric medical images. Our code is released at https://github.com/uni-medical/SAM-Med3D

Molecular Cloning and Characterization of Babesia orientalis

Babesiosis caused by Babesia orientalis is one of the most prevalent infections of water buffalo transmitted by Rhipicephalus haemaphysaloides causing a parasitic and hemolytic disease. The organelles proteins localized in apical membrane especially rhoptries neck and microneme protein form a complex called moving junction important during invasion process of parasites belonging to apicomplexan group, including Babesia species. A truncated fragment coding a 936 bps fragment was cloned in pMD-19T and subcloned into pET32 (a)+ expression vector, expressed in E. coli BL21. Purified recombinant BoRON2 was used to produce polyclonal antibody against BoRON2. Here, we identified the full sequence of gene encoding the rhoptry neck 2 protein that we named BoRON2 which is 4035 bp in full-length open reading frame without introns, encoding a polypeptide of 1345 amino acids. Western blot of rBoRON2 probed with buffalo positive serum analysis revealed a band of around 150 kDa in parasite lysates, suggesting an active involvement during invasion process. These findings most likely are constructive in perspective of ongoing research focused particularly on water buffalo babesiosis prevention and therapeutics and globally provide new information for genes comparative analysis

SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Segment Anything Model (SAM) has achieved impressive results for natural image segmentation with input prompts such as points and bounding boxes. Its success largely owes to massive labeled training data. However, directly applying SAM to medical image segmentation cannot perform well because SAM lacks medical knowledge -- it does not use medical images for training. To incorporate medical knowledge into SAM, we introduce SA-Med2D-20M, a large-scale segmentation dataset of 2D medical images built upon numerous public and private datasets. It consists of 4.6 million 2D medical images and 19.7 million corresponding masks, covering almost the whole body and showing significant diversity. This paper describes all the datasets collected in SA-Med2D-20M and details how to process these datasets. Furthermore, comprehensive statistics of SA-Med2D-20M are presented to facilitate the better use of our dataset, which can help the researchers build medical vision foundation models or apply their models to downstream medical applications. We hope that the large scale and diversity of SA-Med2D-20M can be leveraged to develop medical artificial intelligence for enhancing diagnosis, medical image analysis, knowledge sharing, and education. The data with the redistribution license is publicly available at https://github.com/OpenGVLab/SAM-Med2D