STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Large-scale models pre-trained on large-scale datasets have profoundly advanced the development of deep learning. However, the state-of-the-art models for medical image segmentation are still small-scale, with their parameters only in the tens of millions. Further scaling them up to higher orders of magnitude is rarely explored. An overarching goal of exploring large-scale models is to train them on large-scale medical segmentation datasets for better transfer capacities. In this work, we design a series of Scalable and Transferable U-Net (STU-Net) models, with parameter sizes ranging from 14 million to 1.4 billion. Notably, the 1.4B STU-Net is the largest medical image segmentation model to date. Our STU-Net is based on nnU-Net framework due to its popularity and impressive performance. We first refine the default convolutional blocks in nnU-Net to make them scalable. Then, we empirically evaluate different scaling combinations of network depth and width, discovering that it is optimal to scale model depth and width together. We train our scalable STU-Net models on a large-scale TotalSegmentator dataset and find that increasing model size brings a stronger performance gain. This observation reveals that a large model is promising in medical image segmentation. Furthermore, we evaluate the transferability of our model on 14 downstream datasets for direct inference and 3 datasets for further fine-tuning, covering various modalities and segmentation targets. We observe good performance of our pre-trained model in both direct inference and fine-tuning. The code and pre-trained models are available at https://github.com/Ziyan-Huang/STU-Net

Linking Incomplete Reprogramming to the Improved Pluripotency of Murine Embryonal Carcinoma Cell-Derived Pluripotent Stem Cells

Somatic cell nuclear transfer (SCNT) has been proved capable of reprogramming various differentiated somatic cells into pluripotent stem cells. Recently, induced pluripotent stem cells (iPS) have been successfully derived from mouse and human somatic cells by the over-expression of a combination of transcription factors. However, the molecular mechanisms underlying the reprogramming mediated by either the SCNT or iPS approach are poorly understood. Increasing evidence indicates that many tumor pathways play roles in the derivation of iPS cells. Embryonal carcinoma (EC) cells have the characteristics of both stem cells and cancer cells and thus they might be the better candidates for elucidating the details of the reprogramming process. Although previous studies indicate that EC cells cannot be reprogrammed into real pluripotent stem cells, the reasons for this remain unclear. Here, nuclei from mouse EC cells (P19) were transplanted into enucleated oocytes and pluripotent stem cells (P19 NTES cells) were subsequently established. Interestingly, P19 NTES cells prolonged the development of tetraploid aggregated embryos compared to EC cells alone. More importantly, we found that the expression recovery of the imprinted H19 gene was dependent on the methylation state in the differential methylation region (DMR). The induction of Nanog expression, however, was independent of the promoter region DNA methylation state in P19 NTES cells. A whole-genome transcriptome analysis further demonstrated that P19 NTES cells were indeed the intermediates between P19 cells and ES cells and many interesting genes were uncovered that may be responsible for the failed reprogramming of P19 cells. To our knowledge, for the first time, we linked incomplete reprogramming to the improved pluripotency of EC cell-derived pluripotent stem cells. The candidate genes we discovered may be useful not only for understanding the mechanisms of reprogramming, but also for deciphering the transition between tumorigenesis and pluripotency

Myeloid cell-derived LL-37 promotes lung cancer growth by activating Wnt/β-catenin signaling

Rationale: Antimicrobial peptides, such as cathelicidin LL-37/hCAP-18, are important effectors of the innate immune system with direct antibacterial activity. In addition, LL-37 is involved in the regulation of tumor cell growth. However, the molecular mechanisms underlying the functions of LL-37 in promoting lung cancer are not fully understood. Methods: The expression of LL-37 in the tissues and sera of patients with non-small cell lung cancer was determined through immunohistological, immunofluorescence analysis, and enzyme-linked immunosorbent assay. The animal model of wild-type and Cramp knockout mice was employed to evaluate the tumorigenic effect of LL-37 in non-small cell lung cancer. The mechanism of LL-37 involving in the promotion of lung tumor growth was evaluated via microarray analyses, recombinant protein treatment approaches in vitro, tumor immunohistochemical assays, and intervention studies in vivo. Results: LL-37 produced by myeloid cells was frequently upregulated in primary human lung cancer tissues. Moreover, its expression level correlated with poor clinical outcome. LL-37 activated Wnt/β-catenin signaling by inducing the phosphorylation of protein kinase B and subsequent phosphorylation of glycogen synthase kinase 3β mediated by the toll-like receptor-4 expressed in lung tumor cells. LL-37 treatment of tumor cells also decreased the levels of Axin2. In contrast, it elevated those of an RNA-binding protein (tristetraprolin), which may be involved in the mechanism through which LL-37 induces activation of Wnt/β-catenin. Conclusion: LL-37 may be a critical molecular link between tumor-supportive immune cells and tumors, facilitating the progression of lung cancer

Microbial responses to inorganic nutrient amendment overridden by warming: Consequences on soil carbon stability.

Eutrophication and climate warming, induced by anthropogenic activities, are simultaneously occurring worldwide and jointly affecting soil carbon stability. Therefore, it is of great interest to examine whether and how they interactively affect soil microbial community, a major soil carbon driver. Here, we showed that climate warming, simulated by southward transferring Mollisol soil in agricultural ecosystems from the cold temperate climate zone (N) to warm temperate climate (C) and subtropical climate zone (S), decreased soil organic matter (SOM) by 6%-12%. In contrast, amendment with nitrogen, phosphorus and potassium enhanced plant biomass by 97% and SOM by 6% at the N site, thus stimulating copiotrophic taxa but reducing oligotrophic taxa in relative abundance. However, microbial responses to nutrient amendment were overridden by soil transfer in that nutrient amendment had little effect at the C site but increased recalcitrant carbon-degrading fungal Agaricomycetes and Microbotryomycetes taxa derived from Basidiomycota by 4-17 folds and recalcitrant carbon-degrading genes by 23%-40% at the S site, implying a possible priming effect. Consequently, SOM at the S site was not increased by nutrient amendment despite increased plant biomass by 108%. Collectively, we demonstrate that soil transfer to warmer regions overrides microbial responses to nutrient amendment and weakens soil carbon sequestration

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}

Indigo: a natural molecular passivator for efficient perovskite solar cells

Organic–inorganic hybrid lead halide perovskite solar cells have made unprecedented progress in improving photovoltaic efficiency during the past decade, while still facing critical stability challenges. Herein, the natural organic dye Indigo is explored for the first time to be an efficient molecular passivator that assists in the preparation of high-quality hybrid perovskite film with reduced defects and enhanced stability. The Indigo molecule with both carbonyl and amino groups can provide bifunctional chemical passivation for defects. In-depth theoretical and experimental studies show that the Indigo molecules firmly binds to the perovskite surfaces, enhancing the crystallization of perovskite films with improved morphology. Consequently, the Indigo-passivated perovskite film exhibits increased grain size with better uniformity, reduced grain boundaries, lowered defect density, and retarded ion migration, boosting the device efficiency up to 23.22%, and ˜21% for large-area device (1 cm2). Furthermore, the Indigo passivation can enhance device stability in terms of both humidity and thermal stress. These results provide not only new insights into the multipassivation role of natural organic dyes but also a simple and low-cost strategy to prepare high-quality hybrid perovskite films for optoelectronic applications based on Indigo derivatives.Peer ReviewedPostprint (author's final draft

The miR-15/16 Cluster Is Involved in the Regulation of Vertebrate LC-PUFA Biosynthesis by Targeting pparγ as Demonostrated in Rabbitfish Siganus canaliculatus

Post-transcriptional regulatory mechanisms play important roles in the regulation of long-chain (≥ C20) polyunsaturated fatty acid (LC-PUFA) biosynthesis. Here, we address a potentially important role of the miR-15/16 cluster in the regulation of LC-PUFA biosynthesis in rabbitfish Siganus canaliculatus. In rabbitfish, miR-15 and miR-16 were both highly responsive to fatty acids affecting LC-PUFA biosynthesis and displayed a similar expression pattern in a range of rabbitfish tissues. A common potential binding site for miR-15 and miR-16 was predicted in the 3′UTR of peroxisome proliferator-activated receptor gamma (pparγ), an inhibitor of LC-PUFA biosynthesis in rabbitfish, and luciferase reporter assays revealed that pparγ was a potential target of miR-15/16 cluster. In vitro individual or co-overexpression of miR-15 and miR-16 in rabbitfish hepatocyte line (SCHL) inhibited both mRNA and protein levels of Pparγ, and increased the mRNA levels of Δ6Δ5 fads2, Δ4 fads2, and elovl5, key enzymes of LC-PUFA biosynthesis. Inhibition of pparγ was more pronounced with co-overexpression of miR-15 and miR-16 than with individual overexpression in SCHL. Knockdown of miR-15/16 cluster gave opposite results, and increased mRNA levels of LC-PUFA biosynthesis enzymes were observed after knockdown of pparγ. Furthermore, miR-15/16 cluster overexpression significantly increased the contents of 22:6n-3, 20:4n-6 and total LC-PUFA in SCHL with higher 18:4n-3/18:3n-3 and 22:6n-3/22:5n-3 ratio. These suggested that miR-15 and miR-16 as a miRNA cluster together enhanced LC-PUFA biosynthesis by targeting pparγ in rabbitfish. This is the first report of the participation of miR-15/16 cluster in LC-PUFA biosynthesis in vertebrates