Damage Characteristics of Argillaceous Quartz Sandstone Mesostructure under Different Wetting-drying Conditions

Extensive water–rock interaction in the Three Gorges Reservoir area of the Yangtze River leads to rock mass deterioration along the reservoir banks. However, mineral evolution behavior and its effect on the mesostructure deterioration of rocks under the wetting–drying cycle condition remain unknown. So, the wetting–drying cycle tests were conducted on peculiar argillaceous quartz sandstone in TGRA under neutral (pH = 7) and alkaline (pH = 10) water environments. Here, we provided detailed physical and microscopy images data to determine the control mechanism of mineral behavior on the evolution of sandstone’s mesostructure. Under the neutral condition, repeated “absorption and swelling–dehydration and contraction” of clay minerals leads to the repeated physical action of “squeezing–unloading” in the interior of a rock. This results in the initiation and gradual expansion of cracks in the framework mineral quartz, exhibiting failure mode from the interior to the exterior. In contrast, under the alkaline condition, the dissolution on the surface of quartz particles leads to the expansion and connection of pores, implying that the sandstone exhibits failure mode from the exterior to the interior. Moreover, the internal mechanical analysis indicates the minerals are at high pressure because of the expansion of clay minerals in the neutral solution. However, in an alkaline water environment, the extrusion pressure of framework mineral quartz decreases significantly and is not easily broken due to increased porosity. Thus, the evolution behavior of minerals in different water environments plays an important role in the damage of the rock

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

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}

Phosphoproteomic Analysis of Gossypol-Induced Apoptosis in Ovarian Cancer Cell Line, HOC1a

Ovarian cancer is a major cause for death of gynecological cancer patients. The efficacy of traditional surgery and chemotherapy is rather compromised and platinum-resistant cancer recurs. Finding new therapeutic targets is urgently needed to increase the survival rate and to improve life quality of patients with ovarian cancer. In the present work, phosphoproteomic analysis was carried out on untreated and gossypol-treated ovarian cancer cell line, HOC1a. We identified approximately 9750 phosphopeptides from 3030 phosphoproteins, which are involved in diverse cellular processes including cytoskeletal organization, RNA and nucleotide binding, and cell cycle regulation. Upon gossypol treatment, changes in phosphorylation of twenty-nine proteins including YAP1 and AKAP12 were characterized. Western blotting and qPCR analysis were used to determine expression levels of proteins in YAP1-related Hippo pathway showing that gossypol induced upregulation of LATS1, which phosphorylates YAP1 at Ser 61. Furthermore, our data showed that gossypol targets the actin cytoskeletal organization through mediating phosphorylation states of actin-binding proteins. Taken together, our data provide valuable information to understand effects of gossypol on protein phosphorylation and apoptosis of ovarian cancer cells

Accurate determination of Sr isotopic compositions in clinopyroxene and silicate glasses by LA-MC-ICP-MS

The low-Sr content (generally < 100 \u3bcg g-1) in clinopyroxene from peridotite makes accurate Sr isotopic determination by LA-MC-ICP-MS a challenge. The effects of adding N<inf>2</inf> to the sample gas and using a guard electrode (GE) on instrumental sensitivity for Sr isotopic determination by LA-MC-ICP-MS were investigated. Results revealed no significant sensitivity enhancement of Sr by adding N<inf>2</inf> to the ICP. Although using a GE led to a two-fold sensitivity enhancement, it significantly increased the yield of polyatomic ion interferences of Ca-related ions and TiAr+ on Sr isotopes. Applying the method established in this work, 87Sr/86Sr ratios (Rb/Sr < 0.14) of natural clinopyroxene from mantle and silicate glasses were accurately measured with similar measurement repeatability (0.0009-0.00006, 2SE) to previous studies but using a smaller spot size of 120 \u3bcm and low-to-moderate Sr content (30-518 \u3bcg g-1). The measurement reproducibility was 0.0004 (2s, n = 33) for a sample with 100 \u3bcg g-1 Sr. Destruction of the crystal structure by sample fusion showed no effect on Sr isotopic determination. Synthesised glasses with major element compositions similar to natural clinopyroxene have the potential to be adopted as reference materials for Sr isotopic determination by LA-MC-ICP-MS.Peer reviewed: YesNRC publication: Ye