Methanesulfonate in the firn of King George Island, Antarctica

Methanesulfonate was investigated as a potential contributor to the sulfur budget, based on analysis of a firn core from Collins Ice Cap, King George Island, Antarctica (62°10′ S, 58°50′ W). The anion was found to be present at a mean concentration of 0.17 μeq L−1, with a maximum of 0.73 μeq L−1. Dating based on the δ 18O profile suggests that the principal peaks of methanesulfonate are associated with snow deposited in summer and autumn. A careful examination of MSA, SO4 2− and nssSO4 2− profiles indicates that two of the three peaks in the MSA profile may result mainly from migration and relocation of MSA. The mechanism responsible for this might be similar to that for deep cores from other Antarctic glaciers, supporting the migration hypothesis proposed by prior researchers and extending it to near-temperate ice. Due to the post-depositional modification, the main part of the MSA profile of the firn is no longer indicative of the seasonal pattern of MSA in the atmosphere, and the basis for calculation of the MSA/nssSO4 2− ratio should be changed. The MSA/nssS04 2 ratio obtained by a new computation is 0.22, 10% higher than that ignoring the effect of MSA migration

Biological Factor Regulatory Neural Network

Genes are fundamental for analyzing biological systems and many recent works proposed to utilize gene expression for various biological tasks by deep learning models. Despite their promising performance, it is hard for deep neural networks to provide biological insights for humans due to their black-box nature. Recently, some works integrated biological knowledge with neural networks to improve the transparency and performance of their models. However, these methods can only incorporate partial biological knowledge, leading to suboptimal performance. In this paper, we propose the Biological Factor Regulatory Neural Network (BFReg-NN), a generic framework to model relations among biological factors in cell systems. BFReg-NN starts from gene expression data and is capable of merging most existing biological knowledge into the model, including the regulatory relations among genes or proteins (e.g., gene regulatory networks (GRN), protein-protein interaction networks (PPI)) and the hierarchical relations among genes, proteins and pathways (e.g., several genes/proteins are contained in a pathway). Moreover, BFReg-NN also has the ability to provide new biologically meaningful insights because of its white-box characteristics. Experimental results on different gene expression-based tasks verify the superiority of BFReg-NN compared with baselines. Our case studies also show that the key insights found by BFReg-NN are consistent with the biological literature

Methanesulfonate in the firn of King George Island, Antarctica

Methanesulfonate was investigated as a potential contributor to the sulfur budget, based on analysis of a firn core from Collins Ice Cap, King George Island, Antarctica (62°10\u27 S, 58°50\u27 W). The anion was found to be present at a mean concentration of 0.17 μeq L-1, with a maximum of 0.73 μeq L-1. Dating based on the δ18O profile suggests that the principal peaks of methanesulfonate are associated with snow deposited in summer and autumn. A careful examination of MSA, SO42-and nssSO42- profiles indicates that two of the three peaks in the MSA profile mayresult mainlyfrom migration and relocation of MSA. The mechanism responsible for this might be similar to that for deep cores from other Antarctic glaciers, supporting the migration hypothesis proposed by prior researchers and extending it to near-temperate ice. Due to the post-depositional modification, the main part of the MSA profile of the firn is no longer indicative of the seasonal pattern of MSA in the atmosphere, and the basis for calculation of the MSA/nssSO42- ratio should be changed. The MSA/nssSO42- ratio obtained bya new computation is 0.22, 10% higher than that ignoring the effect of MSA migration

AiluRus: A Scalable ViT Framework for Dense Prediction

Vision transformers (ViTs) have emerged as a prevalent architecture for vision tasks owing to their impressive performance. However, when it comes to handling long token sequences, especially in dense prediction tasks that require high-resolution input, the complexity of ViTs increases significantly. Notably, dense prediction tasks, such as semantic segmentation or object detection, emphasize more on the contours or shapes of objects, while the texture inside objects is less informative. Motivated by this observation, we propose to apply adaptive resolution for different regions in the image according to their importance. Specifically, at the intermediate layer of the ViT, we utilize a spatial-aware density-based clustering algorithm to select representative tokens from the token sequence. Once the representative tokens are determined, we proceed to merge other tokens into their closest representative token. Consequently, semantic similar tokens are merged together to form low-resolution regions, while semantic irrelevant tokens are preserved independently as high-resolution regions. This strategy effectively reduces the number of tokens, allowing subsequent layers to handle a reduced token sequence and achieve acceleration. We evaluate our proposed method on three different datasets and observe promising performance. For example, the "Segmenter ViT-L" model can be accelerated by 48% FPS without fine-tuning, while maintaining the performance. Additionally, our method can be applied to accelerate fine-tuning as well. Experimental results demonstrate that we can save 52% training time while accelerating 2.46 times FPS with only a 0.09% performance drop. The code is available at https://github.com/caddyless/ailurus/tree/main.Comment: Accepted by NeurIPS 202

Preparation of a Nanosized As2O3/Mn0.5Zn0.5Fe2O4 Complex and Its Anti-Tumor Effect on Hepatocellular Carcinoma Cells

Manganese-zinc-ferrite nanoparticles (Mn0.5Zn0.5Fe2O4, MZF-NPs) prepared by an improved co-precipitation method and were characterized by transmission electron microscopy (TEM), X-ray diffraction (XRD) and energy dispersive spectrometry (EDS). Then thermodynamic testing of various doses of MZF-NPs was performed in vitro. The cytotoxicity of the Mn0.5Zn0.5Fe2O4 nanoparticles in vitro was tested by the MTT assay. A nanosized As2O3/Mn0.5Zn0.5Fe2O4 complex was made by an impregnation process. The complex’s shape, component, envelop rate and release rate of As2O3 were measured by SEM, EDS and atom fluorescence spectrometry, respectively. The therapeutic effect of nanosized As2O3/Mn0.5Zn0.5Fe2O4 complex combined with magnetic fluid hyperthermia (MFH) on human hepatocelluar cells were evaluated in vitro by an MTT assay and flow cytometry. The results indicated that Mn0.5Zn0.5Fe2O4 and nanosized As2O3/Mn0.5Zn0.5Fe2O4 complex were both prepared successfully. The Mn0.5Zn0.5Fe2O4 nanoparticles had powerful absorption capabilities in a high-frequency alternating electromagnetic field, and had strong magnetic responsiveness. Moreover, Mn0.5Zn0.5Fe2O4 didn’t show cytotoxicity in vitro. The therapeutic result reveals that the nanosized As2O3/Mn0.5Zn0.5Fe2O4 complex can significantly inhibit the growth of hepatoma carcinoma cells