Recordism: A social-scientific prospect of blockchain from social, legal, financial, and technological perspectives

Blockchain has the potential to reform the architecture of cyberspace and transform the storage, circulation and exchange of information through decentralization, transparency and de-identification. Meaning that ordinary participants can become traders, miners, retailers, and customers simultaneously, breaking the barriers and reducing the information gap between participants in the community, contributing to the futuristic metaverse with an open progressive and equal ideology. Such information transformation empowered by blockchain also profoundly impacts our methodological cognition, legal governance on cyberspace and financial and technological development. This study explores the main question: what are the implications of the blockchain-driven information revolution for society and social sciences? In order to answer this main question, this paper chooses four perspectives, which are methodological, legal, financial and technical. By analysis of these four perspectives, this paper is expected to provide a more comprehensive analysis of the blockchain-driven impact on society, social sciences, and technology to contribute to current scholarships. Additionally, regarding blockchain as an innovative methodological cognition, it grows on top of other technologies while helping advance other technologies. This paper concludes that although there are few frictions between blockchain and current social architecture, blockchain is so much more than the technology itself, that can be a representative of the community, acting as the source of trust, watcher of governance, law enforcer for virtual activities, and an incubator for future technologies

NPS: A Framework for Accurate Program Sampling Using Graph Neural Network

With the end of Moore's Law, there is a growing demand for rapid architectural innovations in modern processors, such as RISC-V custom extensions, to continue performance scaling. Program sampling is a crucial step in microprocessor design, as it selects representative simulation points for workload simulation. While SimPoint has been the de-facto approach for decades, its limited expressiveness with Basic Block Vector (BBV) requires time-consuming human tuning, often taking months, which impedes fast innovation and agile hardware development. This paper introduces Neural Program Sampling (NPS), a novel framework that learns execution embeddings using dynamic snapshots of a Graph Neural Network. NPS deploys AssemblyNet for embedding generation, leveraging an application's code structures and runtime states. AssemblyNet serves as NPS's graph model and neural architecture, capturing a program's behavior in aspects such as data computation, code path, and data flow. AssemblyNet is trained with a data prefetch task that predicts consecutive memory addresses. In the experiments, NPS outperforms SimPoint by up to 63%, reducing the average error by 38%. Additionally, NPS demonstrates strong robustness with increased accuracy, reducing the expensive accuracy tuning overhead. Furthermore, NPS shows higher accuracy and generality than the state-of-the-art GNN approach in code behavior learning, enabling the generation of high-quality execution embeddings

SAMAug: Point Prompt Augmentation for Segment Anything Model

This paper introduces SAMAug, a novel visual point augmentation method for the Segment Anything Model (SAM) that enhances interactive image segmentation performance. SAMAug generates augmented point prompts to provide more information about the user's intention to SAM. Starting with an initial point prompt, SAM produces an initial mask, which is then fed into our proposed SAMAug to generate augmented point prompts. By incorporating these extra points, SAM can generate augmented segmentation masks based on both the augmented point prompts and the initial prompt, resulting in improved segmentation performance. We conducted evaluations using four different point augmentation strategies: random sampling, sampling based on maximum difference entropy, maximum distance, and saliency. Experiment results on the COCO, Fundus, COVID QUEx, and ISIC2018 datasets show that SAMAug can boost SAM's segmentation results, especially using the maximum distance and saliency. SAMAug demonstrates the potential of visual prompt augmentation for computer vision. Codes of SAMAug are available at github.com/yhydhx/SAMAu

Effects of n-3 fatty acid supplements on glycemic traits in Chinese type 2 diabetic patients: A double-blind randomized controlled trial.

SCOPE: To investigate the effects of n-3 fatty acid supplements, both marine and plant-based, on glycemic traits in Chinese type 2 diabetes patients. METHOD AND RESULTS: In a double-blind randomized controlled trial, 185 recruited Chinese type 2 diabetes patients were randomized to either fish oil (FO, n = 63), flaxseed oil (FSO, n = 61), or corn oil group (served as control group, n = 61) for 180 days. The patients were asked to take corresponding oil capsules (four capsules/day), which totally provided 2 g/day of eicosapentaenoic acid + docosahexaenoic acid in FO group and 2.5 g/day of alpha-linolenic acid in FSO group. No group × time interaction was observed for homeostatic model assessment of insulin resistance, fasting insulin, or glucose. Significant group × time interaction (P = 0.035) was observed for glycated hemoglobin A1c (HbA1c), with HbA1c decreased in FO group compared with corn oil group (P = 0.037). We also found significant group × time interactions for lipid traits, including LDL cholesterol (P = 0.043), total cholesterol (P = 0.021), total cholesterol/HDL cholesterol (P = 0.009), and triacylglycerol (P = 0.003), with the lipid profiles improved in FO group. No significant effects of FSO on glycemic traits or blood lipids were observed. CONCLUSIONS: Marine n-3 PUFA supplements may improve glycemic control and lipid profiles among Chinese type 2 diabetic patients.National Basic Research Program of China (973 Program: 2015CB553604)This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1002/mnfr.20160023

Attenuation of PITPNM1 signaling cascade can inhibit breast cancer progression

Phosphatidylinositol transfer protein membrane-associated 1 (PITPNM1) contains a highly conserved phosphatidylinositol transfer domain which is involved in phosphoinositide trafficking and signaling transduction under physiological conditions. However, the functional role of PITPNM1 in cancer progression remains unknown. Here, by integrating datasets of The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer (METABRIC), we found that the expression of PITPNM1 is much higher in breast cancer tissues than in normal breast tissues, and a high expression of PITPNM1 predicts a poor prognosis for breast cancer patients. Through gene set variation analysis (GSEA) and gene ontology (GO) analysis, we found PITPNM1 is mainly associated with carcinogenesis and cell-to-cell signaling ontology. Silencing of PITPNM1, in vitro, significantly abrogates proliferation and colony formation of breast cancer cells. Collectively, PITPNM1 is an important prognostic indicator and a potential therapeutic target for breast cancer

Case Report: Chronic hepatitis E virus Infection in an individual without evidence for immune deficiency

Chronic hepatitis E virus (HEV) infection occurs mainly in immunosuppressed populations. We describe an investigation of chronic HEV infection of genotype 3a in an individual without evidence for immune deficiency who presented hepatitis with significant HEV viremia and viral shedding. We monitored HEV RNA in plasma and stools, and assessed anti-HEV specific immune responses. The patient was without apparent immunodeficiency based on quantified results of white blood cell, lymphocyte, neutrophilic granulocyte, CD3+ T cell, CD4+ T cell, and CD8+ T cell counts and CD4/CD8 ratio, as well as total serum IgG, IgM, and IgA, which were in the normal range. Despite HEV specific cellular response and strong humoral immunity being observed, viral shedding persisted up to 109 IU/mL. After treatment with ribavirin combined with interferon, the indicators of liver function in the patient returned to normal, accompanied by complete suppression and clearance of HEV. These results indicate that HEV chronicity can also occur in individuals without evidence of immunodeficiency

SigRec: Automatic Recovery of Function Signatures in Smart Contracts

Millions of smart contracts have been deployed onto Ethereum for providing various services, whose functions can be invoked. For this purpose, the caller needs to know the function signature of a callee, which includes its function id and parameter types. Such signatures are critical to many applications focusing on smart contracts, e.g., reverse engineering, fuzzing, attack detection, and profiling. Unfortunately, it is challenging to recover the function signatures from contract bytecode, since neither debug information nor type information is present in the bytecode. To address this issue, prior approaches rely on source code, or a collection of known signatures from incomplete databases or incomplete heuristic rules, which, however, are far from adequate and cannot cope with the rapid growth of new contracts. In this paper, we propose a novel solution that leverages how functions are handled by Ethereum virtual machine (EVM) to automatically recover function signatures. In particular, we exploit how smart contracts determine the functions to be invoked to locate and extract function ids, and propose a new approach named type-aware symbolic execution (TASE) that utilizes the semantics of EVM operations on parameters to identify the number and the types of parameters. Moreover, we develop SigRec , a new tool for recovering function signatures from contract bytecode without the need of source code and function signature databases. The extensive experimental results show that SigRec outperforms all existing tools, achieving an unprecedented 98.7 percent accuracy within 0.074 seconds. We further demonstrate that the recovered function signatures are useful in attack detection, fuzzing and reverse engineering of EVM bytecode

Small molecular inhibitors reverse cancer metastasis by blockading oncogenic PITPNM3

Most cancer‐related deaths are a result of metastasis. The development of small molecular inhibitors reversing cancer metastasis represents a promising therapeutic opportunity for cancer patients. This pan‐cancer analysis identifies oncogenic roles of membrane‐associated phosphatidylinositol transfer protein 3 (PITPNM3), which is crucial for cancer metastasis. Small molecules targeting PITPNM3 must be explored further. Here, PITPNM3‐selective small molecular inhibitors are reported. These compounds exhibit target‐specific inhibition of PITPNM3 signaling, thereby reducing metastasis of breast cancer cells. Besides, by using nanoparticle‐based delivery systems, these PITPNM3‐selective compounds loaded nanoparticles significantly repress metastasis of breast cancer in mouse xenograft models and organoid models. Notably, the results establish an important metastatic‐promoting role for PITPNM3 and offer PITPNM3 inhibition as a therapeutic strategy in metastatic breast cancer

A stable aluminosilicate zeolite with intersecting three-dimensional extra-large pores

Anyone can then access the published paper FREE OF CHARGE by clicking on this link. Cualquier persona puede acceder al documento publicado de forma GRATUITA, accediendo a través del enlace: https://www.science.org/stoken/author-tokens/ST-242/full[EN] Zeolites are crystalline porous materials with important industrial applications, including uses in catalytic and adsorption-separation processes. Access into and out of their inner confined space, where adsorption and reactions occur, is limited by their pore apertures. Stable multidimensional zeolites with larger pores able to process larger molecules are in demand in the fine chemical industry and for the oil processing on which the world still relies for fuels. Currently known extra-large-pore zeolites display poor stability and/or lack pore multidimensionality, limiting their usefulness. We report ZEO-1, a robust, fully connected aluminosilicate zeolite with mutually intersecting three-dimensional extra-large plus three-dimensional large pores. ZEO-1 is stable up to 1000°C, has an extraordinary specific surface area (1000 square meters per gram), and shows potential as a catalytic cracking catalys.National Natural Science Foundation of China (grant numbers: 21601004, 21776312, 22078364), the Natural Science Foundation of the Higher Education Institutions of Anhui Province, China (grant numbers: KJ2020A0585), and the Spanish Ministry of Science Innovation and Universities (MICIU) (PID2019-105479RB-I00 project, AEI, Spain and FEDER, EU). The cRED data was collected at the Electron Microscopy Center (EMC), Department of Materials and Environmental Chemistry (MMK) in Stockholm University with the support of the Swedish Research Council (Grant No. 1444205) and the Knut and Alice Wallenberg Foundation (KAW, 2012-0112) through the 3DEM-NATUR project. Use of the Advanced Photon Source at Argonne National Laboratory was supported by the U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. W.F. gratefully acknowledges support from the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, under Award # DE-SC0019170.Peer reviewe

A prognostic index model for assessing the prognosis of ccRCC patients by using the mRNA expression profiles of AIF1L, SERPINC1 and CES1

Background: Kidney carcinoma is a major cause of carcinoma-related death, with the prognosis for advanced or metastatic renal cell carcinoma still very poor. The aim of this study was to investigate feasible prognostic biomarkers that can be used to construct a prognostic index model for clear cell renal cell carcinoma (ccRCC) patients. Methods: The mRNA expression profiles of ccRCC samples were downloaded from the The Cancer Genome Atlas (TCGA) dataset and the correlation of AIF1L with malignancy, tumor stage and prognosis were evaluated. Differentially expressed genes (DEGs) between AIF1L-low and AIF1L-high expression groups were selected. Those with prognostic value as determined by univariate and multivariate Cox regression analysis were then used to construct a prognostic index model capable of predicting the outcome of ccRCC patients. Results: The expression level of AIF1L was lower in ccRCC samples than in normal kidney samples. AIF1L expression showed an inverse correlation with tumor stage and a positive association with better prognosis. ccRCC samples were divided into high- and low-expression groups according to the median value of AIF1L expression. In the AIF1L-high expression group, 165 up-regulated DEGs and 601 down-regulated DEGs were identified. Three genes (AIF1L, SERPINC1 and CES1) were selected following univariate and multivariate Cox regression analysis. The hazard ratio (HR) and 95% confidence intervals (CI) for these genes were: AIF1L (HR = 0.83, 95% CI: 0.76–0.91), SERPINC1 (HR = 1.33, 95% CI: 1.12–1.58), and CES1 (HR = 0.87, 95% CI: 0.78–0.97). A prognostic index model based on the expression level of the three genes showed good performance in predicting ccRCC patient outcome, with an area under the ROC curve (AUC) of 0.671. Conclusion: This research provides a better understanding of the correlation between AIF1L expression and ccRCC. We propose a novel prognostic index model comprising AIF1L, SERPINC1 and CES1 expression that may assist physicians in determining the prognosis of ccRCC patients