Search

Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.

Lactate is used in the food and pharmaceutical industries and is a crucial intermediate for synthesis. Plasma-activated lactate (PAL) in Ringer's solution was recently shown to have effective antitumor action. Small molecule aldehydes, ketones, and organic acids were produced from lactate during plasma exposure, and five-membered conjugated lactone isomers of furanone (C5H6O2) were detected formed by interactions of lactate or its fragments with •OH, organic radicals, and H2O2. 2,3-Dimethyl-tartaric acid may be the effective component in PAL for the selective killing of cancer but not normal cells and possible pathways for its synthesis are provided. Aqueous reaction mechanisms are explained, including dehydration, esterification, hydrolysis, and dimerization. This study will help develop novel cancer therapies and further plasma organic chemistry.

Lactate is used in the food and pharmaceutical industries and is a crucial intermediate for synthesis. Plasma-activated lactate (PAL) in Ringer's solution was recently shown to have effective antitumor action. Small molecule aldehydes, ketones, and organic acids were produced from lactate during plasma exposure, and five-membered conjugated lactone isomers of furanone (C5H6O2) were detected formed by interactions of lactate or its fragments with •OH, organic radicals, and H2O2. 2,3-Dimethyl-tartaric acid may be the effective component in PAL for the selective killing of cancer but not normal cells and possible pathways for its synthesis are provided. Aqueous reaction mechanisms are explained, including dehydration, esterification, hydrolysis, and dimerization. This study will help develop novel cancer therapies and further plasma organic chemistry.

BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC.METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials.RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1.CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of

In this paper we describe the development of a streamlined framework for large-scale ATLAS pMSSM reinterpretations of LHC Run-2 analyses using containerised computational workflows. The project is looking to assess the global coverage of BSM physics and requires running O(5k) computational workflows representing pMSSM model points. Following ATLAS Analysis Preservation policies, many analyses have been preserved as containerised Yadage workflows, and after validation were added to a curated selection for the pMSSM study. To run the workflows at scale, we utilised the REANA reusable analysis platform. We describe how the REANA platform was enhanced to ensure the best concurrent throughput by internal service scheduling changes. We discuss the scalability of the approach on Kubernetes clusters from 500 to 5000 cores. Finally, we demonstrate a possibility of using additional ad-hoc public cloud infrastructure resources by running the same workflows on the Google Cloud Platform., Comment: 8 pages, 9 figures. Contribution to the Proceedings of the 26th International Conference on Computing In High Energy and Nuclear Physics (CHEP 2023)

Linyuan Gui,1,* Qingyuan Ye,2,* Lu Yu,3 Geng Dou,1 Yang Zhou,1 Yang Liu,4 Yanqi Zhang,5 Xiaoshan Yang,1,6 Fang Jin,5 Shiyu Liu,1 Yan Jin,1 Lili Ren1 1State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi International Joint Research Center for Oral Diseases, Center for Tissue Engineering, School of Stomatology, The Fourth Military Medical University, Xi’an, Shaanxi, 710032, People’s Republic of China; 2State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Key Laboratory of Stomatology, Digital Dentistry Center, School of Stomatology, The Fourth Military Medical University, Xi’an, Shaanxi, 710032, People’s Republic of China; 3Department of Periodontology, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration & Shandong Provincial Clinical Research Center for Oral Diseases, Jinan, Shandong, 250012, People’s Republic of China; 4Department of Anesthesiology and Perioperative Medicine, Xijing Hospital, The Fourth Military Medical University, Xi’an, Shaanxi, 710032, People’s Republic of China; 5State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Clinical Research Center for Oral Diseases, Department of Orthodontics, School of Stomatology, The Fourth Military Medical University, Xi’an, Shaanxi, 710032, People’s Republic of China; 6Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, Guangdong, 510280, People’s Republic of China*These authors contributed equally to this workCorrespondence: Lili Ren; Yan Jin, State Key Laboratory of Oral & Maxillofaci

Laminaria japonica (LJ) is a common seaweed, which has been widely used as functional food and medicine, but its pharmaceutical activities in angiogenic regulation are unclear. Here, different lines of studies provide evidence to the suppressive effects of LJ extract in VEGF-medicated angiogenic function. In cellular level, LJ extract significantly inhibited VEGF-mediated endothelial cell proliferation, migration, invasion and tube formation. For in vivo verification, LJ extract suppressed the VEGF-induced formation of sub-intestinal vessels in zebrafish embryo model. In signalling cascades, LJ extract decreased the amount of phosphorylated VEGF receptor-2, as triggered by VEGF, and subsequently some key related downstream elements were obviously inactivated. In parallel, NO production and ROS formation, as induced by VEGF, were significantly attenuated. Thus, these preclinical findings promoted the possible development of LJ extract as a potential therapeutic agent for prevention and clinical treatment of angiogenesis-related diseases. © 2024 The Authors

Yuling Gao,1 Yanrui Zhao,1 Hanzhou Wang,1 Xiaopei Xu,1 Zhexian Cheng,2 Tianchao Lu,1 Junlin Zhou,1 Yang Liu1 1Orthopedics Department, Affiliated Beijing Chaoyang Hospital of Capital Medical University, Bejing, People’s Republic of China; 2Preventive Dentistry Department, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, People’s Republic of ChinaCorrespondence: Junlin Zhou; Yang Liu, Beijing Chaoyang Hospital, Capital Medical University, Gongtinan Road 8#, Beijing, 100020, People’s Republic of China, Email junlinzhou_article@outlook.com; y0ring1996@163.comBackground: This study aimed to create three-dimensional heat map and study the characteristic of fracture lines and represented fragments of OTA/AO type 43C pilon fractures.Methods: CT scan was performed in105 fractures diagnosed with OTA/AO type 43C pilon fractures between January 2017 and December 2022. Three-dimensional pilon fracture maps were created and converted into fracture heat maps. CT scan graphic parameters including the fracture line height, α angle, β angle, the ratio of the area and size of bone fragment represented by the fracture line to the total articular surface were measured.Results: The study included 105 patients with 91 males and 14 females. The fractures included C1 (n=16), C2 (n=23), and C3 (n=66). There was no statistically different among the most parameters except in the fracture-line height of the anterior fracture line (p=0.03) and the sagittal fracture line (p=0.02) between C2 and C3 pilon fractures. The average size of the anterolateral fragment, occupied approximately 13.5% of the articular surface area, was (11.5± 2.8) mm × (20.5± 6.3) mm with the average height of 29.8 mm. The average size of the posterolateral fragment, occupied approximately 13.0% of the articular surface area, was (15.7± 4.6) mm × (19.3± 4.0)

Yang Liu,* Xiaoli Wu,* Siting Chen, Wenxia Meng Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, 510280, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wenxia Meng, Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, 510280, People’s Republic of China, Email mengwx2008@foxmail.comAbstract: Acanthosis nigricans (AN) is a dermatological condition characterised by the symmetrical development of velvety, hyperpigmented plaques predominantly in intertriginous areas such as the axillae, neck, inframammary regions, and groin. The malignant variant of AN is frequently associated with internal malignancies, particularly gastric adenocarcinoma, accounting for 55– 61% of cases. Patients exhibiting characteristic skin lesions are commonly initially evaluated in dermatology departments. This case report details a rare instance of a patient diagnosed with malignant acanthosis nigricans, presenting with only a mild form of florid oral papillomatosis concomitant with ovarian carcinoma. The early identification and management of these subtle clinical manifestations enabled timely intervention for the tumor, resulting in patient survival. There are few reported cases of malignant acanthosis nigricans associated with ovarian cancer. Oral medicine specialists should be cognisant of conditions manifesting as extensive oral papillary hyperplasia, and the possibility of an underlying malignant disease should be considered, particularly in cases of elderly-onset AN presenting exclusively with oral lesions.Keywords: malignant acanthosis nigricans, ovarian cancer, papillary hyperplasia

This study proposed a semi-analytical solution for 2D electro-osmotic consolidation of double-layered soil. The method of Laplace transform is employed to derive the solution in the frequency domain. Then the numerical approach of inverse Laplace transform is used to obtain the explicit solution in the time domain. A finite element simulation is conducted to validate the proposed solution's accuracy. The verification example shows excellent agreement between the two methods. Based on the parametric study, it is found that the value of soil parameters in the second layer has significant influences on the behaviour of electro-osmotic consolidation. The larger the electro-osmosis coefficient in the second layer, the smaller the excess pore pressure at a given time. Additionally, a higher coefficient of volume compressibility in the second layer results in higher excess pore pressure. Compared with the 1D case, the distribution of excess pore pressure at the steady state is non-linear with depth in the 2D electro-osmotic consolidation, rather than a linear distribution. Furthermore, the dissipation rate and steady-state excess pore pressure could be underestimated if the electro-osmotic consolidation is modelled as a 1D case. © 2023 Elsevier Ltd

The COVID-19 pandemic presents challenges to psychological well-being, but how can we predict when people suffer or cope during sustained stress? Here, we test the prediction that specific types of momentary emotional experiences are differently linked to psychological well-being during the pandemic. Study 1 used survey data collected from 24,221 participants in 51 countries during the COVID-19 outbreak. We show that, across countries, well-being is linked to individuals' recent emotional experiences, including calm, hope, anxiety, loneliness, and sadness. Consistent results are found in two age, sex, and ethnicity-representative samples in the United Kingdom (n = 971) and the United States (n = 961) with preregistered analyses (Study 2). A prospective 30-day daily diary study conducted in the United Kingdom (n = 110) confirms the key role of these five emotions and demonstrates that emotional experiences precede changes in well-being (Study 3). Our findings highlight differential relationships between specific types of momentary emotional experiences and well-being and point to the cultivation of calm and hope as candidate routes for well-being interventions during periods of sustained stress. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Federated learning (FL) is a joint training pattern that fully utilizes data information whereas protecting data privacy. A key challenge in FL is statistical heterogeneity, which arises on account of the heterogeneity of local data distributions among clients, leading to inconsistency in local optimization goals and ultimately reducing the performance of globally aggregated models. We propose the Federated Ensemble Learning (FedEL), which makes full use of the heterogeneity of data distribution among clients to train a group of weak learners with diversity to construct a global model, which is a novel solution to the non-independent identical distribution (non-IID) problem. Experiments demonstrate that the proposed FedEL can improve performance in non-IID data scenarios. Even under extreme statistical heterogeneity, the average accuracy of FedEL is 3.54% higher than the state-of-the-art FL method. Moreover, the proposed FedEL reduces model storage and reasoning costs compared with traditional ensemble learning. The proposed FedEL demonstrates good generalization ability in experiments across different datasets, including natural scene image datasets and medical image datasets. © 2023 Elsevier Ltd

This paper explores consumer green consumption practices and considers a set of factors, including cognitive and behavioural level constructs, that influence green consumption. The paper primarily aims to predict the green purchase intention and classify a consumer as a green or non-green consumer. A total of 310 responses were collected and analyzed using machine Learning techniques like Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbour, and Support Vector Machine, and the models were validated using different performance metrics. The paper reveals that the main driving factors for a consumer to consider greener options are green self-identification, followed by environmental knowledge, environmental consciousness, and the impact of social media. The current work will allow better product development and the targeting and positioning of green products/services offerings to customers already classified by the system.

As the world's largest coal consumer, China is facing the dual challenge of implementing strict coal reduction policies while heavily relying on coal. It is crucial to comprehend the pressures exerted on carbon dioxide emissions from coal consumption as China strives to transition towards a carbon-neutral era. This study defines and classifies the pressure of carbon dioxide emissions resulting from coal consumption, referred to as "carboncoal pressure", in 30 provinces (including municipalities and autonomous regions) from 1997 to 2019. This classification enriches the study of the pressure on carbon dioxide emissions by specific energy types. By calculating the centre of gravity of the carbon-coal pressure and its evolution trends, the spatial pattern of the carbon-coal pressure and the evolution characteristics of the centre of gravity of the pressure are revealed. The results demonstrate that, despite the continuous growth in total coal consumption and carbon dioxide emissions in China, the carbon-coal pressure index exhibits a decreasing trend in certain regions, with significant interregional differences. Most provinces fall into the high-pressure and higher-pressure categories. The number of high-pressure provinces has decreased by 33% from 18 to 8, while the number of low-pressure provinces has risen from 0 to 1, and both higher and medium pressure types have increased. The overall stress index decreases from 0.79 to 0.7. The pressure centre displays a similar spatial trend to the overall changes in coal consumption and carbon dioxide emissions centre, albeit with a smaller magnitude of change. Beijing stands out as the only province with low pressure., Funding Agencies|China Scholarship Council Ph.D. program [202206430037]; Key Program of National Social Science Fund of China [23AGL033]

Government policies have focused on carbon emissions from the green supply chain (GSC). To discuss how price decision-making in GSC is affected by the manufacturer's fairness concerns, retailer's countervailing power, and carbon emissions reduction, the Stackelberg model assumes that consumers are environmentally sensitive. To explore this problem considering the type of manufacturer's behavior preferences, we built four models that pertain to the following two situations: the retailer has countervailing power, and the retailer has no countervailing power. The results show that (1) the manufacturer's disadvantageous fairness concerns reduce the overall supply chain profits and utility; influenced by countervailing power, the members witness a win-win situation; (2) moderate advantageous fairness concerns benefit GSC members; furthermore, under advantageous fairness concerns, the manufacturer can "give profits" to the retailer; (3) affected by the retailer's countervailing power, no bargaining agreement is made in the GSC under advantageous fairness concerns. Finally, the interplay between member decisions and consumer sensitivity is explored. This study can help manufacturers achieve emission reduction targets in response to consumer environmental awareness and government regulation., Funding Agencies|Humanities and Social Science Fund of Ministry of Education of the People's Republic of China [21YJC630099]

Five-axis flank milling is prevalent in complex surfaces manufacturing, and it typically consumes high electricity energy. To save energy and improve energy efficiency, this paper proposes a tool path optimisation of five-axis flank milling by meta-reinforcement learning. Firstly, considering flank milling features, a feed angle is defined that guides tool spatial motion and identifies an ideal principal path. Then, machining energy consumption and time are modelled by tool path variables, i.e., feed angle, cutting strip width and path length. Secondly, an energy-efficient tool path dynamic optimisation model is constructed, which is then described by multiple Markov Decision Processes (MDPs). Thirdly, meta-learning integrating with the Soft Actor-Critic (MSAC) framework is utilised to address the MDPs. In an MDP with one principal path randomly generated by a feed angle, cutting strip width is dynamically optimised under a maximum scallop height limit to realise energy-efficient multi-expansions. By quick traversal of MDPs with various feed angles, MSAC enables an energy-efficient path generation and expansion integrated scheme. Experiments show that, regarding machining energy consumption and time, the proposed method achieves a reduction of 69.96% and 68.44% over the end milling with an iso-scallop height, and of 41.50% and 39.80% over the flank milling with an iso-scallop height, with a minimum amount of machining carbon emission, which highlights its contribution to the arena of energy-oriented and sustainable intelligent manufacturing., Funding Agencies|National Natural Science Foundation of China

The green supply chain of agricultural products (GSCAP) is a key link for rural revitalization and sustainable development in China. However, it faces various risks from internal and external environments that threaten its performance and stability. This paper proposes a novel framework and system for identifying and evaluating the main risks in the GSCAP from the perspective of agricultural enterprises. The framework combines social network analysis (SNA) and an improved technique for order preference by similarity to an ideal solution (TOPSIS) method. SNA is used to analyze the correlations and influences among different types of risks, while the improved TOPSIS method is used to rank the risks of different GSCAPs and identify the key risks in each supply chain. The framework and system are verified by a case study of CDYBIT, a leading platform of food safety big data service in China. The results show that the supermarket supply chain has the highest risk, followed by the group catering supply chain, and the five-star hotel supply chain has the lowest risk. The main risk factors for each supply chain are also discussed, and some suggestions for risk management are provided. This paper contributes to the literature by providing a comprehensive and systematic risk assessment framework and system for the GSCAP, which can help agricultural enterprises improve their risk awareness and response capabilities., Funding Agencies|National Social Science Fund of China; [21BGL189]

To compete in emerging markets, a supply chain must perform well. Agile and sustainable sourcing practices can improve supply chain performance; however, their impact needs an optimal evaluation. Although few research studies offer frameworks for integrating agile and sustainable principles, none offer links to implementing these practices in the sourcing networks of a supply chain. The present study seeks to bridge these gaps by developing a framework that identifies and configures the enabling elements for creating agile and sustainable sourcing networks. This study aims to provide an implementable causal model that the plastic industry's supply chain could adopt. In the first phase of the research process, fifteen enablers are identified through literature and validated by Delphi experts. In the second phase, interpretive structural modeling is applied to establish the hierarchical relationships among these enablers and categorize them based on their functionalities. The model is demonstrated based on the real-life case study of a firm manufacturing plastic pipes and fittings. The proposed model identifies the strategic, operational, and performance level enablers and intends to help the managers incorporate the agile and sustainable criteria in their sourcing practices. The findings of this study provide several contributions to the literature and implications for the plastic industry.

This paper explores consumer green consumption practices and considers a set of factors, including cognitive and behavioural level constructs, that influence green consumption. The paper primarily aims to predict the green purchase intention and classify a consumer as a green or non-green consumer. A total of 310 responses were collected and analyzed using machine Learning techniques like Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbour, and Support Vector Machine, and the models were validated using different performance metrics. The paper reveals that the main driving factors for a consumer to consider greener options are green self-identification, followed by environmental knowledge, environmental consciousness, and the impact of social media. The current work will allow better product development and the targeting and positioning of green products/services offerings to customers already classified by the system., CC BY 4.0 DEED© 2024 The AuthorsCorrespondence Address: Y. Liu; Department of Management and Engineering, Linköping University, Linköping, SE-581 83, Sweden; email: yang.liu@liu.se; CODEN: JCROE

Yuanyuan Zhao,1 Yang Liu,1 Cheng Lu,1 Daokuan Sun,2 Shiqi Kang,1 Xin Wang,2,* Laijin Lu1,* 1Department of Hand and Podiatric Surgery, Orthopedics Center, The First Hospital of Jilin University, Changchun, Jilin, People’s Republic of China; 2School of Materials Science and Engineering, Jilin University, Changchun, Jilin, People’s Republic of China*These authors contributed equally to this workCorrespondence: Laijin Lu, Department of Hand and Podiatric Surgery, Orthopedics Center, The First Hospital of Jilin University, Changchun, Jilin, People’s Republic of China, Tel +86-13756661203, Fax +86 431 84808391, Email ljlu@jlu.edu.cn Xin Wang, School of Materials Science and Engineering, Jilin University, Changchun, Jilin, People’s Republic of China, Tel +86-18 626 655 395, Fax +86 431 84 808 391, Email wang_xin@jlu.edu.cnBackground: The treatment of long-gap peripheral nerve injury (PNI) is still a substantial clinical problem. Graphene-based scaffolds possess extracellular matrix (ECM) characteristic and can conduct electrical signals, therefore have been investigated for repairing PNI. Combined with electrical stimulation (ES), a well performance should be expected. We aimed to determine the effects of reduced graphene oxide fibers (rGOFs) combined with ES on PNI repair in vivo.Methods: rGOFs were prepared by one-step dimensionally confined hydrothermal strategy (DCH). Surface characteristics, chemical compositions, electrical and mechanical properties of the samples were characterized. The biocompatibility of the rGOFs were systematically explored both in vitro and in vivo. Total of 54 Sprague-Dawley (SD) rats were randomized into 6 experimental groups: a silicone conduit (S), S+ES, S+rGOFs-filled conduit (SGC), SGC+ES, nerve autograft, and sham groups for a 10-mm sciatic defect. Functional and histological recovery of the regenerated sciatic nerve at 12 weeks after surgery in each group of SD rats were evaluated.Results: rGOFs exhibited

This paper explores consumer green consumption practices and considers a set of factors, including cognitive and behavioural level constructs, that influence green consumption. The paper primarily aims to predict the green purchase intention and classify a consumer as a green or non-green consumer. A total of 310 responses were collected and analyzed using machine Learning techniques like Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbour, and Support Vector Machine, and the models were validated using different performance metrics. The paper reveals that the main driving factors for a consumer to consider greener options are green self-identification, followed by environmental knowledge, environmental consciousness, and the impact of social media. The current work will allow better product development and the targeting and positioning of green products/services offerings to customers already classified by the system., CC BY 4.0 DEED© 2024 The AuthorsCorrespondence Address: Y. Liu; Department of Management and Engineering, Linköping University, Linköping, SE-581 83, Sweden; email: yang.liu@liu.se; CODEN: JCROE

Microbial systems appear to exhibit a relatively high switching capacity of moving back and forth among few dominant communities (taxon memberships). While this switching behavior has been mainly attributed to random environmental factors, it remains unclear the extent to which internal community dynamics affect the switching capacity of microbial systems. Here, we integrate ecological theory and empirical data to demonstrate that structured community transitions increase the dependency of future communities on the current taxon membership, enhancing the switching capacity of microbial systems. Following a structuralist approach, we propose that each community is feasible within a unique domain in environmental parameter space. Then, structured transitions between any two communities can happen with probability proportional to the size of their feasibility domains and inversely proportional to their distance in environmental parameter space—which can be treated as a special case of the gravity model. We detect two broad classes of systems with structured transitions: one class where switching capacity is high across a wide range of community sizes and another class where switching capacity is high only inside a narrow size range. We corroborate our theory using temporal data of gut and oral microbiota (belonging to class 1) as well as vaginal and ocean microbiota (belonging to class 2). These results reveal that the topology of feasibility domains in environmental parameter space is a relevant property to understand the changing behavior of microbial systems. This knowledge can be potentially used to understand the relevant community size at which internal dynamics can be operating in microbial systems.

We examine the descent via membrane nucleation through a landscape of vacua where the cosmological constant is given by a combination of four-form fluxes. It has been shown that this descent can be slowed exponentially for very low curvature vacua close to Minkowski space in a wide class of models satisfying certain parametric conditions, providing a possible solution to the cosmological constant problem. We explore in detail whether or not those parametric conditions are compatible with the membrane weak gravity conjecture. Whilst it is true that there is often tension, we show that this is not always the case and present an explicit model where Minkowski space is absolutely stable and the weak gravity conjecture is satisfied. This corresponds to an extension of the Bousso-Polchinski model into a generalised DBI action for four-forms. We also clarify how the landscape should be populated in a consistent model., Comment: 22 pages, 1 figure

Aims. The aim of this paper is to consider relationship between the decay of sunspots and convection via the motion of the family of granules and how the diffusion mechanism of magnetic field operates in a decaying sunspot. Methods. We report the decay of a sunspot observed by the 1.6m Goode Solar Telescope (GST) with the TiO Broadband Filter Imager (BFI) and the Near-InfraRed Imaging Spectropolarimeter (NIRIS). The analysis was aided by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamic Observatory (SDO). In the first step, we followed the decay of the sunspot with HMI data over three days by constructing its evolving area and total magnetic flux. In the second step, the high spatial and temporal resolution of the GST instruments allowed us to analyze the causes of the decay of the sunspot. Afterward, we followed the emergence of granules in the moat region around the sunspot over six hours. The evolution of the trees of fragmenting granules (TFGs) was derived based on their relationship with the horizontal surface flows. Results. We find that the area and total magnetic flux display an exponential decrease over the course of the sunspot decay. We identified 22 moving magnetic features (MMFs) in the moats of pores, which is a signature of sunspot decay through diffusion. We note that the MMFs were constrained to follow the borders of TFGs during their journey away from the sunspot. Conclusions. The TFGs and their development contribute to the diffusion of the magnetic field outside the sunspot. The conclusion of our analysis shows the important role of the TFGs in sunspot decay. Finally, the the family of granules evacuates the magnetic field.

Recognizing the growing significance of contamination from weak gravitational lensing B-modes induced by Large Scale Structure, we investigate a thorough examination of delensing methods aiming at enhancing the sensitivity of r in primordial B-mode detection experiments. In this study, we focus on two specific delensing approaches. One approach involves computing the gradient-order lensing B-mode template by cross-correlating the E-mode with the lensing potential, and subsequently subtracting it from the observed B-mode signal. Another method entails remapping the observations using the estimated deflection angle. Then demonstrate the delensing efficiency using the simulated maps from future CMB polarization experiments, including two ground-based observations: sub-1 m small aperture telescope and 6 m large aperture telescope, and one future space mission with medium aperture telescope. The results reveal that delensing efficiency will be 40% with ground-based small-aperture only, and increases to 65% when combined with a large-aperture telescope. Furthermore, the future satellite experiment achieves an impressive delensing efficiency of approximately 80%.

We report the test results of several independent foreground-cleaning pipelines used in the Ali CMB Polarization Telescope experiment (AliCPT-1), a high-altitude CMB imager in the Northern hemisphere with thousands of detectors dedicated to the search for a primordial CMB polarization $B$-mode signature. Based on simulated data from 4 detector modules and a single season of observation, which we refer to as DC1 data, we employ different and independent pipelines to examine the robustness and effectiveness of the estimates on foreground parameters and the primordial $B$-mode detection. The foreground-cleaning strategies used in the pipelines include the parametric method of template fitting (TF) and the non-parametric methods of the constrained internal linear combination (cILC), the analytical blind separation (ABS), and the generalized least squares (GLS). We examine the impact of possible foreground residuals on the estimate of the CMB tensor-to-scalar ratio ($r$) for each pipeline by changing the contamination components in the simulated maps and varying the foreground models and sky patches for various tests. According to the DC1 data with the simulation input value $r_{\rm true}=0.023$, the foreground residual contamination levels in the TF/ABS/cILC/GLS pipelines are well within the corresponding statistical errors at the $2\sigma$ level. For a selected patch with relatively stronger foreground contamination, all of the proposed pipelines perform robustly in testing. Furthermore, by utilizing the tension estimator, which helps identify significant residual foreground contamination in the detection of the primordial $B$-mode signal by quantifying the discrepancy between various $r$ measurements, we conclude that the presence of small foreground residuals does not lead to any significant inconsistency in the estimation of $r$.

The Planetary Instrument for X-ray Lithochemistry (PIXL) is a rasterable focused-beam X-ray fluorescence (XRF) spectrometer mounted on the arm of National Aeronautics and Space Administration's (NASA) Mars 2020 Perseverance rover. To ensure that PIXL would be capable of performing accurate in-flight compositional analysis of martian targets, in situ, an elemental calibration was performed pre-flight on the PIXL flight instrument in a simulated martian environment. The details of this calibration, and implications for measuring unknown materials on Mars are the subjects of this paper. The major goals of this calibration were both to align the spectrometer to perform accurate elemental analysis and, to derive a matrix of uncertainties that are applied to XRF measurements of all elements in unknown materials. A small set of pure element and pure compound targets and geologically relevant reference materials were measured with the flight hardware in a simulated martian environment. Elemental calibration and quantifications were carried out using PIXL's XRF quantification software (PIQUANT). Uncertainties generated were implemented into the PIQUANT software version employed by the PIXL's data visualization software (PIXLISE). We outline in this work, a list of factors that impact micro-XRF accuracy, the methodology and steps involved in the calibration, details on the fabrication of the uncertainty matrix, instructions on the use and interpretations of the uncertainties applied to unknowns and an assessment on the limitations and areas open to future improvement as part of subsequent calibration efforts.

The highly sensitive millimeter-wave telescope is an important tool for accurate measurement of Cosmic Microwave Background (CMB) radiation, and its core component is a detector array located in a cryogenic focal plane. The feasibility of utilizing thermal kinetic inductance detectors (TKIDs) for CMB observations has been demonstrated. We propose a pixel design of TKIDs for observing CMB through atmospheric windows for observations in the 90/150 GHz bands. Assuming lossless dielectric, the coupling efficiency of a single pixel is around 90%. This pixel design will be utilized for future large-scale TKIDs array designs for CMB observations.

We present the current state of the art in micro-transfer printing for heterogeneously integrated silicon photonic integrated circuits. The versatility of the technology is highlighted, as is the way ahead to make this technology a key enabler for next-generation photonic systems-on-chip., SCOPUS: sh.j, info:eu-repo/semantics/published

BaTiO3 thin films with a thickness of 400 nm were prepared by spin coating onto a quartz plate and subsequently calcined at 700 °C. The effect of fast (540 K min−1) and slow (10 K min−1) heating on morphology, unit cell parameter and dielectric constant was studied. Fast heating yields tetragonal BaTiO3 films with a semiconductor band gap of 3.54 eV, dielectric constant of 685 and a surface roughness of 12 nm. In contrast, slow heating produces tetragonal BaTiO3 films with a larger band gap of 3.78 eV, a dielectric constant of 219 and a surface roughness of 35 nm. A kinetic constant of 0.0061 min−1 was obtained in the decomposition of methylene blue with UV light over BTO films at 20 °C.

We compare, with data from the quasars, the Hubble parameter measurements, and the Pantheon+ type Ia supernova, three different relations between X-ray luminosity ($L_X$) and ultraviolet luminosity ($L_{UV}$) of quasars. These three relations consist of the standard and two redshift-evolutionary $L_X$-$L_{UV}$ relations which are constructed respectively by considering a redshift dependent correction to the luminosities of quasars and using the statistical tool called copula. By employing the PAge approximation for a cosmological-model-independent description of the cosmic background evolution and dividing the quasar data into the low-redshift and high-redshift parts, we find that the constraints on the PAge parameters from the low-redshift and high-redshift data, which are obtained with the redshift-evolutionary relations, are consistent with each other, while they are not when the standard relation is considered. If the data are used to constrain the coefficients of the relations and the PAge parameters simultaneously, then the observations support the redshift-evolutionary relations at more than $3\sigma$. The Akaike and Bayes information criteria indicate that there is strong evidence against the standard relation and mild evidence against the redshift-evolutionary relation constructed by considering a redshift dependent correction to the luminosities of quasars. This suggests that the redshift-evolutionary $L_X$-$L_{UV}$ relation of quasars constructed from copula is favored by the observations., Comment: 15 pages, 5 figures. Accepted for publication in ApJ

This paper explores consumer green consumption practices and considers a set of factors, including cognitive and behavioural level constructs, that influence green consumption. The paper primarily aims to predict the green purchase intention and classify a consumer as a green or non-green consumer. A total of 310 responses were collected and analyzed using machine Learning techniques like Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbour, and Support Vector Machine, and the models were validated using different performance metrics. The paper reveals that the main driving factors for a consumer to consider greener options are green self-identification, followed by environmental knowledge, environmental consciousness, and the impact of social media. The current work will allow better product development and the targeting and positioning of green products/services offerings to customers already classified by the system., CC BY 4.0 DEED© 2024 The AuthorsCorrespondence Address: Y. Liu; Department of Management and Engineering, Linköping University, Linköping, SE-581 83, Sweden; email: yang.liu@liu.se; CODEN: JCROE

Mengjiao Yang,1,2,* Yang Liu,1,* Yangyang Zhao,3,* Ziwei Wang,4 Jie He,5 Yali Wang,2 Tokie Anme6 1Graduate School of Comprehensive Human Science, University of Tsukuba, Tsukuba, Japan; 2Department of Cardiovascular Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong City, Sichuan Province, People’s Republic of China; 3Department of Blood Transfusion, Affiliated Hospital of North Sichuan Medical College, Nanchong City, Sichuan Province, People’s Republic of China; 4Department of Respiratory and Critical Care Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong City, Sichuan Province, People’s Republic of China; 5Department of Nursing, Affiliated Hospital of North Sichuan Medical College, Nanchong City, Sichuan Province, People’s Republic of China; 6Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan*These authors contributed equally to this workCorrespondence: Tokie Anme, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 3058577, Japan, TelFax +81-29-853-3436, Email tokieanme@gmail.comPurpose: This study aimed to explore the correlation of frailty status with disease characteristics and patient-reported outcomes (PROs) in patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD) and determine the sensitivity and specificity of modified COPD PRO scale (mCOPD-PRO) for detecting frailty.Patients and Methods: This cross-sectional study surveyed 315 inpatients with AECOPD from a tertiary hospital in China from August 2022 to June 2023. Patient frailty and PROs were assessed using the validated FRAIL scale and mCOPD-PRO, respectively. Spearman’s ρ was used to assess the relevance of lung disease indicators commonly used in clinical practice, and ordinal logistic regression analyses were used to identify the variables associated with frailty status. The validity of mCOPD-PRO in discriminating frail or non-frail individuals

Yang Liu,1,2,* Hui Yang,1,2,* Xueshu Fu,1,2 Luting Zhong,1,2 Ping Xu,1,2 Fang Fang,1,2 Ying Liu,1,2 Qing Li,1,2 Ya’nan Yan,1,2 Shanchuang Wei,1,2 Junqing Wang,1,2 Chunhua Zhang1– 3 1Department of Gynaecology, Affiliated Hospital of YangZhou University Huai ‘an Maternal and Child Health Care Center, Huai’an, Jiangsu, People’s Republic of China; 2Department of Gynaecology, The Huai’an Maternity and Child Clinical College of Xuzhou Medical University, Huai’an, Jiangsu, People’s Republic of China; 3Department of Integrated Traditional Chinese & Western Clinical Medicine, Macau University of Science and Technology, Macau, People’s Republic of China*These authors contributed equally to this workCorrespondence: Chunhua Zhang, Department of Gynaecology, The Huai’an Maternity and Child Clinical College of Xuzhou Medical University, Huai’an, Jiangsu, People’s Republic of China, Tel +86-13952326117, Email hafyzch@126.comBackground: Multiple primary cancer (MPC) refers to the presence of more than one cancer in an individual. Triple primary malignancies are uncommon.Case: We report the case of a 50-year-old postmenopausal woman in our gynecology department, diagnosed with endometrial cancer, ovarian cancer, and unilateral breast cancer. She carried germline mutations in BRCA2, PALB2, and RECQL4, along with a somatic pathogenic variant in TP53. Endometrial cancer patients harboring germline pathogenic variants in BRCA2 exhibit a heightened risk of ovarian and breast cancer. BRCA2 is known to play a role in the development of ovarian and breast cancer, while PALB2 is identified as a gene associated with breast cancer susceptibility. RECQL4 has been linked to breast cancer, cervical cancer, and other tumors.Conclusion: Genetic testing may be imperative for identifying MPC in endometrial cancer patients. For individuals with BRCA2 and other gene pathogenic variants, routine examination and monitoring of the endometrium, ovaries, b

Hua-Jing Yuan,1 Hua-Chen Jiao,2 Xiu-Juan Liu,2 Hao Hao,2 Yang Liu,2 Yi-Tao Xue,2 Yan Li2 1Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, 250014, People’s Republic of China; 2Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, 250014, People’s Republic of ChinaCorrespondence: Yi-Tao Xue; Yan Li, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Shanshi East Road, Lixia District, Jinan City, Shandong Province, People’s Republic of China, Tel +86-531-82602943 ; +86-531-68611662, Email xytsdzydfy@126.com; liyan88130@163.comPurpose: The association between serum uric acid (SUA) and atrial fibrillation (AF) has been widely focused on and studied in recent years. However, the exact association between SUA and AF is unclear, and the effect of gender on the association between SUA levels and AF has been controversial. This study aimed to investigate the association between SUA levels and non-valvular AF (NVAF) and the potential effect of gender on it.Patients and Methods: A total of 866 NVAF patients (463 males, age 69.44 ± 8.07 years) and 646 sex-matched control patients in sinus rhythm, with no history of arrhythmia were included in this study. t-test, ANOVA, and chi-square test were used for baseline data analysis. The receiver operating characteristic curve, logistic regression and Pearson correlation analysis were used for correlation analysis.Results: Compared to controls, NVAF patients exhibited higher SUA (P< 0.001). After adjusting for confounders of NVAF, SUA remained significantly associated with NVAF, regardless of gender (OR= 1.31, 95% CI 1.18– 1.43, P< 0.001). SUA demonstrated higher predictability and sensitivity in predicting the occurrence of female NVAF compared to male (area under the curve was 0.68 (95% CI 0.64– 0.72, P< 0.001), sensitivity 87.3%), with the optimal cut-off point identified as 5.72 mg/dL. Furthermore, SUA levels c

Haijun Wei,&ast; Ke Wang,&ast; Wei Huang, Yang Liu Department of Vascular Surgery, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yang Liu, Department of Vascular Surgery, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China, Email zjobliu@163.comAbstract: Pyoderma gangrenosum (PG) is a rare autoinflammatory neutrophilic dermatosis clinically characterized by painful nodules, red papules or plaques that rapidly erode into ulcers. We report a 53-year-old febrile male patient with acute peripheral arterial disease who underwent transtibial amputation after failed thrombolysis. Five days after amputation, an ulcer developed around the cannulation site of the right internal jugular vein that was indicative of pathergy. The patient’s fever did not improve after surgery, and purpuric discoloration and punctate ulcers of the skin near the amputation site became apparent, leading to re-debridement. Finally, consultation with a dermatologist raised the possibility of postoperative PG, and additional laboratory tests revealed positive anticardiolipin autoantibodies consistent with antiphospholipid syndrome. The patient was treated with intravenous glucocorticosteroids and antibiotics, and the amputation wound and cannulation site ulcer were found to have healed at the 2-month follow-up. The current report raises the need for vascular surgeons to be aware of this uncommon etiology of arterial thrombosis, and the postoperative appearance of dermatosis and pathergy should alert for PG.Keywords: antiphospholipid syndrome, case report, peripheral artery disease, pyoderma gangrenosum

In eukaryotes, DNA compacts into chromatin through nucleosomes1,2. Replication of the eukaryotic genome must be coupled to the transmission of the epigenome encoded in the chromatin3,4. Here we report cryo-electron microscopy structures of yeast (Saccharomyces cerevisiae) replisomes associated with the FACT (facilitates chromatin transactions) complex (comprising Spt16 and Pob3) and an evicted histone hexamer. In these structures, FACT is positioned at the front end of the replisome by engaging with the parental DNA duplex to capture the histones through the middle domain and the acidic carboxyl-terminal domain of Spt16. The H2A–H2B dimer chaperoned by the carboxyl-terminal domain of Spt16 is stably tethered to the H3–H4 tetramer, while the vacant H2A–H2B site is occupied by the histone-binding domain of Mcm2. The Mcm2 histone-binding domain wraps around the DNA-binding surface of one H3–H4 dimer and extends across the tetramerization interface of the H3–H4 tetramer to the binding site of Spt16 middle domain before becoming disordered. This arrangement leaves the remaining DNA-binding surface of the other H3–H4 dimer exposed to additional interactions for further processing. The Mcm2 histone-binding domain and its downstream linker region are nested on top of Tof1, relocating the parental histones to the replisome front for transfer to the newly synthesized lagging-strand DNA. Our findings offer crucial structural insights into the mechanism of replication-coupled histone recycling for maintaining epigenetic inheritance.

We report new experimental results on the search for dark photons based on a near-quantum-limited haloscope equipped with a superconducting cavity. The loaded quality factor of the superconducting cavity is $6\times10^{5}$, so that the expected signal from dark photon dark matter can be enhanced by more than one order compared to a copper cavity. A Josephson parametric amplifier with a near-quantum-limited noise temperature has been utilized to minimize the noise during the search. Furthermore, a digital acquisition card based on field programmable gate arrays has been utilized to maximize data collection efficiency with a duty cycle being 100$\%$. This work has established the most stringent constraints on dark photons at around 26.965 $\mu$eV. In the future, our apparatus can be extended to search for other dark matter candidates, such as axions and axion-like particles, and scrutinize new physics beyond the Standard Model.

The rapid evolution of programming languages and software systems has necessitated the implementation of multilingual and scalable clone detection tools. However, it is difficult to achieve the above requirements at the same time. Most existing tools only focus on one challenge. In this work, we propose TGMM, a tree and GPU-based tool for multilingual and multi-granularity code clone detection. By generating parse trees based on user-provided grammar files, TGMM can extract code blocks at a specified granularity and detect Type-3 clones efficiently. In order to show the performance of TGMM, we compare it with seven state-of-the-art tools in terms of recall, precision, and execution time. TGMM ranks first in execution time and precision, while its recall is comparable to the others. Moreover, we analyzed the language extensibility of TGMM across 30 mainstream programming languages. Out of these, a total of 25 languages were supported, while the remaining five currently lack the necessary grammar files. Finally, we analyzed the clone characteristics of nine popular languages at five common granularities, hoping to inspire future researchers. The source code of TGMM is available at: https://github.com/TGMM24/TGMM.git., Comment: 14 pages, 7 figures

Data-Free Knowledge Distillation (DFKD) is a promising task to train high-performance small models to enhance actual deployment without relying on the original training data. Existing methods commonly avoid relying on private data by utilizing synthetic or sampled data. However, a long-overlooked issue is that the severe distribution shifts between their substitution and original data, which manifests as huge differences in the quality of images and class proportions. The harmful shifts are essentially the confounder that significantly causes performance bottlenecks. To tackle the issue, this paper proposes a novel perspective with causal inference to disentangle the student models from the impact of such shifts. By designing a customized causal graph, we first reveal the causalities among the variables in the DFKD task. Subsequently, we propose a Knowledge Distillation Causal Intervention (KDCI) framework based on the backdoor adjustment to de-confound the confounder. KDCI can be flexibly combined with most existing state-of-the-art baselines. Experiments in combination with six representative DFKD methods demonstrate the effectiveness of our KDCI, which can obviously help existing methods under almost all settings, \textit{e.g.}, improving the baseline by up to 15.54\% accuracy on the CIFAR-100 dataset., Comment: Accepted by CVPR24

This study introduces a novel framework for enhancing domain generalization in medical imaging, specifically focusing on utilizing unlabelled multi-view colour fundus photographs. Unlike traditional approaches that rely on single-view imaging data and face challenges in generalizing across diverse clinical settings, our method leverages the rich information in the unlabelled multi-view imaging data to improve model robustness and accuracy. By incorporating a class balancing method, a test-time adaptation technique and a multi-view optimization strategy, we address the critical issue of domain shift that often hampers the performance of machine learning models in real-world applications. Experiments comparing various state-of-the-art domain generalization and test-time optimization methodologies show that our approach consistently outperforms when combined with existing baseline and state-of-the-art methods. We also show our online method improves all existing techniques. Our framework demonstrates improvements in domain generalization capabilities and offers a practical solution for real-world deployment by facilitating online adaptation to new, unseen datasets. Our code is available at https://github.com/zgy600/RetiGen .

Co-salient object detection (CoSOD) aims to identify the common and salient (usually in the foreground) regions across a given group of images. Although achieving significant progress, state-of-the-art CoSODs could be easily affected by some adversarial perturbations, leading to substantial accuracy reduction. The adversarial perturbations can mislead CoSODs but do not change the high-level semantic information (e.g., concept) of the co-salient objects. In this paper, we propose a novel robustness enhancement framework by first learning the concept of the co-salient objects based on the input group images and then leveraging this concept to purify adversarial perturbations, which are subsequently fed to CoSODs for robustness enhancement. Specifically, we propose CosalPure containing two modules, i.e., group-image concept learning and concept-guided diffusion purification. For the first module, we adopt a pre-trained text-to-image diffusion model to learn the concept of co-salient objects within group images where the learned concept is robust to adversarial examples. For the second module, we map the adversarial image to the latent space and then perform diffusion generation by embedding the learned concept into the noise prediction function as an extra condition. Our method can effectively alleviate the influence of the SOTA adversarial attack containing different adversarial patterns, including exposure and noise. The extensive results demonstrate that our method could enhance the robustness of CoSODs significantly., Comment: This paper is accepted by CVPR 2024

Reservoir computing is a recurrent neural network that has been applied across various domains in machine learning. The implementation of reservoir computing, however, often demands heavy computations for activating the reservoir. Configuring physical reservoir networks and harnessing the nonlinearity from the underlying devices for activation is an emergent solution to address the computational challenge. Herein, we analyze the feasibility of employing the nonlinearity from solution-processed molybdenum disulfide (MoS2) devices for reservoir activation. The devices, fabricated using liquid-phase exfoliated MoS2, exhibit a high-order nonlinearity achieved by Stark modulation of the MoS2 material. We demonstrate that this nonlinearity can be fitted and employed as the activation function to facilitate reservoir computing implementation. Notably, owing to the high-order nonlinearity, the network exhibits long-term synchronization and robust generalization abilities for approximating complex dynamical systems. Given the remarkable reservoir activation capability, coupled with the scalability of the device fabrication, our findings open the possibility for the physical realization of lightweight, efficient reservoir computing for, for instance, signal classification, motion tracking, and pattern recognition of complex time series as well as secure cryptography. As an example, we show the network can be appointed to generate chaotic random numbers for secure data encryption.

Smart contracts are decentralized applications built atop blockchains like Ethereum. Recent research has shown that large language models (LLMs) have potential in auditing smart contracts, but the state-of-the-art indicates that even GPT-4 can achieve only 30% precision (when both decision and justification are correct). This is likely because off-the-shelf LLMs were primarily pre-trained on a general text/code corpus and not fine-tuned on the specific domain of Solidity smart contract auditing. In this paper, we propose TrustLLM, a general framework that combines fine-tuning and LLM-based agents for intuitive smart contract auditing with justifications. Specifically, TrustLLM is inspired by the observation that expert human auditors first perceive what could be wrong and then perform a detailed analysis of the code to identify the cause. As such, TrustLLM employs a two-stage fine-tuning approach: it first tunes a Detector model to make decisions and then tunes a Reasoner model to generate causes of vulnerabilities. However, fine-tuning alone faces challenges in accurately identifying the optimal cause of a vulnerability. Therefore, we introduce two LLM-based agents, the Ranker and Critic, to iteratively select and debate the most suitable cause of vulnerability based on the output of the fine-tuned Reasoner model. To evaluate TrustLLM, we collected a balanced dataset with 1,734 positive and 1,810 negative samples to fine-tune TrustLLM. We then compared it with traditional fine-tuned models (CodeBERT, GraphCodeBERT, CodeT5, and UnixCoder) as well as prompt learning-based LLMs (GPT4, GPT-3.5, and CodeLlama-13b/34b). On a dataset of 263 real smart contract vulnerabilities, TrustLLM achieves an F1 score of 91.21% and an accuracy of 91.11%. The causes generated by TrustLLM achieved a consistency of about 38% compared to the ground truth causes.

Federated Learning (FL) heavily depends on label quality for its performance. However, the label distribution among individual clients is always both noisy and heterogeneous. The high loss incurred by client-specific samples in heterogeneous label noise poses challenges for distinguishing between client-specific and noisy label samples, impacting the effectiveness of existing label noise learning approaches. To tackle this issue, we propose FedFixer, where the personalized model is introduced to cooperate with the global model to effectively select clean client-specific samples. In the dual models, updating the personalized model solely at a local level can lead to overfitting on noisy data due to limited samples, consequently affecting both the local and global models' performance. To mitigate overfitting, we address this concern from two perspectives. Firstly, we employ a confidence regularizer to alleviate the impact of unconfident predictions caused by label noise. Secondly, a distance regularizer is implemented to constrain the disparity between the personalized and global models. We validate the effectiveness of FedFixer through extensive experiments on benchmark datasets. The results demonstrate that FedFixer can perform well in filtering noisy label samples on different clients, especially in highly heterogeneous label noise scenarios., Comment: accepted by AAA24

Rug pulls pose a grave threat to the cryptocurrency ecosystem, leading to substantial financial loss and undermining trust in decentralized finance (DeFi) projects. With the emergence of new rug pull patterns, research on rug pull is out of state. To fill this gap, we first conducted an extensive analysis of the literature review, encompassing both scholarly and industry sources. By examining existing academic articles and industrial discussions on rug pull projects, we present a taxonomy inclusive of 34 root causes, introducing six new categories inspired by industry sources: burn, hidden owner, ownership transfer, unverified contract, external call, and fake LP lock. Based on the developed taxonomy, we evaluated current rug pull datasets and explored the effectiveness and limitations of existing detection mechanisms. Our evaluation indicates that the existing datasets, which document 2,448 instances, address only 7 of the 34 root causes, amounting to a mere 20% coverage. It indicates that existing open-source datasets need to be improved to study rug pulls. In response, we have constructed a more comprehensive dataset containing 2,360 instances, expanding the coverage to 54% with the best effort. In addition, the examination of 14 detection tools showed that they can identify 25 of the 34 root causes, achieving a coverage of 73.5%. There are nine root causes (Fake LP Lock, Hidden Fee, and Destroy Token, Fake Money Transfer, Ownership Transfer, Liquidity Pool Block, Freeze Account, Wash-Trading, Hedge) that the existing tools cannot cover. Our work indicates that there is a significant gap between current research and detection tools, and the actual situation of rug pulls.

Recently, automated medical image segmentation methods based on deep learning have achieved great success. However, they heavily rely on large annotated datasets, which are costly and time-consuming to acquire. Few-shot learning aims to overcome the need for annotated data by using a small labeled dataset, known as a support set, to guide predicting labels for new, unlabeled images, known as the query set. Inspired by this paradigm, we introduce MatchSeg, a novel framework that enhances medical image segmentation through strategic reference image matching. We leverage contrastive language-image pre-training (CLIP) to select highly relevant samples when defining the support set. Additionally, we design a joint attention module to strengthen the interaction between support and query features, facilitating a more effective knowledge transfer between support and query sets. We validated our method across four public datasets. Experimental results demonstrate superior segmentation performance and powerful domain generalization ability of MatchSeg against existing methods for domain-specific and cross-domain segmentation tasks. Our code is made available at https://github.com/keeplearning-again/MatchSeg

Heterogeneous Federated Learning (HtFL) enables collaborative learning on multiple clients with different model architectures while preserving privacy. Despite recent research progress, knowledge sharing in HtFL is still difficult due to data and model heterogeneity. To tackle this issue, we leverage the knowledge stored in pre-trained generators and propose a new upload-efficient knowledge transfer scheme called Federated Knowledge-Transfer Loop (FedKTL). Our FedKTL can produce client-task-related prototypical image-vector pairs via the generator's inference on the server. With these pairs, each client can transfer pre-existing knowledge from the generator to its local model through an additional supervised local task. We conduct extensive experiments on four datasets under two types of data heterogeneity with 14 kinds of models including CNNs and ViTs. Results show that our upload-efficient FedKTL surpasses seven state-of-the-art methods by up to 7.31% in accuracy. Moreover, our knowledge transfer scheme is applicable in scenarios with only one edge client. Code: https://github.com/TsingZ0/FedKTL, Comment: Accepted by CVPR2024

Automated Static Analysis Tools (ASATs) have evolved over time to assist in detecting bugs. However, the excessive false warnings can impede developers' productivity and confidence in the tools. Previous research efforts have explored learning-based methods to validate the reported warnings. Nevertheless, their coarse granularity, focusing on either long-term warnings or function-level alerts, which are insensitive to individual bugs. Also, they rely on manually crafted features or solely on source code semantics, which is inadequate for effective learning. In this paper, we propose FineWAVE, a learning-based approach that verifies bug-sensitive warnings at a fine-grained granularity. Specifically, we design a novel LSTM-based model that captures multi-modal semantics of source code and warnings from ASATs and highlights their correlations with cross-attention. To tackle the data scarcity of training and evaluation, we collected a large-scale dataset of 280,273 warnings. We conducted extensive experiments on the dataset to evaluate FineWAVE. The experimental results demonstrate the effectiveness of our approach, with an F1-score of 97.79\% for reducing false alarms and 67.06% for confirming actual warnings, significantly outperforming all baselines. Moreover, we have applied our FineWAVE to filter out about 92% warnings in four popular real-world projects, and found 25 new bugs with minimal manual effort.