Optimal Repair Strategy Against Advanced Persistent Threats Under Time-Varying Networks

Advanced persistent threat (APT) is a kind of stealthy, sophisticated, and long-term cyberattack that has brought severe financial losses and critical infrastructure damages. Existing works mainly focus on APT defense under stable network topologies, while the problem under time-varying dynamic networks (e.g., vehicular networks) remains unexplored, which motivates our work. Besides, the spatiotemporal dynamics in defense resources, complex attackers' lateral movement behaviors, and lack of timely defense make APT defense a challenging issue under time-varying networks. In this paper, we propose a novel game-theoretical APT defense approach to promote real-time and optimal defense strategy-making under both periodic time-varying and general time-varying environments. Specifically, we first model the interactions between attackers and defenders in an APT process as a dynamic APT repair game, and then formulate the APT damage minimization problem as the precise prevention and control (PPAC) problem. To derive the optimal defense strategy under both latency and defense resource constraints, we further devise an online optimal control-based mechanism integrated with two backtracking-forward algorithms to fastly derive the near-optimal solution of the PPAC problem in real time. Extensive experiments are carried out, and the results demonstrate that our proposed scheme can efficiently obtain optimal defense strategy in 54481 ms under seven attack-defense interactions with 9.64$\%$ resource occupancy in stimulated periodic time-varying and general time-varying networks. Besides, even under static networks, our proposed scheme still outperforms existing representative APT defense approaches in terms of service stability and defense resource utilization

Adaptive Split-Fusion Transformer

Neural networks for visual content understanding have recently evolved from convolutional ones (CNNs) to transformers. The prior (CNN) relies on small-windowed kernels to capture the regional clues, demonstrating solid local expressiveness. On the contrary, the latter (transformer) establishes long-range global connections between localities for holistic learning. Inspired by this complementary nature, there is a growing interest in designing hybrid models to best utilize each technique. Current hybrids merely replace convolutions as simple approximations of linear projection or juxtapose a convolution branch with attention, without concerning the importance of local/global modeling. To tackle this, we propose a new hybrid named Adaptive Split-Fusion Transformer (ASF-former) to treat convolutional and attention branches differently with adaptive weights. Specifically, an ASF-former encoder equally splits feature channels into half to fit dual-path inputs. Then, the outputs of dual-path are fused with weighting scalars calculated from visual cues. We also design the convolutional path compactly for efficiency concerns. Extensive experiments on standard benchmarks, such as ImageNet-1K, CIFAR-10, and CIFAR-100, show that our ASF-former outperforms its CNN, transformer counterparts, and hybrid pilots in terms of accuracy (83.9% on ImageNet-1K), under similar conditions (12.9G MACs/56.7M Params, without large-scale pre-training). The code is available at: https://github.com/szx503045266/ASF-former

Rethinking GNN-based Entity Alignment on Heterogeneous Knowledge Graphs: New Datasets and A New Method

The development of knowledge graph (KG) applications has led to a rising need for entity alignment (EA) between heterogeneous KGs that are extracted from various sources. Recently, graph neural networks (GNNs) have been widely adopted in EA tasks due to GNNs' impressive ability to capture structure information. However, we have observed that the oversimplified settings of the existing common EA datasets are distant from real-world scenarios, which obstructs a full understanding of the advancements achieved by recent methods. This phenomenon makes us ponder: Do existing GNN-based EA methods really make great progress? In this paper, to study the performance of EA methods in realistic settings, we focus on the alignment of highly heterogeneous KGs (HHKGs) (e.g., event KGs and general KGs) which are different with regard to the scale and structure, and share fewer overlapping entities. First, we sweep the unreasonable settings, and propose two new HHKG datasets that closely mimic real-world EA scenarios. Then, based on the proposed datasets, we conduct extensive experiments to evaluate previous representative EA methods, and reveal interesting findings about the progress of GNN-based EA methods. We find that the structural information becomes difficult to exploit but still valuable in aligning HHKGs. This phenomenon leads to inferior performance of existing EA methods, especially GNN-based methods. Our findings shed light on the potential problems resulting from an impulsive application of GNN-based methods as a panacea for all EA datasets. Finally, we introduce a simple but effective method: Simple-HHEA, which comprehensively utilizes entity name, structure, and temporal information. Experiment results show Simple-HHEA outperforms previous models on HHKG datasets.Comment: 11 pages, 6 figure

Nested Event Extraction upon Pivot Element Recogniton

Nested Event Extraction (NEE) aims to extract complex event structures where an event contains other events as its arguments recursively. Nested events involve a kind of Pivot Elements (PEs) that simultaneously act as arguments of outer events and as triggers of inner events, and thus connect them into nested structures. This special characteristic of PEs brings challenges to existing NEE methods, as they cannot well cope with the dual identities of PEs. Therefore, this paper proposes a new model, called PerNee, which extracts nested events mainly based on recognizing PEs. Specifically, PerNee first recognizes the triggers of both inner and outer events and further recognizes the PEs via classifying the relation type between trigger pairs. In order to obtain better representations of triggers and arguments to further improve NEE performance, it incorporates the information of both event types and argument roles into PerNee through prompt learning. Since existing NEE datasets (e.g., Genia11) are limited to specific domains and contain a narrow range of event types with nested structures, we systematically categorize nested events in generic domain and construct a new NEE dataset, namely ACE2005-Nest. Experimental results demonstrate that PerNee consistently achieves state-of-the-art performance on ACE2005-Nest, Genia11 and Genia13

Aflatoxin B1 Promotes Influenza Replication and Increases Virus Related Lung Damage via Activation of TLR4 Signaling

Aflatoxin B1 (AFB1), which alters immune responses to mammals, is one of the most common mycotoxins in feeds and food. Swine influenza virus (SIV) is a major pathogen of both animals and humans. However, there have been few studies about the relationship between AFB1 exposure and SIV replication. Here, for the first time, we investigated the involvement of AFB1 in SIV replication in vitro and in vivo and explored the underlying mechanism using multiple cell lines and mouse models. In vitro studies demonstrated that low concentrations of AFB1 (0.01–0.25 μg/ml) markedly promoted SIV replication as revealed by increased viral titers and matrix protein (M) mRNA and nucleoprotein (NP) levels in MDCK cells, A549 cells and PAMs. In vivo studies showed that 10–40 μg/kg of AFB1 exacerbated SIV infection in mice as illustrated by significantly higher lung virus titers, viral M mRNA levels, NP levels, lung indexes and more severe lung damage. Further study showed that AFB1 upregulated TLR4, but not other TLRs, in SIV-infected PAMs. Moreover, AFB1 activated TLR4 signaling as demonstrated by the increases of phosphorylated NFκB p65 and TNF-α release in PAMs and mice. In contrast, TLR4 knockdown or the use of BAY 11-7082, a specific inhibitor of NFκB, blocked the AFB1-promoted SIV replication and inflammatory responses in PAMs. Furthermore, a TLR4-specific antagonist, TAK242, and TLR4 knockout both attenuated the AFB1-promoted SIV replication, inflammation and lung damage in mice. We therefore conclude that AFB1 exposure aggravates SIV replication, inflammation and lung damage by activating TLR4-NFκB signaling

Electrochemical synthesis of phosphorus and sulfur co-doped graphene quantum dots as efficient electrochemiluminescent immunomarkers for monitoring okadaic acid

Abstract(#br)In this study, water-dispersed, uniform-sized phosphorus and sulfur co-doped graphene quantum dots (P, S-GQDs) were prepared by the one-step electrolysis of a graphite rod in an alkaline solution containing sodium phytate and sodium sulfide. Compared with GQDs and mono-doped GQDs (P-GQDs and S-GQDs), the P, S-GQDs dramatically improved the electrochemiluminescence (ECL) performance. Therefore, they were used as bright ECL signaling markers through conjugation with a monoclonal antibody against okadaic acid (anti-OA-MAb). Moreover, as an effective matrix for OA immobilization, the carboxylated multiwall carbon nanotubes-poly(diallyldimethylammonium) chloride-Au nanocluster (CMCNT-PDDA-AuNCs) composite promoted electron transfer and enlarged the surface area. Owing to the multiple amplifications, a competitive indirect ECL immunosensor for highly sensitive quantitation of OA has been developed. Under the optimized conditions, the 50% inhibitory concentration (IC 50 ) of the immunosensor was 0.25 ng mL −1 , and its linear range was 0.01–20 ng mL −1 with a low detection limit of 0.005 ng mL −1 . Finally, the proposed ECL sensor was successfully utilized to detect OA contents in mussel samples. Therefore, this study provides new insights into the designation of ECL luminophores and expands application of co-doped GQDs in fabrication of ECL immunosensors for shellfish toxin determination

Diabetic retinopathy risk in patients with unhealthy lifestyle: A Mendelian randomization study

PurposeThis study aimed to investigate the causal association between unhealthy lifestyle factors and diabetic retinopathy (DR) risk and to determine better interventions targeting these modifiable unhealthy factors.DesignTwo-sample Mendelian randomization (MR) analysis was performed in this study. The inverse variance-weighted method was used as the primary method.MethodOur study included 687 single-nucleotide polymorphisms associated with unhealthy lifestyle factors as instrumental variables. Aggregated data on individual-level genetic information were obtained from the corresponding studies and consortia. A total of 292,622,3 cases and 739,241,18 variants from four large consortia (MRC Integrative Epidemiology Unit [MRC-IEU], Genetic Investigation of Anthropometric Traits [GIANT], GWAS & Sequencing Consortium of Alcohol and Nicotine Use [GSCAN], and Neale Lab) were included.ResultIn the MR analysis, a higher body mass index (BMI) (odds ratio [OR], 95% confidence interval [CI] = 1.42, 1.30–1.54; P < 0.001] and cigarettes per day (OR, 95% CI = 1.16, 1.05–1.28; P = 0.003) were genetically predicted to be causally associated with an increased risk of DR, while patients with higher hip circumference (HC) had a lower risk of DR (OR, 95% CI = 0.85, 0.76–0.95; P = 0.004). In the analysis of subtypes of DR, the results of BMI and HC were similar to those of DR, whereas cigarettes per day were only related to proliferative DR (PDR) (OR, 95% CI = 1.18, 1.04–1.33; P = 0.009). In the MR-PRESSO analysis, a higher waist-to-hip ratio (WHR) was a risk factor for DR and PDR (OR, 95% CI = 1.24, 1.02–1.50, P = 0.041; OR, 95% CI = 1.32, 1.01–1.73, P = 0.049) after removing the outliers. Furthermore, no pleiotropy was observed in these exposures.ConclusionOur findings suggest that higher BMI, WHR, and smoking are likely to be causal factors in the development of DR, whereas genetically higher HC is associated with a lower risk of DR, providing insights into a better understanding of the etiology and prevention of DR

Positive response to trastuzumab deruxtecan in a patient with HER2-mutant NSCLC after multiple lines therapy, including T-DM1: a case report

Human epidermal growth factor 2 (HER2) mutations are uncommon in non-small cell lung cancer (NSCLC), and the lack of established, effective, targeted drugs has resulted in a persistently poor prognosis. Herein, we report the case of a non-smoking, 58-year-old man diagnosed with lung adenocarcinoma (cT3N0M1c, stage IVB) harboring a HER2 mutation (Y772_A775dupYVMA) and PD-L1 (-). The patient’s Eastern Cooperative Oncology Group performance status (PS) score was assessed as 1. He commenced first-line treatment with chemotherapy, followed by immuno-chemotherapy, and with disease progression, he received HER2-targeted therapy and chemotherapy with an anti-angiogenic agent. However, HER2-targeted therapy, including pan-HER tyrosine kinase inhibitors (afatinib, pyrotinib, and pozitinib) and antibody–drug conjugate (T-DM1), produced only stable disease (SD) as the best response. After the previously described treatment, primary tumor recurrence and multiple brain metastases were observed. Despite the patient’s compromised overall physical condition with a PS score of 3-4, he was administered T-DXd in addition to whole-brain radiotherapy (WBRT). Remarkably, both intracranial metastases and primary lesions were significantly reduced, he achieved a partial response (PR), and his PS score increased from 3-4 to 1. He was then treated with T-DXd for almost 9 months until the disease again progressed, and he did not discontinue the drug despite the occurrence of myelosuppression during this period. This is a critical case as it exerted an effective response to T-DXd despite multiple lines therapy, including T-DM1. Simultaneously, despite the occurrence of myelosuppression in the patient during T-DXd, it was controlled after aggressive treatment

High-frequency rTMS over bilateral primary motor cortex improves freezing of gait and emotion regulation in patients with Parkinson’s disease: a randomized controlled trial

BackgroundFreezing of gait (FOG) is a common and disabling phenomenon in patients with Parkinson’s disease (PD), but effective treatment approach remains inconclusive. Dysfunctional emotional factors play a key role in FOG. Since primary motor cortex (M1) connects with prefrontal areas via the frontal longitudinal system, where are responsible for emotional regulation, we hypothesized M1 may be a potential neuromodulation target for FOG therapy. The purpose of this study is to explore whether high-frequency rTMS over bilateral M1 could relieve FOG and emotional dysregulation in patients with PD.MethodsThis study is a single-center, randomized double-blind clinical trial. Forty-eight patients with PD and FOG from the Affiliated Hospital of Xuzhou Medical University were randomly assigned to receive 10 sessions of either active (N = 24) or sham (N = 24) 10 Hz rTMS over the bilateral M1. Patients were evaluated at baseline (T0), after the last session of treatment (T1) and 30 days after the last session (T2). The primary outcomes were Freezing of Gait Questionnaire (FOGQ) scores, with Timed Up and Go Test (TUG) time, Standing-Start 180° Turn (SS-180) time, SS-180 steps, United Parkinson Disease Rating Scales (UPDRS) III, Hamilton Depression scale (HAMD)-24 and Hamilton Anxiety scale (HAMA)-14 as secondary outcomes.ResultsTwo patients in each group dropped out at T2 and no serious adverse events were reported by any subject. Two-way repeated ANOVAs revealed significant group × time interactions in FOGQ, TUG, SS-180 turn time, SS-180 turning steps, UPDRS III, HAMD-24 and HAMA-14. Post-hoc analyses showed that compared to T0, the active group exhibited remarkable improvements in FOGQ, TUG, SS-180 turn time, SS-180 turning steps, UPDRS III, HAMD-24 and HAMA-14 at T1 and T2. No significant improvement was found in the sham group. The Spearman correlation analysis revealed a significantly positive association between the changes in HAMD-24 and HAMA-14 scores and FOGQ scores at T1.ConclusionHigh-frequency rTMS over bilateral M1 can improve FOG and reduce depression and anxiety in patients with PD

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely