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Abstract The pervasive cooperation of a group of UAVs has attracted increasing attention due to the reduced cost and widespread availability. When working in an untrusted or adversarial environment, the mutual authentication of UAVs in the cooperative process is imperative. However, there are some major challenges, including changes in the network environment before and during task performing, and the weak connection network state faced by UAVs. Therefore, a novel task-oriented authentication model for UAVs based on blockchain (ToAM) is proposed, which divides UAVs authentication into group building authentication and intra-group authentication with a two-stage authentication framework. And two lightweight authentication protocols are presented, respectively, corresponding to two stages. Finally, analyses demonstrate that our model realizes secure and lightweight authentication function for the whole process of UAVs requisition and task performing.

Federated learning is a distributed machine learning framework that enables distributed nodes with computation and storage capabilities to train a global model while keeping distributed-stored data locally. This process can promote the efficiency of modeling while preserving data privacy. Therefore, federated learning can be widely applied in distributed conjoint analysis scenarios, such as smart plant protection systems, in which widely networked IoT devices are used to monitor the critical data of plant production to improve crop production. However, the data collected by different IoT devices can be dependent and identically distributed (non-IID), causing the challenge of statistical heterogeneity. Studies have also shown that statistical heterogeneity can lead to a marked decline in the efficiency of federated learning, making it challenging to apply in practice. To promote the efficiency of federated learning in statistical heterogeneity scenarios, an adaptive client selection algorithm for federated learning in statistical heterogeneous scenarios called ACSFed is proposed in this paper. ACSFed can dynamically calculate the possibility of clients being selected to train the model for each communication round based on their local statistical heterogeneity and previous training performance instead of randomly selected clients, and clients with heavier statistical heterogeneity or bad training performance would be more likely selected to participate in the later training. This client selection strategy can enable the federated model to learn the global statistical knowledge faster and thereby promote the convergence of the federated model. Multiple experiments on public benchmark datasets demonstrate these improvements in the efficiency of the models in heterogeneous settings.

With the rapid development of intelligent perception and other data acquisition technologies in the Internet of things, large-scale scientific workflows have been widely used in geographically distributed multiple data centers to realize high performance in business model construction and computational processing. However, insider threats pose very significant privacy and security risks to systems. Traditional access-control models can no longer satisfy the reasonable authorization of resources in these new cross-domain environments. Therefore, a dynamic and semantic-aware access-control model is proposed for privacy preservation in multiple data center environments, which implements a semantic dynamic authorization strategy based on an anomaly assessment of users’ behavior sequences. The experimental results demonstrate that this dynamic and semantic-aware access-control model is highly dynamic and flexible and can improve the security of the application system.

Mobile Crowd-Sensing (MCS) is a dominant sensing paradigm for Internet of Things (IoT), with a lot of potentials as it allows data collection through the user's sensor embedded mobile devices. The participation of people in IoT not only brings greater flexibility and sensing ability but also increases the risk of privacy breaches to the participants. Primarily, a worker's location data is vulnerable to information leaks as the task assignment in MCS is location-based. Most existing mechanisms that preserve worker's location in MCS are designed under the assumption that the platform is trusted, which may be not valid in real-world applications. Besides, the existing studies focus either on task selection problem for workers or task assignment problem for the platform. Therefore, this paper investigates both task bidding and assignment while preserving location privacy. We propose two task selection strategies: Minimize Total Cost (MTC) and Minimize Average Cost (MAC). Each worker submits a cost that is obfuscated using differential privacy to the platform. We propose probability cost-efficient worker selection mechanism (PCE-WSM) to determine winners and probability individual-rationality critical payment mechanism (PIR-CPM) to determine payments to winners. We prove that PIR-CPM is truthful and can achieve probability-individual rationality by theoretical analysis. To evaluate our proposed strategies, we conduct extensive experiments on both synthetic and real-world datasets, and the experimental results validate that PCE-WSM can achieve enough privacy preservation without incurring a high payment.

The incidence of hypertriglyceridemia induced acute pancreatitis (HTAP) continues to rise in China. It has systemic complications and high mortality, making the early assessment of the severity of this disease even more important. Circulating microRNAs (miRNAs) could be novel, non-invasive biomarkers for disease progression judgment. This study aimed to identify the potential role of serum miRNAs as novel biomarkers of HTAP progression. HTAP patients were divided into two groups: moderately severe (HTMSAP) and severe (HTSAP), healthy people were used as control group. The serum miRNA expression profiles of these three groups were determined by microarray and verified by qRT-PCR. The functions and pathways of the targeted genes of deregulated miRNAs were predicted, using bioinformatics analysis; miRNA-mRNA network was generated. Moreover, the correlation between miR-181a-5p and pancreatitis metabolism related substances were studied and the serum concentration of inflammatory cytokines and miRNAs at different time points during the MSAP and SAP were investigated, respectively. Finally, the receiver operating characteristic (ROC) curve of miRNAs was studied. Significant changes in the serum concentration of the following miRNAs of HTAP patients (P

Abstract The aim of the study was to evaluate the concomitant psychiatric disorders of anxiety and depression in patients with epilepsy caused by low-grade brain tumors (LBTs). We retrospectively reviewed the clinical data of patients who underwent preoperative neuropsychological evaluations of anxiety and depression and subsequent epilepsy surgery for LBTs. The univariate and multivariate analyses were conducted to analyze the risk factors of the occurrence of anxiety and depression. Of the 107 patients included in the study, 42 patients (39.3%) were female, and 28 patients (26.2%) were children. The median age at surgery was 22 years (interquartile range [IQR]: 17–27 years old), the median age of seizure onset was 12 years (IQR: 6–18 years old), and the median duration of epilepsy was 84 months (IQR: 42–180 months). In total, 21 patients (19.6%) had psychiatric disorders of anxiety, and 26 patients (24.3%) had psychiatric disorders of depression. Through univariate and multivariate analysis, discordant (vs. concordant) interictal electroencephalogram (EEG) findings were found to be related to the presence of anxiety (P = 0.035, odds ratio [OR] = 3.35). Discordant (vs. concordant) ictal EEG findings (P = 0.015, OR = 4.44) and temporal location of tumor (P = 0.015, OR = 13.52) were found to be associated with the presence of depression. Psychiatric disorders of anxiety and/or depression could frequently occur in patients with epilepsy caused by LBTs. Thus, early screening and mental intervention are necessary, especially for those with discordant or wider epileptic discharges and temporal invasion of tumors.

Abstract The integration of hippocampal oscillations during non-rapid eye movement (NREM) sleep is crucial for memory consolidation. However, how cardinal sleep oscillations bind across various subfields of the human hippocampus to promote information transfer and synaptic plasticity remains unclear. Using human intracranial recordings from 25 epilepsy patients, we find that hippocampal subfields, including DG/CA3, CA1, and SUB, all exhibit significant delta and spindle power during NREM sleep. The DG/CA3 displays strong coupling between delta and ripple oscillations with all the other hippocampal subfields. In contrast, the regions of CA1 and SUB exhibit more precise coordination, characterized by event-level triple coupling between delta, spindle, and ripple oscillations. Furthermore, we demonstrate that the synaptic plasticity within the hippocampal circuit, as indexed by delta-wave slope, is linearly modulated by spindle power. In contrast, ripples act as a binary switch that triggers a sudden increase in delta-wave slope. Overall, these results suggest that different subfields of the hippocampus regulate one another through diverse layers of sleep oscillation synchronization, collectively facilitating information processing and synaptic plasticity during NREM sleep.

Background: Drug-resistant epilepsy (DRE) affects approximately one-third of epilepsy patients who do not achieve adequate seizure control with medication. Vagus nerve stimulation (VNS) is an adjunctive therapy for DRE, but its long-term effects on cortical excitability remain unclear. Objectives: This study aims to elucidate the long-term effects of VNS on electroencephalography (EEG) aperiodic components in patients with DRE. Our objective is to identify biomarkers that can serve as indicators of therapeutic efficacy and provide mechanistic insights into the underlying neural processes. Design: This longitudinal observational study focused on patients with DRE undergoing VNS therapy at Sanbo Brain Hospital. The reduction in seizure frequency rates was quantified over short-term (⩽1 year), medium-term (1–3 years), and long-term (⩾3 years) intervals to assess the therapeutic efficacy of VNS. Both the periodic and aperiodic components of EEG data were analyzed. Methods: Advanced signal processing techniques were utilized to parameterize the periodic and aperiodic components of EEG data, focusing particularly on “offset” and “exponent.” These measures were compared before and after VNS therapy. Correlation analyses were conducted to explore the relationship between these EEG parameters and clinical outcomes. Results: In all, 18 patients with DRE participated in this study. During the long-term follow-up period, the responder rate was 55.56%. Significant decreases were observed in aperiodic offset ( p = 0.022) and exponent ( p = 0.039) among responders. The impact of age on these results was not significant. Correlation analyses revealed a negative association between therapeutic efficacy and a decrease in offset ( R = −0.546, p = 0.019) and exponent ( R = −0.636, p = 0.019). Conclusion: EEG aperiodic parameters, including offset and exponent, have the potential to serve as promising biomarkers for evaluating the efficacy of VNS. An understanding of the regulatory influence of VNS on cortical excitability through these aperiodic parameters could provide a basis for the development of more effective stimulation parameters and therapeutic strategies.

Background:The neuropeptide galanin has been shown to exhibit anticonvulsant effects in animal models. However, only a few studies have attempted to identify the galanin receptor subtype (s) involved in this effect. In the present study, the expression of galanin and that of its receptors in a rat model of temporal lobe epilepsy was studied to reveal the epileptogenesis-related alterations.Methods:Pilocarpine-inducted status epilepticus (SE) rat model was used for temporal lobe epilepsy. Galanin and galanin receptor mRNA was detected by Real-time polymerase chain reaction in dorsal hippocampus of rats at three different time points following SE: at the acute phase (24h), latent period (7 days), and chronic epileptic period (8 weeks). Using immunohistochemistry, galanin positive fibers and bodies were also observed.Results:In the acute seizure phase, a significant up-regulation of the galanin mRNA was observed in the hippocampus. Meanwhile, galanin-positive cells appeared, while galanin-positive fibers disappeared in the hilus of the dentate gyrus, stratum radiatum of CA1, and stratum lucidum of CA3, indicating an increase in galanin synthesis and release during the acute seizure phase. The gene expression analysis also indicated a significant increase in the transcription of galanin receptors GalR1 and GalR3 24h after SE. The increased levels of GalR1 mRNA were also observed in the latent and chronic epileptic periods.Conclusion:In general, these data suggest that adaptive changes occur in the galanin system during epileptogenesis that may affect its antiepileptic effects. Furthermore, these changes should be taken into account when attempting to pharmacologically modulate this system.