Input-To-State Stability for a Class of Switched Stochastic Nonlinear Systems by an Improved Average Dwell Time Method

This paper investigates the input-to-state stable in the mean (ISSiM) property of the switched stochastic nonlinear (SSN) systems with an improved average dwell time (ADT) method in two cases: (i) all of the constituent subsystems are ISSiM and (ii) parts of the constituent subsystems are ISSiM. First, an improved ADT method for stability of SSN systems is introduced. Then, based on that not only a new ISSiM result for SSN systems whose subsystems are ISSiM is presented, but also a new ISSiM result for such systems in which parts of subsystems are ISSiM is established. In comparison with the existing ones, the main results obtained in this paper have some advantages. Finally, an illustrative example with numerical simulation is verified the correctness and validity of the proposed results

RHFedMTL: Resource-Aware Hierarchical Federated Multi-Task Learning

The rapid development of artificial intelligence (AI) over massive applications including Internet-of-things on cellular network raises the concern of technical challenges such as privacy, heterogeneity and resource efficiency. Federated learning is an effective way to enable AI over massive distributed nodes with security. However, conventional works mostly focus on learning a single global model for a unique task across the network, and are generally less competent to handle multi-task learning (MTL) scenarios with stragglers at the expense of acceptable computation and communication cost. Meanwhile, it is challenging to ensure the privacy while maintain a coupled multi-task learning across multiple base stations (BSs) and terminals. In this paper, inspired by the natural cloud-BS-terminal hierarchy of cellular works, we provide a viable resource-aware hierarchical federated MTL (RHFedMTL) solution to meet the heterogeneity of tasks, by solving different tasks within the BSs and aggregating the multi-task result in the cloud without compromising the privacy. Specifically, a primal-dual method has been leveraged to effectively transform the coupled MTL into some local optimization sub-problems within BSs. Furthermore, compared with existing methods to reduce resource cost by simply changing the aggregation frequency, we dive into the intricate relationship between resource consumption and learning accuracy, and develop a resource-aware learning strategy for local terminals and BSs to meet the resource budget. Extensive simulation results demonstrate the effectiveness and superiority of RHFedMTL in terms of improving the learning accuracy and boosting the convergence rate.Comment: 11 pages, 8 figure

Input-To-State Stability for a Class of Switched Stochastic Nonlinear Systems by an Improved Average Dwell Time Method

This paper investigates the input-to-state stable in the mean (ISSiM) property of the switched stochastic nonlinear (SSN) systems with an improved average dwell time (ADT) method in two cases: (i) all of the constituent subsystems are ISSiM and (ii) parts of the constituent subsystems are ISSiM. First, an improved ADT method for stability of SSN systems is introduced. Then, based on that not only a new ISSiM result for SSN systems whose subsystems are ISSiM is presented, but also a new ISSiM result for such systems in which parts of subsystems are ISSiM is established. In comparison with the existing ones, the main results obtained in this paper have some advantages. Finally, an illustrative example with numerical simulation is verified the correctness and validity of the proposed results

NetGPT: A Native-AI Network Architecture Beyond Provisioning Personalized Generative Services

Large language models (LLMs) have triggered tremendous success to empower daily life by generative information, and the personalization of LLMs could further contribute to their applications due to better alignment with human intents. Towards personalized generative services, a collaborative cloud-edge methodology sounds promising, as it facilitates the effective orchestration of heterogeneous distributed communication and computing resources. In this article, after discussing the pros and cons of several candidate cloud-edge collaboration techniques, we put forward NetGPT to capably deploy appropriate LLMs at the edge and the cloud in accordance with their computing capacity. In addition, edge LLMs could efficiently leverage location-based information for personalized prompt completion, thus benefiting the interaction with cloud LLMs. After deploying representative open-source LLMs (e.g., GPT-2-base and LLaMA model) at the edge and the cloud, we present the feasibility of NetGPT on the basis of low-rank adaptation-based light-weight fine-tuning. Subsequently, we highlight substantial essential changes required for a native artificial intelligence (AI) network architecture towards NetGPT, with special emphasis on deeper integration of communications and computing resources and careful calibration of logical AI workflow. Furthermore, we demonstrate several by-product benefits of NetGPT, given edge LLM's astonishing capability to predict trends and infer intents, which possibly leads to a unified solution for intelligent network management \& orchestration. In a nutshell, we argue that NetGPT is a promising native-AI network architecture beyond provisioning personalized generative services

Communication-Efficient Cooperative Multi-Agent PPO via Regulated Segment Mixture in Internet of Vehicles

Multi-Agent Reinforcement Learning (MARL) has become a classic paradigm to solve diverse, intelligent control tasks like autonomous driving in Internet of Vehicles (IoV). However, the widely assumed existence of a central node to implement centralized federated learning-assisted MARL might be impractical in highly dynamic scenarios, and the excessive communication overheads possibly overwhelm the IoV system. Therefore, in this paper, we design a communication efficient cooperative MARL algorithm, named RSM-MAPPO, to reduce the communication overheads in a fully distributed architecture. In particular, RSM-MAPPO enhances the multi-agent Proximal Policy Optimization (PPO) by incorporating the idea of segment mixture and augmenting multiple model replicas from received neighboring policy segments. Afterwards, RSM-MAPPO adopts a theory-guided metric to regulate the selection of contributive replicas to guarantee the policy improvement. Finally, extensive simulations in a mixed-autonomy traffic control scenario verify the effectiveness of the RSM-MAPPO algorithm

Development of Channeled Nanofibrous Scaffolds for Oriented Tissue Engineering

A tissue‐engineering scaffold resembling the structure of the natural extracellular matrix can often facilitate tissue regeneration. Nerve and tendon are oriented micro‐scale tissue bundles. In this study, a method combining injection molding and thermally induced phase separation techniques is developed to create single‐ and multiple‐channeled nanofibrous poly( L ‐lactic acid) scaffolds. The overall shape, the number and spatial arrangement of channels, the channel wall matrix architecture, the porosity and mechanical properties of the scaffolds are all tunable. The porous NF channel wall matrix provides an excellent microenvironment for protein adsorption and the attachment of PC12 neuronal cells and tendon fibroblast cells, showing potential for neural and tendon tissue regeneration. A method combining injection molding and thermally induced phase separation is developed to create single‐ and multiple‐channeled nanofibrous polymer scaffolds. The porous nanofibrous channel wall provides an excellent microenvironment for protein adsorption and cell attachment, showing potential for nerve and tendon regeneration.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92054/1/761_ftp.pd

Linking Cancer Stem Cell Plasticity to Therapeutic Resistance-Mechanism and Novel Therapeutic Strategies in Esophageal Cancer

Esophageal cancer (EC) is an aggressive form of cancer, including squamous cell carcinoma (ESCC) and adenocarcinoma (EAC) as two predominant histological subtypes. Accumulating evidence supports the existence of cancer stem cells (CSCs) able to initiate and maintain EAC or ESCC. In this review, we aim to collect the current evidence on CSCs in esophageal cancer, including the biomarkers/characterization strategies of CSCs, heterogeneity of CSCs, and the key signaling pathways (Wnt/β-catenin, Notch, Hedgehog, YAP, JAK/STAT3) in modulating CSCs during esophageal cancer progression. Exploring the molecular mechanisms of therapy resistance in EC highlights DNA damage response (DDR), metabolic reprogramming, epithelial mesenchymal transition (EMT), and the role of the crosstalk of CSCs and their niche in the tumor progression. According to these molecular findings, potential therapeutic implications of targeting esophageal CSCs may provide novel strategies for the clinical management of esophageal cancer

Genome sequence of the insect pathogenic fungus Cordyceps militaris, a valued traditional chinese medicine

Species in the ascomycete fungal genus Cordyceps have been proposed to be the teleomorphs of Metarhizium species. The latter have been widely used as insect biocontrol agents. Cordyceps species are highly prized for use in traditional Chinese medicines, but the genes responsible for biosynthesis of bioactive components, insect pathogenicity and the control of sexuality and fruiting have not been determined. Here, we report the genome sequence of the type species Cordyceps militaris. Phylogenomic analysis suggests that different species in the Cordyceps/Metarhizium genera have evolved into insect pathogens independently of each other, and that their similar large secretomes and gene family expansions are due to convergent evolution. However, relative to other fungi, including Metarhizium spp., many protein families are reduced in C. militaris, which suggests a more restricted ecology. Consistent with its long track record of safe usage as a medicine, the Cordyceps genome does not contain genes for known human mycotoxins. We establish that C. militaris is sexually heterothallic but, very unusually, fruiting can occur without an opposite mating-type partner. Transcriptional profiling indicates that fruiting involves induction of the Zn2Cys6-type transcription factors and MAPK pathway; unlike other fungi, however, the PKA pathway is not activated.https://doi.org/10.1186/gb-2011-12-11-r11

Stimulated thyroglobulin and pre-ablation antithyroglobulin antibody products can predict the response to radioiodine therapy of TgAb-positive differentiated thyroid cancer patients: a retrospective study

ObjectiveWe aimed to explore the predictive value of stimulated thyroglobulin (sTg) and pre-ablation antithyroglobulin (pa-TgAb) products for the effect of radioiodine therapy (RAIT) on TgAb-positive differentiated thyroid cancer (DTC) patients.MethodsIn this study, we enrolled 265 patients with TgAb-positive DTC who underwent RAIT after total thyroidectomy (TT). Based on the last follow-up result, the patients were divided into two groups: the excellent response (ER) group and the non-excellent response (NER) group. We analyzed the factors related to the effect of RAIT.ResultsThe ER group consisted of 197 patients. The NER group consisted of 68 patients. For the univariate analysis, we found that the maximal tumor diameter, whether with extrathyroidal extension (ETE), bilateral or unilateral primary lesion, multifocality, preoperative TgAb (preop-TgAb), pa-TgAb, sTg × pa-TgAb, initial RAIT dose, N stage, and surgical extent (modified radical neck dissection or not), showed significant differences between the ER group and NER group (all p-values <0.05). The receiver operating characteristic (ROC) curves showed that the cutoff value was 724.25 IU/ml, 424.00 IU/ml, and 59.73 for preop-TgAb, pa-TgAb, and sTg × pa-TgAb, respectively. The multivariate logistic regression analysis results indicated that pa-TgAb, sTg × pa-TgAb, initial RAIT dose, and N stage were independent risk factors for NER (all p-values <0.05). For the Kaplan–Meier analysis of disease-free survival (DFS), the median DFS of the patients with sTg × pa-TgAb < 59.73 and initial RAIT dose ≤ 100 mCi was significantly longer than that of the patients with sTg × pa-TgAb ≥ 59.73 (50.27 months vs. 48.59 months, p = 0.041) and initial RAIT dose >100 mCi (50.50 months vs. 38.00 months, p = 0.030).ConclusionWe found the sTg and pa-TgAb conducts is a good predictor of the efficacy of RAIT in TgAb-positive DTC patients. It can play a very positive and important role in optimizing treatment, improving prognosis, and reducing the burden of patients

CMTCN: a web tool for investigating cancer-specific microRNA and transcription factor co-regulatory networks

Transcription factors (TFs) and microRNAs (miRNAs) are well-characterized trans-acting essential players in gene expression regulation. Growing evidence indicates that TFs and miRNAs can work cooperatively, and their dysregulation has been associated with many diseases including cancer. A unified picture of regulatory interactions of these regulators and their joint target genes would shed light on cancer studies. Although online resources developed to support probing of TF-gene and miRNA-gene interactions are available, online applications for miRNA-TF co-regulatory analysis, especially with a focus on cancers, are lacking. In light of this, we developed a web tool, namely CMTCN (freely available at http://www.cbportal.org/CMTCN), which constructs miRNA-TF co-regulatory networks and conducts comprehensive analyses within the context of particular cancer types. With its user-friendly provision of topological and functional analyses, CMTCN promises to be a reliable and indispensable web tool for biomedical studies