Detection of Fog Network Data Telemetry Using Data Plane Programming

Fog computing has been introduced to deliver Cloud-based services to the Internet of Things (IoT) devices. It locates geographically closer to IoT devices than Cloud networks and aims at offering latency-critical computation and storage to end-user applications. To leverage Fog computing for computational offloading from end-users, it is important to optimize resources in the Fog nodes dynamically. Provisioning requires knowledge of the current network state, thus, monitoring mechanisms play a significant role to conduct resource management in the network. To keep track of the state of devices, we use P4, a data-plane programming language, to describe data-plane abstraction of Fog network devices and collect telemetry without the intervention of the control plane or adding a big amount of overhead. In this paper, we propose a software-defined architecture with a programmable data plane for data telemetry detection that can be integrated into Fog network resource management. After the implementation of detecting data telemetry based on In-Band Network Telemetry (INT) within a Mininet simulation, we show the available features and preliminary Fog resource management based on the collected data telemetry and future telemetry-based traffic engineering possibilities

Association of atopic dermatitis with autoimmune diseases: A bidirectional and multivariable two-sample mendelian randomization study

BackgroundObservational studies have suggested the association between atopic dermatitis (AD) and the risks of autoimmune diseases. It is still unclear, however, whether or in which direction causal relationships exist, because these associations could be confounded.ObjectivesOur study seeks to assess the possibility of AD as a cause of autoimmune diseases, and to estimate the magnitude of the causal effect.MethodsTwo-sample mendelian randomization (MR) analyses were performed using genome-wide association study (GWAS) summary-level statistics. Specifically, bidirectional MR analyses were conducted to examine the direction of association of AD with autoimmune diseases; multivariable MR analyses (MVMR1) were used to test the independence of causal association of AD with autoimmune diseases after controlling other atopic disorders (asthma and allergic rhinitis), while MVMR2 analyses were conducted to account for potential confounding factors such as smoking, drinking, and obesity. Genetic instruments for AD (Ncases=22 474) were from the latest GWAS meta-analysis. The GWAS summary data for asthma and allergic rhinitis were obtained from UK Biobank. The GWAS summary data for smoking, alcohol consumption, obesity and autoimmune diseases (alopecia areata, vitiligo, systemic lupus erythematosus, ankylosing spondylitis, rheumatoid arthritis, and type 1 diabetes) were selected from the largest GWASs available. Causal estimates were derived by the inverse-variance weighted method and verified through a series of sensitivity analyses.ResultsGenetically predicted AD linked to higher risks of rheumatoid arthritis (OR, 1.28; P=0.0068) (ORMVMR1, 1.65; P=0.0020) (ORMVMR2, 1.36; P<0.001), type 1 diabetes (OR, 1.37; P=0.0084) (ORMVMR1, 1.42; P=0.0155) (ORMVMR2, 1.45; P=0.002), and alopecia areata (OR, 1.98; P=0.0059) (ORMVMR1, 2.55; P<0.001) (ORMVMR2, 1.99; P=0.003) in both univariable and multivariable MR. These causal relationships were supported by sensitivity analyses. No causal effect of AD was identified in relation to systemic lupus erythematosus, vitiligo, and ankylosing spondylitis. Concerning the reverse directions, no significant association was noted.ConclusionThe results of this MR study provide evidence to support the idea that AD causes a greater risk of rheumatoid arthritis, type 1 diabetes and alopecia areata. Further replication in larger samples is needed to validate our findings, and experimental studies are needed to explore the underlying mechanisms of these causal effects

The role of tripartite motif-containing 28 in cancer progression and its therapeutic potentials

Tripartite motif-containing 28 (TRIM28) belongs to tripartite motif (TRIM) family. TRIM28 not only binds and degrades its downstream target, but also acts as a transcription co-factor to inhibit gene expression. More and more studies have shown that TRIM28 plays a vital role in tumor genesis and progression. Here, we reviewed the role of TRIM28 in tumor proliferation, migration, invasion and cell death. Moreover, we also summarized the important role of TRIM28 in tumor stemness sustainability and immune regulation. Because of the importance of TRIM28 in tumors, TIRM28 may be a candidate target for anti-tumor therapy and play an important role in tumor diagnosis and treatment in the future

A new species of forest hedgehog (Mesechinus, Erinaceidae, Eulipotyphla, Mammalia) from eastern China

The hedgehog genus Mesechinus (Erinaceidae, Eulipotyphla) is currently comprised of four species, M. dauuricus, M. hughi, M. miodon, and M. wangi. Except for M. wangi, which is found in southwestern China, the other three species are mainly distributed in northern China and adjacent Mongolia and Russia. From 2018 to 2023, we collected seven Mesechinus specimens from Anhui and Zhejiang provinces, eastern China. Here, we evaluate the taxonomic and phylogenetic status of these specimens by integrating molecular, morphometric, and karyotypic approaches. Our results indicate that the Anhui and Zhejiang specimens are distinct from the four previously recognized species and are a new species. We formally described it here as Mesechinus orientalis sp. nov. It is the only Mesechinus species occurring in eastern China and is geographically distant from all known congeners. Morphologically, the new species is most similar to M. hughi, but it is distinguishable from that species by the combination of its smaller size, shorter spines, and several cranial characteristics. Mesechinus orientalis sp. nov. is a sister to the lineage composed of M. hughi and M. wangi from which it diverged approximately 1.10 Ma