New archidermapteran earwigs (Dermaptera) from the Middle Jurassic of Inner Mongolia, China

Two new species of Archidermaptera are described and figured from the Middle Jurassic Jiulonghsan Formation of Daohugou, Inner Mongolia, China. Aneuroderma oiodes gen. & sp. nov. is described in the family Protodiplatyidae and Sinopalaeodermata concavum sp. nov. is established in the family Dermapteridae. Both new species share the typical characters of the extinct suborder Archidermaptera (e.g., pentamerous metatarsi, filiform and multimerous cerci, externalized ovipositor). Aneuroderma gen. nov. is compared with other genera of the Protodiplatyidae, while S. concavum sp. nov. allows us to emend the diagnosis of the genus Sinopalaeodermata. We briefly discuss the diversity of Archidermaptera and challenges to understanding relationships among this mid-Mesozoic diversity

Federated Deep Multi-View Clustering with Global Self-Supervision

Federated multi-view clustering has the potential to learn a global clustering model from data distributed across multiple devices. In this setting, label information is unknown and data privacy must be preserved, leading to two major challenges. First, views on different clients often have feature heterogeneity, and mining their complementary cluster information is not trivial. Second, the storage and usage of data from multiple clients in a distributed environment can lead to incompleteness of multi-view data. To address these challenges, we propose a novel federated deep multi-view clustering method that can mine complementary cluster structures from multiple clients, while dealing with data incompleteness and privacy concerns. Specifically, in the server environment, we propose sample alignment and data extension techniques to explore the complementary cluster structures of multiple views. The server then distributes global prototypes and global pseudo-labels to each client as global self-supervised information. In the client environment, multiple clients use the global self-supervised information and deep autoencoders to learn view-specific cluster assignments and embedded features, which are then uploaded to the server for refining the global self-supervised information. Finally, the results of our extensive experiments demonstrate that our proposed method exhibits superior performance in addressing the challenges of incomplete multi-view data in distributed environments

A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective

Multi-view clustering (MVC) is a popular technique for improving clustering performance using various data sources. However, existing methods primarily focus on acquiring consistent information while often neglecting the issue of redundancy across multiple views. This study presents a new approach called Sufficient Multi-View Clustering (SUMVC) that examines the multi-view clustering framework from an information-theoretic standpoint. Our proposed method consists of two parts. Firstly, we develop a simple and reliable multi-view clustering method SCMVC (simple consistent multi-view clustering) that employs variational analysis to generate consistent information. Secondly, we propose a sufficient representation lower bound to enhance consistent information and minimise unnecessary information among views. The proposed SUMVC method offers a promising solution to the problem of multi-view clustering and provides a new perspective for analyzing multi-view data. To verify the effectiveness of our model, we conducted a theoretical analysis based on the Bayes Error Rate, and experiments on multiple multi-view datasets demonstrate the superior performance of SUMVC

Deep Clustering: A Comprehensive Survey

Cluster analysis plays an indispensable role in machine learning and data mining. Learning a good data representation is crucial for clustering algorithms. Recently, deep clustering, which can learn clustering-friendly representations using deep neural networks, has been broadly applied in a wide range of clustering tasks. Existing surveys for deep clustering mainly focus on the single-view fields and the network architectures, ignoring the complex application scenarios of clustering. To address this issue, in this paper we provide a comprehensive survey for deep clustering in views of data sources. With different data sources and initial conditions, we systematically distinguish the clustering methods in terms of methodology, prior knowledge, and architecture. Concretely, deep clustering methods are introduced according to four categories, i.e., traditional single-view deep clustering, semi-supervised deep clustering, deep multi-view clustering, and deep transfer clustering. Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering

STAT1 as a downstream mediator of ERK signaling contributes to bone cancer pain by regulating MHC II expression in spinal microglia

Major histocompatibility class II (MHC II)-specific activation of CD4+ T helper cells generates specific and persistent adaptive immunity against tumors. Emerging evidence demonstrates that MHC II is also involved in basic pain perception; however, little is known regarding its role in the development of cancer-induced bone pain (CIBP). In this study, we demonstrate that MHC II expression was markedly induced on the spinal microglia of CIBP rats in response to STAT1 phosphorylation. Mechanical allodynia was ameliorated by either pharmacological or genetic inhibition of MHC II upregulation, which was also attenuated by the inhibition of pSTAT1 and pERK but was deteriorated by intrathecal injection of IFNγ. Furthermore, inhibition of ERK signaling decreased the phosphorylation of STAT1, as well as the production of MHC II in vivo and in vitro. These findings suggest that STAT1 contributes to bone cancer pain as a downstream mediator of ERK signaling by regulating MHC II expression in spinal microglia

An aphid-transmitted polerovirus is mutualistic with its insect vector by accelerating population growth in both winged and wingless individuals

The occurrence and increased dispersion of plant viruses and insect vectors are serious global threat to the production of agricultural crops. Facing novel pathogenic plant viruses, the ability to accurately identify plant virus species, and understand the interaction between plant viruses, host plants and their insect vectors would provide an important basis for formulating effective plant virus control measures. In this study, we explored the transmission mechanism, pathogenic symptoms, host range and the interactions between virus and aphid vectors of a novel polero virus from Nicotianatabacum, named Tobacco yellow virus (TYV). The results indicate that TYV can be transmitted by Myzus persicae in a persistent manner, and cause yellowing and shrinking of tobacco leaves. TYV can successfully infect a total of 9 plant species belonging to 3 families. The effect of TYV-infected tobacco plants on M. persicae behavior and life characteristics was found to be stage-dependent. TYV can directly and indirectly manipulate the performance and life history traits of M. persicae vectors to promote their own transmission. These results provide a certain theoretical basis for the possibility of control strategies of the virus, and the in-depth exploration of the interaction among plant virus, vector aphid and host plants

A Major and Stable QTL for Bacterial Wilt Resistance on Chromosome B02 Identified Using a High-Density SNP-Based Genetic Linkage Map in Cultivated Peanut Yuanza 9102 Derived Population

Bacterial wilt (BW) is one of the important diseases limiting the production of peanut (Arachis hypogaea L.) worldwide. The sufficient precise information on the quantitative trait loci (QTL) for BW resistance is essential for facilitating gene mining and applying in molecular breeding. Cultivar Yuanza 9102 is BW resistant, bred from wide cross between cultivated peanut Baisha 1016 and a wild diploid peanut species A. chacoense with BW resistance. In this study, we aim to map the major QTLs related to BW-resistance in Yuanza 9102. A high density SNP-based genetic linkage map was constructed through double-digest restriction-site-associated DNA sequencing (ddRADseq) technique based on Yuanza 9102 derived recombinant inbred lines (RILs) population. The map contained 2,187 SNP markers distributed on 20 linkage groups (LGs) spanning 1566.10 cM, and showed good synteny with AA genome from A. duranensis and BB genome from A. ipaensis. Phenotypic frequencies of BW resistance among RIL population showed two-peak distribution in four environments. Four QTLs explaining 5.49 to 23.22% phenotypic variance were identified to be all located on chromosome B02. The major QTL, qBWB02.1 (12.17–23.33% phenotypic variation explained), was detected in three environments showing consistent and stable expression. Furthermore, there was positive additive effect among these major and minor QTLs. The major QTL region was mapped to a region covering 2.3 Mb of the pseudomolecule B02 of A. ipaensis which resides in 21 nucleotide-binding site -leucine-rich repeat (NBS-LRR) encoding genes. The result of the major stable QTL (qBWB02.1) not only offers good foundation for discovery of BW resistant gene but also provide opportunity for deployment of the QTL in marker-assisted breeding in peanut