Auxiliary Domain Research Articles

How does CHD4 slide nucleosomes?

Chromatin remodelling enzymes reposition nucleosomes throughout the genome to regulate the rate of transcription and other processes. These enzymes have been studied intensively since the 1990s, and yet the mechanism by which they operate has only very recently come into focus, following advances in cryoelectron microscopy and single-molecule biophysics. CHD4 is an essential and ubiquitous chromatin remodelling enzyme that until recently has received less attention than remodellers such as Snf2 and CHD1. Here we review what recent work in the field has taught us about how CHD4 reshapes the genome. Cryoelectron microscopy and single-molecule studies demonstrate that CHD4 shares a central remodelling mechanism with most other chromatin remodellers. At the same time, differences between CHD4 and other chromatin remodellers result from the actions of auxiliary domains that regulate remodeller activity by for example: (1) making differential interactions with nucleosomal epitopes such as the acidic patch and the N-terminal tail of histone H4, and (2) inducing the formation of distinct multi-protein remodelling complexes (e.g. NuRD vs ChAHP). Thus, although we have learned much about remodeller activity, there is still clearly much more waiting to be revealed.

Autoinhibition of ubiquitin-specific protease 8: Insights into domain interactions and mechanisms of regulation

Ubiquitin-specific proteases (USPs) are a family of multi-domain deubiquitinases (DUBs) with variable architectures, some containing regulatory auxiliary domains. Among the USP family, all occurrences of intramolecular regulation presently known are autoactivating. USP8 remains the sole exception as its putative WW-like domain, conserved only in vertebrate orthologs, is autoinhibitory. Here, we present a comprehensive structure-function analysis describing the autoinhibition of USP8 and provide evidence of the physical interaction between the WW-like and catalytic domains. The solution structure of full-length USP8 reveals an extended, monomeric conformation. Coupled with DUB assays, the WW-like domain is confirmed to be the minimal autoinhibitory unit. Strikingly, autoinhibition is only observed with the WW-like domain in cis and depends on the length of the linker tethering it to the catalytic domain. Modelling of the WW:CD complex structure and mutagenesis of interface residues suggests a novel binding site in the S1 pocket. To investigate the interplay between phosphorylation and USP8 autoinhibition, we identify AMP-activated protein kinase as a highly selective modifier of S718 in the 14-3-3 binding motif. We show that 14-3-3γ binding to phosphorylated USP8 potentiates autoinhibition in a WW-like domain-dependent manner by stabilizing an autoinhibited conformation. These findings provide mechanistic details on the autoregulation of USP8 and shed light on its evolutionary significance.

Unprecedented Diversity of the Glycoside Hydrolase Family 70: A Comprehensive Analysis of Sequence, Structure, and Function.

The glycoside hydrolase family 70 (GH70) contains bacterial extracellular multidomain enzymes, synthesizing α-glucans from sucrose or starch-like substrates. A few dozen have been biochemically characterized, while crystal structures cover only the core domains and lack significant parts of auxiliary domains. Here we present a systematic overview of GH70 enzymes and their 3D structural organization and bacterial origin. A representative set of 234 permuted and 25 nonpermuted GH70 enzymes was generated, covering 12 bacterial families and 3 phyla and containing 185 predicted glucansucrases (GS), 15 branching sucrases (BrS), 8 "twin" GS-BrSs, and 51 α-glucanotransferases (α-GT). Analysis of AlphaFold models of all 259 entries showed that, apart from the core domains, the structural variation regarding auxiliary domains is far greater than anticipated, with nine different domain types. We analyzed the phylogenetic distribution and discuss the possible roles of auxiliary domains as well as possible correlations between enzyme specificity, auxiliary domain type, and bacterial origin.

Unsupervised heterogeneous domain adaptation for EEG classification

Objective. Domain adaptation has been recognized as a potent solution to the challenge of limited training data for electroencephalography (EEG) classification tasks. Existing studies primarily focus on homogeneous environments, however, the heterogeneous properties of EEG data arising from device diversity cannot be overlooked. This motivates the development of heterogeneous domain adaptation methods that can fully exploit the knowledge from an auxiliary heterogeneous domain for EEG classification. Approach. In this article, we propose a novel model named informative representation fusion (IRF) to tackle the problem of unsupervised heterogeneous domain adaptation in the context of EEG data. In IRF, we consider different perspectives of data, i.e. independent identically distributed (iid) and non-iid, to learn different representations. Specifically, from the non-iid perspective, IRF models high-order correlations among data by hypergraphs and develops hypergraph encoders to obtain data representations of each domain. From the non-iid perspective, by applying multi-layer perceptron networks to the source and target domain data, we achieve another type of representation for both domains. Subsequently, an attention mechanism is used to fuse these two types of representations to yield informative features. To learn transferable representations, the maximum mean discrepancy is utilized to align the distributions of the source and target domains based on the fused features. Main results. Experimental results on several real-world datasets demonstrate the effectiveness of the proposed model. Significance. This article handles an EEG classification situation where the source and target EEG data lie in different spaces, and what’s more, under an unsupervised learning setting. This situation is practical in the real world but barely studied in the literature. The proposed model achieves high classification accuracy, and this study is important for the commercial applications of EEG-based BCIs.

COMPUTER VISUALIZATION IN TEACHING GENERAL TECHNICAL DISCIPLINES: THEORETICAL AND PRACTICAL ASPECTS

The article investigates the development of visual aids and visualization ideas in the organization of the educational process, particularly in teaching general technical disciplines. The article aims to review the development of visual aids and visualization ideas in education, clarify the essence of computer visualiza- tion, classify its types and kinds, and determine the directions of computer visualization usage in teaching general technical disciplines and the software applications that ensure their development for educational purposes. The study operates methods of retrospective, content, and semantic analysis, classification, gen- eralization, and modelling. The historical context of introducing electronic visualization tools in education has been considered in the study, starting from the first projectors in the 1930s to modern computer visualization technologies such as 2D and 3D visualization, stereoscopic 3D, virtual reality (VR), and augmented reality (AR). The es- sence of computer visualization as a general term for graphic methods of creating, analysing, and transmit- ting information has been defined. Differences between the didactic principles of visual aids and visualiza- tion have been clarified. It has been highlighted that visualization has a more complex structure since it in- cludes actions for constructing images of investigated objects or phenomena. The article identifies the directions for implementing computer visualization, including scientific, soft- ware, and information visualization. It has been established that all visual content can be divided into three subgroups: structured, artistic, and demonstrative visual objects, which can be static or dynamic, in one plane or three-dimensional space. The impact of computer visualization on the methods of teaching general technical disciplines has been analysed. It has been shown that modern approaches to using computer visualization provide new opportunities for more effective and interactive learning, promoting the development of students’ technical and spatial imagination, which is crucial for future engineers and technical specialists. An approximate list of software for creating computer visualization tools, which can be useful in teaching general technical disciplines, has been provided. Among them are applications for creating presentations and infographics, 3D modelling, VR and AR-based applications and services, simulators, and emulators. The main directions for using computer visualization aids in teaching general technical disciplines are identified, including auxiliary synopsis, diagrams, tables, block diagrams, graphs, frames, word clouds, domain structures, memory cards, comics, heuristic sketches, knowledge animation, infographics, multimedia presentations, educational videos, static and dynamic 2D and 3D models, VR and AR objects. It has been concluded, that the didactic effectiveness of computer visualization in teaching general technical disciplines is ensured by certain provisions, namely: the focus on the quick and complete memorization of studied concepts, theories, and ideas by students; adherence to the principles of reliability, quality, completeness, sufficiency, logic, simplicity of perception, optimality, unity of graphics and text, and emphasis on key points; encouraging students to independently analyse and construct new, non-trivial didactic tasks allowing them to explore concepts, phenomena, or objects in terms of independent search and educational experimentation.

LJCD-Net: Cross-Domain Jamming Generalization Diagnostic Network Based on Deep Adversarial Transfer.

Global Navigation Satellite Systems (GNSS) offer comprehensive position, navigation, and timing (PNT) estimates worldwide. Given the growing demand for reliable location awareness in both indoor and outdoor contexts, the advent of fifth-generation mobile communication technology (5G) has enabled expansive coverage and precise positioning services. However, the power received by the signal of interest (SOI) at terminals is notably low. This can lead to significant jamming, whether intentional or unintentional, which can adversely affect positioning receivers. The diagnosis of jamming types, such as classification, assists receivers in spectrum sensing and choosing effective mitigation strategies. Traditional jamming diagnosis methodologies predominantly depend on the expertise of classification experts, often demonstrating a lack of adaptability for diverse tasks. Recently, researchers have begun utilizing convolutional neural networks to re-conceptualize a jamming diagnosis as an image classification issue, thereby augmenting recognition performance. However, in real-world scenarios, the assumptions of independent and homogeneous distributions are frequently violated. This discrepancy between the source and target distributions frequently leads to subpar model performance on the test set or an inability to procure usable evaluation samples during training. In this paper, we introduce LJCD-Net, a deep adversarial migration-based cross-domain jamming generalization diagnostic network. LJCD-Net capitalizes on a fully labeled source domain and multiple unlabeled auxiliary domains to generate shared feature representations with generalization capabilities. Initially, our paper proposes an uncertainty-guided auxiliary domain labeling weighting strategy, which estimates the multi-domain sample uncertainty to re-weight the classification loss and specify the gradient optimization direction. Subsequently, from a probabilistic distribution standpoint, the spatial constraint imposed on the cross-domain global jamming time-frequency feature distribution facilitates the optimization of collaborative objectives. These objectives include minimizing both the source domain classification loss and auxiliary domain classification loss, as well as optimizing the inter-domain marginal probability and conditional probability distribution. Experimental results demonstrate that LJCD-Net enhances the recognition accuracy and confidence compared to five other diagnostic methods.

An Efficient Cross-Domain Recommendation System Based on Clustering Approach and User-Score

Growing consumers and online commercial products have put a great challenge on Recommender systems. Abundant browsing and online shopping across the world have provided immense opportunities to E-commercial giants to effectively learn the behaviour and attract users. However, due to the variety of available products and distinct consumers, crucial issues of data sparsity and cold start problems have been introduced. Single-domain recommendation systems are unable to handle such issues and research has been concentrated on developing more sophisticated cross-domain recommendation systems. Concerning other systems, non-overlapping domains form the most difficult part to solve using the cross-domain approach. This article focuses on partially overlapping domains and suggests a simple and efficient approach based on mapping users' interests by clustering books and movies. The cluster information from the auxiliary domain is transferred to the target domain using the user score and mapped properly to recommend books. The user and item scores in both domains are computed using the transfer frequency-inverse document frequency approach. The proposed recommender system offers low computational complexity and requires less time.

A Dual Perspective Framework of Knowledge-correlation for Cross-domain Recommendation

Recommender System provides users with online services in a personalized way. The performance of traditional recommender systems may deteriorate because of problems such as cold-start and data sparsity. Cross-domain Recommendation System utilizes the richer information from auxiliary domains to guide the task in the target domain. However, direct knowledge transfer may lead to a negative impact due to data heterogeneity and feature mismatch between domains. In this article, we innovatively explore the cross-domain correlation from the perspectives of content semanticity and structural connectivity to fully exploit the information of Knowledge Graph. First, we adopt domain adaptation that automatically extracts transferable features to capture cross-domain semantic relations. Second, we devise a knowledge-aware graph neural network to explicitly model the high-order connectivity across domains. Third, we develop feature fusion strategies to combine the advantages of semantic and structural information. By simulating the cold-start scenario on two real-world datasets, the experimental results show that our proposed method has superior performance in accuracy and diversity compared with the SOTA methods. It demonstrates that our method can accurately predict users’ expressed preferences while exploring their potential diverse interests.

Psychosis superspectrum II: neurobiology, treatment, and implications.

Alternatives to traditional categorical diagnoses have been proposed to improve the validity and utility of psychiatric nosology. This paper continues the companion review of an alternative model, the psychosis superspectrum of the Hierarchical Taxonomy of Psychopathology (HiTOP). The superspectrum model aims to describe psychosis-related psychopathology according to data on distributions and associations among signs and symptoms. The superspectrum includes psychoticism and detachment spectra as well as narrow subdimensions within them. Auxiliary domains of cognitive deficit and functional impairment complete the psychopathology profile. The current paper reviews evidence on this model from neurobiology, treatment response, clinical utility, and measure development. Neurobiology research suggests that psychopathology included in the superspectrum shows similar patterns of neural alterations. Treatment response often mirrors the hierarchy of the superspectrum with some treatments being efficacious for psychoticism, others for detachment, and others for a specific subdimension. Compared to traditional diagnostic systems, the quantitative nosology shows an approximately 2-fold increase in reliability, explanatory power, and prognostic accuracy. Clinicians consistently report that the quantitative nosology has more utility than traditional diagnoses, but studies of patients with frank psychosis are currently lacking. Validated measures are available to implement the superspectrum model in practice. The dimensional conceptualization of psychosis-related psychopathology has implications for research, clinical practice, and public health programs. For example, it encourages use of the cohort study design (rather than case-control), transdiagnostic treatment strategies, and selective prevention based on subclinical symptoms. These approaches are already used in the field, and the superspectrum provides further impetus and guidance for their implementation. Existing knowledge on this model is substantial, but significant gaps remain. We identify outstanding questions and propose testable hypotheses to guide further research. Overall, we predict that the more informative, reliable, and valid characterization of psychopathology offered by the superspectrum model will facilitate progress in research and clinical care.

Privacy-preserving Multi-source Cross-domain Recommendation Based on Knowledge Graph

The cross-domain recommender systems aim to alleviate the data sparsity problem in the target domain by transferring knowledge from the auxiliary domain. However, existing works ignore the fact that the data sparsity problem may also exist in the single auxiliary domain, and sharing user behavior data is restricted by the privacy policy. In addition, their cross-domain models lack interpretability. To address these concerns, we propose a novel multi-source cross-domain model based on knowledge graph. Specifically, to avoid the insufficiency of single auxiliary domain, we construct a knowledge graph comprehensively leveraging items from multiple auxiliary domains. To avoid the leakage of user privacy when user information is transferred to multiple domains, we construct graph for information transfer between items to effectively avoid the propagation of users’ private information between different domains. We implicitly integrate the user–item interaction by transferring the learned item embeddings. To improve the interpretability of cross-domain knowledge transfer, we propose a knowledge graph-based retrieval and fusion method to transfer knowledge derived from multiple auxiliary domains. An attention-based fusion network is designed to enhance the representation of the targeted user and items with the transferred item embedding. We perform extensive experiments on three real-world datasets, demonstrating that our model outperforms the states of the art.

Selectivity filter conductance, rectification and fluctuations of subdomains—How can this all relate to the value of Hurst exponent in the dwell-times of ion channels states?

The Hurst effect in the signals describing ion channels’ activity has been known for many years. This effect is present in the experimental recordings of single-channel currents, but not only. The sequences of dwell times of functionally different channel states also exhibit long-range correlations. We have found that the memory effect within the dwell-time series is related to the coupling between the channel’s activation gate (AG) and selectivity filter (SF), which controls the ion conduction. In this work, we analyzed both the experimental data describing the activity of potassium channels of different types (e.g., BK, mitoBK, mitoTASK-3, mitoKv1.3, TREK-2-like channels) and the series generated according to our previously proposed Hurst effect model. The obtained results suggest that the strength of the allosteric cooperation between the AG and SF determines not only the conductance of the channel – which governs how often ions in SF move or remain blocked – but also modulates the correlations present in the dwell times when sampled with a suitably high sampling rate. Moreover, we found that rectification can interfere with this process, contributing to additional changes in correlations within the channel’s sojourns in subsequent states. Similarly, the correlations may be affected by processes proceeding at longer time scales, like interactions with the channel’s auxiliary domains or lipid surroundings.

Biased collective latent factorization of tensors with transfer learning for dynamic QoS data predicting

The latent factorization of tensors (LFT) develops an efficient way to fulfill the prediction for dynamic quality of service (QoS) data and it has gained much attention in the research field of recommend systems. However, the LFT might perform poorly or even be invalid when data missing and fluctuation occur. To address this problem, a novel LFT called biased collective LFT with transfer learning (BCLFT_TL) is put forward. The main ideas include 1) establishing an auxiliary tensor related to the target tensor and transferring the knowledge from the auxiliary domain to the target domain to mitigate the data sparsity, 2) incorporating linear biases on each dimension of tensors into the objective function to cope with data fluctuations, and 3) a set of learning rates is set delicately to guarantee the non-negativity of the target data and the mini-batch additive gradient descent method is used to reduce the computational burden. Finally, the superiority of the BCLFT_TL-based model is confirmed on six real datasets from two data sources compared with state-of-the-art methods.

An Auxiliary Branch Semi-supervised Domain Generalization Network for Unseen Working Conditions Bearing Fault Diagnosis

An Auxiliary Branch Semi-supervised Domain Generalization Network for Unseen Working Conditions Bearing Fault Diagnosis

A New Progressive Multisource Domain Adaptation Network With Weighted Decision Fusion.

Multisource unsupervised domain adaptation (MUDA) is an important and challenging topic for target classification with the assistance of labeled data in source domains. When we have several labeled source domains, it is difficult to map all source domains and target domain into a common feature space for classifying the targets well. In this article, a new progressive multisource domain adaptation network (PMSDAN) is proposed to further improve the classification performance. PMSDAN mainly consists of two steps for distribution alignment. First, the multiple source domains are integrated as one auxiliary domain to match the distribution with the target domain. By doing this, we can generally reduce the distribution discrepancy between each source and target domains, as well as the discrepancy between different source domains. It can efficiently explore useful knowledge from the integrated source domain. Second, to mine assistance knowledge from each source domain as much as possible, the distribution of the target domain is separately aligned with that of each source domain. A weighted fusion method is employed to combine the multiple classification results for making the final decision. In the optimization of domain adaption, weighted hybrid maximum mean discrepancy (WHMMD) is proposed, and it considers both the interclass and intraclass discrepancies. The effectiveness of the proposed PMSDAN is demonstrated in the experiments comparing with some state-of-the-art methods.

CANDy: Automated analysis of domain architectures in carbohydrate-active enzymes.

Carbohydrate-active enzymes (CAZymes) can be found in all domains of life and play a crucial role in metabolic and physiological processes. CAZymes often possess a modular structure, comprising not only catalytic domains but also associated domains such as carbohydrate-binding modules (CBMs) and linker domains. By exploring the modular diversity of CAZy families, catalysts with novel properties can be discovered and further insight in their biological functions and evolutionary relationships can be obtained. Here we present the carbohydrate-active enzyme domain analysis tool (CANDy), an assembly of several novel scripts, tools and databases that allows users to analyze the domain architecture of all protein sequences in a given CAZy family. CANDy's usability is shown on glycoside hydrolase family 48, a small yet underexplored family containing multi-domain enzymes. Our analysis reveals the existence of 35 distinct domain assemblies, including eight known architectures, with the remaining assemblies awaiting characterization. Moreover, we substantiate the occurrence of horizontal gene transfer from prokaryotes to insect orthologs and provide evidence for the subsequent removal of auxiliary domains, likely through a gene fission event. CANDy is available at https://github.com/PyEED/CANDy.

A multi-constraint transfer approach with additional auxiliary domains for IoT intrusion detection under unbalanced samples distribution

A multi-constraint transfer approach with additional auxiliary domains for IoT intrusion detection under unbalanced samples distribution

Community Preserving Social Recommendation with Cyclic Transfer Learning

Transfer learning-based recommendation mitigates the sparsity of user-item interactions by introducing auxiliary domains. Social influence extracted from direct connections between users typically serves as an auxiliary domain to improve prediction performance. However, direct social connections also face severe data sparsity problems that limit model performance. In contrast, users’ dependency on communities is another valuable social information that has not yet received sufficient attention. Although studies have incorporated community information into recommendation by aggregating users’ preferences within the same community, they seldom capture the structural discrepancies among communities and the influence of structural discrepancies on users’ preferences. To address these challenges, we propose a community-preserving recommendation framework with cyclic transfer learning, incorporating heterogeneous community influence into the rating domain. We analyze the characteristics of the community domain and its inter-influence on the rating domain, and construct link constraints and preference constraints in the community domain. The shared vectors that bridge the rating domain and the community domain are allowed to be more consistent with the characteristics of both domains. Extensive experiments are conducted on four real-world datasets. The results manifest the excellent performance of our approach in capturing real users’ preferences compared with other state-of-the-art methods.

Aero-Engine Remaining Useful Life Prediction Based on Bi-Discrepancy Network

Most unsupervised domain adaptation (UDA) methods align feature distributions across different domains through adversarial learning. However, many of them require introducing an auxiliary domain alignment model, which incurs additional computational costs. In addition, they generally focus on the global distribution alignment and ignore the fine-grained domain discrepancy, so target samples with significant domain shifts cannot be detected or processed for specific tasks. To solve these problems, a bi-discrepancy network is proposed for the cross-domain prediction task. Firstly, target samples with significant domain shifts are detected by maximizing the discrepancy between the outputs of the dual regressor. Secondly, the adversarial training mechanism is adopted between the feature generator and the dual regressor for global domain adaptation. Finally, the local maximum mean discrepancy is used to locally align the fine-grained features of different degradation stages. In 12 cross@-domain prediction tasks generated on the C-MAPSS dataset, the root-mean-square error (RMSE) was reduced by 77.24%, 61.72%, 38.97%, and 3.35% on average, compared with the four mainstream UDA methods, which proved the effectiveness of the proposed method.

Selective transfer subspace learning for small-footprint end-to-end cross-domain keyword spotting

In small-footprint end-to-end keyword spotting, it is often expensive and time-consuming to acquire sufficient labels in various speech scenarios. To overcome this problem, transfer learning leverages the rich knowledge of the auxiliary domain to annotate the unlabeled target data. However, most existing transfer learning methods typically learn a domain-invariant feature representation while ignoring the negative transfer problem. In this paper, we propose a new and general cross-domain keyword spotting framework called selective transfer subspace learning (STSL) that avoid negative transfer and dramatically improve the accuracy for cross-domain keyword spotting by actively selecting appropriate source samples. Specifically, STSL first aligns geometrical relationship and weighted distribution discrepancy to learn a domain-invariant projection subspace. Then, it actively selects appropriate source samples that are similar to the target domain for transfer learning to avoid negative transfer. Finally, we formulate a minimization problem that alternately optimizes the projection subspace and source active selection, giving an effective optimization. Experimental results on 10 groups of cross-domain keyword spotting tasks show that our STSL outperforms some state-of-the-art transfer learning methods and no transfer learning methods.

Structural basis of RNA-induced autoregulation of the DExH-type RNA helicase maleless

RNA unwinding by DExH-type helicases underlies most RNA metabolism and function. It remains unresolved if and how the basic unwinding reaction of helicases is regulated by auxiliary domains. We explored the interplay between the RecA and auxiliary domains of the RNA helicase maleless (MLE) from Drosophila using structural and functional studies. We discovered that MLE exists in a dsRNA-bound open conformation and that the auxiliary dsRBD2 domain aligns the substrate RNA with the accessible helicase tunnel. In an ATP-dependent manner, dsRBD2 associates with the helicase module, leading to tunnel closure around ssRNA. Furthermore, our structures provide a rationale for blunt-ended dsRNA unwinding and 3'-5' translocation by MLE. Structure-based MLE mutations confirm the functional relevance of our model for RNA unwinding. Our findings contribute to our understanding of the fundamental mechanics of auxiliary domains in DExH helicase MLE, which serves as a model for its human ortholog and potential therapeutic target, DHX9/RHA.