Pair Of Modules Research Articles

Gene co-expression analysis reveals conserved and distinct gene networks between resistant and susceptible Lens ervoides challenged by hemibiotrophic and necrotrophic pathogens

As field crops are likely to be challenged by multiple pathogens during their development, the investigation of broad-spectrum resistance in the host is of great interest for crop genetic enhancement. In this study, we attempted to address this question by adopting a weighed gene co-expression approach to study the temporal transcriptome dynamics of resistant and susceptible recombinant inbred lines (RILs) derived from an intraspecific Len ervoides cross during the infection process with either the necrotrophic pathogens Ascochyta lentis or Stemphylium botryosum, or the hemibiotrophic pathogen Colletotrichum lentis. By comparing networks of resistant and susceptible RILs, seven network module pairs were found to possess high correlation coefficients (R > 0.70) and large number of overlapping genes (n > 100). The conserved co-regulation of genes in these network module pairs were involved in plant cell wall synthesis, cell division, cytoskeleton organization, and protein ubiquitin related processes and appeared to be common disease responses against these pathogens. On the other hand, we also identified eight modules with low correlation between resistance and susceptibility networks. Among those, a stronger gene co-expression in R-genes and small RNA processes in the resistant hosts may be enhancing L. ervoides resistance against A. lentis, C. lentis, and S. botryosum, whereas the higher level of synergistic regulation in the synthesis of arginine and glutamine and phospholipid and glycerophospholipids in the susceptible hosts may contribute to increased susceptibility in L. ervoides.

Neural network architecture of a mammalian brain

Connectomics research is making rapid advances, although models revealing general principles of connectional architecture are far from complete. Our analysis of 106 published connection reports indicates that the adult rat brain interregional connectome has about 76,940 of a possible 623,310 axonal connections between its 790 gray matter regions mapped in a reference atlas, equating to a network density of 12.3%. We examined the sexually dimorphic network using multiresolution consensus clustering that generated a nested hierarchy of interconnected modules/subsystems with three first-order modules and 157 terminal modules in females. Top-down hierarchy analysis suggests a mirror-image primary module pair in the central nervous system's rostral sector (forebrain-midbrain) associated with behavior control, and a single primary module in the intermediate sector (rhombicbrain) associated with behavior execution; the implications of these results are considered in relation to brain development and evolution. Bottom-up hierarchy analysis reveals known and unfamiliar modules suggesting strong experimentally testable hypotheses. Global network analyses indicate that all hubs are in the rostral module pair, a rich club extends through all three primary modules, and the network exhibits small-world attributes. Simulated lesions of all regions individually enabled ranking their impact on global network organization, and the visual path from the retina was used as a specific example, including the effects of cyclic connection weight changes from the endogenous circadian rhythm generator, suprachiasmatic nucleus. This study elucidates principles of interregional neuronal network architecture for a mammalian brain and suggests a strategy for modeling dynamic structural connectivity.

Geniposide attenuates influenza virus-induced pneumonia by regulating inflammatory cytokines production. Evidences to elucidate the followed pathway

BackgroundInfluenza virus-induced pneumonia (IVP) is an infectious pulmonary disease characterized by exacerbated pulmonary inflammation caused by invasion of the influenza virus. IVP continues to threaten public health due to its high morbidity and mortality rates. Geniposide is one of the major bioactive constituents of G. jasminoides, which exerts antiviral and anti-inflammatory effects on influenza A virus (IAV) infection. PurposeTo investigate therapeutic effects and comprehensive mechanisms of geniposide on IAV infection and subsequent pneumonia. MethodsICR mice were infected intranasally with H1N1 (A/FM/1/47) to detect the anti-IAV activity of geniposide. Proteomics combined with function-integrated analysis were conducted to gain insight into the comprehensive mechanisms of geniposide. Subsequently, western blot was used to detect the phosphorylation of signal transducer and activator of transcription 1 (STAT1), signal transducer and activator of transcription 2 (STAT2), Interferon regulatory factor 9 (IRF9) and Janus kinase 1 (JAK1) in Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathway in lung tissue. Finally, RT-qPCR was used to detect the levels of interleukin 6 (IL-6), interleukin 17 (IL-17), interferon-γ (IFN-γ) and the STAT1 inhibitor (fludarabine) was used to verify the targeting between STAT1 and geniposide in RAW cells. ResultsGeniposide could significantly reduce the lung index, diminish lung pathology, decrease the virus loads and the inflammatory cytokines expression induced by IAV infection. A total of 411 differentially expressed proteins were identified among control, model, and geniposide-treated group in proteomic analysis. According to function-integrated analysis, 15 KEGG pathways were enriched and divided into 9 groups (modules), including influenza A, NOD-like receptor signaling, RIG-I-like receptor signaling, and so on. Among these modules, the most intensely interacting module pair was the NOD-like receptor signaling and influenza A, in which STAT1 and STAT2 acted as hubs with critical bridgeness role in the target network of geniposide. This indicated that geniposide may mitigate inflammation and alleviate IVP by JAK/STAT signaling pathways. Moreover, validation experiments confirmed that geniposide can significantly inhibit STAT1 and STAT2 phosphorylation as well as down-regulated expression of IL-6, IFN-γ and IL-17 in lung. Furthermore, when RAW cells were treated with the STAT1 inhibitor (fludarabine), the inhibitory effect of geniposide on IFN-γ and IL-6 was attenuated significantly. ConclusionsGeniposide can attenuate IAV-induced pneumonia by regulating inflammatory cytokines production through the JAK/STAT pathway.

Some remarks on two-periodic modules over local rings

In this paper, some properties of finitely generated two-periodic modules over commutative Noetherian local rings have been studied. We show that under certain assumptions on a pair of modules [Formula: see text] with [Formula: see text] being two-periodic, the natural map [Formula: see text] is an isomorphism. As a consequence, we prove that Auslander’s depth formula holds for such a Tor-independent pair. Tor-independence plays a crucial role for the depth formula to hold. Under certain assumptions on the modules, we show that a pair of modules, over a one-dimensional local ring, is Tor-independent if and only if their tensor product is torsion-free. Celikbas et al. recently showed the Huneke–Wiegand conjecture holds for two-periodic modules over one-dimensional domains. We generalize their result to the case of two-periodic modules with rank over one-dimensional local rings.

Cable Eccentricity Detection Method Based on Magnetic Field.

Amid the rapid advancement of electronic information technology, the need for cable eccentricity measurement in the industry is increasing both in China and across the globe. Current detection methods have several flaws, including high costs, insufficient accuracy, and instability. In this paper, we introduce a magnetic field-based detection method for cable eccentricity that provides high precision and cost-effectiveness. We position three pairs of magnetic field-collection modules in a circular array to gather magnetic flux density information induced by the electrified cable. We apply the law of electromagnetic induction to calculate the cable eccentricity. Our method is non-contact, preserving the cable's integrity. Our method outperforms traditional detection methods, not only in achieving greater accuracy and stability but also in significantly lowering the detection cost. Simulations and experiments show that our method's error rate under specified conditions is 0~4%, with a maximum standard deviation of 0.11, confirming its precision and stability in detecting cable eccentricity. The effectiveness of our method is influenced by two factors: lift-off value and loading current intensity. Our method presents a novel concept and a dependable strategy for the progress of cable eccentricity-detection technology.

Laurent family of simple modules over quiver Hecke algebras

We introduce the notions of quasi-Laurent and Laurent families of simple modules over quiver Hecke algebras of arbitrary symmetrizable types. We prove that such a family plays a similar role of a cluster in quantum cluster algebra theory and exhibits a quantum Laurent positivity phenomenon similar to the basis of the quantum unipotent coordinate ring $\mathcal {A}_q(\mathfrak {n}(w))$, coming from the categorification. Then we show that the families of simple modules categorifying Geiß–Leclerc–Schröer (GLS) clusters are Laurent families by using the Poincaré–Birkhoff–Witt (PBW) decomposition vector of a simple module $X$ and categorical interpretation of (co)degree of $[X]$. As applications of such $\mathbb {Z}\mspace {1mu}$-vectors, we define several skew-symmetric pairings on arbitrary pairs of simple modules, and investigate the relationships among the pairings and $\Lambda$-invariants of $R$-matrices in the quiver Hecke algebra theory.

On the bottleneck stability of rank decompositions of multi-parameter persistence modules

A significant part of modern topological data analysis is concerned with the design and study of algebraic invariants of poset representations—often referred to as persistence modules. One such invariant is the minimal rank decomposition, which encodes the ranks of all the structure morphisms of the persistence module by a single ordered pair of rectangle-decomposable modules, interpreted as a signed barcode. This signed barcode generalizes the concept of persistence barcode from one-parameter persistence to any number of parameters, raising the question of its bottleneck stability. We show in this paper that the minimal rank decomposition is not stable under the natural notion of signed bottleneck matching between signed barcodes. We remedy this by turning our focus to the rank exact decomposition, a related signed barcode induced by the minimal projective resolution of the module relative to the so-called rank exact structure, which we prove to be bottleneck stable under signed matchings. As part of our proof, we obtain two intermediate results of independent interest: we compute the global dimension of the rank exact structure on the category of finitely presentable multi-parameter persistence modules, and we prove a bottleneck stability result for hook-decomposable modules. We also give a bound for the size of the rank exact decomposition that is polynomial in the size of the usual minimal projective resolution, we prove a universality result for the dissimilarity function induced by the notion of signed matching, and we compute, in the two-parameter case, the global dimension of a different exact structure related to the upsets of the indexing poset. This set of results combines concepts from topological data analysis and from the representation theory of posets, and we believe is relevant to both areas.

Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression

Late onset Alzheimer’s disease (AD) is a progressive neurodegenerative disease, with brain changes beginning years before symptoms surface. AD is characterized by neuronal loss, the classic feature of the disease that underlies brain atrophy. However, GWAS reports and recent single-nucleus RNA sequencing (snRNA-seq) efforts have highlighted that glial cells, particularly microglia, claim a central role in AD pathophysiology. Here, we tailor pattern-learning algorithms to explore distinct gene programs by integrating the entire transcriptome, yielding distributed AD-predictive modules within the brain’s major cell-types. We show that these learned modules are biologically meaningful through the identification of new and relevant enriched signaling cascades. The predictive nature of our modules, especially in microglia, allows us to infer each subject’s progression along a disease pseudo-trajectory, confirmed by post-mortem pathological brain tissue markers. Additionally, we quantify the interplay between pairs of cell-type modules in the AD brain, and localized known AD risk genes to enriched module gene programs. Our collective findings advocate for a transition from cell-type-specificity to gene modules specificity to unlock the potential of unique gene programs, recasting the roles of recently reported genome-wide AD risk loci.

Endotype-driven Co-module mechanisms of danhong injection in the Co-treatment of cardiovascular and cerebrovascular diseases: A modular-based drug and disease integrated analysis

Ethnopharmacological relevanceCardiovascular and cerebrovascular diseases are the leading causes of death worldwide and interact closely with each other. Danhong Injection (DHI) is a widely used preparation for the co-treatment of brain and heart diseases (CTBH). However, the underlying molecular endotype mechanisms of DHI in the CTBH remain unclear. Aim of this studyTo elucidate the underlying endotype mechanisms of DHI in the CTBH. Materials and methodsIn this study, we proposed a modular-based disease and drug-integrated analysis (MDDIA) strategy for elucidating the systematic CTBH mechanisms of DHI using high-throughput transcriptome-wide sequencing datasets of DHI in the treatment of patients with stable angina pectoris (SAP) and cerebral infarction (CI). First, we identified drug-targeted modules of DHI and disease modules of SAP and CI based on the gene co-expression networks of DHI therapy and the protein–protein interaction networks of diseases. Moreover, module proximity-based topological analyses were applied to screen CTBH co-module pairs and driver genes of DHI. At the same time, the representative driver genes were validated via in vitro experiments on hypoxia/reoxygenation-related cardiomyocytes and neuronal cell lines of H9C2 and HT22. ResultsSeven drug-targeted modules of DHI and three disease modules of SAP and CI were identified by co-expression networks. Five modes of modular relationships between the drug and disease modules were distinguished by module proximity-based topological analyses. Moreover, 13 targeted module pairs and 17 driver genes associated with DHI in the CTBH were also screened. Finally, the representative driver genes AKT1, EDN1, and RHO were validated by in vitro experiments. ConclusionsThis study, based on clinical sequencing data and modular topological analyses, integrated diseases and drug targets. The CTBH mechanism of DHI may involve the altered expression of certain driver genes (SRC, STAT3, EDN1, CYP1A1, RHO, RELA) through various enriched pathways, including the Wnt signaling pathway.

Groupoid Analysis of Pyroxenes

AbstractThe structure and space‐group symmetry of modular structures can be obtained from the knowledge of the structure and subperiodic symmetry of the modules and of the structure‐building operations. This leads to a space groupoid, which contains all the partial operations relating any pairs of modules. Those partial operations that possess a continuation in the whole crystal space are global operations that show up in the space group of the structure. The whole procedure is presented through the example of pyroxenes. The modular structure of these minerals has already been analyzed in the past, but the groupoid construction has only been synthetically described. In this paper, the full step‐by‐step process is presented, which is largely unknown in the crystallographic community.

Nonlocal phase modulation of multimode, continuous-variable twin beams

We investigate experimentally the nonlocal phase modulation of multiple-frequency-mode, continuous-variable entangled twin beams. We use a pair of electro-optical phase modulators to modulate the entangled probe and conjugate light beams produced by four-wave mixing in hot rubidium vapor. A single phase modulator in either one of the twin beams reduces the two-mode squeezing signal. The overall quantum entanglement is preserved, however, as the modulator nonlocally distributes the beam correlations among frequency modes of the multimode fields. The two-mode squeezing can be recovered by reversing the mixing with an additional out-of-phase electro-optical phase modulator (EOM) in the other beam.

Constructing comprehensive and discriminative representations with diverse attention for occluded person re-identification

Occluded person re-identification (Re-ID) is a challenging task that aims to match occluded person images to holistic ones across different camera views. Feature diversity is crucial for achieving high-performance Re-ID. Previous methods rely on additional annotations or hand-crafted rules to achieve feature diversity, which are inefficient or infeasible for occluded Re-ID. To address this, we propose the Diverse Attention Net (DANet) which utilizes attention mechanism to achieve diverse feature mining. Specifically, DANet incorporates a pair of complementary Diverse Parallel Attention Modules (DPAM), which, under the attention decorrelation constraint (ADC), help the model automatically capture diverse discriminative features in a global scope. Additionally, we propose an Efficient Transformer layer that can seamlessly integrate with the proposed DPAM and synergistically enhance the capability to handle occlusions. The resulting DANet construct a set of comprehensive representations that encode diverse discriminative features. Extensive experiments demonstrate DANet achieves state-of-the-art performance on both occluded and holistic ReID benchmarks.

Quantifying the hierarchical adherence of modular documents

Several natural and artificial structures are characterized by an intrinsic hierarchical organization. The present work describes a methodology for quantifying the degree of adherence between a given hierarchical template and a respective modular document (e.g. books or homepages with content organized into modules) organized as a respective content network. The original document, which in the case of the present work concerns Wikipedia pages, is transformed into a respective content network by first dividing the document into parts or modules. Then, the contents (words) of each pair of modules are compared in terms of the coincidence similarity index, yielding a respective weight. The adherence between the hierarchical template and the content network can then be measured by considering the coincidence similarity between the respective adjacency matrices, leading to the respective hierarchical adherence index. In order to provide additional information about this adherence, four specific indices are also proposed, quantifying the number of links between non-adjacent levels, links between nodes in the same level, converging links between adjacent levels, and missing links. The potential of the approach is illustrated respectively to model-theoretical networks as well as to real-world data obtained from Wikipedia. In addition to confirming the effectiveness of the suggested concepts and methods, the results suggest that real-world documents do not tend to substantially adhere to respective hierarchical templates.

A Novel Lightweight Object Detection Network with Attention Modules and Hierarchical Feature Pyramid

Object detection methods based on deep learning typically require devices with ample computing capabilities, which limits their deployment in restricted environments such as those with embedded devices. To address this challenge, we propose Mini-YOLOv4, a lightweight real-time object detection network that achieves an excellent trade-off between speed and accuracy. Based on CSPDarknet-Tiny as the backbone network, we enhance the detection performance of the network in three ways. We use a multibranch structure embedded in an attention module for simultaneous spatial and channel attention calibration. We design a group self-attention block with a symmetric structure consisting of a pair of complementary self-attention modules to mine contextual information, thereby ensuring that the detection accuracy is improved without increasing the computational cost. Finally, we introduce a hierarchical feature pyramid network to fully exploit multiscale feature maps and promote the extraction of fine-grained features. The experimental results demonstrate that Mini-YOLOv4 requires only 4.7 M parameters and has a billion floating point operations (BFLOPs) value of 3.1. Compared with YOLOv4-Tiny, our approach achieves a 3.2% improvement in mean accuracy precision (mAP) for the PASCAL VOC dataset and obtains a significant improvement of 3.5% in overall detection accuracy for the MS COCO dataset. In testing with an embedded platform, Mini-YOLOv4 achieves a real-time detection speed of 25.6 FPS on the NVIDIA Jetson Nano, thus meeting the demand for real-time detection in computationally limited devices.

Numerical and experimental study of a non-frosting thermoelectric generation device for low temperature waste heat recovery

Liquefied natural gas (LNG) has great potential for power generation using thermoelectric generators (TEG) due to the large amount of cold energy released during LNG gasification, but frosting is a major issue which slows the heat transfer between the TEG and the environment and reduces the power generation efficiency. This study describes a non-frosting cryogenic thermoelectric generator for cold energy recovery. This device uses natural gas vapor on the hot side of the TEG to avoid frosting caused by direct contact of the TEG with air. The device characteristics were modeled using a thermodynamic analysis with the predictions verified by experiments to predict the output characteristics. The results show an optimal channel length for the selected design conditions that maximizes the power generation. Increasing the flow rate and enhancing the heat transfer on the hot side significantly increases the power output. For the design conditions of 1000 Nm3/h gasification capacity and 0.4 MPa working pressure, the optimum channel length is 9.8 m with 350 pairs of thermoelectric modules that gives the maximum power generation of 593.94 W with a 1.36% efficiency.

COMA-Net: Towards generalized medical image segmentation using complementary attention guided bipolar refinement modules

Precise medical image segmentation is a crucial step for proper isolation of target regions, such as an organ or lesion for accurate medical diagnosis, prognosis and certain medical procedures. Taking advantage of the available annotated medical image datasets, many CNN-based approaches have been proposed for segmentation over the years. These conventional approaches lack appropriate supervised means to enhance the foreground/target regions relative to background at the feature level for improving the generalizability of these methods to obtain better performance across diverse imaging modalities. In this work, we introduce COMA-Net (COMplementary Attention guided bipolar refinement-based Network), which employs a complementary attention scheme between a pair of positive and negative refinement modules placed on top of two encoder structures for generating refined feature references for the decoder stage in a supervised manner. A four-way feature shifting operation is introduced in conjunction with a set of dilated convolutional layers so that it considers the spatial relationships across a wider footprint leading to better contextual feature extraction. We also formulate a novel Foreground-to-Background Ratio (FBR) index to highlight the differences in signal power between target region and background due to the refinement. Experimental results on five different publicly available medical image segmentation datasets, including BUSI, GLAS, ISIC-2018, MoNuSeg and CVC-ClinicDB reveal that on average, the proposed method can achieve an additional mean F1, IoU, precision, and recall score of +0.97%, +1.25%, +1.11%, and +0.22%, respectively over the state-of-the-art segmentation methods, suggesting its great potential for application on real-world patient image data.

HaptGlove-Untethered Pneumatic Glove for Multimode Haptic Feedback in Reality-Virtuality Continuum.

Novel haptics technologies are urgently needed to bridge the gap between entirely physical world and fully digital environment to render a more realistic and immersive human-computer interaction. Current virtual reality (VR) haptic gloves either deliver limited haptic feedback or are bulky and heavy. The authors develop a haptic glove or HaptGlove, an untethered and lightweight pneumatic glove, that allows users to "physically" interact in a VR environment and enables both kinesthetic and cutaneous sensations naturally and realistically. Integrated with five pairs of haptic feedback modules and fiber sensors, HaptGlove provides variable stiffness force feedback and fingertip force and vibration feedback, allowing users to touch, press, grasp, squeeze, and pull various virtual objects and feel the dynamic haptic changes. Significant improvements in VR realism and immersion areobserved in a user study with participants achieving 78.9% accuracy in sorting six virtual balls of different stiffnesses. Importantly, HaptGlove facilitates VR training, education, entertainment, and socialization in a reality-virtuality continuum.

MANSOR: A module alignment method based on neighbor information for scientific workflow

SummaryFinding similar scientific workflow modules that can substitute essential components of the privacy workflows from public repositories to create a personalized workflow is growing in popularity. This is a cost‐effective and error‐free strategy for the scientific community. Currently, module alignment approaches heavily depend on syntactic information such as modules' names and types. However, the contextual semantic information of modules, which encompasses inputs, outputs, and datalinks connecting modules, can also convey their functions. Unfortunately, this information is scarcely utilized during the module alignment process. In this work, we propose a module alignment method based on neighbor information for scientific workflow (MANSOR). Specifically, we present a rule‐based attribute similarity computation approach for calculating initial module‐pair similarity in two workflows. The relation similarity is then employed to iteratively fine‐tune the matching degree with uncertainty of module pairs by considering the contextual semantics until reaching a steady state. After pairwise comparison of workflows, the module alignment results are determined based on their module‐pair similarity. Experimental results on the human‐curated corpus of ratings indicate that MANSOR outperforms the existing state‐of‐the‐art approaches with statistical significance.

On the Exact Closed-Form ABEP Analysis of Downlink NOMA Over mmWave TWDP Fading

This paper presents the performance analysis of two users’ downlink non-orthogonal multiple access (NOMA) over a millimeter-wave two-wave with diffuse power (TWDP) fading model for successive interference cancellation errors. The new closed-form expressions of average bit error probability (ABEP) for both users are derived for quadrature phase-shift-keying and binary phase-shift-keying modulation pairs. Further, the asymptotic ABEP expressions are derived to provide better insights into the performance analysis. The diversity order of each user is also derived with the aid of asymptotic ABEP expression. The obtained expressions over TWDP fading channels are generalized ABEP expressions as it also shows ABEP expressions for both Rician and Rayleigh fading channels as special cases. Simulation results support the correctness of each analytical ABEP expression.

Image-extension-closed subcategories of module categories of hereditary algebras

We study IE-closed subcategories of a module category, subcategories which are closed under taking Images and Extensions. We investigate the relation between IE-closed subcategories and torsion pairs, and characterize τ-tilting finite algebras using IE-closed subcategories. For the hereditary case, we show that IE-closed subcategories can be classified by twin rigid modules, pairs of rigid modules satisfying some homological conditions. Moreover, we introduce mutation of twin rigid modules analogously to tilting modules, which gives a way to calculate all twin rigid modules for the representation-finite case.