Simple Connection Research Articles

Neurons as Autoencoders.

This letter presents the idea that neural backpropagation is exploiting dendritic processing to enable individual neurons to perform autoencoding. Using a very simple connection weight search heuristic and artificial neural network model, the effects of interleaving autoencoding for each neuron in a hidden layer of a feedforward network are explored. This is contrasted with the equivalent standard layered approach to autoencoding. It is shown that such individualized processing is not detrimental and can improve network learning.

Low-weight high-distance error-correcting fermionic encodings

We perform an extended numerical search for practical fermion-to-qubit encodings with error-correcting properties. Ideally, encodings should strike a balance between a number of the seemingly incompatible attributes, such as having a high minimum distance, low-weight fermionic logical operators, a small qubit to fermionic mode ratio and a simple qubit connectivity graph including ancilla qubits for the measurement of stabilizers. Our strategy consists of a three-step procedure in which we: First generate encodings with code distances up to d≤4 by a brute-force enumeration technique; subsequently, we use these encodings as starting points and apply Clifford deformations to them which allows us to identify higher-distance codes with d≤7; finally, we optimize the hardware connectivity graphs of resulting encodings in terms of the graph thickness and the number of connections per qubit. We report multiple promising high-distance encodings which significantly improve the weights of stabilizers and logical operators compared to previously reported alternatives. Published by the American Physical Society 2024

Graphlet-based hyperbolic embeddings capture evolutionary dynamics in genetic networks.

Spatial Analysis of Functional Enrichment (SAFE) is a popular tool for biologists to investigate the functional organisation of biological networks via highly intuitive 2D functional maps. To create these maps, SAFE uses Spring embedding to project a given network into a 2D space in which nodes connected in the network are near each other in space. However, many biological networks are scale-free, containing highly connected hub nodes. Because Spring embedding fails to separate hub nodes, it provides uninformative embeddings that resemble a "hairball". In addition, Spring embedding only captures direct node connectivity in the network and does not consider higher-order node wiring patterns, which are best captured by graphlets, small, connected, non-isomorphic, induced subgraphs. The scale-free structure of biological networks is hypothesised to stem from an underlying low-dimensional hyperbolic geometry, which novel hyperbolic embedding methods try to uncover. These include coalescent embedding, which projects a network onto a 2D disk. To better capture the functional organisation of scale-free biological networks, whilst also going beyond simple direct connectivity patterns, we introduce Graphlet Coalescent (GraCoal) embedding, which embeds nodes nearby on a disk if they frequently co-occur on a given graphlet together. We use GraCoal to extend SAFE-based network analysis. Through SAFE-enabled enrichment analysis, we show that GraCoal outperforms graphlet-based Spring embedding in capturing the functional organisation of the genetic interaction networks of fruit fly, budding yeast, fission yeast and E. coli. We show that depending on the underlying graphlet, GraCoal embeddings capture different topology-function relationships. We show that triangle-based GraCoal embedding captures functional redundancies between paralogs. https://gitlab.bsc.es/swindels/gracoal_embedding. Supplementary data are available at Bioinformatics online.

A Review of Hypergraph Neural Networks

In recent years, Graph Neural Networks (GNNs) have seen notable success in fields such as recommendation systems and natural language processing, largely due to the availability of vast amounts of data and powerful computational resources. GNNs are primarily designed to work with graph data that involve pairwise relationships. However, in many real-world networks, the relationships between entities are complex and go beyond simple pairwise connections, as seen in scientific collaboration networks, protein networks, and similar domains. If these complex relationships are directly represented as pairwise relationships using graph structures, it can lead to information loss. A hypergraph, as a special kind of graph-structured data, can represent higher-order relationships that cannot be fully captured by graphs, thereby addressing the limitations of graphs. In light of this, researchers have begun to focus on how to design neural networks on hypergraphs, leading to the proposal of hypergraph neural network (HGNN) models for downstream tasks. Therefore, this paper reviews the existing hypergraph neural network models. The review is conducted from two perspectives: spectral analysis methods and neural network methods on hypergraphs, discussing both unfolded and non-unfolded methods, and further subdividing them based on their algorithm characteristics and application scenarios. Subsequently, the design concepts of various algorithms are analyzed and compared, and the advantages and disadvantages of each type of algorithm are summarized based on experimental results. Finally, potential future research directions in hypergraph learning are discussed.

MultiNet 2.0: A lightweight attention-based deep learning network for stenosis measurement in carotid ultrasound scans and cardiovascular risk assessment

BackgroundCardiovascular diseases (CVD) cause 19 million fatalities each year and cost nations billions of dollars. Surrogate biomarkers are established methods for CVD risk stratification; however, manual inspection is costly, cumbersome, and error-prone. The contemporary artificial intelligence (AI) tools for segmentation and risk prediction, including older deep learning (DL) networks employ simple merge connections which may result in semantic loss of information and hence low in accuracy. MethodologyWe hypothesize that DL networks enhanced with attention mechanisms can do better segmentation than older DL models. The attention mechanism can concentrate on relevant features aiding the model in better understanding and interpreting images. This study proposes MultiNet 2.0 (AtheroPoint, Roseville, CA, USA), two attention networks have been used to segment the lumen from common carotid artery (CCA) ultrasound images and predict CVD risks. ResultsThe database consisted of 407 ultrasound CCA images of both the left and right sides taken from 204 patients. Two experts were hired to delineate borders on the 407 images, generating two ground truths (GT1 and GT2). The results were far better than contemporary models. The lumen dimension (LD) error for GT1 and GT2 were 0.13±0.08 and 0.16±0.07 mm, respectively, the best in market. The AUC for low, moderate and high-risk patients’ detection from stenosis data for GT1 were 0.88, 0.98, and 1.00 respectively. Similarly, for GT2, the AUC values for low, moderate, and high-risk patient detection were 0.93, 0.97, and 1.00, respectively.The system can be fully adopted for clinical practice in AtheroEdge™ model by AtheroPoint, Roseville, CA, USA.

Load resistance of masonry infilled panels for steel frames to mitigate progressive collapse caused by middle column missing

The load resistance of masonry infilled panels in upgrading the steel frame to mitigate progressive collapse was quantified experimentally and analytically in the current study. Four 1/2-scale steel subframes were tested, including two bare subframes without infilled panels for reference and two infilled subframes to assess the effects of infilled panels. Two types of connection were investigated in current study: welded connection and top and seat angle connection. The test result showed that infilled panels provided a greater improvement in initial stiffness and peak load of the steel subframe with top and seat angle connections compared to the infilled steel subframe with welded connections. On the premise that the connections retained their integrity, the strut mechanism of infilled panels could change from a diagonal strut mechanism forming in the whole panel to an off-diagonal strut mechanism formed in the remaining intact corner region with increasing vertical deflection. A novel equivalent multi-strut modelling method was proposed for the prediction of the infilled panel behavior with an acceptable level of accuracy. This model incorporates the shifts in the position of plastic hinges, which were influenced by the specific types of connection and the load-resisting mechanisms of the infilled panel. The analysis result demonstrated that the model slightly overestimated the load resistance of infilled panels within the steel frame with simple connections and slightly underestimated the load resistance of infilled panels within steel frame with semi-rigid or rigid connections.

Analysis of fast calcium dynamics of honey bee olfactory coding

Odour processing exhibits multiple parallels between vertebrate and invertebrate olfactory systems. Insects, in particular, have emerged as relevant models for olfactory studies because of the tractability of their olfactory circuits. Here, we used fast calcium imaging to track the activity of projection neurons in the honey bee antennal lobe (AL) during olfactory stimulation at high temporal resolution. We observed a heterogeneity of response profiles and an abundance of inhibitory activities, resulting in various response latencies and stimulus-specific post-odour neural signatures. Recorded calcium signals were fed to a mushroom body (MB) model constructed implementing the fundamental features of connectivity between olfactory projection neurons, Kenyon cells (KC), and MB output neurons (MBON). The model accounts for the increase of odorant discrimination in the MB compared to the AL and reveals the recruitment of two distinct KC populations that represent odorants and their aftersmell as two separate but temporally coherent neural objects. Finally, we showed that the learning-induced modulation of KC-to-MBON synapses can explain both the variations in associative learning scores across different conditioning protocols used in bees and the bees' response latency. Thus, it provides a simple explanation of how the time contingency between the stimulus and the reward can be encoded without the need for time tracking. This study broadens our understanding of olfactory coding and learning in honey bees. It demonstrates that a model based on simple MB connectivity rules and fed with real physiological data can explain fundamental aspects of odour processing and associative learning.

Editorial: Bridges are more than simple connections in transport networks and they must consider their impact on the resilience of networks, systems and regions

Editorial: Bridges are more than simple connections in transport networks and they must consider their impact on the resilience of networks, systems and regions

Survey on Cloud Computing Security

One of the most popular IT subfields today is cloud computing. With a simple Internet connection, it enables users to access cutting-edge information, technology, and infrastructure. Because of the numerous benefits offered, most international organizations have started to move toward cloud computing environments during the last few decades. Companies and organizations find cloud computing appealing because it eliminates the need to plan for provisioning and enables them to start with modest resources and progressively raise them as service demand increases. Cloud computing presents opportunities for research as well as obstacles. The security and privacy of data in cloud systems are important issues. Now that cloud computing technology is developing quickly, it is more convenient for businesses to move their workload there. It asks for a smaller investment and guarantees quick delivery of cutting-edge technologies. Cloud security is of the utmost importance. It is susceptible to cyberattacks that exploit its vulnerabilities in an insecure cloud environment. This puts the cloud environment’s assets, resources, and data in danger. In this paper, we reviewed some cloud computing studies about various attacks on cloud computing services. This paper’s main objective is to give readers a better understanding of security issues and solutions.

Click Chemistry in Detecting Protein Modification.

Click chemistry, also known as "link chemistry," is an important molecular connection method that can achieve simple and efficient connections between specific small molecular groups at the molecular level. Click chemistry offers several advantages, including high efficiency, good selectivity, mild conditions, and few side reactions. These features make it a valuable tool for in-depth analysis of various protein posttranslational modifications (PTMs) caused by changes in cell metabolism during viral infection. This chapter considers the palmitoylation, carbonylation, and alkylation of STING and presents detailed information and experimental procedures for measuring PTMs using click chemistry.

Experimental and numerical analysis of a novel structure obtained by joining two tensegrity-based units using simple construction methods

Structural systems based on tension and compression play a significant role in maximizing the performance of materials. Among them, structures based on the concept of tensegrity have attracted attention owing to their elegant design features and floating feeling. However, the complex construction of tensegrity-based structures hinders their implementation in practical design and architectural applications. Herein, we propose a novel tensegrity-based unit with simple connectivity and manufacturability. The external form of this unit is similar to that of a space-filling polyhedron. The prototype of the proposed unit is constructed, and the simple tension management of the unit is demonstrated. The mechanical behavior of the assembled structure is investigated by performing loading tests and numerical analyses. The behavior can be accurately predicted and general design can be applied to develop structures for practical applications.

Microbiota and Recurrent Pregnancy Loss (RPL); More than a Simple Connection.

Recurrent Pregnancy Loss (RPL) affects 1-2% of women, and its triggering factors are unclear. Several studies have shown that the vaginal, endometrial, and gut microbiota may play a role in RPL. A decrease in the quantity of Lactobacillus crispatus in local microbiota has been associated with an increase in local (vaginal and endometrial) inflammatory response and immune cell activation that leads to pregnancy loss. The inflammatory response may be triggered by gram-negative bacteria, lipopolysaccharides (LPS), viral infections, mycosis, or atypia (tumor growth). Bacterial structures and metabolites produced by microbiota could be involved in immune cell modulation and may be responsible for immune cell activation and molecular mimicry. Gut microbiota metabolic products may increase the amount of circulating pro-inflammatory lymphocytes, which, in turn, will migrate into vaginal or endometrial tissues. Local pro-inflammatory Th1 and Th17 subpopulations and a decrease in local Treg and tolerogenic NK cells are accountable for the increase in pregnancy loss. Local microbiota may modulate the local inflammatory response, increasing pregnancy success. Analyzing local and gut microbiota may be necessary to characterize some RPL patients. Although oral supplementation of probiotics has not been shown to modify vaginal or endometrial microbiota, the metabolites produced by it may benefit patients. Lactobacillus crispatus transplantation into the vagina may enhance the required immune tolerogenic response to achieve a normal pregnancy. The effect of hormone stimulation and progesterone to maintain early pregnancy on microbiota has not been adequately studied, and more research is needed in this area. Well-designed clinical trials are required to ascertain the benefit of microbiota modulation in RPL.

FCSU-Net: A novel full-scale Cross-dimension Self-attention U-Net with collaborative fusion of multi-scale feature for medical image segmentation

Recently, ViT and CNNs based on encoder–decoder architecture have become the dominant model in the field of medical image segmentation. However, there are some deficiencies for each of them: (1) It is difficult for CNNs to capture the interaction between two locations with consideration of the longer distance. (2) ViT cannot acquire the interaction of local context information and carries high computational complexity. To optimize the above deficiencies, we propose a new network for medical image segmentation, which is called FCSU-Net. FCSU-Net uses the proposed collaborative fusion of multi-scale feature block that enables the network to obtain more abundant and more accurate features. In addition, FCSU-Net fuses full-scale feature information through the FFF (Full-scale Feature Fusion) structure instead of simple skip connections, and establishes long-range dependencies on multiple dimensions through the CS (Cross-dimension Self-attention) mechanism. Meantime, every dimension is complementary to each other. Also, CS mechanism has the advantage of convolutions capturing local contextual weights. Finally, FCSU-Net is validated on several datasets, and the results show that FCSU-Net not only has a relatively small number of parameters, but also has a leading segmentation performance.

Ultimate in-plane shear capacity of 3-panel frameless cold-form steel corrugated walls under axial compression

The Frameless building system, developed by BEHLEN Industries LP, is a structural system that incorporates Cold Form Steel Corrugated Wall (CFSCW) components. These include walls, ceilings, and roofs formed by Frameless panels, along with footings, boundary columns, and an optional convex truss. The system employs simple bolt connections, enabling rapid construction without the need for heavy machinery. It is particularly cost-effective and is often utilized in regions with low seismic activity. In high seismic zones, the structural performance of The Frameless building system under combined compression and lateral loads has not been systematically examined. In this study, four full-scale 3-panel Frameless CFSCWs, were tested under combined axial and shear loads. A generic finite element model was proposed to simulate the force-deformation responses and deformed shapes of the 3-panel Frameless CFSCWs. The numerical simulation results were verified using the experimental results. The verified numerical model was then used in parameter study to examine the ultimate shear capacity of 3-panel Frameless CFSCW of different wall thickness, with the presence of axial compression. The results of the study revealed the ultimate shear capacity of 3-panel Frameless CFSCW is linearly correlated with gauge thickness of the wall panel.

Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation

Current state-of-the-art medical image segmentation techniques predominantly employ the encoder–decoder architecture. Despite its widespread use, this U-shaped framework exhibits limitations in effectively capturing multi-scale features through simple skip connections. In this study, we made a thorough analysis to investigate the potential weaknesses of connections across various segmentation tasks, and suggest two key aspects of potential semantic gaps crucial to be considered: the semantic gap among multi-scale features in different encoding stages and the semantic gap between the encoder and the decoder. To bridge these semantic gaps, we introduce a novel segmentation framework, which incorporates a Dual Attention Transformer module for capturing channel-wise and spatial-wise relationships, and a Decoder-guided Recalibration Attention module for fusing DAT tokens and decoder features. These modules establish a principle of learnable connection that resolves the semantic gaps, leading to a high-performance segmentation model for medical images. Furthermore, it provides a new paradigm for effectively incorporating the attention mechanism into the traditional convolution-based architecture. Comprehensive experimental results demonstrate that our model achieves consistent, significant gains and outperforms state-of-the-art methods with relatively fewer parameters. This study contributes to the advancement of medical image segmentation by offering a more effective and efficient framework for addressing the limitations of current encoder–decoder architectures. Code: https://github.com/McGregorWwww/UDTransNet.

Single-cell RNA sequencing reveals the gene expression profile and cellular communication in human fetal heart development

The heart is the central organ of the circulatory system, and its proper development is vital to maintain human life. As fetal heart development is complex and poorly understood, we use single-cell RNA sequencing to profile the gene expression landscapes of human fetal hearts from the four-time points: 8, 10, 11, 17 gestational weeks (GW8, GW10, GW11, GW17), and identified 11 major types of cells: erythroid cells, fibroblasts, heart endothelial cells, ventricular cardiomyocytes, atrial cardiomyocytes, macrophage, DCs, smooth muscle, pericytes, neural cells, schwann cells. In addition, we identified a series of differentially expressed genes and signaling pathways in each cell type between different gestational weeks. Notably, we found that ANNEXIN, MIF, PTN, GRN signalling pathways were simple and fewer intercellular connections in GW8, however, they were significantly more complex and had more intercellular communication in GW10, GW11, and GW17. Notably, the interaction strength of OSM signalling pathways was gradually decreased during this period of time (from GW8 to GW17). Together, in this study, we presented a comprehensive and clear description of the differentiation processes of all the main cell types in the human fetal hearts, which may provide information and reference data for heart regeneration and heart disease treatment.

Мodelling of Passive Coupling of Battery Units of Hybrid Energy Storage System

Due to the development of electric transport and the growth of “green” energy, electric energy storage systems (ESS) are increasingly being used in the world. The growth of the battery market in the last decade has been 20-30%. One of the ways to increase the efficiency of an electric power storage device is its hybridization, i.e. the use of heterogeneous battery units. The paper examines the features of passive coupling of lead-acid and lithium-ion batteries in a hybrid storage device. A model is presented for calculating the electrical characteristics of these units during operation. The possibility of choosing a hybrid drive structure that provides a comparable operating voltage range of the units (operation without voltage converters) is demonstrated. The modes of operation of a hybrid energy storage system are modeled both for simple parallel connection and for switching blocks according to a threshold algorithm. It is demonstrated that in order to equalize the discharge rate of the main and additional units, it is necessary to coordinate the capacity of the ESS, the degree of hybridization, the type of load and the electrical parameters of the batteries, which is impossible without modeling the system. When the threshold switching of the blocks takes place, additional control parameters, making it possible to change the discharge rate of the additional block and increase the economic efficiency of the hybrid ESS. Estimates of the economic efficiency of hybrid ESSs have been made for different values of the threshold switching voltage of the lithium-ion unit, as well as for three characteristic loads: an electric forklift truck, a 30-apartment apartment building and a 300-apartment residential complex. The results demonstrate the features and technical and economic potential of passive hybridization, can be used for the design of hybrid ESSs for small power systems with solar and wind power plants, in the calculation and design of generator – storage – consumer systems.

SaraNet: Semantic aggregation reverse attention network for pulmonary nodule segmentation

Accurate segmentation of pulmonary nodule is essential for subsequent pathological analysis and diagnosis. However, current U-Net architectures often rely on a simple skip connection scheme, leading to the fusion of feature maps with different semantic information, which can have a negative impact on the segmentation model. In response to this challenge, this study introduces a novel U-shaped model specifically designed for pulmonary nodule segmentation. The proposed model incorporates features such as the U-Net backbone, semantic aggregation feature pyramid module, and reverse attention module. The semantic aggregation module combines semantic information with multi-scale features, addressing the semantic gap between the encoder and decoder. The reverse attention module explores missing object parts and captures intricate details by erasing the currently predicted salient regions from side-output features. The proposed model is evaluated using the LIDC-IDRI dataset. Experimental results reveal that the proposed method achieves a dice similarity coefficient of 89.11%and a sensitivity of 90.73 %, outperforming state-of-the-art approaches comprehensively.

A ductile seismic design strategy for the cross-aisle direction of racking systems

Due to their lightness and simple connectivity, steel racking systems are typically considered as “low-dissipative” structures, which is reflected in the modern seismic codes by the absence of capacity design and the adoption of low behaviour factors. This limited capability of stress redistribution significantly increases the vulnerability of racks under beyond-design seismic hazards and raises the demand for more resilient designs. Along these lines, the proposed Plastic Ovalization Strategy (POS) attempts to increase the ductility of the individual upright frames comprising the cross-aisle direction of racks, and at the same time to preserve their low-cost and easy-to-assemble nature. This is achieved by tasking the bearing failure mechanism of the diagonal bolt hole to absorb seismic deformations, while capacity design is employed to keep the rest of the structure in the elastic zone. Following a detailed discussion on the motives and basic principles of the strategy, two high-rise racking systems are designed twice by professional engineers, once using standard approaches and then by additionally employing the proposed POS rules. Finally, the two design solutions are compared by conducting a comprehensive seismic assessment, which employs a phenomenological macro-model comprising elastic elements and nonlinear springs to simulate the bearing failure mechanism.

IoT-driven accessibility: A refreshable OCR-Braille solution for visually impaired and deaf-blind users through WSN

The spectacular concept of integrating OCR with the Internet of Things (IoT) and Wireless Sensor Networks (WSN), presented in this study, intends to increase the autonomy and social inclusion of people with visual impairments and deaf-blindness. This application converts text from photographs into real-time Braille representations by utilizing the capabilities of Optical Character Recognition (OCR) technology, Braille displays, and IoT. Braille is a tactile form of writing which assists the visually impaired by representing letters and numbers with raised dots. Text recognition and conversion through OCR helps people with vision issues by converting printed text into accessible formats like audio or Braille, assisting people in with recognizing things around them. Individuals with visual impairments are given autonomous access to critical information by smoothly integrating modern technologies, boosting their self-confidence and promoting active social involvement. This OCR-Braille approach's primary objective is to minimize the knowledge gap and promote social equality. Users' ease of access to labels, educational resources, and digital information about their environment fosters a sense of empowerment and independence. This ground-breaking study not only eliminates the accessibility gap but also provides the groundwork for a society that is better linked and accessible. Individuals have access to the tools they need for independently navigating their surroundings through simple IoT connectivity and WSN. This research lays the path for a future for assistive technology to catalyze equality and independence, independent of one's visual ability, by fusing assistive technology and IoT connectivity.