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The automatic registration of infrared and visible images in vehicular imaging systems remains challenging in vision-assisted driving systems because of differences in imaging mechanisms. Existing registration methods often fail to accurately register infrared and visible images in vehicular imaging systems due to numerous spurious points during feature extraction, unstable feature descriptions, and low feature matching efficiency. To address these issues, a robust and efficient registration of infrared and visible images for vehicular imaging systems is proposed. In the feature extraction stage, we propose a structural similarity point extractor (SSPE) that extracts feature points using the structural similarity between weighted phase congruency (PC) maps and gradient magnitude (GM) maps. This approach effectively suppresses invalid feature points while ensuring the extraction of stable and reliable ones. In the feature description stage, we design a rotation-invariant feature descriptor (RIFD) that comprehensively describes the attributes of feature points, thereby enhancing their discriminative power. In the feature matching stage, we propose an effective coarse-to-fine matching strategy (EC2F) that improves the matching efficiency through nearest neighbor matching and threshold-based fast sample consensus (FSC), while improving registration accuracy through coordinate-based iterative optimization. Registration experiments on public datasets and a self-established dataset demonstrate the superior performance of our proposed method, and also confirm its effectiveness in real vehicular environments. [ABSTRACT FROM AUTHOR]

Existing lithospheric velocity models exhibit similar structures typically associated with the first-order tectonic features, with dissimilarities due to different data and methods used in model generation. The quantification of model structural similarity can help in interpreting the geophysical properties of Earth's interior and establishing unified models crucial in natural hazard assessment and resource exploration. Here we employ the complex wavelet structural similarity index measure (CW-SSIM) active in computer image processing to analyze the structural similarity of four lithospheric velocity models of Chinese mainland published in the past decade. We take advantage of this method in its multiscale definition and insensitivity to slight geometrical distortion like translation and scaling, which is particularly crucial in the structural similarity analysis of velocity models accounting for uncertainty and resolution. Our results show that the CW-SSIM values vary in different model pairs, horizontal locations, and depths. While variations in the inter-model CW-SSIM are partly owing to different databases in the model generation, the difference of tomography methods may significantly impact the similar structural features of models, such as the low similarities between the full-wave based FWEA18 and other three models in northeastern China. We finally suggest potential solutions for the next generation of tomographic modeling in different areas according to corresponding structural similarities of existing models. [ABSTRACT FROM AUTHOR]

Many non-natural amino acids can be incorporated by biological systems into coded functional peptides and proteins. For such incorporations to be effective, they must not only be compatible with the desired function but also evade various biochemical error-checking mechanisms. The underlying molecular mechanisms are complex, and this problem has been approached previously largely by expert perception of isomer compatibility, followed by empirical study. However, the number of amino acids that might be incorporable by the biological coding machinery may be too large to survey efficiently using such an intuitive approach. We introduce here a workflow for searching real and computed non-natural amino acid libraries for biosimilar amino acids which may be incorporable into coded proteins with minimal unintended disturbance of function. This workflow was also applied to molecules which have been previously benchmarked for their compatibility with the biological translation apparatus, as well as commercial catalogs. We report the results of scoring their contents based on fingerprint similarity via Tanimoto coefficients. These similarity scoring methods reveal candidate amino acids which could be substitutable into modern proteins. Our analysis discovers some already-implemented substitutions, but also suggests many novel ones. [ABSTRACT FROM AUTHOR]

Papain-like protease (PLpro) is a crucial enzyme for SARS-CoV-2 replication and immune evasion. Inhibiting PLpro could be a promising strategy to fight against COVID-19. This study aimed to identify potent inhibitors of PLpro among FDA-approved drugs using an in silico approach. The study also aimed to examine and confirm the binding of the selected compounds to the active pocket of PLpro using a multi-phased in silico approach, involving the screening of 3009 FDA-approved drugs to pinpoint the most similar compounds to, TTT, the co-crystallized ligand TTT of PLpro. The selected compounds were subjected to further analysis, including molecular docking, molecular dynamics simulations, MM-GPSA (molecular mechanics generalized Born surface area), and PLIP (Protein-Ligand Interaction Profiler) studies, to examine and confirm their binding to the active pocket of PLpro. Seven candidates (Vismodegib, Celecoxib, Ketoprofen, Indomethacin, Naphazoline, Valdecoxib, and Eslicarbazepine) showed promising in silico activities against the PLpro. The computational analysis confirmed the binding of Celecoxib to the active pocket of PLpro, suggesting its potential in the fight against COVID-19. This study identified seven FDA-approved drugs as potential inhibitors of PLpro, providing a feasible approach for drug repurposing against COVID-19. The results obtained from the in silico approach hold promise, but further in vitro and in vivo studies are warranted to validate the potential of these compounds. [ABSTRACT FROM AUTHOR]

Although over 300 million protein sequences are registered in a reference sequence database, only 0.2 % have experimentally determined functions. This suggests that many valuable proteins, potentially catalyzing novel enzymatic reactions, remain undiscovered among the vast number of function-unknown proteins. In this study, we developed a method to predict whether two proteins catalyze the same enzymatic reaction by analyzing sequence and structural similarities, utilizing structural models predicted by AlphaFold2. We performed pocket detection and domain decomposition for each structural model. The similarity between protein pairs was assessed using features such as full-length sequence similarity, domain structural similarity, and pocket similarity. We developed several models using conventional machine learning algorithms and found that the LightGBM-based model outperformed the models. Our method also surpassed existing approaches, including those based solely on full-length sequence similarity and state-of-the-art deep learning models. Feature importance analysis revealed that domain sequence identity, calculated through structural alignment, had the greatest influence on the prediction. Therefore, our findings demonstrate that integrating sequence and structural information improves the accuracy of protein function prediction.

Secondary active transporters shuttle substrates across eukaryotic and prokaryotic membranes, utilizing different electrochemical gradients. They are recognized as one of the antimicrobial efflux pumps among pathogens. While primary active transporters within the genome of C. difficile 630 have been completely cataloged, the systematical study of secondary active transporters remains incomplete. Here, we not only identify secondary active transporters but also disclose their evolution and role in drug resistance in C. difficile 630. Our analysis reveals that C. difficile 630 carries 147 secondary active transporters belonging to 27 (super)families. Notably, 50 (34%) of them potentially contribute to antimicrobial resistance (AMR). AMR-secondary active transporters are structurally classified into five (super)families: the p-aminobenzoyl-glutamate transporter (AbgT), drug/metabolite transporter (DMT) superfamily, major facilitator (MFS) superfamily, multidrug and toxic compound extrusion (MATE) family, and resistance-nodulation-division (RND) family. Surprisingly, complete RND genes found in C. difficile 630 are likely an evolutionary leftover from the common ancestor with the diderm. Through protein structure comparisons, we have potentially identified six novel AMR-secondary active transporters from DMT, MATE, and MFS (super)families. Pangenome analysis revealed that half of the AMR-secondary transporters are accessory genes, which indicates an important role in adaptive AMR function rather than innate physiological homeostasis. Gene expression profile firmly supports their ability to respond to a wide spectrum of antibiotics. Our findings highlight the evolution of AMR-secondary active transporters and their integral role in antibiotic responses. This marks AMR-secondary active transporters as interesting therapeutic targets to synergize with other antibiotic activity.

Abstract For light-element materials, X-ray phase contrast imaging provides better contrast compared to absorption imaging. While the Fourier transform method has a shorter imaging time, it typically results in lower image quality; in contrast, the phase-shifting method offers higher image quality but is more time-consuming and involves a higher radiation dose. To rapidly reconstruct low-dose X-ray phase contrast images, this study developed a model based on Generative Adversarial Networks (GAN), incorporating custom layers and self-attention mechanisms to recover high-quality phase contrast images. We generated a simulated dataset using Kaggle’s X-ray data to train the GAN, and in simulated experiments, we achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). To further validate our method, we applied it to fringe images acquired from three phase contrast systems: a single-grating phase contrast system, a Talbot-Lau system, and a cascaded grating system. The current results demonstrate that our method successfully restored high-quality phase contrast images from fringe images collected in experimental settings, though it should be noted that these results were achieved using relatively simple sample configurations.

For light-element materials, X-ray phase contrast imaging provides better contrast compared to absorption imaging. While the Fourier transform method has a shorter imaging time, it typically results in lower image quality; in contrast, the phase-shifting method offers higher image quality but is more time-consuming and involves a higher radiation dose. To rapidly reconstruct low-dose X-ray phase contrast images, this study developed a model based on Generative Adversarial Networks (GAN), incorporating custom layers and self-attention mechanisms to recover high-quality phase contrast images. We generated a simulated dataset using Kaggle's X-ray data to train the GAN, and in simulated experiments, we achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). To further validate our method, we applied it to fringe images acquired from three phase contrast systems: a single-grating phase contrast system, a Talbot-Lau system, and a cascaded grating system. The current results demonstrate that our method successfully restored high-quality phase contrast images from fringe images collected in experimental settings, though it should be noted that these results were achieved using relatively simple sample configurations. [ABSTRACT FROM AUTHOR]

Background: Amyotrophic Lateral Sclerosis (ALS) is a devastating neurological disorder with increasing prevalence rates. Currently, only 8 FDA-approved drugs and 44 clinical trials exist for ALS treatment specifying the lacuna in disease-specific treatment. Drug repurposing, an alternative approach, is gaining huge importance. This study aims to identify potential repurposable compounds using gene expression analysis and structural similarity approaches. Methods: GSE833 and GSE3307 were analysed to retrieve Differentially Expressed Genes (DEGs) which were utilized to identify compounds reversing the gene signatures from LINCS. SMILES of ALS-specific FDA-approved and clinical trial compounds were used to retrieve structurally similar drugs from DrugBank. Drug-Target-Network (DTN) was constructed for the identified compounds to retrieve drug targets which were further subjected to functional enrichment analysis. Results: GSE833 retrieved 13 & 5 whereas GSE3307 retrieved 280 & 430 significant upregulated and downregulated DEGs respectively. Gene expression similarity identified 213 approved drugs. Structural similarity analysis of 44 compounds resulted in 411 approved and investigational compounds. DTN was constructed for 266 compounds to identify drug targets. Functional enrichment analysis resulted in neuroinflammatory response, cAMP signaling, PI3K-AKT signaling, and oxidative stress pathways. A preliminary relevancy check identified previous association of 105 compounds in ALS research, validating the approach, with 172 potential repurposable compounds. [ABSTRACT FROM AUTHOR]

Today, with the expansion of online social networks and their impact on various aspects of human life, investigating the interactions between users and identifying influential users for various advertising applications and accelerating or preventing the dissemination of information has been the focus of researchers. One of the fundamental researches is investigating the fact that the similarity of users' characteristics along with their interests leads to new relationships in the friendship network, a concept known as homophily. The study of homophily can provide significant insight into the flow of information and behaviors in a community to analyze the formation of online communities. In recent years, the emergence of location-based social networks (LBSNs) has created massive datasets by sharing spatial and temporal information better than ever before. This issue enables researchers to analyze the behavioral patterns of users and their impact on their social connections and friends. Throughout the present paper, a framework is being defined to examine the effect of combining structural similarity and homophily in determining users' social influence under two scenarios. The experiments simulate performance of nodes on three LBSNs: Gowalla, Foursquare, and Brightkite. By calculating the correlation coefficient for the similarity methods applied, it can be displayed that with the increase in homophily, the correlation of the proposed method and the social influence increases. A new measure of centrality is also introduced by using the topological structure of the user's communication network, such as the eigenvector centrality along with the values of friendship influence and the number of spatial movements of the user. The results show that our proposed centrality matches up to 85% with baseline methods. [ABSTRACT FROM AUTHOR]

Recently, the analysis of heterogeneous networks has become more popular due to the growing number of social networks. These networks are capable of covering a variety of nodes and edges. The members of these networks usually have metadata whose analysis can lead to the discovery of knowledge. One way to analyze such data is clustered where high-quality clustering requires effective similarity calculation. Most of the existing clustering methods do not pay attention to the use of metadata or the characteristics of network members. On the other hand, they are only able to process small and medium-sized networks due to the amount of memory and execution speed. This paper presents a hybrid approach for heterogeneous network clustering to overcome these problems. The structural similarity in this approach is calculated by the graph embedding method, which we call learning-based. Attribute similarity is calculated by a scoring method that we call similarity-based. In the experimental study, we compared the proposed method with collaborative approaches based on similarity on the real-world networks. The experimental findings demonstrate the superiority of the proposed method in terms of entropy, memory consumption, execution time, and density in certain cases. [ABSTRACT FROM AUTHOR]

In this paper, we present a new objective quality metric designed for immersive video applications—IV-SSIM. The proposed IV-SSIM metric is an evolution of our previous work—IV-PSNR (immersive video peak signal-to-noise ratio)—which became a commonly used metric in research and ISO/IEC MPEG standardization activities on immersive video. IV-SSIM combines the advantages of IV-PSNR and metrics based on the structural similarity of images, being able to properly mimic the subjective quality perception of immersive video with its characteristic distortions induced by the reprojection of pixels between multiple views. The effectiveness of IV-SSIM was compared with 16 state-of-the-art quality metrics (including other metrics designed for immersive video). Tested metrics were evaluated in an immersive video coding scenario and against a commonly used image quality database—TID2013—showing their performance in both immersive and typical, non-immersive use cases. As presented, the proposed IV-SSIM metric clearly outperforms other metrics in immersive video applications, while also being highly competitive for 2D image quality assessment. The authors of this paper have provided a publicly accessible, efficient implementation of the proposed IV-SSIM metric, which is used by ISO/IEC MPEG video coding experts in the development of the forthcoming second edition of the MPEG immersive video (MIV) coding standard. [ABSTRACT FROM AUTHOR]

This paper describes a new version of the IV-PSNR software, developed for the effective objective quality assessment of immersive video. Version 7.1 includes the calculation of structural similarity between compared sequences using the IV-SSIM metric, designed to properly handle the unique characteristics of immersive video, as well as the classic SSIM and MS-SSIM metrics. Moreover, by introducing new modes, IV-PSNR 7.1 is adapted to assess the quality of novel approaches to multiview video processing, based on radiance fields and implicit neural visual representations. Currently, this version of the software is used by the ISO/IEC MPEG VC standardization group for the evaluation of the second edition of the MIV coding standard, and in works aimed at the development of a future standard for radiance field representation and compression.

Background: Amyotrophic Lateral Sclerosis (ALS) is a devastating neurological disorder with increasing prevalence rates. Currently, only 8 FDA-approved drugs and 44 clinical trials exist for ALS treatment specifying the lacuna in disease-specific treatment. Drug repurposing, an alternative approach, is gaining huge importance. This study aims to identify potential repurposable compounds using gene expression analysis and structural similarity approaches. Methods: GSE833 and GSE3307 were analysed to retrieve Differentially Expressed Genes (DEGs) which were utilized to identify compounds reversing the gene signatures from LINCS. SMILES of ALS-specific FDA-approved and clinical trial compounds were used to retrieve structurally similar drugs from DrugBank. Drug-Target-Network (DTN) was constructed for the identified compounds to retrieve drug targets which were further subjected to functional enrichment analysis. Results: GSE833 retrieved 13 & 5 whereas GSE3307 retrieved 280 & 430 significant upregulated and downregulated DEGs respectively. Gene expression similarity identified 213 approved drugs. Structural similarity analysis of 44 compounds resulted in 411 approved and investigational compounds. DTN was constructed for 266 compounds to identify drug targets. Functional enrichment analysis resulted in neuroinflammatory response, cAMP signaling, PI3K-AKT signaling, and oxidative stress pathways. A preliminary relevancy check identified previous association of 105 compounds in ALS research, validating the approach, with 172 potential repurposable compounds.

Abstract The structures of the Fc base of various IgG antibodies have been examined with a view to understanding how this region can be used to conjugate IgG to nanoparticles. The base structure is found to be largely consistent across a range of species and subtypes, comprising a hydrophobic region surrounded by hydrophilic residues, some of which are charged at physiological conditions. In addition, atomistic Molecular Dynamics simulations were performed to explore how model nanoparticles interact with the base using neutral and negatively charged gold nanoparticles. Both types of nanoparticle interacted readily with the base, leading to an adaptation of the antibody base surface to enhance the interactions. Furthermore, these interactions left the rest of the domain at the base of the Fc region structurally intact. This implies that coupling nanoparticles to the base of an IgG molecule is both feasible and desirable, since it leaves the antibody free to interact with its surroundings so that antigen-binding functionality can be retained. These results will therefore help guide future attempts to develop new nanotechnologies that exploit the unique properties of both antibodies and nanoparticles.

Abstract Small molecule identification is a crucial task in analytical chemistry and life sciences. One of the most commonly used technologies to elucidate small molecule structures is mass spectrometry. Spectral library search of product ion spectra (MS/MS) is a popular strategy to identify or find structural analogues. This approach relies on the assumption that spectral similarity and structural similarity are correlated. However, popular spectral similarity measures, usually calculated based on identical fragment matches between the MS/MS spectra, do not always accurately reflect the structural similarity. In this study, we propose TransExION, a Transformer based Explainable similarity metric for IONS. TransExION detects related fragments between MS/MS spectra through their mass difference and uses these to estimate spectral similarity. These related fragments can be nearly identical, but can also share a substructure. TransExION also provides a post-hoc explanation of its estimation, which can be used to support scientists in evaluating the spectral library search results and thus in structure elucidation of unknown molecules. Our model has a Transformer based architecture and it is trained on the data derived from GNPS MS/MS libraries. The experimental results show that it improves existing spectral similarity measures in searching and interpreting structural analogues as well as in molecular networking. Scientific Contribution We propose a transformer-based spectral similarity metrics that improves the comparison of small molecule tandem mass spectra. We provide a post hoc explanation that can serve as a good starting point for unknown spectra annotation based on database spectra.

Noise is an unwanted element that has a negative impact on digital image quality. Salt-and-pepper noise is a type of noise that can appear at any point during the acquisition or transmission of images. It is essential to utilize proper restoration procedures to lessen the noise. This paper proposes a hardware-efficient VLSI architecture for the feedback decision-based trimmed mean filter that eliminates high-density salt-and-pepper noise in the images. The noisy pixels are identified and corrected by considering the neighbouring pixels in a 3 × 3 window corresponding to this noisy centre pixel. Either the mean of the horizontal and vertical noisy pixels or the mean of noise-free pixels in the window is computed. This mean value is fed back and the noisy centre pixel is updated immediately, such that this updated pixel value is used henceforth for correcting the remaining corrupted pixels. It is observed that this procedure helps in removing the noisy pixels effectively even if the noise density is high. Additionally, the designed VLSI architecture is efficient, since the algorithm does not require a sorting process and the computing resources required are less when compared to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The structures of the Fc base of various IgG antibodies have been examined with a view to understanding how this region can be used to conjugate IgG to nanoparticles. The base structure is found to be largely consistent across a range of species and subtypes, comprising a hydrophobic region surrounded by hydrophilic residues, some of which are charged at physiological conditions. In addition, atomistic Molecular Dynamics simulations were performed to explore how model nanoparticles interact with the base using neutral and negatively charged gold nanoparticles. Both types of nanoparticle interacted readily with the base, leading to an adaptation of the antibody base surface to enhance the interactions. Furthermore, these interactions left the rest of the domain at the base of the Fc region structurally intact. This implies that coupling nanoparticles to the base of an IgG molecule is both feasible and desirable, since it leaves the antibody free to interact with its surroundings so that antigen-binding functionality can be retained. These results will therefore help guide future attempts to develop new nanotechnologies that exploit the unique properties of both antibodies and nanoparticles. [ABSTRACT FROM AUTHOR]

Active laser imaging utilizes time-of-flight and echo intensity measurements to generate distance and intensity images of targets. However, scattering caused by cloud and fog particles, leads to imaging quality deterioration. In this study, we introduce a novel approach for improving imaging clarity in these environments. We employed a matched filtering method that leverages the distinction between signal and noise in the time domain to preliminarily extract the signal from one- dimensional photon-counting echo data. We further denoised the data by utilizing the Long Short-Term Memory (LSTM) neural network in extracting features from extended time-series data. The proposed method displayed notable improvement in the signal-to-noise ratio (SNR), from 7.227 dB to 31.35 dB, following an analysis of experimental data collected under cloud and fog conditions. Furthermore, processing positively affected the quality of the distance image with an increase in the structural similarity (SSIM) index from 0.7883 to 0.9070. Additionally, the point-cloud images were successfully restored. These findings suggest that the integration of matched filtering and the LSTM algorithm effectively enhances beam imaging quality in the presence of cloud and fog scattering. This method has potential application in various fields, including navigation, remote sensing, and other areas susceptible to complex environmental conditions. [ABSTRACT FROM AUTHOR]

Feature point matching is a fundamental task in computer vision such as vision simultaneous localization and mapping (VSLAM) and structure from motion (SFM). Due to the similarity or interference of features, mismatches are often unavoidable. Therefore, how to eliminate mismatches is important for robust matching. Smoothness constraint is widely used to remove mismatch, but it cannot effectively deal with the issue in the rapidly changing scene. In this paper, a novel LCS-SSM (Local Cell Statistics and Structural Similarity Measurement) mismatch removal method is proposed. LCS-SSM integrates the motion consistency and structural similarity of a local image block as the statistical likelihood of matched key points. Then, the Random Sampling Consensus (RANSAC) algorithm is employed to preserve the isolated matches that do not satisfy the statistical likelihood. Experimental and comparative results on the public dataset show that the proposed LCS-SSM can effectively and reliably differentiate true and false matches compared with state-of-the-art methods, and can be used for robust matching in scenes with fast motion, blurs, and clustered noise. [ABSTRACT FROM AUTHOR]

Lysosome, the digestive organelle in eukaryotic cells, plays an important role in the degradation and recirculation of cellular products as well as in maintaining the stability of cellular metabolic microenvironment. Surface‐enhanced Raman scattering (SERS) is a molecular fingerprint technology with high detection sensitivity and photostability, suited for revealing various intracellular molecular information by inducing endocytosis of SERS‐active nanoparticles. However, it remains challenging to selectively extract the molecular information of specific organelles (e.g., lysosomes) from a high‐dimensional spectral set. Herein, we proposed a novel paradigm by combining label‐free SERS spectroscopy with confocal fluorescence imaging to investigate the digestion behavior of lysosomes in cells. The structural similarity algorithm was innovatively introduced and exhibited its effectiveness in screening out the wavenumbers in the SERS spectral set with high correlation with the metabolic behaviors of lysosomes. With comprehensive experiments on HeLa single cells, we captured the intracellular macromolecular digestion phenomenon and discovered the changing pattern of cellular SERS spectra after starvation‐induced autophagy, and analyzed the molecular information within the lysosomes in three‐dimensional space. [ABSTRACT FROM AUTHOR]

Small molecule identification is a crucial task in analytical chemistry and life sciences. One of the most commonly used technologies to elucidate small molecule structures is mass spectrometry. Spectral library search of product ion spectra (MS/MS) is a popular strategy to identify or find structural analogues. This approach relies on the assumption that spectral similarity and structural similarity are correlated. However, popular spectral similarity measures, usually calculated based on identical fragment matches between the MS/MS spectra, do not always accurately reflect the structural similarity. In this study, we propose TransExION, a Transformer based Explainable similarity metric for IONS. TransExION detects related fragments between MS/MS spectra through their mass difference and uses these to estimate spectral similarity. These related fragments can be nearly identical, but can also share a substructure. TransExION also provides a post-hoc explanation of its estimation, which can be used to support scientists in evaluating the spectral library search results and thus in structure elucidation of unknown molecules. Our model has a Transformer based architecture and it is trained on the data derived from GNPS MS/MS libraries. The experimental results show that it improves existing spectral similarity measures in searching and interpreting structural analogues as well as in molecular networking. Scientific Contribution: We propose a transformer-based spectral similarity metrics that improves the comparison of small molecule tandem mass spectra. We provide a post hoc explanation that can serve as a good starting point for unknown spectra annotation based on database spectra. [ABSTRACT FROM AUTHOR]

Accurately detecting defects while reconstructing a high-quality normal background in surface defect detection using unsupervised methods remains a significant challenge. This study proposes an unsupervised method that effectively addresses this challenge by achieving both accurate defect detection and a high-quality normal background reconstruction without noise. We propose an adaptive weighted structural similarity (AW-SSIM) loss for focused feature learning. AW-SSIM improves structural similarity (SSIM) loss by assigning different weights to its sub-functions of luminance, contrast, and structure based on their relative importance for a specific training sample. Moreover, it dynamically adjusts the Gaussian window's standard deviation (σ) during loss calculation to balance noise reduction and detail preservation. An artificial defect generation algorithm (ADGA) is proposed to generate an artificial defect closely resembling real ones. We use a two-stage training strategy. In the first stage, the model trains only on normal samples using AW-SSIM loss, allowing it to learn robust representations of normal features. In the second stage of training, the weights obtained from the first stage are used to train the model on both normal and artificially defective training samples. Additionally, the second stage employs a combined learned Perceptual Image Patch Similarity (LPIPS) and AW-SSIM loss. The combined loss helps the model in achieving high-quality normal background reconstruction while maintaining accurate defect detection. Extensive experimental results demonstrate that our proposed method achieves a state-of-the-art defect detection accuracy. The proposed method achieved an average area under the receiver operating characteristic curve (AuROC) of 97.69% on six samples from the MVTec anomaly detection dataset. [ABSTRACT FROM AUTHOR]

The automatic registration of infrared and visible images in vehicular imaging systems remains challenging in vision-assisted driving systems because of differences in imaging mechanisms. Existing registration methods often fail to accurately register infrared and visible images in vehicular imaging systems due to numerous spurious points during feature extraction, unstable feature descriptions, and low feature matching efficiency. To address these issues, a robust and efficient registration of infrared and visible images for vehicular imaging systems is proposed. In the feature extraction stage, we propose a structural similarity point extractor (SSPE) that extracts feature points using the structural similarity between weighted phase congruency (PC) maps and gradient magnitude (GM) maps. This approach effectively suppresses invalid feature points while ensuring the extraction of stable and reliable ones. In the feature description stage, we design a rotation-invariant feature descriptor (RIFD) that comprehensively describes the attributes of feature points, thereby enhancing their discriminative power. In the feature matching stage, we propose an effective coarse-to-fine matching strategy (EC2F) that improves the matching efficiency through nearest neighbor matching and threshold-based fast sample consensus (FSC), while improving registration accuracy through coordinate-based iterative optimization. Registration experiments on public datasets and a self-established dataset demonstrate the superior performance of our proposed method, and also confirm its effectiveness in real vehicular environments.

The absence of a Standard Operating Procedure (SOP) can lead to many problems in operations within organisations. Process mining techniques can discover process models that reflect the actual behaviour of the process implementations by using event logs extracted from information systems. However, the process models discovered by process mining often have too many variations and deviations when compared to the actual SOPs of the processes. This study attempted to compare three prediction methods in finding a process model from process mining that has the closest properties to the actual SOP. The compared methods are Receiver Operating Characteristics (ROC), the four quality dimensions, and similarity measures for structural and behavioural similarities. For the experiment, we designed a synthetic SOP that served as a ground truth for evaluating the performance of the three prediction methods in this study. We used a synthetic event log extracted from a dummy information system we particularly built for this study to test the methods. This study’s results can be useful, e.g. for auditors to save a lot of time from conducting extensive surveys when SOPs are not readily available. [ABSTRACT FROM AUTHOR]

Introduction: Optical and SAR image matching is one of the fields within multisensor imaging and fusion. It is crucial for various applications such as disaster response, environmental monitoring, and urban planning, as it enables comprehensive and accurate analysis by combining the visual information of optical images with the penetrating capability of SAR images. However, the differences in imaging mechanisms between optical and SAR images result in significant nonlinear radiation distortion. Especially for SAR images, which are affected by speckle noises, resulting in low resolution and blurry edge structures, making optical and SAR image matching difficult and challenging. The key to successful matching lies in reducing modal differences and extracting similarity information from the images. Method: In light of this, we propose a structure similarity virtual map generation network (SVGNet) to address the task of optical and SAR image matching. The core innovation of this paper is that we take inspiration from the concept of image generation, to handle the predicament of image matching between different modalities. Firstly, we introduce the Attention U-Net as a generator to decouple and characterize optical images. And then, SAR images are consistently converted into optical images with similar textures and structures. At the same time, using the structural similarity (SSIM) to constrain structural spatial information to improve the quality of generated images. Secondly, a conditional generative adversarial network is employed to further guide the image generation process. By combining synthesized SAR images and their corresponding optical images in a dual channel, we can enhance prior information. This combined data is then fed into the discriminator to determine whether the images are true or false, guiding the generator to optimize feature learning. Finally, we employ least squares loss (LSGAN) to stabilize the training of the generative adversarial network. Results and Discussion: Experiments have demonstrated that the SVGNet proposed in this paper is capable of effectively reducing modal differences, and it increases the matching success rate. Compared to direct image matching, using image generation ideas results in a matching accuracy improvement of more than twice. [ABSTRACT FROM AUTHOR]

Active laser imaging utilizes time-of-flight and echo intensity measurements to generate distance and intensity images of targets. However, scattering caused by cloud and fog particles, leads to imaging quality deterioration. In this study, we introduce a novel approach for improving imaging clarity in these environments. We employed a matched filtering method that leverages the distinction between signal and noise in the time domain to preliminarily extract the signal from one-dimensional photon-counting echo data. We further denoised the data by utilizing the Long Short-Term Memory (LSTM) neural network in extracting features from extended time-series data. The proposed method displayed notable improvement in the signal-to-noise ratio (SNR), from 7.227 dB to 31.35 dB, following an analysis of experimental data collected under cloud and fog conditions. Furthermore, processing positively affected the quality of the distance image with an increase in the structural similarity (SSIM) index from 0.7883 to 0.9070. Additionally, the point-cloud images were successfully restored. These findings suggest that the integration of matched filtering and the LSTM algorithm effectively enhances beam imaging quality in the presence of cloud and fog scattering. This method has potential application in various fields, including navigation, remote sensing, and other areas susceptible to complex environmental conditions.

Abstract Lysosome, the digestive organelle in eukaryotic cells, plays an important role in the degradation and recirculation of cellular products as well as in maintaining the stability of cellular metabolic microenvironment. Surface‐enhanced Raman scattering (SERS) is a molecular fingerprint technology with high detection sensitivity and photostability, suited for revealing various intracellular molecular information by inducing endocytosis of SERS‐active nanoparticles. However, it remains challenging to selectively extract the molecular information of specific organelles (e.g., lysosomes) from a high‐dimensional spectral set. Herein, we proposed a novel paradigm by combining label‐free SERS spectroscopy with confocal fluorescence imaging to investigate the digestion behavior of lysosomes in cells. The structural similarity algorithm was innovatively introduced and exhibited its effectiveness in screening out the wavenumbers in the SERS spectral set with high correlation with the metabolic behaviors of lysosomes. With comprehensive experiments on HeLa single cells, we captured the intracellular macromolecular digestion phenomenon and discovered the changing pattern of cellular SERS spectra after starvation‐induced autophagy, and analyzed the molecular information within the lysosomes in three‐dimensional space.

Abstract The use of a light-weight deep learning Convolutional Neural Network (CNN) augmented with the power of Fuzzy Non-Sample Shearlet Transformation (FNSST) has successfully solved the problem of reducing noise and artifacts in Low-Dose Computed Tomography (LDCT) pictures. Both the Normal-Dose Computed Tomography (NDCT) and the Low-Dose Computed Tomography (LDCT) images from the dataset are subjected to the FNSST decomposition procedure during the training phase, producing high-frequency sub-images that act as input for the CNN. The CNN creates a meaningful connection between the high-frequency sub-images from LDCT and their corresponding residual sub-images during the training operation. The CNN is given the capacity to distinguish between LDCT high-frequency sub-images and expected high-frequency sub-images, which frequently have varying levels of noise or artifacts, especially in a fuzzy setting. The FNSST-CNN then successfully distinguishes LDCT high-frequency sub-images from the expected high-frequency sub-images during the testing phase, thereby reducing noise and artifacts. When compared to other approaches like KSVD, BM3D, and conventional image domain CNNs, the performance of FNSST-CNN is impressive as shown by better peak signal-to-noise ratios, stronger structural similarity, and a closer likeness to NDCT pictures.

Abstract Background Microsporidia are a large taxon of intracellular pathogens characterized by extraordinarily streamlined genomes with unusually high sequence divergence and many species-specific adaptations. These unique factors pose challenges for traditional genome annotation methods based on sequence similarity. As a result, many of the microsporidian genomes sequenced to date contain numerous genes of unknown function. Recent innovations in rapid and accurate structure prediction and comparison, together with the growing amount of data in structural databases, provide new opportunities to assist in the functional annotation of newly sequenced genomes. Results In this study, we established a workflow that combines sequence and structure-based functional gene annotation approaches employing a ChimeraX plugin named ANNOTEX (Annotation Extension for ChimeraX), allowing for visual inspection and manual curation. We employed this workflow on a high-quality telomere-to-telomere sequenced tetraploid genome of Vairimorpha necatrix. First, the 3080 predicted protein-coding DNA sequences, of which 89% were confirmed with RNA sequencing data, were used as input. Next, ColabFold was used to create protein structure predictions, followed by a Foldseek search for structural matching to the PDB and AlphaFold databases. The subsequent manual curation, using sequence and structure-based hits, increased the accuracy and quality of the functional genome annotation compared to results using only traditional annotation tools. Our workflow resulted in a comprehensive description of the V. necatrix genome, along with a structural summary of the most prevalent protein groups, such as the ricin B lectin family. In addition, and to test our tool, we identified the functions of several previously uncharacterized Encephalitozoon cuniculi genes. Conclusion We provide a new functional annotation tool for divergent organisms and employ it on a newly sequenced, high-quality microsporidian genome to shed light on this uncharacterized intracellular pathogen of Lepidoptera. The addition of a structure-based annotation approach can serve as a valuable template for studying other microsporidian or similarly divergent species.

The read-across method is a popular data gap filling technique with developed application for multiple purposes, including regulatory. Within the US Environmental Protection Agency's (US EPA) New Chemicals Program under Toxic Substances Control Act (TSCA), read-across has been widely used, as well as within technical guidance published by the Organization for Economic Co-operation and Development, the European Chemicals Agency, and the European Center for Ecotoxicology and Toxicology of Chemicals for filling chemical toxicity data gaps. Under the TSCA New Chemicals Review Program, US EPA is tasked with reviewing proposed new chemical applications prior to commencing commercial manufacturing within or importing into the United States. The primary goal of this review is to identify any unreasonable human health and environmental risks, arising from environmental releases/emissions during manufacturing and the resulting exposure from these environmental releases. The authors propose the application of read-across techniques for the development and use of a framework for estimating the emissions arising during the chemical manufacturing process. This methodology is to utilize available emissions data from a structurally similar analogue chemical or a group of structurally similar chemicals in a chemical family taking into consideration their physicochemical properties under specified chemical process unit operations and conditions. This framework is also designed to apply existing knowledge of read-across principles previously utilized in toxicity estimation for an analogue or category of chemicals and introduced and extended with a concurrent case study.

In this paper, we present a new objective quality metric designed for immersive video applications—IV-SSIM. The proposed IV-SSIM metric is an evolution of our previous work—IV-PSNR (immersive video peak signal-to-noise ratio)—which became a commonly used metric in research and ISO/IEC MPEG standardization activities on immersive video. IV-SSIM combines the advantages of IV-PSNR and metrics based on the structural similarity of images, being able to properly mimic the subjective quality perception of immersive video with its characteristic distortions induced by the reprojection of pixels between multiple views. The effectiveness of IV-SSIM was compared with 16 state-of-the-art quality metrics (including other metrics designed for immersive video). Tested metrics were evaluated in an immersive video coding scenario and against a commonly used image quality database—TID2013—showing their performance in both immersive and typical, non-immersive use cases. As presented, the proposed IV-SSIM metric clearly outperforms other metrics in immersive video applications, while also being highly competitive for 2D image quality assessment. The authors of this paper have provided a publicly accessible, efficient implementation of the proposed IV-SSIM metric, which is used by ISO/IEC MPEG video coding experts in the development of the forthcoming second edition of the MPEG immersive video (MIV) coding standard.

Single crystals of five compounds with approximate compositions ∼Ru16(Al0.78Si0.22)47, (I), ∼Ru9(Al0.70Si0.30)32, (II), ∼Ru10(Al0.67Si0.33)41, (III), ∼Ru(Al0.57Si0.43)5, (IV), and ∼Ru2(Al0.46Si0.54)9, (V), were obtained from polycrystalline lumps mainly composed of the target compounds, and their crystal structures were determined by means of single-crystal X-ray diffraction. The crystal structure of (I) can be related to that of a cubic rational crystalline approximant to an icosahedral quasicrystal through crystallographic shear and then unit-cell twinning. The crystal structure of (II) is isotypic with that of a phase with composition ∼Fe9(Al,Si)32. The crystal structure of (III) is comprised of edge-sharing Ru(Al,Si)9–11 polyhedra with disordered chains along edges of polyhedra. The crystal structure of (IV) is of the LiIrSn4 type. The crystal structure of (V) can be viewed as a crystallographic shear structure derived from that of (IV).

In our quest to discover effective inhibitors against severe acute respiratory syndrome coronavirus 2 helicase, a diverse set of more than 300 naturally occurring antiviral metabolites was investigated. Employing advanced computational techniques, we initiated the selection process by analyzing and comparing the co-crystallized ligand (VXG) of the severe acute respiratory syndrome coronavirus 2 helicase protein (PDB ID: 5RMM) to identify compounds with structurally similar features and potential for comparable binding. Through structural similarity and pharmacophore research, 13 compounds that shared important characteristics with VXG were pinpointed. Subsequently, these candidates were subjected to molecular docking to identify seven compounds that demonstrated favorable energy profiles and accurate binding to the severe acute respiratory syndrome coronavirus 2 helicase. Among these, mycophenolic acid emerged as the most promising candidate. To ensure the safety and viability of the selected compounds, we conducted ADMET tests, which confirmed the favorable characteristics of mycophenolic acid, and the safety of atropine and plumbagin. Building on these results, we performed additional analyses on mycophenolic acid, including various molecular dynamics simulations. These investigations demonstrated that mycophenolic acid exhibited optimal binding to the severe acute respiratory syndrome coronavirus 2 helicase, maintaining flawless dynamics throughout the simulations. Furthermore, the Molecular Mechanics Poisson–Boltzmann Surface Area tests provided strong evidence that mycophenolic acid successfully formed a stable connection with the severe acute respiratory syndrome coronavirus 2 helicase, with a calculated free energy value of −294 kJ mol−1. These encouraging findings provide a solid foundation for further research, including in vitro and in vivo studies, on the three identified compounds. The potential efficacy of these compounds as treatment options for coronavirus-19 warrants further exploration and may hold significant promise in the ongoing fight against the pandemic. [ABSTRACT FROM AUTHOR]

Patients with multiple sclerosis (MS) often take multiple drugs at the same time to modify the course of disease, alleviate neurological symptoms and manage co-existing conditions. A major consequence for a patient taking different medications is a higher risk of treatment failure and side effects. This is because a drug may alter the pharmacokinetic and/or pharmacodynamic properties of another drug, which is referred to as drug-drug interaction (DDI). We aimed to predict interactions of drugs that are used by patients with MS based on a deep neural network (DNN) using structural information as input. We further aimed to identify potential drug-food interactions (DFIs), which can affect drug efficacy and patient safety as well. We used DeepDDI, a multi-label classification model of specific DDI types, to predict changes in pharmacological effects and/or the risk of adverse drug events when two or more drugs are taken together. The original model with ~34 million trainable parameters was updated using >1 million DDIs recorded in the DrugBank database. Structure data of food components were obtained from the FooDB database. The medication plans of patients with MS (n = 627) were then searched for pairwise interactions between drug and food compounds. The updated DeepDDI model achieved accuracies of 92.2% and 92.1% on the validation and testing sets, respectively. The patients with MS used 312 different small molecule drugs as prescription or over-the-counter medications. In the medication plans, we identified 3748 DDIs in DrugBank and 13,365 DDIs using DeepDDI. At least one DDI was found for most patients (n = 509 or 81.2% based on the DNN model). The predictions revealed that many patients would be at increased risk of bleeding and bradycardic complications due to a potential DDI if they were to start a disease-modifying therapy with cladribine (n = 242 or 38.6%) and fingolimod (n = 279 or 44.5%), respectively. We also obtained numerous potential interactions for Bruton's tyrosine kinase inhibitors that are in clinical development for MS, such as evobrutinib (n = 434 DDIs). Food sources most often related to DFIs were corn (n = 5456 DFIs) and cow's milk (n = 4243 DFIs). We demonstrate that deep learning techniques can exploit chemical structure similarity to accurately predict DDIs and DFIs in patients with MS. Our study specifies drug pairs that potentially interact, suggests mechanisms causing adverse drug effects, informs about whether interacting drugs can be replaced with alternative drugs to avoid critical DDIs and provides dietary recommendations for MS patients who are taking certain drugs. [ABSTRACT FROM AUTHOR]

To improve the robustness of vortex beam transmission and detection in the face of atmospheric turbulence and to guarantee accurate recognition of orbital angular momentum (OAM), we present an end-to-end dynamic compensation technique for vortex beams using an improved cycle-consistent generative adversarial network (CycleGAN). This approach transforms the problem of vortex beam distortion compensation into one of image translation. The Pix2pix and CycleGAN models were extended with a structural similarity loss function to constrain turbulence distortion compensation in luminance, contrast and structure. Experiments were designed to evaluate the compensation performance from subjective and objective indicators. The simulation results demonstrate that the optical OAM intensity map is very similar to that of the target OAM light after compensation. The mean value of structural similarity is close to 1. The recognition accuracy of the OAM is improved by 4.4% compared to no distortion compensation, demonstrating that the improved CycleGAN-based compensation scheme can guarantee excellent detection accuracy without reconstructing the wavefront and saving optical hardware. The method can be implemented in real-time optical communications in atmospheric turbulence environments. [ABSTRACT FROM AUTHOR]

Abstract Background Protein annotation is a major goal in molecular biology, yet experimentally determined knowledge is typically limited to a few model organisms. In non-model species, the sequence-based prediction of gene orthology can be used to infer protein identity; however, this approach loses predictive power at longer evolutionary distances. Here we propose a workflow for protein annotation using structural similarity, exploiting the fact that similar protein structures often reflect homology and are more conserved than protein sequences. Results We propose a workflow of openly available tools for the functional annotation of proteins via structural similarity (MorF: MorphologFinder) and use it to annotate the complete proteome of a sponge. Sponges are highly relevant for inferring the early history of animals, yet their proteomes remain sparsely annotated. MorF accurately predicts the functions of proteins with known homology in $${>}90\%$$ > 90 % cases and annotates an additional $$50\%$$ 50 % of the proteome beyond standard sequence-based methods. We uncover new functions for sponge cell types, including extensive FGF, TGF, and Ephrin signaling in sponge epithelia, and redox metabolism and control in myopeptidocytes. Notably, we also annotate genes specific to the enigmatic sponge mesocytes, proposing they function to digest cell walls. Conclusions Our work demonstrates that structural similarity is a powerful approach that complements and extends sequence similarity searches to identify homologous proteins over long evolutionary distances. We anticipate this will be a powerful approach that boosts discovery in numerous -omics datasets, especially for non-model organisms.

Accurately detecting defects while reconstructing a high-quality normal background in surface defect detection using unsupervised methods remains a significant challenge. This study proposes an unsupervised method that effectively addresses this challenge by achieving both accurate defect detection and a high-quality normal background reconstruction without noise. We propose an adaptive weighted structural similarity (AW-SSIM) loss for focused feature learning. AW-SSIM improves structural similarity (SSIM) loss by assigning different weights to its sub-functions of luminance, contrast, and structure based on their relative importance for a specific training sample. Moreover, it dynamically adjusts the Gaussian window’s standard deviation (σ) during loss calculation to balance noise reduction and detail preservation. An artificial defect generation algorithm (ADGA) is proposed to generate an artificial defect closely resembling real ones. We use a two-stage training strategy. In the first stage, the model trains only on normal samples using AW-SSIM loss, allowing it to learn robust representations of normal features. In the second stage of training, the weights obtained from the first stage are used to train the model on both normal and artificially defective training samples. Additionally, the second stage employs a combined learned Perceptual Image Patch Similarity (LPIPS) and AW-SSIM loss. The combined loss helps the model in achieving high-quality normal background reconstruction while maintaining accurate defect detection. Extensive experimental results demonstrate that our proposed method achieves a state-of-the-art defect detection accuracy. The proposed method achieved an average area under the receiver operating characteristic curve (AuROC) of 97.69% on six samples from the MVTec anomaly detection dataset.

Feature point matching is a fundamental task in computer vision such as vision simultaneous localization and mapping (VSLAM) and structure from motion (SFM). Due to the similarity or interference of features, mismatches are often unavoidable. Therefore, how to eliminate mismatches is important for robust matching. Smoothness constraint is widely used to remove mismatch, but it cannot effectively deal with the issue in the rapidly changing scene. In this paper, a novel LCS-SSM (Local Cell Statistics and Structural Similarity Measurement) mismatch removal method is proposed. LCS-SSM integrates the motion consistency and structural similarity of a local image block as the statistical likelihood of matched key points. Then, the Random Sampling Consensus (RANSAC) algorithm is employed to preserve the isolated matches that do not satisfy the statistical likelihood. Experimental and comparative results on the public dataset show that the proposed LCS-SSM can effectively and reliably differentiate true and false matches compared with state-of-the-art methods, and can be used for robust matching in scenes with fast motion, blurs, and clustered noise.

Fault diagnosis and health monitoring of industrial rotating machines are of paramount importance for ensuring the reliability, safety, and efficiency of modern industrial operations. This paper proposes a Short-Time Fourier Transform (STFT)-based fault diagnosis approach for industrial rotating machinery. In this proposed model, the STFT of the reference vibration signals is evaluated and compared with the STFT of the other testing vibration signals to diagnose the fault types. Three different similarity operators: Euclidean distance, cosine similarity, and structural similarity are used to conclude the similarity index between the reference signal and test signal. By using variable speed vibration data with different fault types, the proposed model can better simulate real-world conditions and improve the accuracy and effectiveness of fault diagnosis. The results from the confusion matrices, heat maps, and t-SNE plots demonstrate the effectiveness of the proposed method for fault diagnosis and monitoring of variable-speed rotating machines using vibration signals. It is concluded that the structural similarity index proved to be a promising approach for accurate fault diagnosis in variable-speed rotating machines. The results are also compared with the existing approaches in the literature and it was concluded that the proposed model attains the highest accuracy for the variable speed rotating machines.

The interest of this paper is in reduction of impulse noise in digital color images. The two main methods used for noise reduction in images are the mean and median filters. These techniques operate by replacing the test pixel in a chosen window by a new filtered pixel value. The window is made to iteratively slide across the entire image to reconstruct a new noise reduced image. The mean filters suffer from the effect of smoothing out color contrast and edges due to leveraging the unrepresentative pixels in the filtering process. The vector median filter and its variants overcome this problem by considering only the most representative pixel in the chosen window. The most representative pixel, i.e. the pixel that is of highest conformity to take the place of the test pixel, is determined by minimizing the aggregate distance from one pixel to every other pixel in the window. The problem in these median filtering approaches is that only one pixel is treated as representative of all the pixels in the chosen window. This conjecture could lead to information loss due to marginalizing other pixels that also are representative of the center pixel. In this paper, we propose a selective mean filtering process to overcome the said problem. The key idea here is to determine the most representative pixels in the window using the method of aggregate distances and then compute the mean of these pixels. This approach will perform better than the vector median filters as now a set of representative pixels are leveraged into the filtering process. Simulation results show that the proposed method performs better than the conventional vector median filtering methods in terms of noise reduction and structural similarity and thus validates the proposed approach. Moreover, the method is tested on real MRI scan images in successfully reducing impulse noise for improved medical diagnosis. [ABSTRACT FROM AUTHOR]

Image quality assessment (IQA) has a very important role and wide applications in image acquisition, storage, transmission and processing. In designing IQA models, human visual system (HVS) characteristics introduced play an important role in improving their performances. In this paper, combining image distortion characteristics with HVS characteristics, based on the structure similarity index (SSIM) model, a novel IQA model based on the perceived structure similarity index (PSIM) of image is proposed. In the method, first, a perception model for HVS perceiving real images is proposed, combining the contrast sensitivity, frequency sensitivity, luminance nonlinearity and masking characteristics of HVS; then, in order to simulate HVS perceiving real image, the real images are processed with the proposed perception model, to eliminate their visual redundancy, thus, the perceived images are obtained; finally, based on the idea and modeling method of SSIM, combining with the features of perceived image, a novel IQA model, namely PSIM, is proposed. Further, in order to illustrate the performance of PSIM, 5335 distorted images with 41 distortion types in four image databases (TID2013, CSIQ, LIVE and CID) are used to simulate from three aspects: overall IQA of each database, IQA for each distortion type of images, and IQA for special distortion types of images. Further, according to the comprehensive benefit of precision, generalization performance and complexity, their IQA results are compared with those of 12 existing IQA models. The experimental results show that the accuracy (PLCC) of PSIM is 9.91% higher than that of SSIM in four databases, on average; and its performance is better than that of 12 existing IQA models. Synthesizing experimental results and theoretical analysis, it is showed that the proposed PSIM model is an effective and excellent IQA model.

The ability to perform automated conversions between different data formats is key to achieving interoperability between heterogeneous systems. Conversions require the definition of mappings between concepts of separate data specifications, which is typically a difficult and time-consuming task. In this article, we present a technique that exploits, in part, semantic web technologies to automatically suggest mappings to users based on both linguistic and structural similarities between terms of different data specifications. In addition, we show how a machine-learned linguistic model created by gathering data from domain-specific sources can help increase the accuracy of the suggested mappings. The approach has been implemented in our prototype tool, SMART (SPRINT Mapping & Annotation Recommendation Tool), and it has been validated through tests using specifications from the transportation domain.

Homologous proteins can display high structural variation due to evolutionary divergence at low sequence identity. This classical inverse relationship between sequence identity and structural similarity, established many years ago, has remained true between homologous proteins of known structure over time. However, a large number of heteromeric proteins also exist in the structural data bank, where the interacting subunits belong to the same fold and maintain low sequence identity between themselves. It is not clear if there is any selection pressure to deviate from the inverse sequence–structure relationship for such interacting distant homologs, in comparison to pairs of homologs which are not known to interact. We examined 12,824 fold pairs of interacting homologs of known structure, which includes both heteromers and multi‐domain proteins. These were compared with monomeric proteins, resulting in 26,082 fold pairs as a dataset of non‐interacting homologous systems. Interacting homologs were found to retain higher structural similarity than non‐interacting homologs at diminishing sequence identity in a statistically significant manner. Interacting homologs are more similar in their 3D structures than non‐interacting homologs and have a preference towards symmetric association. There appears to be a structural constraint between remote homologs due to this commitment.

Activity recognition using data collected with smart devices such as mobile and wearable sensors has become a critical component of many emerging applications ranging from behavioral medicine to gaming. However, an unprecedented increase in the diversity of smart devices in the internet-of-things era has limited the adoption of activity recognition models for use across different devices. This lack of cross-domain adaptation is particularly notable across sensors of different modalities where the mapping of the sensor data in the traditional feature level is highly challenging. To address this challenge, we propose ActiLabel, a combinatorial framework that learns structural similarities among the events that occur in a target domain and those of a source domain and identifies an optimal mapping between the two domains at their structural level. The structural similarities are captured through a graph model, referred to as the dependency graph, which abstracts details of activity patterns in low-level signal and feature space. The activity labels are then autonomously learned in the target domain by finding an optimal tiered mapping between the dependency graphs. We carry out an extensive set of experiments on three large datasets collected with wearable sensors involving human subjects. The results demonstrate the superiority of ActiLabel over state-of-the-art transfer learning and deep learning methods. In particular, ActiLabel outperforms such algorithms by average F1-scores of 36.3 % , 32.7 % , and 9.1 % for cross-modality, cross-location, and cross-subject activity recognition, respectively. [ABSTRACT FROM AUTHOR]