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The native conformation of a protein plays a decisive role in ensuring its functionality. It is established that the spatial structure of proteins may exhibit a greater degree of conservation than the corresponding amino acid sequences. This study aims to clarify structural distinctions between homologous and non-homologous proteins with identical topology. The analysis focuses on secondary structures with special emphasis on their fraction, distribution along the polypeptide chain, and chirality. Three different groups of proteins with identical topology were considered according to the CATH database: a homologous group of Globins, a group of Phycocyanins, which is often considered as a potential relative of globins, and a diverse assembly of other globin-like proteins. Some structural patterns in the distribution of secondary structure have been identified within Globins. A similar profile was observed in Phycocyanins, in contrast to the third group. In addition, a distinguishable structural motif, including structures such as 310-helix and irregular structure, has been found in both Globins and Phycocyanins, which can be proposed as an evolutionary imprint.

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This article aims to provide a comprehensive critical, yet readable, review of general interest to the chemistry community on molecular similarity as applied to chemical informatics and predictive modeling with a special focus on read-across (RA) and read-across structure-activity relationships (RASAR). Molecular similarity-based computational tools, such as quantitative structure-activity relationships (QSARs) and RA, are routinely used to fill the data gaps for a wide range of properties including toxicity endpoints for regulatory purposes. This review will explore the background of RA starting from how structural information has been used through to how other similarity contexts such as physicochemical, absorption, distribution, metabolism, and elimination (ADME) properties, and biological aspects are being characterized. More recent developments of RA's integration with QSAR have resulted in the emergence of novel models such as ToxRead, generalized read-across (GenRA), and quantitative RASAR (q-RASAR). Conventional QSAR techniques have been excluded from this review except where necessary for context.

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BACKGROUND: The Mnemonic Similarity Task (MST) is a popular memory task designed to assess hippocampal integrity. We assessed whether analyzing MST performance using a multinomial processing tree (MPT) cognitive model could detect individuals with elevated Alzheimer's disease (AD) biomarker status prior to cognitive decline. METHOD: We analyzed MST data from >200 individuals (young, cognitively healthy older adults and individuals with mild cognitive impairment [MCI]), a subset of which also had existing cerebrospinal fluid (CSF) amyloid beta (Aß) and phosphorylated tau (pTau) data using both traditional and model-derived approaches. We assessed how well each could predict age group, memory ability, MCI status, Aß, and pTau status using receiver operating characteristic analyses. RESULTS: Both approaches predicted age group membership equally, but MPT-derived metrics exceeded traditional metrics in all other comparisons. DISCUSSION: A MPT model of the MST can detect individuals with AD prior to cognitive decline, making it a potentially useful tool for screening and monitoring older adults during the asymptomatic phase of AD. HIGHLIGHTS: The MST, along with cognitive modeling, identifies individuals with memory deficits and cognitive impairment. Cognitive modeling of the MST identifies individuals with increased AD biomarkers prior to changes in cognitive function. The MST is a digital biomarker that identifies individuals at high risk of AD.

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Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, while aspartic acid, methionine, histidine, asparagine and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models (MQSSMs), which outperformed cutting-edge machine learning (ML)-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, to characterize the chemical space and to discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity.

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Software development teams generally welcome any effort to expose bugs in their code base. In this work, we build on the hypothesis that mobile apps from the same category (e.g., two web browser apps) may be affected by similar bugs in their evolution process. It is therefore possible to transfer the experience of one historical app to quickly find bugs in its new counterparts. This has been referred to as collaborative bug finding in the literature. Our novelty is that we guide the bug finding process by considering that existing bugs have been hinted within app reviews. Concretely, we design the BugRMSys approach to recommend bug reports for a target app by matching historical bug reports from apps in the same category with user app reviews of the target app. We experimentally show that this approach enables us to quickly expose and report dozens of bugs for targeted apps such as Brave (web browser app). BugRMSys 's implementation relies on DistilBERT to produce natural language text embeddings. Our pipeline considers similarities between bug reports and app reviews to identify relevant bugs. We then focus on the app review as well as potential reproduction steps in the historical bug report (from a same-category app) to reproduce the bugs. Overall, after applying BugRMSys to six popular apps, we were able to identify, reproduce and report 20 new bugs: among these, 9 reports have been already triaged, 6 were confirmed, and 4 have been fixed by official development teams.

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Purpose: This study elucidates the relationship between similarity and ethnic psychological compatibility and its underlying psychological mechanisms. According to kin selection theory, similarity can promote ethnic psychological compatibility by enhancing psychological kinship and intergroup contact. Participants: and methods: A questionnaire survey was administered to 1523 participants from 25 ethnic groups in China. Data analysis was carried out via conditional process modelling. A multigroup comparison of mediation models between the ethnic majority and minorities was detected via the Stats Tools Package. Results: Our findings demonstrated that: (1) cultural and attitude similarity both showed a significant positive correlation with ethnic psychological compatibility; (2) psychological kinship and intergroup contact served as mediators in the relationship of attitude and cultural similarity with ethnic psychological compatibility and psychological kinship and intergroup contact were independent mediators, while psychological kinship-intergroup contact showed a significant serial mediation effect; (3) there were no significant differences in mediation effects between different ethnic groups. Conclusion: Our findings expand on kin selection theory and provide valuable paths for psychologically supporting ethnic psychological compatibility.

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Link prediction (LP) is a task for the identification of potential, missing and spurious links in complex networks. Protein-protein interaction (PPI) networks are important for understanding the underlying biological mechanisms of diseases. Many complex networks have been constructed using LP methods; however, there are a limited number of studies that focus on disease-related gene predictions and evaluate these genes using various evaluation criteria. The main objective of the study is to investigate the effect of a simple ensemble method in disease related gene predictions. Local similarity indices (LSIs) based disease related gene predictions were integrated by a simple ensemble decision method, simple majority voting (SMV), on the PPI network to detect accurate disease related genes. Human PPI network was utilized to discover potential disease related genes using four LSIs for the gene prediction. LSIs discovered potential links between disease related genes, which were obtained from OMIM database for gastric, colorectal, breast, prostate and lung cancers. LSIs based disease related genes were ranked due to their LSI scores in descending order for retrieving the top 10, 50 and 100 disease related genes. SMV integrated four LSIs based predictions to obtain SMV based the top 10, 50 and 100 disease related genes. The performance of LSIs based and SMV based genes were evaluated separately by employing overlap analyses, which were performed with GeneCard disease-gene relation dataset and Gene Ontology (GO) terms. The GO-terms were used for biological assessment for the inferred gene lists by LSIs and SMV on all cancer types. Adamic-Adar (AA), Resource Allocation Index (RAI), and SMV based gene lists are generally achieved good performance results on all cancers in both overlap analyses. SMV also outperformed on breast cancer data. The increment in the selection of the number of the top ranked disease related genes also enhanced the performance results of SMV.

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BACKGROUND: Drug repositioning has the potential to reduce costs and accelerate the rate of drug development, with highly promising applications. Currently, the development of artificial intelligence has provided the field with fast and efficient computing power. Nevertheless, the repositioning of traditional Chinese medicine (TCM) is still in its infancy, and the establishment of a reasonable and effective research method is a pressing issue that requires urgent attention. The use of graph neural network (GNN) to compute the similarity between TCM prescriptions to develop a method for finding their new indications is an innovative attempt. METHODS: This paper focused on traditional Chinese medicine prescriptions containing ephedra, with 20 prescriptions for treating external cough and asthma taken as target prescriptions. The remaining 67 prescriptions containing ephedra were taken as to-be-matched prescriptions. Furthermore, a multitude of data pertaining to the prescriptions, including diseases, disease targets, symptoms, and various types of information on herbs, was gathered from a diverse array of literature sources, such as Chinese medicine databases. Then, cosine similarity and Jaccard coefficient were calculated to characterize the similarity between prescriptions using graph convolutional network (GCN) with a self-supervised learning method, such as deep graph infomax (DGI). RESULTS: A total of 1340 values were obtained for each of the two calculation indicators. A total of 68 prescription pairs were identified after screening with 0.77 as the threshold for cosine similarity. Following the removal of false positive results, 12 prescription pairs were deemed to have further research value. A total of 5 prescription pairs were screened using a threshold of 0.50 for the Jaccard coefficient. However, the specific results did not exhibit significant value for further use, which may be attributed to the excessive variety of information in the dataset. CONCLUSIONS: The proposed method can provide reference for finding new indications of target prescriptions by quantifying the similarity between prescriptions. It is expected to offer new insights for developing a scientific and systematic research methodology for traditional Chinese medicine repositioning.

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OBJECTIVE: The purpose of this study was to investigate the extent to which patients feel racially and culturally similar to their therapist, patients' perceptions of their therapist's cultural competence, and how these factors relate to the working alliance in a naturalistic treatment setting. METHODS: Participants were 119 adult patients treated at a large outpatient clinic by clinicians with a range of professional backgrounds (e.g., psychiatric residents, psychologists in training, and staff therapists). Patients were asked to rate the level of racial and cultural similarity between themselves and their therapist and to provide their assessment of their therapist's cultural competency and of the working alliance. RESULTS: Findings suggest that patients' ratings of perceived cultural and racial similarity were not significantly related to the working alliance. However, perceptions of racial and cultural similarity were significantly associated with perceived therapist cultural competence. Perceived cultural competence was also strongly related to the working alliance. Finally, patients' ratings of their therapist's cultural competencies in the areas of awareness and skill, but not knowledge, predicted a strong working alliance after analyses controlled for ratings of racial and cultural similarity. CONCLUSIONS: This study suggests the importance of heightening mental health clinicians' awareness of the influence of culture on the therapeutic relationship and the important role of a therapist's cultural competencies (specifically, awareness and skill) in the working alliance, which may matter more to patients than perceptions of racial or cultural similarity.

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While most studies focused on the impact of intensive agriculture on the barn owl's diet, little is known about the effect of cropland abandonment. We compared the taxon composition/evenness and feeding guild structure of small mammal prey identified in pellets collected before (2004) and after (2012) the abandonment of 9% of cultivated fields within a cultural landscape. Data on prey abundance per pellet were analysed through non-metric multidimensional scaling and permutational, paired tests. Prey taxon evenness in 2012 was significantly lower than in 2004. That induced a shift in prey taxon composition as indicated by the significantly lower dietary similarity compared with the random expectation. The increasing and declining abundance of Murinae and Crocidurinae, respectively, had the largest contribution to the differentiation of the diet spectrum. Insectivorous prey was significantly more abundant in 2004 compared to 2012, while the opposite was true for omnivorous prey. Our results suggest that even a small fraction of abandoned crops in the landscape might induce a detectable shift in the barn owl's food niche. The dietary effects are similar to those observed after agricultural intensification, that is, an increase in the abundance of generalists to the detriment of specialist mammal prey.

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Normal aging is accompanied by changes in brain structure and function associated with cognitive decline. Structural and functional abnormalities, particularly the prefrontal cortex (PFC) and subcortical regions, contributed to cognitive aging. However, it remains unclear how the synchronized changes in structure and function of individual brain regions affect the cognition in aging. Using 3D T1-weighted structural data and movie watching functional magnetic resonance imaging data in a sample of 422 healthy individuals (ages from 18 to 87 years), we constructed regional structure-function coupling (SFC) of cortical and subcortical regions by quantifying the distribution similarity of gray matter volume (GMV) and amplitude of low-frequency fluctuation (ALFF). Further, we investigated age-related changes in SFC and its relationship with cognition. With aging, increased SFC localized in PFC, thalamus and caudate nucleus, decreased SFC in temporal cortex, lateral occipital cortex and putamen. Moreover, the SFC in the PFC was associated with executive function and thalamus was associated with the fluid intelligence, and partially mediated age-related cognitive decline. Collectively, our results highlight that tighter structure-function synchron of the PFC and thalamus might contribute to age-related cognitive decline, and provide insight into the substrate of the thalamo-prefrontal pathway with cognitive aging.

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Most existing intelligent editing tools for music and video rely on the cross-modal matching technology of the affective consistency or the similarity of feature representations. However, these methods are not fully applicable to complex audiovisual matching scenarios, resulting in low matching accuracy and suboptimal audience perceptual effects due to ambiguous matching rules and associated factors. To address these limitations, this paper focuses on both the similarity and integration of affective distribution for the artistic audiovisual works of movie and television video and music. Based on the rich emotional perception elements, we propose a hybrid matching model based on feature canonical correlation analysis (CCA) and fine-grained affective similarity. The model refines KCCA fusion features by analyzing both matched and unmatched music-video pairs. Subsequently, the model employs XGBoost to predict relevance and to compute similarity by considering fine-grained affective semantic distance as well as affective factor distance. Ultimately, the matching prediction values are obtained through weight allocation. Experimental results on a self-built dataset demonstrate that the proposed affective matching model balances feature parameters and affective semantic cognitions, yielding relatively high prediction accuracy and better subjective experience of audiovisual association. This paper is crucial for exploring the affective association mechanisms of audiovisual objects from a sensory perspective and improving related intelligent tools, thereby offering a novel technical approach to retrieval and matching in music-video editing.

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Different organizational structures have been argued to underlie semantic knowledge about concepts; taxonomic organization, based on shared features, and thematic organization based on co-occurrence in common scenes and scenarios. The goal of the current study is to examine which of the two organizational systems are more engaged in the semantic context of a picture naming task. To address this question, we examined the representational structure underlying the semantic space in different picture naming tasks by applying representational similarity analysis (RSA) to electroencephalography (EEG) datasets. In a series of experiments, EEG signals were collected while participants named pictures under different semantic contexts. Study 1 reanalyzes existing data from semantic contexts directing attention to taxonomic organization and semantic contexts that are not biased towards either taxonomic or thematic organization. In Study 2 we keep the stimuli the same and vary semantic contexts to draw attention to either taxonomic or thematic organization. The RSA approach allows us to examine the pairwise similarity in scalp-recorded amplitude patterns at each time point following the onset of the picture and relate it to theoretical taxonomic and thematic measures derived from computational models of semantics. Across all tasks, the similarity structure of scalp-recorded neural activity correlated better with taxonomic than thematic measures, in time windows associated with semantic processing. Most strikingly, we found that the scalp-recorded patterns of neural activity between taxonomically related items were more similar to each other than the scalp-recorded patterns of neural activity for thematically related or unrelated items, even in tasks that makes thematic information more salient. These results suggest that the principle semantic organization of these concepts during picture naming is taxonomic, at least in the context of picture naming.

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Accurate diagnosis of mental disorders is expected to be achieved through the identification of reliable neuroimaging biomarkers with the help of cutting-edge feature selection techniques. However, existing feature selection methods often fall short in capturing the local structural characteristics among samples and effectively eliminating redundant features, resulting in inadequate performance in disorder prediction. To address this gap, we propose a novel supervised method named local-structure-preservation and redundancy-removal-based feature selection (LRFS), and then apply it to the identification of meaningful biomarkers for schizophrenia (SZ). LRFS method leverages graph-based regularization to preserve original sample similarity relationships during data transformation, thus retaining crucial local structure information. Additionally, it introduces redundancy-removal regularization based on interrelationships among features to exclude similar and redundant features from high-dimensional data. Moreover, LRFS method incorporates l2,1 sparse regularization that enables selecting a sparse and noise-robust feature subset. Experimental evaluations on eight public datasets with diverse properties demonstrate the superior performance of our method over nine popular feature selection methods in identifying discriminative features, with average classification accuracy gains ranging from 1.30 % to 9.11 %. Furthermore, the LRFS method demonstrates superior discriminability in four functional magnetic resonance imaging (fMRI) datasets from 708 healthy controls (HCs) and 537 SZ patients, with an average increase in classification accuracy ranging from 1.89 % to 9.24 % compared to other nine methods. Notably, our method reveals reproducible and significant changes in SZ patients relative to HCs across the four datasets, predominantly in the thalamus-related functional network connectivity, which exhibit a significant correlation with clinical symptoms. Convergence analysis, parameter sensitivity analysis, and ablation studies further demonstrate the effectiveness and robustness of our method. In short, our proposed feature selection method effectively identifies discriminative and reliable features that hold the potential to be biomarkers, paving the way for the elucidation of brain abnormalities and the advancement of precise diagnosis of mental disorders.

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A major constraining factor for attentional selection is the similarity between targets and distractors. When similarity is low, target items can be identified quickly and efficiently, whereas high similarity can incur large costs on processing speed. Models of visual search contrast a fast, efficient parallel stage with a slow serial processing stage where search times are strongly modulated by the number of distractors in the display. In particular, recent work has argued that the magnitude of search slopes should be inversely proportional to target-distractor similarity. Here, we assessed the relationship between target-distractor similarity and search slopes. In our visual search tasks, participants detected an oddball color target among distractors (Experiments 1 & 2) or discriminated the direction of a triangle in the oddball color (Experiment 3). We systematically varied the similarity between target and distractor colors (along a circular CIELAB color wheel) and the number of distractors in the search array, finding logarithmic search slopes that were inversely proportional to the number of items in the array. Surprisingly, we also found that searches were highly efficient (i.e., near-zero slopes) for targets and distractors that were extremely similar (≤20° in color space). These findings indicate that visual search is systematically influenced by target-distractor similarity across different processing stages. Importantly, we found that search can be highly efficient and entirely unaffected by the number of distractors despite high perceptual similarity, in contrast to the general assumption that high similarity must lead to slow and serial search behavior.

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To support PTM proteomic analysis and annotation in different species, we developed PTMoreR, a user-friendly tool that considers the surrounding amino acid sequences of PTM sites during BLAST, enabling a motif-centric analysis across species. By controlling sequence window similarity, PTMoreR can map phosphoproteomic results between any two species, perform site-level functional enrichment analysis, and generate kinase-substrate networks. We demonstrate that the majority of real P-sites in mice can be inferred from experimentally derived human P-sites with PTMoreR mapping. Furthermore, the compositions of 129 mammalian phosphoproteomes can also be predicted using PTMoreR. The method also identifies cross-species phosphorylation events that occur on proteins with an increased tendency to respond to the environmental factors. Moreover, the classic kinase motifs can be extracted across mammalian species, offering an evolutionary angle for refining current motifs. PTMoreR supports PTM proteomics in non-human species and facilitates quantitative phosphoproteomic analysis.

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BACKGROUND: Understanding the similarities of patients with cancer is essential to advancing personalized medicine, improving patient outcomes, and developing more effective and individualized treatments. It enables researchers to discover important patterns, biomarkers, and treatment strategies that can have a significant impact on cancer research and oncology. In addition, the identification of previously successfully treated patients supports oncologists in making treatment decisions for a new patient who is clinically or molecularly similar to the previous patient. OBJECTIVE: The planned review aims to systematically summarize, map, and describe existing evidence to understand how patient similarity is defined and used in cancer research and clinical care. METHODS: To systematically identify relevant studies and to ensure reproducibility and transparency of the review process, a comprehensive literature search will be conducted in several bibliographic databases, including Web of Science, PubMed, LIVIVIVO, and MEDLINE, covering the period from 1998 to February 2024. After the initial duplicate deletion phase, a study selection phase will be applied using Rayyan, which consists of 3 distinct steps: title and abstract screening, disagreement resolution, and full-text screening. To ensure the integrity and quality of the selection process, each of these steps is preceded by a pilot testing phase. This methodological process will culminate in the presentation of the final research results in a structured form according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flowchart. The protocol has been registered in the Journal of Medical Internet Research. RESULTS: This protocol outlines the methodologies used in conducting the scoping review. A search of the specified electronic databases and after removing duplicates resulted in 1183 unique records. As of March 2024, the review process has moved to the full-text evaluation phase. At this stage, data extraction will be conducted using a pretested chart template. CONCLUSIONS: The scoping review protocol, centered on these main concepts, aims to systematically map the available evidence on patient similarity among patients with cancer. By defining the types of data sources, approaches, and methods used in the field, and aligning these with the research questions, the review will provide a foundation for future research and clinical application in personalized cancer care. This protocol will guide the literature search, data extraction, and synthesis of findings to achieve the review's objectives. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58705.

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Plant-based dairy alternatives have many benefits in terms of sustainability, animal welfare, and health, but they can only be successful in the market if consumers perceive them as suitable substitutes for conventional dairy. Consumers' expectations for new products are strongly influenced by the food categories into which they place these products. The present study aims to reveal consumers' categorizations of plant-based dairy products to gain insights into their potential as dairy substitutes. In a free sorting task, 100 participants from the German-speaking part of Switzerland sorted a variety of plant-based and conventional animal-based dairy products into groups, indicating their spontaneous similarity perceptions. Additionally, we assessed the participants' characteristics and attitudes toward plant-based dairy products to test potential differences in categorization strategies among consumer groups. Multidimensional scaling and cluster analysis showed that consumers' mental representations of plant-based dairy and conventional animal-based dairy were clearly separated across a wide range of product types. This pattern was even observed among consumers who ate less meat, had higher exposure to vegan dietary styles, and had less negative attitudes toward dairy alternatives. The results suggest that taxonomic distinctions based on plant or animal origin dominate consumers' perceptions and are likely to hinder the substitution of dairy with plant-based dairy. Nevertheless, they also imply that plant-based products that manage to emphasize shared goals and functional properties akin to conventional dairy products are more likely to form a common goal-derived category in consumers' minds and thus have better prospects as substitutes.

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Recently, there has been a major push toward the development of next-generation treatments against snakebite envenoming. However, unlike current antivenoms that rely on animal-derived polyclonal antibodies, most of these novel approaches are reliant on an in-depth understanding of the over 2000 known snake venom toxins. Indeed, by identifying similarities (i.e., conserved epitopes) across these different toxins, it is possible to design cross-reactive treatments, such as broadly-neutralising antibodies, that target these similarities. Therefore, in this project, we built an automated pipeline that generates sequence and structural distance matrices and homology trees across all available snake venom toxin sequences and structures. To facilitate analysis, we also developed a user-friendly and high-throughput visualisation tool, coined "Venom TOxin CluStering" (V-ToCs). This tool allows researchers to easily investigate sequence and structure patterns in snake venom toxins for a wide array of purposes, such as elucidating toxin evolution, and will also hopefully help guide the discovery and development of increasingly broadly-neutralising antivenoms in the near future.

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Humans express emotions through various modalities such as facial expressions and natural language. However, the relationships between emotions expressed through different modalities and their correlations with neural activities remain uncertain. Here, we aimed to unveil some of these uncertainties by investigating the similarity of emotion representations across modalities and brain regions. First, we represented various emotion categories as multi-dimensional vectors derived from visual (face), linguistic, and visio-linguistic data, and used representational similarity analysis to compare these modalities. Second, we examined the linear transferability of emotion representation from other modalities to the visual modality. Third, we compared the representational structure derived in the first step with those from brain activities across 360 regions. Our findings revealed that emotion representations share commonalities across modalities with modality-type dependent variations, and they can be linearly mapped from other modalities to the visual modality. Additionally, emotion representations in uni-modalities showed relatively higher similarity with specific brain regions, while multi-modal emotion representation was most similar to representations across the entire brain region. These findings suggest that emotional experiences are represented differently across various brain regions with varying degrees of similarity to different modality types, and that they may be multi-modally conveyable in visual and linguistic domains.

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