Multidimensional neural representations of social features during movie viewing.

The social world is dynamic and contextually embedded. Yet, most studies utilize simple stimuli that do not capture the complexity of everyday social episodes. To address this, we implemented a movie viewing paradigm and investigated how the everyday social episodes are processed in the brain. Participants watched one of two movies during an MRI scan. Neural patterns from brain regions involved in social perception, mentalization, action observation, and sensory processing were extracted. Representational similarity analysis results revealed that several labeled social features (including social interaction, mentalization, the actions of others, characters talking about themselves, talking about others, and talking about objects) were represented in superior temporal gyrus (STG) and middle temporal gyrus (MTG). The mentalization feature was also represented throughout the theory of mind network, and characters talking about others engaged the temporoparietal junction (TPJ), suggesting that listeners may spontaneously infer the mental state of those being talked about. In contrast, we did not observe the action representations in frontoparietal regions of the action observation network. The current findings indicate that STG and MTG serve as key regions for social processing, and that listening to characters talk about others elicits spontaneous mental state inference in TPJ during natural movie viewing.

Analysis of the Similarity between Injection Molding Simulation and Experiment.

In the plastics industry, CFD simulation has been used for many years to support mold design. However, using simulation as a substitute for experimentation remains a major challenge to this day. This is due to the unknown congruence between simulation and experiment. The present work focuses on a comparison between simulation (generated with the software Moldflow Insight Ultimate from Autodesk Inc., San Francisco, CA, USA) and experiment by using molds of different complexity, where, in contrast to a large number of previous investigations, both the characteristics of the parts and the time series of the process parameters were compared with each other. For this purpose, the high-resolution time series of the process parameters injection pressure, flow rate, and cavity pressure as well as the mass and the dimensions of the manufactured parts were acquired during the experiments and the results were compared with the computations obtained from the simulation. In addition, potential causes like the material data, mesh and solver parameter, and the machine-specific behavior were analyzed to assess which of these causes may be decisive for a deviation between simulation and experiment.

Intergenerational transmission of brain structure and function in humans: a narrative review of designs, methods, and findings.

Children often show cognitive and affective traits that are similar to their parents. Although this indicates a transmission of phenotypes from parents to children, little is known about the neural underpinnings of that transmission. Here, we provide a general overview of neuroimaging studies that explore the similarity between parents and children in terms of brain structure and function. We notably discuss the aims, designs, and methods of these so-called intergenerational neuroimaging studies, focusing on two main designs: the parent-child design and the multigenerational design. For each design, we also summarize the major findings, identify the sources of variability between studies, and highlight some limitations and future directions. We argue that the lack of consensus in defining the parent-child transmission of brain structure and function leads to measurement heterogeneity, which is a challenge for future studies. Additionally, multigenerational studies often use measures of family resemblance to estimate the proportion of variance attributed to genetic versus environmental factors, though this estimate is likely inflated given the frequent lack of control for shared environment. Nonetheless, intergenerational neuroimaging studies may still have both clinical and theoretical relevance, not because they currently inform about the etiology of neuromarkers, but rather because they may help identify neuromarkers and test hypotheses about neuromarkers coming from more standard neuroimaging designs.

An inversion-based clustering approach for complex clusters.

BACKGROUND: The choice of an appropriate similarity measure plays a pivotal role in the effectiveness of clustering algorithms. However, many conventional measures rely solely on feature values to evaluate the similarity between objects to be clustered. Furthermore, the assumption of feature independence, while valid in certain scenarios, does not hold true for all real-world problems. Hence, considering alternative similarity measures that account for inter-dependencies among features can enhance the effectiveness of clustering in various applications. METHODS: In this paper, we present the Inv measure, a novel similarity measure founded on the concept of inversion. The Inv measure considers the significance of features, the values of all object features, and the feature values of other objects, leading to a comprehensive and precise evaluation of similarity. To assess the performance of our proposed clustering approach that incorporates the Inv measure, we evaluate it on simulated data using the adjusted Rand index. RESULTS: The simulation results strongly indicate that inversion-based clustering outperforms other methods in scenarios where clusters are complex, i.e., apparently highly overlapped. This showcases the practicality and effectiveness of the proposed approach, making it a valuable choice for applications that involve complex clusters across various domains. CONCLUSIONS: The inversion-based clustering approach may hold significant value in the healthcare industry, offering possible benefits in tasks like hospital ranking, treatment improvement, and high-risk patient identification. In social media analysis, it may prove valuable for trend detection, sentiment analysis, and user profiling. E-commerce may be able to utilize the approach for product recommendation and customer segmentation. The manufacturing sector may benefit from improved quality control, process optimization, and predictive maintenance. Additionally, the approach may be applied to traffic management and fleet optimization in the transportation domain. Its versatility and effectiveness make it a promising solution for diverse fields, providing valuable insights and optimization opportunities for complex and dynamic data analysis tasks.

Graph semantic similarity-based automatic assessment for programming exercises.

This paper proposes an algorithm for the automatic assessment of programming exercises. The algorithm assigns assessment scores based on the program dependency graph structure and the program semantic similarity, but does not actually need to run the student's program. By calculating the node similarity between the student's program and the teacher's reference programs in terms of structure and program semantics, a similarity matrix is generated and the optimal similarity node path of this matrix is identified. The proposed algorithm achieves improved computational efficiency, with a time complexity of O ( n 2 ) for a graph with n nodes. The experimental results show that the assessment algorithm proposed in this paper is more reliable and accurate than several comparison algorithms, and can be used for scoring programming exercises in C/C++, Java, Python, and other languages.

scEWE: high-order element-wise weighted ensemble clustering for heterogeneity analysis of single-cell RNA-sequencing data.

With the emergence of large amount of single-cell RNA sequencing (scRNA-seq) data, the exploration of computational methods has become critical in revealing biological mechanisms. Clustering is a representative for deciphering cellular heterogeneity embedded in scRNA-seq data. However, due to the diversity of datasets, none of the existing single-cell clustering methods shows overwhelming performance on all datasets. Weighted ensemble methods are proposed to integrate multiple results to improve heterogeneity analysis performance. These methods are usually weighted by considering the reliability of the base clustering results, ignoring the performance difference of the same base clustering on different cells. In this paper, we propose a high-order element-wise weighting strategy based self-representative ensemble learning framework: scEWE. By assigning different base clustering weights to individual cells, we construct and optimize the consensus matrix in a careful and exquisite way. In addition, we extracted the high-order information between cells, which enhanced the ability to represent the similarity relationship between cells. scEWE is experimentally shown to significantly outperform the state-of-the-art methods, which strongly demonstrates the effectiveness of the method and supports the potential applications in complex single-cell data analytical problems.

Scoring alignments by embedding vector similarity.

Sequence similarity is of paramount importance in biology, as similar sequences tend to have similar function and share common ancestry. Scoring matrices, such as PAM or BLOSUM, play a crucial role in all bioinformatics algorithms for identifying similarities, but have the drawback that they are fixed, independent of context. We propose a new scoring method for amino acid similarity that remedies this weakness, being contextually dependent. It relies on recent advances in deep learning architectures that employ self-supervised learning in order to leverage the power of enormous amounts of unlabelled data to generate contextual embeddings, which are vector representations for words. These ideas have been applied to protein sequences, producing embedding vectors for protein residues. We propose the E-score between two residues as the cosine similarity between their embedding vector representations. Thorough testing on a wide variety of reference multiple sequence alignments indicate that the alignments produced using the new $E$-score method, especially ProtT5-score, are significantly better than those obtained using BLOSUM matrices. The new method proposes to change the way alignments are computed, with far-reaching implications in all areas of textual data that use sequence similarity. The program to compute alignments based on various $E$-scores is available as a web server at e-score.csd.uwo.ca. The source code is freely available for download from github.com/lucian-ilie/E-score.

The upside of cumulative conceptual interference on exemplar-level mnemonic discrimination.

Although long-term visual memory (LTVM) has a remarkable capacity, the fidelity of its episodic representations can be influenced by at least two intertwined interference mechanisms during the encoding of objects belonging to the same category: the capacity to hold similar episodic traces (e.g., different birds) and the conceptual similarity of the encoded traces (e.g., a sparrow shares more features with a robin than with a penguin). The precision of episodic traces can be tested by having participants discriminate lures (unseen objects) from targets (seen objects) representing different exemplars of the same concept (e.g., two visually similar penguins), which generates interference at retrieval that can be solved if efficient pattern separation happened during encoding. The present study examines the impact of within-category encoding interference on the fidelity of mnemonic object representations, by manipulating an index of cumulative conceptual interference that represents the concurrent impact of capacity and similarity. The precision of mnemonic discrimination was further assessed by measuring the impact of visual similarity between targets and lures in a recognition task. Our results show a significant decrement in the correct identification of targets for increasing interference. Correct rejections of lures were also negatively impacted by cumulative interference as well as by the visual similarity with the target. Most interestingly though, mnemonic discrimination for targets presented with a visually similar lure was more difficult when objects were encoded under lower, not higher, interference. These findings counter a simply additive impact of interference on the fidelity of object representations providing a finer-grained, multi-factorial, understanding of interference in LTVM.

Anti-inflammatory action of new hybrid N-acyl-[1,2]dithiolo-[3,4-c]quinoline-1-thione.

Most of pharmaceutical agents display a number of biological activities. It is obvious that testing even one compound for thousands of biological activities is not practically possible. A computer-aided prediction is therefore the method of choice in this case to select the most promising bioassays for particular compounds. Using the PASS Online software, we determined the probable anti-inflammatory action of the 12 new hybrid dithioloquinolinethiones derivatives. Chemical similarity search in the World-Wide Approved Drugs (WWAD) and DrugBank databases did not reveal close structural analogues with the anti-inflammatory action. Experimental testing of anti-inflammatory activity of the synthesized compounds in the carrageenan-induced inflammation mouse model confirmed the computational predictions. The anti-inflammatory activity of the studied compounds (2a, 3a-3k except for 3j) varied between 52.97% and 68.74%, being higher than the reference drug indomethacin (47%). The most active compounds appeared to be 3h (68.74%), 3k (66.91%) and 3b (63.74%) followed by 3e (61.50%). Thus, based on the in silico predictions a novel class of anti-inflammatory agents was discovered.

Interpersonal Perceptual Tendencies and Adjustment in Chinese Adolescents: Testing the Mediating Role of Peer Relationship.

Accuracy and assumed similarity are the most central topics in the research area of interpersonal perception. These two interpersonal perceptual tendencies have been demonstrated to have beneficial effects on adults' psychological functioning independently. However, how and why they influence adolescent psychological adjustment is less well-understood. The present research aimed to examine the mediating role of peer relationships in the association between these two interpersonal perceptual tendencies and psychological adjustment in adolescents. In the first study, a sample of adolescents in their first year of college (N = 93, 63% girls, Mage = 17.45 years, SD = 0.52) were recruited from a university located in Eastern China. They completed the likableness ratings of Anderson's 200 adjectives. The top 10 personality traits with the lowest and highest likableness values were selected to measure adolescents' personality profiles. In the second study, a round-robin design in a real-life setting, a sample of Chinese adolescents in grades six to nine were recruited from two middle schools (N = 337, Ndyads = 918, 47% girls, Mage = 13.76, SD = 1.15). They were requested to rate themselves and their peers within the same group on various factors. Results revealed that adolescents could form an accurate perception of their peers' personalities and concurrently assume that their peers' personalities are similar to their own. More importantly, accurate interpersonal perception and a biased tendency towards assumed similarity could, directly and indirectly, benefit adolescent psychological adjustment through peer relationships.

Connectivity analyses for task-based fMRI.

Functional connectivity is conventionally defined by measuring the similarity between brain signals from two regions. The technique has become widely adopted in the analysis of functional magnetic resonance imaging (fMRI) data, where it has provided cognitive neuroscientists with abundant information on how brain regions interact to support complex cognition. However, in the past decade the notion of "connectivity" has expanded in both the complexity and heterogeneity of its application to cognitive neuroscience, resulting in greater difficulty of interpretation, replication, and cross-study comparisons. In this paper, we begin with the canonical notions of functional connectivity and then introduce recent methodological developments that either estimate some alternative form of connectivity or extend the analytical framework, with the hope of bringing better clarity for cognitive neuroscience researchers.

Link prediction in protein-protein interaction network: A similarity multiplied similarity algorithm with paths of length three.

Protein-protein interactions (PPIs) are crucial for various biological processes, and predicting PPIs is a major challenge. To solve this issue, the most common method is link prediction. Currently, the link prediction methods based on network Paths of Length Three (L3) have been proven to be highly effective. In this paper, we propose a novel link prediction algorithm, named SMS, which is based on L3 and protein similarities. We first design a mixed similarity that combines the topological structure and attribute features of nodes. Then, we compute the predicted value by summing the product of all similarities along the L3. Furthermore, we propose the Max Similarity Multiplied Similarity (maxSMS) algorithm from the perspective of maximum impact. Our computational prediction results show that on six datasets, including S. cerevisiae, H. sapiens, and others, the maxSMS algorithm improves the precision of the top 500, area under the precision-recall curve, and normalized discounted cumulative gain by an average of 26.99%, 53.67%, and 6.7%, respectively, compared to other optimal methods.

The link between early adolescents' gender discrimination and gender attitudes about peers: Does gender similarity matter?

Gender is one of the most salient social identities, particularly during early adolescence. However, factors related to adolescents' gender attitudes remain underexamined. We examined links between adolescents' gender discrimination, felt-gender similarity, and intergroup gender attitudes. Participants were 270 adolescents in the United States (Mage = 12.95 years, SD = 1.33; 47.4% adolescent girls; 63.7% White, 12.2% Latinx, 10.7% Black, 4.1% Asian, 5.6% multiracial, and 3% indigenous). Path analyses showed that gender discrimination negatively predicted adolescents' attitudes towards own- and other-gender peers. Felt own-gender similarity positively predicted own-gender attitudes as expected, but other-gender similarity did not predict other-gender attitudes. Further, own- and other-gender similarity did not interact to predict adolescents' gender attitudes. However, adolescents' attitudes towards other-gender peers were more negatively impacted by gender discrimination for those who felt highly similar to own-gender peers than for those with average or low own-gender similarity. Findings inform potential strategies to improve adolescents' gender attitudes.

Autoencoder-Based Unsupervised Surface Defect Detection Using Two-Stage Training.

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.

A Hadal Streptomyces-Derived Echinocandin Acylase Discovered through the Prioritization of Protein Families.

Naturally occurring echinocandin B and FR901379 are potent antifungal lipopeptides featuring a cyclic hexapeptide nucleus and a fatty acid side chain. They are the parent compounds of echinocandin drugs for the treatment of severe fungal infections caused by the Candida and Aspergilla species. To minimize hemolytic toxicity, the native fatty acid side chains in these drug molecules are replaced with designer acyl side chains. The deacylation of the N-acyl side chain is, therefore, a crucial step for the development and manufacturing of echinocandin-type antibiotics. Echinocandin E (ECE) is a novel echinocandin congener with enhanced stability generated via the engineering of the biosynthetic machinery of echinocandin B (ECB). In the present study, we report the discovery of the first echinocandin E acylase (ECEA) using the enzyme similarity tool (EST) for enzymatic function mining across protein families. ECEA is derived from Streptomyces sp. SY1965 isolated from a sediment collected from the Mariana Trench. It was cloned and heterologously expressed in S. lividans TK24. The resultant TKecea66 strain showed efficient cleavage activity of the acyl side chain of ECE, showing promising applications in the development of novel echinocandin-type therapeutics. Our results also provide a showcase for harnessing the essentially untapped biodiversity from the hadal ecosystems for the discovery of functional molecules.

Non-similar bioconvective analysis of magnetized hybrid nanofluid (Ag + TiO2) flow over exponential stretching surface.

Scientists have studied fluid flow over a stretching sheet to explore its potential applications in industries. This study investigates the exponential stretching flow of a bioconvective magnetohydrodynamic (MHD) hybrid nanofluid in porous medium taking into consideration thermal radiations, heat generation, chemical reaction, porosity, and dissipation. Moreover, microorganisms are present in the fluid, so the fluid is more stable, which is crucial in biotechnology, biomicrosystems, and bio-nano coolant systems. Silver and titanium dioxide in a water-based medium are the prototypical nanoparticles. The present study involves a transformation of the governing system into a set of dimensionless, coupled and nonlinear partial differential equations (PDEs) using nonsimilar techniques. The local non-similarity (LNS) technique is used to truncate these equations to ordinary differential equations (ODEs). This technique is also used to estimate transformed equations numerically until the second level of truncation takes place via the bvp4c algorithm, which is a built-in MATLAB solver. Furthermore, tables are provided that presents the drag coefficients, Nusselt numbers, Sherwood numbers, and densities of motile microorganisms. Results show a negative correlation between the velocity and the magnetic field parameter as well as the porosity parameter, as evidenced by a decrease in velocity corresponds to rises in these parameters. The temperature distribution exhibits a positive correlation with the rising values of both radiation parameter and Eckert number. The concentration profiles also exhibit a negative correlation with the increasing values of Lewis and bioconvection Lewis number, chemical reaction parameter, Peclet number and the differences in microbial concentration. This study will improve the future research on hybrid nanofluid regarding industrial applications. There haven't been any previous publications that have investigated the use of this model with the local non-similarity method. The main objective of this article is to enhance the heat transfer performance in a hybrid nanofluid.

Multi-omics bioactivity profile-based chemical grouping and read-across: a case study with Daphnia magna and azo dyes.

Grouping/read-across is widely used for predicting the toxicity of data-poor target substance(s) using data-rich source substance(s). While the chemical industry and the regulators recognise its benefits, registration dossiers are often rejected due to weak analogue/category justifications based largely on the structural similarity of source and target substances. Here we demonstrate how multi-omics measurements can improve confidence in grouping via a statistical assessment of the similarity of molecular effects. Six azo dyes provided a pool of potential source substances to predict long-term toxicity to aquatic invertebrates (Daphnia magna) for the dye Disperse Yellow 3 (DY3) as the target substance. First, we assessed the structural similarities of the dyes, generating a grouping hypothesis with DY3 and two Sudan dyes within one group. Daphnia magna were exposed acutely to equi-effective doses of all seven dyes (each at 3 doses and 3 time points), transcriptomics and metabolomics data were generated from 760 samples. Multi-omics bioactivity profile-based grouping uniquely revealed that Sudan 1 (S1) is the most suitable analogue for read-across to DY3. Mapping ToxPrint structural fingerprints of the dyes onto the bioactivity profile-based grouping indicated an aromatic alcohol moiety could be responsible for this bioactivity similarity. The long-term reproductive toxicity to aquatic invertebrates of DY3 was predicted from S1 (21-day NOEC, 40 µg/L). This prediction was confirmed experimentally by measuring the toxicity of DY3 in D. magna. While limitations of this 'omics approach are identified, the study illustrates an effective statistical approach for building chemical groups.

[Development of Auto Dense-breast Classification on Mammography Images Using Image Similarity].

PURPOSE: In Japan, radiologists perform qualitative visual classification to define four categories of mammary gland density. However, an objective estimation of mammary gland density is necessary. To address this, we developed an automatic classification software using image similarity. METHODS: We prepared 741 cases of mediolateral oblique images (MLO) for evaluation, and they were diagnosed as normal among the mammography images taken at our hospital. Image matching was performed using the evaluation images and an image database for breast density determination. In this study, the image similarity used zero normalized cross-correlation (ZNCC) as an index. In addition, if the breast thickness is less than 30 mm and each breast density category ZNNC has the same value, the category is evaluated on the fat side. We compared the results of qualitative visual classification and automatic classification methods to assess consistency. RESULTS: The agreement with the subjective breast composition classification was 78.5%, and the weighted kappa coefficient was 0.98. One mismatched case was evaluated on the higher density side with the same ZNCC value between categories and a breast thickness greater than 30 mm. CONCLUSION: Image similarity provides an excellent estimation of quantification of breast density. This system could contribute to improving the efficiency of the mammography screening system.

Using the Sensible Heat Flux Eddy Covariance-Based Exchange Coefficient to Calculate Latent Heat Flux from Moisture Mean Gradients Over Snow.

In absence of the high-frequency measurements of wind components, sonic temperature and water vapour required by the eddy covariance (EC) method, Monin-Obukhov similarity theory (MOST) is often used to calculate heat fluxes. However, MOST requires assumptions of stability corrections and roughness lengths. In most environments and weather situations, roughness length and stability corrections have high uncertainty. Here, we revisit the modified Bowen-ratio method, which we call C-method, to calculate the latent heat flux over snow. In the absence of high-frequency water vapour measurements, we use sonic anemometer data, which have become much more standard. This method uses the exchange coefficient for sensible heat flux to estimate latent-heat flux. Theory predicts the two exchange coefficients to be equal and the method avoids assuming roughness lengths and stability corrections. We apply this method to two datasets from high mountain (Alps) and polar (Antarctica) environments and compare it with MOST and the three-layer model (3LM). We show that roughness length has a great impact on heat fluxes calculated using MOST and that different calculation methods over snow lead to very different results. Instead, the 3LM leads to good results, in part due to the fact that it avoids roughness length assumptions to calculate heat fluxes. The C-method presented performs overall better or comparable to established MOST with different stability corrections and provides results comparable to the direct EC method. An application of this method is provided for a new station installed in the Pamir mountains. Supplementary Information: The online version contains supplementary material available at 10.1007/s10546-024-00864-y.

An inclusive multivariate approach to neural localization of language components.

To determine how language is implemented in the brain, it is important to know which brain areas are primarily engaged in language processing and which are not. Existing protocols for localizing language are typically univariate, treating each small unit of brain volume as independent. One prominent example that focuses on the overall language network in functional magnetic resonance imaging (fMRI) uses a contrast between neural responses to sentences and sets of pseudowords (pronounceable nonwords). This contrast reliably activates peri-sylvian language areas but is less sensitive to extra-sylvian areas that are also known to support aspects of language such as word meanings (semantics). In this study, we assess areas where a multivariate, pattern-based approach shows high reproducibility across multiple measurements and participants, identifying these areas as multivariate regions of interest (mROI). We then perform a representational similarity analysis (RSA) of an fMRI dataset where participants made familiarity judgments on written words. We also compare those results to univariate regions of interest (uROI) taken from previous sentences > pseudowords contrasts. RSA with word stimuli defined in terms of their semantic distance showed greater correspondence with neural patterns in mROI than uROI. This was confirmed in two independent datasets, one involving single-word recognition, and the other focused on the meaning of noun-noun phrases by contrasting meaningful phrases > pseudowords. In all cases, areas of spatial overlap between mROI and uROI showed the greatest neural association. This suggests that ROIs defined in terms of multivariate reproducibility can help localize components of language such as semantics. The multivariate approach can also be extended to focus on other aspects of language such as phonology, and can be used along with the univariate approach for inclusively mapping language cortex.