Multidimensional regularity processing in music: an examination using redundant signals effect.

Music is based on various regularities, ranging from the repetition of physical sounds to theoretically organized harmony and counterpoint. How are multidimensional regularities processed when we listen to music? The present study focuses on the redundant signals effect (RSE) as a novel approach to untangling the relationship between these regularities in music. The RSE refers to the occurrence of a shorter reaction time (RT) when two or three signals are presented simultaneously than when only one of these signals is presented, and provides evidence that these signals are processed concurrently. In two experiments, chords that deviated from tonal (harmonic) and acoustic (intensity and timbre) regularities were presented occasionally in the final position of short chord sequences. The participants were asked to detect all deviant chords while withholding their responses to non-deviant chords (i.e., the Go/NoGo task). RSEs were observed in all double- and triple-deviant combinations, reflecting processing of multidimensional regularities. Further analyses suggested evidence of coactivation by separate perceptual modules in the combination of tonal and acoustic deviants, but not in the combination of two acoustic deviants. These results imply that tonal and acoustic regularities are different enough to be processed as two discrete pieces of information. Examining the underlying process of RSE may elucidate the relationship between multidimensional regularity processing in music.

A Novel Detection Method for High-Order SNP Epistatic Interactions Based on Explicit-Encoding-Based Multitasking Harmony Search.

To elucidate the genetic basis of complex diseases, it is crucial to discover the single-nucleotide polymorphisms (SNPs) contributing to disease susceptibility. This is particularly challenging for high-order SNP epistatic interactions (HEIs), which exhibit small individual effects but potentially large joint effects. These interactions are difficult to detect due to the vast search space, encompassing billions of possible combinations, and the computational complexity of evaluating them. This study proposes a novel explicit-encoding-based multitasking harmony search algorithm (MTHS-EE-DHEI) specifically designed to address this challenge. The algorithm operates in three stages. First, a harmony search algorithm is employed, utilizing four lightweight evaluation functions, such as Bayesian network and entropy, to efficiently explore potential SNP combinations related to disease status. Second, a G-test statistical method is applied to filter out insignificant SNP combinations. Finally, two machine learning-based methods, multifactor dimensionality reduction (MDR) as well as random forest (RF), are employed to validate the classification performance of the remaining significant SNP combinations. This research aims to demonstrate the effectiveness of MTHS-EE-DHEI in identifying HEIs compared to existing methods, potentially providing valuable insights into the genetic architecture of complex diseases. The performance of MTHS-EE-DHEI was evaluated on twenty simulated disease datasets and three real-world datasets encompassing age-related macular degeneration (AMD), rheumatoid arthritis (RA), and breast cancer (BC). The results demonstrably indicate that MTHS-EE-DHEI outperforms four state-of-the-art algorithms in terms of both detection power and computational efficiency. The source code is available at https://github.com/shouhengtuo/MTHS-EE-DHEI.git .

Novel embedding model predicting the credit card's default using neural network optimized by harmony search algorithm and vortex search algorithm.

In today's banking and financial system, using a credit card has become indispensable. The credit card industry has existed due to a shift in consumer preferences and a rise in national economic growth. The number of issuing banks, card issuers, and transaction volumes has increased significantly. Nevertheless, owing to the growth in the number of transactions made with credit cards, both the total amount due and the rate of defaults on credit card loans have become issues that cannot be neglected. This issue must be resolved to ensure the continued and prosperous growth of the banking industry in the years to come. Currently, a few optimization algorithms-Whale optimization algorithm (WOA), Harmony Search (HS), Multi-verse optimization (MVO), and Vortex Search (VS)-have been used to achieve this purpose. However, because credit card default data is volatile and unequal, it is challenging for typical optimization algorithms to offer steady approaches with optimal performance. Studies have indicated that optimizing algorithms with suitable properties can significantly improve performance. To improve performance, some tuning was applied to the ANN. This study will assess twenty-three parameters, and the efficacy of all four approaches will be compared using ROC and AUC evaluations. The suggested model's performance is contrasted with a scenario where the classifiers were trained using original data. In contrast, the AUC values for VS-MLP were 0.7407 and 0.7271, while those for HS-MLP were 0.7074 and 0.6997. In the training and testing phases, AUC values of 0.7469 and 0.7329 from MVO-MLP and 0.72 and 0.7185 from WOA-MLP, respectively. The results show that the training accuracy of HS, VSA, MVO, and WOA are similar; MVO has the highest training accuracy. The credit card industry can benefit significantly from this methodology, which may help resolve default probabilities.

Exploring harmonic walking development in children with unilateral cerebral palsy and typically developing toddlers: Insights from walking experience.

This longitudinal study investigated the impact of the first independent steps on harmonic gait development in unilateral cerebral palsy (CP) and typically developing (TD) children. We analysed the gait ratio values (GR) by comparing the duration of stride/stance, stance/swing and swing/double support phases. Our investigation focused on identifying a potential trend towards the golden ratio value of 1.618, which has been observed in the locomotion of healthy adults as a characteristic of harmonic walking. Locomotor ability was assessed in both groups at different developmental stages: before and after the emergence of independent walking. Results revealed that an exponential fit was observed only after the first unsupported steps were taken. TD children achieved harmonic walking within a relatively short period (approximately one month) compared to children with CP, who took about seven months to develop harmonic walking. Converging values for stride/stance and stance/swing gait ratios, averaged on the two legs, closely approached the golden ratio in TD children (R2 = 0.9) with no difference in the analysis of the left vs right leg separately. In contrast, children with CP exhibited a trend for stride/stance and stance/swing (R2 = 0.7), with distinct trends observed for the most affected leg which did not reach the golden ratio value for the stride/stance ratio (GR = 1.5), while the least affected leg exceeded it (GR = 1.7). On the contrary, the opposite trend was observed for the stance/swing ratio. These findings indicate an overall harmonic walking in children with CP despite the presence of asymmetry between the two legs. These results underscore the crucial role of the first independent steps in the progressive development of harmonic gait over time.

Enhancing emotional health and engagement in Chinese English language learners: an approach from teachers' autonomy- supportive behavior, teachers' harmony, and peer support in a two-sample study.

Background: In the evolving landscape of Chinese education, understanding the factors that influence the emotional health and engagement of English language learners is increasingly vital. Against this backdrop, our study delves into how teachers' autonomy-supportive behavior, teachers' harmony, and peer support impact these key educational outcomes. Aim: This study investigates the roles of teachers' autonomy-supportive behavior, teachers' harmony, and peer support in influencing the emotional health and engagement of English language learners in China. Method: Involving a diverse sample of 68 English Language Teachers and their 389 students from various Chinese universities, the study leverages a convenience sampling method. Results: Key findings indicate that students' emotional health is predominantly influenced by peer support, while student engagement is significantly affected by a combination of teachers' autonomy-supportive behavior, peer support, and teachers' harmony. These outcomes highlight the importance of both teacher behavior and peer relationships in educational settings, underscoring their crucial roles in enhancing student well-being and engagement. The study's methodology, incorporating a diverse sample from multiple educational institutions and a comprehensive analytical approach, offers robust insights. However, the limitations of convenience sampling and reliance on self-reported data necessitate a careful interpretation of the findings. Implications: Implications from this research are vital for educational policy and practice, emphasizing the need for interventions that enhance teacher-student relationships and foster supportive peer environments. This study adds to the body of knowledge on factors influencing emotional health and engagement among English language learners, advocating for a comprehensive approach in educational strategies and interventions.

Early Outcomes From a Multicenter Transcatheter Self-Expanding Pulmonary Valve Replacement Registry.

BACKGROUND: Transcatheter pulmonary valve replacement (TPVR) with the self-expanding Harmony valve (Medtronic) is an emerging treatment for patients with native or surgically repaired right ventricular outflow tract (RVOT) pulmonary regurgitation (PR). Limited data are available since U.S. Food and Drug Administration approval in 2021. OBJECTIVES: In this study, the authors sought to evaluate the safety and short-term effectiveness of self-expanding TPVR in a real-world experience. METHODS: This was a multicenter registry study of consecutive patients with native RVOT PR who underwent TPVR through April 30, 2022, at 11 U.S. CENTERS: The primary outcome was a composite of hemodynamic dysfunction (PR greater than mild and RVOT mean gradient >30 mm Hg) and RVOT reintervention. RESULTS: A total of 243 patients underwent TPVR at a median age of 31 years (Q1-Q3: 19-45 years). Cardiac diagnoses were tetralogy of Fallot (71%), valvular pulmonary stenosis (21%), and other (8%). Acute technical success was achieved in all but 1 case. Procedural serious adverse events occurred in 4% of cases, with no device embolization or death. Hospital length of stay was 1 day in 86% of patients. Ventricular arrhythmia prompting treatment occurred in 19% of cases. At a median follow-up of 13 months (Q1-Q3: 8-19 months), 98% of patients had acceptable hemodynamic function. Estimated freedom from the composite clinical outcome was 99% at 1 year and 96% at 2 years. Freedom from TPVR-related endocarditis was 98% at 1 year. Five patients died from COVID-19 (n = 1), unknown causes (n = 2), and bloodstream infection (n = 2). CONCLUSIONS: In this large multicenter real-world experience, short-term clinical and hemodynamic outcomes of self-expanding TPVR therapy were excellent. Ongoing follow-up of this cohort will provide important insights into long-term outcomes.

Is spinopelvic compensation associated with unstable gait?: Analysis using whole spine X-rays and a two-point accelerometer during gait in healthy adults.

BACKGROUND: Pelvic incidence (PI)-lumbar lordosis (LL) mismatch has a significant destabilizing effect on the center of gravity sway in the static standing position. However, the association between spinopelvic alignment and balance during gait in healthy volunteers is poorly understood. RESEARCH QUESTION: The degree of PI-LL mismatch and trunk anterior tilt in the static standing posture influences dynamic balance during gait. METHODS: In this study, 131 healthy volunteers were divided into two groups: harmonious group (PI - LL ≤ 10°; n = 91) and unharmonious group (PI - LL > 10°; n = 40). A two-point accelerometer system was used for gait analysis; accelerometers were attached to the pelvis and upper trunk to measure acceleration in the forward-backward, right-left, and vertical directions so that sagittal (front-back) deviation width, coronal (right-left) width, and vertical width and their ratios were calculated. Measurements were compared between the two groups, and correlations between alignment and accelerometer data were examined. RESULTS: The harmonious group showed a negative correlation between pelvic sagittal width and PI - LL, pelvic tilt (PT), and sagittal vertical axis (SVA) (correlation coefficient ρ = -0.42, -0.38, and -0.4, respectively), and a positive correlation between sagittal ratio and PI - LL (ρ = 0.35). The unharmonious group showed a positive correlation between pelvic sagittal width and PI and PT (ρ = 0.43 and 0.33, respectively) and between sagittal ratio and SVA (ρ = 0.32). The unharmonious group showed a positive correlation between upper trunk sagittal width and PI - LL and PT (ρ = 0.38 and 0.36, respectively). SIGNIFICANCE: The association between spinal alignment and gait parameters differs depending on the presence or absence of PI-LL mismatch. The degree of pelvic compensation and trunk anterior tilt during static standing were associated with unstable gait balance.

Metaheuristic-Based Feature Selection Methods for Diagnosing Sarcopenia with Machine Learning Algorithms.

This study explores the efficacy of metaheuristic-based feature selection in improving machine learning performance for diagnosing sarcopenia. Extraction and utilization of features significantly impacting diagnosis efficacy emerge as a critical facet when applying machine learning for sarcopenia diagnosis. Using data from the 8th Korean Longitudinal Study on Aging (KLoSA), this study examines harmony search (HS) and the genetic algorithm (GA) for feature selection. Evaluation of the resulting feature set involves a decision tree, a random forest, a support vector machine, and naïve bayes algorithms. As a result, the HS-derived feature set trained with a support vector machine yielded an accuracy of 0.785 and a weighted F1 score of 0.782, which outperformed traditional methods. These findings underscore the competitive edge of metaheuristic-based selection, demonstrating its potential in advancing sarcopenia diagnosis. This study advocates for further exploration of metaheuristic-based feature selection's pivotal role in future sarcopenia research.

Science as a calling and as a profession: The wider setting in Weber's scholarly endeavor.

In his 1917 lecture for Munich students (most often entitled in English translation "Science as a Vocation"), Max Weber addressed numerous issues: not only how "profession" and "calling" are related in science and scholarship, but also Entzauberung ("disenchantment"); rationality and its limits; ultimate values; and the field of tension between science and religion. The present essay locates these themes in Weber's oeuvre from 1911 onward, and analyses how they resonate and culminate in Weber's address in 1917. It is in 1911 that he decided to engage with the problem that was to stand central in his thinking until his death in 1920: the nature and causes of certain specific turns in the course of European history which, so he argued, have proven to be of "universal significance." Special attention is given in the present essay to how Weber dealt in this connection with the rise of modern science and the rise of modern tonal harmony. A concluding section explains what, over a century later, makes reading Weber still so rewarding an experience.

Patient Screening for Self-Expanding Percutaneous Pulmonary Valves Using Virtual Reality.

BACKGROUND: In recent years, self-expanding technology to treat pulmonary regurgitation in the native right ventricular outflow tract became Food and Drug Administration approved in the United States and is now routinely used. The current practice for selection of patients who are candidates for these devices includes screening for "anatomic fit," performed by each of the manufacturing companies. Our study aims to validate the use of virtual reality (VR) as a tool for local physician-led screening of patients. METHODS AND RESULTS: This retrospective study from Children's Hospital Colorado included patients who underwent pulmonary valve replacement and had screening for a Harmony TPV or Alterra Prestent performed between September 2020 and January 2022. The data from the commercial companies' dedicated analysis for self-expanding transcatheter pulmonary valve frames evaluation with perimeter analysis were collected. VR simulation was performed blinded by 2 congenital interventional cardiologists using Elucis VR software and an Oculus Quest 2 headset. Among the 27 evaluated cases, the use of a self-expandable valve was recommended by companies' dedicated analysis in 23 cases (85.2%), by VR assessment in 26 cases (96.3), and finally implanted in 25 cases (92.6%). Regarding the level of agreement, both modalities (manufacturer and VR) were good at screening-in patients who received a self-expanding valve (100% versus 96.1%). When it came to screening-out the patients, VR presented good capacity to accurately classify nonsuitable patients (50% versus 100%). CONCLUSIONS: Our institutional experience with VR transcatheter pulmonary valve implantation planning accurately predicted clinical outcomes. This paves the way for routine use of VR in patient selection for self-expanding valve technologies.

The "Starting-Small" Effect in Phonology: Evidence From Biased Learning of Opaque and Transparent Vowel Harmony.

The starting-small effect is a cognitive advantage in language acquisition when learners begin by generalizing on regularities from structurally simple and shorter tokens in a skewed input distribution. Our study explored this effect as a potential explanation for the biased learning of opaque and transparent vowel harmony. In opaque vowel harmony, feature agreement occurs strictly between adjacent vowels, and an intervening "neutral vowel" blocks long-distance vowel harmony. Thus, opaque vowel harmony could be acquired even if learners start with structurally simpler and more frequent disyllabic tokens. Alternatively, transparent vowel harmony can only be observed in longer tokens demonstrating long-distance agreement by skipping a neutral vowel. Opaque vowel harmony is predicted to be learned more efficiently due to its compatibility with local dependency acquired via starting-small learning. In two artificial grammar learning experiments, learners were exposed to both vowel harmony patterns embedded in an equal number of disyllabic and trisyllabic tokens or a skewed distribution with twice as many disyllabic tokens. In Exp I, learners' test performance suggests the consistently biased learning of local and opaque vowel harmony with starting-small learning. Furthermore, in Exp II, the acquired vowel harmony patterns varied significantly by working memory capacity with a balanced but not skewed input distribution, presumably because of the ease of cognitive demand with starting-small learning.

Self-attention-based generative adversarial network optimized with color harmony algorithm for brain tumor classification.

This paper proposes a novel approach, BTC-SAGAN-CHA-MRI, for the classification of brain tumors using a SAGAN optimized with a Color Harmony Algorithm. Brain cancer, with its high fatality rate worldwide, especially in the case of brain tumors, necessitates more accurate and efficient classification methods. While existing deep learning approaches for brain tumor classification have been suggested, they often lack precision and require substantial computational time.The proposed method begins by gathering input brain MR images from the BRATS dataset, followed by a pre-processing step using a Mean Curvature Flow-based approach to eliminate noise. The pre-processed images then undergo the Improved Non-Sub sampled Shearlet Transform (INSST) for extracting radiomic features. These features are fed into the SAGAN, which is optimized with a Color Harmony Algorithm to categorize the brain images into different tumor types, including Gliomas, Meningioma, and Pituitary tumors. This innovative approach shows promise in enhancing the precision and efficiency of brain tumor classification, holding potential for improved diagnostic outcomes in the field of medical imaging. The accuracy acquired for the brain tumor identification from the proposed method is 99.29%. The proposed BTC-SAGAN-CHA-MRI technique achieves 18.29%, 14.09% and 7.34% higher accuracy and 67.92%,54.04%, and 59.08% less Computation Time when analyzed to the existing models, like Brain tumor diagnosis utilizing deep learning convolutional neural network with transfer learning approach (BTC-KNN-SVM-MRI); M3BTCNet: multi model brain tumor categorization under metaheuristic deep neural network features optimization (BTC-CNN-DEMFOA-MRI), and efficient method depending upon hierarchical deep learning neural network classifier for brain tumour categorization (BTC-Hie DNN-MRI) respectively.

This paper proposes a novel approach, BTC-SAGAN-CHA-MRI, for the classification of brain tumors using a Self-Attention based Generative Adversarial Network (SAGAN) optimized with a Color Harmony Algorithm. Brain cancer, with its high fatality rate worldwide, especially in the case of brain tumors, necessitates more accurate and efficient classification methods. While existing deep learning approaches for brain tumor classification have been suggested, they often lack precision and require substantial computational time. The proposed method begins by gathering input brain MR images from the BRATS dataset, followed by a pre-processing step using a Mean Curvature Flow-based approach to eliminate noise. The pre-processed images then undergo the Improved Non-Sub sampled Shearlet Transform (INSST) for extracting radiomic features. These features are fed into the SAGAN, which is optimized with a Color Harmony Algorithm to categorize the brain images into different tumor types, including Gliomas, Meningioma, and Pituitary tumors. This innovative approach shows promise in enhancing the precision and efficiency of brain tumor classification, holding potential for improved diagnostic outcomes in the field of medical imaging.

Early Cannon Development in Females of the "Sanmartinero" Creole Bovine Breed.

The variation in the dimensions of the body of living beings in relation to their size, function, or shape is known as allometry. Allometry studies allow the analysis of the function of body structures and their relationship with the performance and survival of breeds and species. The aim of this study was to describe the ontogenetic characteristics of the weight of certain bone elements of the Sanmartinero creole bovine breed, from an early age (6 months) until maturity. A total of 79 females of the Sanmartinero creole bovine breed with an age range of 0.5 to 10 years were studied. Body measurements were obtained individually using standard procedures: cannon perimeter, thoracic perimeter, body length, withers height, and body weight. Functional and production indices were obtained for animals aged more than 3 years (n = 53): corporal index, anamorphosis index, dactylo thoracic index, and the relative thickness of the cannon. The correlation between body weight and the cannon perimeter showed no trend towards a plateau. In the Sanmartinero creole bovine, therefore, a very early development of the cannon perimeter is detected, which could lead to bone fragility in heavier animals. Therefore, data suggest little harmony with appendicular bone development, i.e., with less robust cannons which are those necessary to support body mass. It is suggested that in genetic improvement programs of the Sanmartinero creole bovine, the measurement of the cannon perimeter, the withers height, and the thoracic perimeter should be considered.

The ABC of happiness: Validation of the tridimensional model of subjective well-being (affect, cognition, and behavior) using Bifactor Polytomous Multidimensional Item Response Theory.

Background: Happiness is often conceptualized as subjective well-being, which comprises people's evaluations of emotional experiences (i.e., the affective dimension: positive and negative feelings and emotions) and judgements of a self-imposed ideal (i.e., the cognitive dimension: life satisfaction). Recent research has established these two dimensions as primary parts of a higher order factor. However, theoretical, conceptual, and empirical work suggest that people's evaluations of harmony in their life (i.e., the sense of balance and capacity to behave and adapt with both acceptance and flexibility to inter- and intrapersonal circumstances) constitutes a third dimension (i.e., the behavioral dimension). This tridemensional conceptualization of subjective well-being has recently been verified using Unidimensional Item Response Theory (UIRT) and Classical Test Theory (CTT). Here, we use a recently developed and more robust approach that combines these two methods (i.e., Multidimensional Item Response Theory, MIRT) to simultaneously address the complex interactions and multidimensionality behind how people feel, think, and behave in relation to happiness in their life. Method: A total of 435 participants (197 males and 238 females) with an age mean of 44.84 (sd = 13.36) responded to the Positive Affect Negative Affect Schedule (10 positive affect items, 10 negative affect items), the Satisfaction with Life Scale (five items), and the Harmony in life Scale (five items). We used Bifactor-Graded Response MIRT for the main analyses. Result: At the general level, each of the 30 items had a strong capacity to discriminate between respondents across all three dimensions of subjective well-being. The investigation of different parameters (e.g., marginal slopes, ECV, IECV) strongly reflected the multidimensionality of subjective well-being at the item, the scale, and the model level. Indeed, subjective well-being could explain 64 % of the common variance in the whole model. Moreover, most of the items measuring positive affect (8/10) and life satisfaction (4/5) and all the items measuring harmony in life (5/5) accounted for a larger amount of variance of subjective well-being compared to that of their respective individual dimensions. The negative affect items, however, measured its own individual concept to a lager extent rather than subjective well-being. Thus, suggesting that the experience of negative affect is a more independent dimension within the whole subjective well-being model. We also found that specific items (e.g., "Alert", "Distressed", "Irritable", "I am satisfied with my life") were the recurrent exceptions in our results. Last but not the least, experiencing high levels in one dimension seems to compensate for low levels in the others and vice versa. Conclusion: As expected, the three subjective well-being dimensions do not work separately. Interestingly, the order and magnitude of the effect by each dimension on subjective well-being mirror how people define happiness in their life: first as harmony, second as satisfaction, third as positive emotions, and fourth, albeit to a much lesser degree, as negative emotions. Ergo, we argue that subjective well-being functions as a complex biopsychosocial adaptive system mirroring our attitude towards life in these three dimensions (A: affective dimension; B: behavioral dimension; C: cognitive dimension). Ergo, researchers and practitioners need to take in to account all three to fully understand, measure, and promote people's experience of the happy life. Moreover, our results also suggest that negative affect, especially regarding high activation unpleasant emotions, need considerable changes and further analyses if it is going to be included as a construct within the affective dimension of a general subjective well-being factor.

Characterizing the water resource-environment-ecology system harmony in Chinese cities using integrated datasets: A Beautiful China perspective assessment.

Integrated management and synergistic improvement of the water system is a topic of widespread concern. This study innovatively integrates three functions of quality assessment, synergy evaluation, and driving influence determination to establish a systematic framework assessing water system harmony. A case study of 336 Chinese cities is further performed by combining multi-scale and multi-source datasets. The results show China's water system quality has improved from 2015 to 2022. Development in the water resource, environment, and ecology subsystems have been differentiated, with 0.05 %, 4.33 %, and -1.64 % changes, respectively. The degradation of water ecology and the weak synergy with the other two subsystems have limited China's water system harmony. Water environment improvement played a contributive role in improving the water system quality. The contribution structure of water resources, environment, and ecology has shifted towards equilibrium in recent years. We found and highlighted the north-south differentiation of water system harmony in Chinese cities. The Beijing-Tianjin-Hebei and its surroundings, the Yangtze River Delta, and the middle reaches of the Yangtze River are identified as priority regions for water system harmony improvement. The primary contribution of this study is to propose an assessing concept of water resource-environment-ecology system harmony, establish well-structured assessment methods, and integrate the multiple data sources. The novel methods and findings, including the indicator system, application of data mining and decomposing methods, and the city-level water system harmony map, deconstruct and quantify the complex and diverse water system, supporting clearer and more efficient water management policymaking.

The development and initial validation of the Earthquake Fear Scale: Its links to personality traits, psychological distress, harmony in life, and mental wellbeing.

Unexpected and unpredictable earthquakes may have a negative impact on people's mental health. The earthquake fear experienced by individuals causes psychological problems. This article is made up of three different studies that look at earthquake fear. The seven item Earthquake Fear Scale (EFS) was confirmed in Study I (N = 407) utilising confirmatory factor analysis. In addition, Item Response Analysis was done, and it was found that all of the items showed a good degree of discrimination. The reliability of the EFS has been proven with different reliability coefficients. In Study II (N = 505), both correlation and network analysis were carried out, and earthquake fear was found to be significantly associated with depression, anxiety, stress, and mental wellbeing. Moreover, neuroticism and conscientiousness, two of the big five personality traits, are significantly associated with earthquake fear. In Study III (N = 382), it was revealed that psychological distress and harmony in life play a serial and fully mediating role in the link between earthquake fear and mental wellbeing. The psychological influence of earthquake fear is emphasised in this pioneering and comprehensive study on earthquake fear.

Dopamine dysregulation in Parkinson's disease flattens the pleasurable urge to move to musical rhythms.

The pleasurable urge to move to music (PLUMM) activates motor and reward areas of the brain and is thought to be driven by predictive processes. Dopamine in motor and limbic networks is implicated in beat-based timing and music-induced pleasure, suggesting a central role of basal ganglia (BG) dopaminergic systems in PLUMM. This study tested this hypothesis by comparing PLUMM in participants with Parkinson's disease (PD), age-matched controls, and young controls. Participants listened to musical sequences with varying rhythmic and harmonic complexity (low, medium and high), and rated their experienced pleasure and urge to move to the rhythm. In line with previous results, healthy younger participants showed an inverted U-shaped relationship between rhythmic complexity and ratings, with preference for medium complexity rhythms, while age-matched controls showed a similar, but weaker, inverted U-shaped response. Conversely, PD showed a significantly flattened response for both the urge to move and pleasure. Crucially, this flattened response could not be attributed to differences in rhythm discrimination and did not reflect an overall decrease in ratings. For harmonic complexity, PD showed a negative linear pattern for both the urge to move and pleasure while healthy age-matched controls showed the same pattern for pleasure and an inverted U for the urge to move. This contrasts with the pattern observed in young healthy controls in previous studies, suggesting that both healthy aging and PD also influence affective responses to harmonic complexity. Together, these results support the role of dopamine within cortico-striatal circuits in the predictive processes that form the link between the perceptual processing of rhythmic patterns and the affective and motor responses to rhythmic music.

Evaluating batch correction methods for image-based cell profiling.

High-throughput image-based profiling platforms are powerful technologies capable of collecting data from billions of cells exposed to thousands of perturbations in a time- and cost-effective manner. Therefore, image-based profiling data has been increasingly used for diverse biological applications, such as predicting drug mechanism of action or gene function. However, batch effects pose severe limitations to community-wide efforts to integrate and interpret image-based profiling data collected across different laboratories and equipment. To address this problem, we benchmarked seven high-performing scRNA-seq batch correction techniques, representing diverse approaches, using a newly released Cell Painting dataset, the largest publicly accessible image-based dataset. We focused on five different scenarios with varying complexity, and we found that Harmony, a mixture-model based method, consistently outperformed the other tested methods. Our proposed framework, benchmark, and metrics can additionally be used to assess new batch correction methods in the future. Overall, this work paves the way for improvements that allow the community to make best use of public Cell Painting data for scientific discovery.

Cognitive and sensory expectations independently shape musical expectancy and pleasure.

Expectation is crucial for our enjoyment of music, yet the underlying generative mechanisms remain unclear. While sensory models derive predictions based on local acoustic information in the auditory signal, cognitive models assume abstract knowledge of music structure acquired over the long term. To evaluate these two contrasting mechanisms, we compared simulations from four computational models of musical expectancy against subjective expectancy and pleasantness ratings of over 1000 chords sampled from 739 US Billboard pop songs. Bayesian model comparison revealed that listeners' expectancy and pleasantness ratings were predicted by the independent, non-overlapping, contributions of cognitive and sensory expectations. Furthermore, cognitive expectations explained over twice the variance in listeners' perceived surprise compared to sensory expectations, suggesting a larger relative importance of long-term representations of music structure over short-term sensory-acoustic information in musical expectancy. Our results thus emphasize the distinct, albeit complementary, roles of cognitive and sensory expectations in shaping musical pleasure, and suggest that this expectancy-driven mechanism depends on musical information represented at different levels of abstraction along the neural hierarchy. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.