Search Results (383)

Objective: This study aimed to investigate the relationship between bronchoalveolar lavage (BAL) cellular profiles, microbiological status, and clinical outcomes such as hospital admission in adult patients with non-cystic fibrosis bronchiectasis. Methods: A retrospective cross-sectional study was conducted on thirty adult bronchiectasis patients. Demographic, clinical, and laboratory data were collected. The cellular components of BAL fluid (macrophages, neutrophils, lymphocytes, and eosinophils) were analyzed. Patients were grouped according to the presence of microbial culture growth and history of hospitalization in the past year. Statistical analyses were performed to determine significant relationships. Results: The median age was 57 years, and the gender distribution was equal. There was no significant difference in BAL cellular profiles between groups with and without culture growth. However, in the group with a hospital admission in the past year, BAL showed a significantly lower percentage of alveolar macrophages (20% vs. 47%, p = 0.011) and a higher percentage of eosinophils (5% vs. 1%, p = 0.036). The hospitalized group also showed a trend toward a higher neutrophil percentage and a lower lymphocyte/neutrophil ratio. Furthermore, surprising associations were noted, such as a higher BAL macrophage count in married individuals and higher BAL eosinophilia in patients with diabetes. Conclusions: BAL cellular analysis provides valuable information beyond routine microbiological investigations in bronchiectasis. The low-alveolar-macrophage and high-eosinophil profile was found to be significantly associated with hospitalization, and this profile has the potential to serve as a prognostic biomarker in defining the “high-risk” phenotype. These findings highlight the complexity of the local inflammatory response and reveal the potential role of BAL in developing personalized treatment strategies for patients with bronchiectasis. Full article

Setting is a fundamental movement in volleyball. While there are several optimal interpreters of the role in professional play, there is a surprising lack of advanced measurement techniques for the evaluation of the movement from a biomechanical perspective. We proposed a comprehensive motion analysis protocol based on kinematics and motor coordination assessment (muscle synergies) for an in-depth analysis of the setting gesture. We also quantified the test–retest performance and discussed in detail the potential of the method. A single experienced player (age 27) tested and retested the protocol. The protocol was quite rapid to perform (about 30 min, including placement of kinematic and electromyography sensors on the patient’s body); we found high test and re-test consistency in different sessions within this participant (ICC > 0.90). These preliminary results suggest that the protocol could support the use of the state-of-the-art methods for motion analysis and biomechanics in volleyball and sports in general. Full article

Emotional Footprint Identification refers to the process of recognizing or understanding the emotional impact that a person, experience, or interaction leaves on others. Emotion Recognition plays an important role in human–computer interaction for identifying emotions such as fear, sadness, anger, happiness, and surprise on the human face during the conversation. However, accurate emotional footprint identification plays a crucial role due to the dynamic changes. Conventional deep learning techniques integrate advanced technologies for emotional footprint identification, but challenges in accurately detecting emotions in minimal time. To address these challenges, a novel Divergence Shepherd Feature Optimization-based Stochastic-Tuned Deep Multilayer Perceptron (DSFO-STDMP) is proposed. The proposed DSFO-STDMP model consists of three distinct processes namely data acquisition, feature selection or reduction, and classification. First, the data acquisition phase collects a number of conversation data samples from a dataset to train the model. These conversation samples are given to the Sokal–Sneath Divergence shuffling shepherd optimization to select more important features and remove the others. This optimization process accurately performs the feature reduction process to minimize the emotional footprint identification time. Once the features are selected, classification is carried out using the Rosenthal correlative stochastic-tuned deep multilayer perceptron classifier, which analyzes the correlation score between data samples. Based on this analysis, the system successfully classifies different emotions footprints during the conversations. In the fine-tuning phase, the stochastic gradient method is applied to adjust the weights between layers of deep learning architecture for minimizing errors and improving the model’s accuracy. Experimental evaluations are conducted using various performance metrics, including accuracy, precision, recall, F1 score, and emotional footprint identification time. The quantitative results reveal enhancement in the 95% accuracy, 93% precision, 97% recall and 97% F1 score. Additionally, the DSFO-STDMP minimized the in training time by 35% when compared to traditional techniques. Full article

With the rapid development of artificial intelligence technology, facial expression recognition (FER) has gained increasingly widespread applications in digital human generation, humanoid robotics, mental health, and human–computer dialogue. Typical FER algorithms based on machine learning have been widely studied over the past few decades, which motivated our survey. In this study, we have surveyed the state of the art in FER across two categories: traditional machine learning-based (ML-based) and deep learning-based (DL-based) approaches. Each category is analyzed based on six subcategories. Then, twelve methods, including four ML-based models and eight DL-based models, are compared to evaluate FER performance across four datasets. The experimental results show that in validation sets, the average accuracy of HOG-SVM is 50.12%, which is the best performance for the four ML-based methods; in contrast, Poster has an average accuracy of 75.98%, which is the best result obtained among the eight DL-based methods. The most difficult expression to recognize is contempt, with recognition accuracies of 10.00% and 40.06% for ML-based and DL-based methods, respectively. The accuracy of the ML-based method for identifying neutral expression is the highest at 35.25%; the DL-based method has the highest accuracy in identifying surprise at 69.56%. From the theoretical analysis and comparative experimental results of existing methods, we can see that FER faces challenges, including inaccurate recognition in complex environments and unbalanced data categories, highlighting several future research directions, especially those involving the latest applications of digital humans and large language models. Full article

Recent studies have compiled all records of new fish species discovered between 2017 and 2025 in two regions of Spanish Mediterranean waters: the Strait of Gibraltar–Alboran Sea (ESAL) and the Levantine–Balearic Sea (LEBA). These studies compared the mean temperatures and thermal ranges (minimum and maximum temperatures) at which these newly recorded species lived in their original distribution zones with the preferred temperature ranges of native species present in Spanish Mediterranean waters prior to 2017. In order to characterize the thermal preferences of Mediterranean native species, previous works used the temperature of waters corresponding to the complete geographical distribution of such species. This comparison identified a tropicalization process in the ESAL region, but not in the LEBA. To further explore and refine these findings, the present work provides a new estimation of the thermal ranges of Mediterranean species occurring in these two regions. This estimation is based on existing temperature climatologies from both international and local databases. However, in the present work, we only use the temperature of waters within the ESAL and LEBA regions, instead of using those of the complete geographical distribution of Mediterranean native fish species. This new comparison offers a more accurate characterization of the thermal conditions under which Mediterranean species live. This updated comparison reveals a surprising result: the Mediterranean species recorded in the LEBA up to 2017 exhibit higher thermal preferences than those reported after this year, a difference that exceeds what had been observed in previous analyses. In contrast, in the ESAL region, the newly recorded species still display higher thermal affinities than native ones, although the differences are smaller than those found in earlier studies. A more detailed analysis, considering both the depth ranges and geographical origins of the new species, reveals a complex scenario. While some species are indeed of tropical origin, contributing to the ongoing tropicalization of the Western Mediterranean, others originate from more northern latitudes or even Mediterranean regions. This suggests that the arrival of invasive species in this part of the Mediterranean is not solely, (or even primarily) driven by the intense warming of the basin, but also by other anthropogenic factors. Full article

This study explores how surprise shocks in Indonesia’s macroeconomic environment—specifically interest rates, inflation, and exchange rates—affect the returns and volatility of key financial assets, including gold, Bitcoin (BTC), stocks (JKSE), and government bonds. Utilizing the EGARCH(1,1) model, this research demonstrates that gold exhibits enduring resilience as a safe-haven during periods of rising inflation and interest rate fluctuations. In contrast, Bitcoin is marked by pronounced speculative dynamics, showing persistent, asymmetric, and extreme volatility, yet delivering attractive gains when market conditions are strong. The findings indicate that stocks and bonds are particularly susceptible to changes in macroeconomic variables, thereby illustrating the vulnerabilities typical of emerging markets. Through portfolio optimization employing the Mean-Variance approach, gold dominates the optimal asset allocation, while Bitcoin provides notable diversification benefits. The results of backtesting using the Kupiec and Basel Traffic Light procedures confirm that GARCH-family risk estimations are robust and meet international regulatory standards. Furthermore, analysis of the Sharpe ratio and cumulative returns reveals that Mean-Variance portfolios consistently outperform equally weighted alternatives by delivering higher risk-adjusted returns and lower overall volatility. By integrating advanced econometric methods with real-world macroeconomic shocks in an Indonesian context, this research offers practical insights for both investors and policymakers addressing asset allocation under uncertainty, while laying the groundwork for future work involving broader asset universes and sophisticated modeling techniques. Full article

This study uses natural language processing (NLP) techniques to analyze the political discourse of the surprise presidential candidate, focusing on linguistic patterns, sentiment distribution, and recurring themes. This study addresses the problem of how TikTok virality and algorithmic amplification mechanisms can influence electoral outcomes in Romania, analyzing whether heuristic boosting strategies can distort traditional political paradigms. The text corpus included over 3915 words extracted from the candidate’s speeches, with the most frequent terms being “sovereignty” (271 occurrences), “democracy” (164 occurrences), and “freedom” (80 occurrences). The analysis revealed that 57.8% of the content was neutral, 10% conveyed positive sentiment, and negative sentiment was absent. A word frequency analysis highlighted the candidate’s strategic emphasis on concepts related to national identity and participatory democracy. Sentiment analysis revealed an intentional use of neutral language to maintain balance, with occasional positive terms maintaining confidence and optimism among voters. Full article

Sensory evaluation, based on cognitive and hedonic dimensions, is crucial in developing innovative foods, such as juices made from less popular but highly nutritious fruits. It enables the creation of products with sensory images tailored to consumers’ needs. The research aimed to determine the optimal sensory image of kiwiberry juice blended with ‘Jonagold’ apple juice (10–50%) which would lead to a positive consumer perception. For this purpose, similarities and differences in the sensory profile of kiwiberry juice blended in various proportions with apple juice were evaluated. In hedonic tests, the degree of liking of the products and the analysis of changes in the type and level of consumer emotional reactions (FaceReader) to the juices were performed. To gain a deeper understanding of consumer perceptions, the assessment included expected liking, experienced liking, and purchase intention. It was found that different proportions of apple juice added to kiwiberry juice evoked positive changes in sensory characteristics in the quantitative and qualitative dimensions. The highest sensory quality was noted in the kiwiberry juice with 50% apple juice. Different levels of apple juice addition also conditioned positive changes in affective responses of consumers. Both experienced liking and purchase intention increased with higher levels of apple juice addition (30–50%). The results of emotional reactions reveal that the ranges of variability among examined emotions varied. The most prominent reaction was neutrality, followed by anger, happiness, disgust, and surprise. An affinity of the degree of liking and emotions to specific juices was noted. The liking attributes were related positively to the sensory image of products with higher content of apple juice and inversely related to the properties of kiwiberry juice which caused negative emotions. Full article

This study examines whether CEO risk tolerance influences a firm’s sustainable practices, as measured by Environmental, Social, and Governance (ESG) scores. The analysis uses facial width–height ratio (fWHR) as a proxy for CEO testosterone and risk-taking behavior. A regression analysis of S&P 500 firms from 2018 to 2022 shows that a greater fWHR is negatively associated with ESG scores, although the economic effect is small. A one standard deviation increase in fWHR decreases ESG by half a point on a 100-point scale. Further investigation into CEO turnover reveals a surprising asymmetry: when a new CEO has a higher fWHR, ESG scores increase significantly compared to firms without a CEO change. This finding, along with other confounding effects, suggests that a certain amount of calculated, strategic risk-taking may be necessary to successfully promote corporate sustainability programs. Full article

Background/Objectives: Facial palsy causes both functional and aesthetic impairments, with asymmetry significantly affecting quality of life. Botulinum toxin injections are increasingly used to restore facial balance by reducing contralateral hyperactivity, but outcome assessments remain largely subjective. The objective of this study was to evaluate the ability of three-dimensional (3D) stereophotogrammetry coupled with root mean square (RMS) surface analysis to objectively quantify improvements in facial symmetry following botulinum toxin treatment and to support clinicians in patient care. Methods: Sixteen adults with long-standing unilateral peripheral facial palsy underwent individualized botulinum toxin injections. Three-dimensional images were acquired using the Vectra H2 system before and 2–3 weeks after injection at peak efficacy. Five facial expressions (neutral, surprise, frown, Mona Lisa smile, and forced smile) were analyzed. RMS values were calculated for the whole face and facial thirds. Clinical assessment included House–Brackmann and Sunnybrook scores. Results: Whole-face RMS values decreased significantly after injection (1.51 ± 0.42 vs. 1.35 ± 0.43, p < 0.001). Improvements were observed across all thirds, most notably in the middle third. During expression, symmetry improved for all movements, with the strongest effects for surprise (d = 1.270), Mona Lisa smile (d = 0.870), and frown (d = 0.832). Conclusions: Three-dimensional stereophotogrammetry with RMS analysis provides an objective and reproducible method to quantify changes in facial symmetry after botulinum toxin treatment. This technique may complement clinical scoring systems and support personalized treatment planning in facial palsy patients. Full article

Background/Objectives: Infant emotional responses during complementary feeding offer key insights into early developmental processes and feeding behaviors. AI-driven facial emotion analysis presents a novel, objective method to quantify these subtle expressions, potentially informing interventions in early childhood nutrition. We aimed to investigate how maternal and infant traits influence infants’ emotional responses during complementary feeding using an automated facial analysis tool. Methods: This multi-center study involved 117 typically developing infants (6–11 months) and their mothers. Standardized feeding sessions were recorded, and OpenFace software quantified six emotions (surprise, sadness, fear, happiness, anger, disgust). Data were normalized and analyzed via Generalized Estimating Equations to identify associations with maternal BMI, education, work status, and infant age, sex, and complementary feeding initiation. Results: Emotional responses did not differ significantly across five food groups. Infants of mothers with BMI > 30 kg/m² showed greater surprise, while those whose mothers were well-educated and not working displayed more happiness. Older infants and those introduced to complementary feeding before six months exhibited higher levels of anger. Parental or infant food selectivity did not significantly affect responses. Conclusions: The findings indicate that maternal and infant demographic factors exert a more pronounced influence on infant emotional responses during complementary feeding than the type of food provided. These results highlight the importance of integrating broader psychosocial variables into early feeding practices and underscore the potential utility of AI-driven facial emotion analysis in advancing research on infant development. Full article

The digital economy is significantly transforming the global economic environment and has emerged as the primary driver behind China’s high-quality development. The comprehensive melding of the cultural and tourism sectors (CTI) serves as a strategic approach to boost regional competitiveness and enhance public welfare. This study investigates the mechanisms and boundary conditions through which the growth of the digital economy across China’s 31 provinces from 2011 to 2023 impacts CTI, aiming to address existing research gaps related to micro-level transmission mechanisms and the analysis of contextual variables. Utilizing a two-way fixed-effects moderated mediation model complemented by instrumental variable (IV-2SLS) regression for testing endogeneity, the research uncovers intricate interactions among the digital economy, CTI, and significant influencing factors. The results strongly suggest that advancements in the digital economy substantially facilitate the integration of cultural and tourism sectors. This beneficial effect is partially mediated through two primary channels: the construction of new infrastructure and enhancements in employment quality, underscoring the critical role of both material and human capital in digital empowerment. Significantly, this research uniquely identifies that new quality productive forces (NQP) have a notable negative moderating impact on the link between the digital economy and cultural–tourism integration. This indicates that in provinces exhibiting high levels of NQP, the positive influence of the digital economy on cultural–tourism integration is considerably diminished. This unexpected finding can be interpreted through mechanisms such as resource dilution, varied integration pathways or maturity effects, along with differences in developmental stages and priorities. Furthermore, it resonates well with the resource-based view, innovation ecosystem theory, and dynamic capability theory. Instrumental variable regression further substantiates the notable positive influence of the digital economy on the integration of cultural tourism. This approach effectively tackles potential endogeneity concerns and reveals the upward bias that may exist in fixed-effects models. The findings contribute significantly to theoretical frameworks by enhancing the understanding of the intricate mechanisms facilitating the digital economy and, for the first time, innovatively designating NQP as a surprising key boundary condition. This enriches theories related to industrial advancement and resource allocation in the digital age. On a practical note, the research provides nuanced and differentiated policy guidance aimed at optimizing pathways for integration across various Chinese provinces at different stages of development. Additionally, it underscores significant implications for other developing nations engaged in digital tourism growth, thereby improving its global relevance. Full article

Calvarial suture skeletal stem cells (Su-SSCs) are a distinct stem cell population for craniofacial bone formation by intramembranous ossification, compared to long bone periosteal SSCs (LB-PSSCs) with endochondral (osteochondrogenic) ossification. However, whether SSC intrinsic or extrinsic factors affect their differentiation process has not been well elucidated. Here, using an inducible Prx1-CreER-EGFP^+/−;Rosa26-tdTomato mouse model, we observed that endogenous Prx1⁺ Su-SSCs and their orthotopic transplantation into calvarial injury do not form cartilage intermediates at the injury sites, while the transplantation of Prx1⁺ LB-PSSCs into LB injury induces osteochondrogenic differentiation, respectively. However, the heterotopic transplantation of Prx1⁺ Su-SSCs (Su-SSCs into LB injury) showed some surprising findings that the transplanted Su-SSCs acquire new chondrocyte differentiation properties at the LB injury sites, although the heterotopic-transplanted Prx1⁺ LB-PSSCs maintained their endochondral ossification properties at the calvarial injury sites. Further, a comparative single-cell transcriptomic analysis of LB-PSSCs and Su-SSCs revealed that Su-SSCs express a higher set of anti-chondrogenic genes, such as Wnt5b, Twist1 while LB-PSSCs highly express chondrogenic Hoxa-9, Hoxc-9, Hoxa-10, Hoxc-10, and Comp genes. We also found that the heterotopic transplantation of LB-PSSCs into calvarial injury enhances bone healing in vivo. Taken together, these findings suggest that LB-PSSCs have high regenerative capability with invariable endochondral ossification even after the heterotopic transplantation but Su-SSCs are more flexible and regulated by the local bone environment. The transplantation of periosteal SSCs will be a promising method for large craniofacial bone defects. Full article

We have evaluated the quality of all 325 deposits in the PDB (as of December 2024) that correspond to (or contain) the catalytic domain of cAMP-dependent protein kinases (PKA). Detailed analysis was possible for 289 deposits of crystal structures that included not only the atomic coordinates but also structure factors. These structures represent 35 years of studies, and it is not surprising that the more recent structures are generally of better quality than the older ones. We did not encounter deposits with very severe problems, although some minor problems were found. To assess whether a uniform method of structure re-refinement, as implemented in the pipeline and website PDB-REDO, leads to significant improvement of structural models, we compared structure quality indicators for the originally refined structures and their counterparts resulting from PDB-REDO refinement. The re-refinement procedure significantly improved only some older structures, while its success was generally limited. We paid particular attention to the quality of small-molecule ligands, finding that most of them fit the electron density very well. This type of analysis helps identify the highest quality structures among many deposits for certain protein families and, thus, could be extended to other groups of proteins as well. Full article

Background: Exploration of the physical environment is an indispensable precursor to information acquisition and knowledge consolidation for living organisms. Yet, current artificial intelligence models lack these autonomy capabilities during training, hindering their adaptability. This work proposes a learning framework for artificial agents to obtain an intrinsic exploratory drive, based on epistemic and achievement emotions triggered during data observation. Methods: This study proposes a dual-module reinforcement framework, where data analysis scores dictate pride or surprise, in accordance with psychological studies on humans. A correlation between these states and exploration is then optimized for agents to meet their learning goals. Results: Causal relationships between states and exploration are demonstrated by the majority of agents. A $15.4\%$ mean increase is noted for surprise, with a $2.8\%$ mean decrease for pride. Resulting correlations of ${\rho }_{surprise}=0.461$ and ${\rho }_{pride}=-0.237$ are obtained, mirroring previously reported human behavior. Conclusions: These findings lead to the conclusion that bio-inspiration for AI development can be of great use. This can incur benefits typically found in living beings, such as autonomy. Further, it empirically shows how AI methodologies can corroborate human behavioral findings, showcasing major interdisciplinary importance. Ramifications are discussed. Full article