Search Results (1,905)

This study explores the motivations, choices, and constraints shaping tourism behavior among transgender individuals living in Italy. Employing a mixed-methods approach, the research begins with quantitative data collection and analyses, followed by qualitative insights to uncover the multifaceted reasons that drive transgender individuals to engage in tourism. These motivations range from seeking personal authenticity to cultural exploration and community connection. This study also examines the intricate interplay of choice and constraint in shaping transgender travelers’ tourism experiences, highlighting the challenges they face and the strategies they use to cope with the obstacles they face within the tourism context. Given Italy’s persistent stereotypes and prejudices against gender and sexual minorities, this research provides a critical examination of the intersectionality of gender identity and tourism within a challenging cultural and legal landscape. The findings contribute to a deeper understanding of transgender travelers’ tourism experiences and offer valuable implications for industry stakeholders, policymakers, and scholars. By amplifying the voices of Italian transgender travelers, this study aims to foster greater inclusivity and recognition of their diverse needs and experiences within the tourism sector. Full article

Professional development involves facing numerous challenges. It is a complex process, susceptible to personal aspects (e.g., health, happiness, productivity), but also contextual aspects (e.g., recognition of the complexity and unpredictability of the labor market, and of the need to have a positive impact on the community). The life design paradigm views individuals as active agents in their career construction. Although this approach strongly emphasizes individual agency, it also underscores the importance of addressing broader issues related to sustainability. Indeed, career counselling can stimulate actions that favor sustainable development, benefiting society and enhancing the well-being of all people. To this end, we developed a training to stimulate reflections on sustainable careers, sense of community, and sustainable behavior. The study involved 44 university students divided into an experimental (n = 22) and a control group (n = 22). The first group participated in 16 online activities, interspersed with three in-person meetings. After the training, the experimental group exhibited improvements in sustainable careers, sense of community, self-efficacy in implementing sustainable behavior, and the perceived importance of promoting sustainability. These findings suggest that career counselling activities can significantly increase the personal resources of university students, equipping them to contribute to society and promote a sustainable world. Full article

Endolysin, a bacteriophage-derived lytic enzyme, has emerged as a promising alternative antimicrobial agent against rising multidrug-resistant bacterial infections. Two novel endolysins LysPEF1-1 and LysPEF1-2 derived from Enterococcus phage PEF1 were cloned and overexpressed in Escherichia coli to test their antimicrobial efficacy against multidrug-resistant E. faecalis strains and their biofilms. LysPEF1-1 comprises an enzymatically active domain and a cell-wall-binding domain originating from the NLPC-P60 and SH3 superfamilies, while LysPEF1-2 contains a putative peptidoglycan recognition domain that belongs to the PGRP superfamily. LysPEF1-1 was active against 89.86% (62/69) of Enterococcus spp. tested, displaying a wider antibacterial spectrum than phage PEF1. Moreover, two endolysins demonstrated lytic activity against additional gram-positive and gram-negative species pretreated with chloroform. LysPEF1-1 showed higher activity against multidrug-resistant E. faecalis strain E5 than LysPEF1-2. The combination of two endolysins effectively reduced planktonic cells of E5 in broth and was more efficient at inhibiting biofilm formation and removing biofilm cells of E. faecalis JCM 7783^T than used individually. Especially at 4 °C, they reduced viable biofilm cells by 4.5 log after 2 h of treatment on glass slide surfaces. The results suggest that two novel endolysins could be alternative antimicrobial agents for controlling E. faecalis infections. Full article

Paragangliomas are rare extra-adrenal neuroendocrine tumors originating from chromaffin tissue that present a diagnostic and therapeutic challenge due to their diverse clinical manifestations and low incidence. While these tumors often manifest as catecholamine-secreting functional tumors, their clinical presentation can vary, leading to delayed diagnosis and challenging management. This study presents the case of a 22-year-old patient with cardiac paraganglioma who initially presented with angina-like symptoms, highlighting the importance of considering this rare condition in young individuals with nonspecific complaints. Diagnostic imaging, including transthoracic echocardiography, CT angiography, and MRI, played a crucial role in identifying the tumor’s location and vascularization. Surgical excision, including pulmonary artery graft and CABG, was the primary management approach, which was accompanied by intraoperative complications that later led to CCU admission, followed by postoperative complications, ultimately leading to the patient’s death. This case highlights the significance of early recognition and management of complications following a surgical approach to treat paragangliomas. Full article

As the sika deer breeding industry flourishes on a large scale, accurately assessing the health of these animals is of paramount importance. Implementing posture recognition through target detection serves as a vital method for monitoring the well-being of sika deer. This approach allows for a more nuanced understanding of their physical condition, ensuring the industry can maintain high standards of animal welfare and productivity. In order to achieve remote monitoring of sika deer without interfering with the natural behavior of the animals, and to enhance animal welfare, this paper proposes a sika deer individual posture recognition detection algorithm GFI-YOLOv8 based on YOLOv8. Firstly, this paper proposes to add the iAFF iterative attention feature fusion module to the C2f of the backbone network module, replace the original SPPF module with AIFI module, and use the attention mechanism to adjust the feature channel adaptively. This aims to enhance granularity, improve the model’s recognition, and enhance understanding of sika deer behavior in complex scenes. Secondly, a novel convolutional neural network module is introduced to improve the efficiency and accuracy of feature extraction, while preserving the model’s depth and diversity. In addition, a new attention mechanism module is proposed to expand the receptive field and simplify the model. Furthermore, a new pyramid network and an optimized detection head module are presented to improve the recognition and interpretation of sika deer postures in intricate environments. The experimental results demonstrate that the model achieves 91.6% accuracy in recognizing the posture of sika deer, with a 6% improvement in accuracy and a 4.6% increase in mAP50 compared to YOLOv8n. Compared to other models in the YOLO series, such as YOLOv5n, YOLOv7-tiny, YOLOv8n, YOLOv8s, YOLOv9, and YOLOv10, this model exhibits higher accuracy, and improved mAP50 and mAP50-95 values. The overall performance is commendable, meeting the requirements for accurate and rapid identification of the posture of sika deer. This model proves beneficial for the precise and real-time monitoring of sika deer posture in complex breeding environments and under all-weather conditions. Full article

Liver cancer, recognized as a significant global health issue, is increasingly correlated with Hepatitis B virus (HBV) infection, as evidenced by numerous scientific studies. This study aims to examine the correlation between HBV infection and the development of liver cancer, focusing on using RNA sequencing (RNA-seq) to detect HBV sequences and applying deep learning techniques to estimate the likelihood of oncogenic transformation in individuals with HBV. Our study utilized RNA-seq data and employed Pathseq software and sophisticated deep learning models, including a convolutional neural network (CNN), to analyze the prevalence of HBV sequences in the samples of patients with liver cancer. Our research successfully identified the prevalence of HBV sequences and demonstrated that the CNN model achieved an exceptional Area Under the Curve (AUC) of 0.998 in predicting cancerous transformations. We observed no viral synergism that enhanced the pathogenicity of HBV. A detailed analysis of sequences misclassified by the CNN model revealed that longer sequences were more conducive to accurate recognition. The findings from this study provide critical insights into the management and prognosis of patients infected with HBV, highlighting the potential of advanced analytical techniques in understanding the complex interactions between viral infections and cancer development. Full article

Deep learning models have demonstrated significant advantages over traditional algorithms in image processing tasks like object detection. However, a large amount of data are needed to train such deep networks, which limits their application to tasks such as biometric recognition that require more training samples for each class (i.e., each individual). Researchers developing such complex systems rely on real biometric data, which raises privacy concerns and is restricted by the availability of extensive, varied datasets. This paper proposes a generative adversarial network (GAN)-based solution to produce training data (palm images) for improved biometric (palmprint-based) recognition systems. We investigate the performance of the most recent StyleGAN models in generating a thorough contactless palm image dataset for application in biometric research. Training on publicly available H-PolyU and IIDT palmprint databases, a total of 4839 images were generated using StyleGAN models. SIFT (Scale-Invariant Feature Transform) was used to find uniqueness and features at different sizes and angles, which showed a similarity score of 16.12% with the most recent StyleGAN3-based model. For the regions of interest (ROIs) in both the palm and finger, the average similarity scores were 17.85%. We present the Frechet Inception Distance (FID) of the proposed model, which achieved a 16.1 score, demonstrating significant performance. These results demonstrated StyleGAN as effective in producing unique synthetic biometric images. Full article

Automatic speech recognition (ASR) aims at understanding naturally spoken human speech to be used as text inputs to machines. In multi-speaker environments, where multiple speakers are talking simultaneously with a large amount of overlap, a significant performance degradation may occur with conventional ASR systems if they are trained by recordings of single talkers. This paper proposes a multi-speaker ASR method that incorporates speaker embedding information as an additional input. The embedding information for each of the speakers in the training set was extracted as numeric vectors, and all of the embedding vectors were stacked to construct a total speaker profile matrix. The speaker profile matrix from the training dataset enables finding embedding vectors that are close to the speakers of the input recordings in the test conditions, and it helps to recognize the individual speakers’ voices mixed in the input. Furthermore, the proposed method efficiently reuses the decoder from the existing speaker-independent ASR model, eliminating the need for retraining the entire system. Various speaker embedding methods such as i-vector, d-vector, and x-vector were adopted, and the experimental results show 0.33% and 0.95% absolute (3.9% and 11.5% relative) improvements without and with the speaker profile in the word error rate (WER). Full article

Background: In addition to being highly distressing, chronic migraine headaches are a major socioeconomic challenge. Like other pain syndromes, migraine headaches are associated with psychological and physiological impairments, including sensorimotor and somatosensory deficits. Aim: This study aims to explore whether patients with high-frequency or chronic migraine differ from a healthy population in the areas of laterality recognition (LAT) and facial emotion recognition (FER) and whether there are correlations between these areas and central sensitization of pain and psychological components like stress, depression, anxiety, and alexithymia. Methods: Using a cross-sectional design, individuals with high-frequency or chronic (ICHD classification) migraine (migraine group MG = 45) and healthy individuals (control group CG = 25) were studied using LAT testing (hand, neck, and face); FER testing; and questionnaires, including the Central Sensitization Inventory (CSI) and the Toronto Alexithymia Scale (TAS-20). Results: Data from 70 participants were collected for analysis. Statistically significant differences were found only in the assessment of central sensitization (p < 0.001). Weak to moderate monotonic correlations were found for the MG, especially between alexithymia detection (TAS-20) and facial emotion recognition (FER test). Discussion: The methodological procedure and its accompanying challenges can be seen as limitations of this study. The lack of significant effects must be mentioned, but the selection of the collected questionnaires, the uniform diagnostics, and the statistical processing of a large amount of data represent methodological strengths. Conclusion: The CSI and the TAS-20 could be used in combination with FER to assess chronic migraine. Implementing the described sensorimotor parameters as a therapeutic intervention requires further investigation. Full article

Liver transplantation is the most effective treatment for end-stage liver disease. Despite improvements in surgical techniques, transplant rejection remains a significant concern. The liver is considered an immune-privileged organ due to its unique microenvironment and complex interactions among various cell types. Alloimmune responses mediated by T cells and antigen-presenting cells (APCs) play crucial roles in transplant rejection. The liver’s dual blood supply and unique composition of its sinusoidal endothelial cells (LSECs), Kupffer cells (KCs), hepatocytes, and hepatic stellate cells (HSCs) contribute to its immune privilege. Alloantigen recognition by T cells occurs through direct, indirect, and semidirect pathways, leading to acute cellular rejection (ACR) and chronic rejection. ACR is a T cell-mediated process that typically occurs within the first few weeks to months after transplantation. Chronic rejection, on the other hand, is a gradual process characterized by progressive fibrosis and graft dysfunction, often leading to graft loss. Acute antibody-mediated rejection (AMR) is less common following surgery compared to other solid organ transplants due to the liver’s unique anatomy and immune privilege. However, when it does occur, AMR can be aggressive and lead to rapid graft dysfunction. Despite improvements in immunosuppression, rejection remains a challenge, particularly chronic rejection. Understanding the mechanisms of rejection and immune tolerance, including the roles of regulatory T cells (Tregs) and hepatic dendritic cells (DCs), is crucial for improving transplant outcomes. Strategies to induce immune tolerance, such as modulating DC function or promoting Treg activity, hold promise for reducing rejection and improving long-term graft survival. This review focuses on the liver’s unique predisposition to rejection and tolerance, highlighting the roles of individual cell types in these processes. Continued research into the mechanisms of alloimmune responses and immune tolerance in liver transplantation is essential for developing more effective therapies and improving long-term outcomes for patients with end-stage liver disease. Full article

The dynamic expressions of emotion convey both the emotional and functional states of an individual’s interactions. Recognizing the emotional states helps us understand human feelings and thoughts. Systems and frameworks designed to recognize human emotional states automatically can use various affective signals as inputs, such as visual, vocal and physiological signals. However, emotion recognition via a single modality can be affected by various sources of noise that are specific to that modality and the fact that different emotion states may be indistinguishable. This review examines the current state of multimodal emotion recognition methods that integrate visual, vocal or physiological modalities for practical emotion computing. Recent empirical evidence on deep learning methods used for fine-grained recognition is reviewed, with discussions on the robustness issues of such methods. This review elaborates on the profound learning challenges and solutions required for a high-quality emotion recognition system, emphasizing the benefits of dynamic expression analysis, which aids in detecting subtle micro-expressions, and the importance of multimodal fusion for improving emotion recognition accuracy. The literature was comprehensively searched via databases with records covering the topic of affective computing, followed by rigorous screening and selection of relevant studies. The results show that the effectiveness of current multimodal emotion recognition methods is affected by the limited availability of training data, insufficient context awareness, and challenges posed by real-world cases of noisy or missing modalities. The findings suggest that improving emotion recognition requires better representation of input data, refined feature extraction, and optimized aggregation of modalities within a multimodal framework, along with incorporating state-of-the-art methods for recognizing dynamic expressions. Full article

Recent mumps outbreaks have been observed in vaccinated young adults due to the mumps virus (MuV) of genotype G, whereas the current vaccine is a mixture of two genotype A strains. These outbreaks could be attributed to waning vaccine immunity or the antigenic differences between the HN and F glycoproteins in the vaccine and circulating MuV. These glycoproteins are essential targets for the immune system, and antigenic variations may reduce the recognition of mumps antibodies, rendering the population susceptible to the MuV. We established stable cell lines expressing the MuV glycoproteins to study cross-reactivity between genotype A and genotype G. Cross-reactivity between the genotypes was evaluated via immunofluorescence using patient sera from vaccinated individuals, infected individuals, and vaccinated individuals infected with genotype G. Titer ratios showed that the vaccinated individuals exhibited a titer 3.68 times higher for the HN protein and 2.3 times higher for the F protein when comparing genotype A with genotype G. In contrast, the infected individuals showed a lower titer for genotype A compared with genotype G, at 0.43 and 0.33 for the HN and F proteins, respectively. No difference in titer ratio was observed for individuals vaccinated and subsequently infected with mumps. These findings suggest that antigenic variations between the two genotypes may potentially result in immune escape of the circulating strain, resulting in individuals susceptible to the MuV. Full article

In a production environment, delayed stress recognition can impact yield. Imaging can rapidly and effectively quantify stress symptoms using indexes such as normalized difference vegetation index (NDVI). Commercial systems are effective but cannot be easily customized for specific applications, particularly post-processing. We developed a low-cost customizable imaging system and validated the code to analyze images. Our objective was to verify the image analysis code and custom system could successfully quantify the changes in plant canopy reflectance. ‘Supercascade Red’, ‘Wave© Purple’, and ‘Carpet Blue’ Petunias (Petunia × hybridia) were transplanted individually and subjected to increasing fertilizer treatments and increasing substrate pH in a greenhouse. Treatments for the first trial were the addition of a controlled release fertilizer at six different rates (0, 0.5, 1, 2, 4, and 8 g/pot), and for the second trial, fertilizer solution with four pHs (4, 5.5, 7, and 8.5), with eight replications with one plant each. Plants were imaged twice a week using a commercial imaging system for fertilizer and thrice a week with the custom system for pH. The collected images were analyzed using an in-house program that calculated the indices for each pixel of the plant area. All cultivars showed a significant effect of fertilizer on the projected canopy size and dry weight of the above-substrate biomass and the fertilizer rate treatments (p < 0.01). Plant tissue nitrogen concentration as a function of the applied fertilizer rate showed a significant positive response for all three cultivars (p < 0.001). We verified that the image analysis code successfully quantified the changes in plant canopy reflectance as induced by increasing fertilizer application rate. There was no relationship between the pH and NDVI values for the cultivars tested (p > 0.05). Manganese and phosphorus had no significance with chlorophyll fluorescence for ‘Carpet Blue’ and ‘Wave© Purple’ (p > 0.05), though ‘Supercascade Red’ was found to have significance (p < 0.01). pH did not affect plant canopy size. Chlorophyll fluorescence pixel intensity against the projected canopy size had no significance except in ‘Wave© Purple’ (p = 0.005). NDVI as a function of the projected canopy size had no statistical significance. We verified the ability of the imaging system with integrated analysis to quantify nutrient deficiency-induced variability in plant canopies by increasing pH levels. Full article

Burnout among healthcare workers has been extensively studied since its initial recognition in 1960, with its defining characteristics established by Maslach in 1982. The syndrome, characterized by emotional exhaustion, depersonalization, and low personal accomplishment, is exacerbated by work-related stress and has profound implications for individual and societal well-being. Methods: A review of the literature, including PubMed searches and analyses of risk factors and protective measures, was conducted to assess the prevalence, impacts, and biomarkers associated with burnout among healthcare workers. Various instruments for evaluating burnout were examined, including the widely used Maslach Burnout Inventory, alongside specific tools tailored to different occupational populations. Results: Healthcare workers, particularly physicians, exhibit significantly higher rates of burnout compared to the general population. Factors such as night shifts, workload, and exposure to biohazards contribute to elevated burnout risk. Biomarkers like cortisol, melatonin, and thyroid hormones have been linked to burnout, highlighting physiological implications. Conclusions: Burnout poses significant challenges to healthcare systems globally, impacting patient care, worker retention, and overall well-being. Identifying and addressing risk factors while promoting protective factors such as resilience and social support are crucial in mitigating burnout. Further research into prevention strategies and biomarker monitoring is warranted to support the mental and physical health of healthcare workers. Full article

Electrocardiogram (ECG) signals are a measure minute electrical signals generated during the cardiac cycle, a biometric signal that occurs during vital human activity. ECG signals are susceptible to various types of noise depending on the data acquisition conditions, with factors such as sensor placement and the physiological and mental states of the subject contributing to the diverse shapes of these signals. When the data are acquired in a single session, the environmental variables are relatively similar, resulting in similar ECG signals; however, in subsequent sessions, even for the same person, changes in the environmental variables can alter the signal shape. This phenomenon poses challenges for person identification using ECG signals acquired on different days. To improve the performance of individual identification, even when ECG data is acquired on different days, this paper proposes an ensemble deep neural network for person identification by comparing and analyzing the ECG recognition performance under various conditions. The proposed ensemble deep neural network comprises three streams that incorporate two well-known pretrained models. Each network receives the time-frequency representation of ECG signals as input, and a stream reuses the same network structure under different learning conditions with or without data augmentation. The proposed ensemble deep neural network was validated on the Physikalisch-Technische Bundesanstalt dataset, and the results confirmed a 3.39% improvement in accuracy compared to existing methods. Full article