Space–air–ground integrated network (SAGIN) is considered as an enabler for the sixth-generation (6G) networks. By integrating terrestrial and non-terrestrial (satellite, aerial) networks, SAGIN seem to be a quite promising solution to provide reliable connectivity everywhere and all the time. Their availability can be further enhanced if hybrid free space optical (FSO)/radio frequency (RF) links are adopted. In this paper, the performance of a hybrid FSO/RF communication system operating in SAGIN has been analytically evaluated. In the considered system, a high altitude platform station (HAPS) is used to forward the satellite signal to the ground station. Moreover, the FSO channel model assumed takes into account the turbulence, pointing errors and path losses, while for the RF links, a relatively new composite fading model has been considered. In this context, a new link selection scheme has been proposed that is designed to reduced the signaling overhead required for the switching operations between the RF and FSO links. The analytical framework that has been developed is based on the Markov chain theory. Capitalizing on this framework, the performance of the system has been investigated using the criteria of outage probability and the average number of link estimations. The numerical results presented reveal that the new selection scheme offers a good compromise between performance and complexity.

The influence of applied loads on free vibrations analysis of plates and shells panels made of functionally graded materials is analyzed in the present work. Formulations for the static analysis considering geometrically nonlinear behavior, as well as linear buckling and free vibrations analyses are considered. The calculation of the through-thickness stress distribution is also performed. A finite element model based on a higher order shear deformation theory and using a non-conforming triangular flat plate/shell element with 3 nodes, and 8 degrees of freedom per node is used in the numerical implementation. The results obtained with this numerical model are presented, discussed, and compared with alternative solutions published by other authors in some benchmark applications.

The aim of the study was to determine the occurrence and characteristics of coagulase-positive Staphylococcus strains in the carcasses of wild birds and aviary capercaillies in south-eastern Poland. Samples from capercaillies and wild birds belonging to 12 orders and 26 species were tested. The resistance of the strains and the presence of selected virulence genes were examined. Similarity analysis of the isolates was performed using pulsed-field gel electrophoresis (PFGE). Coagulase-positive Staphylococcus strains accounted for 5.7% and belonged to the species: Staphylococcus aureus, Staphylococcus pseudintermedius and Staphylococcus delphini. Among isolated strains, 15.8% were multidrug resistant. The most frequently detected virulence genes were hla in 58% of isolates and hlb and hld in 47.4% of isolates., The results of multiplex PCR showed the presence of genes responsible for the production of enterotoxins C, B, E, and J, in single isolates. It can be concluded that coagulase-positive Staphylococcus strains accounted for a small percentage of staphylococci isolated from free-living birds in the study area. The occurrence of multidrug-resistant coagulase-positive Staphylococcus strains in aviary capercaillies suggests that they play a role in the transmission and spread of resistant strains into the environment. Free-living birds may be also a significant reservoir of enterotoxigenic Staphylococcus strains.

This study addresses the influence of working conditions on Work-Related Quality of Life (WRQOL) among Spanish healthcare professionals. The research seeks to elucidate the mediating roles of job engagement and burnout, and the moderating effects of resilience and self-efficacy, in this relationship. Methods: The study included 374 healthcare workers from Spain, encompassing physicians, nurses, and nursing assistants. It employed a methodological approach using validated instruments to assess psychosocial risks, work-life quality, job engagement, burnout, and resilience. Data were gathered through self-reported questionnaires. Results: Key findings indicate a negative association between working conditions and WRQOL. Job engagement and burnout significantly mediated this relationship, accounting for 19% to 33% of the variance in WRQOL. Notably, lack of support emerged as a strong negative factor for work-related quality of life. Additionally, resilience moderated the relationship between lack of support and burnout. Conclusions: The study concludes that improving working conditions could increase WRQOL. Interventions aimed at enhancing engagement and addressing burnout may be effective. The moderating role of resilience suggests that strategies to enhance resilience could also be beneficial. These findings hold significant implications for policy formulation and human resources planning in healthcare, emphasizing the need for enhanced workplace support and the development of resilience among health workers.

De-escalation is currently taking place both in surgical and systemic treatment of breast cancer. The introduction of trastuzumab, the first monoclonal antibody against the HER2 receptor over 20 years ago was a milestone in the treatment of HER2 positive breast cancer and marked the beginning of a new era in targeted tumor therapy. In the sense of de-escalation, omitting non-targeted cytotoxic chemotherapy altogether is often hailed as the ultimate goal of oncological research. Especially in case of small, node-negative, HER2 positive early breast cancer, it remains a challenge for clinicians to establish the safest and most efficient treatment plan while considering the significant potential for toxic side effects associated with chemotherapy and HER2 targeted therapy, and the generally excellent prognosis. In this context, several ongoing studies are currently assessing chemotherapy-free regimens as part of strategies aimed at de-escalating therapy in the field of HER2 positive early breast cancer. Despite promising early results from these studies, combination of anti-HER2 treatment with chemotherapy backbone remains standard of care.

Usually, the vector control of the induction machine (IM) is done by using cascade control structures with conventional linear PI controllers, the inner loop being designed for current control, and the outer loop for rotor flux and speed control. In this paper, starting from the dq model of the IM with the rotor magnetic flux space vector aligned along the d axis, advanced control algorithms are proposed for the two control loops of the cascade structure. For the current inner loop, after the decoupling control of the two dq currents, predictive algorithms with constraints are used to control and limit the currents. Since the outer loop has a nonlinear affine multivariable plant model, a homotopy-based variant of feedback linearization is used to obtain a non-singular decoupling matrix of the feedback transformation even when the rotor flux is zero at the start-up of the motor. During the continuous variation of the homotopy parameter, the model of the plant is variable, in which case the model free algorithms are properly used to control the flux and speed of the IM. The comparative analysis obtained for the cascade structure with conventional PI controllers and advanced algorithms, respectively, shows that the last lead to improve the dynamical performance of the IM.

Amid the deepening imbalance in the supply and demand of allogeneic organs, xenotransplanta-tion can be a practical alternative because it makes an unlimited supply of organs possible. How-ever, to perform xenotransplantation on patients, the source animals to be used must be free from infectious agents. This requires breeding of animals using assisted reproductive techniques, such as somatic cell nuclear transfer, embryo transfer, and cesarean-section, without colostrum de-rived in designated pathogen-free (DPF) facilities. Most infectious agents can be removed from animals produced by these methods, but several viruses known to pass through the placenta are not easy to remove, even with these methods. Therefore, in this review, we look at the characteris-tics of several viruses known to cross the placenta and determine how they can be detected. This review is intended to help maintain DPF facilities by preventing animals infected with the virus from entering DPF facilities and to help select pigs suitable for xenotransplantation.

This comprehensive study delves into the implementation and effectiveness of the flipped classroom model in engineering EMI (English as a Medium of Instruction) courses in Taiwan. Situated within the strategic framework of Taiwan's Bilingual Nation 2023 policy, this research aims to enhance the global competitiveness of Taiwan's semiconducting manufacturing engineering sector. The study’s primary focus is on understanding the perceptions and learning outcomes of EFL (English as a Foreign Language) students in engineering courses. Through a methodical approach using descriptive statistics and chi-square tests, the study analyzes data from 33 junior engineering students specializing in mechanical and mechatronic fields. The findings highlight a significant positive influence of the flipped classroom model on various aspects of student learning, including increased engagement, heightened creativity, and enhanced problem-solving skills. Additionally, the study emphasizes the critical role of timely L1 support in facilitating effective learning in EMI settings. These outcomes not only demonstrate the flipped classroom's viability as a sustainable educational approach but also provide crucial insights for educators and policymakers. By aligning cutting-edge teaching methods with the evolving needs of engineering education, this study contributes to the discourse on educational innovation. It ultimately guides the development of future engineering programs that cater to diverse student needs and fosters the cultivation of skilled engineering professionals.

Prostate cancer (PC) is the second most diagnosed cancer among men. It was observed that early diagnosis of disease is highly beneficial for survival of cancer patients. Therefore, extension and increasing quality of life of PC patients can be achieved by broadening the Cancer Screening programs that are aimed at the identification of cancer manifestation in patients at earlier stages before they demonstrate well-understood signs of the disease. Therefore, there is an urgent need in standard, sensitive, robust, and commonly available screening and diagnosis tools for identification of early signs of cancer pathologies. In this respect, “Holy Grail” of cancer researchers and bioengineers for decades have been molecular sensing probes that would allow diagnosis, prognosis, and monitoring of cancer diseases by their interaction with cell-secreted and cellassociated PC biomarkers, e.g. PSA and PSMA correspondinly. At present, most PSA tests are performed at centralized laboratories using high throughput Total PSA immune analyzers which are suitable for dedicated laboratories and are not readily available for the broad health screening. Therefore, the current trend in detection of PC is developments of the portable biosensors for mobile laboratories and individual use. Phage display since its conception by George Smith in 1985 has emerged as a premier tool in molecular biology with widespread applications. This review describes how the paradigm of molecular evolution and phage display revolutionized the methods of early diagnosis and monitoring of PC.

The incidence of celiac disease and gluten intolerance has been significantly rising globally. Gluten-free product consumption registered a sudden rise also among tolerant people, due to psychosocial factors. Biscuits are popular, low-cost bakery foods, consumed by nearly everyone worldwide. The removal of gluten from the baked product causes some undesirable traits and different textures and tastes. The main goal consists of creating a food product with the same taste and texture as a product with gluten. Moreover, gluten-free bakery products are usually low-grade sources of protein and poor in dietary fiber. Quinoa is a source of TDF and valuable protein. In this study, quinoa flour was used as the main constituent in the formulation of gluten-free c to improve their nutritional properties. Six different recipes with different percentages of quinoa flour have been realized. The formulations were compared, by rheological analysis, of each other and with a wheat flour control shortbread. The recipes with rheological features more similar to control shortbread were subjected to a discrimination analysis and the preferred was characterized from a chemical-physical and sensorial point of view. The experimental shortbreads constituted a good compromise to exploit the good nutritional composition of quinoa maintaining an acceptable sensory profile.

The retrospective noninterventional study investigated the kinetic energy of video images of 18 fertilized eggs (7 were normal and 11 were abnormal) recorded by a time-lapse device leading up to the beginning of the first cleavage. The norm values of cytoplasmic particles were measured by the optical flow method. Three phases profile for normal cases were found regarding the kinetic energy that were 2.199×10⁻²⁴±2.076×10⁻²⁴, 2.369×10⁻²⁴±1.255×10⁻²⁴, and 1.078×10⁻²⁴±4.720×10⁻²⁵ (J) for the phases 1, 2, and 3, respectively. In phase 2, the energies were 2.369 × 10⁻²⁴ ± 1.255 × 10⁻²⁴ and 4.694 × 10⁻²⁴ ± 2.996 × 10⁻²⁴ (J) (mean ± SD, P = 0.0372), and the time required was 8.114 ± 2.937 and 6.018 ± 5.685 (H) (P = 0.0413) for the normal and abnormal cases, respectively. The kinetic energy change was considered a condition for applying the free energy principle that states that, for any self-organized system to be in equilibrium in its environment, it must minimize its informational free energy. The kinetic energy while interpreting it in terms of the free energy principle suggesting clinical usefulness would further our understanding of the phenomenon of fertilized egg development with respect to the birth of human life.

The detection and analysis of small molecules, typically defined as molecules under 1000 Da, is of growing interest ranging from the development of small molecule drugs and inhibitors to the sensing of toxins and biomarkers. However, due to challenges such as their small size and low mass, many biosensing technologies struggle to have the sensitivity and selectivity for the detection of small molecules. Notably, their small size limits the usage of labeled techniques that can change the properties of small molecule analytes. Furthermore, the capability of real-time detection is highly desired for small molecule biosensors’ application in diagnostics or screening. This review highlights recent advances in label-free real-time biosensing technologies utilizing different types of transducers to meet the growing demand for small molecule detection.

Patient-derived tumor organoids (PDTOs) are a novel preclinical model of non-small cell lung cancer (NSCLC) for studying tumor biology and precision medicine, which recapitulates tumor morphology and gene expression profile. However, some issues such as low establishment rates, long-term production, and the loss of the immune microenvironment have hindered their widespread practical use. In this study, we investigated the efficacy of a recently introduced free-floating organoid production method to address these issues. The free-floating approach provided PDTO establishment success rate of over 90 % resulting in organoid formation within a short period of one week. These organoids retained the elements of the parental tumor morphology and contained stroma and immune cell populations, as confirmed by flow cytometry analysis. Importantly, we found that the cytokine and growth factor expression profiles of the free-floating organoids correlated with those of the original tumors in 58 % of cases, indicating the preservation of tumor-associated signaling pathways. Obtained organoids exhibited different responses to anticancer chemotherapeutics that might be a result of variable expression of drug resistance-associated genes. Considering the high establishment rate and short time of free-floating organoid production, this technique provides a valuable tool for a more accurate evaluation of therapeutic strategies and cancer biology studies.

Keywords: Cell-free DNA, sepsis, oxidative stress, immunoredox modulation, cecal ligation and puncture, toll-like receptor

This study aims at the sustainable valorization of eggshells (ES) from different countries of origin in cement concrete as a cement replacement for Hong Kong. For this purpose, sixteen different eggs from different regions across the world were selected from the local market. Firstly, the extent of uniformity in the weight and mineral content of sampled eggshells were assessed by a specific gravity test and thermogravimetric analysis (TGA), respectively. It was found that specific gravity varies between 2.02 and 2.39, with an average value of 2.20 ± 0.01, whereas CaCO₃ varies between 94.65% and 97.23%, with an average value of 96.33%. These values were a bit lower than those of extra-pure limestone (LS) because of the organic part and the porous structure of eggshells. Secondly, three different eggshells were selected for cement replacement in cement concrete, both in the uncalcined and calcined states, based on having the highest, medium, and lowest CaCO₃ content, respectively. To get the calcined eggshells, the eggshells were calcined at 800^oC for three hours. It was found that the strength of eggshells varies with CaCO₃, but the variation was acceptable. The calcined eggshells showed comparatively more compressive strength and were close to the LS because of the free CaO and the absence of an organic part. In general, the variation in the basic properties of eggshells from different regions across the world is negligible and suitable to use as a cement replacement with acceptable variation in strength in Hong Kong.

In this study, we propose an efficient field-free line (FFL) generator for mechanically driven FFL-magnetic particle imaging (MPI) applications. The novel FFL generator comprises pairs of Halbach arrays and bar magnets. The proposed design generates high-gradient FFLs with low-mass permanent magnets, realizing fine spatial resolutions in MPI. We investigate the generated magnetic field using simulations and experiments. Our results show that the FFL generator yields a high gradient of 4.76 T/m at a cylindrical field of view of 30 mm diameter and a 70 mm open bore. A spatial resolution of less than 3.5 mm was obtained in the mechanically driven FFL-MPI.

In the dynamic and complex environment of industrial control rooms, operators are often inundated with a multitude of tasks and alerts, which can lead to an overwhelming situation known as task overload. This state can precipitate decision fatigue and a heavier reliance on cognitive biases, potentially compromising the decision-making process. To mitigate such risks, the implementation of decision support systems becomes crucial. These systems are designed to assist operators in making swift and well-informed decisions, particularly when they sense their own judgment may be faltering. Our research introduces an AI-based framework that leverages dynamic influence diagrams and reinforcement learning to construct an effective decision support system. The cornerstone of this framework is the creation of a robust, interpretable, and efficient tool that supports control room operators during critical process disturbances. By integrating expert knowledge, the dynamic influence diagram forms a comprehensive model that captures the uncertainties inherent in complex industrial processes. It is adept at anomaly detection and recommending optimal actions. Moreover, this model is enhanced through a strategic partnership with reinforcement learning algorithms, which fine-tune the recommendations to be more context-specific and precise. The ultimate goal of our framework is to provide operators with a live, reliable decision support system that can significantly improve their response during process upsets. This paper outlines the development of our AI framework and its application within a simulated control room environment. Our findings indicate that the use of decision support systems can lead to improved operator performance and reduced cognitive workload. However, it also reveals a trade-off with situation awareness, which tends to diminish as operators may become overly reliant on the system’s guidance. Trust emerges as a critical factor in the adoption and effectiveness of decision support systems. Our research underscores the importance of balancing the benefits of decision support with the need for maintaining operator engagement and comprehension during process operations.

This article explores the crucial task of estimating State of Charge (SOC) in Hybrid Electric Vehicles (HEVs) and examines the applicability of various regression models for this purpose. We delve into the strengths and limitations of Linear Regression, Support Vector Regression (SVR), and Neural Network Regression (NNR) in the context of SOC estimation. Linear regression provides a simple and interpretable baseline, SVR extends this to nonlinear relationships, while NNR emerges as a powerful tool with adaptive capabilities. The choice of model depends on factors such as data characteristics, interpretability, and computational resources. As the field evolves, the article advocates for a nuanced approach, possibly incorporating hybrid models, to achieve robust and accurate SOC estimation, contributing to the ongoing enhancement of HEV efficiency and sustainability.

Postpartum depression is a significant health issue during the postpartum period and not only affects the woman but also has important repercussions for the newborn. Group support interventions during the postpartum period have proven effective in helping women cope with depression and improving breastfeeding rates. The aim of this study was to assess the effectiveness of a midwife-led breastfeeding support group intervention on breastfeeding rates, postpartum depression and general self-efficacy. This was a multicentric cluster randomized controlled trial with control and intervention groups and was not blinded. It was conducted in Andalusia (southern Spain) from October 2021 to May 2023. A total of 382 women participated in the study. Results showed a significant difference in exclusive breastfeeding rates at 4 months postpartum between the groups (p < 0.001). Additionally, there was a lower mean score on the Edinburgh Postnatal Depression Scale in the intervention group (12.49 ± 3.6 vs 13.39 ± 4.0; p = 0.044). Similarly, higher scores of general self-efficacy were observed among breastfeeding women at 2 and 4 months postpartum (p = 0.002 and p < 0.001, respectively). In conclusion, midwife-led breastfeeding support groups enhanced self-efficacy, prolonged breastfeeding and reduced postpartum depression 4 months after giving birth.

Background: The proven efficacy of mTOR inhibitors (mTORI), tyrosine kinase inhibitors (TKI) or immune checkpoint inhibitors (ICI) therapies in metastatic renal cell carcinoma (RCC), suggested that these agents should be investigated as adjuvant therapy with the aim of eliminating any residual undetectable microscopic disease after curative resection. Our study aimed to compare the efficacy of these treatments through the application of an innovative method that reconstructs individual patient data. Methods: Nine phase III studies describing different treatment options for adjuvant RCC were selected. Individual patient data were reconstructed from Kaplan–Meier (KM) curves through the “IPDfromKM method”. Disease-free survival (DFS) were compared among combination treatments and control arm (placebo). Results were summarized as multi-treatment KM curves. Standard statistical testing was used, including hazard ratio and likelihood ratio tests for heterogeneity. Results: In the overall population, this study showed that two ICIs (pembrolizumab and nivolumab plus ipilimumab) and one TKI (sunitinib) showed superiority over placebo, whereas both TKI and mTORI did not. As we assessed DFS as the primary endpoint for the adjuvant comparison, the overall survival benefit remains unknown.. Conclusion: This novel approach to investigating survival has allowed us to conduct all of the indirect head-to-head comparisons between these agents in a context where no "real" comparative trials have been conducted.

This work studies the discontinuities features of sedimentary flysch materials in a 100 km2 area belonging to the Basque Arc. Such materials are common in this Spanish Alpine region located in the north of the Iberian Peninsula. A total of 33 outcrops are investigated by an intensive geotechnical investigation including geomechanical stations, boreholes and mechanical laboratory tests. Two flysch units are characterized: the Upper Aguinaga Formation or siliciclastic flysch, and the Lower Itziar Formation or calcareous flysch. Differences between both flysch formation are found. Joints in the siliciclastic flysch formation present an undulated roughness, with a spacing narrower and a persistence lower than in the calcareous flysch formation, which exhibits higher friction angles, although roughness is essentially planar. In addition, the potential of using Artificial Intelligence (AI) techniques, particularly Artificial Neural Networks and Support Vector Machine, to estimate the Geological Strength Index (GSI) from the Rock Quality Design (RQD) and some discontinuities features (spacing, persistence, aperture and roughness) is investigated. AI techniques are found to be satisfactory, being the Support Vector Machine with a linear kernel the technique which achieves the best performance.

Modification the biodegradable matrix was carried by graft-copolymerization polylactide with methacrylic acid of various concentrations. Ultraviolet irradiation with different exposure times to the samples was chosen as the initiator of copolymerization. In the experiment the effect of ultraviolet light on polylactide and graft copolymerization was investigated: the resulting samples were studied by viscosimetry, gravimetry, FTIR, static cation- exchange capacity analysis . The conditions for obtaining grafted copolymers and the dependence of the characteristics on the reaction conditions are established.

In this study, tree selection/plantation DSS (Decision Support Systems) were reviewed and assessed based on essential objectives in the available literature. We reviewed existing DSS using multiple data sources and available online resources such as web interfaces. We compared the existing DSS, focusing primarily on five primary objectives in this study, which DSS may address when it comes to the tree selection, including a) Climate resilience, b) Infrastructure/Space Optimization, c) Agroforestry, d) Ecosystem services, e) Urban sustainability. Climate resilience of tree species and urban sustainability is addressed relatively less in existing systems, which can be holistically internalized in future DSS tools Based on this review, DNN (Deep Neural Networks) is recommended to address achieving trade-offs between complex objectives such as maximizing ecosystem services, climate resilience of tree species, maintaining Agroforestry, and other benefits.

Background: Caring for individuals with mental disorders poses significant challenges for caregivers, often leading to compromised quality of life and mental health issues such as stress, anxiety, and depression. This study aims to assess the extent of these challenges among caregivers in Greece, identifying which demographic factors influence their well-being. Method: A total of 157 caregivers were surveyed using the SF-12 Health Survey for quality of life assessment and the DASS-21 questionnaire for evaluating stress, anxiety, and depression symptoms. T-tests, Kruskal-Wallis tests, Pearson correlation coefficients, and regression analyses were applied to understand the associations between demographics, quality of life, and mental health outcomes. Results: The study found that caregivers, especially women and younger individuals, faced high levels of mental health challenges. Marital status, educational level, and employment status also significantly influenced caregivers' well-being. Depression was the most significant factor negatively correlating with the mental component of quality of life. The magnitude of burden experienced by caregivers highlighted the urgency for targeted social and financial support, as well as strategic treatment programs that consider caregiver well-being. Conclusions: Caregivers of individuals with mental disorders endure significant stress, anxiety, and depression, influencing their quality of life. Demographic factors such as age, gender, marital status, education, and employment status have notable impacts. Findings emphasize the need for society-wide recognition of caregivers' roles and the creation of comprehensive support and intervention programs to alleviate their burden, particularly in the context of the COVID-19 pandemic.

Accurate agricultural commodity price models enable efficient allocation of limited natural resources, leading to improved sustainability in agriculture. Because of climate change, price volatility and uncertainty for the sector are expected to increase in the future, increasing the need for improved price modeling. With the emergence of machine learning (ML) algorithms, novel tools are now available to enhance the modeling of agricultural commodity prices. This research uses both univariate and multivariate ML techniques to perform probabilistic price prediction modelling for the Canadian beef industry, taking into account beef production, commodity market, and international trade features to enhance accuracy. We apply multivariate algorithms by using support vector regression (SVR), random forest (RF), and adaboost (AB) and univariate algorithms by using autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), and the seasonal autoregressive integrated moving average with exogenous factors (SARIMAX). We apply these models to monthly fed steer price data between January 2005 and September 2023 and compare predicted prices with observed prices using several validation metrics. The outcomes indicate that both random forest (RF) and adaboost (AB) in multivariate modeling show superior performance in accurately predicting Alberta fed steer prices in comparison to other algorithms. To better account for the variance of the best model performance, we subsequently adopted a probabilistic approach by considering uncertainty in our best-selected ML model. The beef industry can use these improved price models to minimize resource waste and inefficiency in the sector and improve the long-term sustainability prospects for beef producers in Canada.

Resveratrol is currently considered an option for chemoprevention and treatment of oral cancer, but the instability and unfavorable pharmacokinetics hinder its use in clinical practice. The present study deals with the preparation of resveratrol liposomes by a method that preserves stability and provides payload high enough for chemoprevention and/or treatment of oral cancer. Different temperatures, RSV concentrations and molar fractions, as well as the presence or absence of vitamin C in the hydration media were assayed. Liposome hydrodynamic diameter, polydispersity and zeta potential were evaluated by dynamic light scattering. In vitro biocompatibility, antioxidant activity and permeability were evaluated using TR146 cells and PermeaPad® membranes. Liposomes were included in hydrogels and films, and their suitability for buccal application was evaluated. According to the results, a temperature of 37ºC was optima to efficiently produce the resveratrol liposomes. Vitamin C was found to influence the liposome size, while resveratrol concentration significantly influenced the entrapment efficiency. High cell biocompatibility and potent intracellular antioxidant activity were observed and the inclusion of vitamin C reinforced the antioxidant effects. High permeation across PermeaPad® membranes and efficient uptake by TR146 cells was proved for resveratrol loaded liposomes. Liposomal hydrogels and films appropriated for buccal application were obtained and the payload of the formulations was high enough to produce mucosa concentrations above resveratrol IC50 values reported for cancer cells.

Support effect is an important issue in heterogeneous catalysis while the explicit role of a catalytic support is often unclear for catalytic reactions. A systematic density functional theory computational study is reported in this paper to elucidate the effect of a model boron nitride (BN) support on the first N-H bond activation step of NH3 on Run (n =1, 2, 3) metal clusters. Geometry optimizations and energy calculations were carried out using density functional theory (DFT) calculation for intermediates and transition state from the starting materials undergoing the N-H activation process. The primary findings are summarized as follows. The involvement of the model BN support does not significantly alter the equilibrium structure of intermediates and transition state in the most favorable pathway (MFP). Moreover, the involvement of BN support decreases the free energy of activation, ΔG≠, thus improving the reaction rate constant. This improvement is more obvious at high temperatures like 673 K than low temperatures like 298 K. The BN support effect leading to the ΔG≠ decrease is most significant for the single Ru atom case among all three cases studied. Finally, the involvement of model BN may change the spin transition behavior of the reaction system during the N-H bond activation process. All these findings provide a deeper insight into the support effect on the N-H bond activation of NH3 for supported Ru catalyst in particular and for supported transition metal catalysts in general.

The genetic engineering of plants often relies on the use of antibiotic or herbicide resistance genes for initial selection of primary transgenic events. Nevertheless, the commercial release of genetically modified crops containing any marker gene encounters several challenges stemming from the lack of consumer acceptance. Development of strategies enabled the generation of marker-free transgenic plants is a possible alternative to deal with public concerns over the safety of biotech crops. Leveraging the high in vitro regeneration and transformation capabilities of potato, this study examined the factors associated with the development of transgenic plants without presence of selective substances in medium. To compare the antibiotic-assisted with the antibiotic-free transformation, nodal explants from five highly regenerative potato cultivars were inoculated with the Agrobacterium strain AGL0 carrying the pBIN-mGFP5-ER (35SGFP/nosNPTII) plasmid. The efficiency of transient expression and the formation of chimeric and completely transgenic shoots were assessed through vital GFP observations. Under non selective conditions, the regeneration efficiency of stable non-chimeric transgenic shoots varied from 0.9 (‘Chicago’) to 2.7% (#12-36-42). In order to streamline transformation protocol, a specific timeline for collecting transgenic plantlets was established for each analyzed cultivar. This timeline strategy was implemented to generate transgenic potato plants, which demonstrated the suppression of homologous SteIF4Es family genes as a result of the successful introduction of the marker-free hpRNAi construct.

Remediation of IT issues encoded into domain-specific or user-defined alerts occurring in cloud environments and customer ecosystems in a vast majority of cases suffers from accurate recommendations which could be timely supplied for recovery of performance degradations. That, of course, is hard to realize through furnishing those abnormality definitions with an appropriate expert knowledge which varies from one environment to another. At the same time, in a large proportion of support cases, the reported problems under Global Support Services (GSS) or Site Reliability Engineering (SRE) treatment ultimately go down to the product teams making them waste costly development hours on investigating self-monitoring metrics of our solutions. Therefore, the mean-time-to-resolution (MTTR) rates of problems/alerts are significantly impacted from lack of a systematic approach towards adopting AI Ops. That would imply building, maintaining, and continuously improving/annotating a data store of insights that ML models are trained and generalized across the whole customer base and corporate cloud services. Our ongoing study is in line with such a vision and validates an approach that learns the alert resolution patterns in such a global setting and explains them using interpretable AI methodologies. The knowledge store of causative rules is then applied in predicting potential sources of the application degradation reflected in an active alert instance. In this communication, we share our experiences with a prototype solution and up to date analysis demonstrating how root conditions are discovered with a high accuracy for a specific type of problem. It is validated against the historical data of resolutions performed by heavy manual development efforts. We also offer a Dempster-Shafer theory-based rule verification framework for experts as a what-if analysis tool to test their hypotheses about underlying environment.

There has been a rapid increase in the use of Marine Spatial Planning (MSP) throughout the world recently, partly due to the continued loss of marine biodiversity and habitat. The sustainability of marine resources is threatened in all regions of the world by major events such as climate change, marine pollution, overfishing, illegal, unreported and unregulated fishing both on the high seas and in country waters. Here, we present a comprehensive review and analysis of how economic information has been applied and used to inform decisions about MSP in British Columbia (BC), Canada and other similar jurisdictions. This focus for the paper was selected because important gaps remain in the literature in terms of incorporating economic questions into MSP. We first present different definitions of MSP, and then we extract useful lessons from MSP regimes with well–tested decision support tools, and use this to guide MSP implementation in BC. We finally present and discuss case studies from Australia, South Africa and Belgium. Our review suggests that applying economic information to support the design and implementation of MSPs would lead to better decisions. This in turn would foster livelihoods, attract finance, increase buy–in and advance United Nations Sustainable Development Goal 14: Life Below Water, thereby achieving Infinity Fish, i.e., ensuring that ocean benefits flow to humanity forever.

The strength of laser joined metal and plastic hybrids is based purely on adhesion. Unpolar plastics like polypropylene depend solely on the mechanical adhesion while polar plastics like polyamide 6.6 develop covalent bonds with the metal. In this research the influence of different surface metallizations on the lap shear strength of laser joined hybrids was investigated. Sandblasted AISI 304 was used as base metal and reference. Aluminum and titanium coatings were deposited by the means of Arc-PVD. Polypropylene and polyamide 6.6 were utilized as polar und unpolar plastics. The lap shear strength of all hybrids with polyamide 6.6 was higher than the polypropylene samples. For all investigated variations of the laser power, scanning speed and the cohesive and adhesive failure modes, the samples joined with the sandblasted AISI 304 showed the highest lap shear strength. The reduction in lap shear strength for the coated samples was due to the increased difference in thermal conductivity between the plastics and the coating materials. This lead to increased warping and residual stresses. It can be concluded that the thermal properties of the metallization in laser joined metal and plastic hybrids has a high impact on the lap shear strength.

This paper introduces new improvements to the modified version of the BIRECT (BI secting RECTangles) algorithm referred to as BIRECTv. We explore various approaches, by first including a grouping strategy for hyper-rectangles having almost the same sizes by categorizing them into different classes. Therefore constraining them not to exceed a certain pre-defined threshold (a small positive value to define the tolerance level). This approach allows for more efficient computation and can be particularly useful when dealing with a large number of hyper-rectangles with varying sizes. To avoid over-sampling, and preventing redundant or excessive sampling, at some shared vertices in descendant subregions, a particular vertex database is used to limit the number of samples taken within each subregion to two. The experimental investigation shows that these improvements have a positive impact on the performance of the BIRECTv(imp.) algorithm and the proposal is a promising global optimization algorithm compared to the original BIRECTv algorithm and its variants. Additionally, the BIRECTv(imp.) algorithm showed particular efficacy in solving high-dimensional problems.

Recently, considerable work has been directed at development of a ultra-compact X-ray free-electron laser based on emerging techniques in high field cryogenic acceleration, with attendant dramatic improvements in electron beam brightness, and state-of-the-art concepts in beam dynamics, magnetic undulators, and X-ray optics. A full conceptual design of a 1 nm XFEL with a length and cost over an order of magnitude below current XFELs has resulted from this effort. This instrument has been developed with an emphasis on permitting exploratory scientific research in a wide variety of fields in a university setting. Concurrently, compact FELs are being urgently developed for use as an instrument to enable next generation chip manufacturing through use as a high flux, few-nm lithography source. This new role suggests consideration of XFELs to addressed urgently emerging demands in this sector, as identified by recent national need studies, for new radiation sources aimed at chip manufacturing: a coherent hard X-ray source which enables frontier metrology methods. Indeed, it has been shown that one may use coherent X-rays to perform 10 nm-class resolution surveys of macroscopic, cm-scale structures such as chips, using ptychographic laminography. As the XFEL is a very promising candidate for realizing such methods, we present here an analysis of the issues and likely solutions associated with extending the UCXFEL to harder X-rays (above 7 keV), much higher fluxes and levels of coherence, and methods of applying such a source for ptychographic laminography to micro-electronic device measurements. We discuss the development path to move the concept to rapid realization of a transformative FEL-based application, outlining FEL and metrology system challenges.

Seeds can maintain their quality for a limited time; after that, they will lose their germination ability and vigor. Some physiological and physicochemical changes in the structure of the seeds during storage can decrease the quality of the seeds which is known as aging. Therefore, detection of the strong young seeds from the old ones is a vital issue in the modern agriculture. Conventional methods of detection of the seed viability and germination are destructive, time-consuming and costly. In this research, two peanut cultivars, namely North Carolina 2 (NC-2) and Florispan were selected and three artificial aging levels were induced to them. Hyperspectral images (HSI) of the samples were acquired and the seed viability was evaluated using two pre-trained convolutional neural network (CNN) image processing models, AlexNet and VGGNet. The noise of the reflection spectra of the samples was relatively resolved and modified by combining Preprocessing techniques of moving average (MA) and standard normal variate (SNV). Using principal component analysis (PCA), the dimensions were declined and three principal components (PC) were extracted. These PCs were then used as variables in the classification of support vector machine (SVM) and linear discriminant analysis (LDA). The results showed the high capability of CNN architectures such as AlexNet and VGGNet in detection of the seed viability based on the HIS with no pre-processing and feature extraction. The mentioned architectures reached the accuracy of 0.985 and 0.986, respectively. The combination of feature extraction method of PCA with LDA and SVM classifiers showed that the use of a limited number of PCs instead of all wavelengths can decrease the complexity of modeling, while enhancing the efficiency of the models such that LDA and SVM classifiers achieved the accuracy of 0.983 and 0.986 in classification of peanut sees, respectively.

Celiac disease is an autoimmune disease triggered by oral ingestion of gluten, with certain gluten residues resistant to digestive tract enzymes. Within the duodenum, the remaining peptides incite immunogenic responses, including the generation of autoantibodies and inflammation, leading to irreversible damage. Our previous exploration unveiled a glutenase called Bga1903 derived from the Gram-negative bacterium Burkholderia gladioli. The cleavage pattern of Bga1903 indicated its moderate ability to mitigate the toxicity of pro-immunogenic peptides. The crystal structure of Bga1903, along with the identification of subsites within its active site, was determined. To improve its substrate specificity toward prevalent motifs like QPQ within gluten peptides, the active site of Bga1903 underwent site-directed mutagenesis according to structural insights and enzymatic kinetics. Among the double-site mutants, E380Q/S387L exhibits an approximately 38-fold increase in its specificity constant toward the QPQ sequence, favoring glutamines at the P1 and P3 positions compared to the wild type. Not only does the heightened specificity of E380Q/S387L improve the hydrolytic activity against pro-immunogenic peptides, but also holds this version of the enzyme as a promising candidate as an oral therapy enzyme for celiac disease.

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN works best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least performance of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features.

Human-to-human communication via the computer is mainly done using a keyboard or microphone. In the field of Virtual Reality (VR), where the most immersive experience possible is desired, the use of a keyboard contradicts this goal, while the use of a microphone is not always desirable (e.g. silent commands during task force training) or simply not possible (e.g. if the user has a hearing loss). Data gloves help to increase immersion within the VR as they correspond to our natural interaction. At the same time, they offer the possibility to accurately capture hand shapes, such as those used in non-verbal communication (e.g. thumbs up, okay gesture, ...) and in sign language. In this paper, we present a hand shape recognition system using Manus Prime X data gloves, including data acquisition, data preprocessing, and data classification to enable nonverbal communication within VR. We investigate the impact on accuracy and classification time of using an Outlier Detection and a Feature Selection approach in our data preprocessing. To obtain a more generalized approach, we also studied the impact of artificial Data Augmentation, i.e., we create new artificial data from the recorded and filtered data to augment the training dataset. With our approach, 56 different hand shapes could be distinguished with an accuracy of up to 93.28%. With a reduced number of 27 hand shapes, an accuracy of up to 95.55% could be achieved. Voting Meta-Classifier (VL2) has proven to be the most accurate, albeit slowest, classifier. A good alternative is Random Forest (RF), which was even able to achieve better accuracy values in a few cases and was generally somewhat faster. Outlier Detection has proven to be a effective approach, especially in improving classification time. Overall, we have shown that our hand shape recognition system using data gloves is suitable for communication within VR.

The growing adoption of distributed energy production technologies, and the potential of energy underutilization when it is produced by non-connected groups, has raised interest in ‘sharing economy’ concepts in the electricity sector. We suggest that mechanisms, such as peer-to-peer (P2P) energy trading, will allow users to exchange their surplus energy for mutual benefits, stimulate the adoption of renewable energy, encourage communities to ‘democratically’ control their own energy supplies for local development, and improved energy efficiency, and create many other benefits This approach is receiving increasing attention across the world, particularly in Germany, the Netherlands and Australia. Nevertheless, the actual development and implementation of these platforms is slow and mostly limited to trial activities. This study investigates the challenges and barriers facing P2P energy trading developments based on previous academic and industry studies. We provide a comprehensive multidimensional barrier analysis through a PESTLE approach to assess the barriers from a variety of perspectives, including the political (P), economic (E), social (S), technological (T), legal (L), and environmental (E) aspects. This approach clarifies the many intersecting problem fields of P2P trading in renewable energy, and the paper identifies a list of such barriers, and discusses the prospects for addressing these issues. We also elaborate on the importance of incentive-based P2P market design.

This study provides a comprehensive assessment of Derawan Island's coral reefs over two decades (2003, 2011, and 2021) from a machine learning classification perspective. Employing non-parametric algorithms like Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), our analysis primarily focused on spatial and temporal changes in coral habitats. RF emerged as the most accurate method, demonstrating an accuracy of 71.43% with Landsat 9, 73.68% with Sentinel 2, and 78.28% with Multispectral Aerial Photos. We found that the accuracy of our classification results was significantly influenced by the geographic resolution, as well as the quality of field and satellite/aerial image data. Through spatial clustering, the coral habitats exhibited an Nearest Neighbor Index (NNI) value of 0.8727, indicating specific patterns of distribution. The analysis revealed a decrease in coral reef extent from 2003 to 2011, shrinking to 16 hectares with varying densities, followed by a slight area increase but with more heterogeneous densities between 2011 and 2021. This study not only highlights the dynamic nature of coral reef habitats over two decades but also underscores the critical role of machine learning in environmental monitoring and conservation efforts.

Studies involving vortex in hybrid superconducting devices and their interaction with different components inside the sample are an important issue to reach higher values of their critical parameters in superconducting materials. The vortex distribution on each side of sample with different fundamental parameters, such as temperature T, penetration depth λ, coherence length ξ, electron mass m and the order parameter Ψ, may help to improve the superconducting properties. Thus, in this work, we used the modified Ginzburg-Landau theory to investigate hybrid superconductor (HS), but also to provide a highly tunable and adjustable theoretical tool to theoretically explain the experimental results involving HS, in order to study the vortex behaviour in superconductors of mesoscopic dimensions with extremely differences among their fundamentals parameters. Therefore, we evaluated the influence of HS on vortex configuration and their effects in the field-dependent magnetization. The results show that the during the increment of applied magnetic field H the diamagnetic response of HS, Meissner effect, include additional jumps of magnetization while diamagnetism continues its increase in the sample. In addition, the differences among parameters, create an interface between both components, two different magnitudes of supercurrent and vortex sizes producing a lower degradation of local superconductivity, which increase the upper critical field. On the other hand, this type HS with differences of parameters on both sides can be used to control the vortex movement in the selected superconducting region sample with more accuracy.

This paper proposes a model that combines multi-criteria and multi-faceted risk assessment. Using two data sources, a fuzzy Technique for Order of Preference by Similarity to Ideal Solution FTOPSIS method combined with FMEA assessment. The FMEA method presented in this paper combines the technique of prioritising preferences according to FTOPSIS similarity to the ideal solution and conviction structure to overcome the defects of traditional FMEA indicators. The paper describes the exact process and tool selection, and the results obtained in the study verified accurate data. Finally, a numerical case study was presented on hospital reorganization services and case adaptation to prioritize surgical abandonment in Poland. The problem considered in the publication is the priority of patients’ operations in hospitals. The selection of relevant criteria, their importance and patient preferences are presented. The results obtained from the method provide a viable action plan for the proposed research problem. The proposed method is multi-faceted and can be part of an information system supporting reorganization, restructuring, and modification of the operational process.

This paper investigates if the polar groups induced by a plasma treatment can increase the lap shear strength of laser joined metal and plastic hybrids. Optimal laser joining parameters for cold-rolled AISI304 – Polyamide 6.6 and sandblasted AISI304 - Polypropylene hybrids are developed. The OWRK-method was utilized to identify the change in surface free energy of a low-pressure plasma treatment. Plasma treated samples were joined and tested. The fractured surfaces were investigated via light microscopy and SEM. The arithmetic means of the plasma treated hybrids lap shear strength with polyamide 6.6 varied slightly but all measured values were within the range of the untreated samples. The lap shear strengths of the polypropylene hybrids were significantly reduced. This was attributed to an overaging and the development of low molecular weight oxidized materials which lead to a weak boundary layer. No correlation between surface free energy and the lap shear strength could be found.

The high demands of caring for and raising a child with autism spectrum disorder on a daily basis fatally lead parents to the appearance of physical and mental fatigue. This study aimed to assess the effect of social support and spirituality on the fatigue of parents with children with autistic spectrum disorder. A cross-sectional study with a convenience sample was conducted in Schools of Special Education in Attica (Greece). The sample consisted of 123 parents who completed The Fatigue Assessment Scale (FAS), the Multidimensional Scale of Perceived Social Support (MSPSS), and the Functional Assessment of Chronic Illness Therapy Spiritual Well-Being Scale (FACIT Sp-12) to measure the levels of fatigue, social support, and spirituality, respectively. Demographic data were, also, recorded. Data analysis was applied using IBM SPSS 21.0. The mean age was 47.3 years old, 81.3% were women, and 38.9% stated much / very much religious. The mean of total FAS was 28.4 (SD±7.5), of total FACIT Sp-12 was 31.5 (SD±8.7), and of total MSPSS 5.1 (SD±1.1). Higher levels of total MSPSS and FACIT Sp-12 were associated with lower total FAS (r= -0.50, p <0.001 and r= -0.49, p<0.001 respectively). Social support and spirituality are significant predictors of fatigue.

Abstract Objective Variations of sex characteristics (VSC) are atypical genetic, hormonal, gonadal and anatomical sex traits that require holistic medical management. The Chicago Consensus statement 2006 introduced the requirement that multidisciplinary teams organise the transition of care from pediatric to adult services. Yet, the evidence that the transitional care is in place is scarce. The aim of this paper is to examine the implementation of transition of care for people with VSC. Method Seven focus groups were conducted with health care professionals and peer support groups in care teams in Central, Northern and Western Europe. The data from the focus groups were examined using reflexive thematic analysis. Results The participants in the study stated that the transition of care has been implemented in the last two decades. The quality of care depends on available resources and variation of sex characteristics. However, there are significant hurdles to adequate transition of care as there is lack of time and resources. Lack of adult care providers and psychosocial support leaves young adults and adults with VSC navigating the health care system often alone. Conclusion The outcome of the study shows that the transition of care is organized through the department of pediatric endocrinology. The quality of care varies due to resources and variation. Lack of adult specialists and psychosocial support represent the biggest obstacle for young adults and adults to navigate the healthcare system and for the improvement in provision of healthcare to adults, especially psychosocial support.

Policing is considered a high-stress occupation due to the demanding nature of the job. Addressing police stress requires a detailed understanding of how psychosocial risk factors influence various aspects of their well-being. Moreover, an analysis of the direct effects of work conditions and the mediating effects of cognitive appraisals is also warranted. Using the Demands-Resources-Individual Effects (DRIVE) model of work-related stress [19], this study investigated the direct effects of work conditions on well-being and also examined the intermediate role of perceived job stress and job satisfaction in the relationship between work conditions and well-being. Five hundred and seventy-eight police officers from the Jamaica Constabulary Force (J.C.F.) completed the questionnaire. Data were analyzed using hierarchical regressions and the Hayes Process tool for mediation analysis. Both perceived job stress and satisfaction mediated the relationship between work conditions and general physical health. Perceived job stress was an indirect pathway through which work conditions influenced psychological distress, whereas job satisfaction was not a significant mediator. In contrast, job satisfaction mediated the relationship between work conditions and positive well-being, but perceived job stress did not. These findings provide significant evidence for periodically monitoring and auditing perceptions of stress and job satisfaction as they are likely precursors to subsequent health problems.

Drowning remains a prominent global pediatric health concern, necessitating preventive measures such as educational initiatives for children and caregivers. This study aimed to assess the feasibility and educational effectiveness of an interactive puppet show centered on water safety for children and parents. A 30-minute original theater performance, featuring two actors and three puppets (a girl, a crab, and a lifeguard), was conducted. Subsequently, 185 children (aged 4 to 8) and their 160 parents (134 mothers and 26 fathers) participated in this quasi-experimental study. Pre- and post-show tests were administered to evaluate knowledge and behaviors in aquatic environments. Prior to the puppet show, 78% of children exhibited basic aquatic competency. Only 33% considered swimming alone risky. Following the intervention, 81.6% of children changed their perception of the risks of solo beach activities, showing improved knowledge of emergency number activation (from 63.2% to 98.9%, p<0.001). The intervention increased parents' intention to visit lifeguard-patrolled beaches and improved their CPR knowledge for drowning victims by 58.8%. In conclusion, a drowning prevention puppet show positively impacts children and parents, potentially enhancing safety behaviors during water-related leisure activities, warranting consideration as part of comprehensive drowning prevention strategies.

Background: Malnutrition was associated with worse survival outcomes, impaired quality of life, and deteriorated performance status across various cancer types. We aimed to identify risk factors for malnutrition in patients with epithelial ovarian(EOC) and impact on survival. Methods: In our prospective study, we included the patients with primary and recurrent EOC, tubal or peritoneal cancer conducted. We assessed serum laboratory parameters, body mass index, nutritional risk index, nutritional risk screening score(NRS-2002), and bio-electrical impedance analysis. Results: We recruited a total of 152. Patients >65 years-old, with ascites of >500 ml, or with platinum-resistant EOC showed statistically significant increased risk of malnutrition when evaluated by NRS-2002(p-values = 0.014, 0.001, and 0.007, respectively). An NRS-2002<3 was an independent predictive factor for complete tumor resectability (p=0.009). The patients with NRS-2002≥3 had a median overall survival(OS) of seven months (95% CI=0-24 months), as compared to the patients with NRS-2002<3 where median OS was 46 months(p=0,001). Phase angle(PhAα)≤ 4.5 was the strongest predictor of OS. Conclusion: Malnutrition is an independent predictor of incomplete cytoreduction in study population. It is an independent prognostic factor for poor OS. Preoperative nutritional assessment is an effective tool in identification of high-risk groups within EOC characterized by poor clinical outcome.

Background: This study retrospective evaluates patients with stage IV melanoma treated with Nivolumab and Ipilimumab combination therapy from two regional oncology centers from Romania between the years of 2019 up to the end of 2022. Methods: The data were analyzed in SAS for Windows, V9.4. The survival curves were estimated using the Kaplan-Meier method, and survival distributions were compared with log-rank test. The effects of the main clinical and pathological variables on OS and PFS were investigated with Cox regression. Results: Kaplan-Meier curve of OS in all evaluable patients enrolled in the study resulted in a median OS of 346 days (95% CI: 150-NA) and a median PFS of 211 days (95% CI: 113-430). 45.3% of the patients experienced adverse events during the Nivolumab + Ipilimumab treatment with some of them having multiple organ systems involved. Discussion: The OS values were lower than that reported in approval clinical trials, but the results show a marked improvement when comparing to results obtained by chemotherapy regimens previously used in these scenarios. Conclusion: This study provides real-world insights into the survival data and safety profiles of combination therapy with anti-PD-1 antibodies and anti-CTLA-4 antibodies.

Repeated exposure to pathogens leads to evolutionary selection of adaptive traits. Many species transfer immunological memory to their offspring to counteract future immune challenges. Transfer factor such as that found in colostrum is among the many mechanisms where transfer of immunologic memory from one generation to the next can be achieved for enhanced immune response. Here, a library of 100 plants with high protein contents were screened to find plant-based proteins that behave like a transfer factor moiety to boost human immunity. Aqueous extracts from candidate plants were tested on a human peripheral blood mononuclear cell (PBMC) cytotoxicity assay using human cancerous lymphoblast cells - K562 cells as a target and Natural Killer cells as an effector. Plant extracts that caused PBMCs to exhibit enhanced killing beyond the capability of the colostrum-based transfer factor were considered hits. Primary screening yielded an 11% hit rate. Protein content of these hits were tested by Bradford Assay and Coomassie stained-SDS-PAGE, where three extracts were confirmed to have high protein contents. Plants with high protein contents underwent C18 column fractionation using methanol gradients followed by membrane ultrafiltration to isolate proteins with molecular weight &lt;3 kDa, 3-30 kDa, and &gt;30 kDa fractions. It was found that the 3-30 kDa and &gt;30 kDa fractions had high activity in the PBMC cytotoxicity assay. The 3-30 kDa ultrafiltrates from the top two hits, seeds from Raphanus sativus and Brassica juncea, were then selected for protein identification by mass spectrometry. The majority of the proteins in the fractions were found to be seed storage proteins, with low abundance of proteins involved in plant defense and stress response. These findings suggest that Raphanus sativus or Brassica juncea extracts could be considered for further characterization and immune functional exploration with a possibility of supplemental use to bolster recipients’ immune response.

India ranks among the top ten nations in the world for grape production. Fungal pathogens inflict damage to crop plants in turn making cultivators bear huge economical losses. With an output of 1.21 million tons (about 2% of 57.40 million tons produced globally). 1.2% of the nation’s total fruit cropland is covered by grapes. But due to fungal diseases the effect of the yield produced ranges from 5-80% depending on the severity of diseases which will affect the yield of grape vineyard. In precision agriculture, new sensing technologies and artificial intelligence could be used to automatically identify grapevine and disease pest symptoms. Traditional manual disease-monitoring methods are inefficient, labor-intensive, and ineffective. Timely effective and precise evaluation of grape diseases is admitted as a critical step in the field management. In this paper, we are explaining about different optical sensing methods applied for RGB, Multispectral and Thermal cameras. Section-wise we will be describing environmental set up for image-aquation, data-preprocessing, different modelling methods, evaluation matrix, result, and reviewer’s comment.

Research has shown a protective association between social support and depression, depression among stroke patients, and health impacts of depression. Yet not much is known on the effect of social support on depression among stroke patients. This review aims to summarize the current research examining the association between social support and depression among stroke patients. A literature search was performed in PubMed to find original peer-reviewed journal articles from 2016 to Mar. 12, 2023 that examined the association between social support and depression among stroke patients. The search terms were depression and "social support" and stroke, which lead to 172 articles. After abstract review, seven observational studies that studied the target association among stroke patients were selected. One additional study was found using PsycINFO as a complementary source with the same search strategy and criteria. Overall, a negative association was found between social support and depression among stroke patients in seven studies, with more social support leading to lower rates of depression post-stroke. One study found that social support was positively related with depression, but the result was nonsignificant. Overall, the results of recent studies suggest that social support is negatively associated with depression among stroke patients. In most studies, this association was statistically significant. The findings suggest the importance of improving social support perceived by stroke patients in the prevention of depression after the occurrence of stroke.

Band structure calculations using the spin-polarized relativistic Korringa-Kohn-Rostoker (SPRKKR) band structure method have been performed to determine the intrinsic magnetic properties (magnetic moments, magneto-crystalline anisotropy -MAE and Curie temperatures) of the Fe5-x-yCoxMySiB2 (M = Re, W) alloys. The general gradient approximation for ex-change-correlation potential and the atomic sphere approximation (GGA ASA) have been em-ployed. Previous studies showed that the Co doping is turning the in-plane into axial anisotropy for a certain doping range, whilst the 5d doping enables a strong spin-orbit coupling of Fe-3d and M-5d states which is needed to enhance the MAE. The theoretical calculations aim to find the dependence of the anisotropy constant K1 for combined Co and M doping, building a two-dimensional (2D) map of K1 for 0 ≤ x ≤ 2 and 0 ≤ y ≤ 1. Similar theoretical 2D maps for magnetization and Curie temperature vs. Co and M content (M = W and Re) have been built, allowing to select alloy compositions with enhanced values of uniaxial anisotropy, magnetization and Curie temperature. Magnetic properties of Fe4.1W0.9SiB2 alloy that meets the selection criteria for axial anisotropy K1 > 0.2 meV/f.u., Curie temperature Tc > 800 K determined by mean field approach and magnetization µ0Ms > 1 T are discussed.

Cancer remains as a worldwide cause of death. Investigational research efforts have included the administration of tumors-derived extracts to healthy animals. Having previously demonstrated that administration of non-transmissible, human cancers-derived homogenates induced malignant tumors in mice; we examined here the consequences of administering 50 or 100 µg of protein of crude homogenates from samples of mammary carcinoma, pancreatic adenocarcinoma, and melanoma. The protocol conceived six inoculations per week during three months. Concurrent control group received homogenates of healthy donor skin cosmetic surgery fragments. Mammary carcinoma homogenate administration did not provoke animals’ deterioration or mortality. Multiple foci of lung adenocarcinomas with broad expression of malignity histomarkers, coexisting with small cell-like carcinomas were found. Disseminated cells, positive to classic epithelial markers were detected in lymphoid nodes. Pancreas tumor and melanoma homogenates administrations severely deteriorated animals’ health. Pancreas tumor induced lung poorly-differentiated adenocarcinomas, as a remarkable pancreatic islets hyperplasia. Melanoma affected lungs with atypical adenomatous hyperplasia and solid pseudopapillary adenocarcinoma. Giant atypical hepatocytes with variegated nuclei were also observed. Kidney parenchyma exhibited dispersed foci of neoplastic cells within a desmoplastic matrix. A noticeable nuclear overlapping with hyperchromatic nuclei, mitotic figures, and prominent nuclear atypia was identified in epidermal cells. None of these changes were ever detected in any mouse of the control groups. This study confirms and extends our hypothesis that tumor homogenates contain and may act as a vector for “malignancy drivers”, which ultimately implement a carcinogenesis process in otherwise healthy mice.

.Celiac disease (CeD) is a chronic, autoimmune disorder triggered by the ingestion of gluten, affecting around 1% of the global population. It is a multifactorial disease involving both genetics and environ-mental factors. Nowadays, the only available treatment for CeD is a life-long gluten-free diet (GFD), which can cause a significant burden for patients, since symptoms and mucosal injury can persist de-spite apparent compliance with a GFD. That could also lead to psychological consequences and affect the quality of life of these patients. Thankfully, recent advances in understanding the pathogenesis of CeD and the availability of various targets have made it feasible to explore pharmaceutical treatments specific to CeD. Recently, the FDA has highlighted the unmet needs of adult patients on GFD who expe-rience ongoing symptoms attributed to CeD and also show persistent duodenal villous atrophy. This re-view will outline the limitations of a GFD, describe the targets of potential novel treatment of CeD and provide an overview of the primary clinical trials involving oral and injectable agents for a non-dietary treatment of CeD.

Accurate pathogenic detection in wastewater is critical for safeguarding public health and the environment. However, the presence of free nucleic acids in wastewater samples poses significant challenges to molecular detection accuracy. This comprehensive review explores the current status and future potential of pretreatment methods to remove free nucleic acids from wastewater samples. The study contributes a comprehensive analysis of the mechanisms, strengths, and limitations of various pretreatment approaches, including physical, chemical, and enzymatic processes. The effect of various factors on the removal efficiency of these pretreatment methods is also discussed. This review enhances our comprehension of pretreatment techniques and their vital role in achieving precise pathogenic detection in complex wastewater matrices. Furthermore, it outlines future perspectives and developments for improving the speed and effectiveness of pathogenic detection, contributing significantly to disease surveillance, early warning systems, and environmental protection.

In recent years, computer-aided analysis techniques have emerged as valuable tools in assisting dermatologists by providing objective and efficient analysis of skin cancer images. This paper utilizes the combination of the Contourlet Transform (CT) and Local Binary Pattern (LBP) techniques for accurately recognizing borders, contrast changes, and shapes of skin cancer images. These results often contain many features, leading to high computational costs and potential over-fitting issues. Hence, we applied Particle Swarm Optimization (PSO) to select the most informative and discriminating features, reducing the dimensionality while retaining important information for accurate classification. After reducing the feature set with PSO, we applied these sets to Machine learning classification algorithms: Support Vector Machine (SVM), Random Forest (RF), and Neural Networks (NN). The results show that SVM has the lowest time complexity of 0.0458 seconds, followed by the Neural Network at 0.08730 seconds, and the Random Forest model has the highest time complexity of 0.1622 seconds. The SVM and Neural Network models are faster to train than the Random Forest model, making them more suitable for real-time or latency-sensitive applications. We also compared our proposed model with the state-of-the-art models and obtained the accuracy of 86.9%, which is the highest among the models.

Serologic measures of tissue transglutaminase (tTG) immunoglobulin A (IgA) and deamidated gliadin peptide (DGP) IgA and immunoglobulin G (IgG) are hallmark tests utilized when diagnosing individuals for celiac disease (CeD) and for monitoring adherence to a gluten-free diet (GFD), currently the only available treatment for CeD. We address two issues in this study: (i) the relapse to seropositivity for CeD patients who resume a gluten containing diet and (ii) the correlation between two different tTG-IgA assays near the upper limit of normal (ULN) designated thresholds. Regarding the first issue, often a suspected CeD individual is put back on a gluten diet to return to their serologic levels. However, we show it requires a substantial amount of gluten for serology to return to a positive level. For example, in one study of 22 patients treated with placebo and taking 84 g of gluten over 6 weeks, only 2 converted from seronegative to seroposi-tive for tTG-IgA. Regarding the second topic, we compare the relationship for different serologic assays, namely tTG-IgA AB (recombinant, ULN=4 Units/mL) vs. tTG-IgA (non-recombinant, ULN=20 Units). There is a strong correlation by both measurements as evidenced by a Pearson coefficient of R = 0.8584. However, we observed that the cross-correlation in terms of sensitivity and specificity improved substantially by using an ULN value of 3 instead of 4 for the tTG-IgA AB (recombinant) assay. This result suggests that assay thresholds used for initial diagnosis in patients who have not yet started a GFD may need to be adjusted for monitoring and in the setting of a diagnostic gluten challenge.

Keywords: Artificial Neural Network (ANN), Support Vector Machine (SVM), Support Vector Regression (SVR), Lightweight Gradient Boosting Machines (Light GBM), Machine Learning, Solar Irradiance (SI), Solar forecasting

The emergence of antimicrobial resistance remains one of the greatest public health concerns. Biofilm formation has been postulated as a mechanism of microbial pathogens to resist antimicrobial agents. Lactic Acid Bacteria (LAB) and their metabolites have been proposed to combat bacterial biofilms, due to their antimicrobial activity. In this vein, the aim of the present study was to investigate the biofilm removal potential of cell-free supernatant (CFS) of five wild-type Lacticaseibacillus rhamnosus LAB strains, isolated from Greek natural products, in comparison to the commercial available L. rhamnosus GG strain, formed by common foodborne pathogens (Salmonella Enteritidis, Salmonella Typhimurium, Escherichia coli, Listeria monocytogenes, and Staphylococcus aureus). Biofilm removal activity of LAB was assessed on two-days mature biofilm using a microtiter plate-based procedure. Both non-neutralized and neutralized CFSs removed biofilms in a concentration-dependent manner. Biofilm removal activity of the non-neutralized CFSs was significantly higher compared to the neutralized CFSs, as expected, ranging 40 – 90% and 10 – 78%, respectively. Biofilm removal efficiency of L. rhamnosus OLXAL-3 was signifanctly higher among the wild-type L. rhamnosus strains tested (20 – 100% v/v). In conclusion, our results suggested the great potential of application of wild-type Lacticaseibacillus rhamnosus strains CFSs as effective natural agents against pathogenic bacterial biofilms.

The well-being in the people is a key aspect of the field of psychology. Hence, it is important to an-alyse the variables that are related to life satisfaction and happiness as perceived by individuals and that, therefore, increase their overall well-being. The main objective of this study was to ana-lyse the predictive capacity of emotional intelligence and perceived social support on both the level of life satisfaction and perceived happiness. A total of 380 psychology students completed the Trait Meta Mood Scale, the Multidimensional Scale of Perceived Social Support, the Satisfaction With Life Scale, and the Subjective Happiness Scale. The results show that both emotional intelligence and social support are related to and predictive of subjective happiness and life satisfaction. The importance of developing the components of emotional intelligence and promoting an adequate social network in young people is highlighted.

The increading emission of carbon dioxide to the atmosphere has urged the scientific community to investigate alternatives to alleviate such emissions being the principal contributor to the greenhouse gas effect. One major alternative is carbon capture and utilisation (CCU) towards the production of value-added chemicals using diverse technologies. This work aims at the study of the catalytic potential of different cobalt-derived nanoparticels for methanol syntheis from carbon dioxide hydrogenation. Thanks to its abundance and cost-efficacy, cobalt can serve as an economical catalyst compared to noble-metal-based catalysts. In this work, we present a systematic comparison among different cobalt and cobalt oxide nanocomposites in terms of their efficiency as catalysts for carbon dioxide hydrogenation to methanol as well as how different supports can enhance their catalytic capacity. The oxygen vacancies in the cerium oxide act as carbon dioxide adsorption and activation sites, which facilitates a higher methanol production yield.

Aryl chlorides and bromides in the presence of NHC palladium hydroxo dimers of the type [{Pd(µ-OH)Cl(NHC)}2] (where NHC = IPr, SIPr, IMes, SIMes) undergo an efficient and selective Sonogashira cross-coupling with terminal acetylenes. For the coupling of 4-chlorotoluene with phenylacetylene TON = 560000 was observed. The procedure enables high-throughput and selective synthesis of a broad spectrum of unsymmetrically substituted 1,2-diarylacetylenes. Nearly a quantitative amount of HCl was detected within cross-coupling products. Water plays the role of the base and is regenerated in the process so that it does not have to be used in stoichiometric amounts (relative to the limiting reactant). Generation of the active Pd(0) complex proceeds via ethanol oxidation.

Spatial interpolation of meteorological data can have immense implications on risk management and climate change planning. Kriging with external drift (KED) is a spatial interpolation variant that uses auxiliary information in the estimation of target variable at unobserved locations. However, the traditional KED methods with linear trend functions may not be able to capture the complex and non-linear interdependence between target and auxiliary variables, which can lead to an inaccurate estimation. In this work, a novel KED method using least squares support vector regression (LSSVR) is proposed. This machine learning algorithm is employed to construct trend functions regardless of the type of variable interrelations being considered. To evaluate the efficiency of the proposed method (KED with LSSVR) relative to the traditional method (KED with a linear trend function), a systematic simulation study for estimating the monthly means temperature and pressure in Thailand in 2017 was conducted. The KED with LSSVR is shown to have superior performance over the KED with the linear trend function.

The work carried out first-principles calculations within the framework of density functional theory to study the structural stability of the CsSnI₃ compound and the influence of phase transitions on their electronic and optical properties. Using the GGA and SCAN functionals, the relaxed structures of the CsSnI₃ phases were obtained and their geometric characteristics were assessed. Using the Phonopy code based on VASP, calculations of phonon and thermodynamic properties were performed, and the temperatures of phase transitions of CsSnI₃ were determined. The temperature dependences of the thermodynamic parameters α-, β-, γ- and δ-phases of CsSnI₃ were analyzed. The trends in free energy, entropy, enthalpy, heat of formation energy and heat capacity were justified in terms of the pattern of changes in the total energy of the four phases of CsSnI₃ from VASP calculations. It is shown that at 0 K the non-perovskite structure of the CsSnI₃ compound (δ-CsSnI₃) is the most stable (followed by γ-CsSnI₃), and the tetragonal phase (β) is quite unstable, having the highest energy among the perovskite phases. It was revealed that at temperatures above 450 K the tetragonal phase becomes stable, and when the temperature drops, it transforms into the cubic phase (α-CsSnI₃). The phase transition between the β and γ phase of perovskite occurs in the range of 300-320 K, and at 320 K a black-yellow transformation of CsSnI₃ occurs in which the cubic phase (black perovskite) undergoes a phase transition to a non-perovskite conformation (yellow phase). The presence of temperature phase transitions between two orthorhombic phases of CsSnI₃ at 360 K was discovered, although direct transitions of the α⟷γ and γ⟷δ types have not yet been reported in any experiment, except for γ→δ transitions under the influence of moisture. Based on well-relaxed structures (from the SCAN calculations), the band gap widths for four CsSnI₃ phases were calculated and compared with experimental measurements. Electronic properties and Fermi level shifts as a result of phase transformations of CsSnI₃ were assessed using the HSE06 functional and machine learning prediction. The values of the complex dielectric constant and the refractive index of all phases of the CsSnI₃ were determined.

Effective sports training should be attuned to the athlete's specific conditionings and characteristics. In motor learning research, two often neglected factors that influence this resonance are the learner's athletic background and the structural diversity of exercises (e.g., relative similarity). This study examines the effects of three learning approaches (i.e., contextual interference (CI), differential learning (DL), and free-play control condition (CO)) on the parallel learning of Handball (HB), Volleyball (VB), and Basketball (BB) skills, considering participants' prior sport backgrounds. Forty-five males (15 HB, 15 VB, and 15 BB players) with a mean age of 22±1.4 years and at least 6 years of experience in the mastered discipline voluntarily participated in this study. A pre-post-retention test design including a 6-week-intervention program was employed. In terms of short-term (pre-post) gain, only the DL- group significantly improved their performance in both non-mastered disciplines (p=0.03 for the BB free-throw and p=0.05 for the HB shooting tests), with a trend (ES=0.53) towards an improvement in the performance of the mastered VB underarm-pass skill. In terms of relatively permanent gains, the CI group significantly improved their performances from pre- to retention-test only in the non-mastered BB free-throw skill (p=0.018). In contrast, the DL group significantly improved their performance at retention compared to the pre-test in both non-mastered BB (p=0.004) and HB (p=0.003) skills, with a trend (ES=0.4) towards improvement in the mastered VB test. In both short, and relatively permanent terms, higher composite score gains were observed in DL compared to CI (p=0.001 and 0.002) and CO (p=0.001 and <0.0001). In conclusion, the present findings provide additional support for the potential advantages of the DL model over those of CI. These findings can serve as the basis for tailored training and intervention strategies and provide a new perspective for addressing various issues related to individual and situational learning.

Dacomitinib is an irreversible epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor indicated for the treatment of patients with advanced non-small cell lung cancer (NSCLC) and EGFR-activating mutations. Proton pump inhibitors decreased dacomitinib exposure. This anal-ysis summarizes the effect of H2RA on dacomitinib exposure. A within-patient comparison of steady-state trough concentrations (Ctrough,ss) of dacomitinib and its active metabolite and active moiety with and without concomitant use of H2RA was conducted using a linear mixed effects model with pooled data from 11 clinical studies in patients with NSCLC. An oral absorption PBPK model was constructed and verified using clinical PK data after a single dose of dacomitinib in healthy volunteers to estimate the effect of gastric pH altered by an H2RA on dacomitinib PK. The adjusted geometric mean of dacomitinib Ctrough,ss of dacomitinib parent, metabolite and active moiety following co-administration with H2RA was approximately 86%, 104% and 100% relative to that following dacomitinib 45 mg administration without H2RA (P>0.05). The PBPK modeling showed negligible change in dacomitinib Cmax and AUC over 0-24 hours after H2RA administra-tion, when compared with those administered dacomitinib alone. Co-administration of an H2RA with dacomitinib is not expected to have any clinically relevant effect on dacomitinib exposure.

Cardiovascular diseases (CVD) are chronic diseases associated with a high risk of mortality and morbidity. Early detection of CVD is crucial to initiating timely interventions, such as appro- priate counseling and medication, which can effectively manage the condition and improve patient outcomes. Preventive measures should be implemented at the general public level, promoting a healthy lifestyle, and at the individual level, that is, in people with moderate to high risk of CVD or patients already diagnosed with CVD by addressing an unhealthy lifestyle. Personalized early diagnostic systems based on artificial intelligence (AI), ontologies, and other medical information processing systems may prove to be a great preventive measure. In this paper, we focus on the use of ontology-inspired database models in the diagnosis of cardiovascular disease, as well as their potential for use in web application development.

In the seasonally frozen regions, during the grouting of prestressed bridge ducts in low-temperature environments, incompletely cured grout materials undergo volumetric changes due to the freeze-thaw cycling, resulting in structural cracks along the prestressing ducts of the bridge, thereby diminishing the bridge's operational lifespan. To investigate the frost heaving characteristics of grout materials under freeze-thaw cycles and propose potential enhancements, this study was devised to conduct frost heaving stress tests on grout materials. Through this research, we gained insights into how various additives affect the frost heaving stress characteristics of grout materials under freeze-thaw cycles. Incorporating analyses of free water content, setting time, compressive strength, as well as macroscopic examinations such as XRD and SEM, this study delves into the mechanisms for enhancing frost heaving characteristics of grout materials. Furthermore, it proposes a research approach that utilizes multiple admixtures to improve the frost heaving properties of grout materials. Through frost heaving stress tests and other macroscopic experiments, the study investigates the influence of these combined admixtures on frost heaving stress in grout materials and elucidates the underlying improvement mechanisms. The results indicate that reducing the water-cement ratio, incorporating calcium formate, sulfoaluminate cement, air-entraining agents, and carbamide all have a positive impact on mitigating frost heaving stress in grout materials. However, the improvement mechanisms differ, and employing a single measure alone is insufficient to effectively reduce frost heaving stress while meeting performance criteria such as compressive strength, setting time, and flowability. Free water content emerges as a crucial indicator determining the magnitude of frost heaving stress in grout materials, with 11.5% of free water content representing the critical threshold for frost heaving in grout materials. Utilizing composite admixtures can simultaneously decrease free water content, lower the freezing point of free water, and alleviate frost heaving deformation, resulting in more efficient reduction of frost heaving stress. When the admixture content reaches 9.9%, frost heaving stress is eliminated, and the comprehensive performance parameters including compressive strength, setting time, and flowability meet the specified requirements.

This research investigates the utilization of human driving models in autonomous vehicles, particularly in scenarios with minimal or no interactions with other vehicles. Human driving models provide valuable insights into driver behavior and play a crucial role in shaping the behavior of autonomous vehicles, enhancing their performance and user experience. The primary focus of the study is the creation of a planning model for autonomous vehicles when navigating roundabouts in the absence of traffic. This model seeks to emulate human driving behavior, ensuring predictability, safety, optimization of traffic flow, and adaptation to various roundabout geometries. To achieve this, the research introduces a trajectory model that takes into account geometric attributes and speed variations within roundabouts. The model is calibrated using empirical data and generalizes parameters through statistical regression methodologies. This model is referred to as "MRoundabout" and is evaluated for its consistency in generating plans that closely mimic human driving behavior within roundabouts. While the study presents a promising approach, it acknowledges limitations related to the model's reliance on geometric attributes and its inability to account for external factors like weather conditions. This research underscores the importance of bridging the gap between theoretical research and practical application, with the aim of enhancing safety and the overall user experience in real-world driving scenarios.

(1) Background: Most patients with mycosis fungoides (MF), a form of cutaneous T-cell lym-phoma (CTCL), develop relapsed/refractory (R/R) disease following front-line systemic therapy. This report describes treatment patterns and outcomes from the subpopulation with R/R MF. (2) Methods: This observational, retrospective, cohort study analysed patient records (1984–2016) from 27 clinical sites in Eurpe. Outcomes included treatments received, response to first-, second- and third-line treatment, overall survival (OS), and progression-free survival (PFS). (3) Results: Of 104 patients with MF, 100 received second-line and 61 received third-line therapy. Median (range) times from start of first-line therapy to first R/R MF and from first to second R/R MF were 11.2 (0.3–166.5) and 13.5 (0.0–174.6) months, respectively. Second-and third-line treatment options varied and comprised systemic therapies (85% and 79% of patients, respectively), radio-therapy (32% and 34%, respectively) and topical therapies (48% and 36%, respectively). Median (95% confidence interval [CI]) OS from the diagnosis of first R/R MF was 11.5 (6.5–not reached [NR]) years and was higher with non-chemotherapy (NR) versus chemotherapy (6.5 years); es-timated median PFS (95% CI) from the time of first R/R MF was 1.3 (1.0–2.1) years. (4) Conclu-sion: High rates of R/R disease were observed after second- and third-line treatments in this re-al-world cohort, with longer median OS in patients receiving non-chemotherapy versus chemo-therapy. Following the standard management of MF and using recently approved targeted therapies can help improve patient outcomes in advanced-stage MF.

This article presents the possibilities of using ferrite absorption material for shielding rooms with medical devices in order to additionally protect them against the limits of the electric component of the E field, which are up to 61 V/m. These values may appear in connection with the currently launched 5G system operating in the high-band frequency range, i.e. 28 GHz and 38 GHz, and may cause negative effects on the operation of medical devices. The possibilities of using ferrite absorption material for shielding rooms with medical devices were determined based on the results of measurements of the electrical properties of the ferrite material performed using two methods: the reflection method and the free space method. In the article, the complex electrical permittivity and complex magnetic permeability were determined for a ferrite plate based on measurements of the reflection coefficient and the transmission (attenuation) coefficient. The presented calculation and measurement results were used to show the properties of the ferrite material in the frequency range from 15 GHz to 45 GHz and to show the possibility of using it for shielding rooms as a material absorbing electromagnetic waves in this frequency range.

One-step oxidative esterification of 2, 5-furandiformaldehyde (DFF) derived from biomass to prepare Dimethyl Furan-2, 5-dicarboxylate (FDMC) not only simplifies the catalytic process and increases purity of product, but also avoids the polymerization of 5-hydroxymethylfurfural (HMF) at high temperature condition. Gold supported on a series of acidic oxide, alkaline oxide and hydrotalcite were prepared by colloidal deposition to explore the effect of support on the catalytic activities. The Au/Mg3Al-HT catalyst exhibited the best catalytic activity in all catalysts, 97.8% selectivity of FDMC at 99.9% conversion of DFF. And this catalyst is suitable to oxidative esterification of benzaldehyde and furfural as well. X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and CO2 temperature programmed desorption (CO2-TPD) were performed to characterize the catalysts. The results indicated that the medium and strong basic sites in the heterogeneous catalysts benefited for the absorption of intermediate spices, further facilitated the oxidative esterification of aldehyde groups. While, neutral or acidic support tended to aldol condensation reaction. It was worth noting that basicity on the support surface reduce electronic state of the Au nanoparticle (Auδ-), thus enhance the catalytic selectivity of oxidative esterification. This finding demonstrated the support plays crucial role in oxidative esterification.

Compared to tin lead alloys, lead-free tin alloys have lower toxicity, making them more widely used. In this study, Sn-20Bi-0.3Al-xY solders alloys were prepared. The effects of the addition of Y on its microstructure, micro-hardness, wettability and mechanical properties was investigated. The result showed that the addition of Y had significant effect on the refinement of Sn-20Bi-0.3Al-xY. From the spreading tests results, Y increased the wettability of the solders, however when the fraction of Y gradually increased, the wettability reduced. The ultimate tensile and hardness of Sn-20Bi-0.3Al-xY solders alloys were increased as the content of Y increasing.

Modern imaging strategies are paramount to studying living systems such as cells, bacteria, and fungi and their response to pathogens, toxicants, and nanomaterials as modulated by exposure and environmental factors. The need to understand the processes and mechanisms of damage, healing and cell survivability of living systems continues to motivate the development of alternative imaging strategies. Of particular interest is the use of label-free techniques that minimize interference of biological processes by foreign marking substances and reduce intense light exposure and potential photo-toxicity effects. This review focus on the potential synergic capabilities of atomic force microscopy (AFM) as a well-developed and robust imaging strategy with demonstrated applications to unravel intimate details in biomedical applications, with the label-free, fast, and enduring Holotomographic Microscopy (HT) strategy. We first review the operating principles that form the basis for the complementary details provided by these techniques regarding the surface and internal information provided by HT and AFM is essential and complimentary for the development of several biomedical areas studying the interaction mechanisms of nanomaterials with living organisms. First, AFM can provide superb resolution on surface morphology and biomechanical characterization. Second, the quantitative phase capabilities of HT enable superb modeling and quantification of the volume, surface area, protein content, and mass density of the main components of cells and microorganisms, including morphology of cells in microbiological systems. These capabilities result from directly quantifying refractive index changes without requiring fluorescent markers or chemicals. As such, HT is ideally suited for long-term monitoring of living organisms in conditions close to their natural settings. We present a case-based review of the principal uses of both techniques and their essential contributions to nanomedicine and nanotoxicology, emphasizing cancer and infectious disease control. The synergic impact of the sequential use of these complementary strategies provides a clear drive for adopting these techniques as interdependent fundamental tools.

While persons with schizophrenia find steady employment difficult to sustain, many of the women take on the most difficult task of all – motherhood. The aim of this paper is to review the challenges of motherhood in this population and the treatment strategies needed to keep mothers and children safe, protecting health and fostering growth. This is a narrative review reflecting the author’s life experience and a non-systematic search of the recent literature. Topics added are the stigma against motherhood in the context of schizophrenia, mothers’ painful choices, issues of contraception, abortion, and child custody, fostering and kin placement of children, the effects of antipsychotics, specific perinatal delusional syndromes, and parental supports. Recommendations are to work collaboratively with mothers who struggle with serious mental illness, take note of their strengths as well as their failings, offer a wide array of family services, monitor closely for safety, and appreciate the challenges these women daily face.

Keywords: Requirements prioritization, Next release plan, Software product planning, Decision support, MoSCoW, AHP, K-Menas, GMM, BIRCH, PAM, Hierarchical, Clustering, Clusters Evaluation

Objectives: This study aimed to assess the impact of a predictive model for detecting extracapsular extension on pathology (pECE+) on biochemical recurrence-free survival (BCRFS) within 4 years after robotic-assisted radical prostatectomy (RARP). Methods: Retrospective data analysis from a single center between 2015 to 2022. Variables under consideration included prostate-specific antigen (PSA) levels, patient age, prostate volume, MRI semantic features and Gleason score (GS). We also assessed the influence of pECE+ and positive surgical margins on BCRFS. using the Kaplan-Meier survival function and Cox regression model were assessed. Additionally, we analyzed the MRI features on BCR (biochemical recurrence) in low/intermediate risk patients. Results: 177 participants with a follow-up exceeding 6 months post-RARP were included. The 1-year, 2-year, and 4-year risks of BCR after curative prostatectomy were 5%, 13%, and 21%, respectively. The survival analysis showed that adverse MRI features as macroscopic ECE on MRI (mECE+), capsular disruption, high tumor capsular contact length (TCCL), GS≥8, positive surgical margins (PSM), and pECE+ on pathology were risk factors for BCR. In low/intermediate-risk patients (pECE- and GS &lt;(4+4)) the presence of adverse MRI features, has been shown to increase the risk of BCR. Conclusions: The study highlights the importance of incorporating predictive MRI features for detecting extracapsular extension pre-surgery in influencing early outcomes and clinical decision-making; mECE+, TCCL, capsular disruption, and GS≥8 based on pre-surgical biopsy were independent prognostic factors for early BCR. The presence of adverse features on MRI can assist in identifying low/intermediate-risk patients who would benefit from closer monitoring.

This research mapped significant social networks of victims of childhood sexual abuse, identifying relationships between childhood trauma and social bonds in adult life. Ten adult women, aged between 20 and 40 years, in psychiatric care for the consequences of trauma suffered in childhood participated in the research. For data collection, the map of significant social networks was used and analyzed through the structure, functions and attributes of the bonds. The network is composed of people from the Friendship, Family, Community, Work/Study and Health Teams group. The networks of friendships, family, co-workers or studies play a predominant role of emotional support, while the Health Teams also offer emotional support, but primarily provide clinical support in overcoming trauma and its consequences. The network of relationships has specific functions that maintain psychosocial integrity and help women to overcome the abuse suffered.

Pseudo-nitzschia spp. blooms are a recurrent problem in many coastal areas globally imposing some significant threats to the health of human, ecosystems and economy. Monitoring programmes have been established, where feasible, to mitigate the impacts caused by Pseudo-nitzschia spp. and other harmful algae blooms. The detection of such blooms from satellite data could really provide timely information on emerging risks but the development of taxa-specific algorithms from available multispectral data is still challenged by coupled optical properties with other taxa and water constituents, availability of ground data and generalisation capabilities of algorithms. Here, we developed a new set of algorithms (PNOI) for the detection and monitoring of Pseudo-nitzschia spp. blooms over the Galician coast (NW Iberian Peninsula) from Sentinel-3 OLCI reflectances using support vector machine (SVM). Our algorithm was trained and tested with reflectance data from 260 OLCI images and 4607 Pseudo-nitzschia spp. match up data points, of which 2171 were of high quality. The performance of no bloom/bloom model in the independent test set was robust, showing values of 0.80, 0.72 and 0.79 for area under the curve (AUC), sensitivity and specificity, respectively. Similar results were obtained by our below detection limit/presence model. We also present different model thresholds based on optimisation of true skill statistic (TSS) and F1-score. PNOI outperforms linear models, while its relationship with in-situ chlorophyll-a concentrations is weak demonstrating poor correlation with the phytoplankton abundance. We showcase the importance of PNOI algorithm and OLCI sensor for monitoring the bloom evolution between the weekly ground sampling and during periods of ground data absence, such as due to Covid-19.

Introduction: The objective of the study is to assess the failure of therapies with HFNO (High-Flow Nasal Oxygen), CPAP, Bilevel or combined therapy in patients with hypoxemic acute respiratory failure due to SARS-CoV-2 during their hospitalization. Methods: Retrospective and observational study of SARS-CoV-2 positive patients who required non-invasive respiratory support (NIRS), at the Reina Sofía General University Hospital of Murcia between March 2020 and May 2021. Results: Of 7355 patients, 197 (11,8%) were included. 95 of them failed this therapy (48,3%). We objectivated that during hospitalization in the ward the combined therapy with HFNO and CPAP had a lower failure rate overall and the highest treatment purely with Bilevel (p = 0,005). In the comparison of failure in therapy without two levels of airway pressure, HFNO, CPAP and combined therapy of HFNO with CPAP, (35,6% of patients) presented a 24,2% failure compared to those who did have two levels of pressure with Bilevel and combined therapy of HFNO with Bilevel (64,4% of patients) with 75,8% associated failure (OR: 0, 374; CI 95%: 0,203-0,688. p=0,001).Conclusions: The use of NIRS in conventional hospitalization is safe and effective in patients with respiratory failure secondary to SARS-CoV-2 infection. The therapeutic strategy with Bilevel increases the probability of failure, with the combined therapy strategy with CPAP and HFNO being the most promising option.

This study uses explainable artificial intelligence to analyze major predictors of poor self-rated health, dementia and their comorbidity, which include various aspects of social support. Data came from the Korean Longitudinal Study of Ageing (2016-2018), with 5527 participants aged 56 or more. The dependent variables were: (1) poor self-rated health (SRH) with 5 categories (very good, good, middle, poor, very poor); (2) dementia (no, yes); and (3) their comorbidity with 4 categories (SRH very good, good or middle, dementia no; SRH very good, good or middle, dementia yes; SRH poor or very poor, dementia no; SRH poor or very poor, dementia yes). The 49 demographic, socioeconomic and health-related predictors were included. The accuracy and area under the receiver operating characteristic curve (AUC) were compared for logistic regression and the random forest. Logistic regression and the random forest registered similar AUCs with the range of 0.80-0.87. Based on random forest variable importance, 18 out of top-20 predictors were identical for SRH, dementia and their comorbidity: previous SRH, life satisfaction - health, age, body mass index, income, chronic diseases, life satisfaction - economic, life satisfaction - overall, grandchildren alive, friendship meeting, brothers/sisters alive, class identity, children alive, social activity - friendship, education, children in weekly contact, religion and drinker. However, some variations were found among the three dependent variables, i.e., previous SRH for comorbidity (1st) and SRH (1st) compared to dementia (11th), grandchildren alive for SRH (6th) and dementia (4th) compared to comorbidity (9th), chronic diseases for comorbidity (6th) compared to SRH (10th) and dementia (10th). In terms of Shapley Additive Explanation (SHAP) values, the probability of dementia is expected to decrease by 5.57% in case a predictor grandchildren alive is included to the model. This study highlights the importance of social support for the prediction of poor self-rated health, dementia and their comorbidity. Specifically, the promotion of family support and friendship activity for elders would be vital for the prevention and management of their self-rated health and dementia.

Background: Glioblastoma is the most common primary brain neoplasm in adults, with still a poor prognosis despite a constant effort to improve patients’ survival. Some neuroradiological volumetric parameters seem to play a predictive role on Overall Survival (OS) and Progression Free Survival (PFS). The aim of this study is to analyze the impact that the volumetric areas of contrast-enhancing tumor and perineoplastic edema have on survival of patients treated for glioblastoma; Methods: A series of 87 patients who underwent surgery was retrospectively analyzed; OS and PFS were considered as the end points of the study. For each patient a multidisciplinary revision was conducted in collaboration with the Neuroradiology and Neuro-Oncology board. A manual and semi-automatic measurement were adopted to perform the radiological evaluation: contrast Enhancement Preoperative (CE-PTV) and Postoperative Tumor Volume (CE-RTV), Edema/Infiltration Preoperative (T2/FLAIR-PV) and Postoperative Volume (T2/FLAIR-RV); necrosis volume inside the tumor (NV); total tumor volume, including necrosis (TV); Results: The median OS value was 9 months and the median PFS value was 4 m; the mean values were respectively 12,3 m and 6,9 m. Multivariate analysis showed that the OS related factors were: adjuvant chemo-radiotherapy (p &lt; 0,0001), CE-PTV &lt; 15 cm³ (p=0,03), surgical resection &gt; 95% (p=0,004) and the presence of a “pseudo-capsulated” radiological morphology (p=0,04); Conclusions: maximal safe resection is one of the most relevant predictive factors for patients’ survival. The semi-automatic pre-operative MRI evaluation could play a key role in prognostically categorizing these tumors.

Trans-arterial radioembolization (TARE) is a form of radiation therapy performed for hepatocellular carcinoma (HCC) via selective intra-arterial injection of Yttrium-90 loaded microspheres. This was a multicenter retrospective study of consecutive patients with HCC who underwent TARE between July 2009 and May 2019. Using pre-treatment computed tomography imaging, the total cross-sectional area (cm2) of the abdominal skeletal muscle at the third lumbar vertebra was measured. The skeletal muscle index (SMI) was calculated by normalizing the muscle area to patient height. In total, 347 patients (median age, 65 years; 284 male) were included in the study. A total of 108 (31.1%) patients had portal vein tumor thrombus (PVTT) and 126 (36.3%) were classified as sarcopenic. The median overall survival (OS) was 28.1 months (95% CI, 24.8-35.7) and median progression-free survival was 8.0 months (95% CI, 6.4-9.4). Multivariate Cox regression analysis revealed that sarcopenia (hazard ratio [HR], 1.36; 95% CI, 1.00-1.85, p = 0.05), PVTT (HR, 1.82; 95% CI, 1.33-2.49, p &lt; 0.01), alpha-fetoprotein (AFP) (≥200 ng/mL) (HR 1.41; 95% CI, 1.04-1.92, p = 0.03), and albumin-bilirubin grade (2-3) (HR 1.74; 95% CI, 1.24-2.43, p &lt; 0.01) were independently associated with poor OS. TARE provided favorable long-term outcomes for patients with advanced HCC. Pre-treatment sarcopenia independently associated with survival, suggesting its utility as a surrogate biomarker for identifying TARE candidates.

Previous studies to recognize negative emotions (e.g. disgust, fear, sadness) for mental health care have used heavy equipment directly attaching electroencephalogram (EEG) electrodes to the head, making it difficult to use in daily life, and they have proposed binary classification methods to determine whether negative emotion or not. To tackle this problem, we propose a negative emotion recognition system to collect multimodal biosignal data such as five EEG signals in an EEG headset and heart rate, galvanic skin response, and skin temperature in a smart band for classifying multiple negative emotions. It consists of android Internet of Things (IoT) application, an oneM2M-compliant IoT server, and a machine learning server. The android IoT application upload the biosignal data to the IoT server. By using the biosignal data stored in the IoT server, the machine learning server recognizes the negative emotions of disgust, fear, and sadness using a multi-class support vector machine (SVM) model with a radial basis function kernel (RBF). The experimental results showed that the multi-class SVM model achieved 93% accuracy when considering all the multimodal biosignal data. Moreover, when considering only data in the smart band, it could achieve 98% accuracy by optimizing the hyper-parameter of the RBF kernel.

Determination of the fetal gender in the first trimester is important in twin pregnancy cases of familial X-linked genetic syndromes and helps determine chorionicity. We assessed and compared the accuracy of first-trimester ultrasound scans, and circulating cell-free fetal DNA (cffDNA) performed by non-invasive prenatal screening (NIPS) in determining fetal gender in the first trimester of twin pregnancies. For this purpose, we enrolled prospectively pregnant women with twin during the first trimester, gestational week (GA) 11-13. The fetal gender was determined using both ultrasound scans and cffDNA screening. Both results were compared to the newborn gender at delivery. A total of 113 women with twin pregnancies were enrolled. There was 100% sensitivity and specificity in Y chromosome detection using cffDNA. The accuracy of gender assignment using ultrasound in any first-trimester scans was 79.7%. Accuracy level increased from 54.2% in CRL 45-54 mm to 87.7% in CRL 55-67 mm and 91.5% in CRL 67-87 mm. Male fetuses had significantly higher chances of a gender assignment error compared to female fetuses, odds ratio = 23.57 (CI 7.356 - 75.66). This is the first study that compared the accuracy of gender determination in the first trimester by cffDNA and by ultrasound scanning in twin pregnancy. It was concluded that the cffDNA is highly sensitive and specific in determining the presence of the Y chromosome in twin pregnancies in the first trimester. Between CRL 67-87 mm, ultrasound scanning offers a highly accurate determination of fetal gender in twin pregnancies. Larger cohorts of multi-center studies are warranted.

The amino acids (AAs) and vitamins imbalances are observed in celiac disease (CD). This study evaluated the plasma profile of vitamin A and AAs and the expression level of IL-2, IL-4, IL-10, IL-12 and TGFβ in CD patients. A total of 60 children and adults with CD and 40 healthy controls (HCs) were included. Plasma profile of Vitamin A and AAs and the mRNA expression levels of target genes were assessed. Active adult patients exhibited a decrease in Vitamin A levels (p=0.04) and an increase in IL-2 (p=0.008) and IL-12 (p=0.007) mRNA expression compared to HCs. Treated adult patients showed elevated Serine (p=0.003) and Glycin (p=0.04) levels, as well as increased IL-12 (P&lt;0.0001) mRNA expression, and a decrease in Tryptophan (p=0.04) levels relative to the controls. Additionally, treated adult patients had higher plasma levels of Threonine compared to both active (p=0.04) and control (p=0.02) subjects, and increased mRNA expression of IL-4 (p=0.01) in comparison to active patients. In active children with CD, IL-2 mRNA level was found to be higher than in controls (P&lt;0.0001) and in treated children (p=0.005). Treated children with CD exhibited decreased plasma levels of Tryptophan (p=0.01) and Isoleucine (p=0.01) relative to the controls, and increased mRNA expression of TGFβ (p=0.04) relative to active patients. Managing Retinoic acid levels is crucial to alleviate intestinal inflammation of CD patients. Threonine, tryptophan, and Isoleucine are potential therapeutic options for treating CD, and targeting serine, histidine, tyrosine, and aspartate may hold promise as therapeutic targets for CD patients.

Rollover prevention of partially-filled tank trucks is an industry teaser, with the core challenge being real-time perception and observation of the liquid state inside the tank. In order realize re-liable observation of sloshing liquid, this article firstly proposes a sloshing modeling method based on multi-degree-of-freedom pendulum model, and derives the double mass trammel pendulum model (DMTP, 2DOF) accordingly, which accurately reflects the sloshing dynamics under more pervasive working conditions. Secondly, a free surface fluctuation sensor is designed based on magnetostriction, capable of measuring the inclination and height of the liquid level inside tanks filled with hazardous chemical. Finally, the unscented Kalman filter (UKF) is utilized to synthesize the information of the two, establishing a credible real-time observation of the sloshing liquid. Verified through vehicle-fluid coupled co-simulation, under condition of con-secutive double lane change, observation error of the proposed method is only 25.9% that of the open-loop calculation, providing a secure guarantee for the observation of the state variables of the single pendulum model (SP) used for most kinds of anti-rollover controller.

Abstract: The ever-increasing use of motor vehicles causes a number of traffic safety and community issues, which are particularly severe in cities, accompanied with scarcity of parking spaces and challenges encountered in the road layout alteration projects. The commonly applied solutions include designation of through streets and implementation of on-street parking on residential streets and retrofitted traffic calming measures (TCMs). This article presents the results of the study conducted on a two-way street where Metered Parking System MPS was implemented together with diagonal and parallel parking spaces, refuge islands, horizontal deflection and lane narrowing by a single-sided chicane. The aim of this study was to identify those TCMs that effectively helped to reduce the island approach speed. Heuristic method was applied to assess the effect of the respective TCMs on reducing the island approach speed and the key speed reduction determinants were defined using cause and effect diagram and Pareto chart. Comparative analyses were carried out to rate the respective TCMs as effective, moderately effective or ineffective. Section 1 of this article presents the output of the literature review on urban parking analyses and TCM efficacy. Section 2 presents the study site and the applied heuristic method. The study results are presented in Section 3. Section 3 defines the determinants on the cause and effect diagram and analyses the determinants using the Pareto chart. The final conclusions and comments are given in Section 5. Although the study was limited to a single street in Poland, the findings may hold true in other countries where similar TCMs are used.

Keywords: Electrical power systems, Support vector machines, Random Forest, Machine learning, Wavelet transform, Transmission lines fault, Electrical power quality, Short circuit, Classification of faults, Localization of faults, Decision trees, Ensemble learning, K-nearest neighbors.

This paper utilizes a life cycle assessment to evaluate the energy resources and environmental impacts of three heating systems. In the first step, life cycle assessments (LCAs) are prepared for the renewable, conventional, and combined systems. The system boundary of the LCA method is "cradle to gate," which means it includes the entire life cycle of a product or process. This includes energy supply, transport, energy generation, and operational energy use. The system scenarios were compared based on ecological and energy loads in the second step. Primary energy, resources, emissions, and environmental potentials are calculated for the system scenarios using the life cycle assessment methods CML 2016 and ReCiPe 2016. Finally, models for environmental reliability and complex decision-support tools have been developed. This work addresses two main questions: how to characterize the environmental and energy impacts of the systems under study, and what is the optimal scenario for the system? The research results show that the gas boiler system has a greater environmental impact. The most significant differences between the systems relate to the degrada-tion of abiotic fossil fuels when using a gas boiler and the process of acidification when using an electric heat pump. The results can be used to develop sustainable heating systems with reduced environmental impacts and enhanced energy efficiency.

The unsupervised domain-adaptive vehicle reidentification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain, however there are knowledge differences between the target domain and the source domain. To reduce domain differences, existing unsupervised domain-adaptive rerecognition methods generally require access to the source domain data to assist in re-training the target domain model. But for security reasons, in many cases, data between different domains cannot communicate. To this end, this paper proposes an unsupervised domain-adaptive vehicle re-identification method based on source-free knowledge transfer. First, by constructing a passive domain knowledge migration module, the target domain is consistent with the source domain model output to train a generator to generate the "source-like samples". Then, it can effectively reduce the model knowledge difference and improve the model generalization performance. According to the experiment and testing of VeRi776 and VehicleID, two mainstream public data sets in this field, the rank-k and mAP indexes obtained are both improved, and are suitable for object re-recognition tasks without source data.

In this paper, we present an innovative technique that improves the convergence order of iterative schemes that do not require the evaluation of Jacobian matrices. Using this procedure, we achieve a remarkable increase in the order of convergence, raising it from p to p + 3 units, which results in a remarkable improvement in the overall performance. We have conducted comprehensive numerical tests in various scenarios to validate the theoretical results, showing the efficiency and effectiveness of the new Jacobian-free schemes.

Mechanical circulatory support has proven effective in managing postcardiotomy cardiogenic shock by stabilizing patients' hemodynamics and ensuring adequate organ perfusion. Among the available device modalities, the combination of extracorporeal life support and a microaxial flow pump for left ventricular unloading has emerged as a valuable tool in the surgical arma-mentarium. In this publication, we provide recommendations for the application and weaning of temporary mechanical circulatory support in cardiogenic shock patients, derived from a con-sensus among leading cardiac centers in German-speaking countrie

In Philadelphia chromosome positive B-cell (Ph+) acute lymphoblastic leukemia (LLA), growing evidence has ac-cumulated about the efficacy of low-intensity and chemo-free regimens. Our objective was to analyze all recent trials evaluating these treatments and to compare them in terms of efficacy. We applied the Shiny method, an artificial intelligence technique, to analyze Kaplan-Meier curves and reconstruct patient-level data. Reconstructed patient data were then evaluated through standard survival statistics and subjected to indirect head-to-head treatment comparisons. The endpoint was progression-free survival (PFS). Based on 848 reconstructed patients, 9 trials were analyzed. The survival data from these trials were pooled into three types of treatments: i) treatments based on ty-rosine kinase inhibitors (TKIs) combined with reduced-intensity chemotherapy (denoted as TKICHE); ii) TKIs as-sociated to steroids with no chemotherapy (TKISTE); iii) chemotherapy-free combinations of blinatumomab plus TKIs (TKIBLI). According to the Shiny method, the three PFS curves were reported in a single Kaplan-Meier graph and subjected to survival statistics. In terms of PFS, TKIBLI ranked first, TKICHE second, and TKISTE third; the differences between these three regimens were statistically significant. This multi-treatment Kaplan-Meier graph, generated through the Shiny method, summarized the current evidence on these treatments in both qualitative and quantitative terms.

Landslide is one of the most common geological disasters in China, which is characterized by suddenness and uncertainty, and it is difficult to realize accurate identification, early warning and forecasting of landslide disaster by conventional means. With the development of high-resolution remote sensing satellites and InSAR surface deformation monitoring technology, the traditional means of landslide monitoring data sources are limited, and there is a lack of effective methods to excavate the characteristics of the spatial distribution of landslide hazards and their triggering factors and other issues. In this paper, the area extending 10 km outside the VII isobar of the Gengma earthquake is taken as the study area, and 13 evaluation factors are screened out by integrating the factors of InSAR surface deformation, topography and geological environment, and the Bayesian Optimized Convolutional Neural Network (BO-CNN) is used for the evaluation of landslide susceptibility, and the BO-RF and PSO-SVM models are selected for the comparative analysis. The model accuracy evaluation was carried out by three indexes: ROC curve, AUC value and FR value, in which the ROC curves of PSO-SVM, BO-RF and BO-CNN were all close to the upper-left corner of the corner, and the AUC values were 0.9388, 0.9529, and 0.9535, respectively, and the FR value of landslide in the high susceptibility area of BO-CNN was as high as 14.9, and was higher than that of PSO-SVM and BO-RF, respectively. SVM and BO-RF model is 4.55 and 3.69 higher, the experimental results show that the BO-CNN model used in this paper has a better effect in landslide susceptibility evaluation, and the research results of the local government's disaster prevention and mitigation measures are of great significance.

The complex and interconnected challenges of climate change, water stress, disasters, and health crises have far-reaching implications for sustainable development and global sustainability agendas, such as the Sustainable Development Goals (SDGs). However, one critical issue that has been overlooked is the nexus between climate change impacts and mental health (CCMH). Recognizing and addressing the negative emotions associated with this global phenomenon is essential to fostering a holistic approach to climate action planning and building long-term resilience. In this assessment, we present a set of narratives to argue that CCMH research requires a collaborative, transdisciplinary approach that integrates socio-economic and socio-cultural complexities. For this assessment, we used a case study approach to elucidate that the mental health impacts of climate change are unequally distributed, disproportionately affecting vulnerable groups based on age, gender, race, and socioeconomic status. The assessment presented in this study concluded that adequate mental health support programs are limited due to sociocultural stigmas and limited socioeconomic resources in some regions. Existing climate-related mental health services mechanisms lack coordination and specific action plans, leaving affected populations underserved. Unlike traditional understandings of the climate-health nexus, this research calls for experts from multiple fields to work together and for enhanced attention to and investment in CCMH research to bridge the gap between scientific knowledge and practical solutions. Such solutions will lead to scalable and lasting change as communities can implement research findings to support those in need and enhance disaster resilience. Furthermore, by collectively recognizing the climate and mental health nexus, global commitments such as the SDGs and the Paris Declaration can advance awareness and action in climate-related mental health, ultimately promoting a healthier relationship between humanity and nature.

In the framework of mean field approximation, we consider a spin system consisting of two interacting sub-ensembles: spins interactions with in each sub-ensemble are ferromagnetic, while the inter-ensemble interactions are antiferromagnetic. We define the effective number of the nearest neighbors, and show that if the two sub-ensembles have the same effective number of the nearest neighbors, the classical form of critical exponents (, , , ) gives way to the non-classical form (, , , ), and the scaling function changes simultaneously. We demonstrate that this system allows for two second-order phase transitions and two first-order phase transitions. We observe that an external magnetic field does not destroy the phase transitions, but only shifts their critical points, allowing for control of the system’s parameters. We discuss the regime when the magnetization as a function of the magnetic field develops a low-magnetization plateau, and show that the height of this plateau abruptly rises to the value of one when the magnetic field reaches a critical value. Our analytical results are supported by a Monte Carlo simulation of a three-dimensional layered model.

Considering the aging society and budget constraints, a strategy for reforming public transportation welfare policies is essential for sustainability. Among the various transportation welfare policies, fare discount policies are provided to specific age groups, and most countries offer benefits to several public transportation. South Korea provides a fare-free policy limited to subways, and the need for policy modification is emerging due to sustainability problems caused by an aging society. Hence, the scope of the policy should be expanded to all travel modes, but the discount rate should be reduced, or the target group should be reduced in consideration of the budget. This study proposes a travel mode to focus on by city through the preference of the elderly. The latent class model is employed to classfy stated preference data collected from the survey. The estimation results show a significant preference heterogeneity depending on the level of subway supply by region, and a policy focused on subways would be more reasonable in cities with sufficient subway infrastructure. Hence, regarding sustainability, it may be more appropriate to focus on specific means than to provide the same discount policy for all public transport.

Background: Metastatic oral squamous cell carcinoma (OSCC) is associated with poor patient prognosis. Metastasis is a complex process involving various proteins, tumor cell alterations including changes caused by the epithelial-to-mesenchymal transition (EMT) process, and interactions with the tumor microenvironment (TME). In this study, we investigate a combined protein marker system consisting of connexin 43 (Cx43), EMMPRIN (CD147), E-cadherin and vimentin during the invasive metastatic process of OSCC and the possibility of using this system for prognosis prediction. Methods: The protein expression profiles of Cx43, EMMPRIN, E-cadherin and vimentin were investigated by immunohistochemistry in tissue samples from 24 OSCC patients. The metastatic process was mapped through different regions of interest (ROI) of adjacent healthy oral mucosa (OM), center of primary OSCC, invasive front (IF), and local cervical lymph node metastases (LNM). Disease-free survival (DFS) and overall survival (OS) were the primary clinical endpoints. Results: Significant changes in the expression profiles of the different marker proteins were detected between the different ROIs (all p values &lt; 0.05). Multivariable Cox regression analysis revealed a significant effect of increased EMMPRIN expression towards IF on DFS (p = 0.019) and OS (p = 0.023). The combined predictive analysis showed a significant predictive value of the marker system for DFS (p = 0.0017) and OS (p = 0.00044). Conclusions: The combined marker system was able to significantly predict patient prognosis. An increase in EMMPRIN expression towards IF showed the strongest effect and could be an interesting new antimetastatic therapy approach.

Pregnant women have been considered a high-risk group for SARS-CoV-2 infection; the impact of the disease on the health of a mother and her child is still being studied. The emotional impact of the pandemic on pregnant women has been extensively studied. Emotional distress is proposed as a perspective to explain the emotional manifestations of women during this stage as something common rather than pathological. The objective of this study was to know the emotional experience of women who tested positive for SARS-CoV-2 towards the end of their pregnancy, during the first and second waves of COVID in Mexico. A qualitative study was carried out: 18 pregnant women with COVID were interviewed. A thematic analysis of the data was performed, resulting in three main themes and 14 subthemes. The COVID-infected mothers-to-be experienced mild to moderate emotional distress. It was more intense for those with comorbidities. This distress was aggravated during obstetrical complications and comorbidities, as well as during COVID and postpartum. The emotional distress was appeased by both the perception of medical care and social support. The emotional distress of pregnant women with COVID requires emotional accompaniment to reduce its impact.