Search

Identifying and understanding visitor needs and expectations is of the utmost importance for a number of stakeholders and policymakers involved in the touristic domain. Apart from traditional forms of feedback, an abundance of related information exists online, scattered across various data sources like online social media, tourism-related platforms, traveling blogs, forums, etc. Retrieving and analyzing the aforementioned content is not a straightforward task and in order to address this challenge, we have developed the StreetLines platform, a novel information system that is able to collect, analyze and produce insights from the available tourism-related data. Its highly modular architecture allows for the continuous monitoring of varying pools of heterogeneous data sources whose contents are subsequently stored, after preprocessing, in a data repository. Following that, the aforementioned data feed a number of independent and parallel processing modules that extract useful information for all individuals involved in the tourism domain, like place recommendation for visitors and sentiment analysis and keyword extraction reports for professionals in the tourism industry. The presented platform is an outcome of the StreetLines project and apart from the contributions of its individual components, its novelty lies in the holistic approach to knowledge extraction and tourism data mining.

The impoundment of Aoos springs was created in 1990 to facilitate hydroelectric production, and fish fauna has been enriched through the years due to deliberate or accidental fish stockings, with certain invasive alien fish species arising (i.e., Lepomis gibbosus, Carassius gibelio), some of which are ranked among the most dangerous species for global biodiversity. A three-year monitoring survey was conducting to map the nesting areas of Pumpkinseed, L. gibbosus, to assess the effectiveness of electrofishing as an active method to reduce the spread of the corresponding species and to identify the impact of the L. gibbosus on native species. The largest percentage of nests was found in areas with silty-sand bottoms (53.4%) and low vegetation. The potential nesting area was estimated at 1.97 km2 and the area of confirmed nesting was 0.33 km2. The species appeared to nest in a small area, compared to the potential nesting area. The results also indicate that a significant percentage of the target species (71.6% of the total number of species) was removed, with an average time per sampling of 1.2 ± 1 h, whereas the impact on the other native species was minimal. These results are important for designing and implementing programs for the eradication or reduction of L. gibbosus in the impoundment of Aoos springs.

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

An experimental study was conducted to identify the behaviour of neutrally buoyant microplastics (specific density, 0.94) in different hydrodynamic conditions while focusing on combined wave–current conditions and the mixing across the hyporheic zone. For in-water-column microplastics, it was observed that the streamwise dispersion of neutrally buoyant microplastics is comparable to solute dye in both slow open-channel flow conditions and combined wave–current conditions. However, for in-bed microplastics, when compared to soluble tracers, the longer timespans associated with the hyporheic exchange process allowed the density effects to enhance the vertical exchange when compared to solutes.

Evidence suggests that COVID-19 infection increases the risk of type 1 (T1D) and type 2 diabetes (T2D). Diabetes, in turn, increases COVID-19 susceptibility and contributes to increased COVID-19 morbidity and mortality. Oxidative stress has emerged as a common factor driving the pathogenesis of diabetes and COVID-19 caused by the severe acute respiratory syndrome coronavirus. The mechanistic links between oxidative stress, diabetes, and COVID-19 have primarily been studied in adults and will be summarized in this review. However, we suggest that studying these interconnections in children and young adults is critical since early intervention is optimal for improving outcomes. At the height of the pandemic, COVID-19 was a leading cause of death in children and young people, and people in this age group are as susceptible to COVID-19 as adults and the elderly. Glutathione is the primary water-soluble intracellular antioxidant and can be deficient in both diabetes and COVID-19. Glutathione is a tripeptide containing cysteine, glutamic acid, and glycine. Strategies to increase glutathione levels may be beneficial in helping to manage COVID-19-induced diabetes and diabetes-induced COVID-19 risk. Dietary supplementation with glycine plus n-acetyl-l-cysteine may be optimal since it contains two metabolic glutathione precursors.

Ongoing modern computational advancements continue to make it easier to collect increasingly large and complex datasets, which can often only be realistically analyzed using models defined by intractable likelihood functions. This Stats invited feature article introduces and provides an extensive simulation study of a new approximate Bayesian computation (ABC) framework for estimating the posterior distribution and the maximum likelihood estimate (MLE) of the parameters of models defined by intractable likelihoods, that unifies and extends previous ABC methods proposed separately. This framework, copulaABCdrf, aims to accurately estimate and describe the possibly skewed and high-dimensional posterior distribution by a novel multivariate copula-based meta-t distribution based on univariate marginal posterior distributions that can be accurately estimated by distribution random forests (drf), while performing automatic summary statistics (covariates) selection, based on robustly estimated copula dependence parameters. The copulaABCdrf framework also provides a novel multivariate mode estimator to perform MLE and posterior mode estimation and an optional step to perform model selection from a given set of models using posterior probabilities estimated by drf. The posterior distribution estimation accuracy of the ABC framework is illustrated and compared with previous standard ABC methods through several simulation studies involving low- and high-dimensional models with computable posterior distributions, which are either unimodal, skewed, or multimodal; and exponential random graph and mechanistic network models, each defined by an intractable likelihood from which it is costly to simulate large network datasets. This paper also proposes and studies a new solution to the simulation cost problem in ABC involving the posterior estimation of parameters from datasets simulated from the given model that are smaller compared to the potentially large size of the dataset being analyzed. This proposal is motivated by the fact that, for many models defined by intractable likelihoods, such as the network models when they are applied to analyze massive networks, the repeated simulation of large datasets (networks) for posterior-based parameter estimation can be too computationally costly and vastly slow down or prohibit the use of standard ABC methods. The copulaABCdrf framework and standard ABC methods are further illustrated through analyses of large real-life networks of sizes ranging between 28,000 and 65.6 million nodes (between 3 million and 1.8 billion edges), including a large multilayer network with weighted directed edges. The results of the simulation studies show that, in settings where the true posterior distribution is not highly multimodal, copulaABCdrf usually produced similar point estimates from the posterior distribution for low-dimensional parametric models as previous ABC methods, but the copula-based method can produce more accurate estimates from the posterior distribution for high-dimensional models, and, in both dimensionality cases, usually produced more accurate estimates of univariate marginal posterior distributions of parameters. Also, posterior estimation accuracy was usually improved when pre-selecting the important summary statistics using drf compared to ABC employing no pre-selection of the subset of important summaries. For all ABC methods studied, accurate estimation of a highly multimodal posterior distribution was challenging. In light of the results of all the simulation studies, this article concludes by discussing how the copulaABCdrf framework can be improved for future research.

The topic of consent within interpersonal relationships is sensitive and complex. A serious game can provide a safe medium for the exploration of the topic of consent. In this paper, we aim to alleviate the challenges of designing a serious game artefact with the implicit goal of exploring the topic of consent. The resulting artefact, “Case By Case”, is a VR-based serious game targeting university students, which uses an allegory-based approach to achieve its goal. The participants play the role of a detective who is tasked with determining if individuals have committed theft, which serves as an allegory for breach of consent. “Case By Case” provides the users an opportunity to reflect on their decisions within the game and apply them to the complex situations of consent such as victim-blaming and bystander awareness. To evaluate the effectiveness of the game in achieving its implicit goal, we ran a user study (n = 24). The results show that “Case By Case” provided a safe environment for the users to reflect on the concept of consent and increase their understanding about the topic further.

Retroperitoneal tumors are uncommon and may reach a large size prior to causing symptoms or being noticed by the patient or physician. A middle-aged female consulted us for care during her “terminal” illness. She had already undergone four previous retroperitoneal resection surgical procedures. She presented with a large recurrent protruding mass from the right side of the abdomen and related a history of a previous cholecystectomy, right nephrectomy, right colectomy, and repeated resection of a recurrent retroperitoneal liposarcoma. She thus came to us for consultation and terminal care in order to be away from her friends during treatment for this terminal condition. After our consultation, she elected to have repeated surgical excisions of the tumor. The surgical excisions yielded a giant recurrent tumor mass, which overflowed and covered all margins of the 21-inch-wide surgical scrub basin. Over the next eleven years, she had multiple surgical resection procedures involving both the right and left retroperitoneum (a splenectomy, a left colectomy, and a colostomy). Recovery from each of these resection procedures (the final combined resection weight was 120 pounds) was without complications. However, the tumor finally encased the pancreas and the left kidney. If the tumor encasement were to be palliated and resected, she would require hemodialysis. At this time, the patient elected to have no further resection surgeries, no dialysis, nor any palliative chemoradiation treatment. Over a period of sixteen years from her first resection and twelve years from our first resection, the patient had continued to work at her medical administrative and leadership position and led a functional life after our consultation, except for her surgical period. The patient was not cured but benefited from repeated palliative surgeries, prolonging her life and improving her job performance.

In open-field agricultural environments, the inherent unpredictable situations pose significant challenges for effective human–robot interaction. This study aims to enhance natural communication between humans and robots in such challenging conditions by converting the detection of a range of dynamic human movements into specific robot actions. Various machine learning models were evaluated to classify these movements, with Long Short-Term Memory (LSTM) demonstrating the highest performance. Furthermore, the Robot Operating System (ROS) software (Melodic Version) capabilities were employed to interpret the movements into certain actions to be performed by the unmanned ground vehicle (UGV). The novel interaction framework exploiting vision-based human activity recognition was successfully tested through three scenarios taking place in an orchard, including (a) a UGV following the authorized participant; (b) GPS-based navigation to a specified site of the orchard; and (c) a combined harvesting scenario with the UGV following participants and aid by transporting crates from the harvest site to designated sites. The main challenge was the precise detection of the dynamic hand gesture “come” alongside navigating through intricate environments with complexities in background surroundings and obstacle avoidance. Overall, this study lays a foundation for future advancements in human–robot collaboration in agriculture, offering insights into how integrating dynamic human movements can enhance natural communication, trust, and safety.

Aim: This study aimed to identify self-compassion among staff nurses after the COVID-19 pandemic in Qatar. Design: Descriptive cross-sectional survey design. Methods: Anonymous data were collected through an online survey using Microsoft Forms from 300 nurses in 14 health facilities in Qatar. The organization had almost 10,000 nursing staff working in different facilities. Data were gathered using a structured online questionnaire and included socio-demographic information, and the Self-Compassion Scale—Short Form was used to collect the remaining data. Correlation, t-test, and ANOVA analyses were conducted. Results: Nurses in the study showed high self-compassion. Among the sub-domain ‘mindfulness’, they showed comparatively high scores (7.96 ± 1.55), and the lowest score was for ‘isolation’ (6.15 ± 1.99). The score for ‘self-kindness’ was 7.29 ± 1.55, that for ‘self-judgement’ was 6.79 ± 2.01, that for ‘common humility’ was 6.62 ± 1.47, and that for the sub-domain ‘over-identified’ was 6.47 ± 1.91. Mindfulness scores were high among the nurse leaders. Moreover, over-identified scores were high among the nurses who were currently working under COVID-19 at the time of data collection. Conclusions: Nurses faced many difficulties while working during the COVID-19 pandemic, including a heavy workload and tension. The current study’s findings add to our understanding of how COVID-19 affected the development of self-compassion. A rise in mindfulness, which aids nurses in managing stress at work and building resilience, further underscores an increase in nurses’ acceptance of the COVID-19 pandemic. The findings also highlight how crucial it is to encourage self-compassion in individuals and offer them emotional support at such times, especially when there is a significant risk factor for mental health, such as COVID-19.

Background: Accurate and rapid testing for COVID-19 is critical for effective disease management and control. The One-Step-COVID-2019-Test was developed as a rapid serological test to detect antibodies against SARS-CoV-2. Objective: To estimate the accuracy of the rapid serological test for COVID-19 using One-Step-COVID-2019. Methods: We conducted a population-based serological survey with a stratified sampling of 593 adults between October and December 2020, prior to mass vaccination and during a period of limited availability of rapid tests. Participants provided 7.5 mL of serum, which was tested using the One-Step-COVID-2019-Test for IgM-IgG antibodies without distinction, as well as an in-house ELISA for IgG against the spike protein. Statistical analysis accounted for sampling weights, with accuracy assessed through sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), Youden index, and kappa coefficient, using ELISA as the reference standard. McNemar’s test identified significant differences between the test results. Results: The ELISA-based prevalence of infection was 11.1%. The One-Step-COVID-2019-Test showed low sensitivity (27.0–30.8%) but high specificity (89.9–96.6%), with poor agreement (kappa: 0.290–0.337), particularly among asymptomatic individuals. Conclusions: The One-Step-COVID-2019 rapid test for COVID-19 demonstrated inadequate performance, characterized by low sensitivity and poor reliability, making it unsuitable for effective serological surveillance.

The anti-tumoral activity of somatostatin has been demonstrated in both animal experiments and human tumors. Clinical trials have reported conflicting results. We therefore hypothesized that somatostatin might have different effects in various hepatocellular carcinoma cells. Their clarification would possibly allow for the better selection of patients suitable for the optimal treatment results. We studied the mRNA and protein expression of TNF receptors and the TNFa-induced apoptosis using the HepG2 and the Hep3B human hepatocellular carcinoma cells after incubation with the somatostatin analog octreotide. RT-PCR, Western blot, and parameters associated with apoptosis (NF-kB nuclear translocation, P65 Ser536 and P65 Ser468 phosphorylation, DNA fragmentation) were assessed. Only TNFR1 was constitutively present in the two cell lines. Octreotide incubation led to an earlier reduction in TNFR1 mRNA and protein in HepG2 compared to Hep3B cells (1 h and 6–12 h, respectively). NF-kB translocation to the nucleus was induced by TNFa and was more prominent in Hep3B. Translocation was unaffected by octreotide. Serine phosphorylation was significantly induced by TNFa and was more evident in the Hep3B cells. TNFa-induced Ser536 phosphorylation was inhibited by octreotide only in the HepG2 cells. DNA fragmentation was not influenced by either octreotide or TNFa in the HepG2 cells, but TNFa induced fragmentation in the Hep3B cells (1.8-fold increase) verified by the TUNEL index (43 compared to 19 for the HepG2 cells). Octreotide and TNFa co-incubation induced apoptosis in the HepG2 cells (1.7-fold increase compared to controls) but inhibited apoptosis in the Hep3B cells. We conclude that: (1) octreotide reduced TNFR1 receptor expression in both cell lines, (2) parameters of apoptosis were differentially affected by octreotide in the two cell lines, and (3) this might be a partial explanation for the conflicting results of somatostatin analog treatment in human hepatocellular carcinoma trials.

Coagulation/Flocculation (C/F) process aims to efficiently eliminate turbidity, TSS, COD, BOD, toxic metals, phosphates, and UV254nm from wastewater. Both natural and synthetic coagulants, used alone or in conjunction with flocculants, play crucial roles in this treatment. This review summarizes recent trends in coagulants for wastewater treatment, highlighting a wide array of inorganic and organic coagulants that have demonstrated significant efficacy based on reviewed studies. Notably, Crab Shell Bio-Coagulant (CS) excels in turbidity remov5al, achieving a remarkable 98.91% removal rate, while oak leaves protein shows superior performance in TSS and COD removal. Synthetic inorganic coagulants like PALS, PSiFAC1.5:10:15, and PAPEFAC1.5-10-15 demonstrate outstanding turbidity removal rates, over 96%. POFC-2 coagulant stands out for efficiently removing TSS and COD from domestic wastewater, achieving up to 93% removal for TSS and 89% for COD. Moreover, the utilization of FeCl3 as an inorganic coagulant alongside chitosan as an organic flocculant shows promise in reducing turbidity, COD, and polyphenols in wastewater from vegetable oil refineries. PE-2, a novel organic coagulant, demonstrates exceptional efficacy in eliminating turbidity, TSS, COD, and BOD from sugar industry wastewater. Chitosan shows effectiveness in removing TOC and orthophosphates in brewery wastewater. Additionally, CTAB shows high efficiency in removing various toxic metal ions from wastewater. The hybrid coagulants: PAAP0.1,0.5 and PPAZF accomplish exceptional turbidity removal rates, approximately 98%.

The field of sports analytics has grown rapidly, with a primary focus on performance forecasting, enhancing the understanding of player capabilities, and indirectly benefiting team strategies and player development. This work aims to forecast and comparatively evaluate players’ goal-scoring likelihood in four elite football leagues (Premier League, Bundesliga, La Liga, and Serie A) by mining advanced statistics from 2017 to 2023. Six types of machine learning (ML) models were developed and tested individually through experiments on the comprehensive datasets collected for these leagues. We also tested the upper 30th percentile of the best-performing players based on their performance in the last season, with varied features evaluated to enhance prediction accuracy in distinct scenarios. The results offer insights into the forecasting abilities of those leagues, identifying the best forecasting methodologies and the factors that most significantly contribute to the prediction of players’ goal-scoring. XGBoost consistently outperformed other models in most experiments, yielding the most accurate results and leading to a well-generalized model. Notably, when applied to Serie A, it achieved a mean absolute error (MAE) of 1.29. This study provides insights into ML-based performance prediction, advancing the field of player performance forecasting.

Heritage and culture tourism involve features that commemorate a valued past. Mining heritage tourism allows visitors to experience the past, guided by former mining landscapes and engaging interactively with material artifacts. This paper introduces the CoalHeritage European project, focusing on the promotion of coal mining heritage through the production and design of the European Visual Map Journal (EVMJ). The EVMJ is a user-friendly, web-based, interactive storytelling platform that supports the transfer of industrial and geoheritage from former coal mining areas. It aims to collect and disseminate heritage assets from post-mining coal areas, informing stakeholders and promoting these sites as tourist destinations. To further enhance public awareness, several ESRI StoryMaps web apps are being created to highlight specific features of each case study across Europe. The aim of this work is to introduce coal mining heritage as a new term, present the coal heritage platform and its importance for disseminating coal heritage aspects to the public, describe the methodology used for its design, and provide a brief overview of its evolving content.

This review aims to streamline the approach to assessing the most used valorization methods for fruit and vegetable waste (FVW) that are eco-friendly, cost-effective, and sustainable within a circular economy framework. Green processing technologies for the extraction of bioactive compounds from FVW, their applications, and the technico-economical assessment of FVW’ biorefinery to support circular economy are highlighted. Important value-added products generated by FVW include bioactive compounds, pectin, protein isolates, such as soy, natural pigments such as anthocyanins, quinones, carotenoids, betalains, and chlorophyll. At this time, the prospects of using FVW have increased in the food supplements, bioactive and edible food packaging, agriculture, energy, and water purification fields. The findings report that proper management of FVW not only minimizes their addition to landfills in the absence of composting, but also promotes the efficient utilization of resources for the development of innovative materials with a wide range of beneficial applications. Implementing the possible solutions described in this paper would not only reduce environmental impact, but also open up new economic opportunities through the valorization of FVW.

This paper aims to conduct an acoustic analysis through noise measurements of a hybrid propulsion system intended for implementation on a drone, from which the main noise sources can be identified for further research on noise reduction techniques. Additionally, the noise was characterized by performing spectral analysis and identifying the tonal components that contribute to the overall noise. The propelling force system consists of a micro-turboshaft coupled with a gearbox connected to an electric generator. The propulsion system consists of a micro-turboshaft coupled with a gearbox connected to an electric generator. The electric current produced by the generator powers an electric ducted fan (EDF). The engineturbo-engine was tested in free-field conditions for noise generation at different speeds, and for this, an array of microphones was installed, positioned polarly around the system and near the intake and exhaust. Consequently, based on the test results, the acoustic directivity was plotted, revealing that the highest noise levels are at the front and rear of the engine. The noise level at a distance of 1.5 m from the turboengine exceeds 90 dBA at all tested speeds. Spectral analyses of both the far-field acoustic signals (measured with a polar microphone array) and the near-field signals (microphones positioned near the intake and exhaust) revealed that the primary contributors to the overall noise are the micromotor’s compressor, specifically the gas dynamic phenomena in the fan (BPF and 2× BPF). Thus, it was determined that at the intake level, the main noise contribution comes from the high-frequency components of the compressor, while at the exhaust level, the noise mainly originates from the combustion chamber, characterized by low-frequency components (up to 2 kHz). The findings from this study have practical applications in the design and development of quieter drone propulsion systems. By identifying and targeting the primary noise sources, engineers can implement effective noise reduction strategies, leading to drones that are less disruptive in urban environments and other noise-sensitive areas. This can enhance the acceptance and deployment of drone technology in various sectors, including logistics, surveillance, and environmental monitoring.

In the present paper, the use of programmable microprocessors to develop a computerized stand for the preparation of a mixture of flammable/toxic/explosive gases in order to obtain mixtures at concentrations in explosive range is presented. The operating principle of the stand is based on the mixing of two volumetric flows, controlled with the help of microprocessors, where gases are stored and circulated at atmospheric pressure through cylindrical injectors, driven by stepper motors so that the gas circuit does not require valves. The exit of the stand is a homogenization chamber, with agitator and sensor to confirm the desired concentration of the mixture. This automated stand eliminates mechanical, electric or pneumatic valves from the gas circuits, avoiding elements with high mechanical resistance suitable for high pressures/depressions, removing the possibility of the return of the gas flow, without sensitivity to sudden pressures variations.

Background: Lymph node involvement (N+) in bladder cancer indicates a poor prognosis. Current preoperative evaluations of N+ are often inaccurate. We aimed to develop a predictive model for N+ using basic clinical variables and assess the diagnostic accuracy of Computed Tomography (CT). Methods: A retrospective cohort study was conducted. We include 62 MIBC patients who underwent radical cystectomy (RC) from 2010 to 2019 in our center. We evaluated diagnostic concordance between CT and histopathology for extravesical extension (T3a≥) and N+. Univariate and multivariate logistic regressions were used to create a predictive model, with an ROC curve and nomogram developed. Results: We found 59% sensitivity and 69% specificity for CT for staging cT3≥ and a sensitivity of 22% and a specificity of 21% for N+. NLR > 2.60 (OR 6.03, p = 0.02) and lymphovascular invasion (LVInv) in the TURB sample (OR 9.26, p = 0.04) were correlated with N+. Both fundus lesions (OR 0.21, p = 0.04) and creatinine > 0.94 mg/dL (OR 0.17, p = 0.025) were associated with reduced risk. The ROC curve of the model showed 80.4% AUC. Conclusions: A predictive model with good diagnostic performance for N+ can be developed from basic clinical data. CT sensitivity and specificity for the detection of N+ patients are limited.

This study examines the relationship between Bitcoin market dynamics and user activity on the r/cryptocurrency subreddit. The purpose of this research is to understand how social media activity correlates with Bitcoin price and trading volume, and to explore the sentiment and topical focus of Reddit discussions. We collected data on Bitcoin’s closing price and trading volume from January 2021 to December 2022, alongside the most popular posts and comments from the subreddit during the same period. Our analysis revealed significant correlations between Bitcoin market metrics and Reddit activity, with user discussions often reacting to market changes. Additionally, user activity on Reddit may indirectly influence the market through broader social and economic factors. Sentiment analysis showed that positive comments were more prevalent during price surges, while negative comments increased during downturns. Topic modeling identified four main discussion themes, which varied over time, particularly during market dips. These findings suggest that social media activity on Reddit can provide valuable insights into market trends and investor sentiment. Overall, our study highlights the influential role of online communities in shaping cryptocurrency market dynamics, offering potential tools for market prediction and regulation.

Background: The lack of knowledge about physical responses to pickleball creates a clear gap about performance in this sport. The purpose of this study was to investigate neuromuscular fatigue caused by playing doubles pickleball. Methods: Recreational pickleball players (n = 32, mean age = 60.0 years) were recruited to perform sets of four countermovement jumps (CMJs) on a force plate before and after doubles pickleball matches. Results: For players who had not played a match prior to testing, there was a significant learning effect across trials within the baseline set of jumps for five outcomes from the CMJ test, including propulsive peak force (p = 0.005); however, there was no significant learning effect for jump height. There were significant improvements in the large effect size for all except one dependent variable (propulsive phase time) between the first and second set of jumps (i.e., after one match). Neither further increases nor decreases were seen after the second set of jumps. Conclusions: Participants saw significant increases in CMJ performance across trials after one pickleball match, indicating learning and potentiation effects. After three matches of doubles pickleball, no fatigue effect was detected.

This paper elicits and analyzes the main requirements for Smart Pedestrian applications designed to enhance the pedestrian experience in urban environments by offering optimized walking routes, improved accessibility, and support for social inclusion and connectivity. Utilizing a mixed-methods approach, the research combines qualitative insights with quantitative data analysis based on surveys conducted in two strategically selected urban areas of Nicosia, Cyprus. Through the survey, the requirements and potential use of Smart Pedestrian apps are investigated while accounting for the quality of service of the urban infrastructure in a medium-sized city context. Additionally, the study contrasts the current smartphone applications, as they predominantly facilitate vehicular transportation, with the potential use of ICT/ITS to support pedestrians for sustainable mobility. The findings reveal a significant demand for a Pedestrian Smartphone app, driven by its ability to provide relevant information on optimum pedestrian routes, as well as act as a citizen’s voice for spotting infrastructure problems and improving the pedestrian network. Further, it is also revealed that limitations in the pedestrian infrastructure substantially restrict walking preferences, emphasizing the need for urgent city-level urban planning solutions to support active mobility. Additionally, the research carried out underscores the importance of a sustainable business model to support the successful deployment of Smart Pedestrian apps. Ultimately, the results of the study suggest prioritizing a smart technology leverage with a crowdsourcing social network business model to promote pedestrian mobility, thereby reducing vehicular dependence, enhancing public health, and improving the quality of life. Such an approach would act as catalyst for policymakers to concentrate on sustainability by investing in digital technology for integrated pedestrian networks, fostering the emergence of genuine smart cities.

Obesity is marked by excessive fat accumulation in the adipose tissue, which disrupts metabolic processes and causes chronic systemic inflammation. Commonly, body mass index (BMI) is used to assess obesity-related risks, predicting potential metabolic disorders. However, for a better clustering of obese patients, we must consider molecular and epigenetic changes which may be responsible for inflammation and metabolic changes. Our study involved two groups of patients, obese and healthy donors, on which routine analysis were performed, focused on BMI, leukocytes count, and C-reactive protein (CRP) and completed with global DNA methylation and gene expression analysis for genes involved in inflammation and adipogenesis. Our results indicate that obese patients exhibited elevated leukocytes levels, along with increased BMI and CRP. The obese group revealed a global hypomethylation and upregulation of proinflammatory genes, with adipogenesis genes following the same trend of being overexpressed. The study confirms that obesity is linked to systematic inflammation and metabolic dysfunction through epigenetic and molecular alterations. The CRP was correlated with the hypomethylation status in obese patients, and this fact may contribute to a better understanding of the roles of specific genes in adipogenesis and inflammation, leading to a better personalized therapy.

Reinforced Concrete (RC) members in existing RC structures are susceptible to shear-critical due to their under-reinforced design. Thus, implementing a retrofitting technique is essential to eliminate the casualties that could arise from sudden and catastrophic collapses due to these members’ brittleness. Among other proposed techniques, using Carbon-Fiber Reinforced Polymers (C-FRP) ropes to increase the shear strength of RC structural elements has proved to be a promising reinforcement application. Moreover, an Electro-Mechanical Impedance (EMI-based) method using Lead Zirconate Titanate (PZT-enabled) was employed to assess the efficiency of the strengthening scheme. Initially, the proposed technique was applied to C-FRP rope under the subjection of pullout testing. Thus, a correlation of the rope’s tensile strength with the EMI responses of the PZT patch was achieved using the Root Mean Square Deviation (RMSD) metric index. Thereafter, the method was implemented to the experimentally acquired data of C-FRP ropes, used as shear reinforcement in a rectangular deep beam. The ropes were installed using the Embedded Through Section (ETS) scheme. Furthermore, an approach to evaluate the residual shear-bearing capacity based on the EMI responses acquired by being embedded in and bonded to the ropes’ PZTs was attempted, demonstrating promising results and good precision compared to the analytical prediction of the C-FRP ropes’ shear resistance contribution.

Optimizing the prediction of maize (Zea mays L.) yields in smallholder farming systems enhances crop management and thus contributes to reducing hunger and achieving one of the Sustainable Development Goals (SDG 2—zero hunger). This research investigated the capability of unmanned aerial vehicle (UAV)-derived data and machine learning algorithms to estimate maize yield and evaluate its spatiotemporal variability through the phenological cycle of the crop in Bronkhorstspruit, South Africa, where UAV data collection took over four dates (pre-flowering, flowering, grain filling, and maturity). The five spectral bands (red, green, blue, near-infrared, and red-edge) of the UAV data, vegetation indices, and grey-level co-occurrence matrix textural features were computed from the bands. Feature selection relied on the correlation between these features and the measured maize yield to estimate maize yield at each growth period. Crop yield prediction was then conducted using our machine learning (ML) regression models, including Random Forest, Gradient Boosting (GradBoost), Categorical Boosting, and Extreme Gradient Boosting. The GradBoost regression showed the best overall model accuracy with R2 ranging from 0.05 to 0.67 and root mean square error from 1.93 to 2.9 t/ha. The yield variability across the growing season indicated that overall higher yield values were predicted in the grain-filling and mature growth stages for both maize fields. An analysis of variance using Welch’s test indicated statistically significant differences in maize yields from the pre-flowering to mature growing stages of the crop (p-value < 0.01). These findings show the utility of UAV data and advanced modelling in detecting yield variations across space and time within smallholder farming environments. Assessing the spatiotemporal variability of maize yields in such environments accurately and timely improves decision-making, essential for ensuring sustainable crop production.

Generation Z’s members are considered to have a strong preference for streaming and on-demand media only. This article is dedicated to Generation Z and comes to investigate the triptych of attitudes, opinions, and behaviors regarding radio and music preferences of its members in Greece through an Internet survey. The research data were collected through a web-based questionnaire, while for the analysis, descriptive and inductive statistics were applied from and through Internet applications and services. The research results and findings confirm previous empirical studies and research regarding the radio, the genealogical characteristics, habits, and ethos of Generation Z as well as that Generation Z can also be characterized as a sound generation. Finally, these research results and findings are considered encouraging and could be leveraged primarily by the radio media ecosystem with the aim of reorganizing or decentralizing the radio for its future form.

Chronic neck pain (CNP) is one of the most common musculoskeletal conditions, is considered the second leading cause of pain, and is among the leading causes of disability. Cognitive Functional Therapy (CFT) is a novel behavioral therapy for individualizing the management of spinal pain targeting the multidimensional aspect of musculoskeletal pain. This study outlines the protocol for an assessor-blind randomized controlled trial (RCT) designed to compare an individualized Cognitive Functional Therapy (CFT) intervention with usual care in terms of pain and disability. Aiming for a pragmatic intervention, the CFT group will receive 16 sessions based on patient’s condition characteristics, and clinical presentation and progression. The control group will receive 16 sessions of standardized usual care (electrotherapy, massage, posture exercise, and educations). Both groups will have the same intervention duration. Patients will be randomly allocated into groups and will be assessed at baseline, at the 8th session, at the 16th session, and 3 months after randomization. Primary outcomes will be pain, disability, cervical range of motion, and neck muscle isometric strength. To our knowledge, this study will be the first RCT to compare the clinical effectiveness of CFT compared to UC for adults with CNP. The study results will provide information about the use of CFT in clinical practice.

Crowdsourcing applications that integrate storytelling and geotagging capabilities offer new avenues for engaging the public in cultural heritage. However, standardised evaluation frameworks are lacking. This paper presents an applied evaluation methodology involving the analysis of relevant web-based tools. Towards this goal, this paper presents the development of crowdsourcing applications using, as a case study, the collection of myths and legends associated with the monumental heritage site of the Palace of the Grand Master of the Knights of Rhodes in Greece. Additionally, the paper presents an evaluation conducted through a criteria-based approach and user-based survey. The study reviews the concepts of crowdsourcing and digital storytelling within digital heritage, along with current concepts of living heritage and folklore, and examines relevant initiatives. The evaluation follows a four-stage methodology: (i) initial web-based tool selection based on the minimum requirements of web compatibility, crowdsourced data display, and map-based storytelling capability; (ii) application development using the selected web-based tools; (iii) a five-criteria assessment, based on the factors of open access, usability/tool support, participatory content/story creation, metrics provision and metadata model usage; and (iv) a crowd-based survey, indicating the most effective option. Findings from 100 respondents reveal limited exposure to participatory storytelling applications but interest in contributing content. Social media and influential figures serve as key channels for promoting crowdsourcing open calls. The results highlight gaps in understanding user expectations and perceptions, suggesting future research for gaining insights into engagement rates.

MicroGrowthPredictor is a model that leverages Long Short-Term Memory (LSTM) networks to predict dynamic changes in microbiome growth in response to varying environmental perturbations. In this article, we present the innovative capabilities of MicroGrowthPredictor, which include the integration of LSTM modeling with a novel confidence interval estimation technique. The LSTM network captures the complex temporal dynamics of microbiome systems, while the novel confidence intervals provide a robust measure of prediction uncertainty. We include two examples—one illustrating the human gut microbiota composition and diversity due to recurrent antibiotic treatment and the other demonstrating the application of MicroGrowthPredictor on an artificial gut dataset. The results demonstrate the enhanced accuracy and reliability of the LSTM-based predictions facilitated by MicroGrowthPredictor. The inclusion of specific metrics, such as the mean square error, validates the model’s predictive performance. Our model holds immense potential for applications in environmental sciences, healthcare, and biotechnology, fostering advancements in microbiome research and analysis. Moreover, it is noteworthy that MicroGrowthPredictor is applicable to real data with small sample sizes and temporal observations under environmental perturbations, thus ensuring its practical utility across various domains.

Tenacibaculum discolor develops biofilm in marine aquaculture production tanks and is identified as one of the causative agents of tenacibaculosis, a bacterial disease that causes significant losses in marine aquaculture production. In this study, the biofilm characteristics of T. discolor strain FMCC B487 were evaluated. Cell growth and biofilm formation and development were studied in miniaturized assays to assess the effect of different levels of environmental factors temperature and salinity, as well as the presence of monosaccharides potentially found in aquaculture hatcheries. The ability of the strain to grow and develop strong biofilms in ambient to high temperatures and at salinities above 20 g/L was shown. Mannose was the monosaccharide with the most prominent impact on the T. discolor strain FMCC B487 biofilm. The composition of planktonic cell extract, biofilm extracts, and extracellular polymeric substances (EPS) produced by T. discolor strain FMCC B487 were investigated by means of colorimetric and fluorometric assays as well as analyses by electrophoresis, gas chromatography, and high-performance size-exclusion chromatography coupled with a multiangle light scattering detector, revealing the dominance of proteins and lipids and the absence of high-molecular-weight polysaccharides. This information may serve as a basis for considering anti-biofilm strategies against the pathogen T. discolor.

Background: Emergency medicine in Romania has developed fast since inception. The need for faster diagnostic capabilities due to the high workload pre- and in-hospital made point-of-care ultrasound (POCUS) a logical next step. The advantages of POCUS are well known, but implementation presents challenges. Our goal was to study how a straightforward method of implementation would work locally. Methods: Two prospective observational studies were conducted at 6 months (prehospital) and 4 months (in-hospital). The protocol used was extended focused assessment sonography in trauma (eFAST), and the shock index (SI) was used to stratify patients. Voluntary sampling was conducted by emergency physicians. The primary outcomes were patient numbers, type of case use, results, and accuracy. Results: The prehospital study registered 34 patients: 41% traumas, 35% cardiac arrest, 18% shock, and 6% acute respiratory distress. The in-hospital study patients were 78: 36% traumas, 6% cardiac arrests, 41% shock, and 17% acute respiratory distress. A total of 88.5% of the cases were confirmed with definitive imagistic findings. Conclusion: The studies mark an increase in POCUS usage and use in complicated cases. Providing supervision and feedback into clinical practice resulted in a further increase in POCUS usage, the second study having an 88.5% accuracy when compared to the final diagnostic proving the increased efficiency of a longitudinal training approach.

Background: Postoperative ileus (POI) is a common complication after colorectal surgery, leading to increased hospital stay and costs. This study aimed to explore patient comorbidities that contribute to the development of POI in the colorectal surgical population and compare machine learning (ML) model accuracy to existing risk instruments. Study Design: In a retrospective study, data were collected on 316 adult patients who underwent colorectal surgery from January 2020 to December 2021. The study excluded patients undergoing multi-visceral resections, re-operations, or combined primary and metastatic resections. Patients lacking follow-up within 90 days after surgery were also excluded. Eight different ML models were trained and cross-validated using 29 patient comorbidities and four comorbidity risk indices (ASA Status, NSQIP, CCI, and ECI). Results: The study found that 6.33% of patients experienced POI. Age, BMI, gender, kidney disease, anemia, arrhythmia, rheumatoid arthritis, and NSQIP score were identified as significant predictors of POI. The ML models with the greatest accuracy were AdaBoost tuned with grid search (94.2%) and XG Boost tuned with grid search (85.2%). Conclusions: This study suggests that ML models can predict the risk of POI with high accuracy and may offer a new frontier in early detection and intervention for postoperative outcome optimization. ML models can greatly improve the prediction and prevention of POI in colorectal surgery patients, which can lead to improved patient outcomes and reduced healthcare costs. Further research is required to validate and assess the replicability of these results.

Land use/land cover (LULC) is a fundamental concept of the Earth’s system intimately connected to many phases of the human and physical environment. LULC mappings has been recently revolutionized by the use of high-resolution imagery from unmanned aerial vehicles (UAVs). The present study proposes an innovative approach for obtaining LULC maps using consumer-grade UAV imagery combined with two machine learning classification techniques, namely RF and SVM. The methodology presented herein is tested at a Mediterranean agricultural site located in Greece. The emphasis has been placed on the use of a commercially available, low-cost RGB camera which is a typical consumer’s option available today almost worldwide. The results evidenced the capability of the SVM when combined with low-cost UAV data in obtaining LULC maps at very high spatial resolution. Such information can be of practical value to both farmers and decision-makers in reaching the most appropriate decisions in this regard.

Background: The COVID-19 pandemic has disrupted global daily life, including the world of elite athletes. This paper examines the multifaceted impact the COVID-19 pandemic had on elite swimmers and water polo athletes, specifically their mental health, their concerns over the virus, their intentions of getting vaccinated, and sleep disturbances that they may have faced. Methods: We conducted a cross-sectional study on elite swimmers and water polo players, using an anonymous questionnaire. Results: A total of 200 elite athletes participated. The majority of the participants reported a negative impact on their mental health, screened positive for insomnia (n = 107 (53.5%), with females (n = 101; 57.7%), swimmers (n = 100, 66.7%), and university students (n = 71, 71.7%) being more vulnerable (p < 0.001). Concerns about contracting the disease especially during important training or tournament periods and potential career disruption also affected their psychological well-being. While the majority (75%) had the intention of getting vaccinated, an alarming percentage was yet uncertain over its decision. Conclusions: This study highlights the significant psychological distress faced by elite aquatic athletes during the pandemic. It emphasizes the difficulties faced by elite swimmers and water polo athletes and determines not only the importance of addressing the vaccination intentions of athletes, but also how critical it is to confront the challenges they face both for their personal health and for the restoration of world sports to their pre-pandemic state. More large-scale studies are required to inform policies targeted at minimizing disruption to the athletes’ career, provision of information on preventive measures and vaccination, and improvement in psychological well-being in case of similar major public health issues in the future. Additionally, this study calls for further research to explore the unique challenges faced by aquatic athletes, such as those related to their training environments and fear of contagion, to better support them in future public health crises.

The stability of rock cliffs is a longstanding issue and is of practical significance. This case study demonstrates the application and use of advanced 3D modeling techniques, concentrating on the geological formations of the Xrobb l-Ġħaġin peninsula on the south-east coast of Malta, where the Xrobb l-Ġħaġin Neolithic site is located. In order to utilize a static and dynamic analysis of the investigated scenario, a 3D finite element model (FEM) of the geological formation in which the monument is set had to be created. To this end, 3D scanning, unmanned aerial vehicles (UAVs), and oblique photogrammetry were first used with state-of-the-art commercial packages for mesh reconstruction. As a result, a geometric and finite element model (FEM) was created, suitable for both static and dynamic analysis. In the second stage, a parametric investigation of the material properties of the structural system of the geological substrate was sought. The structural response of the system was evaluated for different loading scenarios assuming nonlinear finite element analysis. Collapse case scenarios were investigated for standard and weakened materials, predicting which components would collapse first and under which case of weakened materials the collapse occurs. Among other aspects, the main novelty of this paper lies in the integrated approach and multidisciplinary paradigm that supplement the available historical knowledge for this specific cultural heritage Neolithic site towards its conservation.

The distinct spin, optical, and coherence characteristics of solid-state spin defects in semiconductors have positioned them as potential qubits for quantum technologies. Both bulk and two-dimensional materials, with varying structural properties, can serve as crystalline hosts for color centers. In this study, we conduct a comparative analysis of the spin–optical, electron–nuclear, and relaxation properties of nitrogen-bound vacancy defects using electron paramagnetic resonance (EPR) and electron–nuclear double resonance (ENDOR) techniques. We examine key parameters of the spin Hamiltonian for the nitrogen vacancy (NV−) center in 4H-SiC: D = 1.3 GHz, Azz = 1.1 MHz, and CQ = 2.53 MHz, as well as for the boron vacancy (VB−) in hBN: D = 3.6 GHz, Azz = 85 MHz, and CQ = 2.11 MHz, and their dependence on the material matrix. The spin–spin relaxation times T2 (NV− center: 50 µs and VB−: 15 µs) are influenced by the local nuclear environment and spin diffusion while Rabi oscillation damping times depend on crystal size and the spatial distribution of microwave excitation. The ENDOR absorption width varies significantly among color centers due to differences in crystal structures. These findings underscore the importance of selecting an appropriate material platform for developing quantum registers based on high-spin color centers in quantum information systems.

This study aspires to evaluate the antibacterial and inhibitory effects of carbohydrate digestive enzymes in tree leaves that are widely distributed in the Mediterranean region. Leaves were sequentially extracted with solvents of increasing polarity. The results demonstrated a wide range of phenolic (3.5–770.7 mg gallic acid equivalent g−1) and flavonoid (0.2–321.3 mg catechin equivalent g−1) contents in leaf extracts. The minimum inhibitory and bactericidal concentration of leaf extracts was determined for six bacteria using the broth microdilution method. The polar extracts of carob, lentisk, and white mulberry leaves exerted strong antibacterial potency against Gram-positive bacteria, while the susceptibility of Escherichia coli on relative apolar extracts of carob, fig, and olive leaves was also observed. In parallel, the inhibitory effects of leaf extracts on carbohydrate digestive enzymes were evaluated. A robust inhibition of α-glucosidase was found for carob and lentisk leaf extracts, followed by extracts produced by white mulberry and olive leaves. Carob and lentisk leaves also act as a-amylase inhibitors at high concentrations. Overall, this study provides valuable data for the nutraceutical value of the “forgotten” treasure of Mediterranean tree leaves and assesses these plants as potential sources of antibacterial and carbohydrate digestive enzyme inhibitory agents for drug discovery.

(1) Background: Breast cancer is the leading malignancy worldwide, and in Ghana, it has a poor overall survival rate. However, approximately 50% of cases are cases of early-stage disease, and with advances in breast cancer treatment and improvements in survival, quality of life (QOL) is becoming as important as the treatment of the disease. (2) Methodology: This was a cross-sectional study of survivors who had breast-conserving surgery (BCS), mastectomy only (M) and mastectomy with breast reconstruction (BRS) from 2016 to 2020 at a tertiary hospital in Ghana, comparatively assessing their QOL using EORTC QLQ C-30 and EORTC QLQ BR-23. (3) Results: The study participants had an overall global health status (GHS) median score of 83.3 [IQR: 66.7–91.7] with no significant differences between the surgery types. The BRS group had statistically significant lower median scores for the functional scale (82.8 and 51.0) and the highest scores for the symptomatic scale (15.7 and 16.5). Body image was significantly lowest for the BRS group (83.3) [68.8–91.7] and highest (100) [91.7–100] for the BCS group (p < 0.001). (4) Conclusion: There is a need to develop support systems tailored at improving the QOL of breast cancer survivors taking into consideration the type of surgery performed.

Understanding pedestrian perceptions and attitudes is crucial for promoting walking as a daily transportation mode for sustainable mobility and the effective development of smart cities. Pedestrian preferences, shaped by factors such as age, gender, and urban infrastructure, play a pivotal role in travel behaviors. Based on a survey study, this paper examines the impact of individual and urban factors on pedestrian perceptions and attitudes towards walking in the cities of Bologna and Porto. Results reveal that individuals generally value short, safe, and green walking routes, appreciating walking for physical activity, cost savings, and time efficiency. Disliked aspects include adverse weather conditions and walking on inadequate sidewalks. Through carrying out Chi-square statistical analysis tests, a variety of significant correlations between individual and urban variables have been revealed on what people like or dislike about walking. For instance, males, young individuals, and students were more likely to prefer short pedestrian routes, while adults and seniors favored safe and green routes. These findings can assist urban planners in identifying factors that make walking both convenient and enjoyable and in supporting sustainable urban mobility policies.

Sentinel-2 data are crucial in mapping flooded areas as they provide high spatial and spectral resolution but under cloud-free weather conditions. In the present study, we aimed to devise a method for mapping a flooded area using multispectral Sentinel-2 data from optical sensors and Geographical Information Systems (GISs). As a case study, we selected a site located in Northern Italy that was heavily affected by flooding events on 3 October 2020, when the Sesia River in the Piedmont region was hit by severe weather disturbance, heavy rainfall, and strong winds. The method developed for mapping the flooded area was a thresholding technique through spectral water indices. More specifically, the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI) were chosen as they are among the most widely used methods with applications across various environments, including urban, agricultural, and natural landscapes. The corresponding flooded area product from the Copernicus Emergency Management Service (EMS) was used to evaluate the flooded area predicted by our method. The results showed that both indices captured the flooded area with a satisfactory level of detail. The NDWI demonstrated a slightly higher accuracy, where it also appeared to be more sensitive to the separation of water from soil and areas with vegetation cover. The study findings may be useful in disaster management linked to flooded-area mapping and area rehabilitation mapping following a flood event, and they can also valuably assist decision and policy making towards a more sustainable environment.

When studying hazardous materials such as spent nuclear fuel (SNF), the minimisation of sample volumes is essential, together with the use of chemically-similar surrogates where possible. For example, the bulk behaviour of urania (UO2) can be mimicked by appropriately-engineered thin films of sufficient thickness, and inactive materials such as ceria (CeO2) can be used to study the effects within radioactive systems used to fuel nuclear fission. However, thin film properties are sensitive to the preparative method, many of which require the use of highly toxic precursors and specialised apparatus (e.g., chemical vapour deposition). To address this, we present the development of a flexible, tuneable, scalable method for the preparation of thin-film CeO2 SIMFUEL models with a thickness of ≈5 μm. The effects of γ irradiation (up to 100 kGy) and dopants including trivalent lanthanides (Ln3+) and simulant ε-particles on the structure and long-term leaching of these systems under SNF storage conditions were explored, alongside the context of this within further work. It was found that the sensitivity of CeO2 films to reduction upon irradiation, particularly in the presence of simulant ε-particles, resulted in increased leaching of Ce (as CeIII), while trivalent lanthanides (Nd3+ and Eu3+) had a minimal effect on Ce leaching.

Pharmaceuticals are used to improve the lives of people across the globe. The high demand for their fabrication and use causes a very serious environmental threat since their presence is ubiquitous in aqueous matrices. For this reason, the synthesis, characterisation, and efficiency of three chitosan-based materials to eliminate pharmaceutical mixtures from aqueous solutions were examined in the present study. The target mixture comprised seven widely used drugs: carbamazepine, cyclophosphamide, adefovir, levofloxacin, metronidazole, glibenclamide, and trimethoprim. The grafting of poly(ethylene imine) and poly(acrylamide) on the chitosan structure allowed its physical characteristics to be controlled. An adsorption assessment was performed at different pH values, and it was concluded that pH = 4 was the optimum value. The adsorption kinetics revealed that the adsorption of a drug mixture involves a combination of physical and chemical adsorption. The adsorption process appeared to be finished after 1 h for all compounds of the studied mixture, with CS-AMI exhibiting the fastest kinetics. Mass adsorption experiments were also carried out to determine its effects. Overall, the grafting process significantly increased the adsorption capacity over the pristine material. Specifically, the highest capacity increase for CS-PEI was ~220% for carbamazepine, and for CS-AMI, it was 158% for trimethoprim. FT-IR, SEM, and XRD were used for the characterisation of the polymers. Based on the findings, the three materials are suggested as very effective adsorbents for the elimination of medicine residues from aqueous matrices.

Plants possess endless structural and chemical diversity, which is peerless with any synthetic library of small biomolecules, inspiring novel drug discovery. Plants are widely applied to encounter global health challenges such as antimicrobial resistance and diabetes. The objective of this work was to evaluate the antibacterial and antidiabetic potency of native plants grown in Cyprus. All plants were sequentially extracted with solvents of increasing polarity, namely hexane, acetone, methanol, and water. First, the phenolic and flavonoid contents of the extracts were assessed. Afterwards, the bacteriostatic and bactericidal potency of plant extracts were tested against a panel of six bacteria using the broth microdilution method, whereas the inhibitory effects on alpha-glucosidase and alpha-amylase enzymes were also determined with the employment of microplate assays. The results highlighted the superiority of Sarcopoterium spinosum as a potential enzyme inhibitor, while a knowledge base was also acquired for the inhibitory potential of all plants. Daucus carota, Ferula communis, and Tordylium.aegyptiacum displayed additionally outstanding bacteriostatic and bactericidal effects on Gram-positive bacteria at concentrations of 250 µg mL−1 and 500 µg mL−1. Overall, the present study describes the antibacterial and inhibitory activity against carbohydrate digestive enzymes of native plants grown in Cyprus delivering the first reports for many plant species.

The modeling of many problems of practical interest leads to nonlinear ill-posed equations (for example, the parameter identification problem (see the Numerical section)). In this article, we introduce a new source condition (SC) and a new parameter choice strategy (PCS) for the Tikhonov regularization (TR) method for nonlinear ill-posed problems. The new PCS is introduced using a new SC to compute the regularization parameter (RP) before computing the regularized solution. The theoretical results are verified using a numerical example.

Open burning poses a significant threat to human health and the environment by releasing hazardous chemicals and exacerbating plastic pollution. Urgent action is required to address its pervasive impact and the substantial release of gaseous pollutants. Limited research has explored the demographic aspect of open burning behavior, with none specifically conducted in Kentucky. An analysis of open burning complaints reported to the Kentucky Division for Air Quality in 2015, 2019, and 2021 revealed no significant differences in reported incidents by month and county. Binary logistic regression analyses identified the urban vs rural divide as significant predictors of open burning incidents, while violations were influenced by both urban and rural factors and average household income. Unemployment rates and the percentage of individuals with less than a high school diploma did not significantly predict open burning violations. Targeted interventions at the state and local level, focusing on rural areas and economically disadvantaged communities, can effectively address and mitigate open burning issues.

This study combines theoretical modeling and experimental validation to explore anaerobic digestion comprehensively. Developing a computational model is crucial for accurately simulating a digester’s performance, considering various feedstocks and operational parameters. The main objective was to adapt the anaerobic digestion model 1 (ADM1) simulation code to align with the laboratory-scale anaerobic digestion reactor’s specifications, especially regarding the liquid–gas transfer process. Within this computational framework, users may define model parameters and elucidate processes occurring in compartments reflecting the physical design. The model accurately predicts total concentrations of chemical oxygen demand (COD) as well as the produced biogas, with an average difference of less than 10% between experimental and simulated data. This consistency underscores the reliability and effectiveness of the adapted model in capturing anaerobic digestion nuances under specified conditions.

Type I interferon signaling (IFN-I) perturbations are major drivers of COVID-19. Dysregulated IFN-I in the brain, however, has been linked to both reduced cognitive resilience and neurodegenerative diseases such as Alzheimer’s. Previous works from our group have proposed a model where peripheral induction of IFN-I may be relayed to the CNS, even in the absence of fulminant infection. The aim of our study was to identify significantly enriched IFN-I signatures and genes along the transolfactory route, utilizing published datasets of the nasal mucosa and olfactory bulb amygdala transcriptomes of COVID-19 patients. We furthermore sought to identify these IFN-I signature gene networks associated with Alzheimer’s disease pathology and risk. Gene expression data involving the nasal epithelium, olfactory bulb, and amygdala of COVID-19 patients and transcriptomic data from Alzheimer’s disease patients were scrutinized for enriched Type I interferon pathways. Gene set enrichment analyses and gene–Venn approaches were used to determine genes in IFN-I enriched signatures. The Agora web resource was used to identify genes in IFN-I signatures associated with Alzheimer’s disease risk based on its aggregated multi-omic data. For all analyses, false discovery rates (FDR)

Osteosarcoma malignancy currently represents a major health problem; therefore, the need for new therapy approaches is of great interest. In this regard, the current study aims to evaluate the anti-neoplastic potential of a newly developed phosphinic acid derivative (2-carboxyethylphenylphosphinic acid) and, subsequently, to outline its pharmaco-toxicological profile by employing two different in vitro human cell cultures (keratinocytes—HaCaT—and osteosarcoma SAOS-2 cells), employing different techniques (MTT assay, cell morphology assessment, LDH assay, Hoechst staining and RT-PCR). Additionally, the results obtained are compared with three commercially available phosphorus-containing compounds (P1, P2, P3). The results recorded for the newly developed compound (P4) revealed good biocompatibility (cell viability of 77%) when concentrations up to 5 mM were used on HaCaT cells for 24 h. Also, the HaCaT cultures showed no significant morphological alterations or gene modulation, thus achieving a biosafety profile even superior to some of the commercial products tested herein. Moreover, in terms of anti-osteosarcoma activity, 2-carboxyethylphenylphosphinic acid expressed promising activity on SAOS-2 monolayers, the cells showing viability of only 55%, as well as apoptosis features and important gene expression modulation, especially Bid downregulation. Therefore, the newly developed compound should be considered a promising candidate for further in vitro and in vivo research related to osteosarcoma therapy.

Over the last decades, the analysis of complex microbial communities by high-throughput sequencing of marker gene amplicons has become routine work for many research groups. However, the main challenges faced by scientists who want to make use of the generated sequencing datasets are the lack of expertise to select a suitable pipeline and the need for bioinformatics or programming skills to apply it. Here, we present MetaXplore, an interactive, user-friendly platform that enables the discovery and visualization of amplicon sequencing data. Currently, it provides a set of well-documented choices for downstream analysis, including alpha and beta diversity analysis, taxonomic composition, differential abundance analysis, identification of the core microbiome within a population, and biomarker analysis. These features are presented in a user-friendly format that facilitates easy customization and the generation of publication-quality graphics. MetaXplore is implemented entirely in the R language using the Shiny framework. It can be easily used locally on any system with R installed, including Windows, Mac OS, and most Linux distributions, or remotely via a web server without bioinformatic expertise. It can also be used as a framework for advanced users who can modify and expand the tool.

This study investigates the differential expression of miRNA gene subtypes in tumoral versus benign nevi in individuals with melanoma, aiming to identify clinically significant correlations that could serve as reliable markers for assessing tumor stage and progression. Conducted between 2019 and 2022, this descriptive, quantitative observational research analyzed 90 formalin-fixed paraffin-embedded (FFPE) samples from the Pius Brinzeu County Emergency Clinical Hospital, Timisoara, including 45 samples of advanced-stage melanoma and 45 samples of pigmented nevi. miRNA purification and analysis were performed using the miRNeasy Kit and the Human Cancer PathwayFinder miScript miRNA PCR Array, with statistical analysis (including logistic regression) to determine associations with cancer staging, such as high Breslow index risk, number of mitoses, and vascular invasion. After the analysis and comparison of 180 miRNA gene subtypes, we selected 10 of the most upregulated and 10 most downregulated genes. The results revealed that hsa-miR-133b, hsa-miR-335-5p, hsa-miR-200a-3p, and hsa-miR-885-5p were significantly upregulated in melanoma samples, with fold changes ranging from 1.09 to 1.12. Conversely, hsa-miR-451a and hsa-miR-29b-3p showed notable downregulation in melanoma, with fold changes of 0.90 and 0.92, respectively. Additionally, logistic regression analysis identified hsa-miR-29b-3p (OR = 2.51) and hsa-miR-200a-3p (OR = 2.10) as significantly associated with an increased risk of a high Breslow index, while hsa-miR-127-3p and hsa-miR-451a were associated with a reduced risk. Conclusively, this study underscores the significant alterations in miRNA expression in melanoma compared to benign nevi and highlights the potential of specific miRNAs as biomarkers for melanoma progression. The identification of miRNAs with significant associations to melanoma characteristics suggests their utility in developing non-invasive, cost-effective diagnostic tools and in guiding therapeutic decisions, potentially improving patient outcomes in melanoma management.