This study aimed to identify whether the combined use of functional electrical stimulation (FES) reduces the motor torque of a gait exercise rehabilitation robot in spinal cord injury (SCI) and to verify the effectiveness of the developed automatic assist level adjustment in people with paraplegia. Acute and chronic SCI patients (1 case each) performed 10 min of gait exercises with and without FES using a rehabilitation robot. Reinforcement learning was used to adjust the assist level automatically. The maximum torque values and assist levels for each of the ten walking cycles when walking became steady were averaged and compared with and without FES. The motor's output torque and the assist level were measured as outcomes. The assist level adjustment allowed both the motor torque and assist level to decrease gradually to a steady state. The motor torque and the assist levels were significantly lower with the FES than without the FES under steady conditions in both cases. No adverse events were reported. The combined use of FES attenuated the motor torque of a gait exercise rehabilitation robot for SCI. Automatic assistive level adjustment is also useful for spinal cord injuries.

This study examines the relationship between chondroitin sulphate, proteinoids, and computational neuron models, with a specific emphasis on the Izhikevich neuron model. We investigate the effect of chondroitin sulphate-proteinoid complexes on the behaviour and dynamics of simulated neurons. Through the use of computational simulations, we provide evidence that these biomolecular components have the power to regulate the responsiveness of neurons, the patterns of their firing, and the ability of their synapses to change within the Izhikevich architecture. The findings suggest that the interactions between chondroitin sulphate and proteinoid cause notable alterations in the dynamics of membrane potential and the timing of spikes. We detect adjustments in the features of neuronal responses, such as shifts in the thresholds for firing, alterations in spike frequency adaptation, and changes to bursting patterns. The findings indicate that chondroitin sulphate and proteinoids may have a role in precisely adjusting neuronal information processing and network behaviour. This study offers new and valuable information about the complex connection between the many components of the extracellular matrix, protein-based structures, and the functioning of neurons. In addition, our analysis of the proteinoid-chondroitine system using game theory uncovers a significant Prisoner’s Dilemma scenario. The system’s inclination towards defection, due to the appeal of cheating and the significant penalty for cooperation, with a mean voltage of −9.19 mV, indicates that defective behaviors may prevail in the long-term dynamics of these neuronal interactions.

Background/Objectives: Asthma is a chronic inflammatory disorder of the airways affecting over 10% of the global population. It is characterized by airway inflammation, mucus hypersecretion, and bronchial hyperresponsiveness, driven predominantly by type 2 helper T cells (Th2) and type 2 innate lymphoid cells (ILC2s) in a subset of patients. However, a significant portion of asthmatic individuals present with "type 2-low" asthma, often refractory to standard inhaled corticosteroid (ICS) therapy. Therefore, developing innovative therapeutic strategies has become essential. Recent studies have highlighted cannabidiol (CBD) as a promising anti-inflammatory agent capable of modulating immune responses. This study investigates the therapeutic potential of a high-CBD extract (CBD-X) in asthma; Methods: We evaluated the effects of CBD-X on cells involved in asthma pathogenesis using primary human Th2 cells, neutrophils and asthma mouse model; Results: Our findings indicate that CBD-X extract inhibits Th2 differentiation and reduces the secretion of IL-5 and IL-13, which are crucial cytokines in asthma. Additionally, CBD-X significantly reduces pro-inflammatory cytokines IL-8 and IL-6 in neutrophils and impairs their migration, a critical step in airway inflammation. In a murine asthma model, CBD-X administration led to marked downregulation of IgE and pro-asthmatic cytokines, along with reduced leukocyte, eosinophil, and neutrophil infiltration in lung tissues; Conclusions: These results suggest that CBD-X extract could offer a novel, complementary approach to managing both type 2-high and type 2-low asthma by targeting key inflammatory pathways and modulating immune cell behavior.

This study investigates the correlation between gut microbiota diversity and various hematological and meat quality parameters in different cattle breeds, including Angus, Simmental, Braunvieh, Brahman and F1(SMxBR). Using 16S rRNA and 18S rRNA gene sequencing, microbial communities were profiled to discern breed-specific impacts on gut microbiota. Significant correlations were identified between gut microbiota diversity and health parameters, emphasizing the role of microbial diversity in influencing skin condition, fat deposition, and muscle quality. Key bacterial genera such as Blautia, Christensenellaceae_R-7_group, and Coprococcus exhibited significant correlations with meat quality traits. In fungi, genera like Rhodotorula exhibited positive correlations with muscle quality, while protist genera such as Cryptosporidium negatively correlated with meat quality parameters. These findings suggest that host genetics significantly shape the gut microbiota and highlight the potential for targeted microbiota management to optimize gut health, enhance feed efficiency, and improve meat quality in livestock.

Maritime transport has adapted to recent political and economic shifts by addressing stringent pollution reduction requirements, redrawing transport routes for safety, reducing onboard technical incidents, managing data security risks and transitioning to autonomous vessels. The integration of AI-based technologies for fault diagnosis and decision-making, coupled with proper personnel training, has become essential for optimal ship operation, with AI-assisted predictive maintenance emerging as the key strategy to ensure onboard systems function within desired parameters and enhance equipment availability. This study proposes a machine-learning algorithm for evaluating onboard systems performance by analyzing sensor data and mapping it to fault patterns to estimate functional states, validated through tests on a seawater cooling system from an oil tanker, demonstrating operational efficiency and reliability. The immediate advantage is reduced time for fault diagnosis by rapidly assessing the system using operational data, the algorithm enabling real-time monitoring and fault data management to minimize efforts as part of predictive maintenance assisted by machine learning tools.

Nitrogen loss in water from farmland has become a highly concerned environment issue due to the different nitrogen-fertilized input, which is the main cause of agricultural non-point source pollution in the Lake Basin. However, characteristics of nitrogen forms loss from farmland and how the root system responds is lacking. The effectiveness of 100–0% (T1), 75–25% (T2), 50–50% (T3), 25–75% (T4), and 0–100% (T5) nitrate-ammonium nitrogen fertilizer treatment was investigated to determine nitrogen loss in water and root morphological parameters of tobacco in Mile and Chengjiang Counties. Two sites result suggested that: Compared with the T1, T4, and T5 treatments, the TN loss of the T2 and T3 treatments in runoff was reduced by 4.67–27.32%, 11.85–23.20%, and 9.56–31.43%, respectively. Similar results in the nitrogen loss of infiltration were observed with that from runoff. Correlation analysis suggested that the root biomass was negatively correlated with the nitrogen loss. T2 and T3 treatments were larger than those of the T1, T4, and T5 treatments in terms of root biomass, root surface area, and root spatial distribution. This study suggested that 50–50% nitrate-ammonium nitrogen fertilizer can facilitate the root growth of tobacco and reduce nitrogen loss, which provides a reference for agricultural sustainable development.

Africa's health landscape is rapidly changing, requiring new solutions such as a focus on health literacy. However, there is currently a limited overview of the development and application of health literacy in African countries and societies. To inform progress, a scoping review was conducted, including a systematic literature search of research approaches, historical developments, geographic origins, target populations and settings, and topical interests. 336 articles were retrieved from six databases following PRISMA guidelines. Sources identified included reports, policy briefs, book chapters, and research publications. The analysis provided the most comprehensive overview of the current scope and scale of health literacy in Africa to date. The publications represented 38 of the 54 African Union countries and dated back to 2001, although most were published in the last decade. The content analysis identified 13 broad themes, including mental health, communicable diseases, non-communicable diseases, maternal health, digital health, information and communication, health care, prevention and health promotion, conceptual perspectives, cultural perspectives, outcomes and measurement. The stakeholder analysis revealed a wide range of actors involved in different settings, mostly in health care or community settings. These comprehensive and novel findings can be used to prioritize future actions for public and professional capacity building, policy development and improved practice to improve health literacy for all in Africa.

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of DNA repair gene-X (X, a particular gene/protein) at 2nd order level after drug administration. These rankings reveal which Wnt-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and determine the mechanism of functioning between the Wnt and X. In this re- search work, we cover combinations of RAD with X-ray repair cross complementing (XRCC) family, 5’-3’ exoribonuclease 2 (XRN2), NFKB repressing factor (NKRF), B cell CLL/lymphoma (BCL) family, exosome component (EXOSC) family, FA comple- mentation group (FANC) family and XRCC with EXOSC.

In the context of energy conversion from renewable sources to distribution grids (insulated or not), a converter is often required to transfer energy from a low voltage source, towards three-phase grids. This paper presents the HW design, the simulation results and the conversion performance of a CSI converter intended to interface low voltages renevable sources to three-phase grids. The main focus of this paper is to obtain the best performance in terms of voltage increase towards the output stage while maximizing the conversion efficiency. In comparison with the currently used energy conversion systems for small photovoltaic systems, hereafter some solutions were adopted to level and maximize the energy flow from the source to the DC-link and improve the quality of current supplied in terms of harmonic distortion. The proposed system is composed of two conversion stages: the first, voltage-to-current, the second current-to-current via a three-phase CSI bridge modulated with SVM technique. The stages are not completely decoupled from an electrical point of view, therefore, in order to mitigate the effects of these interactions, synchronization strategies have been adopted.

This study examines the relationship between Gross Domestic Product (GDP) and income inequality in Greece across three key periods: the pre-crisis period (2003-2008), the crisis and memoranda period (2009-2014), and the post-crisis period (2015-2020). Using data from the World Bank Open Data database, the study employs correlation analysis, and linear regression models to analyze how economic changes influence income distribution. The findings reveal that during the pre-crisis period, economic growth was moderately correlated with increasing inequality, although this relationship was not statistically significant, indicating uneven distribution of growth benefits. In the crisis period, a strong and statistically significant negative relationship between GDP and the Gini coefficient was identified, highlighting the sharp rise in inequality as the economy contracted due to austerity measures. In the post-crisis period, the relationship between GDP and inequality weakened, showing a statistically insignificant correlation, suggesting that economic recovery alone did not significantly reduce inequality. The study concludes that economic growth is insufficient to address income inequality without targeted policy interventions, particularly during periods of crisis. These findings underscore the importance of comprehensive social policies to mitigate inequality and suggest areas for future research on the impact of specific interventions on income distribution.

This scoping review explores the intersection of Extended Mind Theory and Extended Reality (XR) technologies, focusing on how Virtual Reality, Augmented Reality, and Mixed Reality reshape human cognition and interaction. XR enables users to offload cognitive tasks and engage in embodied experiences, extending cognition beyond the brain into digital environments. The review highlights a wide range of XR applications, from immersive learning in STEM education and medical training, neuropsychological assessment to therapeutic interventions, arts and entertainment, professional skills development, retail and e-commerce, remote work, sports training, architecture and urban planning, and cultural heritage preservation. XR's integration with modalities like haptics, eye-tracking, face- and body-tracking, and brain-computer interfaces further enhances cognitive extension and user engagement. However, alongside these advancements come significant ethical, psychological, and societal challenges, such as data privacy concerns, the psychological effects of prolonged immersion, and social inequality arising from disparate access to XR technologies. This review emphasizes the need for robust ethical frameworks that address these challenges, ensuring that XR technologies enhance human development while maintaining autonomy, privacy, and mental well-being. As XR continues to evolve and integrate with artificial intelligence and other emerging technologies, its role in expanding human cognition will depend on responsible implementation and governance.

Background. Sarcopenia is characterized by progressive loss of muscle mass and function. It is often associated with frailty, a syndrome linked to physical disability and shortened survival in various diseases, including cancer patients. Low serum alanine aminotransferase (ALT) values, representing low muscle mass (sarcopenia), is a routine blood test that was shown to be associated with increased frailty and subsequently shortened survival in several cancers. In the current study, we aimed to test the association between low ALT and shorter survival in renal cell carcinoma (RCC) patients and survivors. Methods. This was a retrospective analysis of RCC patients and survivors, both in and out-patients. We defined patients with sarcopenia as those presenting with ALT < 17 IU/L. Results. We identified records of 3,012 patients diagnosed with RCC. The final cohort included 1,830 eligible patients (mean age 65.6 ± 13.3 years, 68% were men) of whom only 179 underwent surgical treatment. Out of the eligible cohort, 811 patients (44.3% ( had ALT < 17 IU/L. The mean ALT value of patients within the low ALT group was 11.79 IU/L while the mean value in the higher ALT level group was 24.44 IU/L (p < 0.001). Patients in the lower ALT group were older (67.9 vs. 63.7 years; p < 0.001) and had lower BMI (26.6 vs. 28; p < 0.001). In addition, patients with low ALT had lower hemoglobin values (12.14 vs. 12.91 g/dL; p < 0.001), higher serum creatinine (1.49 vs. 1.14; p < 0.001) and higher platelets to lymphocytes ratio (178 vs. 156; p < 0.001). In a univariate analysis, low ALT levels were associated with a 72% increase in mortality (95% CI 1.46-2.02, P < 0.001). In a multivariate model controlled for age, male gender, hemoglobin, platelets, LDH, neutrophil to lymphocytes ratio and platelets to lymphocytes ratio, low ALT levels were associated with a 27% increase in mortality (HR = 1.27, 95% CI 1.08 – 1.51; p = 0.005]. Conclusion. Low ALT values, indicative of sarcopenia and frailty, are associated with shortened survival of RCC patients and survivors and could potentially be applied for optimizing individual treatment decisions.

Microglial cells are highly specialized central nervous system (CNS) cells that can promote the inflammation and neurodegeneration seen in Multiple Sclerosis (MS). MS is a neurodegenerative autoimmune disease characterized by inflammation, demyelination, and axonal degeneration. In MS patients, demyelination is associated with activated microglia. Endothelin-1 (ET-1) is a potent vasoconstrictor that induces severe and prolonged cerebral vasoconstriction and inflammation. However, the mechanism of how ET-1 activates a proinflammatory response in the CNS is unknown. To investigate ET-1’s role in microglia activation, HMC3 cells were treated with ET-1 in the presence or absence of endothelin receptor B antagonist, BQ788. TNFα and IL-6 levels were measured using ELISA. Nitric Oxide (NO) production was measured using Griess Reagent. Reactive oxygen species (ROS) production was measured using the MUSE Oxidative Stress kit. ET-1 increases TNFα levels by 56% (p= 0.0003) and IL-6 levels by 86% (p= 0.0111) in HMC3 cells, and it was decreased to basal levels in the presence of BQ788. NO and ROS production is induced by ET-1 (p&lt;0.05), and treatment with BQ788 was able to decrease them. ET-1 increases STAT-1 activation by 3.5 folds compared to control (p &lt;0.0001) in microglia cells. Moreover, to study ET-1 levels in MS, we used C57BL/6 mice brains with or without induced experimental autoimmune encephalomyelitis (EAE). The brain ET-1 levels were measured by qPCR and ELISA. We found that the ET-1 gene and protein were upregulated by 1.5 folds (p= 0.0199) in EAE mice compared to the control. These data suggest that in vitro administration of ET-1-activated microglia significantly increased inflammatory cytokine levels and NO and ROS formation. This novel mechanism of microglial cell activation will provide key information to understand MS pathogenesis.

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of ATP- binding cassette (ABC) transporters-X (X, a particular gene/protein) at 2nd order level after drug administration. These rankings reveal which ABC-X combinations might be working synergistically in CRC. If found true, oncologists can further test the com- bination of interest in wet lab and determine the mechanism of functioning between the ABC and X. In this research work, we cover combinations of ABC with ubiquitin- conjugating enzymes (UBE2), Interleukin (IL), BCL and Caspase (CASP).

Mandible is a bony structure of neuroectodermal origin with unique characteristics that support dentition and jaw movements. In the present study we investigated the effects of gestational exposure to a mixture of endocrine disrupting chemicals (EDCs) on mandibular growth in mice. The mixture under study (Mixture N1) has been associated with neurodevelopmental effects in both a human cohort and animal studies. Pregnant mice were exposed throughout gestation to 0.5x (times of pregnant women exposure levels), 10x, 100x and 500x of Mixture N1, or the vehicle, and the mandibles of the male offspring were studied in adulthood. Micro-CT analysis showed non-monotonic effects of Mixture N1 in the distances between specific mandibular landmarks and in the crown width of M1 molar, as well as changes in the mandibular bone characteristics. The alveolar bone volume was reduced, and the trabecular separation was increased in the 500X exposed mice. Bone volume in the condyle head was increased in all treated groups. The Alcian blue stained area of pre-hypertrophic chondrocytes in condyle’s head was increased in 0.5x, 10x and 100x groups, while the Safranin-O-stained area of mature hypertrophic chondrocytes was respectively reduced. This is the first indication that prenatal exposure to an epidemiologically defined EDC mixture, associated with neurodevelopmental impacts, can also affect mandibular growth in mammals.

Nanocomposites (NCs) consisting of zero-valent iron nanoparticles (nZVI) immobilized in chitosan (CS) were prepared and employed, for the removal of hexavalent chromium (Cr(VI)) from both synthetic and real wastewater. Medium (MCS) and high (HCS) molecular weight chitosan and stabilization with carboxymethylcellulose (CMC), and different nZVI load were explored. Characterization through Scanning Electron Microscopy with Energy Dispersive X-ray Analysis (SEM-EDS) and X-ray Diffraction (XRD) revealed millimeter-sized spheres with micrometer-sized nZVI clusters randomly distributed. Better nanoparticle dispersion was observed in NCs from the CMC-MCS and HCS combinations. Fourier-transform infrared spectroscopy (FTIR) analysis indicated that CS binds to Fe(II) or Fe(III) on the surface of nZVI through its functional groups -CONH-, -N-H and -C-OH and through the -COO- functional group of CMC, forming a bidentate bridge complex. Through experiments with synthetic waters, it was found that the elimination of Cr(VI) is favored by lowering the pH, obtaining the maximum percentage of Cr(VI) removal at pH 5.5. With real waters, it was shown that increasing the mass of NCs also improves the removal of Cr(VI), following a pseudo-second-order adsorption kinetics. The synthesized materials show great potential for applications in environmental remediation, showing good efficiency in the removal of Cr(VI) in wastewater.

Entropy production in stochastic thermodynamics is defined as the average over all the possible system trajectories of a quantity (observable) which measures the divergence between a trajectory and its time-reversal. In this way a quantitative measure of the irreversible evolution of a system out of equilibrium can be given. In this note we consider a Markov system and substitute the linear average with a more general notion of average over the trajectories (Kolmogorov-Nagumo mean) which corresponds to substituting Shannon entropy with Rény entropy. We show that, unlike the linear average case, the resulting notion of entropy production depends on how the observable is defined.

Background/Objectives: Periodontitis (P), a chronic inflammatory condition that affects the supportive tissues around the teeth, is 3 to 4 times more prevalent in individuals with diabetes mellitus (DM), with a direct correlation between its severity and the levels of glycosylated haemoglobin (HbA1c). This study aimed to evaluate the periodontal clinical parameters following non-surgical periodontal treatment (NSPT) in patients with and without type 2 DM and P. Methods: Forty patients with P were divided into two groups: Group DM/P and Group P. All patients were assessed at baseline, and 90 and 180 days after receiving NSPT. The parameters evaluated included HbA1c levels, plaque index (PI), probing pocket depth (PPD), clinical attachment level (CAL), and bleeding on probing (BoP). Statistical analysis was performed with a significance level set at α = 5%. Results: There were significant differences in HbA1c levels between the DM/P and P groups at baseline, 90, and 180 days, as expected. Importantly, HbA1c levels did not change after NSPT. Group P showed a significant reduction in both PI and BoP values at 90 and 180 days (p &lt; 0.05). In contrast, group DM/P demonstrated a significant increase in the percentage of sites with PPD ≥ 5mm at 180 days (p &lt; 0.05). Additionally, group P exhibited an increase in sites with PPD ≤ 4mm and a decrease in sites with PPD ≥ 5mm at both 90 and 180 days (p &lt; 0.05). Conclusions: Our findings suggest that DM may compromise the effectiveness of NSPT, potentially hindering favorable outcomes during the follow-up period.

Automatic Milking Systems (AMS) are revolutionizing the dairy industry by boosting herd effi-ciency, primarily through increased milk yield per cow and reduced labor costs. The performance of milking machines, whether traditional or automated, can be evaluated using advanced vacuum meters through dynamic testing. This process involves scrutinizing the system and milking rou-tine to identify critical points, utilizing the VaDia™ logger (BioControl AS, Rakkestad, NO). Vacuum recordings were downloaded and analyzed using the VaDia Suite™ software under the guidance of a milking specialist. Access to data from AMS across various manufacturers and herds facilitated a retrospective study aimed at describing and comparing key milk emission parameters for different AMS brands while identifying potential mastitis risk factors. Using proper statistical procedures of SPSS 29.1 (IBM Corp, USA), researchers analyzed data from 4,878 individual quarter milkings from cows in 48 dairy herds. Results indicated significant variability in milking pa-rameters associated to quarter milk yield and AMS brand. Notably, despite AMS standardizing teat preparation and stimulation, the study revealed a surprisingly high frequency of two major mastitis risk factors—bimodality and irregular vacuum fluctuations—occurring more frequently than in conventional milking systems. This study, one of the few comparing different AMS brands and their performance, highlights the crucial role of dynamic testing in evaluating AMS perfor-mance under real-world conditions.

Peroxydisulfate activation has garnered a lot of interest due to its benefits of broad adaptability, strong anti-interference ability, and powerful oxidation capacity. Due to their low cost and potent action, carbon materials are new green catalysts that have garnered interest. In order to effectively degrade organic contaminants in water by activating peroxydisulfate, carbon compounds are currently commonly used. Better metal ions for activating peroxydisulfate include Fe2+ and Co2+, and these metal ions can be encapsulated in carbon compounds made by calcinating metal organic framework（MOFs ）as precursors. The synthesis of carbon compounds from MOF precursors as well as their effectiveness and mechanism in reducing organic pollutants in peroxydisulfate systems. The following are the specific research contents: NH2-MIL-101 (Fe/Co) bimetallic MOFs material was synthesized by hydrothermally altering the Fe3+ to Co2+ ratio, followed by the carbonization of Fe/Co-CNs. XRD, SEM, TEM, FT-IR, and XPS were used to analyze the materials' morphology, structure, and chemical make-up. The results demonstrate that Fe/Co-CNs-2 (Co2+ doping concentration approaches 20%) has a stable three-dimensional carbon structure and effectively incorporated Co ions into the carbon material. The simultaneous encapsulation of both Fe and Co metals by the three-dimensional carbon skeleton ensures material stability and reaction activity. Tetracycline hydrochloride was reported to degrade more quickly in Fe/Co-CNs-2 than in other Co2+ doping concentrations within 30 minutes of peroxymonosulfate（PMS） activation. The degradation rate of Fe/Co-CNs-2 for 20 mg/L TC was determined to be 93.34% within 30 minutes under the following conditions: Fe/Co-CNs-2 concentration of 0.1 g/L, PMS concentration of 0.2 g/L, and pH of 7.33. The catalyst also demonstrates good stability. Last but not least, the Fe/Co-CNs-2 free radical quenching experiment revealed that singlet oxygen (1O2) is the primary active species in the reaction system, and a potential mechanism was examined by combining degradation and XPS analysis.

Predicting credit default risk is important to financial institutions, as accurately predicting the likelihood of a borrower defaulting on their loans will help to reduce financial losses, thereby maintaining profitability and stability. Although machine learning models have been used in assessing large applications with complex attributes for these predictions, there is still a need to identify the most effective techniques and to also address the issue of data imbalance. In this research, we conducted a comparative analysis of random forest, decision tree, SVM (Support Vector Machine), XGBoost (eXtreme Gradient Boosting), ADABoost (ADAptive Boosting) and multilayered perceptron with three-hidden layers, to predict credit default using loan data from LendingClub. Additionally, we also combined the model predictions using voting and stacking ensemble methods to enhance the models' performance. Furthermore, various sampling techniques was explored to handle the issue of class imbalance observed in the dataset, with the result showing that the balanced data performs better than the imbalanced data. Our proposed model achieved an accuracy of 93.7%, a precision of 95.6% and a recall of 95.5%, which shows the potential of ensemble methods in improving credit default predictions and can provide lending platforms with the tool to reduce default rates and financial losses. In conclusion, the findings from this study have broader implications for financial institutions, offering a robust approach to risk assessment beyond the Lend-ingClub dataset.

(1) Background: To further investigate the link between inadvertent tooth movements (ITMs) and mechanical properties of different retainers, the response upon vertical loading was compared in three different retainers (two stainless steel and one nickel-titanium). The influence of different reference teeth was also considered. (2) Methods: Three retainers (R1, R2, R3) were tested in a newly developed biomechanical analysis system (FRANS). They were bonded to 3D-printed models of the lower anterior jaw and vertically displaced up to 0.3mm. Developing forces and moments were recorded at the center of force. (3) Results: The vertical displacement caused vertical forces (Fz) and labiolingual moments (My) to arise. These were highest in the lateral incisors (up to 2.35±0.59N and 9.27±5.86Nmm for R1; 1.69±1.06N and 7.42±2.65Nmm for R2; 3.28±1.73N and 15.91±9.71Nmm for R3) for all analyzed retainers, and with the R3 retainer for all analyzed reference teeth, while the lowest Fz and My values were recorded with the R1 retainer. (4) Conclusions: Displacements of 0.2mm and larger provided forces and moments which could be sufficient to cause unwanted torque movements, such as ITMs in all analyzed retainers. Clinicians must be mindful of these risks and perform posttreatment checkups on patients with retainers of all materials.

The fundamental period is one of the most important parameters for the design of new structures , as well as the estimation of the capacity of existing ones. However, its calculation requires the solution of an eigenvalue problem. Especially for new buildings, this can involve material and geometric parameters that are not known beforehand. Thus, to estimate it, various design codes and researchers have adopted a several approximate analytical equations based on a number of key structural parameters. To this end, the present study introduces a novel methodology for the derivation of analytical equations for the fundamental period of Reinforced Concrete (RC) structures. The methodology is based on Machine Learning (ML) explainability techniques and specifically the so-called SHapley Additive exPlanations (SHAP) values. The novel methodology allows these equations to be constructed sequentially and in an informed manner, controlling the balance between accuracy and complexity. An extended dataset comprised of 4026 data points is employed. The results showed excellent accuracy, with a coefficient of determination R2≈0.95. This demonstrates the potential applicability of the proposed methodology in a wide array of similar engineering challenges.

Estrogen receptor α (ERα) is a nuclear hormone receptor that mediates the biological effects of estrogen and is known to be phosphorylated at multiple sites. Despite several reports highlighting serine-167 (S167) of ERα as a common phosphorylation site for various kinases, there have been contradictory data regarding its relevance in the context of ERα-expressing luminal A breast cancer. In this study, we show that S167 is targeted by the oncogenic PIM family kinases in these cells. Loss of PIM expression or catalytic activity reduces S167 phosphorylation and restricts ERα signaling, while PIM overexpression has the opposite effect. Nonetheless, we found no evidence that PIMs protect breast cancer cells from ERα blockade, as both PIM protein levels and ERα S167 phosphorylation levels were substantially lower in populations of cells that had developed tamoxifen resistance as compared to untreated cells. However, this does not rule out the possibility that PIMs provide protection against ERα-targeting therapies in such breast cancer cells in which they are overexpressed, warranting further investigations.

Metaverse represents a multifaceted digital universe integrating diverse technologies to create immersive, interconnected virtual environments. Key components foundational to its structure include the infrastructure layer for hardware and network support, the human interface layer utilizing virtual reality (VR) and augmented reality (AR) devices, and a decentralized layer leveraging blockchain for secure digital asset management. Further components encompass spatial computing for realistic 3D environments, the creator economy for content generation and monetization, and discovery mechanisms for navigation within Metaverse. VR, AR, blockchain, AI, the fifth-generation (5G) networks, and the Internet of Things (IoT) are central to Metaverse functionality, enabling real-time interactions and data integration. Real-Time Metaverse aims to replicate instantaneous real-world interactions, requiring advanced technological infrastructure for seamless data exchange. Critical challenges include ensuring low latency, high bandwidth, scalability, and robust security measures. Metaverse’s potential spans various domains, promising to revolutionize social interactions, entertainment, workplaces, and education through dynamic, immersive experiences. Ongoing advancements in high-performance computing, cloud services, and AI-driven technologies are essential for addressing existing barriers and achieving a cohesive, secure, and inclusive Metaverse ecosystem. Future research must focus on enhancing user experience, accessibility, and standardized interoperability to fully realize the transformative capabilities of a Metaverse.

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of BCL-X (X, a particular gene/protein) at 2nd order level after drug administration. These rank- ings reveal which BCL-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and deter- mine the mechanism of functioning between the BCL and X. In this research work, we cover combinations of BCL with Interleukin (IL), Selenbp1, TP53, caspase (CASP), mucin (MUC) and exosome (EXOSC).

The capability of large food retail chains to respond quickly to changes in consumer behaviour and their dominant market position affects all food market players and often conflicts with the interests of national food producers, which can reduce the presence of locally sourced products in the food product mix in the country. In Latvia, there have been several proposals to limit retailers' opening hours on weekends and public holidays to increase the viability of small local food producers, retailers, and catering enterprises; however, limiting retailers' opening hours without research-based arguments is unacceptable. The research studies conducted in Europe also do not give a unified conclusion. Household consumer behaviour in the food market is viewed as an essential argument for increasing domestic demand and economic stability. Accordingly, the present research aims to identify the impacts of diversification of opening hours of food supermarkets on consumer shopping habits and the implications for creating an advantage for small and medium agri-food producers in selling their products. The research applied a quantitative approach to identify the main trends in society (n=2738), with a survey including 31 variables to quantify consumer behaviour, values, and opinions and seven socio-demographic variables. If a decision were made in Latvia to close grocery shops on Sundays or reduce their opening hours on weekends, 85% of consumers indicated that they would be unlikely to change their usual shopping location and would plan to shop at a supermarket on other days. The choice between farmers' markets and local food shops on Sundays would be made by 45% of consumers, with more than half (53%) of them shopping at local food shops at least a few times a month. It could, therefore, be argued that reducing the opening hours of supermarkets and closing them on Sundays and public holidays do not provide a competitive advantage and do not divert consumers to small and medium agri-food producers.

Muscle ultrasound quantification is a valuable complementary diagnostic tool for diabetic peripheral neuropathy (DPN). DPN significantly impacts the lives of individuals with diabetes, leading to pain, lower limb amputation, and disability, affecting patient quality of life. This work develops a computer-aided diagnostic (CAD) system based on muscle ultrasound that integrates the bag of features (BOF) and an ensemble subspace k-nearest neighbour (KNN) algorithm for DPN detection. The BOF creates a histogram of visual word occurrences to represent the muscle ultrasound images and trains an ensemble classifier through cross-validation, determining optimal parameters to improve classification accuracy for the ensemble diagnosis system. The dataset includes ultrasound images of six muscles from 53 subjects, consisting of 27 control and 26 patient cases. An empirical analysis was conducted for each binary classifier based on muscle type to select the best vocabulary tree properties or K values for BOF. The result indicates that ensemble subspace KNN classification, based on the bag of features, achieved an accuracy of 97.23%. This study suggests muscle ultrasound as a promising diagnostic tool with the potential for image recognition and interpretation. CAD systems can accurately diagnose muscle pathology, helping to overcome limitations and identify issues in individuals with diabetes.

Wave and tidal energy, recognized as vital renewable resources, harness the ocean's kinetic and potential power. This study aims to provide an in-depth patent analysis of the technological landscape within these sectors. We applied a dual approach: first, a descriptive analysis was conducted to explore patent publication trends, technology lifecycle stages, patent activity by country, top assignees, and IPC classifications. Our analysis provided a detailed overview of the sector's growth and the key players involved. Second, we utilized topic modeling, specifically BERTopic enhanced with large language models (LLMs), to identify and fine-tune key technolog-ical themes within the patent data. In this study, we identified seven distinct clusters each for wave and tidal energy using this approach. This method led to a novel categorization of the pa-tents, revealing latent themes within the patent data. Although our categorization differs from traditional methods, it provides deeper insights into the thematic focus of the patents, highlight-ing emerging trends and areas of innovation within wave and tidal energy technologies to better exploit and optimize ocean energy conversion infrastructure.

Background/Objectives: Nephrolithiasis is a common urologic condition and a significant source of patient morbidity and healthcare expenditure. Despite the higher prevalence of metabolic risk factors for nephrolithiasis among Black women, there have been few epidemiologic studies of kidney stones focusing on this group. Methods: We describe demographic and health characteristics, diagnostic, and metabolic profiles of women with self-reported kidney stones among participants in the Black Women’s Health Study (BWHS), a large prospective cohort of U.S. Black women. Results: Among the 2,750 BWHS participants who completed an online supplemental questionnaire assessing urologic health, 201 women reported nephrolithiasis. Of this number, 62% had completed ≥16 years of education and 82% reported access to health care. Overall, 39% reported experiencing ≥2 stones in their lifetime and 29% required surgery to treat the condition. Thirty-two percent reported having completed a metabolic evaluation, while 70% had undergone a CT-scan to diagnose nephrolithiasis. The frequency of metabolic evaluation increased with the number of metabolic components reported: 3% (0 components) to 43% (3-4 components). Conclusions: Our findings are consistent with reports of lower rates of metabolic evaluation among Black patients despite their having multiple risk factors for nephrolithiasis. Further study is needed to identify the barriers and facilitators of metabolic and diagnostic workup of nephrolithiasis in Black women.

Polymeric nanoparticles (NPs) have garnered significant interest due to their potential in drug delivery. Specifically, nanopolymers functionalized with molecules such as proteins or peptides serve as versatile vehicles for this purpose. Curcumin (Cur) is a widely studied anticancer compound known for its positive effects on human health. Nevertheless, its insolubility and low bio-distribution hinder the exploitation of its beneficial traits. In this study, our team developed a nanodelivery system using a nanopolymer called poly(ε-caprolactone)-poly(ethylene glycol) (PCL-PEG), functionalized with A6 peptide to enable targeted delivery of Cur. The designed system exhibited favorable characteristics, including a stable zeta potential of -13.8 mV, a small size of 76.4 nm, uniform surface morphology, and high hydrophilicity with a contact angle of 31.53°. Additionally, the targeted delivery system demonstrated high encapsulation efficiency (EE%) (93 ± 0.89%), drug loading (DL%) (16.7 ± 0.9%), and a slow and gradual release profile with a release of approximately 83.1 ± 0.12%. The MTT assay results showed significant cell death in MDA-MB-231 cancer cells (IC₅₀ = 21.3 ± 1.57 µM) when treated with the Cur-NPs-A6 formulation, while the toxicity of the designed NPs was reduced on non-cancerous MCF-10A cells. Moreover, both the RT-qPCR and invasion assays demonstrated the system's effectiveness in activating apoptosis pathways and inhibiting cell invasion. These findings suggest that the engineered intelligent delivery system, equipped with the A6 peptide, which has a strong affinity for CD44 (a protein with increased expression in malignancy), could be an exceptionally effective strategy for cancer treatment.

In this paper, the distribution of prime numbers is expressed based on proving the Riemann hypothesis. The relationship between three numbers, three, six, and nine, and the modality to the distribution of prime numbers, is one of the results of Riemann's zeta function. Prime numbers are classified into six groups of single-digit numbers. There are no prime numbers in groups of three, six, and nine. The groups are made based on the sum of the internal digits. And for each set, there is an angle in the complex plane. The distance between the prime numbers in each group has a regular pattern. This pattern is a multiple of the numbers three, six, and nine. According to Euler's number, for an angle of 60 degrees, the real part of the cosine is 1/2. Accordingly, all prime numbers are related to angles greater than 60 degrees to 90 degrees. As a result, based on the relationship between the golden spiral and the complex conjugate of the zeta function, the function in The 1/2 point becomes zero based on the prime numbers.

The continues evolution of coronaviruses has gained considerable attention prior to the outbreak of COVID 19. Although, COVID-19 is currently not recognized as a global pandemic, the virus's ability to evolve and cause disease outbreaks in both humans and animals necessitates ongoing research into its genetic and structural characteristics. Therefore, understanding the molecular origin, rapid evolution, as were as the immunogenicity of coronaviruses is a critical step towards preparing for and preventing future outbreaks. This study aimed to uncover the genetic diversity and evolutionary relationships among the main viral proteins of SARS-CoV-2 and other related coronaviruses. Amino acid sequences of structural viral proteins spike glycoprotein, nucleocapsid protein, membrane protein, and envelope protein were retrieved from the NCBI database for SARS-CoV-2 and 13 other coronaviruses. Here, we identified that SARS-CoV-2 is closely related to BatCoV RaTG13 and Pangolin-CoV, suggesting their potential role in the emergence of the virus. Additionally, we predicted novel epitopes, glycosylation sites, and cleavage sites, identifying three novel glycosylation sites (NATR, NXSN, and NGTK) and six new CTL epitopes (XSNQVAVLY, NSFTRGVYY, NATRFASVY, STQDLFLPF, NSASFSTFK, and ASFSTFKCY). Our comparative analysis revealed that BatCoV RaTG13 has the closest cleavage site to SARS-CoV-2, with significant similarities also observed with Pangolin-CoV. The study highlighted the unique features of SARS-CoV-2, including its furin cleavage site, which may account for its high infectivity during the pandemic. These findings contribute to the understanding of SARS-CoV-2 evolution and its relationship with other coronaviruses, providing critical insights for the development of broad-spectrum vaccines and antiviral therapies.

This paper explores the concept of agency behaviour within complex adaptive systems, leveraging the Mindset Agency Theory (MAT) framework. The connection between agents within an agency population reveal a remarkable capacity for adaptation. MAT can help one understand the "why" behind adaptability through the key dualities: affect-cognition and tangible-intangible. The affect-cognition duality acknowledges the interplay between emotions and rational thought, enabling understanding of how internal states like emotions influence behaviour and shape the social structures that agents inhabit. Similarly, the tangible-intangible duality illuminates how agencies interact with their environment, recognising that observable behaviours are just one piece of the puzzle. Are also important, and previously missing from MAT are beliefs, both core beliefs and self-efficacy beliefs. These can be formalised as traits, taking value states from within a spectrum ranging between tangible and intangible poles. This becomes relevant in the digital age, where navigating information complexities requires acknowledging the dynamics of beliefs and values. With respect to environmental influences on agency, technology is special since it acts as an amplifier with the potential to intensify both positive and negative aspects of the proposed dualities by elaborating emotional responses (affect) while simultaneously creating echo chambers that limit exposure to diverse viewpoints (cognition). By understanding how core beliefs and self-efficacy beliefs interact, strategies can develop to navigate these complexities.

Global dust events have become more frequent due to climate change and increased human activity, significantly impacting air quality and human health. Previous studies have often focused on simulating cross-regional dust transport based on atmospheric parameters; however, comprehensive monitoring of dust events remains challenging. Therefore, this study utilized Fengyun-4A (FY-4A) satellite Advanced Geostationary Radiation Imager (AGRI) imagery, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar, and other auxiliary data to conduct three-dimensional spatiotemporal monitoring and cross-regional transport analysis of two typical dust events in the Beijing–Tianjin–Hebei (BTH) region of China using four dust intensity indices (Icsd, Dust Detection Index (DDI), Dust value (DV), and Dust Strength Index (DSI)) and the HYSPLIT model. We found that among the four indices, DDI was the most suitable for studying dust in the BTH region, with a detection accuracy (POCD) of > 88% at all times and reaching a maximum of 96.14%. Both the 2021 and 2023 dust events originated from large-scale deforestation in southern Mongolia and the border area of Inner Mongolia, with dust plumes distributed between 2 and 12 km being transported across regions to the BTH area. Further, when dust aerosols are primarily concentrated below 4 km and PM10 concentrations consistently exceed 600 µg/m³, large dust storms are more likely to occur in the BTH region. The findings of this study provide valuable insights into the sources, transport pathways, and environmental impacts of dust aerosols.

Objectives: To develop a risk prediction model for the identification of features involved in the prediction of erectile dysfunction (ED) at 12 months following COVID-19 recovery. Methods: We performed an observational prospective multicentre study. Participants were classified according to their history of COVID-19: I) Patients with a past history of COVID-19; and II) Patients without a previous microbiological diagnosis of COVID-19. A total of 361 patients (Past history of COVID-19, n=166; No past history of COVID-19, n=195) were assessed from January 2022 to March 2023. Patients with a past history of COVID-19 were assessed at 12 months following COVID-19 recovery. The primary outcome measure was ED, assessed through the 5-item International Index of Erectile Function (IIEF-5). Data concerning epidemiologic variables, comorbidities and active treatment were also collected. We performed a binary logistic regression to develop a risk predictive model. Among the models developed, we selected the one with the higher Area Under the Curve (AUC). Results: The median age was 55 years in both groups. The ED prevalence was 55.9% in patients with past history of COVID-19 and 44.1% in those with no past history of COVID-19. The best predictive model developed for ED comprised 40 variables and had an AUC of 0.8. Conclusions: We developed a regression model for the prediction of ED 12 months after COVID-19 recovery. The application of our predictive tool in a community setting could eventually prevent the adverse effects of ED on cardiovascular health, and the associated unfavourable economic impact.

There are few longitudinal studies on the association between dietary vitamin C and sleep disorders. The aim of this study was to investigate the association between dietary vitamin C intake and various types of sleep disorders, including sleep apnea and insomnia, as well as recommended dietary intakes. A total of 68,221 participants from the UK Biobank were enrolled in the study. Dietary vitamin C intake was calculated by a 24-hour dietary recall questionnaire. We used Cox regression model to assess the association between dietary vitamin C and sleep disorders and restricted cubic spline models to explore potential nonlinear relationships. Stratified analyses and sensitivity analyses were also performed to further test the results. The results showed that vitamin C intake at the Q4 level (132.88-191.51 mg/d) was significantly protective against sleep disorders with an HR (95% CI) of 0.78 (0.65-0.94), and against sleep apnea with an HR (95% CI) of 0.75 (0.62-0.92). The protective effect remained significant in stratified analyses of men and age ≤60 years, and the results were robust in sensitivity analyses. Adequate dietary vitamin C intake may have a protective effect in preventing sleep disorders.

Neuropathic pain (NeP) is a complex and debilitating condition that impacts millions of people globally. Although various treatment options exist, their effectiveness is often limited, and they can be accompanied by significant side effects. In recent years, there has been increasing interest in targeting the N-methyl-D-aspartate receptor (NMDAR) as a potential therapeutic approach to alleviate different types of neuropathic pain. This narrative review aims to provide a comprehensive examination of NMDAR antagonists, specifically ketamine, memantine, methadone, amantadine, carbamazepine, valproic acid, phenytoin, dextromethorphan, riluzole, and levorphanol, in the management of NeP. By analyzing and summarizing current preclinical and clinical studies, this review seeks to evaluate the efficacy of these pharmacologic agents in providing adequate relief for NeP.

This article introduces a novel two-step fifth-order Jacobian-free iterative method aimed at efficiently solving systems of nonlinear equations. The method leverages the benefits of Jacobian-free approaches, utilizing divided differences to circumvent the computationally intensive calculation of Jacobian matrices. This adaptation significantly reduces computational overhead and simplifies the implementation process while maintaining high convergence rates. We demonstrate that this method achieves fifth-order convergence under specific parameter settings, with a broad applicability across various types of nonlinear systems. The effectiveness of the proposed method is validated through a series of numerical experiments which confirm its superior performance in terms of accuracy and computational efficiency compared to existing methods.

Exposure to cadmium sulfate (CdSO4) can lead to neurotoxicity. Nevertheless, the precise molec-ular mechanisms underlying this phenomenon remain unclear, and effective treatment strategies are scarce. This study explored the protective effects of ellagic acid (EA), a natural polyphenolic compound, against CdSO4 exposure-induced neurotoxicity in HT22 cells. Additionally, the inves-tigation sought to elucidate the molecular mechanisms underlying this phenomenon. Our findings demonstrated that exposure of HT22 cells to CdSO4 resulted in apoptosis, which was effectively reversed by EA supplementation. EA supplementation also decreased reactive oxygen species (ROS) and mitochondrial ROS productions, reduced malondialdehyde (MDA) levels, and restored the activities of superoxide dismutase (SOD) and catalase (CAT). Additionally, EA supplementation at 5–20 μM significantly counteracted Cd-induced mitochondrial membrane potential and ATP losses and reduced the ratio of Bax/Bcl-2 and cleaved-caspase-3 protein expression. Furthermore, EA supplementation upregulated Nrf2 and HO-1 protein expression while downregulating the phosphorylation of JNK and p38 proteins. Pharmacological inhibition of JNK partially inhibited CdSO4-induced Nrf2/HO-1 activation and exacerbated its cytotoxicity. In conclusion, our results indicate that EA supplementation provides protection against CdSO4-induced cell apoptosis in HT22 cells by inhibiting oxidative stress and activating the Nrf2 pathway. Activation of the JNK pathway plays a protective role in CdSO4-induced cell apoptosis in HT22 cells.

The electric network frequency (ENF), often referred to as the industrial heartbeat, plays a crucial role in the power system. In recent years, it has found applications in multimedia evidence identification for court proceedings and audio-visual temporal source identification. This paper introduces a ENF region classification model named UniTS-SinSpec within the UniTS framework. The model integrates the si-nusoidal activation function and spectral attention mechanism while also redesigning the model frame-work. Training is conducted using a public dataset on the open science framework platform (OSF), with final experimental results demonstrating that after parameter optimization, the UniTS-SinSpec model achieves an average validation accuracy of 97.47%, surpassing current state-of-the-art and baseline models. Accurate classification can significantly aid in ENF temporal source identification. Future re-search will focus on expanding dataset coverage and diversity to verify the model's generality and ro-bustness across different regions, time spans, and data sources. Additionally, it aims to explore extensive application potential of ENF region classification in preventing crimes such as telecommunications fraud, terrorism, and child pornography.

A prospective clinical-epidemiological study was conducted to evaluate the dynamics of biomarkers in hospitalized patients with moderate to severe COVID-19 (according to the National Institute of Health severity scale) treated with Anakinra, compared to those receiving standard treatment. The study included 65 patients with a mean age of 75.51±9.54 years, ranging from 44 to 99 years. Among the study participants, 38 (58.5%) were male and 27 (41.5%) were female. The patients were divided into two groups: control group (standard treatment): 24 patients (36.9%) and target group (Anakinra treatment): 41 patients (63.1%). The results showed a significant relationship between the dynamics of leukocytes, ASAT, LDH, D-dimer, ferritin, and IL-6 and the administration of Anakinra. In the control group, no significant dynamics were observed in leukocyte count, D-dimer, ferritin, or IL-6. A statistically significant decrease in ASAT and LDH values in the control group was noted only on Day 7. In the Anakinra group, significantly (or with borderline statistical significance, p&lt;0.1) lower mean values were observed for leukocytes, NLR, D-dimer, ferritin, and IL-6 on Days 3 and 7 compared to the control group. Additionally, significantly lower CRP values were detected in the Anakinra group on Day 7 compared to the control group.

This paper employs the Finite Element Method (FEM) to simulate and analyze the effects of corrosion defect parameters on the stress and failure pressure of pipelines. It investigates how the boundary conditions of the pipeline model influence stress and examines the sensitivity of failure pressure to corrosion defect parameters. A nonlinear regression equation has been developed from a dataset obtained through simulation experiments to predict the failure pressure of corroded pipelines. To validate the effects of corrosion defect parameters on failure pressure, a hydrostatic test platform for an API 5L X65 pipeline with corrosion defects was established to measure stress levels and failure pressures across varying corrosion defects. The study reveals that failure pressure is negatively correlated with corrosion length and depth, while positively correlated with corrosion width. Among these parameters, corrosion depth exerts a more significant influence on the pipeline's failure pressure than corrosion length and width. Within the range of corrosion defect parameters examined, the maximum deviation of the prediction equation's results from the simulation results is 8.71%, with an average deviation of 5.81%. The standard deviation of the fitted residuals is 0.01837. Additionally, the maximum deviation between the predicted results and experimental measurements is 8.39%, with an average deviation of 7.77%. The strong agreement between the predicted results from the equation and the actual measured data underscores the effectiveness of the nonlinear regression equation.

Most available studies on the mechanical properties of apples lack the comprehensive results needed for the construction and validation of FEM models. Researchers typically focus on either the flesh and epidermis or the whole fruit, often overlooking the maturity stage of the examined apples. They usually report only firmness or the starch index as indicators of maturity. Furthermore, many studies use store-bought apples, which is impractical for industrial applications since this fruit have already undergone various treatments before reaching the shelf. This article aims to determine the mechanical properties of apples necessary for constructing FEM models that are both adequate and useful for the industry. The new Polish apple variety, Chopin, was selected as the research material. The study was conducted for three stages of apple maturity: development, ripening, and senescence. Mechanical properties of the flesh and skin were determined as material data for FEM models. Force-displacement curves and pressure-force functions were examined for future model validation. Using micro-computed tomography, the bruise volumes of fruit subjected to 20%, 50%, and 80% of the destructive force were determined. Significant differences were found between apples in the senescence stage and those in the development and ripening stages. Results of Micro-Ct and the results of modified and real compression tests of whole fruit have allowed us to formulate the research hypothesis regarding the influence of flesh cracking (characterized by local drops of force), influencing the bruise visibility and detection.

Lignin is nature's second most abundant vascular plant biopolymer, playing significant roles in mechanical support, water transport, and stress responses. This study identified 90 lignin biosynthesis genes in the rice based on phylogeny and motif constitution, and they belong to PAL, C4H, 4CL, HCT, C3H, CCoAOMT, CCR, F5H, COMT, and CAD families. Duplication events contributed largely to the expansion of these gene families, such as PAL, CCoAOMT, CCR, and CAD families mainly attributed to tandem and segmental duplication, respectively. Microarray data of 33 tissue samples covering the entire life cycle of rice suggested, fairly high PAL, HCT, C3H, CCoAOMT, CCR, COMT, and CAD gene expression, and rather variable C4H, 4CL, and F5H expression. Some members of lignin-related genes (OsCCRL11, OsHCT1/2/5, OsCCoAOMT1/3/5, OsCOMT, OsC3H, OsCAD2, and OsPAL1/6) were expressed in all tissues examined. The expression patterns of lignin-related genes can be divided into two major groups with eight subgroups, each showing a distinct co-expression in tissues representing typically primary and secondary cell wall constitutions. Some lignin-related genes were strongly co-expressed in tissues typical of secondary cell walls. The combined HPLC analysis showed increased lignin monomers (H, G, and S) in five genotypes from young to old growth stages. Based on 90 genes' microarray data, 27 genes were selected for qRT-PCR gene expression analysis. Four genes (OsPAL9, OsCAD8C, OsCCR8, and OsCOMTL4) were significantly negatively correlated with lignin monomers. Furthermore, eleven genes were co-expressed in certain genotypes during secondary growth stages. Among them, six genes (OsC3H, OsCAD2, OsCCR2, OsCOMT, OsPAL2, and OsPAL8) were overlapped with microarray gene expressions, highlighting their importance in lignin biosynthesis.

The Sentinel-1 SAR Extra Wide swath mode with its 40 meters pixel size allows accurate lead mapping in Arctic sea ice areas. We present an improved winter lead detection algorithm based on a modified U-Net convolutional neural network. We introduce a preprocessing procedure that balances normalized radar cross section (NRCS) values between sub-swaths. This results in more consistent Sentinel-1 SAR images as input for the classifier, which is especially important for the cross-polarization HV channel. In turn, image consistency is essential for automatic image semantic segmentation. We develop a new lead detection algorithm and compare it with a previously published version. We produce pan-Arctic, 40 meters resolution lead maps and present pan-Arctic lead area fraction on a 12 km grid for January 2019. Within different Arctic regions, the lead area fraction is stable and varies only within the expected natural variability during the one month studied here. The average Arctic-wide lead area fraction is 2.44 % with 0.25 % standard deviation during January 2019. The improved lead detection algorithm leads to a better lead discrimination and extends the applicability area to the entire Arctic.

In this work, an attempt is made to the theoretically substantiate the experimentally known facts of the influence of halogen concentration on the catalytic properties of the neodymium-based Ziegler–Natta system. Based on the structural and thermochemical data obtained using modern methods of quantum chemistry, the process of the 1,3-butadiene cis-1,4-polymerization under the model active centers of the neodymium Ziegler-Natta catalysts with different content of chloride ions was studied. Results are presented that explain the increase in the cis-stereospecificity and activity of the polymerization system with an increase in the content of the chloride ions in the neodymium catalytic system. There were established the reasons for the decrease in the concentration of active centers relative to the introduced Nd(III) with an excess of chloride ions and the occurrence of the anti-syn isomerization as a source of the formation of the trans-1,4-structures in the cis-1,4-polybutadiene.

Avian Metapneumovirus (aMPV) is a significant poultry pathogen with a global presence, primarily causing respiratory issues in turkeys. It also affects chickens, although the severity of its impact is often lessened in this species. In Morocco, aMPV has been detected in broiler flocks, prompting the need to deeply analyze circulating strains to understand better the epidemiology and develop control measures accordingly. This research focuses on the sequencing and molecular characterization of aMPV in these flocks. Additionally, aMPV isolated from turkeys displaying Turkey Rhinotracheitis (TRT) signs, were included in the study to compare the findings. RNA extracted from positive swabs was subjected to nested PCR, targeting the attachment protein of the G gene, followed by gel electrophoresis. Amplicons were purified and sequenced using the Sanger method. Bioinformatics tools facilitated sequence analyses, including BLAST for similarity searches and Mega® for phylogenetic analysis using the maximum likelihood method with 1000 bootstrap replicates. The investigation unveiled the existence of three distinct clades of the same aMPV/B strain, two of which originated from used vaccines and one from field recombination, all circulating in broilers and turkeys and indicating potential virus transmission between both poultry species. This article presents the first-ever molecular characterization of aMPV isolated from Moroccan broilers and turkeys, encompassing comprehensive investigations on its presence and subtype, and genetic characterization.

Introduction: Inflammation is a significant contributor to cardiovascular disease (CVD) in people with HIV (PWH), who face twice the risk of CVD compared to the general population. The presence of co-existing rheumatic disease (RD) may further exacerbate inflammation and increase the incidence of CVD events in this population. Methods: We conducted a retrospective study using electronic health record (EHR) data from the Veterans Affairs Medical Center in Atlanta, covering the period from 2000 to 2019. The study included 3,930 veterans with HIV, and assessed the impact of rheumatic disease therapies (RDT) on CVD outcomes. The primary outcome was the first occurrence of a CVD event. Results: Rheumatic disease was significantly associated with an increased risk of CVD events (OR = 2.67; p < 0.001). Additionally, exposure to multiple RDTs (aHR = 2.121, p = 0.047), NSAIDs (aHR = 1.694, p = 0.003), glucocorticoids (aHR = 2.332, p < 0.0001), and hypouricemic agents and colchicine (aHR = 3.445, p < 0.0001) were all significantly associated with increased CVD events. Conclusion: The co-existence of HIV infection and rheumatic disease, along with the use of RDTs, may amplify the risk of CVD events in PWH. These findings underscore the need for further investigation into the relationship between RD, RDTs, and CVD risk in larger, controlled studies, given the potential implications for treatment decisions in this patient population.

GR4J (Genie Rural a 4 parame`tres Journalier) is a rainfall-runoff model widely used to modelling rainfall into discharge. In this model, the input data used are daily rainfall and evapotranspiration data, calibrated using daily observed discharge data. This model seeks the optimum parameter value that produces the minimum deviation or error. The Nash-Sutcliffe Coefficient and Relative Volume Error (RVE) are used to estimate the errors that occur. One of the modelling parameters, namely X4 (peak time), has a value that does not match the observed hydrograph peak time (Tp). For this reason, it is necessary to adjust the modelling equation so that the unit hydrograph of the model appropriately in terms of value and shape with observed unit hydrograph. This study examines ten watersheds in Java Island so that it is expected to represent catchment conditions in tropical areas and developing countries. A relationship was obtained between the difference peak time from modelling (X4) and observed Tp. Adjustments Formula can be made to the general GR4J modelling equation for unit hydrograph in Java Island watersheds.

The optimal combination of five components to develop a concrete for high-temperature applications was studied through a response surface methodology known as mixture design of experiment. The selected components were water, an alkali-activated binder based on ferronickel slag activated with potassium silicate and potassium hydroxide, and three aggregate sizes corresponding to 0-4 mm, 4-8 mm, and 8-16 mm. Selected response parameters were slump and compressive strength before and after heat exposure. Six cubes of side 150 mm were cast for each concrete mix, three for ambient and three for residual (post-heat exposure) compressive strength. All 20 concrete mixes were exposed to a thermal load of 600 °C for two hours. The optimal concrete mix was adapted for large-scale applications, by increasing slump and replacing limestone with olivine aggregates for enhanced residual compressive strength. The adapted mix resulted in concrete with 76 MPa and 32 MPa compressive strength before and after high-temperature exposure. The CO2 emissions were found to be 77 % lower when compared to traditional OPC concrete.

This narrative review synthesizes findings from empirical studies to explore the factors influencing the adoption of ChatGPT in higher education. The review highlights several critical determinants, both confirmed and debatable, of ChatGPT acceptance among students and educators. Previous studies have predominantly utilized theories such as the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Diffusion of Innovations (DoI) theory. Confirmed factors include performance expectancy, effort expectancy, social influence, facilitating conditions, and trust, which significantly predict user attitudes and intentions toward adopting ChatGPT. Perceived usefulness and ease of use are core components influencing adoption positively. However, some factors have shown inconsistent impacts. While generally critical, effort expectancy and facilitating conditions vary based on context. Social influence, demographic characteristics (age, gender), extrinsic motivation, and technology readiness exhibit inconsistent effects, suggesting that intrinsic motivations may be more critical. Data collection primarily used online surveys with structured questionnaires, varying widely in sample sizes from small pilot studies to large-scale surveys and covering diverse academic disciplines. Practical implications suggest enhancing perceived usefulness and ease of use through training and technical support, building trust by addressing privacy concerns, and leveraging social influence to drive adoption. The review underscores the need to refine technology acceptance models to incorporate broader psychological and contextual factors, integrate intrinsic motivation theories, and adopt a systems thinking approach. Addressing limitations such as potential selection bias and the quality and scope of existing studies, future research can develop robust models and provide deeper insights into ChatGPT adoption dynamics in higher education, supporting its effective integration to enhance learning outcomes and foster educational innovation.

The optimization of ligand-receptor binding affinities is essential for enhancing therapeutic efficacy and specificity, particularly in the design of peptide-based drugs. ziconotide, a potent blocker of the N-type calcium channel CaV2.2, has demonstrated therapeutic potential in the treatment of moderate to severe chronic pain. However, there is limited structural and binding affinity data available for scaling the design of ziconotide analogues with improved efficacy and specificity. In this study, I present a structural biophysics-based approach to develop a scalable in silico framework for generating and analyzing ligand-receptor binding affinity landscapes, focusing on ziconotide and its receptor, CaV2.2. This framework integrates advanced computational structural tools and binding affinity calculations to produce high-accuracy structural and intermolecular binding data. The insights gained from this scalable approach will guide the design of ziconotide analogues with enhanced efficacy, offering a powerful computational workflow for high-throughput ligand optimization. Additionally, when combined with artificial intelligence (AI) algorithms, this computational workflow generates high-accuracy structural and biophysical data, towards an AI- and structural biophysics-driven paradigm shift in peptide discovery and design.

Soil stabilization using Portland cement is a widely adopted technique. Previous research has demonstrated that molasses, which contains sugars, enhances the reaction between cement and aggregates. This study investigates the impact of adding molasses to soil stabilized with Ordinary Portland Cement (OPC) on geotechnical properties. Expansive clay soil samples from Taru Jabba, District Nowshera, Pakistan, were treated with various combinations of molasses and cement. The concentrations of each stabilizer were varied at 0%, 4%, 8%, and 12% by dry weight of the soil. Additionally, the soil was treated with constant molasses contents of 4%, 8%, and 12%, while varying the cement content at 4%, 8%, and 12% by dry weight. Geotechnical tests, including Proctor compaction, Atterberg limits, Unconfined Compressive Strength (UCS), California Bearing Ratio (CBR), and swelling potential, were conducted to assess the effects of the stabilizers. The results indicated that the addition of molasses improved soil strength, mitigated shrinkage cracks, and reduced brittleness. Specifically, the CBR value increased from 3.2% in the native soil to 12.3% with 12% molasses and 12% cement. The Plasticity Index (PI) decreased from 14.23% to 8.12%, and the CBR swell value reduced from 9.66% to 3.82%. Furthermore, the UCS of the stabilized soil increased by 64.7% compared to the untreated soil after a 7-day curing period.

In today’s rapidly evolving digital landscape, the significance of password guessing techniques in both offensive and defensive strategies is paramount. Passwords serve as a crucial line of defense for both individuals and corporations, safeguarding their sensitive systems and data. Therefore, assessing the effectiveness of these access credentials is a critical task. However, existing research in this field often encounters limitations, such as a lack of sufficient training data and extended model training times. Current methods often struggle with limited training data and lengthy training times. This paper introduces BBPE-AE, a novel Auto Encoder Network (AE) designed for password guessing. BBPE-AE utilizes Byte-level Byte Pair Encoding (BBPE) to extract frequent tokens from password datasets without length restrictions, employing a dynamic window technique to capture complex patterns. Experimental results on Hotmail and Myspace datasets demonstrate exceptional performance, achieving high similarity rates (BLEU-Unigram: 0.90, BLEU-Bigram: 0.82 for Hotmail; BLEU-Unigram: 0.90, BLEU-Bigram: 0.81 for Myspace). BBPE-AE generates realistic passwords that meet HIBP (Have I Been Pwned) standards, minimizing duplication. These findings highlight the effectiveness of BBPE-AE in enhancing security by generating realistic passwords, ultimately safeguarding sensitive systems and data.

With the increased global attention to environmental protection, social responsibility and corporate governance (ESG) standards, integrating ESG into supply chain management (SCM) is becoming a strategic requirement for companies to achieve competitiveness and sustainable development. This study reviews the impact factors of ESG practices on supply chain competitiveness, and empirically examines the mechanism of ESG performance on supply chains through a questionnaire survey with a sample of Chinese SMEs. It was found that normative pressure, coercive pressure and imitative pressure in SMEs have a significant positive effect on cognition and attitude and perceived behavioral control; good corporate cognition and attitude, perceived behavioral control and corporate pressure also enhance the level of ESG practices. Further analysis shows that cognition and attitude and perceived behavioral control play a mediating role in the impact of ESG practices, and ESG performance promotes sustainable development of SMEs. The article puts forward the following suggestions: (1) close supply chain partnerships to enhance green innovation; (2) the government should give more resource support to ESG-invested enterprises and support the development of advantageous enterprises; (3) improve the corporate disclosure mechanism and strengthen the supervision; and (4) strengthen the training of professionals in supply chain ESG management.

Sick leave is influenced by various modifiable lifestyle factors and sex differences. This study examines the impact of body mass index, fruit and vegetable consumption, physical activity, smoking, and alcohol consumption on sick leave among Serbia's working population, focusing on sex differences. Data from the 2019 National Health Survey of Serbia were analyzed, focusing on a representative sample of 4,652 individuals aged 18-65. Chi-square tests and logistic regression models assessed the relationships between lifestyle factors and sick leave, adjusting for sociodemographic, work-related, and health-related variables. Among respondents, 15.8% reported sick leave in the past 12 months, with higher rates among women in both, short-term (13.9% vs. 10.6%) and long-term sick leave (3.4% vs. 2.2%). For men, obesity (OR = 2.6), poor dietary habits (fruit OR = 2.1; vegetables OR = 2.8), smoking (OR = 1.9), and risky alcohol consumption (OR = 4.1) significantly increase the likelihood of sick leave. In women, smoking (OR = 1.8) and risky alcohol consumption (OR = 3.1) are major predictors, while BMI and physical activity show less impact. Fruit and vegetable consumption and physical activity have inconsistent effects on sick leave, indicating the need for further research. This study identifies obesity, poor dietary habits, smoking, and alcohol consumption as key predictors of sick leave in men. In women, smoking and risky alcohol consumption are major predictors. Smoking is a risk factor for both sexes, while risky alcohol consumption emerges as a prominent predictor and is a significant predictor overall. These findings highlight the need for targeted public health interventions to address these lifestyle factors and reduce sick leave rates. Further research is needed to clarify the roles of dietary habits and physical activity in influencing sick leave.

Harvesting is a serious issue in oil palm plantations after 15-20 years owing to the increased height of the trees (&gt;20 feet). The slow vertical growth of the oil palm dura genotypes is desired for increasing the DxP progenies productivity and economic life span upto ten years. Reduced height increment has a long-term impact on harvesting costs. The current study assessed 308 genotypes generated from African germplasm. Over a three year period, the biometric properties of eleven DxD crosses were evaluated in order to quantify genetic parameters, phenotypic correlations, and principal component analysis for genetic attributes of the better performing dwarf progenies in terms of yield. The evaluated genotypes have a highly significant influence (p &lt; 0.01) on the majority of characteristics. The progenies yielded between 165 and 208 kg of fresh fruit bunches (FFB) per palm every year. The Height increment (HI) varied between 17% and 19%, with an overall average of 18%. Genotypes G8, G300, and G221 had the lowest yearly height increments, measuring 28.98, 29.19, and 30.87 cm, respectively. The outcome of the present study shows that they are slow height increment genotypes with a high FFB yield (&gt; 25 T/Ha). The creation of dura parents with slow height increment in combination with a high bunch weight helps for prolonging the productive life of the palm to more than 35 years, adding value to obtaining distinct oil palm varieties. Overall, this targeted breeding effort towards developing dwarf oil palm hybrids reflects a strategic approach to addressing specific challenges in oil palm cultivation, ultimately helping to promote the oil palm sector globally.

The energy supply for isolated systems remains a current challenge in Brazil and worldwide, particularly when known solutions are compared regarding cost, the ability to supply energy on demand, and sustainability. In this context, green hydrogen, which has been considered one of the main paths for the decarbonization of the energy chain, is also seen as a potential solution for energy supply in isolated systems. Based on a literature review of articles addressing the topics of green hydrogen and isolated systems, an analysis is conducted on the application of green hydrogen as an energy supply solution for isolated systems. The review identified and collected data on important parameters for comparing solutions, such as CAPEX, OPEX, energy cost, and the price per kilogram of hydrogen. Using these data, analyses were performed to provide an overview of the application of green hydrogen in isolated systems. One of the objectives of this work is to present a comprehensive analysis of the green hydrogen chain and its application in the context of electricity generation, particularly for isolated systems in Brazil.

: We report the synthesis and characterization of Mn(III) chloride (MnIIICl3) complexes coordinated with N-oxide ylide ligands, namely trimethyl-N-oxide (Me3NO) and pyridine-N-oxide (PyNO). The compounds are reactive and, while isolable in the solid-state at room temperature, readily decompose into Mn(II). For example, “[MnIIICl3(ONMe3)n]” decomposes into the 2D polymeric network compound complex salt [MnII(µ-Cl)3MnII(µ-ONMe3)]n[MnII(µ-Cl)3]n•(Me3NO•HCl)n (4). The reaction of MnIIICl3 with PyNO forms varied Mn(III) compounds with PyNO coordination and these react with hexamethylbenzene (HMB) to form the chlorinated organic product 1-cloromethyl-2,3,4,5,6-pentamethylbenzene (8). In contrast to N-oxide coordination to Mn(III), the reaction between [MnIIICl3(OPPh3)2] and 2,2,6,6-tetramethyl-1-piperidinyloxy (TEMPO) resulted in electron transfer forming d5 manganate of the [TEMPO] cation instead of TEMPO–Mn(III) adducts. The reactivity affected by N-oxide coordination is discussed through comparisons with other L–MnIIICl3 complexes within the context of reduction potential.

Acute Respiratory Distress Syndrome (ARDS) is a severe lung condition with a high mortality rate for which there are no effective therapeutics. The failure of the alveolar-capillary barrier, composed of lung endothelial (EC) and alveolar epithelial (AEC) cells, is a critical factor leading to excessive inflammation and edema characteristics of acute lung injury (ALI). Phosphodiesterases (PDE) are enzymes well-recognized for their roles in regulating endothelial permeability and inflammation. Although PDE inhibitors are used as therapeutics for inflammatory diseases like COPD (chronic obstructive pulmonary disease), their efficacy in treating ARDS has not yet been established. In this study, we investigate the effects of ensifentrine, an FDA-approved novel dual PDE 3/4 inhibitor, on lung endothelial and epithelial dysfunction caused by methicillin-resistant S. aureus (MRSA), a pathogen involved in bacterial ARDS. Human primary lung endothelial cells and alveolar epithelial cell lines (A549 and immortalized AEC) were treated with heat-killed MRSA, and their responses were assessed in the presence or absence of ensifentrine. Ensifentrine given either pre- or post exposure attenuated MRSA-induced increased lung endothelial permeability. VE-cadherin junctions, which serve to stabilize the EC barrier, were disrupted by MRSA; however, ensifentrine effectively prevented this disruption. Pre-treatment with ensifentrine protected against MRSA-induced EC pro-inflammatory signaling by inhibiting the expression of VCAM-1, ICAM-1, and by reducing the IL-6 release. In AEC, MRSA caused upregulation of ICAM-1, activation of NF-kB and production of IL-8, all of which were inhibited by ensifentrine. These results indicate that dual inhibition of phosphodiesterases 3 and 4 by ensifentrine is barrier protective and attenuates MRSA-induced inflammation in both lung endothelial and epithelial cells. The PDE3/4 inhibitor ensifentrine may represent a promising novel strategy for the treatment of ALI.

In recent times, the adoption of cloud computing by small and medium-sized 9 enterprises (SMEs) has become a crucial issue. These businesses face substantial difficulties in 10 accessing reliable and affordable IT infrastructure due to economic constraints and technological 11 restrictions. Hybrid cloud computing solutions which incorporate Infrastructure as a Service 12 (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) offer a feasible approach to 13overcoming these challenges. This study proposes a systematic literature review of cloud 14 computing's impact on SME performance emphasizing cost-efficiency, reliability, and 15 competitive advantages, using the 2020 Preferred Reporting Items for Systematic Reviews and 16 Meta-Analyses (PRISMA) framework. The inclusion criteria are bounded by (1) publication date 17 between 2014 and 2024, (2) English-language publications, (3) research focused on cloud 18 computing for SMEs, and (4) studies with a clear analytical framework for evaluating SME 19 performance. Following this process, 90 eligible research studies were included. The average 20 total cost for cloud services varies significantly, with operational costs reflecting the scalability 21 of the services and the specific needs of SMEs. This analysis offers valuable guidance for SMEs 22 and stakeholders in optimizing cloud-based strategies for enhanced performance.

Wheat is threatened by global warming. This study aimed to characterize based on DNA profile and identify heat-tolerant wheat genotypes. Fifteen wheat genotypes were sown in Nov 21 and Dec 21 and evaluated within 13 SSR markers. A dendrogram classified the genotypes into two main groups. The highest genetic distances (0.93) were drawn between BAW 1290 and BARI Gom 28, and the lowest one between Nadi 2 and BAW 1147. Heat susceptibility index (HSI) values for 1000-grain weight (TGW) and grain yield indicated moderate heat tolerance in Cluster I genotypes (HSI 0.50-1.00), closely matching Group A's Cluster II members. In Group B, Cluster III genotypes had higher HSI (1.04-1.23) and significant reductions in TGW and grain yield under heat stress, indicating heat susceptibility. The varieties BARI Gom 25, 26, 27, 28, 29, and 30 were identified as heat-stress tolerant. This research provides insights for future heat stress-molecular breeding programs.

Lymphoma is one of the most common cancers worldwide, categorized into Hodgkin lymphoma and non-Hodgkin lymphoma. 18F-fluorodeoxyglucose positron emission tomography (FDG PET) has become an essential imaging tool for evaluating patients with lymphoma in terms of initial diagnosis, staging, prognostication, and treatment response assessment. Recent advancements in imaging technology and methodologies, along with the development of artificial intelligence, have revolutionized the evaluation of complex imaging data, enhancing the diagnostic and predictive power of PET in lymphoma. However, FDG is not cancer-specific, but it primarily reflects glucose metabolism, which has prompted the investigation of alternative PET tracers to address this limitation. Novel PET radiotracers, such as fibroblast activation protein inhibitors targeting the tumor microenvironment, have recently shown promising results in evaluating various malignancies compared to FDG PET. Furthermore, with the rapid advancements in immunotherapy and the favorable imaging properties of 89Zr, immunoPET has emerged as a promising modality, offering insights into the functional and molecular status of the immune system. ImmunoPET can also facilitate the development of new antibody therapeutics and radioimmunotherapy by providing pharmacokinetic and pharmacodynamic data. This review provides comprehensive insights into the current clinical applications of FDG PET in lymphoma, while also exploring novel PET imaging radiotracers beyond FDG, discussing their mechanisms of action and potential impact on patient management.

This study investigated the impact of six high-intensity interval training (HIIT) running sessions at 1% or 10% slope on various physiological and performance parameters in 25 men. The partic-ipants underwent assessments of VO2max, time to exhaustion at 1% slope (TLim1%), and time to exhaustion at 10% slope (TLim10%) in the initial three visits. They were then randomly assigned to control (CON), HIIT at 1% slope (GT1%), or HIIT at 10% slope (GT10%) groups. Over three weeks, participants performed six HIIT sessions with equalized workload based on their indi-vidual maximal oxygen uptake (vVO2max). The sessions comprised 50% of TLim, with a 1:1 ratio of exercise to recovery at 50% vVO2max. Results indicated significant improvements in VO2max and peak velocity (VPeak) after HIIT at both slopes. Heart rate (HR) behavior differed between sessions for GT1%, while no significant differences were observed for GT10%. Rating of perceived exertion (RPE) significantly reduced for GT1% after the third session, with a similar trend for GT10%. In summary, six sessions of 1% or 10% slope HIIT effectively enhanced VO2max and VPeak, but there was no improvement in TLim performance, suggesting no adaptive transfer between training groups.

Traditional chemical research often relies on trial-and-error synthesis and characterization methods. Now, modern machine learning (ML) offers data-driven approaches for predicting properties, like water solubility, directly from chemical structure. But with various data representation schemes for molecular structure and model approaches to select from, it can be difficult for non-experts to determine best practices for utilizing ML. To clarify this landscape of choices, this study uses the ESOL molecular solubility dataset to compare the performance of a selection of different models on different data representations. First, we compare three classical regression methods (linear, ridge, LASSO) on three common data representations (RDKit fingerprint, Morgan fingerprint, intuition-selected molecular features). Then, we demonstrate how two distinct deep learning approaches (multilayer perceptron, graph convolution) can achieve accurate predictions even when prior intuition about feature-property correlations are absent. Finally, we outline a modern attention-based approach, inspired by successes in language modeling and fine-tuned by Bayesian optimization, to achieve a prediction methodology that is more general and performant than the previous approaches. This attention-based approach operates directly on the common SMILES string molecular representation, without requiring as many model parameters as other deep learning approaches or preprocessing into a representative fingerprint or vector of intuitively selected features. In short, when selected molecular features are known to likely correlate with a feature of interest, it may be possible to achieve good predictive modeling without turning to massive deep learning approaches. When particular features are not known a priori, graph approaches are a common solution, and we further demonstrate how a modern hyperparameter optimized attention approach can perform even better.

Due to the inherent limitations of a single viewpoint, reconstructing 3D human meshes 1 from a single image has long been a challenging task. While deep learning networks enable us to 2 approximate the shape of unseen sides, capturing the texture details of the non-visible side remains 3 difficult with just one image. Traditional methods utilize Generative Adversarial Networks (GANs) 4 to predict the normal maps of the non-visible side, thereby inferring detailed textures and wrinkles 5 on the model’s surface. However, we have identified challenges with existing normal prediction 6 networks when dealing with complex scenes, such as a lack of focus on local features and insufficient 7 modeling of spatial relationships.To address these challenges, we introduce EMAR—Enhanced 8 Multi-scale Attention-driven Single Image 3D Human Reconstruction. This approach incorporates 9 a novel Enhanced Multi-Scale Attention (EMSA) mechanism, which excels at capturing intricate 10 features and global relationships in complex scenes. EMSA surpasses traditional single-scale attention 11 mechanisms by adaptively adjusting the weights between features, enabling the network to more 12 effectively leverage information across various scales.Furthermore, we have improved the feature 13 fusion method to better integrate representations from different scales. This enhanced feature fusion 14 allows the network to more comprehensively understand both fine details and global structures 15 within the image. Finally, we have designed a hybrid loss function tailored to the introduced attention 16 mechanism and feature fusion method, optimizing the network’s training process and enhancing the 17 quality of reconstruction results.Our network demonstrates significant improvements in performance 18 for 3D human model reconstruction. Experimental results show that our method exhibits greater 19 robustness to challenging poses compared to traditional single-scale approaches.

Storied Rocks (Tumpituxwinap) is a term of reference used by the Numic Speaking tribal elders whom we have worked with for over 60 years on an estimated 200 ethnographic studies. Key in this analysis are the protocols for approaching, interacting, and using the places where Storied Rocks have been located. Concomitant with these traditional protocols are ones established to resolve the curiosity of non-Natives about why they are in a particular place and what they mean. This analysis shares the cultural understandings of Tribal representatives who participated in these ethnographic studies. Studies used in the analysis were funded by U.S. federal agencies, supported by federally recognized Native American tribal governments, and composed with the cultural understandings shared and made public by tribally appointed elders to clarify the conundrums that are Storied Rocks.

Previous research has indicated that ultra-high pressure (UHP) technology is useful in speeding up the extraction process of cold brew (CB) coffee. However, the impact of different degrees of roasting on UHP CB coffee is still unclear. Therefore, this study aimed to investigate the effects of three different degrees of roasting (light, medium, and dark) on the physicochemical and flavor characteristics of UHP CB coffee. The results of this study showed that most physicochemical characteristics of UHP coffee were similar to those of conventional CB coffee, regardless of the degree of roasting. However, the majority of physicochemical characteristics, non-volatile components, and sensory evaluation significantly increased with an increase in roasting degree. The HS-SPME-GC/MS analysis showed that most volatile components, such as pyrazines, furans (except furfural), esters, pyrrole, pyridine, and most aldehydes, increased from light to dark roasting, and this was significantly evident in CB coffee compared with UHP. The content of volatile components in UHP coffee was significantly (P<0.05) higher than that in CB coffee at the same degree of roasting, especially with dark roasting. The combination of orthogonal partial least squares-discriminant analysis (OPLS-DA) and principal component analysis (PCA) indicated that the distance between coffee samples at different degrees of roasting under CB coffee was greater than that under UHP coffee. However, the distance between these two extraction methods at medium roasting was evidently closer than that at light and dark roasting. Moreover, 15 representative differential compounds, such as were Hazelnut pyrazine, linalool, butane-2,3-dione, 3- methylbutanal found by the calculation of odor-active values (OAVs) indicating the main contributing compounds to odor. Overall, the effect of roasting degrees on UHP coffee was less than that on CB coffee, which was consistent with the results of physicochemical characteristics, volatile components, and sensory evaluation.

Recently concluded, large-scale cancer genomics studies involving multiregion sequencing of primary tumors and paired metastases appear to indicate that many or most cancer patients have one or more “clonal" mutations in their tumors. Clonal mutations are those that are present in all of a patient’s cancer cells. Clonally mutated proteins can potentially be targeted by inhibitors or E3 ligase small molecule glues, but developing new small molecule drugs for each patient is not feasible currently. Achilles Therapeutics is currently the only company specifically targeting clonal mutations. However, they are doing so with tumor-derived T cells. To address the potential limitations of immunotherapy, I have devised another approach for exploiting clonal mutations, which I call “Oncolytic Vector Efficient Replication Contingent on Omnipresent Mutation Engagement” (OVERCOME). The ideal version of OVERCOME would likely employ a bioengineered facultative intracellular bacterium. The bacterium would initially be attenuated, but (transiently) reverse its attenuation upon clonal mutation detection.

The proliferation of intelligent monitoring devices has led to the widespread adoption of background extraction technology across a multitude of domains, including intelligent transportation, video surveillance, human-computer interaction, and medical diagnosis. In this work, the model employs a multi-layer convolutional neural network structure, which enables the extraction and fusion of scene features from different scales in a layer-by-layer manner. This approach facilitates the comprehensive capture of complex scene information. The convolutional network comprises multiple layers, with each layer responsible for extracting features at a specific scale. The shallower layers capture more detailed feature information, whereas the deeper layers focus on more global scene features. The strategy of multi-scale feature fusion allows the model to fully capture multi-level and multi-dimensional information in the context of large-scale scenes. Furthermore, the incorporation of an attention mechanism enables the model to adaptively allocate attention to salient regions, thereby enhancing its capacity to discern intricate scenes by assigning greater significance to pivotal features. The experimental results demonstrate that the proposed method is effective in practice, as evidenced by its performance on public datasets.

Cancer and infectious diseases are two of the world's major public health problems. Gemcitabine (GEM) is an effective chemotherapeutic agent against several types of cancer. In this study, we developed macrocapsules by incorporating GEM into a chitosan matrix blended with magnetite and zeolite. The physico-chemical characterization of the precursor materials and the new delivery system was carried out by HRTEM-ED, XRD, FESEM-EDS, FT-IR, TGA, encapsulation efficiency (%E.E.) and release profiles at pH 7.4 and 5.0. Cell viability tests on A549 and H1299 cell lines and microbiological properties against staphylococcal strains were performed. Our results revealed the successful production of hemispherical capsules with an average diameter of 1.22 mm, a rough surface and characteristic FT-IR material interaction bands. GEM encapsulation rates exceeded 80%, and release profiles were well-controlled over 24 hours at tested pH levels. Notably, cell viability in H1299 cells was lower than the free GEM control. Moreover, the formulation exhibited potent antimicrobial activity against Staphylococcus epidermidis, highlighting its dual action as a chemotherapeutic and antimicrobial agent. This dual effect underscores the potential of these macrocapsules as innovative delivery system for treating lung cancer in patients who are also at risk of infectious diseases and who are immunosuppressed.

European Union (EU) regulations on in vitro diagnostics (IVD) and on serious cross-border threats to health provide for the establishment of European Reference Laboratories (EURLs) and their harmonisation and cooperation with National Reference Laboratories (NRLs). While the EURLs under the IVD Regulation will be operational by 1 October 2024, the EURLs under the Regulation on serious cross-border threats to health will be operational by January 2025. Although NRLs may have been operating for a long time on the basis of national legislation, they should now cooperate with each other and with EURLs in a network of centres of excellence for the authorisation and post-market surveillance of IVDs and for the epidemiological surveillance and control of communicable diseases. The term "reference laboratory" has long been used colloquially to refer to many kinds of laboratories, regardless of their tasks, competencies, responsibilities and designation. To clarify the roles and functioning of EURLs and NRLs, we evaluated the relevant current EU provisions and compared the findings with those of reference laboratories designated by other organisations, calibration (reference) laboratories and referral laboratories, which are simply referred to as "reference laboratories".

Drought and water scarcity are significant factors limiting the development of China's maize industry. Deep sowing, as a method to mitigate drought and preserve soil moisture and seedlings, can effectively mitigate the adverse effects of drought stress on their growth. However, deep sowing can influence the emergence of maize seedlings and has a certain impact on their growth and development. To investigate the physiological and transcriptomic changes in maize inbred lines under deep sowing stress, and the alleviation effects of exogenous hormones, this study investigated the response of mesocotyl in deep-seeding tolerant maize inbred line Qi319 and deep-seeding sensitive maize inbred line Zi330 to deep-seeding stress under exogenous MeJA treatment. The physiological and transcriptomic mechanisms underlying the alleviation of deep-seeding stress by exogenous MeJA were elucidated. Results showed that compared to normal sowing depth, mesocotyl length (MES), mesocotyl and coleoptile length (MESCOL) significantly increased under deep-seeding stress for both inbred lines, while seedling length (SDL) was significantly inhibited. However, after adding 1.5 μmol·L^-1 MeJA, MES, MESCOL, and SDL significantly increased compared to deep-seeding stress alone. Exogenous MeJA promoted cell elongation, enhanced mesocotyl length, and improved seedling emergence ability. RNA-Seq sequencing revealed differentially expressed genes (DEGs). Under deep-seeding stress compared to normal sowing depth conditions, 1248 DEGs were identified in Qi319 and 1288 DEGs were identified in Zi330. After adding exogenous MeJA under deep-seeding stress compared to normal sowing depth with exogenous MeJA treatment conditions, 538 DEGs were identified in Qi319 and 2284 DEGs were identified in Zi 330. GO enrichment analysis showed that differentially expressed genes were mainly concentrated in biological processes, cell components and molecular functions. KEGG enrichment analysis showed that differentially expressed genes were mainly concentrated in transport and catabolism, signal transduction, amino acid metabolism and lipid metabolism processes. Compared with the deep-seeded treatment, 12 differentially expressed genes were co-expressed in the two inbred lines under exogenous MeJA treatment, and 10 key genes, including ornithine decarboxylase, terpene synthase 7 and ethylene reaction transcription factor 11, were screened out. Our findings elucidate the molecular mechanism underlying maize's tolerance response to deep seeding and offer valuable insights into candidate gene resources for improving deep seeding tolerance in maize.

Telemedicine has become an interesting research topic in recent years, especially since the start of the COVID-19 pandemic. This article conducts research using a systematic literature review to identify paradoxes in telemedicine services. The research method follows the steps in a systematic literature review, by accessing the Scopus journal database to search for relevant articles between 2018 and 2024. The selected articles were selected based on certain inclusion and exclusion criteria, resulting in 28 relevant articles which were then analyzed to understand findings related to paradoxes in telemedicine services. The research results show four main clusters of paradoxes in telemedicine research, namely health services, health quality, the COVID-19 pandemic, and technology. Each cluster shows complex opportunities and challenges in efforts to improve health services through telemedicine. Understanding the clusters for each article can then be mapped to the organizational categories in the Dynamic Equilibrium Model. In conclusion, this study provides a comprehensive insight into the paradoxes in telemedicine services, highlighting the importance of understanding the various aspects involved in the adoption and implementation of this technology. With a better understanding of this paradox, it is hoped that society and health practitioners can overcome the challenges and exploit the opportunities offered by telemedicine to improve the accessibility and quality of health services in the future.

Background: The absence of interdental papillae in dental prosthetics often results in unsatisfactory esthetic outcomes, such as black triangles and elongated clinical crowns. Although previous research has shown that gingival papillae can regenerate in a coronal direction, the mechanisms behind this phenomenon are not fully understood. Various theories have been proposed as potential explanations. Materials and Methods: This study evaluated several factors potentially influencing papilla growth, including adjacent elements, buc-cal-lingual papilla width, contact point position, convergent neck, crown overcontour, and intraoral negative pressure. A conceptual map was created to organize these factors, followed by their classification using a modified Overton Window. The principle of parsimony (Occam's Razor) was applied to identify the most plausible cause-related factors, categorizing them as causal relationship factors, filtered out factors, essential factors, or influencing factors. A mind map was used for visual grouping. Results: The analysis identified intraoral negative pressure as the most plausible cause-related factor for spontaneous papilla growth. Factors such as inflammation and drug-induced hyperplasia were excluded due to their association with unhealthy tissue. Other factors were classified as essential or influencing, but without direct causal links to papilla growth. Conclusions: Intraoral negative pressure is likely a key contributor to spontaneous papilla growth around implants. This finding, supported by abductive reasoning and the exclusion of less plausible factors, offers a new direction for enhancing esthetic outcomes in dental prosthetics, though further research is needed to confirm this causal relationship.

Olivine-like NaFePO4 glasses and nanocomposites are promising materials for cathodes in sodium batteries. Our previous study concerned the influence of high pressure on DC electric conductivity of NaFePO4 glasses. This work presents the results of AC measurements of glasses and nanocomposites from pressure treatment. Depending on the temperature range, the electronic conduction mechanism changed from variable range hopping (between Fe2+/Fe3+centers) to phonon-assisted hopping. For DC electric conductivity the portrayal via the classic Mott’s Variable Range Hopping is limited to low temperatures. Spectra of AC conductivity for glasses and pressure-formed nanocomposites fulfill Jonscher’s scaling law with power exponents ranging from 0.3 to 0.9 depending on temperature and the electron hopping mechanism. Due to the decrease in distance between hopping centers, an increase of AC conductivity around 2-3 orders of magnitude was observed after high pressure-high temperature treatment. Up to 200 K temperature dependencies of AC conductivity follow the pattern suggested by Mott's theory. Above that temperature corresponds to phonon-assisted hopping with activation energies EAC≈0.40 eV (for glass: amorphous solid) and EAC≈0.18 eV (for glass + nanocrystallites nanocomposite). Notable that they are considerably lower than values of EDC for DC conductivity. Results obtained show that high-temperature compressing, just below the glass temperature, significantly leads to a stable decrease in distance between hopping centers related to nanocrystallites in a glassy matrix. Consequently, it leads to an essential rise in electric conductivity and volumetric capacity. AC spectra of electronic conductivity are disturbed by the conduction of Na+ mobile ions, which impacts polaron conductivity and can be a limiting factor for conductivity.

Small wind turbines placed at City locations are affected by variable speed winds that frequently also change the direction. Architectural constructions, buildings of different height and abrupt orography of Cities make the winds that occur at City locations, more variable than in flat lands or at sea. However, performance of Small-wind turbines under this type of variable winds is not deeply studied in specialized literature. Therefore, this article analyses this important issue, also considering three types of power conversion configurations utilized in small-wind turbines; the generally used Maximum Power Point Tracking (MPPT) configuration, the simple only-rectifier configuration and an intermediate configuration in terms of complexity called Pseudo-MPPT. This general-purpose analysis is applied to a specific type of wind turbine, i.e. the Ayanz wind turbine with screw blades, which presents very adequate characteristics for city locations such as; safety, reduced visual and acoustic impacts and birds’ casualties avoidance. Thus, a wide simulation and experimental tests-based analysis is carried out, identifying which are the main factors affecting to the maximization of energy production of small-wind turbines in general and the Ayanz turbine in particular. It is concluded that depending how the wind turbine is designed in terms of these identified main factors, the simple Pseudo-MPPT power conversion configuration could even extract more energy than more complex MPPT configurations, especially in quickly varying wind situations.

Transcriptomes from stem-inoculated Scots pine saplings were analyzed to identify unique and enriched H. annosum transcripts in the early stages of infection. Comparing different time points since inoculation identified 131 differentially expressed H. annosum gene with p values ≤ 0.01. Our research supports the results of previous studies on the Norway spruce – Heterobasidion annosum s.l. pathosystem, indicating the role of carbohydrate- and lignin degradation genes in pathogenesis at different timepoints post inoculation and the role of lipid metabolism genes (including but not limited to the delta-12 fatty acid desaturase gene previously reported to be an important factor). The results of this study indicate that malic enzyme could be a potential gene of interest in the context of H. annosum virulence.

This study examines stress distributions in adhesive joints under various loading and temperature conditions. Finite element analysis (FEA) was employed to compute the peel and shear stresses at the adhesive interface and bondline mid-section. Dependency analysis shows that mid-section peel stress significantly impacts the experimental shear strength of SLJs more than shear stress. This insight highlights the need to carefully analyze peel stress and bending moment factors. The analytical solutions proposed by Goland and Reissner were analyzed with modifications by Hart-Smith and Zhao. Hart-Smith’s approach performed more effectively, especially when the adhesive layer thickness (t_a) is 0.5 mm and the overlap length to thickness ratio (c/t_a) is ≥20. FEA revealed stress distributions at the adhesive/adherend interface and bondline mid-section. DP490 adhesive joints exhibited lower stresses than EA9696. Temperature variations significantly affected joint behavior, particularly above the adhesive’s glass transition temperature (T_g). Both EA9696 and DP490 adhesive joints displayed distinct responses to stress and temperature changes. The parabolic and biquadratic solutions for functionally graded adhesive (FGA) joints were compared. The biquadratic solution consistently yielded higher shear and peel stress values, with an increase ranging from 15% to 71% compared to the parabolic solution at various temperatures because of larger gradient of the Young's modulus distribution near the overlap ends. The ratio of peak peel stress to peak shear stress suggests selecting an adhesive with a superior peel strength or primarily reducing the peak peel stress by functionally grading is advisable, particularly if the adhesive is brittle. Comparison of stress distributions emphasize the importance of selecting adhesives based on stress type, temperature, and solution methods in optimizing adhesive bonding applications. These findings provide valuable insights for thermomechanical applications where thermal stimuli may be used for controlled debonding.

A comparative study of the photocatalytic decoloration of methylene blue (MB) dissolved in water was conducted, using three semiconductor oxides: zinc oxide (ZnO), tin oxide (SnO2) and titanium dioxide (TiO2) in the form of thin films and varying their thickness ~ 100, 200, and 300 nm for each film. All the films were deposited using the cost-effective dip-coating technique, at moderate temperature processing (400 °C) with sol-gel solutions on cheap glass substrates. The structural, morphological, and compositional properties of the films were analyzed by X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Raman spectroscopy, respectively. The degradation of MB was studied by varying both thickness and type of semiconductor oxides through measurements of optical absorbance of decolorated liquid by photocatalytic process. Structural analysis shows that ZnO and SnO2 films were polycrystalline with preferential growth of (002) and (110) planes respectively, whereas, an amorphous structure was observed for TiO2 films. In addition, morphological properties confirms that the surfaces of all the films are conformed with nano grains in round and irregular shapes. Based on all results reported in this work, that authors believe that the degradation efficiency of MB depends on the thickness of the film and the type of semiconductor material used as a catalyst. The highest degradation efficiencies were recorded with an exposure time of 120 min and with an initial concentration of 10 mg/L for ZnO, SnO2, and TiO2 films were: 100, 88 %, and 76 %, respectively.

This case report demonstrates the rare but potentially serious complication of pulmonary vein stenosis (PVS) diagnosed several years after radiofrequency ablation for atrial fibrillation. Pulmonary vein stenosis can remain asymptomatic until significant occlusion occurs, after which it can present with a variety of symptoms, mimicking numerous cardiovascular and pulmonary pathologies. Early diagnosis and treatment rely on consistent follow-up using appropriate diagnostic imaging modalities, which are paramount in preventing severe complications.

Burnout in healthcare affects the quality of patient care. In 2020, Mayo Clinic implemented Advanced Care at Home (ACH), a new hospital-at-home program. We aimed to assess burnout levels among ACH providers, analyzing data from quarterly surveys. Physicians and nurse practitioners registered in ACH completed a 16-question anonymous survey distributed via email. Ten questions from the original survey used the Maslach Burnout Inventory to measure the three domains associated with burnout: depersonalization, emotional exhaustion, and personal achievement. We conducted a descriptive analysis using frequency distribution and percentages. A total of 27 providers participated, with response rates of 38%, 23%, and 66% in the first, second, and third quarters. Over 75% of the participants responded satisfactorily to nine of the ten questions associated with the risk of burnout. Moreover, 77% of providers felt joyful at work "always or most of the time." Of the 74% of participants who responded to the question about feeling burned out from work, 48% of the participants "strongly” or “somewhat” disagreed, while only 26% "strongly” or “somewhat” agreed. Mayo Clinic's ACH has demonstrated low levels of burnout among providers, positively impacting the well-being of healthcare workers and contributing to a high standard of patient care.

This study explores the industrial property market in Hong Kong, a sector characterized by its unique features and atypical market behaviour. Particularly, this research employs machine learning techniques to predict property prices based on a set of features, including location, square footage, floor level, accessibility to mass transit railway station and the like. To ensure transparency and understanding, the Shapley value is employed to quantify the relative importance of each feature in predicting property prices. Our analysis reveals the existence of non–linear relationships among these features, as demonstrated by the wide distribution of SHAP values for most features, which are illustrated in a beeswarm plot that span both sides of the baseline. This finding indicates a complex interaction among such features as square footage, age, floor level, carpark, proximity to mass transit railway stations, location, and property prices. The results contribute valuable insights into the relationships between industrial property characteristics and their corresponding values, thereby equipping stakeholders with enhanced understanding of the market to support informed decision–making.

In physics we construct idealized mathematical models in order to explain various phenomena which we observe or create in our laboratories. In this article, I recall how sophisticated mathematical models evolved from the concept of a number created thousands years ago and I discuss some challenges and open questions in quantum foundations and in the standard model. We liberated nuclear energy, landed on the Moon and built ‘quantum computers’. Encouraged by these successes many believe, that when we reconcile the general relativity with the quantum theory then we will have the correct theory of everything. Perhaps, we should be much more humble. Our perceptions of reality are biased by our senses and by our brain bending them to meet our priors and expectations. Our abstract mathematical models describe only in an approximate way different layers of physical reality. To describe the motion of a meteorite we can use a concept of a material point but the point-like approximation breaks completely, when the meteorite hits the Earth. Similarly, thermodynamic, chemical, molecular, atomic, nuclear, elementary particle layers of physical reality are described using specific abstract mathematical models and approximations. In my opinion, the theory of everything does not exist.

The development of the finite element method (FEM) combined block polynomial interpolation with the concepts of finite difference formats and the variation principle. Because of this combination, FEM overcomes the shortcomings of traditional variation methods while maintaining the benefits of current variation methods and the flexibility of the finite difference method. As a result, FEM is an advancement above the traditional variation methods. The aim of the study is to experimentally determine the thermal behavior of two stomatognathic systems, one control and the other presenting orthodontic treatment by means of a fixed metallic orthodontic appliance, both being subjected to several thermal regimes. In order to carry out this experimental study, we examined the case of a female subject, who was diagnosed with Angle class I malocclusion. The patient underwent a bimaxillary CBCT investigation before initiating the orthodontic treatment. A three-dimensional model with fully closed surfaces was obtained by using the InVesalius and Geomagic programs. Like the tissues examined in the patient, bracket and tube-like components, as well as orthodontic wires, can be included to these models. Once it is finished and geometrically accurate, the model is exported to a FEM-using program, such as Ansys Workbench. The intention was to study the behavior of two stomatognathic systems (with and without a fixed metallic orthodontic appliance) subjected to very hot food (70 ⁰C) and very cold food (-18 ⁰C). From the analysis of the obtained data, it was concluded that, following the simulations carried out in the presence of the fixed metallic orthodontic appliance, significantly higher temperatures were generated in the dental pulp.

Extracorporeal membrane oxygenation (ECMO) is increasingly used in intensive care units; however, ECMO can modify drug pharmacokinetics (PK), leading to underexposure and associated treatment failure. The aim of the present study was to determine the effect of ECMO on the PK of dexmedetomidine (DEX). In vivo rat models were developed, both of which involved a bolus injection of DEX and concentration monitoring. In the in vivo model experiment, rats which underwent ECMO were compared with a control group. PK analysis of in vivo data (noncompartmental analysis and nonlinear mixed-effects modelling) was carried out to determine the effect of ECMO. In vivo PK exploration showed that ECMO induced a decrease in DEX clearance. Because the effect of ECMO on the PK of DEX is not clinically significant, normal DEX dosing for critically ill patients should be effective for patients undergoing ECMO.

The LTTD process for highly concentrated crude oil-contaminated soil was studied to evaluate the remediation efficiency. In addition, the environmental impact difference between HTTD and LTTD was studied. OpenLCA software obtained from PE International was used to perform the analyses. The total ADP environmental impact of the HTTD process was 1.63E-04 MJ, whereas the total GWP impact was 4.14E+02 kg CO2-eq. In contrast, the LTTD process exhibits total ADP and GWP environmental impacts of 1.29E-04 MJ and 2.78E+02 kg CO2 eq, respectively. Analysis of the overall environmental impact reduction revealed that replacing the HTTD process with the LTTD process resulted in a total ADP reduction of 3.40E-05 MJ (20.5%) and a GWP reduction of 1.36E+02 kg CO2 -eq (32.9%). It is inferred that the LTTD treatment of contaminated soil can significantly reduce the environmental impact compared with conventional incineration and HTTD processes. Additionally, the LTTD-treated soil with coke or carbonized residue could act like biochar as a soil amendment. Approximately 50% of organic hydrocarbon contaminants are sequestered into coke or carbonized residues on the soil surface. With additional analyses of various oil-contaminated soils, the potential utility of LTTD-treated soils is expected to be maximized.

Background/objective: Tools to accurately and efficiently measure the dietary intake of Nigeria are lacking. We aimed to develop and assess the usability of a new online dietary assessment tool for Nigeria, myfood24 West Africa. Methods: We developed myfood24 West Africa database using data from existing food composition tables, back-of-pack labels of packaged foods and research articles. A 7-step approach was used: identified data sources, selected foods, processed/cleaned data, calculated nutrient content of recipes, created/allocated portion sizes, quality-checked database, and developed food accompaniments. To pilot myfood24 West Africa, we recruited 179 university staff living in Nigeria using a cross-sectional design; usability was assessed using a questionnaire that incorporated the System Usability Scale (SUS) and feedback session. Results: The database included 924 foods with up to 54 nutrients and 35 portion-size images allocated to foods. 60% of the data were sourced from the 2019 West Africa Food Composition Table, 17% from back-of-pack labels, 14% from the 2017 Nigeria Food Composition Table, 5% from generated recipes and 4% items from published literature. 30% (n=53) self-recorded their diet on their own and 1345 entries were made. The mean SUS score of 74 (95% CI: 68,79) indicated good usability. The feedback showed the tool was easy to use, educative, and included a variety of locally foods consumed. Conclusion: This new tool will enhance the dietary assessment of the Nigerian population. More work will cover more foods from other regions of Nigeria and West African countries.

The spread of false information can significantly harm public opinion, underscoring the importance of accurately identifying untrustworthy news. This paper presents an innovative machine learning (ML) tool, TMining, designed to evaluate news credibility and facilitate various text-mining tasks. By examining a range of ML methodologies alongside preprocessing techniques, we aim to boost the system's effectiveness. Our research meticulously assesses different datasets, highlights the impact of applying stemming techniques, and employs Local Interpretable Model-Agnostic Explanations (LIMEs) to shed light on the rationale behind model predictions. The outcomes reveal a notable enhancement in both the precision and clarity of the news verification process. The ultimate version of the model has been made available as an Application Program Interface (API), and its source code has been shared openly, encouraging further exploration and collaboration within the scientific community. This initiative advances our ability to discern manipulative context from fictitious content and promotes transparency and understanding in the domain of ML applications.

The purpose of this study was to investigate the effects of cognitive dual-tasking on gait initiation and sit-to-walk. Twenty-eight healthy young adults performed gait initiation and sit-to-walk under two conditions: while engaging in serial subtraction (dual-task) and without any additional task (single-task). Motion data were collected using a 10-camera optoelectronic motion capture system, synchronized with force plates and surface electromyography. We analyzed spatiotemporal parameters, center of mass displacement, center of pressure trajectory, and lower limb muscle activation patterns. We found that dual-task conditions significantly affected both gait initiation and sit-to-walk patterns, increasing the duration of transitions and mediolateral center of mass displacements, while reducing vertical center of mass displacements and forward propulsion. We also observed a more constrained and less efficient center of pressure path, with reduced posterior displacement during the weight shift phase. Muscle activation, particularly in the tibialis anterior and biceps femoris, decreased during dual-task conditions, indicating altered neuromuscular strategies. These findings suggest a shift in postural control demands and motor performance during dual-task transitions.

Keywords: construction; processes; optimization; polymers, carbon; emission; strategy.

An oceanographic cruise from southern Adriatic to the northern Ionian basins, during 2013 spring period, allowed us to describe spatial abundance and distribution of decapod crustacean larval assemblages with a multidisciplinary approach. Seventeen locations on the Apulian and Albanian shelves and offshore waters, including the Strait of Otranto, were sampled by BIONESS electronic multinet. In May 2013 a swarm of zoeae (11 Brachyura taxa, mostly at first instar, with Xantho granulicarpus at 87%) was recorded in the neuston of the Italian side. Decapod larvae were concentrated in the first 20-30 m surface layer, strongly linked to the thermocline and generally above the DCM, suggesting that they are involved in surface water circulation. The migratory behavior of decapod larvae in coastal stations is quite regular between 20 and 60 m depth and daily time independent. In offshore stations, migration is compatible with the day-night cycle, where a minimum WMD value is evident at about 20m at night. The availability of four satellite-tracked surface drifters in the same area and period of the larvae presence, represented a possibility to discuss the geographic dispersal of larvae linked to the surface circulation in successive days. Only one drifter crossed the south Adriatic passing from the Italian to the Balkan neritic area, employing about 40 days. The actual genetic homogeneity of many Brachyura coastal species populations on the opposite sides of the Adriatic Sea, suggests the existence of a genetic connection which should not rely exclusively upon larvae circulation and needs to be fueled by additional strategies of biological communication.

Objective: To develop and validate the short version of the JAEN Scale for use in women with Fibromyalgia Syndrome (FMS) to make the balance disorder measurement process more efficient. Methods: A Cross-sectional observational validation study was conducted. Fifty-six women with FMS and 44 healthy control were included. Certain items of the original tool were selected with the aim of 1) Improving internal consistency by reducing item redundancy 2) Obtaining a diagnostic capacity with an Area Under the ROC Curve (AUC) greater than 0.70 for discriminating FMS patients and fallers. The internal consistency, factorial validity, concurrent validity and diagnostic capacity of the new tool were analyzed. Results: Factorial analysis showed a two-factor structure that explained 72% of variance. Cronbach alpha coefficients of 0.904 were obtained for the total score of the JAEN-10 items. Concurrent validity analysis showed strong correlations of the JAEN-10 with other instruments that measured quality of life, postural balance or disability related to dizziness. The score of the JAEN-10 items showed an AUC of 0.858 with a sensitivity of 64.29 and a specificity of 95.45 for discriminating between FMS and healthy controls, and an AUC of 0.835 with a sensitivity of 90.48 and a specificity of 67.24 for discriminating between fallers and non-fallers.; Conclusions: The 10-item JAEN scale is a valid instrument for discriminating between subjects with or without FMS and between fallers and non-fallers. Its psychometric properties are good and similar to those of the original 20-item scale. Moreover, it is quicker to complete, which may be relevant in subjects with a tendency to experience fatigue.

The ECM is composed of a considerable number of biochemically and structurally diverse constituents. ECM is a highly dynamic system that constantly receives and sends biological, chemical and mechanical signals. Several studies suggest that mechanical signals derived from the extracellular microenvironment regulate skin regeneration and wound healing. We evaluated effects of a mixture of biomolecules extracted and purified from bovine colostrum on restoring the mechanical properties of ECM. Test measuring collagen contraction showed a significant difference in contraction activation in samples treated with the 2% colostrum derivative mixture compared to the control. The analysis of the supernatant showed an inhibition of metalloproteinase-2 expression and an increase in collagen secretion by fibroblasts in treatment samples. Our hypothesis is that the molecules extracted and purified from bovine colostrum restore the ECM environment qualitative and quantitative characteristics, thus guaranteeing through a mechanical action the restoration of the wound due to the transduction of the signal activated by the integrins.

This paper aims to present a robust computational technique utilizing finite difference schemes for accurately solving time fractional reaction-diffusion models, which are prevalent in chemical and biological phenomena. The time-fractional derivative is treated in the Caputo sense, addressing both linear and nonlinear scenarios. The proposed schemes were rigorously evaluated for stability and convergence. Additionally, the effectiveness of the developed schemes was validated through various linear and nonlinear models, including the Allen-Cahn equation, the KPP-Fisher equation, and the Complex Ginzburg-Landau oscillatory problem. These models were tested in one, two, and three-dimensional spaces to investigate the diverse patterns and dynamics that emerge. Comprehensive numerical results were provided, showcasing different cases of the fractional order parameter, highlighting the schemes' versatility and reliability in capturing complex behaviors in fractional reaction-diffusion dynamics.

Cell viability refers to the proportion of living, healthy cells within a given population. This information is crucial in any cell-based research, especially in toxicology. Over the past century, numerous methods for assessing cell viability have been developed. These methods can be categorized into five main types: non-invasive cell structure damage, invasive cell structure damage, cell growth, cellular metabolism, and cell membrane potential. Each method operates on different principles and has its own advantages and disadvantages, making it essential for researchers to choose the method that best fits their experimental design. Despite the variety of cell viability methods available, most rely on indirect measurements of cell viability, which may compromise their accuracy. In this sense, there is insufficient information regarding their accuracy, highlighting the need for researchers to investigate the accuracy of both existing and future methods.

CTC numbers in the blood of cancer patients can indicate the progression and invasiveness of tumors and their prognostic and predictive value has been repeatedly demonstrated. However, the standard baseline CTC count at the beginning of treatment, while informative, is not completely reliable and may not adequately reflect the state of the disease. The growing number of studies indicate that long-term monitoring of CTC numbers in the same patient provides more comprehensive prognostic data and should be incorporated into clinical practice, as a factor that contributes to therapeutic decisions. This Review describes the current status of CTC enumeration as a prognostic and predictive factor, highlights the shortcomings of current solutions and advocates for longitudinal CTC analysis as a more effective method of the evaluation of developing disease, treatment efficacy, and long term-monitoring of the minimal residual disease.

Due to model errors caused by local variations in cloud precipitation processes, there are still significant uncertainties in current predictions and simulations of short-duration heavy rainfall. To tackle this problem, the effects of warming on cloud-precipitation efficiency was analyzed utilizing a weather research and forecasting (WRF) model. The analysis focused on a rainstorm corridor event that took place in July 2020. Rainstorm events from July 4 to 6 formed a narrow rain belt with precipitation exceeded 300 mm in the middle and lower reaches of the Yangtze River. Temperature sensitivity tests revealed that warming intensified the potential temperature gradient between north and south, leading to stronger upward motion on the front. It also strengthened the southwest wind, which also resulting in more pronounced precipitation peaks. Warming led to a stronger accumulation and release of convective instability energy. Convective available potential energy (CAPE) and convective inhibition (CIN) increased correspondingly with the temperature. The precipitation efficiency increased sequentially with 2℃ warming to 27.4%, 31.2% and 33.1%. Warming can affect the cloud precipitation efficiency by both promoting and suppressing convective activity, which may be one of the reasons for the enhancement of extreme precipitation under global warming. The diagnostic relationship between upward moisture flux and lower atmospheric stability during precipitation evolution was also revealed.

Incense is essential in religious ceremonies, even in relatively new religious movements such as New Age and Neopaganism. These garner little attention from ethnobotanists, although they trigger an international trade in wild-harvested plants. In this paper, we studied the botanical ingredients of smudge sticks (dried plant bundles burned for purification) in the Netherlands, and people’s motivations to use them posing the following questions: what plant species are included in smudge sticks? what are they used for? and are exotic plants preferred over native Dutch plant species? We visited online and physical shops in Dutch cities, acquiring a total of 29 different smudge sticks containing at least 15 species. We held semi-structured interviews with 11 users, vendors, and herbal experts, and collected data from 33 questionnaires. Salvia apiana L. was most frequently found, along with North American species of the genus Artemisia. The rise of the New Age movement resulted in North American ritual plant species being easily available in (online) shops and it being used for personal protection and cleansing. Despite its commercial demand there is no data regarding the pressure on wild populations, therefore, we call for the need to monitor the harvesting of these species in the wild.