A number of standard molecules are used for the molecular and histological characterization of lymphatic endothelial cells (LECs) such as Lyve1, Podoplanin (D2-40), VEGFR3, Prox1 and CD31. The number of molecules whose mutations cause lymphatic malformations or primary congenital lymphedema is considerable, but the majority of these diseases have not yet been characterized at the molecular level. Therefore, there is still considerable scope for molecular and functional studies of the lymphatic vasculature. Using RNASeq, we have previously characterized LECs under normoxic and hypoxic conditions. We used this information to compare it with immunohistochemical data. We carried out some of the immunohistology ourselves, and systematically studied the Human Protein Atlas (https://www.proteinatlas.org/). Here we describe molecules that are expressed at RNA and protein levels in LECs, hoping to stimulate future functional studies of these molecules.

Globally, the fuel demand is increasing rapidly and the conventional method to produce fuel is toxic which causes environmental degradation and climate crisis. Therefore, biodiesel emerges as the substitute to overcome the conventional fuel problems because biodiesel is renewable and non-toxic. Biodiesel synthesis is the process of using fats and oils to produce biodiesel ester and glycerol. Biodiesel can be produced using edible and non-edible feedstock but using the edible feedstocks problems like fuel vs food, nutritional value and economic considerations were raised. Therefore, non-edible feedstocks are being used to overcome problems arising from edible feedstock. As non-edible feedstocks can easily be grown anywhere on land, doesn’t cause imbalance to food economy and has low cost. Many non-consumable seeds like Mahua, Jatropha, Castor, Neem etc. are used to produce biodiesel. Now a days, researchers are using nanoparticles to enhance the stability, quality, and properties of biodiesel. In future, researchers can use algae and microbial oils for production of biodiesel. Research can also use biojet fuel in such a way that it reduces the carbon emission. In future, production of biodiesel can be integrated into circular economy models to reduce environmental waste and impact. In this review we will discuss the production methods for biodiesel, catalysts for biodiesel production, and non-edible feedstock properties of biodiesel.

Train fault detection primarily relies on the comparative analysis of collected images with standard reference images, making the accurate identification of the collected image types crucial for effective fault detection. Most current systems achieve indirect classification of carriage images by using Automatic Equipment Identification (AEI) devices to recognize carriage identification number information while capturing the carriage images. However, damaged RF tags or blurred characters can hinder these devices, obstructing further train fault analyses. The unique features of carriage linear array images, such as high resolution, extreme aspect ratios, and local non-linear distortions, pose significant challenges to recognition algorithms. This paper proposes a method specifically designed to recognize these types of images. First, we implement an object detection algorithm to locate the positions of key components in carriage linear array images. By leveraging the spatial layout mapping between these key components and the carriage type classification, we simplify the complex image recognition problem into a sparse point set alignment task. To ensure effective registration despite local distortions in the linear array images, we propose a weighted radial basis function. To address the issues of unknown matching relationships and quantity mismatches during point set alignment, the proposed objective function is designed to maximize the similarity between Gaussian mixtures of point sets, thereby determining the RBF weights. Extensive experiments have demonstrated that our algorithm achieves 100% recognition accuracy under local nonlinear scale distortions of less than 15%. The algorithm remains effective even in the presence of some false positives or missed detections in key component object detection results. It can accurately identify the target category from 79 image categories within 24 milliseconds on an i7 CPU without GPU support. It is of great significance to reduce system cost and promote automatic exterior fault detection for train rolling stock.

This systematic review explores the cross-cultural validation of neuropsychological assessments and their clinical applications in cognitive behavioral therapy (CBT), focusing on culturally adapted CBT (CA-CBT) across diverse populations and settings. Methods: Following the PRISMA guidelines, a comprehensive search was conducted in multiple academic databases, including PubMed, PsycINFO, Scopus, and Web of Science. Keywords related to cognitive-behavioral therapy, cultural adaptation, and specific populations were used. Inclusion criteria encompassed randomized controlled trials (RCTs) and pilot studies that assessed CA-CBT for various mental health conditions. Results: The review included studies involving Chinese Americans, Latino caregivers, Syrian refugees, Jordanian children, Malaysian Muslims, Afghan refugees, Iraqi women, Japanese children and adolescents, and Tanzanian and Kenyan children. CA-CBT demonstrated significant effectiveness in reducing symptoms of depression, anxiety, PTSD, and psychosis. For instance, Research has shown that CA-CBT is more effective than standard CBT in reducing depressive symptoms among Chinese Americans and significantly lowering PTSD symptoms in Syrian refugee women. The method has been well-received and feasible in diverse populations, such as Jordanian children and Afghan refugees. Long-term benefits are promising, with sustained improvements reported in various studies. Additionally, digital and remote delivery methods have demonstrated potential for expanding the accessibility of CA-CBT. Conclusions: CA-CBT is a valuable and effective intervention for diverse cultural populations, significantly improving mental health outcomes. However, future research must address limitations such as small sample sizes, short follow-up periods, and variability in assessment tools. Studies should include larger and more diverse sample sizes, longer follow-up periods, rigorous control groups, and comprehensive outcome measures to further validate and enhance the application of CA-CBT across different cultural contexts.

Chemical vapor deposition (CVD) is a vital process for depositing thin films of various materials with precise control over thickness, composition, and properties. Understanding the heat and mass transfer mechanisms during CVD is essential for optimizing process parameters and ensuring high-quality film deposition. This review provides an overview of recent advancements in the heat and mass transfer modeling for chemical vapor deposition processes. It explores innovative modeling techniques, recent research findings, emerging applications, and challenges in the field. Additionally, it discusses future directions and potential areas for further advancement in CVD modeling.

Pelagic stingray (Pteroplatytrygon violacea) is the only species of stingray (Dasyatidae) that utilizes both pelagic and demersal habitats. It is the main bycatch species in pelagic longline fisheries targeting bluefin tuna in the Gulf of Lions. In the Mediterranean Sea, their stock structure, behavioural ecology and movements are unknown. For the first time in the Mediterranean, 17 individuals (39 to 60 cm disc width) were tagged with pop-up satellite archival transmitting tags using a novel method of tag attachment, to investigate horizontal and vertical movements. The tags were attached for between two and 60 days. Between the months of July and October, pelagic stingray occupied a temperature range of 12.5 to 26.6°C, and a depth range extending from the surface to 480 m. Monthly trends in catch-per-unit-effort (CPUE) of pelagic stingray peaked in August and decreased by late autumn. Pelagic stingray may aggregate on the continental shelf during summer, and move southwards in early autumn, and this movement pattern is considered in relation to the reproductive cycle and overwintering. Future work and options for bycatch mitigation are discussed.

In view of the fact that the field monitoring information can more intuitively and accurately reflect the stability state of slopes, this paper takes the warning threshold of slope deformation rate monitoring as the slope stability evaluation standard, and puts forward a method for solving the safety coefficient of rocky slopes. The discrete element method 3DEC, which is suitable for rocky slopes, is selected as the numerical calculation tool, and the convergence criterion of its strength reduction method is modified to the slope deformation rate threshold, and the method is realized by the bifurcation method through its built-in FISH programming language. The results of the classical case show that, by selecting the slope deformation rate threshold in the appropriate interval, the results of this paper's method are very close to those of the finite unit stress method and the limit equilibrium method, which verifies the reliability of this paper's method. Further, the method of this paper is applied to an open-pit mine slope project, based on the slope deformation on-site monitoring data, through the time series prediction method to determine the slope deformation rate warning threshold, using the threshold as an evaluation criterion to solve the slope's coefficient of safety, the calculation results show that: the slope's coefficient of safety in natural working conditions is 1.086, basically stable, suffered from continuous rainfall, the slope body is gradually saturated, the slope's coefficient of safety is reduced to 0.987, the slope's safety is reduced to 0.987. After continuous rainfall and gradual saturation of the slope, the coefficient of safety decreased to 0.987, destabilizing and destroying, which is consistent with the site conditions.

The escalation of mining activities in the karst regions of Guizhou Province has heightened the occurrence of water-inrush incidents in deep coal mines. This study focused on water-inrush phenomena within the Xinhua mining area of Jinsha County, Guizhou Province, aiming to investigate the sources of these incidents. The findings indicated that the overlying limestone of the Changxing Formation in the coal seam served as a vulnerable aquifer under certain conditions, leading to water inrushes. The analysis of the spatiotemporal distribution patterns of water-inrush incidents at the working face indicated that previous mining operations damaged the shallow Changxing Formation limestone, resulting in the accumulation of goaf water and the formation of numerous mining-induced fractures. These fractures served as rapid conduits for water inrushes from both atmospheric precipitation and underground sources at the deep working face. The examination of surface water and mine water quality demonstrated that both exhibited similar characteristics, predominantly featuring bicarbonate, sulfate, and sodium compositions. Investigation into the relationship between mine water inflow and atmospheric precipitation established that atmospheric precipitation influenced the mine water supply cycle, with a replenishment period of ~10 months during the operational phase of the Jinyuan Coal Mine and about one month post-closure. The fractures induced by mining activities within the Changxing Formation limestone facilitated water flow, with atmospheric precipitation serving as the primary water source for the mine. This study offered a valuable scientific foundation for addressing water-related damage resulting from atmospheric precipitation in mines susceptible to water inrushes under analogous hydrogeological conditions.

Intraoperative load sensors have been developed with the aim of balancing the soft tissue in the knee during Total Knee Replacements (TKRs). Literature has shown success in soft tissue bal-ancing however, concerns regarding the accuracy of these sensors remain. For this reason, pre-vious research has attempted to create robust sensors for intraoperative use. The design of the sensor in this research has aimed to address the concerns outlined in the literature to create a new and novel smart-sensor for soft-tissue balancing. This new intraoperative load sensor included design features to increase accuracy while artificial intelligence allowed for comprehensive sensing across the entirety of the sensor, providing unparalleled insight during the operation. Notably, the sensor was designed to withstand loads of at least 450 N, ensuring robust perfor-mance of the sensor. To optimize the design process while minimizing cost, Finite Element Analysis was employed. This approach ensured the design features fulfilled their function while maintaining structural integrity to withstand loads. Moreover, the novelty of using training data from simulated data will reduce the iterative process of the labor-intensive collection of training data for the artificial intelligence while reducing human error associated with it. Results from the evaluation demonstrated the ability of this design to successfully bridge the current gap in the market by fulfilling all essential design criteria established within the literature. This innovative smart-sensing tool will in turn enhance patient outcomes and alleviate financial burdens to pa-tients and the healthcare systems by reducing the need for early revision surgeries associated with improper joint tension.

During the high-speed flight of a vehicle in the atmosphere, surface friction with the air generates aerodynamic heating. The aerodynamic heating phenomenon can create extremely high temperatures near the surface. These high temperatures impact material properties and the structure of the aircraft, so thermal deformation measurement is essential in aerospace engineering. This paper revisits high-temperature deformation measurement using the digital image correlation (DIC) technique under high-intensity blackbody radiation with a precise speckle pattern fabrication and a heat haze reduction method. The effect of speckle pattern on the DIC measurement has been thoroughly studied at room temperature, but high-temperature measurement studies have not reported such effects so far. We found that the commonly used methods to reduce the heat haze effect could produce incorrect results. Hence, we propose a new method to mitigate heat haze effects. An infrared radiation heater was employed to make an experimental setup that could heat a specimen up to 950 ℃. First, we mitigated image saturation using a short-wavelength bandpass filter with blue light illumination, a standard procedure for high-temperature DIC deformation measurement. Second, we studied how to determine the proper size of the speckle pattern under a high-temperature environment. Third, we devised a reduction method for the heat haze effect. As proof of the effectiveness of our developed experimental method, we successfully measured the deformation of stainless steel 304 specimens from 25 ℃ to 800 ℃. The results confirmed that this method can be applied to the research and development of thermal protection systems in the aerospace field.

This bibliographic study addresses key aspects related to fishing, product safety, and climate change in the Adriatic Sea region. The examination of product safety focuses on the assessment of contaminants originating from human activities such as industry, mining, agriculture, and household waste disposal. The contamination of the aquatic environment has emerged as a pressing global concern, extending to the Adriatic subbasin. Aquatic organisms, including fish, are prone to accumulating pollutants directly from polluted water sources and indirectly through the food web. The bio-accumulation of potentially hazardous substances, particularly heavy metals, pesticides, PCBs, PAHs, and antibiotic resistance in aquatic organisms, poses a significant threat to human health. Climate change effects will deplete our seafood supply in terms of quantity and safety owing to negative consequences such as higher levels of pollution, parasites, viruses, infections, acidification, and toxicities such as shellfish poisoning. Global food safety strategies should be developed to reduce greenhouse gas emissions and promote environmentally friendly technology, which indirectly affects seafood quality and microbiological safety, especially for Adriatic Sea, which is part of the Mediterranean Sea, characterized by the most polluted waters in the world.

Cell-culture meat is a novel concept to replace traditional meat produced from animals, and to satisfy rising food demands due to the world population growth. It is still a young field and requires to solve technical issues including cell sources and types, efficient culture for large scale production, taste and texture for customer satisfaction. The current review summarized traditional beef meat production and evaluation methods for customer satisfaction. In particular, serum biomarkers for beef meat evaluation are discussed, which can be applicated for cell-culture meat production.

The numerical implementation of the controllers allows the use of very complex algorithms with ease. However, in practice, due to its proven advantages, the PID controller (and its variants) is widely used in industrial control systems as well as in many other applications that require continuous control. Most of the methods for tuning the parameters of PID controllers are based on time-invariant linear models of the processes, which in practice can lead to poor performance of the control system. The paper presents an application of reinforcement learning algorithms in tuning of PID controllers for the control of some classes of continuous nonlinear systems. Tuning the parameters of PID controllers is done with the help of Twin Delayed Deep Deterministic Policy Gradients (TD3) algorithm which presents a series of advantages compared to other similar methods from Machine Learning dedicated to continuous state and action spaces. TD3 algorithm is an off-policy Actor-Critic based method and was used as it does not require a system model. The presented technique is applied for control of a biotechnological system which has a strongly nonlinear dynamic. The proposed tuning method is compared to the classical tuning methods of PID controllers. The performance of the tuning method based on the TD3 algorithm is demonstrated through simulation illustrating the effectiveness of the proposed methodology.

Promoting the sustainable development of resource-based cities is of great significance to the ecological protection and high-quality development of the Yellow River Basin. Combining the environmental status and economic development, we objectively evaluate the ecological safety level of resource-based cities in the Yellow River Basin, compare and analysis the main factors affecting the ecological safety of different resource-based cities, and formulate precise and differentiated ecological safety protection measures in river basins to achieve high-quality development. Taking the Yellow River Basin as an example, this paper analyses the ecological level of the Yellow River Basin based on the theory of sustainable development, calculates the ecological safety level of 30 resource-based cities in the Yellow River Basin from 2006 to 2020 by TOPSIS model, and makes convergence analysis by classification. Through the DID empirical test of sustainable development planning policy on ecological safety factors, distinguish the development characteristics of different resource-based cities. The results show that: (1) the ecological safety level of resource-based cities in the Yellow River Basin is generally developing well, and there are differences among different types of resource-based cities; (2) the kinetic energy of sustainable development of resource-based cities in the Yellow River Basin is mainly industrial transformation, and the power of science and technology is insufficient; (3) local governments pay limited attention to environmental protection, and the sustainable development planning of resource-based cities in 2013 mainly promotes the adjustment of industrial structure of resource-based cities.

To stabilize the Earth’s climate a large-scale transition is needed to non-carbon emitting renewable energy technologies like wind and solar energy. Although these renewable energy sources are now lower-cost than fossil fuels, their inherent intermittency make them unable to supply a constant load without storage. To address these challenges rechargeable electric batteries are currently the most promising option; however, their high capital costs limit current deployment velocities. To both reduce the cost as well as improve performance 3D printing technology has emerged as a promising solution. This literature review provides the state-of-the-art enhancements of battery properties with 3D printing including efficiency, mechanical stability, energy and power density, customizability and sizing, production process efficiency, material conservation and environmental sustainability as well as the progress in solid-state batteries. The principles, advantages, limitations, and recent advancements associated with the most common types of 3D printing are reviewed focusing on their contributions to the battery field. 3D printing batteries offer design flexibility, material flexibility, reduces pack weight, minimizes material waste, increases the range of applications and has the potential to reduce costs. As 3D printing technologies become more accessible, the prospect of cost-effective production for customized batteries is extremely promising.

The uncertainty in the secondary path of an Active Noise Control (ANC) headphone affects the waterbed effect and stability of the feedback system. This study focuses on the uncertainty of the secondary path when real users wear headphones, and proposes a new uncertainty constraint based on the measured results of the secondary path transfer function under different wearing conditions of dummy head and limited subjects. This constraint and a cascaded second-order infinite impulse response filter with fixed coefficients are used to formulate a control strategic function, which is optimized by the Improved Grey Wolf Optimizer (IGWO) algorithm to obtain the optimal controller with better noise reduction performance. The proposed method and simulation model are validated based on the experimental test results. The results demonstrate that the safety factor and waterbed suppressing factor contained in the proposed uncertainty constraint ensures more stable noise reduction and effective suppression of the waterbed effect for new subjects without a priori data.

The rapid increase in metabolic diseases occurred in the last three decades in both industrialized and developing countries has been related to the rise in sugar-added foods and sweetened beverages consumption. An emerging topic in the pathogenesis of metabolic diseases related to modern nutrition is the role of Advanced Glycation Endproducts (AGEs). AGEs can be ingested with high temperature processed foods, but also endogenously formed as consequence of a high dietary sugars intake. Animal models of high sugars consumption, in particular fructose, have reported AGEs accumulation in different tissues in association with peripheral insulin resistance and lipid metabolism alterations. The in vitro observation that fructose is one of the most rapid and effective glycating agent when compared to other sugars has prompted the investigation of the in vivo fructose-induced glycation. In particular, the widespread employment of fructose as sweetener has been ascribed by many experimental and observational studies for the enhancement of lipogenesis and intracellular lipid deposition. Indeed, diet-derived AGEs have been demonstrated to interfere with many cell functions such as lipid synthesis, inflammation, antioxidant defences, and mitochondrial metabolism. Moreover, emerging evidences also in humans suggest that this impact of dietary AGEs on different signalling pathways can contribute to the onset of organ damage in liver, skeletal and cardiac muscle, and brain, affecting not only metabolic control, but global health. Indeed, the here reviewed most recent reports on the effects of high sugars consumption and diet-derived AGEs on human health suggest the need to limit the dietary sources of AGEs, including added sugars, to prevent the development of metabolic diseases and related comorbidities.

Effective and rapid mixing is essential for various chemical and biological assays. The present work reports a simple and low-cost micromixer based on magnetofluidic actuation. The device takes advantage of magnetoconvective secondary flow, a bulk flow induced by an external magnetic field, for mixing. A paramagnetic stream of diluted ferrofluid and a non-magnetic stream are introduced to a straight microchannel. A permanent magnet placed next to the microchannel induced a non-uniform magnetic field. The magnetic field gradient and the mismatch in magnetic susceptibility between the two streams create a body force, which leads to rapid and efficient mixing. The micromixer reported here could achieve a high throughput and a high mixing efficiency of 90 % in a relatively short microchannel.

This paper seeks to model the crack propagation in of AAs (aluminum-alloys) 2524-T3 and 7050-T7452 subjected to fatigue loading at low temperature. Fatigue crack growth tests were performed on AAs 2524-T3 and 7050-T7452 subjected to constant-amplitude and actual random-spectra loading histories at room temperature of about 25 °C and at cryogenic temperature of -70 °C, respectively, to determine their crack growth properties and residual lives. The interaction mechanisms between cryogenic temperature and the fatigue load were deduced on the basis of the results of fractographic analysis. Temperature-dependent residual lives under actual random-spectrum load history were modeled based on a modified accumulation damage rule by accounting for the load interaction. Good correlation was achieved between the predictions and actual experiments, demonstrating the practical and effective use of the proposed method.

Bio-molecular reagents like antibodies required in experimental biology are expensive and their effectiveness, among other things, is critical to the success of the experiment. Although such resources are sometimes donated by one investigator to another through personal communication between the two, there is no previous study to our knowledge on the extent of such donations, nor a central platform that directs resource seekers to donors. In this paper, we describe, to our knowledge, a first attempt at building a web-portal titled Bio-Resource Exchange that attempts to bridge this gap between resource seekers and donors in the domain of experimental biology. Users on this portal can request for or donate antibodies, cell-lines and DNA Constructs. This resource could also serve as a crowd-sourced database of resources for experimental biology. Further, in order to index donations outside of our portal, we mined scientific articles to find instances of donations of antibodies and attempted to extract information about these donations at the finest granularity. Specifically, we extracted the name of the donor, his/her affiliation and the name of the antibody for every donation by parsing the acknowledgements sections of articles. To extract annotations at this level, we propose two approaches – a rule based algorithm and a bootstrapped relation learning algorithm. The algorithms extracted donor names, affiliations and antibody names with average accuracies of 57% and 62% respectively. We also created a dataset of 50 expert-annotated acknowledgements sections that will serve as a gold standard dataset to evaluate extraction algorithms in the future. Contact: madhavi@pitt.edu, madhavi@cs.cmu.edu Database URL: http://tonks.dbmi.pitt.edu/brx Supplementary information: Supplementary data are available at Database online.

Acoustic Radiation Forced Impulse (ARFI) is an ultrasound integrated method, measuring stiff-ness by point shear wave elastography. To evaluate the diagnostic performance of ARFI of the liver and of the spleen, combined with spleen dimension and platelet count, in predicting clinical events in cirrhosis, a prospective and cross-sectional study was conducted between February 2017 and February 2021. The following ratio scores with ARFI measurements were performed: ALSDP (ARFI Liver-Spleen Diameter-to-Platelets ratio score), ASSDP (ARFI Spleen-Spleen Diam-eter-to-Platelets ratio score), ASSAP (ARFI Spleen-Spleen Area-to-Platelets ratio score), ALSAP (ARFI Liver-Spleen Area-to-Platelets ratio score). In 100 enrolled subjects, spleen ARFI, ASSDP, ASSAP, were significantly associated with high-risk oesophageal varices (HRVs) in the prospec-tive short- and long-term follow-up and in the cross-sectional study (p 0.7). ARFI-ratio scores can predict, in cirrhotic patients, the risk of developing HVRs in short and long-term period.

Rapid urbanization has facilitated socioeconomic development but has also exacerbated land use/cover change (LUCC), significantly impacting ecological environments. Timely, objective, and quantitative assessments of ecological quality changes resulting from LUCC are essential for safeguarding the natural environment and managing land resources. However, limited research has explored the potential interrelationships between the spatio-temporal heterogeneity of LUCC and ecological quality during urbanization. This study focuses on Xiong’an New Area, a region experiencing rapid urbanization, utilizing the remote sensing-based ecological index (RSEI) to monitor ecological quality dynamics from 2017 to 2023. To address the computational challenges associated with large-scale regions, a streamlined RSEI construction method was developed using Landsat imagery and implemented via Google Earth Engine (GEE). A geographically weighted regression (GWR) analysis, integrated with Sentinel-2 land use data, was employed to examine the influence of LUCC on ecological quality. The findings reveal the following: (1) Ecological quality in Xiong’an New Area exhibited an overall positive trajectory, with improvements elevating the ecological status to above moderate levels. (2) Urban expansion resulted in a 17% reduction in farmland, primarily converted into construction land, which expanded by approximately 12%. (3) Ecological protection policies have facilitated the conversion of farmland into wetlands and urban green areas, which have emerged as the principal contributors to ecological quality enhancement. (4) A positive correlation was observed between changes in ecological land and ecological quality, while a negative correlation was identified between shifts in construction land and farmland and ecological quality. This research provides valuable scientific insights into ecological conservation and land use management, thereby establishing a foundation for the development of rational land resource planning and sustainable ecological development strategies in the Xiong’an New Area.

Multiple myeloma (MM) is a plasma cell malignancy that resides within the bone marrow microenvironment, relying heavily on interactions with its cellular components. Among these, endothelial cells (ECs) play a pivotal role in MM progression and the development of therapeutic resistance. In this study, we analyzed publicly available single-cell RNA sequencing data to identify unique pathway activations distinguishing ECs from MM patients and healthy donors. We developed a novel protocol to isolate and culture endothelial progenitor cells (EPCs) and ECs directly from MM patient bone marrow, demonstrating their ability to promote myeloma cell proliferation. Validation studies confirmed that these MM-derived ECs exhibit angiogenic potential as well as expression of characteristic endothelial lineage markers. These findings underscore the critical role of bone marrow ECs in the MM tumor microenvironment and highlight potential new therapeutic targets to disrupt MM progression.

Mismatch repair (MMR) deficiency can be indicative of Lynch syndrome (LS) and guide treatment with immune checkpoint inhibitors. Colorectal cancers (CRCs) and endometrial cancers (ECs) are routinely screened to identify LS, primarily using immunohistochemistry (IHC) or Microsatellite Instability (MSI) testing, but concordance between methods is variable in ECs. Here, we investigate this variability by using the amplicon sequencing based Newcastle MSI assay (NCL_MSI) to re-analyse 361 ECs from the Ohio OCCPI/OPTEC (n=196) and Manchester PETALS (n=165) trials. NCL_MSI was consistent with Ohio results (94% and 97% concordance with IHC and original MSI assays respectively), and increased concordance in the Manchester cohort from 78% to 86% (MSI) and 84% (IHC). Among discordant Manchester samples, NCL_MSI was significantly associated with MLH1 promoter methylation status (p=0.0028), and had the highest concordance with methylation, 62/69 samples (90%), indicating utility as a screening tool in this tumour type. However, tumours with germline MSH6 defects were only detected efficiently with IHC; 7/8 LS tumours classified as MSS by either MSI assay had isolated MSH6 loss, compared to 4/12 classified as MSI-H by both (p=0.028). Furthermore, reduced MSI signal was observed in tumours with isolated MSH6 loss (p=0.009 Ohio, p=6.2x10-5 Manchester), and in both ECs and CRCs with germline defects although this only reached significance in CRCs (p=0.002). These results provide further evidence that ECs with MSH6 loss in particular, and LS tumours in general, have an attenuated MSI signal, and supports current guidelines specifically recommending IHC for LS detection and immune checkpoint therapy assessment in EC.

Background: In response to the COVID-19 pandemic, the European Association of Urology (EAU) Guidelines defined priority groups to guide the prioritization of surgery for urological malignancies. The objective of this study was to evaluate the impact of the COVID-19 prioritization recommendations on tumor pathological characteristics in a real-world setting at our academic referral Institution. Methods: We compared baseline and pathological tumor features of all patients with urological malignancies treated during the pandemic period (2020-2021) versus in the post-pandemic period (2022-2023). Our institution adhered to the International recommendations and prioritized those cases defined as “high-risk”. Results: Data of 9196 patients treated for urological malignancies were reviewed and grouped according to period of surgery (4401 in the pandemic vs 4785 in non-pandemic period). The overall number of surgical procedures was comparable for all diseases except for the number of radical prostatectomy (1117 vs 1405; p=0.03) and partial nephrectomy (609 vs 759; p=0.02) which were significantly lower in the pandemic period. Regarding tumors’ pathological features, none of the recorded variable was found to differ according to period of surgery, including disease stage, tumor grading, presence of necrosis, lymphovascular invasion, histological variants. Conclusion: A correct policy of prioritization of oncological pathologies during emergency periods and a centralization of oncological cases in reference centers reduces the possible risk of worsening cancer disease features related to the reorganization of healthcare resources.

This study checked whether vitamin D (Vit-D) supplementation improves the efficacy of resistance training (RT) in terms of increasing muscle strength and lean body mass (LBM) and influencing cardiorespiratory fitness (VO2max) in Vit-D insufficient middle-aged healthy men. Participants (n = 28) were quasi‐randomly assigned to one of the two groups, which, in a double‐blind manner, supplemented their diet daily with either Vit‐D (8000 IU; VD) or placebo (PLC) during participation in a 12‐week supervised RT program. During the intervention, serum Vit‐D concentrations increased 2.6‐fold (p < 0.001) in the VD group, while no changes occurred in the PLC group. Muscle strength gains (p < 0.001) as measured in seven exercises performed on RT equipment and increases (p < 0.001) in LBM were similar in the two groups. Total fat mass, percent total fat, and percent android fat decreased (p < 0.05) to a similar extent in both groups, but there was no change in VO2max in either group. In conclusion, in healthy Vit‐D insufficient middle-aged men engaged in resistance training, Vit‐D supplementation increases serum 25(OH)D levels but does not enhance gains in muscle strength and LBM or decreases in fat mass and fat percentage and does not affect cardiorespiratory fitness.

The main purpose of our research is the evaluation of the efficacity of solvent polarity on phenolic composition, as well as the antibacterial, and antioxidant activities of extract of Moroccan Mentha aquatica L., leaves. To attempt these objectives, the HPLC-DAD was used to identify the biomolecules. The total amount of phenolic and flavonoid content was determined using the Folin-Ciocalteu and aluminum trichloride processes as well as the DPPH, RP, and TAC tests were used to evaluate the antioxidant capabilities. In addition, by employing both in vitro and in silico analysis through the microdilution method, the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of the extracts were assessed against multidrug-resistant bacteria. Furthermore, the High-Performance Liquid Chromatography (HPLC) analysis indicated that all extracts of M. aquatica were abundant in noteworthy phytochemical compounds, especially gallic acid, vanillic acid, chlorobenzoic acid, quercetin, and kaempferol. Furthermore, the results indicated that the total phenolic content was higher in hydroethanolic extract (62.19 ± 1.19 mg GAE/g DW) in contrast to the acetonic and aqueous extracts (22.22 ± 0.64, and 22.44± 0.57 mg GAE/g DW) respectively. Similarly, the aqueous ethanol extract contained the greatest flavonoid concentration, with values of 29.15± 0.09 mg QE/g DW when compared with the other extracts. The seam extract poses excellent antioxidant power with an IC50 of 0.06 ± 0.00 mg/mL for the DPPH test, and lower RP with EC50 of 80± 0.00 µg/ml when compared with acetonic and aqueous extract (370± 0.00 and 460± 0.01 µg/mL) respectively. Moreover, the hydroethanolic extract has a high level of TAC 75.09± 2.00 mg EAA/g DW, followed by aqueous extract carrying a value of 28.53 ± 1.36 mg EAA/g DW. The antibacterial capacity is ranged between 0.78 ± 0.05 mg/mL and 12.60 ± 0.00 mg/L. The results of the in-silico study demonstrated that quercetin, vanillic acid, and naringenin components contribute to the antioxidant capability. While gallic acid, kaempferol, and quercetin components are attributed to the antibacterial capacity.

Currently, one of the main challenges is the mitigation of the effects of climate change on the agricultural sector. Conventional agriculture, with the intensive use of herbicides and pesticides to control weeds and pests, and the improper use of mineral fertilizers, contributes to climate change by causing increased greenhouse gases and groundwater pollution. Therefore, more innovative technologies must be used to overcome these problems. One possible solution is nanotechnology, which has the potential to revolutionize the conventional agricultural system. Active nanoparticles can be used both as a direct source of micronutrients and as a delivery platform for bioactive agrochemicals to improve crop growth, yield and quality. The use of nanoparticle formulations, including nanopesticides, nanoherbicides, nano-fertilizers and nano-emulsions, has been extensively studied to improve crop health and shelf life of agricultural products. Comprehensive knowledge of the interactions between plants and nanoparticles opens up new opportunities to improve cropping practices through the enhancement of properties such as disease resistance, crop yield, and nutrient use. The main objective of this review is to analyze the main effects of climate change on conventional agricultural practices, such as the use of pesticides, herbicides and fertilizers. It also focuses on how the introduction of nanoparticles into conventional practices can improve the efficiency of chemical pest control and crop nutrition. Finally, this review examines in depth the last 10 years (2014-2024) of scientific literature regarding the use of nanoparticles in agriculture to mitigate the effects of climate change.

Background: We evaluate the genetic and phenotypic features of a cohort of 12 Italian patients affected by Retinitis Pigmentosa (RP) associated with RP1 sequence variants.; Methods: Retrospective, single center study (Careggi Hospital, Florence), including a cohort of twelve Italian patients (Five male and seven female) affected by RP carrying pathogenic variants of the RP1 gene. A complete ophthalmic assessment and pedigree analysis was conducted with focus on the onset of disease symptoms, the patient’s age at the first diagnosis, the follow up duration and the possible presence of comorbidities; Results: The mean age at onset of symptoms was 31,0 ± 24,1 years old, with a mean follow-up period of 9,1± 2,4 years. The main symptoms at presentation were hemeralopia and visual field constriction. Fundus evaluation revealed a classic type of RP. Fundus autofluorescence (FAF), Optical Coherence Tomography (OCT), Electroretinogram (ERG), and visual field confirmed in most of the cases the typical features of classic retinitis pigmentosa.; (4) Conclusions: This single-center cohort of Italian patients provided information on the clinical and genetic features of RP1-associated RP. By comprehensively clarifying the genetic variations and their associated clinical manifestations, we can better tailor therapeutic interventions aimed at targeting specific genetic abnormalities. This ultimately promises to improve the prognosis and quality of life for individuals with RP-associated RP1.

The gut microbiota (GM) communicates with the brain via biochemical signaling constituting the gut-brain axis, which significantly regulates the body's physiological processes. The GM dysbiosis can impact the digestive system and the functioning of the central nervous system (CNS) linked to the onset of neurodegenerative diseases. In this review, the scientific data compiled from diverse sources primarily emphasizes the neuropathological characteristics linked to the accumulation of modified insoluble proteins (such as β-Amyloid peptides and hyperphosphorylated tau proteins) in Alzheimer’s Disease (AD) and the potential impact of gut microbiota (GM) on AD susceptibility or resilience. The specific GM profile of human beings may serve as an essential tool for preventing or progressing neurodegenerative diseases like AD. This review focuses mainly on the effect of gut microfauna on the gut-brain axis in the onset and progression of AD. The GM produces various bioactive molecules that may serve as proinflammatory or anti-inflammatory signaling, contributing directly or indirectly to the repression or progression of neurodegenerative disorders by modulating the response of the brain axis. Human studies must focus on further understanding the gut-brain axis and venture to clarify microbiota-based therapeutic strategies for AD.

Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography re-view paper provides a systematic review of computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The systematic review is meticulously carried out, adhering closely to the preferred reporting items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. Image processing and machine learning-based methods are compre-hensively reviewed. The evaluation of a deep learning architecture based on self-extracted features for classification tasks demonstrated outclass performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations' sustainability de-velopment goals.

Nowadays, Multi-needle electrospinning has used to be an efficient method for producing nanofiber membranes. But in the process, the fluctuation errors in electrospinning fluid flow rate have directly influence the membrane quality, which bringing instability and still have not been solved yet. For finding out the solution to the inaccurate flow control in multi-needle, it is necessary to develop a multi-stage flow runner spinneret for large-scale production of nanofiber membrane. At first, the paper uses the finite element software COMSOL to numerically simulate the polymer flow in the runner of spinneret. Based on it, the velocity field distribution curve was draw and the velocity instability coefficient of the polymer flow was attained, which are providing theoretical guidance for the optimal design of spinneret. Next, the response surface analysis method (RSM) was used to establish a regression model between the average diameter of the nanofiber membrane and the process parameters. Then, above regression model used to perform variance data processing between the average diameter and the process parameter configuration. After that, the 2D and 3D response surface profiles and the significance analysis were completed. Finally, the stability of the nanofibers production was improved, which attained the optimal adjustment of process configuration in multi-needle electrospinning.

Objectives: To assess the short-term impact of a single global stretching session on plantar pressures and lumbar range of motion (RoM) over one week in sedentary university students. Design: A randomised controlled trial with two parallel groups. Participants: Sixty-four university students were randomly assigned to a stretching group or control group. The stretching group received a single session of global stretching and the control group remained seated for the same length of time as the stretching session. Main outcome measures: Lumbar RoM and plantar pressures were assessed before, immediately after, 48 hours after, and 7 days after intervention. Results: Group-by-time interaction was significant for lumbar lateral flexion (p = 0.044), right maximal pressure (p = 0.003), left maximal pressure (p = 0.008), right mean pressure (p = 0.025), left mean pressure (p = 0.002), and maximal pressure minus mean pressure in right foot (p = 0.007). The model with plantar pressures showed a significant difference for group-by-time interaction (p = 0.010).Conclusion: Global stretching seems to counteract the ill-effects of prolonged sitting up to seven days with respect to plantar pressure in standing without improving lumbar ROM

One of the most concerning global environmental issues is the pollution of agricultural soils by heavy metals (HMs), especially cadmium (Cd), which not only affects human health by taking Cd-containing foods but also impacts the quality of rice. The soil's nitrification and denitrification processes, coupled with the release of volatile organic compounds by plants, raise substantial concerns. The understanding of three aspects of paddy soil under conditions of heavy-metal pollution and water management is currently limited. In this review, we summarized recent literature related to the deleterious effects of Cd on both soil processes related to the N cycle and rice quality, particularly aroma, in different water management practices. Under both continuous flooding (CF) and alternate wetting and drying (AWD) conditions, cadmium (Cd) has been observed to reduce both nitrification and denitrification processes. The adverse effects are more pronounced in continuous flooding (CF) compared to alternate wetting and drying (AWD). Similarly, the alteration in rice aroma is more significant in AWD than in CF. The precise modulation of volatile organic compounds (VOCs) by Cd, remains unclear based on the available literature. Nevertheless, HM accumulation is higher in AWD conditions compared to CF, leading to a detrimental impact on volatile organic compounds (VOCs). The literature concluded that AWD practices should be avoided in Cd contaminated fields to decrease accumulation and maintain the quality of rice. In the future, rhizospheric engineering and plant biotechnology can be used to decrease the transport of HMs from the soil to the plant’s edible parts.

Starting from accelerated industrial development, industrial disasters (chemical spills, explosions, nuclear accidents, etc.), which fall under man-made (technological) disasters, increasingly threaten human lives, the environment, and property. Their complexity and far-reaching consequences, both at the local and global levels, require effective management of industrial risks and the disasters themselves (preparedness, mitigation, response, and recovery). For these reasons, the review paper aims to provide a holistic description of the diverse causes, dynamics, and multidimensional consequences of disasters. Additionally, it highlights and explains the key factors that directly or indirectly contribute to their occurrence. Furthermore, it could be said that the paper analyzes existing strategies for managing risks from industrial disasters and systematizes recommendations for improving preventive and reactive measures in high-risk industries. On the other side, the paper utilizes a comprehensive literature review, which involves the systematic identification, review, analysis, and critical evaluation of existing scientific papers, studies, reports, and regulatory documents on industrial disasters. Besides that, it also includes a detailed analysis of well-known industrial disaster cases such as Seveso (Italy, 1976), Bhopal (India, 1984), Chernobyl (Ukraine, then USSR, 1986), and Fukushima (Japan, 2011). The paper emphasizes that industrial disasters are often the result of the combined influence and interaction of technical failures, human errors, and inadequately developed safety procedures. Moreover, it can be highlighted that the design and implementation of preventive measures, such as regular maintenance of technical systems and systematic training and education of employees, are crucial for reducing the likelihood of such catastrophic events. The implications of this review paper are multifaceted and point to the following: the need to reassess current safety practices in industries; reconsider the potential for introducing innovations in preventive technical-technological solutions; examine the thorough implementation of innovative early warning systems, safety procedures, and modern equipment in facilities; improve cooperation between engineers, safety managers, and regulatory workers; reassess the need for additional updates and tightening of regulations regarding the storage, transport, and use of hazardous materials in industry; and improve the management of risks from industrial disasters.

The recent development of Wearable Exoskeletons has stressed their effectiveness in assisting humans for rehabilitation and augmentation purposes. These devices interact with the user, therefore their actuators and power transmission mechanisms are fundamental for improving physical Human-Robot Interaction (pHRI). The enhanced advancement of 3D-printing technology as a valuable approach for creating light and efficient gearboxes allows for exploring multiple reducers’ designs. However, to the authors’ knowledge, only sporadic implementations with relatively low reduction ratios are reported, and the respective experimental validations usually differ from each other, preventing a comprehensive evaluation of different design and implementation choices. In this paper, we design, develop, and examine experimentally multiple 3D-printed gearboxes conceived for wearable assistive devices. Two relevant transmission ratios (1:30 and 1:80) and multiple designs, that include single and double-stage compact cam cycloidal drives, compound planetary gearboxes, and the series of cycloidal and planetary architectures have been compared to assess the worth of 3D-printed reducers in Human-Robot Interaction applications. The resulting prototypes were examined by evaluating their weight, cost, back-driveability, friction, regularity of the reduction ratio, gear play, and stiffness. The results show that the developed gearboxes represent valuable alternatives for actuating wearable exoskeletons in multiple applications.

This study aims to address the inefficiencies and time-consuming nature of traditional hot-air anti-icing system designs by introducing reduced order models (ROM) and machine learning techniques to predict anti-icing surface temperature distributions. This study compares several classic neural networks, ultimately proposing two models: POD-AlexNet and multi-CNNs with GRU (MCG). Design variables of the hot-air anti-icing cavity are used as inputs, and the corresponding surface temperature distribution data serve as outputs. The performance of these models is evaluated on the test set. The POD-AlexNet model achieves a mean prediction accuracy of over 95%, while the MCG model reaches 96.97%. Both models significantly outperform traditional numerical simulation methods, delivering faster predictions. The proposed models achieve fast prediction of anti-icing surface temperature distribution while ensuring acceptable prediction accuracy. These models greatly enhance the prediction efficiency over existing numerical simulation approaches, contributing to the design of aircraft hot-air anti-icing systems based on optimization methods such as genetic algorithms.

It is crucial to conduct quality control tests to assess the quality and consistency of medicinal plant products before they are used. Products can often be placed on the market without registration, but some countries may require manufacturers to present product labels to authorities before selling them. These labels should confirm the composition of the product, particularly the concentrations of cannabidiol (CBD). The information on the label should provide unambiguous details about the qualitative composition and concentration of primary cannabinoids, such as CBD and THC. However, since there is a lack of regulatory control, it is possible to find inaccurate information on the label. This study aimed to create and validate a high-performance liquid chromatography method for identifying active compounds (cannabinoids) in CBD oil. The main compounds identified included Cannabidivarin (CBDV), Cannabidiolic Acid (CBD-A), Cannabigerolic Acid (CBG-A), Cannabigerol (CBG), Cannabidiol (CBD), Tetrahydrocannabivarin (THCV), Cannabinol (CBN), ∆9- Tetrahydrocannabinol (d9 -THC) ∆8- Tetrahydrocannabinol (d8-THC), Cannabicyclol (CBL), Cannabichromene (CBC), and Tetrahydrocannabinolic Acid (THCA-A), determined in line with the International Conference on Harmonization's (ICH) guidelines. The method was validated for linearity, accuracy, precision, limit of detection (LOD), and limit of quantitation (LOQ). It was determined to be linear, with a correlation coefficient (R2) ranging from 0.99937 to 0.99973. The LOD and LOQ values calculated from the calibration curve ranged from 0.05 to 0.13 and 0.50 to 0.61 µg/mL, respectively. The method also exhibited acceptable precision, with relative standard deviation values lower than or equal to 2%. The method's accuracy was assessed through recovery percentages and fell within an acceptable range (98.12–102.39%). This study's rigorous methodology and comprehensive findings significantly contribute to cannabinoid analysis. This validated protocol was used to analyze the cannabinoid concentration levels in 14 CBD oil products in the Republic of North Macedonia. The performance parameters indicate that the developed and validated method is suitable for quantitatively determining cannabinoids in CBD oil. The analysis of 14 CBD oil products revealed no significant derogation regarding the labeling, i.e., they were by the producers' declared variability.

Overtourism in national parks and protected areas can cause irreversible damage to the environment, compromising the quality of soil, water, wildlife, and vegetation. Thus, it is critical for park managers to have detailed information on visitor activities and spatial dynamics in order to prioritize actions capable of mitigating undesirable impacts in the most frequently visited areas. In this article, we use georeferenced trails and photographs from Wikiloc and Flickr web platforms to determine the spatial visitation patterns in the Atlantic Islands of Galicia National Park (AINP) from 2008 to 2023. Maps showing trail usage intensity and the distribution of photographs according to land use allowed us to identify the most frequented land uses by visitors and the areas of highest tourist pressure within the AINP. The results show that distribution patterns vary between platforms. Shrubland (37%) and marine cliffs (27%) were the most photographed land uses by visitors, while artificial areas (14%) were the most frequented by Wikiloc users. Cíes island emerges as the most popular tourist destination, as evidenced by the greater number of trails and photographs compared to Ons, Sálvora, and Cortegada. This study shows how social media data, specifically trails and geotagged photographs from Wikiloc and Flickr, can support and complement the monitoring of visitor use and impact in protected areas.

The development of new materials for the controlled release of molecules represents a topic of primary importance in medicine as well as in food science. Herein, three eutectic systems respectively composed by choline chloride and ethylene glycol (1:2 molar ratio), methyltriphenylphosphonium bromide and ethylene glycol (molar ratio 1:5), and choline chloride and glycerol (molar ratio 1:1.5) were tested as materials for the controlled release of an essential oil derived from citrus lemon leaves.

Natural components are sparking a lot of interest in the field of drug formulation and delivery. Anastatica hierochuntica is a small plant, extremely rich in its phytochemical content including flavonoids, phenolics, and several minerals. For that, it is widely used in folk medicine in a plethora of conditions such as headache, asthma, menstrual pain, and childbirth. The aim of this research is to investigate its potential antimicrobial effects when loaded onto nanofiber as field leading. The extract-loaded nanofibers were prepared via electrospinning. The prepared nanofibers were rod-shaped with nanometric size as revealed by scanning electron microscopy. The loaded NFs showed increased hydrophilicity compared to the plain NFs as evidenced by reduced WCA and increased percent water uptake. Further, they exhibited adequate tensile properties compared to the plain NFs as evidenced by increased strain and stress. Based on the highest hydrophilicity (WCA 43.1±0.6º and % swelling 216.67±2.36% after 1 h) and the best mechanical properties (strain and stress of 67.6% and 0.0486 N/mm2, respectively), NF20 was selected for further investigations. The selected formulation (NF20) showed a bi-phasic, sustained drug release pattern (73.40±1.31% after 72 h). More importantly, testing the antimicrobial effects of the extract-loaded nanofibers highlighted significant reduction in MIC, MBC, and MFC values when compared with Silymarin standard against Acinetobacter baumannii, Pseudomonas aeruginosa, Staphylococcus aureus, and Aspergillus niger. Finally, wound healing assay confirmed the accelerated wound closure and fibroblasts migration in case of the fabricated NF20, suggesting the potential of the bio- fabricated nanofiber as a novel promising wound dressing.

Design and multi-objective optimization of auxetic sandwich panels (ASPs) are performed to enhance the blastworthiness of armored fighting vehicles (AFVs). Various metastructures in the form of four auxetic geometries are proposed as the sandwich core: re-entrant honeycomb (REH), double-arrow honeycomb (DAH), star honeycomb (SH), and tetra-chiral honeycomb (CH). This paper employs a combination of finite element (FE) and machine learning (ML) methodologies to evaluate blastworthiness performance. Optimization is carried out using the non-dominated sorting genetic algorithm II (NSGA-II) method. The optimization results show significant improvements in blastworthiness performance, with notable reductions in permanent displacement and enhancements in specific energy absorption (SEA). Global sensitivity analysis (GSA) using SHapley Additive exPlanations (SHAP) reveals that cell thickness is the most critical factor affecting blastworthiness performance, followed by the number of cells and corner angle or radius for CH. The application of optimized ASP on AFVs shows promising results, with no failure occurring in the occupant floor. Furthermore, AFVs equipped with the optimized ASP DAH significantly reduce maximum displacement and acceleration by 39.00% and 43.56%, respectively, and enhance SEA by 48.30% compared to optimized aluminum foam sandwich panels (AFSPs). This study concludes that ASPs have potential applications in broader engineering fields.

Northern Australia is considered a ‘lifeboat’ region for globally threatened sharks and rays (elasmobranchs), although much of the region is understudied. The Roper River in the Northern Territory’s Gulf of Carpentaria has been inadequately surveyed, with most elasmobranch data gained opportunistically through freshwater fish surveys. This study aimed to determine the occurrence of elasmobranch species in the Roper River through targeted field surveys conducted between 2016 and 2023 and a review of data from other sources. Four euryhaline species were recorded comprising two sharks, a sawfish, and a stingray. Records of the globally Vulnerable Speartooth Shark (Glyphis glyphis) represented a significant range extension and the documentation of a new breeding population. Records of the Critically Endangered Largetooth Sawfish (Pristis pristis) extended almost 400 km upstream, highlighting extensive use of the river system. A predominantly marine species, the Critically Endangered Giant Guitarfish (Glaucostegus typus) was observed in brackish estuarine waters, approximately 15 km upstream from the river mouth. Further research is required to develop a more thorough understanding of abundance, life history, and population connectivity for these species in this river system. Given the documentation of threatened species, the potential impacts of fisheries, water allocations, and climate change requires assessment.

Imprinted polymers with imprints of Zn2+ ions (ZnIP) and reference polymers without imprints (NIP) were synthesized based on humic and methacrylic acids. Comparative studies of the physical parameters of ZnIP and NIP were carried out using elemental analysis, IR spectroscopy and electron microscopy. Kinetic parameters of Zn2+ ion sorption on ZnIP and NIP were determined. It was found that the sorption kinetics on both ZnIP and NIP are approximated by a pseudo-first-order equation. The sorption rate constant for ZnIP is higher than that of NIP, indicating a higher adsorption rate of Zn2+ ions onto the surface of ZnIP. This may be due to more accessible sites on ZnIP. Adsorption of Zn2+ ions is best described by the Langmuir model, indicating monolayer adsorption on a homogeneous surface. The Langmuir model also shows that ZnIP is effective at low adsorbate concentrations. The Freundlich model describes the adsorption with less accuracy, which may indicate monolayer adsorption and less surface homogeneity. ZnIP shows better adsorption capacity and higher efficiency compared to NIP and can be proposed as a selective sorption material for Zn2+ ions.

Purpose Using Chatbots to seek healthcare information is becoming more popular. Misinformation and gaps in knowledge exist regarding the risks and benefits of Testosterone Replacement Therapy (TRT). We aimed to assess and compare the quality and readability of responses generated by four AI chatbots. Materials and Methods ChatGPT, Google Bard, Bing Chat, and Perplexity AI were asked the same eleven questions regarding Testosterone Replacement Therapy. The responses were evaluated by four reviewers using DISCERN and Patient Education Materials Assessment Tool (PEMAT) questionnaires. Readability was assessed using the Readability Scoring system v2.0 to calculate the Flesch-Kincaid Reading Ease Score (FRES) and Flesch-Kincaid Grade Level (FKGL). Kruskal-Wallis statistics were completed using GraphPad Prism V10.1.0. Results Google Bard received the highest DISCERN and PEMAT. Perplexity AI received the highest FRES and best FKGL. Significant differences were found in understandability between Bing and Google Bard, DISCERN scores between Bing and Google Bard, FRES between ChatGPT and Perplexity, and FKGL scoring between ChatGPT and Perplexity AI. Conclusion ChatGPT and Google Bard were top performers based on their quality, understandability, and actionability. Despite Perplexity scoring higher in readability, the generated text still maintained an eleventh-grade complexity. Perplexity stood out for its extensive use of citations, however, it offered repetitive answers despite the diversity of questions posed to it. Google Bard demonstrated a high level of detail in its answers, offering additional value through visual aids.

In the study of mountainous areas, mountain peaks play a vital role as geomorphic features that have significant influence on hydrological processes, vegetation distribution, and other important topographic characteristics. From the perspectives of geomorphology and hydrology, the river network water system is an interconnected entity that serves as a crucial component in describing regional topography, geomorphology, and hydrological features. The primary objective of this article is to extract water system information and mountain peak data in Chongli District, Hebei Province. To extract mountain peaks, coarse-resolution Digital Elevation Model (DEM) data is utilized to estimate their positions and initiate data sampling based on DEM elevations. Deep learning models are employed to avoid explicit feature extraction and instead implicitly learn from training data. By using weight sharing techniques, the training parameters of the network are reduced, achieving more accurate extraction and recognition of mountain peaks in Chongli District, along with their classification. For the extraction of the water system, the fast regional convolutional neural network algorithm from target detection is introduced to enhance the efficiency and accuracy of extracting water system information through image processing. This algorithm rapidly identifies the type, location, and width of the water systems. Additionally, the AlexNet transfer learning method is applied to locate the river skeleton within the extracted water system area. Morphological methods are then used to extract river morphology and calculate its length and width. Finally, a developed algorithm for water system information extraction detects the presence of water system coverage areas in Chongli District. Through experiments, a total of 2792 mountain peak data points were successfully extracted. In terms of water system information extraction, the Faster R-CNN and CNN-based methods demonstrate high efficiency and low detection rates. This study provides information services and data support for the operation and maintenance of the Chongli Winter Olympics. Furthermore, it serves as a reference for future research conducted by scholars in the Chongli area, offering insights into various fields such as environmental changes, vegetation classification, and geomorphic features.

Arbutus unedo (strawberry tree) is a small Mediterranean tree capable of vigorous regrowth after disturbances like fire. Traditionally used for biomass fuel, its bark and branches hold potential for higher-value products through ecovalorisation into liquid mixtures that could replace petro-leum-based materials. This study aimed to explore the chemical composition of various compo-nents of Arbutus unedo and to produce a liquefied material from its internal (IB) and external bark (EB). Chemical compositions of internal and external bark were determined using TAPPI standards including ash, extractive content, lignin, and cellulose. Metal cations were analyzed by ICP. Liquefaction of bark was optimized in a PARR reactor, evaluating factors such as particle size, temperature, and time and the best polyols were monitored by FTIR-ATR. Polyurethane foams were made with internal and external bark liquefied materials by polymerization with isocyanate, a catalyst and water as blowing agent. Results showed that EB has a higher extractive and lignin content, while IB contains more cellu-lose. Liquefaction yields were higher for IB (74%) than EB (68%), with IB yielding polyols that produced stronger and more resilient foams. Mechanical properties of the foams were influenced by the NCO/OH ratio and catalyst levels. Overall, the internal bark demonstrated superior performance for foam production, highlighting its potential as an eco-friendly alternative to petroleum-derived materials.

Situational awareness detection and characterization of mental states have a vital role in medicine and many other fields. An electroencephalogram (EEG) is one of the most effective tools for identifying and analyzing cognitive stress. Yet, the measurement, interpretation, and classification of EEG sensors is a challenging task. This study introduces a novel machine learning-based approach to assist in evaluating situational awareness detection using EEG signals and spiking neural networks (SNNs) based on a unique spike-continuous-time-neuron (SCTN). The implemented biologically inspired SNN architecture is used for effective EEG feature extraction by applying time-frequency analysis techniques and allows adept detection and analysis of the various frequency components embedded in the different EEG sub-bands. The EEG signal undergoes encoding into spikes and is then fed into an SNN model which is well-suited to the serial sequence order of the EEG data. We utilize the SCTN-based resonator for EEG feature extraction in the frequency domain which demonstrates a high correlation with the classical FFT features. A new SCTN-based 2-D neural network is introduced for efficient EEG feature mapping aiming to achieve a spatial representation of each EEG sub-band. To validate and evaluate the performance of the proposed approach a common publicly available EEG dataset is used. Experimental results show that by using the extracted EEG frequencies features and the SCTN-based SNN classifier the mental state can be accurately classified with an average accuracy of 96.8% for the common EEG dataset. Our proposed method outperforms existing machine learning-based methods and demonstrates the advantages of using SNN for situational awareness detection and mental state classifications.

Optimal upper extremity motor control and range of motion are necessary to achieve even the basic Activities of Daily Living (ADL) function. Stroke, with resulting hemiparesis, can significantly and negatively impact an individual’s ADL function. Functional Independence Measure (FIM) self-care score can provide assessment of what aspects and to what degree ADL functions are impaired. FIM self-care assessment can also track changes in ADL function during stroke recovery and rehabilitation. Recently, sensor-acquired 3D motion analysis of stroke patients’ upper extremity has shown promise as a potential alternative to assess ADL function. This observational study evaluates whether the sensor-acquired upper extremity reachable workspace (RWS) measure correlates with clinician-evaluated FIM self-care score in stroke patients. Seventeen patients with stroke were enrolled in the study. FIM self-care, NeuroQoL upper extremity, and reachable workspace outcome measures (relative surface area, RSA) were collected upon rehabilitation hospital admission, at discharge, and at 3-month visit. Pearson and Spearman’s rank correlation coefficients as well as multiple linear regression analyses were used to determine the relationships between FIM self-care, NeuroQoL, and reachable workspace RSAs. Moderately strong correlation between total reachable workspace RSA and total FIM self-care score at discharge and at 3-month were noted (r=0.619, r=0.661, p&lt;0.05), and similarly strong correlation was also noted with the upper extremity NeuroQoL total score (r=0.690, r=0.815, p&lt;0.05). Multiple linear regression analyses revealed a change in bilateral total RSA of 0.1 unit from admission to 3-month follow up correlated with respective change in FIM self-care score of 2.011 points (95%CI: 0.663-3.360). Longitudinal improvement in ADL function during stroke rehabilitation and recovery process is correlated with improvement in reachable workspace.

This paper presents an 8x8 channel optoelectronic readout array (ORA) realized in a 180-nm CMOS process for the applications of short-range light detection and ranging (LiDAR) sensors. We propose several circuit techniques in this work including an amplitude-to-voltage (A2V) converter that reduces the notorious walk-errors by intensity compensation, and a time-to-voltage (T2V) converter that acquires the linear slope of the output signals by exploiting a charging circuit, thus extending the input dynamic range significantly from 5 uApp to 1.1 mApp, i.e. 46.8 dB. These results correspond to the maximum detection range of 8.2 meters via the action of the A2V converter and the minimum detection range of 56 centimeters with the aid of the proposed T2V converter. Optical measurements utilizing an 850-nm laser diode confirm that the proposed 8 x 8 ORA with 64 on-chip avalanche photodiodes (APDs) can successfully recover the narrow 5-ns light pulses even at the shortest distance of 56 centimeters. Hence, this work provides a potential CMOS solution for low-cost, low-power short-range LiDAR sensors.

Peri-implant infective diseases seem to be the most common cause for the late dental implant failure. Therefore, peri-implant microbiota has been intensively studied in modern implantology. Many studies showed a presence of extremely varied microflora in cases of peri-implant infective diseases. The main scope of this observational single-blind study was to analyze the presence of some microbial species in a group of patients with clinical signs of peri-implant mucositis compared to two other groups (patients treated with dental implants but without any clinical signs of peri-implant inflammation and patients treated with no dental implants). The identification of microorganisms was followed by an antibiogram in order to find their sensitivity to various antibiotics. The results showed a microbiota not necessarily specific for each situation (peri-implant infective diseases, healthy tissues around dental implants and healthy natural teeth and gums) with species like Candida albicans, Neisseria mucosa, Prevotella, Staphylococcus aureus, Streptococcus and others. Peri-implant infective diseases presented more bacterial species than other conditions.

In this study, in order to determine the effect of matrices shape on percolation threshold values, computer simulations were performed using the Monte Carlo method for a 200x200 square-shaped matrix and rectangular matrices containing the same number of nodes as the square matrix. Based on the simulations, the average values of percolation thresholds and standard deviations for current flow along and across the matrices were determined. It was determined that for a square-shaped matrix, the average values of percolation thresholds in both directions of current flow were the same. Extending the rectangular matrix while reducing its height causes the average value of the percolation threshold in the direction of current flow along the matrix to increase from 0.592740 to 0.759847, while in the transverse direction it decreases from 0.592664 to 0.403614. The values of the classical asymmetry coefficients of the probability distributions of percolation thresholds for both directions of current flow were determined. Histograms of the probability distributions of percolation threshold values for square-shaped matrix and rectangular matrices were made and compared with normal distributions. It was found that the occurrence of two percolation thresholds in rectangular layers should be considered when analyzing electrical conductivity measurements of nanocomposite thin films.

Over 3.0 billion people of the 7.75 billion globally suffer from malnutrition due to deficiencies in micronutrients, particularly iron, zinc, and vitamins (such as vitamin A, B complex, E, and folate, etc.). In response, there is growing consumer interest in foods that provide traditional nutrients and contain additional compounds beneficial to health and well-being. Wheat is the most important cereal crop in terms of both production and consumption, contributing about 30% of total cereal consumption worldwide. It provides a significant source of carbohydrates and gluten protein (around 12-14% protein), which supply substantial energy. Wheat grain is also rich in various nutrients, such as macro-elements like phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg); microelements like iron (Fe), zinc (Zn), manganese (Mn), and vitamins B complex: thiamin (B1), riboflavin (B2), niacin (B3), pantothenic acid (B5), pyridoxine (B6), biotin (B7), folate (B9), additionally soluble fiber and other trace minerals which are higher than in other major cereals. The inner bran coats, phosphates, and other mineral salts in wheat contribute to its beneficial effects on bowel movements. Despite its nutritional benefits, the quality of wheat's nutrients is a topic of ongoing discussion. Exploring ways to enhance the nutritional quality of wheat could provide valuable insights for scientists working to improve global nutrition status.

This study identified the effects of substituting fishmeal (FM) with soy protein concentrate (SPC) supplemented with lysine and methionine in the olive flounder (Paralichthys olivaceus) diet on the expression of genes related to growth, stress, immunity, and digestion. Three experimental diets were prepared; the control diet (Con) contained 60% FM, and 25% (SPC25) and 50% (SPC50) FM were replaced by SPC. After the 140-day feeding period, weight gain, specific growth rate, feed availability, biological indices and chemical composition of the muscle were not significantly affected by the dietary treatments. The gene expression levels of growth-related genes (IGF, GFB-3), immune-related genes (IL-8 and caspase), stress-related genes (SOD, GPX, PRX, TRX) were higher in Con fish than in those fed the SPC25 and SPC50 diets. Chymo-TRY2, TRY2 and TRY3 in the stomach of fish fed the SPC25 diet were higher than fish fed the Con or SPC50 diets. TRY2 and lipase of fish fed the SPC25 diet were higher than those of fish fed the other diets but TRY3 was not significantly affected by the treatments in the intestine. In conclusion, substituting 25% FM with SPC was the most effective for the expression of growth, immune, stress, and digestive enzyme genes.

After decades of studies, the pathogenesis of Autism Spectrum Disorder (ASD) remains unclear and there still lackseffective treatment strategies for core symptoms. Our recent work, combined with others’ research in a diversity ofmodels and clinical trials, suggested a concise, but potentially rational theory that can illustrate the key of the pathogenesis and treatment of ASD from the aspect of damage control.

This study presents a reproducible and scalable method for synthesizing silica nanoparticles (SNPs) with controlled sizes below 200 nm, achieved by systematically varying three key reaction parameters: ammonium hydroxide concentration, water concentration, and temperature. SNPs with high monodispersity and controlled dimensions were produced by optimizing these factors. The results indicated a direct correlation between ammonium hydroxide concentration and particle size, while higher temperatures resulted in smaller particles with increased polydispersity. Water concentration also influenced particle size, with a quadratic relationship observed. This method provides a robust approach for tailoring SNP sizes, with significant implications for biomedical applications, particularly in drug delivery and diagnostics. Using eco-friendly solvents such as ethanol further enhances the sustainability and cost-effectiveness of the process.

Background: This study was designed to adapt the Delis-Kaplan Executive System (D-KEFS) version of the Color-Word Interference (CWIT) and Trail making Tests (TMT) in Greek adult population from 20 to 49 years old, since it is of research as well as clinical importance to detect executive functions’ impairment in young adults with neurological or/and psychiatric conditions. Aims: Norms for the Greek adult population have been calculated to be available for neuropsychologists and health professionals who work in relevant settings. Methods: The study sample consisted of 101 healthy adults (41% male and 60% female), aged 20 to 49 years (M=32.16, SD=11.57) with education from 12 to 19 years of schooling (M=14.51, SD= .89). Pearson correlation test as well as chi square test were conducted to examine potential associations between gender, age, education, and participants’ performance. Afterwards, we calculated normative data using raw scores and transformed them to percentile scores. Finally, Greek norms were compared to the original raw scores, which were transformed in scaled scores by Delis et al. (2001). Results: The findings showed that age was the only variable which affected CWIT, whereas level of education as well as age were predictive factors for most TMT conditions, except for the visual scanning test (Condition 1). Gender did not affect both tests. Finally, D-KEFS norms for CWIT and TMT are available for Greek adult population to help clinicians detect executive functions’ deficits and therefore adjust tailored therapeutic strategies. Additionally, it is of great importance to use these tests for research purposes. Conclusion: Given that executive functions are assumed as high- level skills, which are highly related to everyday functionality, adapted tests contribute not only to assess the progression of any existing neurological as well as psychiatric disorders, but they can also be used to evaluate patients’ ability to live independently, as well as their access to work.

Glioblastoma (GBM) is the most common malignant primary brain tumor, with a median survival rate of less than two years. Currently, there is no cure for GBM, underscoring the urgent need for innovative treatment approaches. Vaccine design emerges as a crucial strategy, offering a safe and effective means for both preventive and therapeutic interventions against GBM. In this study, we targeted four GBM-associated mutated surface proteins—urokinase plasminogen activator sur-face receptor (PLAUR), integrin beta-3 (ITGB3), and the B-41 alpha chain (HLA-B) and A-24 alpha chain (HLA-A) of the HLA class I histocompatibility antigens—to design a peptide-based vaccine. The vaccine construct includes cytotoxic T lymphocyte (CTL) and T helper cell (Th cell) epitopes, and was meticulously evaluated for antigenicity, allergenicity, and toxicity. The results indicate that the vaccine is antigenic and non-allergenic, making it a promising candidate. Additionally, the physico-chemical properties of the vaccine suggest stability and suitability for further de-velopment. Immune simulation studies predict efficient immune responses upon vaccine ad-ministration. Our vaccine shows promise as a potential tool in the fight against GBM, offering new hope for patients facing this devastating disease.

The inhibition of kynurenine aminotransferase II (KATII) is a target for treating several diseases with an excess of kynurenic acid (KYNA). KATII inhibitors exist, but they have adverse side effects, given their irreversible inhibition mechanism. This study aimed to discover potent and safe inhibitors of KATII by using computational and in vitro methods. We identified herbacetin and (-)-Epicatechin as reversible KATII inhibitors from a small library of natural compounds, whose Glide docking scores (-8.66 kcal mol-1 and-8.16 kcal mol-1, respectively) and MM/GBSA binding energies (-50.30 kcal mol-1 and -51.35 kcal mol-1, respectively), which are higher scores than the standard inhibitor, PF-04859989 (-7.12 kcal mol-1 and -38.41 kcal mol-1, respectively). The ADMET analysis showed that herbacetin and (-)-Epicatechin have suitable pharmacokinetic parameters, are moderately bioavailable, and have a safe toxicity profile. Their KATII inhibitory activity is in the range of IC50 values of 5.98± 0.18 µM and 8.76 ± 0.76 µM, respectively. Furthermore, the MTT assay for cell toxicity showed that the two compounds do not alter HepG2 cell viability at their required concentration for KATII inhibition. These results show that herbacetin and(-)-Epicatechin are suitable for KATII inhibition and appropriate candidates for further development of KATII inhibitors.

Pain diagnosis remains a challenging task due to its subjective nature, the variability in pain expression among individuals, and the difficult assessment of the underlying biopsychosocial factors. In this complex scenario, artificial intelligence (AI) can offer the potential to enhance diagnostic accuracy, predict treatment outcomes, and personalize pain management strategies. This review aims at dissecting the current literature on computer-aided diagnosis methods. It also discusses how AI-driven diagnostic methods can be integrated into multimodal models that combine various data sources, such as facial expression analysis, neuroimaging, and physiological signals, with advanced AI techniques. Despite the significant advancements in AI technology, its widespread adoption in clinical settings faces crucial challenges. Ethical considerations related to patient privacy, biases, and lack of reliability and generalizability are the main issues. Furthermore, there is a need for high-quality real-word validation as well as the development of standardized protocols and policy rules to guide the implementation of these technologies in diverse clinical settings.

Integrated nutrient management (INM) is an approach that optimizes the benefits derived from various nutrient sources and beneficial soil microorganisms in an integrated manner to increase crop productivity while maintaining soil fertility. An open-field experiment was conducted at Cabuyao, Laguna, Philippines to investigate the effects of combining chemical fertilizers, vermicompost, arbuscular mycorrhizal fungi (AMF), and nitrogen-fixing bacteria (Azospirillum spp.) on the growth, nutrient uptake and yield of tomato. The experiment was arranged in a randomized complete block design replicated four times. The treatments were recommended rate of chemical fertilizer (RRC), combined 50% RRC and 4 t ha-1 vermicompost (INM 1), AMF inoculation with combined nutrient sources (INM 2), and dual inoculation with AMF and Azospirillum in synergy with combined nutrient sources (INM 3). Results revealed that the growth parameters were not significantly affected by the treatments, but the highest data were observed in the treatments under INM. AMF inoculation significantly increased phosphorus, potassium, calcium, and magnesium uptake of tomato while INM 3 recorded the highest nitrogen uptake. Treatments with vermicompost application recorded significantly higher uptake of copper. INM 1, INM 2, and INM 3 gave a significantly higher fruit yield than the RRC with an increase of 7.98%, 13.40%, and 13.65%, respectively. The percentage of mycorrhizal root colonization and number of rhizosphere spores were higher in mycorrhizal plants. Fruit yield and AMF root colonization are positively correlated with uptake of various nutrients. The results imply that these INM strategies can improve growth of tomato, increase fruit yield, reduce the amount of chemical fertilizers applied, and potentially lead to economic, soil health and environmental benefits.

Continuous-flow microwave heating stands out for its ability to rapidly and uniformly heat substances, making it widely applicable in chemical production. However, in practical applications, the permittivity of the heated liquid changes dramatically as the reaction progresses, affecting the efficiency and uniformity of continuous-flow heating. Herein, this work presents a novel microwave heating device based on coaxial structure for high-performance heating. Our approach commenced with the development of a multi-physical field model, incorporating a spiral polytetrafluoroethylene (PTFE) as a water channel and the coaxial waveguide as a container was established. The analysis shows that the uniform distribution of the sectional electric field of electromagnetic waves in the TEM mode within the coaxial structure can enhance heating uniformity. Then, a continuous-flow microwave heating system for different liquid loads was established, and experimental measurements were conducted. The heating efficiency for all loads exceeded 90%, which basely matched the simulation results, validating the accuracy of the model. Finally, the heating efficiency and uniformity under different permittivity loads were analyzed, as well as the impact of channel radius on heating efficiency. The device demonstrated high heating efficiency and stable heating uniformity across different loads. This continuous-flow microwave device is suitable for chemical research and production, contributing to the promotion of microwave energy applications in the chemical industry.

With the significant annual increase in the market demand for biopesticides, the industrial production demand for predatory mites, which hold the largest market share among biopesticides, has also been rising.To achieve efficient and low energy consumption control of predatory mite breeding environmental parameters, accurate estimation of breeding environmental parameters is necessary. This paper collects and pre-processing hourly time-series data of temperature and humidity from industrial breeding environments. Time series prediction models such as SVR, LSTM, GRU, and LSTNet are applied to model and predict historical data of the breeding environment. Experiments validate that the LSTNet model is more suitable for such environmental modeling. To further improve prediction accuracy, the training data for the LSTNet model is enhanced using hierarchical clustering of time-series features. After augmentation, the root mean square error (RMSE) of temperature prediction decreased by 27.3%, and the RMSE of humidity prediction decreased by 32.8%, significantly improving the accuracy of multi-step predictions and providing substantial industrial application value.

Of the 100,000 railroad bridges in the United States, 50% are over 100 years old. Many of these bridges do not meet the minimum vertical clearance standards, making them susceptible to impacts from over-height vehicles. Impacts can cause structural damage and unwanted disruption to railroad bridge services; rapid notification of the railroad authorities is crucial to ensure that the bridges are safe for continued use and to affect timely repairs. Therefore, researchers have developed approaches to identify these impacts to railroad bridges. Some recent approaches use machine learning to more effectively identify impacts from the sensor data. Typically, the collected sensor data is transmitted to a central location for processing. However, the challenge with this centralized approach is that the transfer of data to a central location can take considerable time, which is undesirable for time-sensitive events like impact detection that require rapid assessment and response to potential damage. To address the challenges posed by the centralized approach, this study develops a framework for edge implementation of machine learning predictions on wireless smart sensors. Wireless sensors are used because of their ease of installation and lower costs compared to their wired counterparts. The framework is implemented on the Xnode wireless smart sensor platform, thus bringing artificial intelligence models directly to the sensor nodes and eliminating the need to transfer data to a central location for processing. This framework is demonstrated using data obtained from events on a railroad bridge near Chicago; results illustrate the efficacy of the proposed edge computing framework for such time-sensitive structural health monitoring applications.

Background: Anxiety disorders can be both an independent risk factor of many somatic conditions and the cause of complications of various organic disorders leading to poor prognosis. Objectives: The purpose of this study is to compare the heart rate variability (HRV) recorded both during the day and at night in patients with anxiety disorders and healthy individuals. Methods: The study was conducted on fifty consecutive outpatients with anxiety disorders who participated in intensive group psychotherapy. The diagnosis was made according to DSM-IV-TR criteria using the PSE-10 questionnaire. Out of 50 patients under study, 17 were diagnosed with panic disorder (PD) and 21 with generalized anxiety disorder (GAD). The control group consisted of 40 healthy people. The resulting three groups were compared in terms of circadian HRV using the Oxford Medilog Suprima Holter System. Results: The HRV analysis showed the nocturnal falls in the time and frequency parameters related to parasympathetic activity (rMSSD, pNN50, HF). The most unfavourable alterations were observed in the PD group, which comprised the disappearance of day vs. night amplitude of the majority of HRV parameters. Hence, the PD group was particularly susceptible to the arrhythmic events. It was noticeable that nocturnal vagotonia in the PD group was significantly lower as compared with the GAD and control group. Conclusions: The innovative aspect of the present study was to find a distinct profile of HRV with regard to the types of anxiety disorders because of the existing considerable differences between the nature of PD and GAD.

This study investigates the impact of vertical greenery systems (VGS) applied to heavyweight walls on indoor thermal conditions in a building module situated in the Mediterranean climate of Catania, Italy. Using dynamic simulations in TRNSYS, the research compares the thermal behavior of walls constructed from hollow clay blocks (Poroton) and lava stone blocks with a lightweight wall setup already in place at the University of Catania. The primary focus is on evaluating the VGS’s effect on reducing peak inner surface temperatures and moderating heat flux fluctuations entering the building. The findings indicate that adding a vertical greenery layer to heavyweight walls can decrease the peak inner surface temperature by up to 1.0°C compared to the same wall without vegetation. However, the greenery’s impact on mitigating heat flux fluctuations is less pronounced than in the case of the lightweight wall. This research underscores the potential of green facades in enhancing indoor thermal comfort in regions with climates similar to the Mediterranean, providing valuable insights for sustainable building design and urban planning.

This study aims to propose a method for constructing basic digital twin data in South Korea by adhering to international standards and utilizing publicly available data. Specifically, the study focuses on designing and proposing a digital twin data model for buildings, which are the most applicable among the basic digital twin data. To achieve this, the first section provides essential background information, introduces concepts and requirements related to digital twin basic data, and offers a brief overview of CityGML. The second section explains the methodology and data used in this study. The third section presents the main findings: the selection of public data (buildings) for constructing digital twin basic data, the mapping process using CityGML, and the creation of UML diagrams. The fourth section discusses these findings. Finally, the conclusion and suggestions for future research are provided. This approach enhances the accuracy of building-related digital twin data and supports the use of digital twin services in both public and private sectors by enabling various spatial analyses.

Shape memory alloys (SMA) are becoming a more important factor in actuation technology. Due to their unique features, they bring the potential to safe weight, installation space and reduce energy consumption. The system integration of the generally small diameter NiTi wires is an important cornerstone for the emerging technology. Crimping, a common method for the mechanical and electrical connection of SMA wires, has several drawbacks when it comes to miniaturization and high force outputs. For high force applications for example, multiple SMA wires in parallel are needed to keep actuation frequencies high, while scaling up the actuation force. To meet these challenges, the proposed study deals with the development of a resistance welding process for manufacturing NiTi wire bundles. The wires are welded to sheet metal substrate, resulting in promising functional properties and high joint strengths. The welding process benefits from low cost, easy to control parameters and good automation potential. A method for evaluating the resistance welding process parameters is presented. With these parameters in place, a manufacturing process for bundled wire actuators is discussed and implemented. The welded joints are examined by peel tests, microscopy, and fatigue experiments. The performance of the manufactured bundle actuators is demonstrated by comparison to a single wire with the same accumulated cross-sectional area.

Oxidative stress plays an essential role in the pathological processes that trigger various chronic liver diseases and contributes to the development of hepatocarcinogenesis. Antioxidants from natural sources trap free radicals by converting them into non-harmful products and minimize oxidative stress, playing a crucial function in the treatment of free radical-induced diseases. However, the activity of these antioxidants is limited due their poor bioavailability, which is why various nano-drug delivery systems have been developed to improve their stability, promote smooth release, and targeted delivery for liver cancer treatment. Specifically, this review aims to report recent scientific findings of novel nano-formulations containing natural antioxidant molecules targeted for the treatment of liver cancer.

Genetic similarity determines the extent to which two genotypes share common genetic material. It can be measured in various ways, such as comparing DNA sequences, proteins, or other genetic markers. The significance of genetic similarity is multifaceted and encompasses various fields, including evolutionary biology, medicine, forensic science, animal and plant breeding, and anthropology. Genetic similarity is an important concept with wide application across different scientific disciplines. In the presented study, six measures for calculating genetic similarity were compared: Euclidean, Jaccard, Kulczyński, Sokal and Michener, Nei, and Rogers. The research material included 21 rapeseed genotypes (ten interspecific Brassicaceae hybrids of F2 generation, and 11 of their parental forms) and 146 alleles, obtained using 21 ISSR molecular markers. The results indicate that the research material was divided into two identical groups using any of the proposed methods, despite differences in the values of genetic similarity coefficients. Two of the presented measures of genetic similarity (Sokal and Michener, and Euclidean) were the same.

The breast holds significant importance in women's lactation process. The breast has diverse tissues such as glandular, Cooper’s ligament, lactiferous ducts, adipose, muscles, and skin with various mechanical properties. Most soft tissues exhibit nonlinear, viscoelastic, and anisotropic mechanical behaviour. Stiffness is one of the commonly used mechanical properties for measuring mechanical behaviour in soft tissues. However, this is typically used in simplifying cases where analysis is limited to within the linear elastic region. Young’s modulus is the prevailing measure for quantifying stiffness in tissues. This systematic review examines commonly used techniques for mechanically characterising breast soft tissues to close the gap in the literature. Additionally, this systematic review summarises the mechanical behaviour of the breast tissues available in the literature and highlights the uncovered areas to understand the mechanical behaviour of breast tissues fully. These include a) more mechanical behaviour studies, allowing more accurate modelling for other applications, b) Viscoelastic analysis studies, and c) mechanical analysis on other breast tissues, such as the lactiferous duct, also known as the milk duct. The data obtained in the review can be used as inputs in computational models to analyse the behaviour of the breast tissues.

Electric tractors have many advantages, including high torque, excellent controllability, energy efficiency, simple structure, and electric interface for expansion. However, a significant limitation lies in their endurance. This study presents the design of an extended-range power supply system to ensure continuous endurance of electric tractor. The objective is to provide a continuous power source for our self-developed electric tractor while preserving the benefits of electric propulsion. Extended-range power systems utilize a primary mover, typically an oil-fueled internal combustion engine, to drive the generator for electricity generation, and the generated AC form electricity is subsequently converted into stable DC bus voltage by a power electronic converter. The hardware and control design of the extended-range power supply system have been finalized and validated through experimental trials. Results demonstrate the system’s capability to sustain a stable DC bus voltage amidst disruptions such as sudden load shifts and fluctuations in prime mover speed. Even with 50% sudden load change, the voltage drop is within 12% and could recover to ±3% within 4 seconds. The range extender can be used alone without battery to power the electric tractor, or it can used in parallel with other extended-range extenders or battery thanks with the droop control ability for power sharing.

This study focuses on predicting PM2.5 levels at the University of Petroșani by employing advanced machine learning techniques to analyze a dataset that encapsulates a wide array of air pollutants and meteorological factors. Utilizing data from IoT sensors and established environmental monitoring stations, the research leverages Random Forest, Gradient Boosting Machines, and Support Vector Regression models to forecast air quality, emphasizing the complex interplay between various pollutants. The models demonstrate varying degrees of accuracy, with the Random Forest model achieving the highest predictive power, indicated by an R2 score of 0.82764. Our findings highlight the significant impact of specific pollutants such as NO, NO2, and CO on PM2.5 levels, suggesting targeted mitigation strategies could enhance local air quality. Additionally, the study explores the role of temporal dynamics in pollution trends, employing time-series analysis to further refine the predictive accuracy. This research contributes to the field of environmental science by providing a nuanced understanding of air quality fluctuations in a university setting and offering a replicable model for similar environments seeking to reduce airborne pollutants and protect public health.

The proliferation of fake news on social media platforms poses significant challenges to information authenticity and public trust. In this study, the impact of various word embedding techniques on the performance of both machine learning and deep learning models for fake news detection is systematically evaluated. The comprehensive TruthSeeker dataset, which spans over a decade of labeled news articles and social media posts, is utilized. Three popular word embedding techniques are analyzed: TF-IDF, Word2Vec, and FastText. These embeddings were tested with multiple machine learning classifiers, including logistic regression, naive Bayes, K-nearest neighbors, support vector machines, multilayer perceptrons, decision trees, and random forests, as well as deep learning models, specifically convolutional neural networks (CNNs). Among machine learning models, the Support Vector Machine (SVM) with TF-IDF achieved the highest accuracy of 99.03%, precision of 98.92%, recall of 99.15%, and F1 score of 99.03%. The Multilayer Perceptron (MLP) also demonstrated strong performance with Word2Vec, reaching an accuracy of 95.24%, precision of 94.95%, recall of 95.50%, and F1 score of 95.07%. For deep learning models, CNNs combined with TF-IDF embeddings showed outstanding results, achieving an accuracy of 98.99%, precision of 98.56%, recall of 99.48%, and F1 score of 99.02%. These results highlight the critical role of word embeddings in enhancing model accuracy and reliability in misinformation detection.

The goal is the optimal synthesis of the parameters of the control law for a nonlinear satellite attitude control system under constraints related to the saturation effect of reaction wheels. The maximum stability degree of the control system is considered as a criterion for the optimality of the parameters of the control law, and the absolute values of the control torques and angular velocities of the reaction wheels, achievable within the limits of the physical characteristics of the drives, are considered as constraints. The solution to the optimal synthesis problem is based on the transformation of the nonlinear control system model into a linear model, within the framework of the problem of global asymptotic stability of the control system is also guaranteed. The problem is decomposed into a subproblems of obtaining the optimal transient process shape in the control system and considering the constraints. A method of synthesizing the optimal transient process shape based on the criterion of maximum stability degree had been developed. An analytical method of determining the optimal values of the control law parameters, based on decomposing the normalized characteristic polynomial in the linear model into three identical factors had been proposed. A method with regards to the constraints of the control torque and angular speed of the reaction wheels, based on transitioning from relative to real time is developed. An algorithm for calculation of the transition scale from relative to real time, ensuring the fulfillment of all constraints in the optimal synthesis problem, is proposed. An example demonstrating the efficiency and simplicity of the proposed methods and algorithm for targeted selection of technical characteristics of the control system, considering the maximum absolute values of angular speeds and control torques of the reaction wheels, is provided.

In this work, we propose an in-house simulation tool designed for the analysis and optimization of bifacial photovoltaic (PV) modules, which are currently under the spotlight in the renewable energy scenario. The tool is conceived to support researchers and engineers by providing fast and accurate predictions of the PV module yield under various operating and environmental conditions. For a chosen geographical site, the impact of module orientation, tilt, albedo, sky conditions, ambient temperature, and so on, can be effortlessly determined. In case of nonuniformity across the cells dictated by localized architectural shading, dirt, bird drops, defects, a circuit-based cell-level approach can be activated to compute the module production. An extensive simulation campaign is performed by assuming that the panels are installed in Naples without loss of generality. Results are shown to give detailed insights into the performance of bifacial modules, thus providing unambiguous guidelines for their correct installation. Further analyses are conducted to demonstrate the tool capability to quantify the detrimental influence of a poorly-irradiated cell on the backside, as well as of cracked cells.

This paper introduces a novel decision-making approach grounded in insights into human visual perception of change. Modern technologies such as internet of things (IoT) provide us with large amounts of sensor data that need to be processed in real time and decisions made with a high degree of accuracy and reliability. Artificial intelligence (AI) methods are welcome in this context and need to be upgraded to meet actual challenges. While modern computing capabilities facilitate rapid data processing, the real-time demands of vast sensor data necessitate swift responses across the cyber chain, often leading to compromises in solution quality to circumvent combinatorial search complexities. Determining the adequacy of a solution entails varied approaches, often relying on heuristic methodologies. We illustrate our original approach with an example of a selected detail of a differential evolution algorithm, where we have to make a decision to adopt the best solution so far. We propose an approach inspired by human perceptual features that exploits the Weber-Fechner law to emulate human judgements, and offers a promising way to improve decision making in AI applications and real-time requirements fulfillment. Our proposed methodology demonstrates applicability across diverse AI scenarios involving numerical data, effectively mirroring human perception abilities.

Transition metal dichalcogenides (TMDs) monolayers exhibit unique valleytronics properties due to the dependency of the coupled valley and spin state at the hexagonal corner of the first Brillouin zone. Precisely controlling valley spin-polarization via manipulating the electron population enables its application in valley-based memory or quantum technologies. This study uncovered the uncompensated spins of the antiferromagnetic oxide (NiO) serving as the ferromagnetic (FM) order to induce valley spin-polarization in molybdenum disulfide (MoS₂) monolayers via the magnetic proximity effect (MPE). A spin-resolved photoluminescence spectroscopy (SR-PL) was employed to observe MoS₂, where the spin-polarized trions appear to be responsible for the MPE, leading to a valley magnetism. Results indicate that local FM order from the uncompensated surface of NiO could successfully induce significant valley spin-polarization in MoS₂ with the depolarization temperature approximately at 100 K, which is relatively high compared to related literature. This study reveals new perspectives and potential of AFM materials in the field of exchange-coupled van der Waals heterostructures.

Traditional 3D reconstruction models have consistently encountered a challenge: attaining high recall of object edges while preserving precision. In this paper, we introduce a post-processing method Multi-Head Attention Refiner (MA-R) aimed at tackling this challenge by integrating a multi-head attention mechanism within the U-Net style refiner module. The 3D reconstruction model applying this method demonstrates excellence in parsing intricate image details, resulting in significant enhancement of boundary predictions and increased recall rates. In our experiments, our method significantly enhances the reconstruction performance of Pix2Vox++ when multiple images are utilized as input. Specifically, it achieves an enhanced Intersection Over Union score of approximately 1.1% compared to the original Pix2Vox++ model when 16-view images are employed.

Structural and functional materials are subjected to harsh oxidizing environments that lead to a rapid decline in their surface chemical stability. At high-temperatures, the diffusion of atoms is erratic; thus, the reactive elements (REs) get a chance to migrate to the surface and look for oxygen-reactive sites. Their migration from the lattice sites creates vacancies in the matrix and thus weakens the bond, making the material prone to environmental degrading factors. Subsequently, the inward diffusion of oxygen, sulfur, nitrogen, or carbon may occupy these vacancies, resulting in undesired precipitation at the grain boundary, which is usually brittle. The solubility and dissociation of attacking species and corrosion products play the most critical role in corrosion in high-temperature water. To evade such situations, Ti, Al, and Cr are added in Ni/Fe-based alloys, forming a dense oxide layer on the surface that impedes further surface migration of ions. The stability of the alloys is then determined by the capability of these oxide layers to heal or reform during spalling/cracking. This review attempts to understand the migration of REs at the interfaces to prepare structural materials in any unexpected set of oxidizing service conditions. This review will also try to establish the interaction mechanism of REs novel/conventionally added in the alloys with the oxidizing elements in the environments to design better degradation-resistant high-temperature alloys.

Vitiligo is a chronic autoimmune disease characterized by progressive depigmentation of the skin caused by the selective destruction of melanocytes. Although its etiopathogenesis remains unclear, multiple factors are believed to be involved, including genetic and metabolic factors linked to cellular oxidative stress, melanocyte adhesion defects, innate and adaptive immunity. The aim of this review is to provide an overview of the current state of knowledge regarding the role of different cellular mechanisms, including cytokines and chemokines interactions in the pathogenesis of vitiligo. Although there is no definitive cure for vitiligo, excessive progress has been made and several treatments have shown favourable results. A thorough understanding of the basis of the disease, uncover promising drug targets for future research, providing clinical researchers with valuable insights for developing improved treatment options.

In the ongoing shift towards electric vehicles (EVs) primarily utilizing lithium-ion battery technology, a significant population of hybrid electric vehicles (HEVs) remains operational, reliant on established NiMH battery systems. Over the last twenty years, these HEVs have generated a substantial quantity of NiMH batteries that are either inoperable, experiencing performance degradation, or approaching the end of their service life. This results in a twofold challenge: a growing volume of environmentally hazardous waste due to the difficulty of NiMH battery reclamation, and escalating maintenance costs for HEV owners necessitated by replacement battery purchases. Patent WO2015092107A1, published in 2015, proposed a "Method for regenerating NiMh batteries." This method claims the ability to restore NiMH batteries to their original functionality. However, a comprehensive review of relevant scientific literature fails to identify any empirical evidence supporting the efficacy of the regeneration technique detailed in the patent. Within this context, this paper presents a detailed analysis and evaluation of the feasibility of the aforementioned patented method. To achieve this objective, the authors propose a prototype constructed utilizing NiMH battery cells sourced from a Toyota Prius. The experimental results obtained from this prototype demonstrate the potential for battery regeneration using the proposed method. These findings offer a promising solution for mitigating the challenges associated with NiMH battery disposal and maintenance within the HEV domain.

Neurofibromas, rare benign tumors of the peripheral nerve sheath, present diagnostic challenges, particularly in diabetic patients with toe ulcers. This case involves a 55-year-old female with type 2 diabetes mellitus who developed an enlarging ulcer on her right second toe. The initial evaluation suggested a diabetic ulcer; however, advanced imaging revealed a mass-like lesion. Partial excision and biopsy confirmed a neurofibroma with spindle cells within the myxoid stroma and S100 protein expression. One month later, total excision and Z-plasty reconstruction were performed under general anesthesia. The patient’s postoperative recovery was uneventful, and the patient was discharged without complications. Follow-up revealed successful healing with no recurrence or functional issues. This case highlights the importance of considering neurofibromas in the differential diagnosis of diabetic toe ulcers to avoid misdiagnosis and ensure appropriate management.

Infantile Neuroaxonal Dystrophy (INAD) is a rare neurodegenerative disorder, affecting 1:1,000,000 population. It results from pathogenic variants in the PLA2G6 gene located on chromosome (22q13.1). The onset of symptoms usually occurs between 6 to 18 months, causing developmental regression leading to debilitating symptoms such as muscle weakness, dementia, and loss of basic skills. Eventually, it progresses to life-threatening symptoms including breathing difficulties, which limits the life expectancy to 5-10 years. While potential genetic therapy for treatment is are being developed, they are yet to be approved for use, and management remains essentially supportive. This case report is of a 9-year-old Pakistani girl with INAD. She presented with recurrent chest infections, developmental regression, loss of speech, paralysis, hypertension, and eventually breathing difficulties. Brain magnetic resonance imaging and genetic testing con-firmed the diagnosis. This case posed diagnostic challenges in view of its overlapping clinical presentation. Through this report, we aim to raise awareness about the condition among practitioners, outline the importance of genetic counseling in susceptible couples and suggest potential areas of further research.

This paper investigates five different GARCH models (GARCH, EGARCH, IGARCH, Component GARCH (CGARCH) and GJR-GARCH) along with six distributions (Normal, Student’s t, GED and their skewed forms), which are used to forecast the volatility for the Bulgarian Stock index SOFIX. We use a GARCH model to predict how much time it will take, after the latest crisis, for the SOFIX index to reach its historical peak once again. The empirical data covers the period between the years 2000 and 2024, including the 2008 financial crisis and the COVID-19 pandemic. The purpose is to answer which of the five models is the best at forecasting the volatility of SOFIX and which distribution is most appropriate. The results, based on BIC and AIC information criteria, show that the ARMA(1,1)-CGARCH(1,1) specification with the Student t-distribution is preferred for forecasting. With the help of 5000 simulations, it is estimated that the chances of Index SOFIX reaching its historical peak value of 1976.73 (08.10.2007) are higher than 90% at 13.08.2087.

The development of one technology can be portrayed by common methods like Gartner’s hype cycle or S-curve, however, there is no method to characterize the feature distributions of multiple technologies within a specific domain. This study proposes a big data-based method in terms of four proposed features, namely versatility, significance, commerciality, and disruptiveness, to characterize the technologies within a given domain. The features of technologies are quantitively portrayed using the representative keywords and volumes of returned search results from Google and Google Scholar in two-dimensional analytical spaces of technique and application. We demonstrate the applicability of this method using 452 technologies in the domain of intelligent robotics. The results of our assessment indicate that the versatility values are normally distributed, while the values of significance, commerciality, and disruptiveness follow power-law distributions, in which few technologies possess higher feature values. We also show that significant technologies are more likely to be commercialized or causing potential disruption, as such technologies have higher scores in these features. Further, we validly prove the robustness of our approach via comparing historical trends with literature and characterizing technologies in reduced analytical spaces. Our method can be widely applied in analyzing feature distributions of technologies in different domains, and it can potentially be exploited in decisions like investment, trade, and science policy.

Smartwatches are one of the most relevant fitness trends of the past two decades, and they collect increasing amounts of health and movement data. The accuracy of these data may be questionable and requires further investigation. Therefore, the aim of the present study is to validate smartwatches for use in triathlon training. Ten different smartwatches were tested for accuracy in measuring heart rate, distance (via global navigation satellite systems, GNSS), swim stroke rates and the number of swim laps in a 50 m Olympic-size pool. The optical heart rate measurement function of each smartwatch was compared to that of a chest strap. Thirty participants (15 females, 15 males) ran five 3-minute intervals on a motorised treadmill to evaluate the accuracy of the heart rate measurements. Moreover, for each smartwatch, running und cycling distance tracking was tested over six runs of 4,000 m on a 400 m tartan stadium track, six hilly outdoor runs over 3.4 km, and four repetitions of a 36.8 km road bike course, respectively. Three swimming protocols ranging from 200 m to 400 m were performed in triplicate in a 50 m Olympic-size pool, evaluating tracked distance and detected number of strokes. The mean absolute percentage errors (MAPEs) for the average heart rate measurements varied between 3.1% and 8.3%, with the coefficient of determination ranging from 0.22 to 0.79. MAPE results ranged from 0.8% to 12.1% for the 4,000 m run on the 400 m track, from 0.2% to 7.5% for the 3.4 km outdoor run, and from 0.0% to 4.2% for the 36.8 km bike ride. For the swimming tests, in contrast, the deviations from the true distance varied greatly, starting at a 0.0% MAPE for the 400 m freestyle and reaching 91.7% for the 200 m medley with style changes every 25 m. In summary, for some of the smartwatches, the measurement results deviated substantially from the true values. Measurements taken while road cycling over longer distances with only a few curves were in relative terms more accurate than for outdoor runs and even more accurate than for the 400 m track. In the swimming exercises, the accuracy of the measured distances was severely deteriorated by the medley changes among the majority of the smartwatches. Altogether, the results of this study should help in assessing the accuracy and thus suitability of smartwatches for general triathlon training.

Australia’s avian species are unique and diverse. Due to the dramatic changed in distribution, physiology, and behaviour caused by both changing climatic conditions and human activities, Australian bird species becomes highly vulnerable to health-related factors such as impact injury and vehicle trauma, as well as environmental-related stressors such as animal attack and abnormal behaviours. This is reflected in increasing number of avian patients received by wildlife hospitals. The most common outcome of avian species in wildlife hospitals was euthanasia, only few of them were released back into their ecosystem. During the process of bird rescue and rehabilitation, stress can be caused by various stressors such as injury, infection, trauma, shock and stressful environment in wildlife hospitals and rehabilitation centres. Therefore, in order to better understand the causes of the high rates of mortality and improve the management and conservation strategies, it is important to study the species responses to environmental changes. Similar with other species, in birds, the HPA axis plays an essential role in maintaining homeostasis by producing glucocorticoids (corticosterone in birds). In this study, we sampled a total of 30 individuals humanely euthanised at the Adelaide Koala and Wildlife Centre (AKWC), and noted their clinical information. Feather samples were collected from all 30 individuals, and then were processed and analysed for corticosterone levels using enzyme-immunoassay (EIA). The biological relevance of feather CORT assay was evaluated by comparing the absolute levels of CORT between birds. We also analysed the effects of different stressors by grouped the diagnosis into acute and chronic categories. Although results suggest that stress levels were variable between the diagnosis, no statistically significant difference was found between these two categories in this study due to the limited sample size. We also obtained patient records from the 2022-2023 period from AKWC for retrospective analysis. Records were categorized and analysed based on the diagnoses, species and admission dates. Retrospective analysis suggests that in 2022, the Rainbow Lorikeet had the highest admission rate (192/544, 35.29%), followed by Magpie (124/544, 22.79%). Most admissions occurred in Summer. Musculoskeletal injury was the most prevalent diagnosis across all species with Magpies having the highest rates (73.39%) across all diagnoses and Rainbow Lorikeets the second highest (36.46%). Beak and Feather disease (BFD) was another common diagnosis for lorikeets. Data collected at 2023 followed the similar trend. This is the first study that uses non-invasive method to determine stress levels of rescued Australian native avian species, aims at improving and implementing native avian species management and conservation initiatives. This research also provides a basis for future studies which allow for more detailed analyses of wild populations and a broader range of species.

Temperature Cycle Induced Deracemization (TCID) offers a promising approach to obtain enantiopure solids from racemic mixtures. By combining rapid racemization in solution and temperature swings, homochirality is theoretically achieved. Despite theoretical expectations of doubled yields compared to traditional chiral separation methods, such as in Preferential Crystallization, experimental validation remains lacking. We applied TCID to (1-(4-chlorophenyl)-4,4-dimethyl-2-(1H-1,2,4-triazol-1-yl) pentan-3-one) (Cl-TAK), introducing a post-TCID cooling step to enhance yield and a washing step to augment enantiopurity. This refinement yielded an 89.8% mass yield with 99.1% enantiomeric excess in the crystal phase (c.e.e.) within 24 hours on an 8.75g scale, showcasing improved performance with insignificant process duration extension. Additionally, we explored the stochasticity of deracemization, observing the development from low initial crystal enantiomeric excesses(1-6% c.e.e0) at a 2.5 g scale. Kinetic analysis revealed that a 2% c.e.e0 effectively mitigates chiral flipping risks and induction time in our system. Our study underscores the potential for reduced initial c.e.e. to expedite deracemization and presents a straightforward method to optimize yield and purity, facilitating industrial application.

Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from January 1, 2013, to December 22, 2023. Articles were collected using key search terms such as “artificial intelligence” and “machine learning.” Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. 8 studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.

Aim: This study aimed to evaluate the effectiveness of a plyometric training program designed to enhance agility, speed, and social interaction among boys with mild intellectual disability (ID) aged 10-12, within the Special Olympics framework. Participants: Thirty-two boys (mean age = 10.69 ± 0.8 years; mean IQ = 61 ± 7) from three inclusive education centers participated in the study. Methods: The intervention consisted of a 10-week plyometric training program, conducted twice a week. Pre- and post-intervention assessments included the Reaction Time Test, 4-Corner Agility Test, 10-Meter Speed Run, 4x5 Meter Relay Test, and the Friendship Activity Scale (FAS). Results: The study found significant improvements across all measures. Reaction time decreased by 0.83 seconds (t = 7.44, p < 0.001), 4-corner agility improved by 0.77 seconds (t = 11.93, p < 0.001), 4x5 meter speed improved by 0.61 m/s (t = 17.37, p < 0.001), and 10-meter run speed improved by 1.55 m/s (t = -12.76, p < 0.001). Additionally, the FAS score increased by 0.81 points (t = 9.59, p < 0.001), indicating enhanced social interactions. Conclusion: The plyometric training program significantly improved agility, speed, and social interaction in children with mild ID, demonstrating the effectiveness of inclusive, adaptive physical education strategies within the Special Olympics framework.

The aim of this study was to investigate the relationship between postprandial glycemic levels based on flash-monitoring and the production of intestinal hydrogen (H2) and methane (CH4) gases based on measurement of the amount of these gases in exhaled air. We studied 14 subjects with type 2 diabetes mellitus (T2DM) and 10 individuals without diabetes (control) with 3 food-load tests with simultaneous determination of gases in exhaled air and assessment of glucose levels. In 15.4% of patients with T2DM, there was a non-significant increase in exhaled H2. How-ever, all subjects in the control group had significant increase in exhaled H2. Moreover, the level of H2 in exhaled breathafter food load in patients with T2DM was lower than in normoglycemic subjects. Also, in normoglycemic subjects there was an inverse correlation between maximum glucose rise and maximum H2 in exhaled air after food load. At the same time in patients with T2DM the level of CH4 in exhaled air directly correlated with the parameters of postprandial glycemia in the test with lactulose. Confirmation of a causal relationship between decreased H2 production, increased intestinal CH4 production, and more severe postprandial glycemia may identify new therapeutic targets in the correction of postprandial glycemia in patients with T2DM.

Pedestrian trajectories are crucial for self-driving cars to plan their paths effectively. The sensors implanted in these self-driving vehicles, despite being state-of-the-art ones, often face inaccuracies in the perception of surrounding environments due to technical challenges for adverse weather conditions, interference from other vehicles sensors and electronic devices, and signal reception failure, leading to incompleteness in trajectory data. But for real-time decision making for autonomous driving, the trajectory imputation is no less crucial. Previous attempts to address this issue such as statistical inference and machine learning approaches have shown promise. Yet, the landscape of deep learning is rapidly evolving with new and more robust models emerging. In this research, we have proposed an encoder-decoder architecture, Human Trajectory Imputation Model, coined as HTIM, to tackle these challenges. This architecture aims to fill in the missing parts of pedestrian trajectories. The model has been evaluated using the Intersection drone inD dataset, containing trajectory data at suitable altitudes preserving naturalistic pedestrian behavior with varied dataset sizes. To assess the effectiveness of our model, we have utilized L1, MSE, Quantile and ADE loss. Our experiments have demonstrated that HTIM outperforms the majority of the state-of-the-art methods in this field, thus indicating its superior performance in imputing pedestrian trajectories.

A comprehensive modeling framework for the thermoforming of polymer matrix woven laminate composite was developed. Two numerical indicators, the slip path length and traction magnitude, have been identified to be positively correlated to matrix smearing and wrinkling defects. The material model has been calibrated with picture-frame experimental results, and the prediction accuracy for intra-ply shear and thickness distribution were examined with measurements on the physically formed parts. Finally, a parametric study was conducted to determine the relationship between various process parameters and the quality of the formed part. For the trapezoidal part, orienting the laminate at 45-degrees to the mold axis reduces the likelihood of matrix smear and wrinkling defects. Although this laminate orientation yielded a greater spatial variation of part thickness, the thickness deviation is lower than that for the 0-degree orientation case. Higher forming rates were found to result in more defects due to increased slip path lengths and traction magnitudes. Optimal forming speeds should balance rapid shaping within the operating temperature window while avoiding tear. It was discovered that shallower molds benefit from faster ramp rates, while deeper molds require slower rates to manage extensive shearing, stretching, and bending. Faster forming rates lead to smaller thickness increases at high intra-ply shear regions, indicating a shift from intra-ply shear to out-of-plane bending due to the visco-plastic effect of the molten laminate, and can negatively impact part quality. Lastly, it was shown that a well-conceived strategy using darts could improve the part quality by reducing the magnitude of the defect indicators.

Kisspeptin, a key neuropeptide derived from the KISS1 gene, is renowned for its critical role in regulating the hypothalamic-pituitary-gonadal axis and reproductive hormone secretion. Beyond its primary function in reproductive biology, emerging research has illuminated its influence in various cancers, mediating significant effects through its interaction with the G-protein coupled receptor, Kisspeptin receptor. This interaction has been implicated in modulating cellular pro-cesses such as proliferation and metastasis, making it a potential target for therapeutic interven-tion. Our study initially screened ten kisspeptin-10 analogs through cytotoxic effects of kisspep-tin-10 (KP10) and its analogs in several cancer types, including cervical, prostate, breast, and gastric cancers, with a particular focus on cervical cancer where the most profound effects were observed. Further exploration using kinase array assays revealed that these analogs specifically alter key kinases involved in cancer progression. Migration assays demonstrated a substantial decrease in cell motility, and Bioluminiscence Resonance Energy Transfer assays confirmed these analogs strong interactions with the kisspeptin receptor. Overall, our results indicate that these KP10 analogs not only hinder cervical cancer cell proliferation but also curtail migration through targeted modulation of kinase signaling, suggesting their potential as therapeutic ayfgents in managing cervical cancer progression. This comprehensive approach underscores the therapeutic promise of exploiting kisspeptin signaling in cancer treatment strategies.

Both bacteria product flagellin and macrophages are implicated in HIV infection/disease progression. However, the impact of their interaction on HIV infection and the associated mechanisms remain to be determined. We thus examined the effect of the flagellins on HIV infection of primary human macrophages. We observed that the pretreatment of macrophages with the flagellins from the different bacteria significantly inhibited HIV infection. The mechanistic investigation showed that the flagellin treatment of macrophages downregulated the major HIV entry receptors (CD4 and CCR5) and upregulated the CC chemokines (MIP-1α, MIP-1β, and RANTES), the ligands of CCR5. These effects of the flagellin could be compromised by a toll like receptor 5 (TLR5) antagonist. Given the important role of flagellin as a vaccine adjuvant in TLR5 activation-mediated immune regulation and in HIV infection of macrophages, future investigations are necessary to determine the in vivo impact of flagellin-TLR5 interaction on macrophage-mediated innate immunity against HIV infection and the effectiveness of flagellin adjuvant-based vaccines studies.

Feature selection is predominantly used in machine learning tasks, such as classification and regression. It selects a subset of features (relevant attributes of data points) from a larger set that contributes as optimally as possible to the informativeness of the model. There are exponentially many subsets of a given set, and thus, the exhaustive search is only practical for problems with at most a few dozen features. In the past, there have been attempts to reduce the search space using dynamic programming. However, models that consider similarity in pairs of features besides the quality of individual features do not provide the required optimal substructure. As a result, algorithms, which we will call suboptimal dynamic programming algorithms, find a solution that may deviate significantly from the optimal one. In this paper, we propose a dynamic programming algorithm with an iterative upgrade where the order of feature processing is inverted in each iteration. Such an alternating approach allows for improving the optimization function by using the score from the previous iteration to estimate the contribution of unprocessed features. The iterative process is proven to converge and terminate when the solution does not change in three successive iterations or when the number of iterations reaches the threshold. Results in more than 90 % of tests align with those of the exhaustive search, being competitive and often superior to the reference greedy approach. The validation was done by comparing the scores of output feature subsets and examining the accuracy of different classifiers learned on these features. In the context of feature selection, the proposed algorithm can be characterized as a filter method. However, we expect that the idea of alternating suboptimal optimization will soon be generalized to tasks beyond feature selection.

This work presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The control system will aim to prevent contaminated water from escaping the network, especially during periods of heavy rainfall, that can cause combined sewer overflows (CSO), while maintaining the flow of water towards the wastewater treatment plant (WWTP) as close as possible to its nominal value. The main purpose of the work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when there may be anomalies affecting the elements of the control system, mainly sensors and actuators. Comparing the results obtained considering various types of faults, with situations of normal controlled operation and with the behavior of the sewer network when no control is applied, will allow some conclusions to be drawn at the end.

Age-related diseases place an intense and growing stress on healthcare systems. Numerous theories of ageing exist. Yet prediction, prevention and treatment limitations of age-related disease persist because we lack a complete aetiological understanding of their origins. Here we propose a new theory that aims to provide a blueprint-type understanding of ageing and disease aetiology, working across multiple scientific disciplines. Supported by specific examples, we demonstrate that the primary driver of much of age-related diseases is the futile triggering of specific biological pathways in a pathological manner, which we term pathophysiological pathways or patho-pathways. Patho-pathways induce pathological cell, tissue and system changes in the form of dysactivity (out of context activity that is at the wrong time and/or place), which is characteristic of age related disease. As part of patho-pathways, all wild-type (normal) genes have the potential to contribute to age-related diseases (though specific risk varies and can be formularised). In a domino-style effect, one patho-pathway typically triggers others, resulting in complex, but traceable, cascades. Utility, in terms of changing clinical and scientific practice, is derived from mapping these cascades to create Blueprint Maps that allow identification of biomarkers and therapeutic targets far upstream in the causal chain of events. Finally, our theory provides testable hypotheses concerning predictions of links between otherwise seemingly disparate diseases as well as new interventions to reduce ageing.

Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios.