Autoimmune hemolytic anemias (AIHAs) are conditions whereby a person produces an antibody to their own red blood cells (RBCs). These conditions can be primary with unknown cause or secondary, associated with diseases or infections. There are seven different categories of AIHA, warm-antibody AIHA (wAIHA); cold-antibody AIHA that includes cold agglutinin syndrome (CAS) and cold agglutin disease (CAD); mixed AIHA, also called combined cold and warm AIHA; paroxysmal cold hemoglobinuria (PCH) also termed Donath-Lansteiner test-positive AIHA; direct antiglobulin test-negative AIHA (DAT-neg AIHA); drug-induced immune hemolytic anemia (DIIHA); and passenger lymphocyte syndrome. This review will discuss each of these AIHAs and provide information on diagnosis and treatment.

This study explores the role of inflammation and oxidative stress, hallmarks of COVID-19, in accelerating cellular biological aging. We investigated early molecular markers—DNA methylation age (DNAmAge) and telomere length (TL)—in blood leukocytes, nasal cells (NC), and induced sputum (IS) one-year post-infection in pauci- and asymptomatic healthcare workers (HCWs) infected during the first pandemic wave (February-May 2020), compared to COPD patients, model for "aged lung". Data by questionnaires, Work Ability Index (WAI), blood analyses, autonomic cardiac balance assessments, heart rate variability (HRV), and pulmonary function tests were collected. Elevated leukocyte DNAmAge significantly correlated with advancing age, male gender, daytime work, and an aged phenotype characterized by chronic diseases, elevated LDL and glycemia levels, medications affecting HRV, and declines in lung function, WAI, lymphocyte count, hemoglobin levels, and HRV (p<0.05). Increasing age, LDL levels, job positions involving intensive patient contact, and higher leukocyte counts collectively contributed to shortened leukocyte TL (p<0.05). Notably, HCWs exhibited accelerated biological aging in IS cells compared to both blood leukocytes (p≤0.05) and NC (p<0.001), and were biologically older than COPD patients (p<0.05). These findings suggest the need for monitoring aging in pauci- and asymptomatic COVID-19 survivors, who represent the majority of the general population.

Accurate classification of objects in 3D point clouds is a significant problem in several applications, such as autonomous navigation and augmented/virtual reality scenarios, which has become a research hot spot. In this paper, we presented a deep learning strategy for 3D object classification in augmented reality. The proposed approach is a combination of the GRU and LSTM. LSTM networks learn longer dependencies well, but due to the number of gates, it takes longer to train; on the other hand, GRU networks have a weaker performance than LSTM, but their training speed is much higher than GRU, which is The speed is due to its fewer gates. The proposed approach used the combination of speed and accuracy of these two networks. The proposed approach achieved an accuracy of 0.99 in the 4,499,0641 points dataset, which includes eight classes (unlabeled, man-made terrain, natural terrain, high vegetation, low vegetation, buildings, hardscape, scanning artifacts, cars). Meanwhile, the traditional machine learning approaches could achieve a maximum accuracy of 0.9489 in the best case.

We present a case of adult-onset systemic chronic active EBV disease (CAEBV) in a 40-year-old woman with chronic HBV hepatitis. Initial symptoms resembled a viral illness, progressing to recurrent fever, transaminitis, and anasarca. Investigations revealed high-level EBV viremia and an abnormal T-cell population in liver and bone marrow, indicative of CAEBV. Liver biopsy showed CD3+ T-cells lacking TCRbeta and displaying dim/negative CD5, with elevated EBV-infected T-cells. Next-generation sequencing identified rare variants in CREBBP, SPEN, TP73, and PLCG2, suggesting potential contributions to disease pathogenesis. This case underscores diagnostic challenges and management complexities of adult-onset CAEBV, particularly with underlying chronic HBV infection. Genomic profiling offers crucial insights into the molecular landscape of rare lymphoid malignancies, highlighting the importance of personalized treatment strategies. The distinct immunophenotypic features underscore the heterogeneity in EBV-associated T-cell LPDs, urging further research for optimized clinical management

Background: Endovascular treatment of popliteal aneurysms (PA) has increased in the last few years, quickly becoming the main treatment in many vascular centers, based on the acceptable and promising outcomes reported in the literature. However, endograft infections are the most dangerous complication as a serious local compromise and usually require open surgical conversion and device explantation to preserve the affected extremity. Case report: We reported two patients admitted to the emergency room for pain and edema in the lower leg. Both patients have undergone exclusion of ruptured PA a few years before by endovascular graft. CTA showed a significant volume of fluid-corpuscular collection related to perianeurysmal abscess collection in both cases. Blood cultures and drained material cultures were positive for Staphylococcus Capitis in the first case and S. Aureus in the second. The antibiotics prophylaxis was performed for 10 days, then, patients underwent an open surgical conversion with a complete explantation of endovascular material and a femoro-popliteal bypass using an autologous vein in the first case and biological bovine pericardium prosthesis in the other case. Furthermore, we have questioned the literature and found eight more cases described in case report article referred to popliteal graft infections by different microorganisms, mostly presenting acute limb ischemia as the first symptom and suggesting endograft explantation with open conversion and autologous vein bypass as the commonest therapeutic choice. Conclusions: Open surgical conversion of popliteal endograft infection is the best strategy to manage peripheral infection of the popliteal stent.

Plato defined a quality as “some degree of perfection” [49]. Studies on quality have unveiled the internal and external quality factors [17] that significantly impact product quality [4]. Internal factors are invisible to the end customer and are dependent on overall work organisation by the development team [17]. This work organisation is called “the process” [11],[33]. In this article, we will discuss how to address the drone-dedicated software development process to support system verification and ensure product quality by supporting quality characteristics and therefore increase software development awareness and control. We will discuss the process's influence on overall work activities and relationships between those activities by addressing the software development process utilised in the drone-dedicated GNSS-denied navigation system development project. The result was observable after three weeks of starting the software development. It was focused on better team awareness, faster task completion, lower number of development mistakes, and better and faster change response than we observed in other projects that didn’t follow the predefined process.

This study optimizes the placement and farm layout of Vertical Axis Wind Turbines (VAWTs) using a neural fuzzy network, an advanced machine learning system, and meta-heuristic algorithms. The methodology introduces a novel approach to enhance turbine efficiency and overall farm performance. The objective is to determine the optimal configuration of vertical axis wind turbines using a neural-fuzzy system and meta-heuristic optimizers. Turbine efficiency and power generation rates are assessed through three-dimensional numerical simulations based on the Reynolds-averaged Navier-Stokes equations. This innovative design, featuring straight blades with bevel-angle vortex generators, represents a significant advancement over traditional straight-blade VAWTs. Comparative analyses demonstrate that the oblique configuration exhibits higher average output moment and power. A novel concept integrates the strengths of Darrieus VAWTs, enhancing efficiency through vortex generator integration. Simulation results are validated with empirical data, confirming the superior aerodynamic efficiency and increased power generation of the oblique configuration. Inspired by the optimal group movements of birds, this study recommends adopting the oblique arrangement for improved wind turbine performance.

Using numerical simulation method, combined with the pre-branch material characteristics of the measurement results, based on the LS-DYNA software to simulate the branch crushing process, the analysis found that not only the existence of the cutter cutting in the branch destruction process, there is also the cutting of the opposing surface of the bending fracture, with the increase in the speed of the knife rollers, the cutter cutting resistance increases, the cutting resistance reaches 2690 N when the cutter at 2500 r/min, the cutter cutting resistance is the smallest in the cutting simulation at the blade angle of 55°, the blade angle is more suitable for the cutting of the branch. In the cutting simulation under different cutting edge angles, the cutting resistance of the tool is the smallest when the edge angle is 55°, which is 1860 N. This edge angle is more suitable for branch crushing and cutting. Using Fluent software to analyse the airflow characteristics of the crushing device, it was found that with the increase of the knife roller speed, the inlet flow and negative pressure of the crushing chamber are increasing, the speed of 2500 r/min, the inlet flow and negative pressure were 1.92 kg/s and 37.16 Pa, which will be conducive to the feeding of branches, but the speed is too high and it will also lead to the enhancement of the vortex of part of the area in the crushing device, which will affect the feeding of branches and the throwing of the crushed material. affect the feeding of branches and the throwing out of crushed materials, and finally determined the speed range of the crushing knife roller is 1800 ~ 2220 r/min. Based on ANSYS/Model module modal analysis of the crushing knife roller, the knife roller of the first six orders of the intrinsic frequency and vibration pattern, crushing knife roller of the lowest order of the modal intrinsic frequency of 137.42 Hz, much larger than the crushing knife roller operating frequency of 37 Hz, the machine will not resonate during operation.

In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking.

Establishing systemic communications between the project plan, stakeholders, and the organization through integrated management meets stakeholders' demands and managerial expectations in project-oriented organizations. Performance assessment can be defined as a control system in any company that reviews its daily operations and evaluates how well it has achieved its goals. This study aims to investigate the impact of an integrated project management system on the financial performance of the Organization for Renovation, Development, and Equipment of Schools. This applied research is descriptive-analytical. The statistical population comprises 2,700 employees of the organization, with a sample size of 340 individuals examined. The reliability of the designed questionnaire was confirmed by experts in the organization using Cronbach's alpha (α=0.91). Data were analyzed using SPSS version 20 and AMOS 18 software through descriptive statistics and confirmatory factor analysis. The results indicate that the seven dimensions of the integrated project management system (cost management, quality management and continuous control, change management, systems and information management, human resources management, interaction and communication management, procurement management) have a positive and significant impact on the financial performance of the Organization for Renovation, Development, and Equipment of Schools.

CO2 emission activity of xylotrophic fungi response to an increase in temperature in the range of 10-30 °C on the example of pure dikaryotic cultures of Fomes fomentarius s. str., F. inzengae, Fomitopsis betulina, F. pinicola, Phellinus igniarius was analyzed. Emission activity was assessed by the difference in CO2 concentration in 0.5-liter exposure chambers with Petri dishes with mycelium growing on agar at the beginning of exposure and an hour later using a Gasmet DX-4030 FTIR spectrometer (Gasmet Technologies Oy, Finland), error measurements ±50 ppm. Specific (μg CO2/cm2/h) and total (μg CO2/h) emission activity and its relationship with temperature and size (area) of the mycelium were assessed. It has been shown that in the range of 10-30 °C, the specific and total CO2 emission activity of the mycelium is closely and positively related to temperature. Specific emission, which is an indicator of the respiratory activity of the mycelium, does not depend on its size; its only driver is temperature, the relationship with which is linear: an increase in temperature by 10 °C causes an increase in the specific emission activity of the mycelium by 1.7 times. The total CO2 emission activity, which is an indicator of the total amount of CO2 emitted, is directly proportional to the specific emission activity and the size of the mycelium. In the range of 10-30 °C, an increase in temperature causes an almost equal increase in both the specific emission activity of the mycelium (Q10 1.7) and its growth (Q10 1.5) and causes an exponential increase in the total emission of CO2. This must be taken into account when predicting CO2 emissions from woody debris under climate change, as it could potentially contribute to accelerating climate change.

Although, promotion of urbanization culture in recent decades has made inevitable development of cities in the world, however, the development can be guided in a direction that leave, to the extent possible, minimum socioeconomic and environmental impacts. For this, it is required to first forecast auto-spreading orientation of cities and suburbs in rural areas over time and then avoid shapeless growth of cities. This paper is an attempt to develop a dynamic hybrid model based on logistic regression (LR), Markov chain (MC), and cellular automata (CA) for prediction of future urban sprawl in fast-growing cities. The model was developed using 12 widely-used urban development criteria, whose significant coefficient was determined by logistic regression, and validated by relative operating characteristic (ROC) analysis. The validated model was run in Guilan, a tourist province in northern Iran with a very high rate of urban development. For this, changes in the area of urban land use were detected over the period of 1989 to 2013 and then, future sprawl of the province was forecasted by the years 2025 and 2037. The analysis results revealed that the area of urban land use was increased by more than 1.7 % from 36012.5 ha in 1989 to 59754.8 ha in 2013, and the area of Caspian Hyrcanian forestland was reduced by 31628 ha. The results also predicted an alarming increase in the rate of urban development in the province by the years 2025 and 2037, during which urban land use is predicted to develop 0.9 % and 1.38 %, respectively. The development pattern is expected to be uneven and scattered, without following any particular direction. The development will occur close to the existing or newly-formed urban basements as well as around major roads and commercial areas. This development, if not controlled, will lead to the loss of 13863 ha of Hyrcanian forests and if the trend continues, 21013 ha of Hyrcanian forests and 20208 ha of Barren/open lands are expected to be destroyed by the year 2037. In general, the proposed model is an efficient tool for the support of urban planning decisions and facilitates the process of sustainable development of cities by providing decision-makers with an overview on future development of cities where the growth rate is very fast.

This study evaluates audiogenic epilepsy in Krushinsky-Molodkina rats using a wireless EEG system to overcome the limitations of traditional methods that restrict animal movement. Nine male KM rats, prone to audiogenic seizures, were implanted with electrodes in areas critical for seizure activity (inferior colliculus, ventrolateral periaqueductal gray, dorsal area of the secondary auditory cortex, and motor cortex). This setup allowed for the observation of seizures without movement restriction. Results showed that targeted neural intervention via electrode implantation reduced the intensity of convulsive seizures in half of the subjects, suggesting a potential therapeutic approach. The severity of seizures was influenced by the pre-phonostimulation EEG background activity in specific brain regions, identifying them as key in the genesis and generalization of seizures. Observations of dual generalized seizure waves and induced hyperkinesis challenge conventional epilepsy models and suggest new directions for research and therapy development.

Chronic kidney disease patients treated by hemodialysis present a high cardio-vascular morbidity and mortality. There is an imperative need for novel biomarkers in identifying and to offer possible therapeutically intervention in these patients. We performed a prospective observational cohort study on 77 patients in the period October 2021 – October 2023. We measured serum plasma levels of Interleukin 1-Beta, Galectin 3, human suppression of tumorigenicity factor 2, bone morphogenetic protein 2 and fibroblastic growth factor 23 at the inclusion. We evaluated the correlations of these biomarkers with cardiac function and structure evaluated by echocardiography. Mean age was 61.02 (±11.81) years, 45 (56.2%) males and with a dialysis vintage of 4.95 (2.4-7.8) years. Median ejection fraction was 51 (43-54 %) and more than two thirds of the patients presented valvular calcifications. Overall mortality was 22%. Interleukin 1-Beta correlated positively with ejection fraction and global longitudinal strain and negatively with left atrium diameter and left ventricle tele systolic diameter. Galectin 3 values negatively correlated with aortic valve fibrosis and mitral valve calcifications and human suppression tumorigenity factor 2 negatively correlated with mitral valve calcifications. Some of these novel biomarkers could be used to better asses the cardio-vascular disease in patients on maintenance hemodialysis.

Sparse training (ST) aims to ameliorate deep learning by replacing fully connected artificial neural networks (ANNs) with sparse or ultra-sparse ones, such as brain networks are, therefore it might benefit to borrow brain-inspired learning paradigms from complex network intelligence theory. Here, we launch the ultra-sparse advantage challenge, whose goal is to offer evidence on the extent to which ultra-sparse (around 1% connection retained) topologies can achieve any leaning advantage against fully connected. Epitopological learning is a field of network science and complex network intelligence that studies how to implement learning on complex networks by changing the shape of their connectivity structure (epitopological plasticity). One way to implement Epitopological (epi- means new) Learning is via link prediction: predicting the likelihood of non-observed links to appear in the network. Cannistraci-Hebb learning theory inspired the CH3-L3 network automata rule for link prediction which is effective for general-purpose link prediction. Here, starting from CH3-L3 we propose Epitopological Sparse Meta-deep Learning (ESML) to apply Epitopological Learning to sparse training. In empirical experiments, we find that ESML learns ANNs with ultra-sparse hyperbolic (epi-)topology in which emerges a community layer organization that is meta-deep (meaning that each layer also has an internal depth due to power-law node hierarchy). Furthermore, we discover that ESML can in many cases automatically sparse the neurons during training (arriving even to 30% neurons left in hidden layers), this process of node dynamic removal is called percolation. Starting from this network science evidence, we design Cannistraci-Hebb training (CHT), a 4-step training methodology that puts ESML at its heart. We conduct experiments on 7 datasets and 5 network structures comparing CHT to dynamic sparse training SOTA algorithms and the fully connected counterparts. The results indicate that, with a mere 1% of links retained during training, CHT surpasses fully connected networks on VGG16, GoogLeNet, ResNet50, and ResNet152. This key finding is an evidence for ultra-sparse advantage and signs a milestone in deep learning. CHT acts akin to a gradient-free oracle that adopts CH3-L3-based epitopological learning to guide the placement of new links in the ultra-sparse network topology to facilitate sparse-weight gradient learning, and this in turn reduces the convergence time of ultra-sparse training. Finally, CHT offers the first examples of parsimony dynamic sparse training because, in many datasets, it can retain network performance by percolating and significantly reducing the node network size. Our code is available at: https://github.com/biomedical-cybernetics/Cannistraci-Hebb-training.

Modern climatic changes have an impact on the bearing capacity of permafrost soils at the base of the foundations of buildings and structures in the urbanized territories of the Arctic and Subarctic. The activation of cryogenic processes leads to the destruction of the infrastructure of human settlements and, as a result, to social, economic, environmental consequences for the population. Based on the results of geothermy of frozen and thawing soil, GPR profiling of the territory of the city of Anadyr, it was concluded that the main risks of permafrost degradation are associated with the spread of hydrogenic melt zones. Maps of soil temperature in imaginary cross-sectional with a depth of 3, 5 and 10 m were compiled; maps of the capacity of thawing soils and the permafrost aquifer; a map of zones of dangerous spread of exogenous cryogenic processes. A permafrost monitoring system in the territory of Anadyr has been substantiated, which will consist of an automated network of observations of soil temperature in 35 wells at the border and at the epicenter of 20 zones of dangerous development of exogenous cryogenic processes and 12 control GPR profiles at the intersection of linear hydrogenic taliks.

Graft injury affects over 50% of liver transplant (LT) recipients, but non-invasive biomarkers to diagnose and guide treatment are currently limited. We aimed to develop a biomarker of graft injury by integrating serum metabolomic profiles with clinical variables. Serum from 55 LT recipients with biopsy confirmed metabolic-dysfunction associated steatohepatitis (MASH), T-cell mediated rejection (TCMR) and biliary complications was collected and processed using a combination of LC-MS/MS assay. The metabolomic profiles were integrated with clinical information using a multi-class Machine Learning (ML) classifier. The efficacy of the model was assessed through the evaluation of the Out-of-Bag (OOB) error estimate. Our ML model yielded an overall accuracy of 79.66% with an OOB estimate of the error rate at 19.75%. The model exhibited a maximum ability to distinguish MASH, with an OOB error estimate of 7.4% compared to 22.2% for biliary and 29.6% for TCMR. The metabolites serine and serotonin emerged as the topmost predictors. When predicting binary outcomes using 3 models: Biliary (biliary vs. rest), MASH (MASH vs. rest) and TCMR (TCMR vs. rest), the AUCs were 0.882, 0.972 and 0.96 respectively. Our ML tool integrating serum metabolites with clinical variables shows promise as a non-invasive, multi-class serum biomarker of graft pathology.

Background: Inflammatory plays critical role in myocardial infarction as a critical process in the development of heart failure, involving the development of cardiac fibrosis. Colchicine is a well-established anti-inflammatory drug, but its scientific application in controlling post-acute myocardial infarction remodelling processes has not been established. IL-10, a key cytokine in modulating inflammatory responses, underscoring its potential as a crucial therapeutic target of colchicine. Objectives: To explore the protective role of IL-10 modulated by colchicine in myocardial healing and repair following AMI, particularly cardiac fibrosis. Materials and Methods: The predicted protein of Colchicine was assessed using WAY2DRUG PASS as probability active value. Proteins associated with Colchicine, cardiac fibrosis and acute myocardial infarction were analysed with DisGeNET and Open Target databases. Analysis and visualization of protein-protein interactions were conducted using STRING and Cytoscape. A 3T3 cell line treated with CoCl2 was used to mimic hypoxic. HIF-1α and IL-10 expression were measured by flow cytometry and analysed using a one-way ANOVA test. This observational clinical trial examined acute myocardial infarction patients undergoing immediate and delayed primary percutaneous coronary interventions. Subjects were randomized into control groups receiving placebo and intervention groups treated with Colchicine. Assessments occurred at 24 hours and five days after the intervention. IL-10 expression in the clinical trial was measured by ELISA assay and analysed using a T-test. Results: Colchicine demonstrates promising bioactivity in treating acute myocardial infarction, with notable probability active values as a tubulin antagonist (0.744), beta-tubulin antagonist (0.673), and NOS2 inhibitor (0.529). Its primary action targets IL-10, with protein-protein interactions analysis indicating interactions between IL-10 and key inflammatory mediators—IL-1β, IFN-γ, CCL2, TNF, and TGF-β1—during acute myocardial infarction and cardiac fibrosis. Hypoxic conditions in the CoCl2-induced 3T3 cell model show significantly elevated HIF-1α compared to controls (p<0.0001). Acute myocardial infarction patients were predominantly from high-risk populations, with notable smoking prevalence in the early Colchicine group (72%). Hypertension prevalence was significantly different, with 67.31% in the late Colchicine group compared to 38% in the early Colchicine and 39.80% in the early placebo groups. Both early and late Colchicine groups exhibited a higher prevalence of diabetes mellitus. Colchicine use, either early or late, did not significantly alter major adverse cardiovascular events incidence compared to placebo (p>0.05). However, Colchicine significantly increased IL-10 expression in CoCl2-treated cells (p<0.0001) and in acute myocardial infarction patients within five days (p<0.05). Conclusions: Colchicine may bolster the anti-inflammatory response post-myocardial infarction by activating IL-10 pathways in fibroblasts and in clinical settings, potentially reducing major adverse cardiovascular events. Further investigation into broader aspects of this pathway, particularly in cardiac fibroblasts, is required.

Using the example of a generator model with dedicated inertia, a theoretical study of two coupled self-oscillators with capacitive coupling, their sequential single-frequency synchronization, chaos and two-frequency synchronization with an adiabatic change in the magnitude of the coupling between partial self-oscillators was carried out. The parameters of self-oscillators and the values of the coupling coefficient at which the specified operating modes of coupled self-oscillators exist are determined. The results of numerical studies, illustrating the conditions for excitation of single-frequency, chaotic and dual-frequency oscillations in a system of coupled self-oscillators are presented.

The occurrence of chaotic instability of oscillations at the output of a single-stage amplifier on a powerful bipolar microwave transistor when a biharmonic signal is amplified is considered. It has been established that at a high level of the input signal at the output of the amplifier, the signal contains a significant chaotic component. Cases were experimentally studied when the frequency distance between the partial frequency components of a biharmonic signal is close to the gain band and when it is much smaller than this band. It has been established that in the first case, the chaos in oscillations is associated with the emergence of a sequence of trains of a forced oscillatory process with arbitrary initial phases and durations. In the second case, the appearance of a chaotic pedestal is associated with the chaos in the passive underexcited mode of the nonlinear circuit formed by the capacitance of the p-n junction and the input matching circuits of the amplifier stage.

The speed-density or flow-density relationship is a fundamental aspect of traffic flow theory. Single-regime models often face challenges in consistently fitting empirical data under diverse traffic conditions. This paper addresses the inaccuracies of single-regime models, attributing them not only to their functional forms but also to data dispersion and sample selection bias. To mitigate these issues, the data is segmented into day and night periods, leading to the creation of separate models for each. Calibration results, conducted on data from two freeways, Tehran-Karaj and Tehran-Qom, reveal distinct challenges, with the former exhibiting high dispersion and the latter showing low dispersion. Surprisingly, single-regime models demonstrate notable effectiveness when applied to distinct day and night data. Further analysis, categorizing data into day and night, results in reduced errors (20-60% improvement in R-Square), emphasizing the potential for enhanced accuracy by considering distinct regimes. Overall, the study underscores the significance of regime-specific considerations for accurate traffic flow modeling. Upon thorough evaluation, the study highlights the superiority of the neural network Multi-layer Perceptron (MLP) over traditional models. The research discusses the implications of incorporating day and night parameters into the models, emphasizing their potential for accuracy enhancement.

Dynamic sparse training is an effective strategy to alleviate the training and inference demands of artificial neural networks. However, current sparse training methods face a challenge in achieving high levels of sparsity while maintaining performance comparable to that of their fully connected counterparts. The Cannistraci-Hebb training (CHT) method produces an ultra-sparse advantage compared to fully connected training in various tasks by using a gradient-free link regrowth method, which relies solely on the network topology. However, its rigid selection based on link prediction scores may lead to epitopological local minima, especially at the beginning of the training process when the network topology might be noisy and unreliable. In this article, we introduce the Cannistraci-Hebb training soft rule (CHTs), which applies a flexible approach to both the removal and regrowth of links during training, fostering a balance between exploring and exploiting network topology. Additionally, we investigate the network topology initialization using several approaches, including the bipartite scale-free and bipartite small-world network models. Empirical results show that CHTs can surpass the performance of fully connected networks with MLP architecture by using only 1% of the connections (99% sparsity) on the MNIST, EMNIST, and Fashion MNIST datasets and can provide remarkable results with only 0.1% of the links (99.9% sparsity). In some MLPs for image classification tasks, CHTs can reduce the active neuron network size to 20% of the original nodes (neurons), demonstrating a remarkable ability to generalize better than fully connected architectures, reducing the entire model size. This represents a relevant result for dynamic sparse training. Finally, we present evidence from larger network models such as Transformers, with 10% of the connections (90% sparsity), where CHTs outperform other prevalent dynamic sparse training methods in machine translation tasks.

Parasomnias are a group of sleep disorders characterized by abnormal and unpleasant motor, verbal or behavioral events that occur during sleep or during transitions between wake and sleep states. They disrupt sleep and can have a detrimental impact on the individual experiencing them. Our goal was to identify types of parasomnias and their prevalence in the post-secondary student population and to explore students' coping strategies for parasomnias they found distressing. Seventy-seven post-secondary students completed the 21-item Munich Parasomnia Screening (MUPS) frequency scale. They also rated, on a 10-point scale, how disturbing each parasomnia was. Not only did 92% percent of students report at least one parasomnia, but our results also indicate that the vast majority of students experienced more than one parasomnia. This led us to investigate the likelihood of the co-occurrence of different parasomnias. With respect to the level of subjectively experienced distress, the most prevalent parasomnias were not always the more disturbing ones. Coded open-ended responses about what students do about the disturbing parasomnias indicate that grounding strategies and physical manipulation of one’s body were the most common, although most participants indicated that in spite of distress, they do nothing to cope. In conclusion, our study found a strikingly high prevalence of parasomnias in this sample of young adults and a lack of knowledge about effective means of dealing with these.

Background. Gene rearrangements affecting KMT2A are frequent in acute myeloid leukemia (AML) and are often associated with a poor prognosis. KMT2A gene fusions are often detected by chromosome banding analysis and confirmed by fluorescence in-situ hybridization. However, small intragenic insertions, termed KMT2A partial tandem duplication (PTD), are particularly challenging to detect using standard molecular and cytogenetic approaches. Methods. We have validated the use of a custom hybrid capture based next-generation sequencing (NGS) panel for comprehensive profiling of AML patients seen at our institution. This NGS panel targets the entire consensus coding DNA sequence of KMT2A; to deduce the presence of a PTD we used the relative ratio of KMT2A exons coverage. We sought to corroborate the KMT2A-PTD NGS results using (1) multiplex-ligation probe amplification (MLPA) and (2) optical genome mapping (OGM). Results. We analyzed 932 AML cases and identified 41 individuals harboring a KMT2A-PTD. MLPA, NGS and OGM confirmed the presence of a KMT2A-PTD in 22 of the cases analyzed where orthogonal testing was possible. Two false positive PTD calls by NGS could be explained by the presence of cryptic structural variants impacting KMT2A and interfering with PTD analysis. OGM revealed the nature of these previously undetected gene rearrangements in KMT2A, while MLPA yielded in-conclusive results. MLPA analysis for KMT2A-PTD is limited to exon 4, whereas NGS and OGM resolved KMT2A-PTD sizes and copy number levels. Conclusion. KMT2A-PTDs are complex gene rearrangements that cannot be fully ascertained using a single genomic platform. MLPA, NGS panels and OGM are complementary technologies applied in standard of care testing for AML patients. MLPA and NGS panels are designed for targeted copy number analysis; however, our results show that integration of concurrent genomic alterations is needed for accurate KMT2A-PTD identification. Unbalanced chromosomal rearrangements overlapping with KMT2A can interfere with the diagnostic sensitivity and specificity of copy number based KMT2A-PTD detection methodologies.

For the first time, the effect of a significant change in the resistance of the aquatic environment of a population of protozoan microorganisms under the influence of radiofrequency electromagnetic radiation from cell phones has been experimentally studied. The results of the experiment demonstrate the biological nature of the changes occurring in the “water population of protozoa” system, which indicates that the impact of radiation directly affects the cell. This conclusion is confirmed by the same order of magnitude time of information exchange between individual individuals of the population in the calculation and in the experiment. An extremely low concentration of protozoa in the experiment was established, equal to 1000 pcs./liter, at which the indicated effect is observed.An unambiguous dependence of the biological effect in the “water – amoeba population” system on the average power of electromagnetic radiation of cell phones, both in standby mode and in talk mode, has been established. It is concluded that the presented method of changing the physical properties of the aquatic environment of a population of protozoa under the influence of electromagnetic fields can be a model experiment in identifying the mechanisms of interaction of biological systems with electromagnetic radiation, determining the response time of biological systems to external electromagnetic influence, and establishing the degree of influence of radiation in the 4G, 5G ranges.

Many studies utilize sensors to measure foot plantar pressure and analyze gait patterns, often focusing on assessing weight-bearing symmetry or asymmetry compared to control groups. However, a critical question arises: does the selection of the control group significantly impact study outcomes? Does chronic low back pain affect foot plantar pressure measurements? This experimental study involved 23 participants, including healthy individuals and those without foot disorders but with chronic low back pain. Dynamic foot pressure measurements were conducted using the Ortopiezometr, and data were analyzed using the "Steps" program. Participants were divided into two groups: Group 1 comprised completely healthy individuals, while Group 2 consisted of individuals without foot problems but with moderate chronic low back pain. The findings highlight a prevalence of energy release in the forefoot sensors compared to the hindfoot for both the right and left legs in most cases. Even among healthy volunteers, achieving perfect symmetry in the maximum values on the sensors for the right and left feet was a challenge. Results showed no statistically significant differences in energy released on specific sensors or duration of gait phases between the groups. Essentially, this suggests that foot pressure patterns are comparable between healthy individuals and those with low back pain. The use of sensor-equipped inserts for pedobarographic examinations indicates that, in cases of degenerative changes in the lumbar and sacral spine without accompanying acute pain, gait patterns resemble those of the healthy population.

Omsk hemorrhagic fever virus (OHFV) is a member of the tick-borne encephalitis virus (TBEV) complex of the Flaviviridae family. Currently, there is no data on cross-reactivity of antibodies to the NS1 proteins of OHFV and TBEV. Such data are of major interest for monitoring viral encephalitis of unknown etiology due to the increasing geographical distribution of OHFV. In this study, recombinant OHFV NS1 protein was produced using Escherichia сoli expression system and purified. The recombinant OHFV NS1 protein was recognized by specific mice immune ascetic fluids to the native OHFV NS1 protein. Western blot analysis and ELISA of recombinant NS1 proteins of OHFV and TBEV were used to study the cross-reactivity of antibodies from immune ascites fluid obtained from OHFV-infected mice and mAbs against TBEV NS1. Anti-TBEV NS1 mouse monoclonal antibodies (mAbs) have been shown not to be cross-reactive to OHFV NS1 protein. Sera from patients with confirmed tick-borne encephalitis (TBE) were examined by ELISA using recombinant OHFV NS1 and TBEV NS1 proteins as antigens. It was shown for the first time that cross-reactive antibodies to OHFV NS1 protein were not detected in the sera of TBE patients whereas the sera contained antibodies to TBEV NS1 protein.

The main objective of this study was to compare the removal efficiency of nutrients using Lactuca sativa, Medicago sativa and Phragmites australis in subsurface flow constructed wetlands with horizontal flow. In order to test water quality, fabricated reactors designed and the plants cultivated in the soil while their root were inside the wastewater. A long time study carried out from spring till end of autumn (9 months) in order to evaluate the difference in removal rate based on the seasonal changes. The highest removal rate was during summer which followed by spring and autumn. Thus, the effect of plants on the removal efficiency of organic matter (COD, BOD), TSS and nutrient (P and TN) appeared to be dependent on the seasonal growth. Phragmites australis the most sensitive species in order the removal of nutrient from wastewater.

BackgroundLiving donor liver transplantation offers comparable long-term results to cadaveric donor transplantation. This study analyzes the selection process of living liver donors at a Western transplant center that utilizes both donation forms. MethodsThis retrospective analysis examined potential living donors, collecting medical data and reasons for refusal, and divided patients by indication and urgency for transplantation. ResultsFrom January 2013 to December 2023, 594 potential living donors were assessed at Jena University Hospital. Of these, 124 living donor transplants were performed. During outpatient evaluation, 357 potential donors were excluded. Subsequently, 238 individuals underwent inpatient assessment, with 100 more being excluded. Main reasons for donor refusal included medical or psychological issues (n=160), disease progression in recipients (n=90), and recipient refusal or donor withdrawal (n=54). The evaluation process showed a trend towards considering more distant relatives and acquaintances as donor candidates with increasing severity of the recipient's disease.

This paper describes a system that is used to gather data from Smart Home sensors measuring energy consumption, analyze and predict future consumption by using machine learning algorithm for prediction. It focuses on the architecture of the system as well as the need for such system to exist in practice – why the consumption should be measured at all and how that measurement can be put to good use for achieving energy efficiency.

The p53 tumor suppressor protein is an activator of transcription. Diverse stress factors lead to various sets of posttranslational modifications of p53 what results in different sets of upregulat-ed genes. We noticed that actinomycin D and nutlin-3a (A+N) synergize in inducing activating phosphorylations of p53 and upregulation of selected p53-target genes. Here we found that one of these genes is DUSP13, which codes for poorly-studied, dual-specificity phosphatase having at least two isoforms, one expressed in testis and the other in skeletal muscles. In cancer cells ex-posed to A+N, DUSP13 is expressed from an alternative promoter in intron, what results in ex-pression of isoform named TMDP-L1. The luciferase reporter tests demonstrated that this pro-moter is activated by both endogenous and ectopically expressed p53. We showed for the first time that mRNA expressed from this promoter actually produces the protein, which can be de-tected by Western blotting in all examined cancer cell lines with wild-type p53 exposed to A+N. In some cell lines it is also induced by clinically relevant camptothecin or by nutlin-3a acting alone. This isoform, fused with green fluorescent protein localizes in perinuclear region of cells. Thus, TMDP-L1 isoform may be an important element of p53-regulated stress response system.

The principal aim of this descriptive-survey investigation is to ascertain the factors influencing individuals' choice to purchase organic commodities in Tehran. The statistical populace for this examination comprised all patrons of food retail establishments in Tehran, with the sample size determined using the Cochran formula for an unknown community size, involving the distribution of 30 questionnaires to customers (n=439). Data collection was carried out through a random sampling technique. The survey instrument consisted of a questionnaire with two sections consisting of general queries (7 questions) and specific inquiries (23 items) categorized into four dimensions (product, price, place, and promotion) for the clientele. The questionnaire's validity was confirmed through face and content validation, while its reliability was assessed utilizing Cronbach’s alpha. Data analysis was conducted using SPSS25 software, encompassing both descriptive and inferential statistical analyses. The results revealed a positive and significant correlation between customers' inclination to consume organic goods and the characteristics of the product, price, and promotion. However, no association was found between the propensity to consume organic products and location characteristics. Concerning contextual variables, a solely positive and significant correlation was identified between the willingness to purchase organic items and consumer income levels. No significant relationships were detected between other contextual variables and organic product consumption. A notable contrast was observed between respondents' gender and their readiness to buy organic products, while no significant variances were noted regarding age, income, occupation, and education in relation to the propensity to purchase organic commodities.

The signaling complex around voltage-gated sodium (Nav) channels includes accessory proteins and kinases crucial for regulating neuronal firing. Previous studies showed that one such kinase, WEE1—critical to the cell cycle—selectively modulates Nav1.2 channel activity through the accessory protein fibroblast growth factor 14 (FGF14). Here, we tested whether WEE1 exhibits crosstalk with the AKT/GSK3 pathway for coordinated regulation of FGF14/Nav1.2 channel complex assembly and function. Using the in-cell split luciferase complementation assay (LCA), we found that the WEE1 inhibitor II and GSK3 inhibitor XIII reduce FGF14/Nav1.2 complex formation, while the AKT inhibitor triciribine increases it. However, combining WEE1 inhibitor II with either one of the other two inhibitors abolished its effect on FGF14/Nav1.2 complex formation. Whole-cell voltage-clamp recordings of sodium currents (INa) in HEK293 cells co-expressing Nav1.2 channels and FGF14-GFP showed that WEE1 inhibitor II significantly suppresses peak INa density, both alone and in the presence of triciribine or GSK3 inhibitor XIII, despite the latter inhibitors’ opposite effects on INa. Additionally, WEE1 inhibitor II slowed the tau of fast inactivation, and caused depolarizing shifts in the voltage dependence of activation and inactivation. These phenotypes either prevailed or were additive when combined with triciribine but were outcompeted when both WEE1 inhibitor II and GSK3 inhibitor XIII were present. Concerted regulation by WEE1 inhibitor II, triciribine and GSK3 inhibitor XIII were also observed on long-term inactivation and use-dependence of Nav1.2 currents. Overall, these findings suggest a complex role for WEE1 kinase—in concert with the AKT/GSK3 pathway—in regulating the Nav1.2 channelosome.

We study L_2-approximation problems in the weighted Hilbert spaces in the worst case setting. Three interesting weighted Hilbert spaces appear in this paper, whose weights are equipped with two positive parameters $\ga_j$ and $\az_j$ for $j=1,\,2,\dots,\,d$. We consider the worst case error of algorithms that use finitely many arbitrary continuous linear functionals. We discuss the exponential convergence-(t,s)-weak tractability (EC-(t,s)-WT) of these L_2-approximation problems under the absolute or normalized error criterion. In particular, we obtain the sufficient and necessary conditions for EC-(1,1)-WT and EC-(t,1)-WT with t

The European Green Deal poses a two-pronged challenge for the automotive industry: migrating to solutions based in light structures, requiring light weight concepts and light materials, while at the same time avoiding dependence towards the importation of these advanced materials. Aluminium alloys are lightweight and cost-effective materials that can successfully cover the requirements of many structural applications, however their production requires from bauxite and other Critical Raw Materials (CRM), such as Si and Mg. Aluminium alloys are fully recyclable, but scrap is usually contaminated and its use is related to an increment of impurities, tramp element and undesired inclusions. Traditionally, the use of secondary alloys has been restricted to low performance applications. The present work analyses the effect that the use of scrap has in the quantity of inclusions present in the alloy and in other properties relevant for the material processing. The study was carried out for common alloys belonging to 3 of the most common aluminium processes used in the car manufacturing industry: High Pressure Die Casting (HPDC) (EN AC-43500), extrusion (6063) and sheet metal forming (5754 and 6181). Reference alloy were mixed with different level of scrap (0, 20, 40, 60, 80 and 100%) trying to keep the chemical composition as unaffected as possible. The inclusions level of the alloy was characterized with the Prefil Foot-printer® test. In addition, the obtained materials, after being cast in an open mould, were subjected to metallographic characterization. Relevant properties to assess the formability quality of the alloys for the corresponding transforming process, were obtained: flowability test for HPDC alloy and high temperature compression for extrusion alloys. The results obtained suggest that the number of inclusions present in the melt highly increase with the amount of scrap used to produce the alloy. These inclusions are also related with an important lost of flowability, but do not have a noticeable impact on microstructure.

Bats are natural hosts for a wide variety of viruses and could therefore be the reservoir, source and spillover for several mammalian viruses, including adenoviruses. We examined faecal samples from 12 bats (five species of the family Vespertilionidae) from central Russia (Moscow Region) and found that two of them (16%) were positive for mastadenoviruses. One of the identified viruses was isolated from faeces of Nyctalis noctule and named BatAdV/MOW15-Nn19. The genome of the BatAdV/MOW15-Nn19 was 37,915 bp in length. Comparative studies have shown significant divergence of the nucleotide sequence of the BatAdV/MOW15-Nn19 genome from European mastadenoviruses, the highest genome sequence similarity was to isolate WA3301 from an Australian bat. Phylogenetic analysis, as well as a comparative analysis of the protein coding genes to homologous available from public database, showed evidence of novel species within Mastadenovirus genus (preliminary named «K»). Analysis of the short part of the DNA polymerase gene of BatAdV/MOW15-Nn19 reveals few NCBI records from Europe with high identity to the new virus, i.e. mastadenoviruses related to the new species are circulating in European bats. The partial genome of another mastadenovirus from Pipistrellus nathusii was identified, it represented bat adenovirus species B, which is widely distributed in European Vespertilionidae bats. New data on the genetic diversity of bat adenoviruses may help to better understand how they evolved in mammals and spread across Europe.

Healthcare professionals constantly face situations that reflect ecosystems’ degradation. These can negatively affect their mental health. Research suggests that hope and resilience can play an important role in this scenario, since they are related to/predict mental health in highly heterogeneous samples (considering geography, age, profession, health, etc.). In this context, the aims of the present study are: to characterize and explore the relationship between hope, resilient coping and mental health of Portuguese healthcare professionals. Using Google Forms, 276 healthcare professionals answered to the GHQ-28, the (adult) Trait Hope Scale, and the Brief Resilient Coping Scale (cross-sectional study). The minimum and maximum possible scores were reached, with the exception of the maximum score of GHQ-28-Total. Regarding Hope, 19.6% scored below the midpoint (M=43.46, SD=11.97); 29.3% revealed low resilience (M=14.93, SD=4.05); and the average of 4 of the 5 Mental Health scores (exception: Severe Depression) indicates the probability of a psychiatric case. Hope correlated with Social Dysfunction and GHQ-28-Total; resilient coping proved to be a (weak) predictor of 4 of the 5 GHQ-28 indicators (exception: Severe depression). The results support the need to promote the sample's mental health, hope and resilient coping. They also suggest that stimulating resilient coping may contribute to improving healthcare professionals’ mental health.

Underwater visual detection technology plays a pivotal role in fields such as marine exploration. With the increasing demand for underwater monitoring, the quest for efficient and reliable methods for underwater target recognition has become particularly significant. To address this requirement, this study developed an innovative underwater object detection architecture based on YOLOv8, named YOLOv8-MU, aimed at significantly enhancing detection accuracy.By integrating the LarK module proposed in UniRepLKNet to optimize the backbone network, YOLOv8-MU aims to achieve a larger receptive field without increasing the model’s depth. Further, this research introduces C2fSTR, an innovative method that combines Swin Transformer with the C2f module. Additionally, we have incorporated the SPPFCSPC_EMA module, which combines Cross-Stage Partial Fast Spatial Pyramid Pooling (SPPFCSPC) with attention mechanisms, significantly improving the detection accuracy and robustness of various biological targets. Moreover, by introducing a fusion block based on DAMO-YOLO into the neck of the model, we further enhanced the model’s capability in multi-scale feature extraction. Finally, the adoption of the MPDIoU loss function, designed around vertex distance, effectively tackles the challenges of localization accuracy and boundary clarity in underwater organism detection. Experimental results on the URPC2019 dataset demonstrate that the YOLOv8-MU model achieved an [email protected] of 78.4%, marking improvements of 5.6%, 1.1%, and 4.0% over YOLOv5s, YOLOv7, and YOLOv8n respectively, indicating the leading performance (SOTA) of this method. On the other hand, further evaluation on the URPC2020 dataset confirmed the generalization capability of the YOLOv8-MU architecture, with its [email protected] reaching 80.4%, surpassing various models including YOLOv5x and YOLOv8n, showcasing the wide applicability of our proposed improved model architecture.

Proteomics research has become one of the most important topics in the fields of life science and natural science. At present, research on protein–protein interaction networks (PPINs) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate function module detection mechanisms in PPIN, including open databases, existing detection algorithms and recent solutions. After that, we describe the proposed solution based on simplified swarm optimization (SSO) algorithm and gene ontology knowledge. The proposed solution implements SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species dataset for experiment: Fruitfly, Mouse, Scere, and Human. The testing and analysis result show that the proposed solution is feasible, efficient and could achieve a higher accuracy of prediction than existing approaches.

(1) Background: Multiple alterations of cellular metabolism have been documented in experimental studies of autosomal dominant polycystic kidney disease (ADPKD) and are thought to contribute to its pathogenesis. (2) Methods: To elucidate the molecular pathways and transcriptional regulators associated with the metabolic changes of renal cysts in ADPKD, we compared global gene expression data from human PKD1 renal cysts, minimally cystic tissues (MCT) from the same patients, and healthy human kidney cortical tissue samples. (3) Results: We found PKD1 renal cysts displayed the Warburg effect with gene pathway changes favoring increased cellular glucose uptake and lactate production, instead of pyruvate oxidation. Additionally, mitochondrial energy metabolism was globally depressed, associated with downregulation of gene pathways related to fatty acid oxidation (FAO), branched-chain amino acid (BCAA) degradation, the Krebs cycle, and oxidative phosphorylation (OXPHOS) in renal cysts. Activation of mTORC1 and its two target proto-oncogenes, HIF-1α and MYC, was predicted to drive the expression of multiple genes involved in the observed metabolic reprogramming (e.g., GLUT3, HK1/HK2, ALDOA, ENO2, PKM, LDHA/LDHB, MCT4, PDHA1, PDK1/3, MPC1/2, CPT2, BCAT1, NAMPT); indeed, their predicted expression patterns were confirmed by our data. Conversely, we found AMPK inhibition was predicted in renal cysts. AMPK inhibition was associated with decreased expression of PGC-1α, a transcriptional coactivator for transcription factors PPARα, ERRα, and ERRγ, all of which play a critical role in regulating oxidative metabolism and mitochondrial biogenesis. (4) Conclusions: These data provide a comprehensive map of metabolic pathway reprogramming in ADPKD and highlight nodes of regulation that may serve as targets for therapeutic intervention.

BACKGROUND Detecting total cholesterol in dried blood spots could aid in identifying individuals with a high likelihood of familial hypercholesterolemia and could be used as a screening measure. This study aims to assess the diagnostic accuracy of dried blood spots on Whatman 903 paper cards using a manual enzymatic technique. METHODS A total of 394 samples collected as serum and dried blood spots were compared. Cholesterol was determined in serum using the automated reference method, while cholesterol on paper was measured using a manual enzymatic method. Within- and between-day diagnostic variability were analyzed. The correlation between both methods was assessed using Passing-Bablok regression and Bland-Altman plot. Internal validation of our correlation formula was performed on 149 samples, along with external validation of the formula proposed by Corso et al. RESULTS The within- and between-day coefficient of variation was found to be lower than 10.14% and 14.09%, respectively. Method comparison yielded an area under the curve of 0.976, with a standard error of 0.009. Passing-Bablok regression indicated a precision of 0.803 and an accuracy of 0.96. Internal validation precision was measured at 0.716. Regarding internal validation, calculated cholesterol with our regression line did not differ from serum cholesterol analysis, while significant differences were observed in calculated cholesterol levels using the external formula. CONCLUSIONS Total cholesterol analysis in dot blood spots demonstrates high precision and reproducibility. This method reliably enables the incorporation of this biological marker into neonatal screening for familial hypercholesterolemia detection.

This study explores fault-tolerant consensus in leader-following heterogeneous multi-agent systems, focusing on actuator failures in uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs). An optimization-based fault-tolerant consensus algorithm is proposed. The algorithm utilizes the Euler-Lagrange formula to ensure system consistency under actuator failures, with the Lyapunov stability theory proving the asymptotic stability of the consistency error. The algorithm is applied to heterogeneous multi-agent systems of UAVs and UGVs to derive optimal fault-tolerant consensus control laws for each vehicle type. Simulation experiments give evidence for the feasibility of the proposed control strategy.

The interest in the Spark Plasma Sintering (SPS) technique has continuously increased over the last few years. This article shows the possibility of the development of a SPS device used for material processing and synthesis both in both scientific and industrial applications. This work presents an example of processing Arc-Meleted (half-Heusler, cobalt triantimonide) and SHS-synthesized semiconductors (bismuth telluride) materials with SPS device. The system functionalities expansion is presented, showing the possible way of increasing the information amount obtained about SPS processes and using the SPS apparatus for conducting synthesis of materials and increasing reproducibility and accuracy of process parameters control.

Early detection of neurological conditions is critical for timely diagnosis and treatment. Identifying cellular-level changes is essential for implementing therapeutic interventions prior to symptomatic disease onset. However, monitoring brain tissue directly through biopsies is invasive and high-risk. Bodily fluids such as blood or cerebrospinal fluid contain information in many forms, including proteins and nucleic acids. Particularly, cell-free DNA (cfDNA) has potential as a versatile neurological biomarker. Yet, our knowledge of cfDNA released by brain tissue and how cfDNA changes in response to deleterious events within the brain is incomplete. Mapping changes in cfDNA to specific cellular events is difficult in vivo, where many tissues contribute to circulating cfDNA. Organoids are tractable systems for examining specific changes consistently in a human background. However, few studies have investigated cfDNA released from organoids. Here, we examined cfDNA isolated from cerebral organoids. We found that cerebral organoids release quantities of cfDNA sufficient for downstream analysis with droplet-digital PCR and whole-genome sequencing. Further, gene ontology analysis of genes aligning with sequenced cfDNA fragments revealed association with terms related to neurodevelopment and autism spectrum disorder. We conclude that cerebral organoids hold promise as tools for the discovery of cfDNA biomarkers related to neurodevelopmental and neurological disorders.

Previously, we described a 19-base pair double-stranded RNA with 3'-trinucleotide overhangs, acting as immunostimulatory RNA (isRNA). This molecule demonstrated notable antiproliferative effects on cancer cells, inhibited tumor growth, and elicited immunostimulatory and antiviral responses by inducing cytokine and interferon production. Within this study, we compared the efficiency of lung fibrosis development, initiated in mice by BLM or LPS using different schemes of induction. Then we compared the effect of isRNA used in a preventive or therapeutic regimen on the development of fibrosis in selected BLM- and LPS-induced mouse models and showed that isRNA can be used in pathological conditions accompanied by the development of inflammation and the risk of fibrosis formation, without adverse side effects. Prophylactic regimen of isRNA application is beneficial for prevention of the development of pulmonary fibrosis.

Responsible, efficient, and environmentally conscious energy consumption practices are increasingly essential for ensuring the reliability of the modern electricity grid. This study focuses on leveraging time series analysis to improve forecasting accuracy, crucial for various application domains where real-world time series data often exhibit complex, non-linear patterns. Our approach advocates for utilizing Long Short Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models for precise time series forecasting. To ensure a fair evaluation, we compare the performance of our proposed approach with traditional neural networks, time-series forecasting methods, and conventional decline curves. Additionally, individual models based on LSTM, Bi-LSTM, and other machine learning methods are implemented for a comprehensive assessment. Experimental results consistently demonstrate that our proposed model outperforms all benchmarking methods in terms of mean absolute error (MAE) across most datasets. Addressing the imbalance between activations by consumer and prosumer groups, our predictions show superior performance compared to several traditional forecasting methods, such as the Autoregressive Integrated Moving Average model (ARIMA) and Seasonal Autoregressive Integrated Moving Average model (SARIMA). Specifically, the root mean square error (RMSE) of Bi-LSTM is 5.35%, 46.08%, and 50.6% lower than LSTM, ARIMA, and SARIMA, respectively, on the May test data.

The accurate forecasting of solar power output is essential for ensuring the stable and efficient operation of grid-connected photovoltaic (PV) systems, especially when faced with variable weather patterns. This research focuses on the integration of Machine Learning (ML) methodologies and climatic parameters to predict solar panel energy generation, with a specific emphasis on addressing consumption-production imbalances. Leveraging a dataset sourced from the Kaggle platform, the study is conducted in the context of Estonia, aiming to optimize solar energy utilization in this geographic region. The dataset, obtained from Kaggle, encompasses comprehensive information on climatic variables, including sunlight intensity, temperature, and humidity, alongside corresponding solar panel energy output. Leveraging machine learning algorithms, including XGBoost regression and neural networks, our model aims to identify complex patterns and relationships within the datasets. By tailoring the model to Estonia's climatic nuances, we seek to enhance the accuracy of energy production forecasts and, consequently, better manage the challenges associated with consumption-production imbalances. Furthermore, the research investigates the adaptability of the proposed model to diverse climatic conditions, ensuring its applicability for similar endeavors in other geographical locations. The performance of the forecasting model will be evaluated using error metrics like mean absolute error (MAE) and root mean squared error (RMSE). This study leverages a comprehensive dataset from Kaggle and utilizes advanced machine learning techniques to generate valuable insights. These insights can inform the development of sustainable energy policies and practices, ultimately leading to a more efficient and reliable renewable energy infrastructure.

Volatile organic compounds (VOCs) released from plants have been correlated with disease-status. Analysis of VOCs using GC-MS is time-consuming, laboratory-based, and requires specialist training. Electronic nose devices (E-nose) provide a portable alternative. Three different E-nose devices were compared to assess how accurately they could detect Mango Twig Tip Dieback and Panama disease in banana. The devices were initially trained on known volatiles, then pure cultures of Pantoea sp., Staphylococcus sp., and Fusarium odoratissimum, and finally, on infected and healthy mango leaves and field-collected, infected banana pseudo-stems. The experiments were repeated three times with six replicates for each host-pathogen pair. The variation between healthy and infected host materials was evaluated by principal component and linear discriminant analysis, cross-validation and chemometric data analysis. GC-MS analysis was conducted contemporaneously and identified an 80% similarity between healthy and infected plant material. The portable C 320 was 100% successful in discriminating known volatiles but had a low capability in differentiating healthy and infected plant substrates. The advanced devices (PEN 3 / MSEM 160) successfully detected healthy and diseased samples with a high variance. The results suggest that E-nose devices are more sensitive and accurate in detecting changes of VOCs between healthy and infected plants compared to headspace GC-MS.

The aim of this paper is to investigate the amenability modulo an ideal of Banach algebras with emphasis on applications to homological algebras. In doing so, we show that amenability modulo an ideal of A^** implies amenability modulo an ideal of A. Finally, for a large class of semigroups, we prove that l¹(S)^** is amenable modulo Iσ^** if and only if an appropriate group homomorphic image of S is finite where Iσ is the closed ideal induced by the least group congruence σ.

The FAS ligand (FASLG) is expressed on lymphocytes, which employ it to activated death receptors on targets cells. Cancer cells are generally resistant to apoptosis triggered by FASLG. In this work we found a way to circumvent this resistance by treatment with actinomycin D (ActD) and nutlin-3a (Nut3a). We selected this drug combination based on our transcriptomic data showing strong activation of proapoptotic genes, including the ones for receptor mediated apoptosis, in cells exposed to actinomycin D and nutlin-3a. To test our hypothesis, we pre-exposed cancer cell lines to this drug combination for 45h and next we treated them with recombinant FASLG. This almost instantaneously killed most cells. Actinomycin D and nutlin-3a strongly synergized in the sensitization because the effect of the drugs acting solo was not as spectacular as the drug combination, which together with FASLG killed more than 99% of cells. Based on the caspase activation pattern (caspase-8, caspase-9, caspase-10), we conclude that both extrinsic and intrinsic pro-apoptotic pathways were engaged. In engineered p53-deficient cells this pro-apoptotic effect was completely lost. Therefore, the combination of ActD+Nut3a activates p53 in an extraordinary way, which overcomes the resistance of cancer cells to apoptosis triggered by FASLG. Interestingly, other combinations of drugs, e.g., etoposide + nutlin-3a, actinomycin D + RG7112 and actinomycin D + idasanutlin had similar effect. Moreover, normal human fibroblasts are less sensitive to death induced by ActD+Nut3a+FASLG. Our findings create the opportunity to revive the abandoned attempts of cancer immunotherapy employing the recombinant FAS ligand.

Groove pancreatitis represents a chronic focal form of pancreatitis affecting the zone between the pancreatic head and the duodenal „C” loop, known as the groove area. This is a rare condition that affects the pancreatic periampullary part, including the duodenum and the common bile duct, associated usually with long-term alcohol and tobacco misuse and being more frequent in men than in women. The most common clinical symptoms of groove pancreatitis include weight loss, acute abdominal pain, nausea, and jaundice. This report is about a 66 year old woman with a history of heavy smoking, presenting with weight loss, nausea, and upper abdominal pain. Contrast enhanced computed tomography revealed the existence of a chronic pancreatitis as well as the dilatation of the main pancreatic duct, a cyst of the pancreatic head and enlargement of the biliary tract. Conservatory treatment was initiated but with no improvement of symptoms. Since there was no indication for endoscopic retrograde cholangiopancreatography, we decided to perform a pancreatoduodenectomy, as surgery appears to be the single effective treatment.

With the rapid growth of large language models (LLMs), there is increasing demand for memory and computation in LLMs. Recent efforts on post-training pruning of LLMs aim to reduce the model size and computation requirements, yet the performance is still sub-optimal. In this paper, we present a plug-and-play solution for post-training pruning of LLMs. The proposed solution has two innovative components: 1) Relative Importance and Activations (RIA), a new pruning metric that jointly considers the weight and activations efficiently on LLMs; and 2) Channel Permutation, a new approach to maximally preserve important weights under N:M sparsity. The two proposed components can be readily combined to further enhance the N:M semi-structured pruning of LLMs. Our empirical experiments show that RIA alone can already surpass all existing post-training pruning methods on prevalent LLMs, e.g., LLaMA ranging from 7B to 65B. Furthermore, N:M semi-structured pruning with channel permutation can even outperform the original LLaMA2-70B on zero-shot tasks, together with practical speed-up on specific hardware. Our code is available at: https://github.com/biomedical-cybernetics/Relative-importance-and-activation-pruning

Network Function Virtualization (NFV) allows for the dynamic provisioning of Virtualized Network Functions, adapting services to the complex and real-time network environment for enhanced network performance. Despite the benefits of NFV, virtual network functions (VNFs) migration and the energy consumption pose significant challenges due to the dynamic nature of the physical network, especially in the edge-cloud continuum network. Currently, people tackle this challenge by predicting the network traffic and then proactively migrating the VNFs using the predicted values. There are two challenges here. First, when SFCs belong to different network service providers, the historical network traffic data is held by the service providers, and they are reluctant to share the historical data due to privacy concerns. Without the historical network traffic data of each SFC, the resource provider that owns the underlying network cannot effectively predict the network traffic; in addition, without the feedback of the migration results of the underlying physical network, the network traffic prediction will be difficult to match the needs of the physical network. In this manuscript, we solve the problem of network traffic prediction in the NFV-based edge-cloud continuum network for multiple network service providers. We aim to improve migration performance and reduce energy consumption while ensuring resource and latency requirements. To protect the privacy of each network service provider, we apply an FL algorithm. After that, to improve the migration performance, we tailor the loss function. In the loss function, factors such as the number of migrations, the number of migration failures, and the energy consumption of the NFV-based edge-cloud continuum network are added. Additionally, to obtain the results of the SFC migration, we develop a mathematical model for the problem of multiple network service providers in the NFV-based network. The effectiveness of our algorithm is evaluated through extensive simulations, and the results show a significant reduction in the number of migrated nodes and energy consumption while improving the SFC acceptance ratio compared to state-of-the-art schemes which only use the difference between predicted and actual flows to define the loss function.

Background: Acute kidney disease (AKD) is a known risk factor for increased mortality and evolution towards chronic kidney disease (CKD) in adults. The data regarding children are scarce. The purpose of our study is to explore the risk factor for AKD and impact of AKD following an acute kidney injury (AKI) episode in children on mortality and new-onset CKD. Methods: We retrospectively evaluated 736 children with ages between 2 and 18 years old with AKI admitted in a tertiary care hospital from the west Romania over a 9 years period. Results: AKD incidence following AKI was 17%. Patients that developed AKD were older, with higher baseline serum creatinine values, urea, C reactive protein and lower proteins, haemoglobin and sodium. They were more often exposed to sepsis, critical illness and nephrotoxic medications and presented certain susceptibilities like CKD, neoplasia, anemia, heart failure, arterial hypertension, stem cell transplant and female gender. AKI causes that increased the risk of AKD were intrinsic ones. AKD increased the overall mortality by three times. The factors that independently increased the risk of evolution towards CKD were AKD, acute tubular necrosis and higher baseline serum creatinine values. Conclusions: AKD is an independent risk factor for mortality and new-onset CKD in children.

Background and Objectives: Knee osteoarthritis (KOA) is a degenerative disease that is targeting continuously people of different ages, but especially the elderly population, the number of which tends to increase continuously at the global level. Apart from age, excess weight can influence the evolution of the disease, with obesity being associated with a weak inflammation stage, and an imbalance between pro-inflammatory and anti-inflammatory cytokines. The present work aimed to identify specific biomarkers alike ACRP-30, IL-10, TNF-α, and IL-6 in synovial fluid and correlate them with patients’ clinical data. Materials and Methods: 24 subjects with KOA and over 50 years of age are involved in the present study. All patients performed a supine X-ray at the target knee. Ultrasound was performed using an ESAOTE MyLab Omega (ESAOTE Spa, Italy) ultrasound device and a 6-19 MHz linear array transducer. Results: The obtained results highlighted a significant correlation between age and adiponectin level (p=0.0451, r=-0.412). Also, the IL-10 values are lower in cases where the intensity of the pain is more pronounced (p=0.0405, r=-0.421). In addition, analyzing the data by gender, it was observed that in the case of males, stiffness is more related to age (p=0.0079, r=0.7993), compared to women (p=0.0203, r=0.6223). In the case of women, the progression of the disease tends to increase more intensively total WOMAC score’s values (p=0.00031, r=0.8342), compared with men (p=0.0289, r=7013). Regarding interleukins and BMI, significant correlations were observed only in the case of men. Conclusions: A significant correlation between age and adiponectin, and adiponectin and IL-6, suggests that advanced age may contribute to adiponectin reduction. Comparing men with women, it was observed that men's age is more related to rigidity, and IL-6 and IL-10 are directly correlated to BMI and women seem to be more sensitive to pain and stiffness.

The planned closure of potash mines, achieved through the injection of highly mineralized brines into the worked-out area, is a complex and not fully studied process. A critical concern arises when brines obstruct the aerodynamic connections between the flooded mine's airspace and the atmosphere, potentially leading to the formation of closed cavities where explosive gases can accumulate. To address this hazard, it is imperative to develop systems capable of extracting the gas-air mixture from the unflooded domed portions of the worked-out area. This is the focus of this study, in which we analyze gas-dynamic processes within the potassium mine worked-out area during planned flooding. Two distinct scenarios are examined: the first involves controlled flooding with saturated brines, while the second contemplates flooding resulting from a hypothetical breakthrough of supra-salt strata, leading to the ingress of groundwater into the worked-out area. A novel mathematical model is introduced to predict the evolution of gas-air mixture parameters in the unflooded dome segment of the worked-out area. Utilizing this model, we assess the effectiveness of proposed measures designed to eliminate explosive gases from the worked-out area. Specifically, a pipeline system is proposed for the removal of gases, followed by their dilution below the maximum permissible concentrations in the effluent jet. The findings from this study contribute valuable insights into ensuring the safe and efficient closure of potash mines, shedding light on potential risks and effective mitigation strategies for gas-related hazards during planned flooding.

In this paper, the boiling flow inside a nanochannel with 700000 argon particle has been simulated by molecular dynamics (MD) simulation. This approach has been employed to analysis the superheated flow and its heat transfer pattern as well. For all simulations an external thrust force varying from 1 PN to 12 PN is exerted on inlet nanoparticles along the channel to have the forced annular boiling flow. Computations reveal that saturation condition and superheat degree have significant impacts on the liquid-vapor interface. Furthermore, because of the major influence of surface tension throughout a nanochannel, the x-velocity of liquid film and vapor core has not considerable fluctuations and stay smooth. All provided results show the behaviors completely similar to the available outcomes in the literature.

Familial Mediterranean fever (FMF) is a systemic autoinflammatory disorder caused by inherited mutations in the MEFV (Mediterranean FeVer) gene located on chromosome 16 (16p13.3) and coding pyrin protein. Despite the existing data on MEFV mutations, the exact mechanism of their effect on the development of pathological processes leading to autoinflammation observed in FMF, remains unclear. Induced pluripotent stem cells (iPSCs) are considered an important tool for studying the molecular genetic mechanisms of various diseases due to their ability to differentiate into any cell type, including macrophages, which contribute to the development of FMF. In this study, we developed iPSCs of an Armenian patient with FMF having M694V, p.(Met694Val) (c.2080A>G, rs61752717) pathogenic mutation in exon 10 of the MEFV gene. As a result of direct differentiation, macrophages expressing CD14 and CD45 surface markers were obtained. In addition, we found that the morphology of patient macrophages derived from iPSCs with MEFV mutation was significantly different from that of differentiated from iPSCs of a healthy donor carrying wild-type MEFV gene.

The incidence of diabetes mellitus (DM) continues to rise worldwide and one of the most serious microvascular complications is diabetic kidney disease (DKD), which is the leading cause of end-stage renal disease. Current bi-omarkers such as urinary albumin excretion rate have limitation for early detection of DKD. In our study we used ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry (UHPLC-QTOF-ESI+-MS) techniques to quantify previously analyzed metabolites, such as tryptophan, kynurenic acid, taurine, l-acetylcarnitine, glycine, and tiglylglycine. We performed the targeted analy-sis of metabolites from urine and serum samples, collected from 110 subjects. Of these, 90 patients with type 2 DM (T2DM) were divided according to the urinary albumin/creatinine ratio (UACR) into normoalbuminuria 300 mg/g groups, respectively, while 20 subjects were rep-resented by healthy controls. Through various validation methods, we identified several metabolites as potential biomarkers, such as kynurenic acid, glycine, and l-tryptophan in serum and l-acetylcarnitine, tiglylglycine, and tau-rine in urine.

River flow prediction is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the climatic variables e.g., precipitation has a significant impact on the temporal distribution of the river discharge in recent days making the discharge forecasting even more complicated for diversified water-related issues e.g., flood prediction and irrigation planning. To predict the discharge, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-driven predictions, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, Long Short-term Memory (LSTM) neural network regression model is trained using over 80 years of daily data to forecast the discharge time series up to 3 days ahead of time. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.09. Higher performance is achieved through the increase in the number of epochs and hyper parameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time river discharge prediction.

Despite the availability of COVID-19 vaccines, the global pandemic remains a significant health challenge. Timely detection of the virus is crucial for effective containment efforts, prompting the exploration of machine learning (ML) models for COVID-19 detection. highlights the ongoing significance of COVID-19 as a global health crisis despite the availability of vaccines. To address the need for timely virus detection, the study explores the efficacy of machine learning (ML) models, including DNN, Voting, Bagging, SVC_rbf, SVC_linear, SVC_polynomial, and SVC_sigmoid, in predicting COVID-19 infection probability. With a dataset comprising 4000 samples split into 3200 for training and 800 for testing, the analysis reveals that DNN and Voting models demonstrate the highest performance, achieving an impressive accuracy of 89%. These results underscore the potential of ML techniques, particularly DNN and ensemble methods like Voting, in facilitating early COVID-19 detection and contributing to effective pandemic management. The study emphasizes the importance of continued research to further refine and optimize ML-based COVID-19 detection systems.

Aiming at the problems of inaccurate segmentation of long object and information loss of small object in real-time semantic segmentation algorithm, this paper proposes a lightweight multi-branch real-time semantic segmentation network based on BiseNetV2. The new auxiliary branch makes full use of spatial details and context information to cover the long object in the field of view. Meanwhile, in order to ensure the inference speed of the model, the asymmetric convolution is used in each stage of the auxiliary branch to design a structure with low computational complexity. In the multi-branch fusion stage, the alignment and fusion module is designed to provide guidance information for deep and shallow feature mapping, so as to make up for the problem of feature misalignment in the fusion of information at different scales, and thus reduce the loss of small target information. In order to further improve the model’s awareness of key information, a global context module is designed to capture the most important features in the input data. The proposed network uses NVIDIA GeForce RTX 3080 Laptop GPU experiment, on the road street view data set Cityscapes and CamVid average occurring simultaneously ratio reached 77.1% and 78.4% respectively, with running speed of 127 frames/s respectively and 112 frames/s. The experimental results show that the proposed algorithm can achieve real-time segmentation and improve the accuracy significantly, showing good semantic segmentation performance

Recent advancements in vascular organoid (VO) and vessel-on-chip (VoC) technologies have revolutionized our approach to studying cardiovascular diseases (CVDs), offering unprecedented insights through more physiologically relevant models. VOs generated from human pluripotent stem cells exhibit remarkable self-organization capabilities, forming complex three-dimensional structures that closely mimic human blood vessel architecture and function, while VoCs engineered with groundbreaking microfluidic systems that meticulously recreate the physical and functional attributes of blood vessels. These innovative constructs serve as powerful tools for investigating vascular development, disease progression, and therapeutic efficacy. By enabling the creation of patient-specific VOs and VoCs, they pave the way for personalized medicine approaches, allowing researchers to delve into genetic variations, intricate cellular interactions, and dynamic processes with exceptional resolution. The synergy between VOs and VoCs with cutting-edge technologies such as single-cell sequencing and high-resolution imaging has further amplified their potential, unveiling novel mechanisms underlying CVDs and identifying promising therapeutic targets. Herein, we summarize different types of VOs and VoCs and present an extensive overview on the generation and applications of VOs and VoCs in CVDs. We will also highlight clinical and translational challenges and future perspectives around VOs and VoCs.

In this work SiC-Ti3AlC2 and TiC-Ti3AlC2 composites produced by additive manufacturing are investigated. The fused granules fabrication (FGF) method as one of the material extrusion additive manufacturing (MEAM) technology is used to obtain composite samples. Composites with different ratios between components and different powder:polymer ratios are investigated. The technological features of the additive formation of composites are investigated, as well as their structure and properties. The optimal sintering temperature to form the best mechanical properties for both composites is 1300 °C. The composites have a regulatable porosity. Ti3AlC2 content, sintering temperature, and polymer content in the feedstock are the main parameters that regulates the porosity of FGF samples.

Some medical professional organizations have advocated for including the menstrual cycle as a vital sign in adolescence, but not in adulthood. However, documenting menstrual cycle patterns is not routine clinical or research practice. Vital signs are used to predict health outcomes, indicate needed treatment, and monitor a clinical course. They can help identify pathologies, affirm wellness, and are responsive to exposures. Here we review the scientific evidence showing how the menstrual cycle meets these criteria and should therefore be treated as a vital sign. Using key words and controlled vocabulary terms, we carried out multiple literature searches, prioritizing the inclusion of systematic reviews, meta-analyses, and clinical practice guidelines. This review describes how the menstrual cycle is a health indicator, can cyclically impact health conditions, and its associations with long-term post-menopausal health outcomes. We review exposures influencing the menstrual cycle, evidence underlying its use to optimize wellness, and available tools for documenting cycles. Supplementary materials include patient handouts on menstrual cycle tracking, and an index of related clinical practice guidelines and reviews by subject. The menstrual cycle is a vital sign from menarche through menopause, an underutilized but powerful tool for understanding gynecological and general health.

Complexity science studies physical phenomena that cannot be explained by the mere analysis of the single units of a system but requires to account for their interactions. A feature of complexity in connected systems is the emergence of mesoscale patterns in a geometric space, such as groupings in bird flocks. These patterns are formed by groups of points that tend to separate from each other, creating mesoscale structures. When multidimensional data or complex networks are embedded in a geometric space, some mesoscale patterns can appear respectively as clusters or communities, and their geometric separability is a feature according to which the performance of an algorithm for network embedding can be evaluated. Here, we introduce a framework for the definition and measure of the geometric separability (linear and nonlinear) of mesoscale patterns by solving the travelling salesman problem (TSP), and we offer experimental evidence on embedding and visualization of multidimensional data or complex networks, which are generated artificially or are derived from real complex systems. For the first time in literature the TSP’s solution is used to define a criterion of nonlinear separability of points in a geometric space, hence redefining the separability problem in terms of the travelling salesman problem is an innovation which impacts both computer science and complexity theory.

Fundus image registration plays a crucial role in the clinical evaluation of ocular diseases, such as diabetic retinopathy and macular degeneration, necessitating meticulous monitoring. The alignment of multiple fundus images enables the longitudinal analysis of patient progression, widening the visual scope, or augmenting resolution for detailed examinations. Currently, prevalent methodologies rely on feature-based approaches for fundus registration. However, certain methods exhibit high feature point density, posing challenges in matching due to point similarity. This study introduces a novel fundus image registration technique integrating U-Net for the extraction of feature points employing FIVES for its training and evaluation, a novel and large dataset for blood vessels segmentation, prioritizing point distribution over abundance. Subsequently, the method employs medial axis transform and pattern detection to obtain feature points characterized by the Fast Retina Keypoint (FREAK) descriptor, facilitating matching for transformation matrix computation. Assessment of the vessel segmentation achieves 0.7559 for Intersection Over Union (IoU), while evaluation on the Fundus Image Registration Dataset (FIRE) demonstrates the method’s comparative performance against existing methods, yielding a registration error of 0.596 for area under the curve, refining similar earlier methods and suggesting promising performance comparable to prior methodologies.

Civic participation is of great significance to urban management decision-making. In order to facilitate citizens to participate in city management decision-making, this paper proposes a large-scale group decision-making (LSGDM) method based on multi-granular probabilistic linguistic preference relations (MG-PLPRs). First, each decision maker selects a language terms set from the multi-granularity language terms set to represent individual preference relations and the MG-PLPRs are obtained by statistical calculation to represent sub-group’s preferences information. Then, an optimization model based on the expected consistency of PLPR and consensus measure of groups is established to achieving consensus reaching processes, which can ensure satisfactory individual consistency and group consensus. Finally, the validity and applicability of the proposed method is verified by a case of a city "shared garden" site selection with the participation of citizens.

This paper critically reviews the correlation between the built environment and sociology as it pertains to residential segregation and social housing policy in Iran. The objective is to identify design strategies that minimize the risk of social isolation, promote social interaction, and foster a sense of community within affordable housing zones. The literature review includes existing research on urban segregation and social sustainability, with a focus on architectural solutions. Case studies are utilized to exemplify both positive and negative social outcomes related to urban segregation and identify effective design elements. The findings highlight the complex nature of residential segregation and its impact on social isolation, social capital, and social exclusion. The built environment contributes to the problem by facilitating the creation of isolated communities, and architects have a crucial role in addressing them. The results suggest that a comprehensive social housing policy is necessary to improve the quality of social housing and reduce residential segregation in Iran. Finally, the paper provides practical design strategies to promote social interaction, enhance accessibility and mobility, create mixed-use developments, and incorporate sustainable design features in social housing policy. By using design strategies that support social sustainability and inclusiveness, architects can create thriving and sustainable urban environments that prioritize the needs of marginalized communities.

Polycentric governance, as conceptualized by the Nobel Laureate Elinor Ostrom, has been widely celebrated as a promising approach to fostering inclusive innovation ecosystems in the Global South. By emphasizing the coordination of multiple decision-making centers and the engagement of diverse stakeholders, polycentric systems are believed to enhance the inclusivity and responsiveness of innovation processes, crucial for addressing societal challenges and promoting equitable development. However, this article argues that the theoretical and practical application of polycentric governance may not adequately address the inherent power structures and complexities within inclusive innovation ecosystems. Drawing on critical analysis, empirical evidence, and complementary theoretical perspectives, this paper interrogates the limitations of polycentric governance and proposes a more nuanced, multidimensional framework for inclusive innovation governance in the context of the Global South.

Artificial intelligence (AI) is a current trend in computer science, which extends itself its amazing capacities to other technologies such as mechatronics and robotics. Going beyond technological applications, the philosophy behind AI is that there is a vague and potential convergence of artificial manufacture and natural world although the limiting approach may be still very far away, but why? The implicit problem is that Darwin theory of evolution focuses on natural world where breeding conservation is the cornerstone of the existence of creature world but there is no similar concept of breeding conservation in artificial world whose things are created by human. However, after developing for a long time until now, AI issues an interesting concept of generation in which artifacts created by computer science can derive their new generations inheriting their aspects / characteristics. Such generated artifacts make us look back on offsprings by the process of breeding conservation in natural world. Therefore, it is possible to think that AI generation, which is a recent subject of AI, is a significant development in computer science as well as high-tech domain. AI generation does not help us to reach near biological evolution even in the case that AI can combine with biological technology but, AI generation can help us to extend our viewpoint about Darwin theory of evolution as well as there may exist some uncertain relationship between man-made world and natural world. Anyhow AI generation is a current important subject in AI and there are two main generative models in computer science: 1) generative model that applies large language model into generating natural language texts understandable by human and 2) generative model that applies deep neural network into generating digital content such as sound, image, and video. This technical report focuses on deep generative model (DGM) for digital content generation, which is a short summary of approaches to implement DGMs. Researchers can read this work as an introduction to DGM with easily understandable explanations.

Following the adoption of the Kunming-Montreal Global Biodiversity Framework (KM-GBF), with the Convention on Biological Diversity serving as its guiding treaty, the European Union (EU) has just reached an agreement on an EU Nature Restoration Regulation. This study carries out a systematic cross-walk between the restoration regulation and the KM-GBF, focusing on their implications for forest ecosystems. This paper identifies areas of alignment, divergence, and potential gaps related to habitat restoration, species protection, climate resilience, and the sustainable use of natural resources. The methodology adopts a grounded approach, starting with the 23 targets outlined in the KM-GBF and proceeding to the 28 articles set out in the restoration regulation. The results highlight the need for better alignment between the KM-GBF, the restoration regulation, and other forest-relevant EU policy instruments. The study stresses the need for a coherent and integrated EU policy approach that can address the diverse challenges and policy objectives facing forests. It concludes that amendments to the restoration regulation have significantly diluted its potential impact, limiting the EU Members States accountability and ability to meet KM-GBF goals and targets. It further stresses the need for strategies that can reconcile divergent EU policy pathways, support forest management and restoration efforts, while aligning with global biodiversity objectives.

Coronavirus disease 2019 (COVID-19) continues to cause morbidity and mortality worldwide; therefore, effective treatments remain crucial to controlling it. As interferon-alpha (IFN-α) and -beta (β) have been proposed as COVID-19 treatments, we sought to assess their effectiveness on respiratory, cardiovascular, neurological, and psychiatric signs and symptoms, as well as PASC and death, in hospitalized COVID-19 patients without multiple sclerosis (MS). Using a federated data research network (TriNetX), we performed a retrospective cohort study of hospitalized COVID-19 patients without MS who received IFN-α or -β treatment, comparing them to a similar cohort who did not receive treatment. Following propensity score-matched analyses, we demonstrate that hospitalized COVID-19 patients who were treated with IFN-α or -β had significantly higher odds of death. In contrast, there was no significant difference in any other outcomes between 1-30 days or 1 day to anytime afterward. Overall, hospitalized COVID-19 patients without MS who were treated with IFN-α or -β had similar short- and long-term sequelae (except for mortality) as those who did not receive treatment. The potential benefits of utilizing IFN-α or -β treatment as therapeutics remain to be realized, and our research highlights the need to explore repurposing drugs for COVID-19 using real-world evidence.

Despite global efforts to improve vaccination coverage, the number of zero dose and under immunized children has increased in the African continent, particularly in Nigeria where more than 2.3 million unvaccinated children currently reside, making it the country with highest number of unvaccinated children on the African continent. It is thus imperative to identity barriers and facilitators of immunization in Nigeria with a view to formulate and design targeted interventions that can effectively bridge the immunization gap in Nigeria. In this review, we systematically mapped and summarized existing literature and data on key barriers to vaccine uptake, and facilitators influencing immunization practices in Nigeria. The electronic databases were systematically searched and articles that reported determinants of routine immunization uptake in Nigeria from inception of the databases up till October 2023 were included. The eligible articles were evaluated using a set of pre-defined criteria and the extracted data were analysed and visualized. This scoping review identified barriers spanning logistical, socio-economic, cultural, and healthcare system-related aspects, highlighting the need for a multi-pronged approach to address these challenges. While considerable progress has been made over the years, persistent barriers like distance between area of residence of caregivers and immunization centers, vaccine shortages, poor interpersonal relationship between healthcare workers and caregivers and cultural/religious influences persist. Lastly, collaborative efforts among stakeholders and communities remain paramount in ensuring improved routine immunization coverage across the Nigeria’s diverse socio-cultural landscape.

In the olive orchard on the western part of Istria, Croatia, twig and branch dieback was observed on several olive trees from the local cultivar ‘Buza’. In total, seven samples from symptomatic trees were collected. Samples were analyzed, and four fungal isolates showed morphological similarities to the species Phaeoacremonium iranianum. One isolate, chosen as a representative, was taken for molecular identification and pathogenicity tests. Based on DNA sequence data of ITS1/ITS4, βt2a/βt2b, EF1-728F/EF1-986R, the isolate was identified as P. iranianum. Pathogenicity tests were conducted on detached olive branches and olive trees in the greenhouse, and identified fungal species was pathogenic on olive. To the best of our knowledge, this is the first report of twig and branch dieback on olive causing by phytopathogenic specie of fungus Phaeoacremonium iranianum.

This critical study examines the social consequences of neoliberal urbanism, with a focus on housing policies by implementing qualitative approach. The main goal of the study is to identify downsides of the neoliberalism in residential areas and shed light on the potential social challenges caused by this. After reviewing the existing literature in this field and introducing some cases, the findings highlight that the prioritization of market mechanisms in urban development, which is the key characteristic of neoliberalism, has led to unaffordable housing, gentrification, and displacement of marginalized communities, exacerbating socio-economic inequalities. The commodification of housing under neoliberal urbanism has transformed homes into financial assets, and instead of addressing the structural roots of housing insecurity, strategies such as implementing public-private partnerships (PPPs), urban mega projects and deregulation, have perpetuated housing crises in urban areas. The results suggest that alternative methods such as inclusionary zones for having a mix of market-rate and affordable housing in new developments, as well as strengthening tenant protections through rent control, and tenant rights advocacy programs, can remarkably help address systemic inequalities. By prioritizing the needs of vulnerable populations, policymakers can work towards mitigating the detrimental social effects of neoliberal urbanism and fostering more just and inclusive urban environments. Finally, critical engagement with social justice issues is essential to creating diverse and inclusive urban communities.

Neurology is a quickly evolving specialty that requires clinicians to make precise and prompt diagnoses and clinical decisions based on the latest evidence-based medicine practices. In all Neurology subspecialties, Stroke and Epilepsy in particular, clinical decisions affecting patient outcomes depend on neurologists accurately assessing patient disability. Artificial intelligence (AI) can predict the expected neurological impairment from an AIS (Acute Ischemic Stroke), the possibility of ICH (IntraCranial Hemorrhage) expansion, and the clinical outcomes of comatose patients. This review article informs readers of artificial intelligence principles and methods. The article introduces the basic terminology of artificial intelligence before reviewing current and developing AI applications in neurology practice. AI holds promise as a tool to ease a neurologist’s daily workflow and supply unique diagnostic insights by analyzing data simultaneously from several sources, including neurological history and examination, blood and CSF laboratory testing, CNS electrophysiologic evaluations, and CNS imaging studies. AI-based methods are poised to complement the other tools neurologists use to make prompt and precise decisions that lead to favorable patient outcomes.

Computer simulation of normal and diseased human heart activity requires a 3D anatomical model of the myocardium, including myofibres. For clinical applications, such a model has to be constructed based on routine methods of cardiac visualisation such as sonography. Symmetrical models are shown to be too rigid, so an analytical non-symmetrical model with enough flexibility is necessary. Based on previously made anatomical models of the left ventricle, we propose a new, much more flexible spline-based analytical model. The model is fully described and verified against DT-MRI data. We show a way to construct it on the basis of sonography data. To use this model in further physiological simulations, we propose a numerical method to utilise finite differences in solving the reaction-diffusion problem together with an example of scroll wave dynamics simulation.

This paper explores the concept of feminist architecture and its potential to challenge gender norms and stereotypes in urban design. Women face specific challenges in urban spaces, including safety concerns, lack of accessibility, and discrimination. Feminist architecture offers a potential solution to these challenges by providing a framework that prioritizes the needs and experiences of marginalized groups, including women. The study employs a mixed-methods approach to conduct a review of relevant literature, case studies, and examples of feminist architecture from around the world to address research questions related to barriers and benefits of implementing feminist architecture in mainstream design practices. After identifying obstacles, this paper offers concrete strategies to overcome these barriers and the findings suggest that by prioritizing inclusivity, accessibility, sustainability, and democracy, feminist architecture principles offer a framework for designing urban environments that are just, equitable, and empowering for all individuals. However, despite the potential gains of incorporating feminist architecture into the design process, there are several barriers and limitations that designers may face. Therefore, to overcome these barriers and limitations, implementing feminist architecture principles require a commitment to understanding how gender operates in society, financial support, policy advocacy, and collaborative approaches among designers. By creating inclusive, gender-neutral, and supportive spaces and promoting diversity and inclusion within learning and practice, feminist architecture has the capacity to transform the built environment and make it more inclusive, equitable, and just for everyone.

Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research towards the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we introduce a set of metrics considering both quantitative and qualitative aspects of the road network. Compared to traditional neural networks, our HAN model demonstrates superior accuracy and F1-score, particularly for road types with comparable samples. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.

This paper provides an overview of current literature on community-led development initiatives in urban contexts, their role in addressing urban inequality, and the challenges they face. This study examines effective strategies of community-led initiatives, such as empowering communities, building partnerships, mobilizing resources, focusing on local needs, and participatory design methods. Case studies from around the world demonstrate how successful community-led development initiatives have been able to overcome obstacles to create more inclusive and equitable urban environments. Institutional support, including technical assistance, capacity building, and regulatory frameworks, are crucial for promoting the success and sustainability of these initiatives. Challenges to community-led initiatives include limited resources, capacity building needs, inadequate infrastructure, lack of institutional support, and lack of trust among community members. Therefore, the paper suggests that it is essential to address these challenges to enable the success of community-led development initiatives. Furthermore, this study recommends that future research should explore how emerging technologies and innovative approaches can effectively support community-led development initiatives and advance more just and sustainable urban development.

In atherosclerosis patients, vascular endothelial dysfunction is commonly observed with damage of vascular endothelial glycocalyx, an extracellular matrix-bound to and encapsulating the endothelial cell lining the blood vessel wall. Unfavorable lifestyle; smoking and physical inactivity, also induces glycocalyx degradation. Moreover, the vascular endothelial glycocalyx is damaged by various unfavorable disease conditions like as dehydration, acute infectious disease, trauma, sepsis, ARDS, Kawasaki disease, preeclampsia, gestational diabetes mellitus, hypertension, diabetes, chronic kidney disease, atherosclerosis, stroke, dementia, microvascular angina, acute coronary syndrome, and heart failure. The vascular endothelial glycocalyx has been shown to be important not only as a physical cytoprotective barrier for vascular endothelial cells but also as a mechanism that regulates intracellular cell signaling. Therefore, vascular endothelial glycocalyx has great potential to explore new strategies for assessing the benefit conditions of our healthy vasculature.

New technologies have resulted in a better understanding of blood and lymphatic vascular heterogeneity at the cellular and molecular levels. Meanwhile, we still need to learn more about the heterogeneity of the cardiovascular and lymphatic systems between different species at the anatomical and functional levels. Even the deceptively simple question of the functions of fish lymphatic vessels has yet to be conclusively answered. The most common interpretation assumes a similar dual setup of the vasculature in zebrafish and mammals: on the one hand, there is the cardiovascular, circulatory system, and on the other hand, the lymphatic vascular system, in which the unidirectional flow is derived from surplus interstitial fluid and returned into the cardiovascular system. A competing interpretation questions the identity of the lymphatic vessels in fish as at least some of them receive their flow from arteries via specialised anastomoses, neither requiring an interstitial source for the lymphatic flow nor stipulating unidirectionality. In this alternative view, the “fish lymphatics” are a specialised subcompartment of the cardiovascular system, called the “secondary vascular system” (SVS). Many of the contradictions found in the literature appear to stem from the fact that the SVS develops in part or its entirety from an embryonic lymphatic vascular system by transdifferentiation. Future research needs to establish the extent of embryonic transdifferentiation of lymphatics into SVS blood vessels. Similarly, more insight is needed into the molecular regulation of vascular development in fish. Most fish do feature more than the five VEGF genes and three VEGF receptor genes that we know from mice or humans, and the relative tolerance of fish to whole genome and gene duplications could underlie the evolutionary diversification of the vasculature. This review discusses the key elements of the fish lymphatics versus the SVS and attempts to draw a picture coherent with the existing data, including the phylogenetic knowledge.

Hyperspectral image and LiDAR image fusion plays a crucial role in remote sensing by capturing spatial relationships and modeling semantic information for accurate classification and recognition. However, existing methods, like Graph Convolutional Networks (GCNs), face challenges in constructing effective graph structures due to variations in local semantic information and limited receptiveness to large-scale contextual structures. To overcome these limitations, we proposed a invariant attribute-driven binary bi-branch classification (IABC) method which is a unified network that combines binary Convolutional Neural Network (CNN) and GCN with invariant attributes. Our approach utilizes a joint detection framework that can simultaneously learn features from small-scale regular regions and large-scale irregular regions, resulting in an enhanced structured representation of HSI and LiDAR images in the spectral-spatial domain. This approach not only improves the accuracy of classification and recognition but also reduces storage requirements and enables real-time decision-making, which is crucial for effectively processing large-scale remote sensing data. Extensive experiments demonstrates the superior performance of our proposed method in hyperspectral image analysis tasks. The combination of CNNs and GCNs allows for accurate modeling of spatial relationships and effective construction of graph structures. Furthermore, the integration of binary quantization enhances computational efficiency, enabling real-time processing of large-scale data. Therefore, our approach presents a promising opportunity for advancing remote sensing applications using deep learning techniques.

In the famous medieval magical manual called Picatrix, the unknown author describes the phenomenon of magic with the term nigromantia. As is well known, the original meaning of the Greek term necromantia has a more concise meaning. It is used for a special kind of divination, i.e., divination through the parts of a cadaver and the conjured spirit of the dead. Seemingly in the Picatrix, no necromantic ritual can be found; moreover, the author stresses that his main goal is pious, i.e., to find the path leading to the ultimate source of the universe, the one and only God. In my paper, I show that on some pages of the Picatrix, there are traces of divinatory practices that may be connected to the original meaning of the term nigromantia. In the third book of the manual, descriptions of some interesting rituals attributed to the pagan Sabeans of Harran and their teacher, the god Hermes can be found. During the practices, the magician involved conjured spirits of the heavenly bodies and powers as well. Because of this, by looking closely at and analyzing the given text, it is possible to piece together a complex web of necromantic and demonic divinatory rituals.

Background: Both hypertension and carotid atherosclerosis are independent risk factors for cardiovascular disease. Understanding their combined impact could enhance risk assessment and management strategies. Hypothesis: We hypothesized that the synergistic effects of hypertension and carotid plaques would increase the risk of both cardiovascular events and all-cause mortality. Methods: A follow-up study was conducted at the Preventive Cardiology department of Vilnius University Hospital Santaros klinikos between 2012 and 2021. The study recruited participants aged 40-60 who did not have overt CVD and were part of the Lithuanian High Cardiovascular Risk primary preventive program. The study collected demographic and clinical data, including an ultrasound assessment of carotid plaque. The participants were monitored for 4-10 years for CVD events and all-cause mortality. Results: Among 6,138 participants, 954 (16%) experienced cardiovascular disease (CVD) events. The presence of carotid plaque on both sides was significantly associated with CVD events, myocardial infarction, and all-cause mortality. However, the combination of hypertension and carotid plaque did not significantly increase the risk for CVD events, stroke, or all-cause mortality. Conclusion: The combination of hypertension and carotid plaque did not significantly increase the risk of CVD events or all-cause mortality. CV events depend on the spread of the atherosclerosis process in the carotid vessels.

This project designed a set of network security mechanisms for cloud applications in VANETs. At present, cloud computing is one of the government’s development priorities in the industry. We divide cloud into public, private, and hybrid clouds in this project. Vehicles or passengers can access road conditions or public transport information through the public cloud; public transportation can access current driving records and users can access relevant enterprise information on the private cloud and hybrid cloud is a combination of public and private clouds. Both cloud computing or VANETs require network and information security. At present, many researches on VANETs security only focus on message communication, and neglect information storage. Researches on cloud computing security only focus on information protection, and neglect user privacy and anonymity. This project designed a set of network and information security mechanisms in line with the requirements of Confidentiality, Authentication, Non-repudiation, Conditional Anonymity, and Conditional Untraceability. This project primarily needs to achieve the following: 1.an authentication mechanism to verify the identity of each other between the passenger and the vehicle and to verify the identity with Single Sign-On; 2. vehicle or user privacy and anonymity, which need to be able to replace the anonymous ID and related parameters for the vehicle or the user; 3. a private communication mechanism, which enables any vehicle or user to communicate privately; and 4. an information security encryption method, which can encrypt the information on a cloud server to avoid unauthorized access by internal personnel or hackers.

OBJECTIVE: To investigate the value of serum inflammatory factor levels in acute ischemic stroke patients after revascularization therapy in assessing acute prognosis. METHODS: 94 patients with acute ischemic stroke who underwent revascularization therapy admitted to our hospital from January 01, 2022 to January 01, 2024 were studied. The main evaluation index was modified Rankin Scale score (mRS) after 3 months, and patients with mRS score >2 were categorized into the poor prognosis group, and the rest were the good prognosis group. As well as the treatment of revascularization in all patients was used as a division into a thrombus extraction group and an intravenous thrombolysis group. Logistic regression analysis was applied to explore the independent risk factors for the prognosis of patients treated with recanalization for acute ischemic stroke. RESULTS: Two groups with good (59 cases) and poor (35 cases) prognosis were divided according to the mRS score. There were some differences in age, gender, emergency GLU, INR, whether they had diabetes or hypertension and other contents between the two groups, but the differences were not statistically significant. The NIHSS score, hospitalization days, previous cardiac history, hospitalization days, LDL, fasting glucose, IL-6, and TNF-a at the time of admission were lower than those in the poor prognosis group, and the difference was statistically significant. In the patients in the embolization group, there were some differences in age, gender, emergency GLU, INR, whether they had diabetes or hypertension and other contents between the two groups, but the differences were not statistically significant. The pre-hospital NHISS score, IL-6, and TNF-a of patients in the good prognosis group were lower than those in the poor prognosis group, and the difference was statistically significant. CONCLUSION: IL-6 and TNF-α may be used as predictors of prognosis after revascularization therapy for acute ischemic stroke, and this conclusion also applies to patients in the embolization group.

In recent years, extensive research has been conducted on the IoT, one of its challenges has been the field security and penetration in these networks. security solutions it Require more planning and awareness to ensure system security and privacy protection.it has also been shown that adjusting the weights of neural networks can help improve the accuracy of detection to what extent. the challenge in the attack detection field is to increase the accuracy of attack detection by machine learning methods, in this paper, a new method is presented to adjust weights of the random neural network for attack detection. Evaluations of the proposed method show that compared to random neural network methods, nearest neighbor, and support vector machine, the accuracy of attack detection improves up to 99.49 percent, and the random neural network method has been able to improve by 99.01 percent, placing the best methods together in these experiments in the form of a multi-learning model showed that the results also had an increase in accuracy up to 99.56 percent, while the training time in the proposed model is less compared to the random neural network method.

The purpose of this study is to examine the cognitive processes underlying the listening comprehension section of IELTS and to investigate if they vary in terms of difficulty. For this purpose, a checklist of possible cognitive operations was prepared based on the literature and the candidates’ feedback. The checklist consisted of six cognitive operations. A sample of IELTS listening test was given to 310 upper intermediate and advanced students of English. Linear logistic test model was employed to analyse the data. Findings showed that keeping up with the pace of the speaker and understanding reduced forms were the most difficult operations for the listeners. Altogether, the six operations explained 72% of the variance in item difficulty estimates. Implications of the study for the testing and teaching of listening comprehension are discussed.

Keywords: Vascular Malformation, Diode Laser, Angioma, Blue light, Conservative

The increase in population and the challenges caused by it are always among the concerns of decision-makers and policymakers in large countries. One of the most serious concerns is in the field of energy consumption. Optimizing energy use is one of the most effective ways to manage limited resources and energy. Energy consumption in residential houses constitutes a significant percentage of the total energy consumption worldwide; therefore, current research focusing on mathematical models aims to provide a predictive multi-objective linear mathematical model for residential house consumption. For this purpose, the consumption of water, electricity, and gas for 80 residential houses, including 10 household appliances and equipment on a weekly basis, has been considered. The results showed that the mixed-integer multi-objective predictor model is able to optimize household consumption plans compared to the actual state of household energy consumption. Since the goals of household satisfaction and energy consumption costs in the model conflict with each other, the influence coefficient was calculated to demonstrate the simultaneous achievement of these goals. The optimal value achievable with the default model for the model's purposes is 69%.

Hepatitis C virus (HCV) is a major public health threat associated with serious clinical consequences worldwide. The WHO reports an estimated 58 million people globally living with chronic hepatitis C virus (HCV) infection, with 1.5 million new cases and over 350,000 deaths annually due to HCV-related diseases. Despite the existence of multiple diagnostic methods, the accessibility of diagnosis and treatment is often hindered across the continent by limited resources, insufficient healthcare infrastructure, and low public awareness of Hepatitis C virus Hepatitis C virus infection is a major public health concern in African countries, which have the highest prevalence rates of HCV in the world (1–26%). Egypt, African country which had the highest prevalence of Hepatitis C virus infection in the world. Its national hepatitis C elimination initiative emerges as an exemplary case of success an having screened over 60 million individuals, and treated more than 4 million, thereby markedly diminishing both the prevalence and incidence of the disease. The strategies employed by Egypt offer critical insights and adaptable frameworks for other African Countries facing analogous challenges. The Aim of this Review is to highlight the triumphs and hurdles of Egypt's approach, while also providing strategic guidance for African Countries to accelerate the journey towards the eradication of hepatitis C.

One of the factors in the mobility of a nation's economy is the stock exchange which plays a decisive role in sustainable economic development. Accordingly, and for the special place of the stock exchange in the economy, we decided to investigate the relationship between the interest and exchange rates and the Tehran stock exchange dividend and price index (TEDPIX). Using the autoregressive distributed lag method, the present study is conducted utilizing the annual data over the period of 1991 to 2020. The results indicate that the exchange rate has a positive long-run relationship with the TEDPIX, but no statistically significant relationship was found between the TEDPIX and the interest rate. Furthermore, the results of the estimation of the error correction model show that 30.786% of the disequilibrium in each period is adjusted to the long-run equilibrium.

The impact of systemic vascular resistance (SVR) in hypertrophic (HCM) and amyloid cardio-myopathies has been seldom investigated. We studied pressure-strain loop derived myocardial work (MW) indices and SVR in patients with either sarcomere HCM or wild type transthyretin cardiac amyloidosis (ATTR), presented with heart failure and preserved left ventricular ejection fraction (HFpEF) at the outpatient cardiology units at Messina and Catania University Hospitals (Italy). Patients were in sinus rhythm and needed to have good transthoracic ultrasound imaging quality to undergo transthoracic strain-echocardiography. Other than conventional parameters, SVR and GWI (global work index) were measured. Based on cross-tabulation (a way to assess the ventricular-arterial coupling), the patients were then classified in 4 classes according to the cut-off values suggested by previous studies of 1,440 dyne/s/cm-5 for SVR (Stefadouros et al., 1973) and 1576 mm Hg% for GWI (Olsen et al., 2022). Sixty patients, 30 in each group, aged 61±16 years, 78% males, were studied. HCM were younger than ATTR patients and in a better clinical status (23% HCM vs 77% ATTR were NYHA class 3, p<0.001). Overall, 51 patients (85%) showed high SVR, of whom 21 were HCM (70%) and 30 ATTR (100%) (p<0.005). Both, SVR and GWI were impaired (the poorest class C) in 43% HCM patients (with greater left ventricular concentric hypertrophy) vs 93% of ATTR patients (in advanced NYHA class) (p<0.001). In conclusion, assessing SVR and GWI in HFpEF patients with HCM or ATTR can help clinicians to interpret the pathophysiological and clinical features of such complex diseases.

The central nervous system (CNS) is the final frontier in drug delivery because of the blood-brain barrier (BBB), which poses significant barriers to the access of most drugs to their targets. Kynurenic acid (KYNA), a tryptophan (Trp) metabolite, plays an important role in behavioral functions and abnormal KYNA levels have been observed in neuropsychiatric conditions. The current challenge lies in delivering KYNA to the CNS owing to its polar side chain. Recently, C-3 side chain modified KYNA analogs have been shown to cross the BBB; however, it is unclear whether they retain the biological functions of the parent molecule. This study examined the impact of KYNA analogs, specifically SZR-72, SZR-104, and the newly developed SZRG-21, on behavior. The analogs were administered intracerebroventricularly (i.c.v.), and their effects on the motor domain were compared with those of KYNA. Specifically, open field (OF) and rotarod (RR) tests were employed to assess motor activity and skills. SZR-104 increased horizontal exploratory activity in the OF test at a dose of 0.04 μmol/4 μL, while SZR-72 decreased vertical activity at doses of 0.04 and 0.1 μmol/4 μL. In the RR test, however, neither KYNA nor its analogs showed any significant differences in motor skills at either dose. Side chain modification affects affective motor performance and exploratory behavior, as the results show for the first time. In this study, we showed that KYNA analogs alter emotional components such as motor-associated curiosity and emotions. Consequently, drug design necessitates the development of precise strategies to traverse the BBB while paying close attention to modifications in their effects on behavior.

The electroencephalogram (EEG) is a record of brain activity. Brain Computer Interface (BCI) technology formed by the EEG signal has become one of the hotspots at present. How to extract the feature signal of EEG is the most basic research of BCI technology. In this paper, A new method of recognizing fatigue, conscious, concentrated state of human brain is proposed by the combination of discrete wavelet transform and the neural network based on EEG signal. First of all, the law signal is preprocessed by the wavelet denoising method because the law EEG signal contains a large number of high frequency noise, which is decomposed into multi-layer high frequency signal and low frequency signal. thus, δ wave, θ wave, α wave, β wave are obtained by the wavelet transform. And then, frequency band energy of the different wave is regards as the feature signal of EEG. In the experiment, the feature signal is classified by radial basic function (RBF) and annealed chaotic competitive learning network (ACCLN). RBF and ACCLN networks are trained with 500 sets of sample data and are tested by 100 sets of samples in different mental states. The experimental results show that the average accuracy of RBF network under three conditions are 88.75%, 88.25%, 88.5%, respectively, and the correct rate of ACCLN network is 97%, 98%, 98%, respectively.

Money demand is the amount of real money balance with people and they can decide at any time what part of their assets to keep in cash. In this study, money was assumed to be a durable good that leads to the flow of some kind of service. Therefore, money became a function of utility. Using AIDS elasticity demand function, income, price and cross elasticities were evaluated. The statistics of this study are related to the years 2003-2022. The results showed that, based on the assumption of three goods, the demand includes bills and negotiable instruments, demand deposits, and time deposits. Based on the limitation of symmetry, collectability and homogeneity, the tensions can be calculated. The results showed that the price elasticity of demand for banknotes and muskox with term deposits is not significant. On the other hand, the cross tension between sight deposits and term deposits is negative and significant.

General phytochemical screening of the aerial parts of Consolida orientalis revealed the presence of steroids, terpenes, phenolic compounds, saponins, fatty acids, alkaloids. This study was conducted to investigate the bioactivities of extracts, isolation and identification the compounds from aerial parts of C.orientalis. The main goal of the present study is identifying and characterizing the antioxidant activity of the Consolida orientalis and biological isolation of active terpenoid. Aerial parts of the plant were dried at room temperature and reduced to small pieces, followed by using extraction with ethyl acetate percolation. Tree complementary analysis system was used, DPPH free radical scavenging test, total phenolic metabolites and FRAP. The total phenolic content was 38.83±2.09 mg gallic acid corresponding to g-1 extract with regarding to standard curve (y=0.0054x+0.0488, r2=0.995). IC50 value for DPPH radical – scavenging was 987.11±28.66 mgml-1. The extract was exhibited a medium reducing power compared with Vit C. The isolation and purification was afforded white crystalline powder which was subjected to physical, chemical and spectral identification by IR, ¹H- and ¹³C- NMR and GC-MS. Isolated compound was identified as β-sitosterol. That is a terpenoid with melting point 133.4-134.5 ͦ c and with molecular formula C₂₉H₅₀O.

The way people innovate and create new ideas and bring them to the market is undergoing a fundamental change from closed innovation to open innovation. Why and how do firms perform open innovation? Firms’ open innovation is measured through the levels of firms’ joint patent applications. Next, we analyze network structures and characters of firms’ joint patent applications such as betweenness and degree centrality, structure hole, and closure. From this research, we drew conclusions as follows. First, the structure of collaboration networks has both direct and indirect effects on firms’ innovative performance. Second, in the process of joint patent applications, there is a long tail phenomenon in networks of joint patent applications. Third, the number of patents and International Patent Classification (IPC) subclasses together constitute a meaningful measure of the innovation performance of firms.