The pressing process and density profile of particleboards produced with addition of waste wood containing phenol-formaldehyde (PF) resin was presented. The particles were melamine-urea-phenol-formaldehyde (MUPF) resinated. Recovered particles were obtained from post-industrial waste plywood. The press closing time recorded during the pressing of mats containing only recovered particles in the core layer (100%), at a unit pressure of 3 MPa, in which the face layer rate was 50%, resin load for face and core layer was 12% and 10% was shorter by 29% than for the industrial particle mats. Regardless of the level of variability of independent factors the overheating time of mats to a temperature of 100 °C, containing recovered particles was 10% - 20% shorter than the time of overheating mats with industrial particles. The greatest impact (52%) on maximum density of the face layer of particleboards was demonstrated by the content of the recovered particles and the resin load (32%). The maximum density area of the face layer was located closer to the surface in particleboards produced with a higher (80%, 100%) content of the recovered particles, a higher (i.e. 12% and 10%, respectively, for face and core layer) resin load, a lower (35%) face layer rate and a higher (3 MPa ) unit pressure.

The frequent occurrence of rib fractures in slaughtered lambs results in significant economic loss to producers and processors in South Australia and raises concerns about the welfare of the animals. The prevalence of rib fractures became evident through the Enhanced Abattoir Surveillance (EAS) program introduced in 2007. The present study aimed to investigate the association between rib fracture prevalence in lambs at slaughter and their nutrition and management on the farm of origin. The slaughter of 30,055 lambs from 60 farms was monitored at a South Australian abattoir in November 2016. Rib fracture prevalence was 4% (range 0 - 18% per consignment), with all consignments of more than 240 lambs having at least one with fractured ribs. Traceback investigation on 56 of the 60 farms suggests that rib fractures most probably occur during routine husbandry procedures and, based on soil mineral analysis, are associated with calcium inadequacy. There was substantial variation between lines of lambs from different farms, and factors contributing to the differences included line size, region, and soil characteristics, including available calcium. There is a need to modify sheep husbandry and management practices to improve sheep welfare and reduce the cost of rib fractures to the sheep industry.

In this paper, we present a new parametric class of optimal fourth-order iterative methods to estimate the solutions of nonlinear equations. After the convergence analysis, we study the stability of this class by using the tools of complex discrete dynamics. This allows us to select those elements of the class with lower dependence on initial estimations. We verify by means of numerical tests that the stable members on quadratic polynomials perform better than the unstable ones, when applied to other non-polynomial functions. We also compare the performance of the best elements of the family with known methods, such as the schemes by Newton or Chun, among others, showing robust and stable behaviour.

Due to the particular structure of the beveloid gear, it can’t be hobbed directly by an ordinary gear hobbing machine. The existing processing method is complex and high-cost. Therefore, the mass production and industrialization of beveloid gears are limited. To improve the machining efficiency and accuracy of processing beveloid gears, we proposed a hobbing method via the modification of ordinary hobbing machines. At first, we completed the derivation and calculation of the relevant processing parameters of the beveloid gear based on the study of the structural characteristics of the beveloid gear and the principle of hobbing machining. Then, we proposed and designed a beveloid gear hobbing method, and the modification of the ordinary hobbing machine was completed by using a hanging wheel mechanism in synchronous belt type. Finally, we completed the actual hobbing of the beveloid gear, and the feasibility of the proposed method was verified. After that, we analyzed the machining error of the trial-produced beveloid gear, the results showed that the accuracy of the trial-produced beveloid gear met the 6-level standard, which also verified the accuracy of the proposed method.

Leaf area estimation is a critical component in the study of plant growth and productivity within agricultural systems. This research introduces the LeafArea package, a specialized tool designed to calculate the leaf area of six distinct Andean fruit species: S. quitoense, S. betaceum, P. peruviana, R. fruticosus, P. ligularis and P. edulis. Leveraging response variables such as species type, leaf length and width, the package employs advanced machine learning algorithms to estimate leaf area accurately. The primary focus of the study is to identify the most effective model for describing the relationship between leaf width, length, and area for each plant species. Currently, the LeafArea package utilizes four different machine learning algorithms, namely generalized linear model (GLM), generalized linear mixed model (GLMM), Random Forest and XGBoost. Among these, XGBoost stands out as a top-performing algorithm, exhibiting exceptional predictive accuracy. The evaluation metrics employed in the program provide valuable insights for researchers, aiding in informed decision-making. Specifically, XGBoost demonstrates significantly lower prediction errors and approaches a near-perfect R2 value, emphasizing its potential to enhance predictive accuracy. These results underscore the efficacy of machine learning techniques, as a compelling choice for researchers seeking precise and robust predictions in leaf area estimation. The LeafArea package thus represents a valuable tool for advancing our understanding of plant growth dynamics, resource allocation, and overall productivity within agricultural ecosystems.

The study’s objective is to analyse the mechanical properties of steel pipe piles as a part of a trestle bridge subjected to five years of natural marine corrosion degradation. Sixteen tensile specimens were extracted from the steel pipe piles in the splash, tidal, and immersion zones. The experimental tensile test results are used to establish regression equations defining elastic modulus, yield strength, strain hardening index, and strength coefficient for the true stress-strain curves of the three regions. A non-linear time-dependent mathematical model is exploited to identify the corrosion degradation using the data from one single corrosion degradation measurement campaign. The analysis indicated that the splash zone experiences the most severe corrosion degradation, and there are progressive losses in the mechanical properties of each zone as the corrosion degradation progresses. The established relationships of the mechanical properties, as a function of the ratio of corroded plate thickness to the as-built one, can be used as a fast-engineering approach to identify the mechanical properties of severely corroded piles. The corrosion degradation allowance is also defined using the First Order Reliability Method, accounting for existing uncertainties covered by the partial safety factors. By examining the impact of marine corrosion on the mechanical properties of marine structures and developing predictive models to assess the corrosion’s effect on material strength and corrosion allowance, the study aims to improve offshore structures’ safety, design, and maintenance.

The chemical reaction of ion-adsorption type rare earth during in-situ leaching process is accompanied by ion migration and charge movement, making the leaching process electrochemical in nature. The chemical reaction rate plays an important role in the leaching rate of rare earth elements. In this work, electrochemical impedance spectroscopy (EIS) was used to reveal characteristics of electrical resistance alteration and leaching rate of rare earth elements during in-situ leaching. The equivalent circuit model of the leaching process was established, and two critical parameters of solution resistance Rs and charge transfer resistance Rt were analyzed to reflect the electrochemical characteristics. According to the characteristics of electrical resistance alteration, the leaching process was divided into four stages: wetting, reaction, equilibrium, and top water stage. The resistance parameters Rs and Rt decreased first and then increased during the leaching process. The maximum value of Rs was 1330Ω∙cm2 at the end of the top water stage, and the minimum value was 135Ω∙cm2 at the beginning of the equilibrium stage. The maximum value of Rt was 8310Ω∙cm2 at the beginning of the leaching stage, and the minimum value was 21Ω∙cm2 at the end of the reaction stage. Rs and Rt were negatively correlated with the pore size and chemical reaction rate during leaching. With increasing of pore size and reaction rate , the resistance parameter decrease. This study provide a new idea for intelligent monitoring of rare earth ore.

The South of France is a biodiversity hotspot within Europe. Here, we present a comprehensive review of surveys conducted in the Cent Fonts aquifer, an overlooked subterranean biodiversity hotspot embedded in a region rich in stygobiotic species and threatened by climate change and water abstraction projects. Key studies, spanning from 1950 to 2006, show a progression in survey methods and results, although troglobiotic species remain poorly documented. With 43 stygobiotic species recorded, the Cent Fonts is the second richest stygobiont hotspot in Europe. Most species are regional endemics, a quarter of which are considered vulnerable by the IUCN. The Cent Fonts also hosts several relict species and is the type locality of four species. Such a high biological value clearly deserves to be preserved. Our analysis warns of a possible decline in biodiversity, as 8 of the species recorded in the 20th century were absent from the 2006 survey, suggesting potential threats of unknown origin. The capture of the Cent Font springs for water abstraction is discussed as a potential threat to this ecosystem and its unique biodiversity. Three new species of stygobiotic molluscs are described, one of which was collected in the Cent Fonts.

Localized jawbone invasion is a milestone in the progression of oral squamous cell carcinoma (OSCC). The factors that promote this process are not well understood. Sclerostin is known to be involved in bone metabolism and there are preliminary reports of its involvement in bone tumors and metastasis. To identify a possible involvement of sclerostin in the bone invasion process of OSCC, sclerostin expression was analyzed in vitro in two different human OSCC tumor cell lines by qRT-PCR, and the effect of recombinant human (rh)-sclerostin treatment on tumor cell capabil-ities was evaluated using proliferation, migration, and invasion assays. Undifferentiated human mesenchymal stem cells (hMSCs) were osteogenically differentiated and co-cultured with OSCC tumor cells to demonstrate potential interactions on migration characteristics. Sclerostin expres-sion was evaluated in clinical cases by immunohistochemistry at the OSCC jawbone interface in a cohort of 15 patients. Sclerostin expression was detected in both OSCC tumor cell lines in vitro and was also detected at the OSCC jawbone interface in clinical cases. Tumor cell proliferation rate, migration and invasion ability were increased by rh-sclerostin treatment. The migration rate of tumor cells co-cultured with osteogenically differentiated hMSCs was increased. The re-sults presented are the first data suggesting a possible involvement of sclerostin in the bone inva-sion process of OSCC, which needs to be further investigated

In order to analyze the spatial distribution characteristics of the cultural landscape of the Yangtze River (Nanjing section) and show the role of the Yangtze River culture in promoting regional economic development and cultural radiation, Based on the Knowledge graph technology and 3S technology, this paper conducts geographical positioning and spatial analysis of the Yangtze River cultural landscape in Nanjing, and analyzes the surrounding environment by combining remote sensing images and topographic data. Researches show that: The Yangtze River cultural landscape in Nanjing includes 74 landscape points of seven landscape types, and the distribution of landscape points shows a clustering trend, but the spatial distribution and aggregation degree of different types of cultural landscapes are different. Due to the influence of climate environment and water resources distribution, the surrounding cultural landscape has a low altitude and high vegetation coverage, showing obvious distribution characteristics along the river. This study is helpful to improve the coordination degree between cultural landscape protection and planning management, and can provide useful reference for the protection of cultural landscape of the Yangtze River.

The ability of deep learning to process vast data and uncover concealed malicious patterns has spurred the adoption of deep learning methods within the cybersecurity domain. Nonetheless, a notable hurdle confronting cybersecurity researchers today is the acquisition of a sufficiently large dataset to train deep learning models effectively. Privacy and security concerns associated with using real-world organization data have made cybersecurity researchers seek alternative strategies, notably focusing on generating synthetic data. Generative Adversarial Networks (GANs) have emerged as a prominent solution, lauded for their capacity to generate synthetic data spanning diverse domains. Despite their widespread use, the efficacy of GANs in generating realistic cyber attack data remains a subject requiring thorough investigation. Moreover, the proficiency of deep learning models trained on such synthetic data to accurately discern real-world attacks and anomalies poses an additional challenge that demands exploration. This paper delves into essential aspects of generative learning, scrutinizing their data generation capabilities, and conducts a comprehensive review to address the above questions. Through this exploration, we aim to shed light on the potential of synthetic data in fortifying deep learning models for robust cybersecurity applications.

Creating environmentally sustainable oral healthcare culture requires embedding contents in dental curriculum. This study aimed to explore the current knowledge and drivers among dental students and educators regarding environmentally sustainable dentistry (ESD) in the United Arab Emirates (UAE) and identify barriers and enablers to embrace it. A cross-sectional survey using online questionnaire was carried out in undergraduate dental educational institutes. Data analysis included descriptive statistics and thematic analysis. Most students and educators were not aware of ESD content in their curriculum. However, the majority of educators were familiar with ESD concept, while students were mostly unfamiliar. Nonetheless, students largely agreed on its im-portance and their interest in learning it, as they view it relevant to their future practice. Educators agreed that the dental profession has a responsibility to be environmentally friendly and expressed desire in introducing ESD content into the curricula. Several barriers were reported such as lack of knowledge, curriculum space, educational resources, and time. Meanwhile, enablers included providing training and resources. There is no explicit presence of ESD content in the dental curricula. Regardless of the lack of in-depth knowledge among educators and more so with students, they both have positive views toward incorporating ESD into dental curricula.

Febrile infections are a common cause of presentation to the emergency department (ED) in chil-dren. While viral infections are usually self-limiting, sometimes bacterial illnesses may lead to sepsis and severe complications. Inflammatory biomarkers such as C reactive protein (CRP) and procalcitonin are usually the first blood exams performed in the ED to differentiate bacterial and viral infections; the better understanding of immunochemical pathways has nowadays led to the discovery of new and more specific biomarkers that could play a role in the emergency setting. The aim of this narrative review is to provide the most recent evidence on biomarkers and predictor models combining them for serious bacterial infections (SBI) diagnosis in febrile children. Litera-ture analysis showed that inflammatory response is a complex mechanism in which many bio-chemical and immunological factors contribute to host response in SBI. CRP and procalcitonin still represent the most used biomarkers in the pediatric ED for the diagnosis of SBI. Their sensibility and sensitivity increase when combined and for this reason it is reasonable to take them both into consideration in the evaluation of a febrile children. The potential of machine learning tools, who represent a real novelty in medical practice, in conjunction with routine clinical and biological in-formation, may improve accuracy of diagnosis and target therapeutic options in SBI. However, studies on this matter are not yet validated in younger populations, making their relevance in pe-diatric precision medicine still uncertain. More data from further researches are needed to improve clinical practice and decision making using these new technologies.

This study centered on examining 60 different antibiotics by assessing their primary toxicity parameters through the pkCSM database. The parameters included Max. Tolerated Dose (Human) (log mg/kg/day), Oral Rat Acute Toxicity (LD50) (mol/kg), Oral Rat Chronic Toxicity (LOAEL) (log mg/kg_bw/day), and Minnow Toxicity (LC50) (log mM). The results highlight four potential antibiotics—cefotaxime, ceftriaxone, imipenem, and meropenem—that exhibited lower toxicity effects compared to other compounds. These findings imply a relatively favorable safety profile for these antibiotics concerning the specified toxicity parameters. However, it is crucial to interpret these outcomes within the context of the specific clinical application and to consider additional factors for a comprehensive safety assessment. It's essential to acknowledge that evaluating toxicity is intricate, and the determination of what is considered "less toxic" can rely on specific criteria and the context of use. Furthermore, individual responses to antibiotics may vary, and the selection of an antibiotic should be based on diverse factors, including the specific infection being treated, the patient's health, and potential side effects.

Bandung and Lembang are the cities that are chosen by many tourists (local and foreign) as their destinations. Geographically, these cities are placed side-by-side and each city has its own characteristics. As in Bandung, there are many hotels and culinary spots, meanwhile, in Lembang, there are many scenery tourism spots. Tourists usually have limited time to visit all the destinations on holiday, which makes them choose several tourist destinations to visit. In this paper, the tourism itinerary recommendation system is proposed by calculating the most optimal route between destinations and approaching the Vehicle Routing Problem with Time Windows. The optimal route is defined by using Dijkstra the shortest path algorithm by using the collaboration between the road information (road length, traffic condition, travel time, and weather condition) and criteria weight that is determined using the Analytics Hierarchy Process as the graph weights. According to the simulation results, the criteria weights are 6.9%, 62.7%, 18.6%, and 11.9% for Route Length, Traffic Condition, Travel Time, and Weather Condition respectively. Moreover, the optimal number of tourism itinerary plans for the tourist in this research is 4 destinations. As the usage of computational resources, it takes 31.8% of CPU and 61.9% of Memory usage. For the time processing, it increases exponentially as the increment of number of requested stops. The output of this research is expected to be a solution to the tourist itinerary plan, especially in Bandung and Lembang.

This research provides a computationally efficient numerical tool for predicting 3-D turbulent flows over hydrofoils. It addresses a gap in existing numerical methods by extending the capabilities of the laminar VIScous Vorticity Equation (VISVE) solver to handle 3-D turbulent flow scenarios. The implementation integrates the k − ω SST model into the 3-D VISVE solver using the Finite Volume Method (FVM), extending its capability to handle turbulent flows. The turbulence model is also parallelized to improve computational efficiency. The application of the newly developed model to 3-D hydrofoil cases expands its scope and serves as preparatory work for future rotational propeller applications. Rigorous testing includes a convergence analysis concerning grid numbers and time step sizes, and validation against a Reynolds-Averaged Navier-Stokes (RANS) solver ensures the reliability and accuracy of the enhanced VISVE solver for turbulent flow simulations. The turbulent VISVE solver offers advantages such as reduced computational domain and costs through a vorticity-based approach. Turbulence concentration within boundary layers and free shear flows does not compromise the advantages of the small computational domain. The simplified meshing process, automatically generated by denoting the number of panels on the hydrofoil, makes the solver accessible for researchers and engineers.

Nuclear fusion reactors are composed by several complex components whose behaviour may be not certain a priori. This uncertainty may have a significant importance in the evolution of fault transients in the machine, causing unexpected damages to its components. For this reason a suitable method for the uncertainty propagation during those transient is required. The Monte Carlo method would be the reference option, but it is, in most of the cases, not applicable due to the large amount of required simulations to be repeated. In this context the Polynomial Chaos Expansion has been considered as a valuable alternative. It allows to create a surrogate model of the original one in terms of orthogonal polynomials. Then, the uncertainty quantification is performed repeatedly calling this much simpler and faster model. Using the fast current discharge in the Divertor Tokamak Test Toroidal Field (DTT TF) coils as a reference scenario, this method has been applied: the uncertainty on the parameters of the Fast Discharge Unit (FDU) varistor disks is propagated to the simulated electrical and electro-magnetic relevant effects. Eventually two worst case scenarios have been analyzed from the thermal-hydraulic point of view with the 4C code, simulating a fast current discharge as a consequence of a coil quench. Eventually it has been demonstrated that the uncertainty on the inputs (varistor parameters) strongly propagates obtaining a wide range of possible scenarios in case of accidental transients. This result underlines the necessity to take into account and propagate all possible uncertainties in the design of a fusion reactor, according to the Best Estimate Plus Uncertainty approach.

The Digital Twin is one of the big technology trends of the last decade. In the course of its rapid expansion into the various fields of application, many different definitions emerged, tailored to the respective applications. Taxonomies can cluster the diversity and define application-specific archetypes. This paper presents a characterization of the Digital Twin in the context of structural mechanics and lightweight design. It begins by exploring various definitions of the Digital Twin concept and its application in different industries. The paper further analyzes the product life cycle of structures, dividing it into the design and operational phases and identifying the requirements for a Digital Twin in each phase. The structural design process is examined, including the definition of a structure and the stages of the product development process. Additionally, the paper discusses the operational phase of structures, highlighting the need for continuous monitoring, evaluation, and adaptation through Structural Health Monitoring (SHM) systems. The paper proposes two archetypes of Digital Twins for structures: a structure-designing Digital Twin for the design phase and a structure-monitoring Digital Twin for the operational phase. The findings provide a framework for implementing Digital Twins in structural mechanics, enabling digital design and monitoring of structures for improved performance and maintenance strategies.

Stroke remains one of the leading causes of death and disability worldwide. Current reperfusion treatments for ischaemic stroke are limited due to their narrow therapeutic window in rescuing ischaemic penumbra. Stem cell therapy offers a promising alternative. As a regenerative medicine, stem cells offer a wider range of treatment strategies, including long-term intervention for chronic patients, through the reparation and replacement of injured cells via mechanisms of differentiation and proliferation. The purpose of this review is to evaluate the therapeutic role of stem cells for ischaemic stroke. The paper discusses the pathology during acute, subacute, and chronic phases of cerebral ischaemic injury, highlights the mechanisms involved in mesenchymal, endothelial, haematopoietic, and neural stem cell-mediated cerebrovascular regeneration and evaluates the pre-clinical and clinical data concerning the safety and efficacy of stem cell-based treatments. Treatment of stroke patients with different type of stem cells appears to be safe and efficacious even at relatively higher concentrations irrespective of the route and timing of administration. Priming or preconditioning of cells prior to administration appears to help augment their therapeutic impact. However, larger patient cohorts and later phase trials are required to consolidate these findings.

Background and Objectives: Although adult spinal deformity (ASD) surgery brought about the improvement of quality of life in patients, it accompanied with a high invasiveness of surgery and several complications. Specifically, mechanical complications of rod fracture, instrumentation failures, and pseudarthrosis are still unsolved issues. To better improve these problems, the oblique lateral interbody fusion at L5/S1 (OLIF51) was introduced since 2015 in my institution. The objective of this study was to compare the clinical and radiographic outcomes of anterior-posterior combined surgery for ASD between the use of OLIF51 versus transforaminal interbody fusion (TLIF) at L5/S1. Materials and Methods :A total of 117 ASD patients received anterior-posterior correction surgeries either use of OLIF51 (35 patients) or L5/S1 TLIF (82 patients). In both groups, L1-5 OLIF and minimally invasive posterior procedures of hybrid or circumferential MIS were employed. The sagittal and coronal spinal alignment and spino-pelvic parameters are recorded preoperatively and at follow-up. The quality of life parameters and visual analogue scale were evaluated as well as surgical complications at follow-up. Results: The average follow-up period was thirty months (13-84). The average fused segments were eight (4-12). The operation time and estimated blood loss were significantly smaller in OLIF51 that those in TLIF. The PI-LL mismatch, LLL, L5/S1 segmental lordosis, and L5 coronal tilt were significantly better in OLIF51 than TLIF. The complication rate was statistically equivalent between two groups. Conclusion: The introduction of OLIF51 for adult spine deformity surgery led to the decrease of operation time and estimated blood loss as well as the improvement of sagittal and coronal correction when compared to TLIF.

Abstract: Rural digital governance is an inevitable requirement to improve the efficiency of rural governance, and is also an important means to realize the modernization of rural governance. In the context of the digital rural development strategy, the index measurement system of rural digital governance level is built around five key governance areas of "digital economy, digital ecology, digital culture, digital people's livelihood, and digital government affairs". The entropy weight Topsis model is used to measure and evaluate the level of rural digital governance in 31 provinces in China in 2021. The results show that there is a large gap in the level of digital governance in China's counties and villages, and the level of each region presents a decreasing spatial distribution from "east - middle - west". In terms of digital economy, the eastern region has a high score and good development, while the central and western regions have poor development. In terms of digital ecology, only the eastern region is higher than the national average; In terms of digital culture, only the central region is higher than the national average; In terms of digital livelihood and digital government, the central and eastern regions are slightly higher than the national average; The top three provinces in overall scores are Zhejiang, Guangdong and Jiangsu.

Penrose’s singularity from 1965 predicted that in the center of the black hole space-time curvature is infinite and consequently gravity force there is infinite. In 2014 NASA measured universal space has Euclidean shape. This means that stellar objects cannot curve universal space and that the space-time singularity model has no physical existence. In the center of black holes energy density of superfluid space is so low that the electromagnetic properties of space are changed. This causes electromagnetic forces between the nucleus and orbiting electrons to become unstable. In the center of a black hole, atoms are falling apart into elementary particles. Black holes are rejuvenating systems of the universe, they transform old matter into fresh energy in the form of elementary particles. Astrophysical jets are the outcome of this process.

Bambara groundnut (BG) (Vigna subterranean L. verdc) is a highly nutritious and economically important leguminous crop of African origin that forms symbiotic relationships nitrogen fixing rhizobia, but it has a low yield. Currently, there is paucity of information on effective rhizobium inoculant, (to improve the yield), for BG which are cheaper and safer alternatives to chemical fertilizers. Indigenous rhizobia strains were trapped from 54 farm soils collected at three Nigerian states (Niger, Kaduna and Kano). Six selected rhizobium strains (Bradhyrhizobium spp) were identified using 16S rDNA sequencing, applied as inoculant in field experiment using completely randomized block design and compared with USDA110 strain (Bradhyrhizobim japonicum), urea fertilizer and un-inoculated plants on 2 selected BG varieties (TVSU1248 and TVSU 631). Nutrient utilization and nitrogen fixation in plants were determined at 6 weeks while leaf chlorophyll was determined forth-nightly. Seed yield was determined at maturity. The strains increased the %nitrogen (1.33±0.10 - ≥1.37±0.05), and %phosphorous (0.48±0.02 - ≥0.51±0.02), %nitrogen fixed (21.07±2.38 - ≥61.30±6.13), nodulation (72.50±21.075 - 154.00±23.79) and leaf chlorophyll were ≥49.74±1.47mg/L, ≥51.82±1.86mg/L and ≥49.65±1.56mg/L at 2nd, 4th and 6th week after planting respectively and the yield of BG (from average of 398.6 Kgha-1 to 1454.725 Kgha-1). Seed yield was highest (1869.85±273.68 Kgha-1) in BG variety TVSU1248 using BN5 strain. Indigenous Bambara-symbiotic strains significantly increased the seed yields, and plant nutrient, showing their potential as use for inoculant to improve the yield of Bambara groundnut.

C-peptide is used as a measure of endogenous insulin production. Given that insulin and C-peptide are produced in equal amounts, C-peptide is typically used to differentiate between external and endogenously produced insulin in insulin-treated type 1 diabetes mellitus (T1DM). In a clinical setting, a decline in C-peptide is regarded as a loss of beta cell function. However, physiological conditions may also be associated with low C-peptide levels. The authors of this paper use a low-carbohydrate diet, the so-called paleolithic ketogenic diet (PKD), in the treatment of various conditions and observed that C-peptide is typically low on this diet. In order to characterize C-peptide levels on this diet, we designed a study to retrospectively assess C-peptide levels in 100 non-T1DM subjects. We found that 55% of the subjects had a C-peptide level below the standard reference range. C-peptide levels correlated with glucose levels. A significant correlation was found between C-peptide and age, with younger subjects having lower C-peptide levels. Males also showed lower C-peptide levels than females. Given the increasing number of patients using low-carbohydrate diets worldwide, physicians should be aware of laboratory correlates of low-carbohydrate diets, including low C-peptide levels, most importantly to prevent incorrect T1DM diagnosis.

In the rapidly evolving landscape of wireless communication, each successive generation of networks has achieved significant technological leaps, profoundly transforming the way we connect and interact. From the analog simplicity of 1G to the digital prowess of 5G, the journey of mobile networks has been marked by constant innovation and escalating demands for faster, more reliable, and more efficient communication systems. As 5G becomes a global reality, laying the foundation for an interconnected world, the quest for even more advanced networks leads us to the threshold of the sixth-generation (6G) era. This paper presents a hierarchical exploration of 6G networks, poised at the forefront of the next revolution in mobile wireless technology. This study delves into the technological advancements that underpin the need for 6G, examining its key features, benefits, and the key enabling technologies. We dissect the intricacies of cutting-edge innovations like terahertz communication, ultra-massive MIMO, artificial intelligence (AI), machine learning, quantum communication, and re-configurable intelligent surfaces. Through a meticulous analysis, we evaluate the strengths, weaknesses, and state-of-the-art research in these areas, offering a wider view of the current progress and potential applications of 6G networks. Central to our discussion is the transformative role of AI in shaping the future of 6G networks. By integrating AI and machine learning, 6G networks are expected to offer unprecedented capabilities, from enhanced mobile broadband to groundbreaking applications in areas like smart cities and autonomous systems. This integration heralds a new era of intelligent, self-optimizing networks that promise to redefine the parameters of connectivity and digital interaction. This paper also addresses critical challenges in the deployment of 6G, from technological hurdles to regulatory concerns, providing a holistic assessment of potential barriers. By highlighting the interplay between 6G and AI technologies, this study not only maps out the current landscape but also lights the path forward in this rapidly evolving domain. This paper aims to be a cornerstone resource, providing essential insights, addressing unresolved research questions, and stimulating further investigation into the multifaceted realm of 6G networks. By highlighting the synergy between 6G and AI(Artificial Intelligence) technologies, we aim to illuminate the path forward in this rapidly evolving field.

Transitions from seed to seedling represent a critical developmental step in the life cycle of higher plants, dramatically affecting plant ontogenesis and stress tolerance. The release from dormancy to acquiring the germination ability is defined by a balance of phytohormones, with a substantial contribution of abscisic acid (ABA), which inhibits germination. We studied the embryonic axes of Pisum sativum L. before and after radicle protrusion. Our previous work compared RNA sequencing-based transcriptomics in the embryonic axes isolated before and after radicle protrusion. The current study aims to analyze ABA-dependent gene regulation during the transition of embryonic axes from germination to post-germination stage. First, we determined the levels of abscisates (ABA, phaseic acid, dihydrophaseic acid, and neo-phaseic acid) using the ultra-high-performance liquid chromatography-tandem mass spectrometry. Second, we made a detailed annotation of ABA-associated genes using RNA sequencing-based transcriptome profiling. Finally, we analyzed the DNA methylation pattern in the promoters of the PsABI3, PsABI4, and PsABI5 genes. We showed that changes in the abscisate profile are characterized by the accumulation of ABA catabolites, and the ABA-related gene profile is accompanied by the upregulation of genes controlling the seedling development and the downregulation of genes controlling the water deprivation. The expression of ABI3, ABI4 and ABI5, which encode the crucial transcription factors during late maturation, was downregulated by more than 20-fold, and their promoters exhibited a high level of methylation already at the late germination stage. Thus, although ABA remains important, other regulators seems to be involved in the transition from seed to seedling.

Base excision repair (BER) is the predominant pathway to remove most forms of hydrolytic, oxidative and alkylative DNA damage. The precise functioning of BER is achieving by the regulation of each step by regulatory/accessory proteins, with the most important of which being poly(ADP-ribose) polymerase 1 (PARP1). The PARP1 regulatory functions extend to many cellular processes including regulation of mRNA stability and decay. PARP1 can therefore affect BER both at the level of the BER proteins and at the level of their mRNAs. Systematic data on how the PARP1 content affects the activities of key BER proteins and the levels of their mRNAs in human cells are extremely limited. In this study the CRISPR/Cas9 based technique has been used to knocked out the PARP1 gene in the human HEK 293FT line. The obtained cell clones with putative PARP1 deletion were characterized by several approaches including: PCR analysis of deletions in genomic DNA, Sanger sequencing of genomic DNA, qPCR analysis of PARP1 mRNA, western-blot analysis of whole-cell-extract proteins with anti-PARP1 antibodies, and the PAR synthesis in the whole-cell-extracts. qPCR analysis of mRNAs coding the BER related proteins: PARP2, uracil DNA glycosylase 2, AP endonuclease 1, DNA polymerase β, DNA ligase III and XRCC1 did not reveal crucial influence of PARP1 knockout. The corresponding catalytic activities in the whole-cell extracts (WCEs) evaluated in parallel did not differ significantly between mutant and parental cell lines. No considerable influence of poly(ADP-ribose) synthesis on the activity of the above WCE enzymes was revealed.

Crystalline phase engineering is a prominent strategy for synergistically optimizing the sur-face-body phases of a catalyst. In this work, TiO2 nanosheets assembled into nanotubes (TNSTs) with two-phases of anatase and rutile were firstly synthesized via crystal engineering by simple thermal annealing, and subsequently loaded with Ru nanoparticles to create efficient benzene hydrogenation catalyst Ru/TNSTs. The well- designed nanosheet-tubes structure boasts large specific surface area and excellent transmission channels, which effectively prevents the ag-glomeration and deactivation of loaded Ru nanoparticles, as well as promotes the internal diffu-sion in the reaction process of benzene hydrogenation to cyclohexene. Furthermore, titanium dioxide nanosheet-tubes contain numerous Ti3+ defects, which not only improves the overall generation rate of cyclohexene, but also enhances the suppression of cyclohexene adsorption. Most importantly, the two-phases of titanium dioxide increases the ratio of the electron defi-ciencies of Ru and promotes cyclohexene desorption. These synergistic properties enhance the selectivity and efficiency of the Ru/TNSTs catalysts, resulting in excellent performance in the hydrogenation of benzene to cyclohexene. Especially the Ru/TNSTs-4 catalyst achieves a 95% in-itial selectivity and 51% yield of cyclohexene in the reaction, outperforming most supported Ru-based catalysts. This work may provide new perspectives for designing efficient benzene hydrogenation catalysts via crystalline phase engineering.

In order to utilize high-alumina coal gangue (HACG) as auxiliary cementitious admixture, it is of fundamental significance to thermally activating Al2O3 effectively in HACG and promoting formation of hydratable products containing Al2O3. In this paper, a new preparation technology is developed to make HACG auxiliary cementitious admixture by thermal activation of HACG-Ca(OH)2 (CH) mixture. HACG powders mixed with 20wt.% CH were calcined within a temperature range of 600-900oC, the thermal transformation and mineral phase formation were analyzed by thermogavimetric analyzer (TGA), X-ray diffractometer (XRD), scanning electron microscope (SEM) and energy dispersive X-ray spectrometer (EDS). The hydration reaction between activated HACG-CH mixture and cement was also investigated. HACG experienced a conventional transformation from kaolinite to metakaolin at 600oC and finally to mullite at 900oC. However, CH underwent an unexpected transformation process from CH to CaO, then to CaCO3, and finally to CaO again with increasing calcining temperature. These substances states were associated with dehydroxylation of CH, chemical reaction between CaO and CO2 generating from combustion of carbon in HACG, and decomposition of CaCO3, respectively. It is the formation of a large amount of CaO above 800oC that favors the formation of hydratable products containing Al2O3 in the calcining process and C-A-H gel in hydration process. The mechanical properties of HACG-cement mortar specimens were measured, from which the optimal calcination temperature for preparing HACG auxiliary cementitious admixture was determined to be 850oC. As compared with pure cement mortar specimens, the maximum 28-d flexural and compressive strength of HACG-cement mortar specimens increased by 5.4% and 38.2%, respectively.

Several life contingency agreements are based on the assumption that policyholders have impaired life expectancy attributable to factors such as lifestyle, social class, or preexisting health issues. Quantifying two crucial variables, augmented death probabilities and the discount rate of projected cash flows, is essential for pricing such agreements. Information regarding the correct values of these parameters is subject to vagueness and imprecision, which further intensifies if impairments must be considered. This study proposes modelling mortality and interest rates using a generalization of fuzzy numbers (FNs), known as intuitionistic fuzzy numbers (IFNs). Consequently, this paper extends the literature on life contingency pricing with fuzzy parameters, where uncertainty in variables, such as interest rates and death probabilities, is modelled using FNs. While FNs introduce epistemic uncertainty, the use of IFNs adds bipolarity to the analysis by incorporating both positive and negative information regarding actuarial variables. Our analysis focuses on two agreements involving policyholders with impaired life expectancy: determining the annuity payment in a substandard annuity and pricing a life settlement over a whole life insurance policy. In particular, we emphasize modelling interest rates and survival probabilities using triangular intuitionistic fuzzy numbers (TIFNs) owing to their ease of interpretation and implementation.

Through the refinement of approaches to applying visual augmentation to virtual environments, the possibilities of utilising this process in robotics continues to be explored. However these endeavours have only yielded a collaborative relationship between the two technologies, where the augmented reality (AR) component is separate from the robot functionality. Regarding telepresence robots, research has shown that their perceived usefulness wanes in comparison to that of video conferencing tools as they do not offer a level of immersion and modified visual experience to justify their use. Therefore this paper contributes a novel virtually augmented robot (VAR) model that embeds AR functionality into telepresence, through the use of augmentation and facial recognition sub-models and algorithms. Experimental results show that using pulse width modulation (PWM), duty cycles, digital signals and accelerometer angles provides an approach for stabilising robot motion and the transference of human to robot head movement. The results also show the benefit of having a robot model with intrinsic AR functionality, as it offers a greater level of immersion and visual control of an environment.

Antimicrobial resistance (AMR) is a growing public health issue worldwide that affects the world economy. The concern associated with this issue increased during the COVID-19 pandemic. The misuse of antibiotics during the COVID-19 pandemic will have catastrophic implications for the control of AMR. In 2019, the coronavirus illness COVID-19 first appeared in China. COVID-19 infection is a surface-to-surface communicable disease. It is considered the most vital global health disaster of the century. It has rapidly spread around the world and is the greatest challenge for humankind. This chapter discusses the origin, symptoms, transmission, treatment, and recommendations of the COVID-19 disease.

A new approach for predicting the geometrical characteristics of the mixing behavior of an inclined dense jet for angles ranging from 15° to 85° is proposed in this study. This approach is called group method of data handling (GMDH) which is based on the artificial neural network (ANN) technique. The proposed model was trained and tested using existing experimental data reported in literature. The model was then evaluated using statistical indices as well as compared with analytical models from previous studies. The results of the coefficient of determination (R2) indicate a high accuracy of the proposed model with values of 0.9719 and 0.9513 for training and testing for the dimensionless of the distance from the nozzle to the return point xr/D, and 0.9454 and 0.9565 for training and testing for the dimensionless of the terminal rise height yt/D . Moreover, four previous analytical models were used to evaluate the GMDH model. The results showed the superiority of the proposed model in predicting the geometrical characteristics of the inclined dense jet for all tested angles. Finally, the standard error of estimate (SEE) was applied to demonstrate which model performed the best in terms of getting closer to the actual data. The results illustrate that all fitting lines of the GMDH model performed very well for all geometrical parameter predictions and was the best model with approximately 10% error, which was the lowest value of error among the models. Therefore, this study confirms that the GMDH model can be used to predict the geometrical properties of the inclined negatively buoyant jet with high performance and accuracy.

A symbolic analysis of Archimedes’ periodical number system is developed, from which a natural link emerges with the modern positional number systems with zero. After the publication of Fibonacci’s Liber Abaci, the decimal Indo-Arabic positional system was the basis of the algorithmic and algebraic trend of modern mathematics, but even if zero plays a crucial role in algebra and mathematical analysis, zeroless positional systems show the same capability of producing efficient arithmetical algorithms based on operation tables over digits. The crucial role of digits is assessed, by considering a representation of numbers based on strings in lexicographic order. A new algorithm for the determination of decimal periods is presented,, by remarking the cruciality of this topic in number theory. Periods of ordinal numbers, and enumerations of recursive enumerability are shortly recalled. Concluding remarks are formulated about the deep relationship among numbers and information, which shed new light on a red line passing through the whole history of mathematics

This study delves into the therapeutic potential of two natural compounds, Amentoflavone and Bilobetin, by investigating their interactions with the BPIFB4 protein. BPIFB4, a member of the BPI fold-containing superfamily, has been associated with various biological processes, particularly in longevity and cardioprotection( LAV-BPIFB4). Molecular docking analyses revealed promising binding energies for Amentoflavone and Bilobetin with BPIFB4, suggesting favorable interactions.Additionally by pKCSM database are showed the predicted toxicity properties of Amentoflavone and Bilobetin respectively.This research contributes to the growing body of knowledge on natural compounds and their therapeutic implications in the context of vital biological processes. However, these findings underscore the need for further investigations, including in vivo studies and clinical trials, to validate the observed effects and explore the translational potential of Amentoflavone and Bilobetin in promoting cardiovascular health and longevity.

In vertebrates, oligodendrocytes (OLs) are glial cells of the central nervous system (CNS) responsible for the formation of the myelin sheath that surrounds the axons of neurons. The myelin sheath plays a crucial role in the transmission of neuronal information by promoting the rapid saltatory conduction of action potentials and providing neurons with structural and metabolic support. Saltatory conduction, first described in the peripheral nervous system (PNS), is now generally recognized as a universal evolutionary innovation to respond quickly to the environment: myelin helps us think and act fast. However, the function of myelin in the CNS, particularly in the brain, is not necessarily to act quickly, but rather to act correctly. In this respect, myelin should primarily play a role in synchronizing the different neuronal networks, a synchrony that occurs in the form of oscillations (or rhythms) relevant for specific information processing. Interestingly, myelin has been directly involved in different types of cognitive processes relying on brain oscillations, and myelin plasticity is currently considered to be part of the fundamental mechanisms for memory formation and maintenance. However, despite ample evidence showing the involvement of myelin in cognition and neurodevelopmental disorders characterized by cognitive impairments, the link between myelin, brain oscillations, cognition and disease is not yet fully understood. In this review, we aim to highlight what is known and what remains to be explored to understand the role of myelin in high order brain processes.

Biofilm-related infections play a significant role in the development and persistence of ventilator-associated pneumonia. Pseudomonas aeruginosa frequently causes biofilm-related infections associated with ventilator tubing. Glucosamine gold nanoparticles (AuNPs) may exhibit antibiofilm properties; however, more studies, including combinatorial therapy with antibiotics, are needed to explore their potential applications in clinical settings. This study aimed to investigate the biofilm inhibition properties of glucosamine AuNPs in combination with meropenem against Pseudomonas aeruginosa ATCC 9027 on the endotracheal tube. A biofilm inhibition assay of glucosamine AuNPs at 0.02 mg/ml, both singly and in combination with meropenem at 1 mg/ml, was carried out against Pseudomonas aeruginosa ATCC 9027 on an endotracheal tube using the tissue culture plate method. Scanning electron microscopy was performed for visualization. Glucosamine AuNPs at 0.02 mg/ml combined with meropenem at 1 mg/ml showed greater biofilm inhibition (72%) than glucosamine nanoparticles at 0.02 mg/ml alone (26%) on the endotracheal tube (p=0.001). The scanning electron microscopic visualization revealed that the untreated Pseudomonas aeruginosa biofilm was denser than the glucosamine nanoparticles-treated biofilm either combined with meropenem or glucosamine nanoparticles alone. The combination of glucosamine AuNPs and meropenem may have the synergistic effect to inhibit biofilm production of Pseudomonas aeruginosa on the endotracheal tube in patient with mechanical ventilation. Conducting additional experiments to explore the impact of combining glucosamine-coated gold nanoparticles (AuNPs) with meropenem on the inhibition of biofilm production by clinical Pseudomonas aeruginosa isolates would be beneficial.

This study investigates the socio-economic determinants correlated with Post-Traumatic Stress Disorder (PTSD) symptoms amid the 'Guardian of the Walls' operation, in the Israel-Hamas conflict. Our investigation primarily focused on assessing the effects of missile strikes on Israeli civilians. By conducting an in-depth statistical analysis of individual experiences, we sought to unravel the correlation between socioeconomic status and PTSD susceptibility.Our findings reveal a stark disparity in PTSD risks across different socio-economic groups. Individuals from lower socio-economic backgrounds exhibited a notably higher propensity for PTSD symptoms, suggesting that socio-economic adversity intensifies the psychological toll of conflict exposure. Conversely, higher education and stable family environments appeared to mitigate PTSD risks, underscoring the protective role of certain socio-economic factors.The study's implications extend far beyond the immediate context of the Israel-Hamas conflict. By elucidating the heightened vulnerability of lower socio-economic groups to PTSD, it calls for a nuanced approach to mental health intervention in conflict zones. This approach should not only address the direct psychological impact of conflict but also consider the broader socio-economic fabric that shapes individual experiences of trauma.This research contributes to a growing body of knowledge on PTSD in conflict settings, offering valuable insights for policymakers, mental health professionals, and humanitarian organizations. By highlighting the socio-economic dimensions of PTSD, it advocates for more comprehensive, context-sensitive mental health strategies. Such strategies are crucial for addressing the complex array of factors influencing mental health in conflict zones, ultimately paving the way for more resilient and supportive communities.

Small island developing states (SIDS) traditionally face a set of challenges like the narrow economic structure, environmental issues, and dependence on a few ranges of economic activities forcing them to open the economy to the exterior. Therefore, their development model, like in Cape Verde depends on migration, remittances, dependence on aid, tourism, and state employment. The current research offers an insight about the nature of Cape Verde’s economy as a SIDS and the degree to which its economy depends on tourism and government services, both of which are supported through aid programs. Understanding Cape Verde’s development model is important to clarify the challenges the country faces, and its development needs to gather a long-term resilience and to understand if it is changing from a MIRAB (Migrations, Remittances, Aid and Bureaucracy) model into another one.

When applying deep learning methods to detect micro defects on low-contrast LCD surfaces, there are challenges related to the imbalance in samples dataset, as well as the complexity and laboriousness of annotating and acquiring target image masks. In order to solve these problems, a method based on sample and mask auto-generation for deep generative network models is proposed. We first generate an augmented dataset of negative samples using a generative adversarial network(GAN), and then highlight the defect regions in these samples using the training method constructed by the GAN to generate masks for the defect images automatically. Experimental results shows the effectiveness of our proposed method, as it allows for the simultaneous generation of LCD image samples and their corresponding image masks. Through a comparative experiment on the deep learning method Mask R-CNN, we demonstrate that the automatically obtained image masks have high detection accuracy.

Thermoplastic composites are continuously replacing thermosetting composites in lightweight structures. However, the accomplished work on the fatigue behavior of thermoplastics is quite limited. In the present work, we propose a numerical modeling approach for simulating fatigue delamination growth and predicting the residual tensile strength of quasi-isotropic TC 1225 LM PAEK thermoplastic coupons. The approach is supported and validated by tension and fatigue (non-interrupted and interrupted) tests. Fatigue delamination growth is simulated using a mixed-mode fatigue crack growth model, which is based on the cohesive zone modeling method. All interfaces between the thermoplastic plies were modelled using cohesive elements. Quasi-static tension analyses on pristine and fatigued coupons were performed using a progressive damage model implementing a set of Hashin-type strain-based failure criteria and a damage mechanics-based material property degradation module. Using the fatigue model, delamination growth was predicted as a function of number of cycles at all interfaces. The results agree well with C-scan images taken on fatigued coupons during interruptions of fatigue tests. An unequal and unsymmetric delamination growth was predicted due to the quasi-isotropic lay-up. Moreover, the combined models capture the decrease in the residual tensile strength of the coupons. In the quasi-static tension analysis of the fatigued coupons, the driving failure mechanisms are the fast propagation of the pre-existing delamination and the severe matrix cracking.

Lophomonas are flagellated protozoa which have been increasingly associated with upper and lower airway infection in humans. The prevalence and characterization of this disease in the critically ill remains poorly understood. We present a series of eleven ICU patients with confirmed Lophomonas spp. identification in respiratory samples.

Disposable surgical gowns are usually made from petroleum-based synthetic fibers that do not naturally decompose, impacting the environment. A promising approach to diminish the environmental impact of disposable gowns involves utilizing natural fibers and/or bio-based synthetic fibers. In this study, composite web from polylactic bicomponent fiber and natural fibers, cattail and kapok fiber, were prepared by hot-press method. Only the sheath region of the PLA bicomponent fiber melts, acting as an adhesive that enhanced the strength and reduced the thickness of the composite web compared to its state before hot-pressing. Mechanical and physical properties of composite webs were evaluated. Composite webs created from kapok fiber displayed a creamy yellowish-white color, while those made from cattail fiber showed a yellowish-brown color. Composite webs exhibit hydrophobic properties when adding the natural fiber. The maximum natural fiber content, at a ratio of 30, can be incorporated into composite webs while preserving mechanical properties and proper water vapor permeability. This nonwoven material presents an alternative with the potential to replace petroleum-based surgical gowns.

Using metaheuristics, such as the Evolutionary Algorithm (EA), within control structures is a realistic approach for certain optimal control problems. Their role is often predicting the optimal control values over a prediction horizon using a process model (PM). The big computational effort sometimes causes problems. Our work addresses a new issue: whether a machine learning (ML) algorithm could “learn” the optimal behaviour of the couple (EA, PM). A positive answer is given by proposing datasets apprehending this couple's optimal behaviour and appropriate ML models. Following a design procedure, a number of closed-loop simulations will provide the sequences of optimal control and state values, which are collected and aggregated in a data structure. For each sampling period, datasets are extracted from the aggregated data. The ML algorithm experiencing these datasets will produce a set of regression functions. Replacing the EA predictor with the ML model, new simulations are carried out, proving that the state evolution is almost identical. The execution time decreases drastically because the PM’s numerical integrations are totally avoided. The performance index equals the best-known value. In different case studies, the ML models succeeded in capturing the optimal behaviour of the couple (EA, PM) and yielded efficient controllers.

Polysaccharide from Ficus carica L. (FCP) exert multiple biological activities, as a biological macromolecule, the available knowledge about the specific structures and mechanisms of biological activity of purified 'Brunswick' fig polysaccharide is currently limited. In the present study, by chemical purification and characteristics identified via specific instruments, and then the impact of FCP on the immunomodulation activity in vitro and in vivo was examined. Structural char-acteristics showed that the molecular weight of the FCP was determined to be 127.5 kDa, the primary monosaccharides present in the FCP were galacturonic acid (GalA), arabinose (Ara), galactose (Gal), rhamnose (Rha), glucose (Glc), and xylose (Xyl) at a ratio of 0.321:0.287:0.269:0.091:0.013:0.011. Based on the investigation of in vitro immunomodulatory ac-tivity, FCP was found to stimulate the production of NO, TNF-α, and IL-6 and increased the pi-nocytic activity of macrophages. Further analysis revealed that FCP activated macrophages by interacting with toll-like receptor 4 (TLR4). Moreover, the in vivo test results indicate that FCP showed significant increase serum pro-inflammatory factors in immunosuppressed mice. Overall, this study suggests that FCP has the potential to be utilized as a novel immunomodulator in the pharmaceutical and functional food industries.

This article aims to delve into the Equity, Diversity and Inclusivity (EDI) issues prevalent in the engineering disciplines in Canada and Spain, shedding light on the common obstacles faced by underrepresented individuals and highlighting potential strategies to foster a more inclusive and diverse engineering community in these nations. Two strategic lines have been identified: (a) facilitating university education access to underrepresented and minority groups, and (b) Accompanying and guiding such students during university training, setting them up for successful future careers. The article also shows the sets of strategies employed in Canada and Spain, clearly distinguishing the approach taken in the two countries. While in Canada, there is a more decentralized approach wherein the universities device their strategies and agenda to address EDI issues, in Spain there is a stronger and direct involvement of the government to ensure a comprehensive, system-wide approach to tackling EDI issues in academia.

While machine learning (ML) has been quite successful in the field of structural health monitoring (SHM), its practical implementation has been limited. This is because ML model training requires data containing a variety of distinct damaged instances captured from a real structure and the experimental generation of such data is challenging. One way to tackle this issue is by generating training data through numerical simulations. However, simulated data cannot capture the bias and variance of experimental uncertainty. To overcome this problem, this work proposes a deep learning-based domain transformation method for transforming simulated data to the experimental domain. Use of this technique has been demonstrated for debonding location and size prediction of stiffened panels using a vibration-based method. The results are satisfactory for both, debonding location and size prediction. This domain transformation method can be used in any field where experimental data for training machine learning models is scarce.

This article introduces a new physically based Lyapunov function difinition, which can be used as acoustic energy for finite amplitude pressure oscillations in reacting systems especially in combustion chambers. Reacting flow is seen as an open, non-equilibrium (parameters are distributed unevenly locally) thermodynamic system. This Lyapunov function is defined as the maximum mechanical work, which could be extracted by a heat engine from studied system, if this system would be disconnected from inlet and outlet and from any other surrounding environment, and the engine could transfer to the surrounding environment only mechanical work.

From the mid-19th century, Collegiate Gothic architecture gained prominence among American universities. Subsequently, through the efforts of American missionaries, the Collegiate Gothic style spread to Christian universities in Asia. Specifically, this study concentrates on the nuanced relationship between the transnational missionary routes and adopting the Collegiate Gothic style, primarily focusing on Anderson Hall at Soochow University, China. The investigation meticulously dissects the multifaceted factors influencing the choice of architectural styles, considering the perspectives of missionaries, architects, and other key stakeholders. Through an intricate analysis of Anderson Hall, this research illuminates the unique characteristics of Collegiate Gothic architecture. By unraveling the complex layers of East Asian campus heritage, this study enriches our comprehension of its origins. It establishes a robust foundation for guiding the preservation and revitalization of architectural styles within university campuses. This scholarly inquiry holds particular significance for experts in academic heritage conservation.

The temporo-spatial theory of consciousness postulates that the brain implements its own inner time and space for conscious processing of outside world. In line with this hypothesis, I present a mechanistic model of mutually connected processes that encode the inner space and time. The model is used to elaborate the binding mechanism between two sets of processes representing the inner space and time, respectively. Further, a stochastic version of the model is developed to investigate the interplay between the binding strength and noise. The spectral entropy is used to characterize the noise effects on the systems of processes when the binding strength is varied. The stochastic modeling results reveal that the spectral entropy values for strongly bound systems have similar entropy values for weakly bound or even decoupled systems. Thus, the analysis performed in this study allows to conclude that the binding mechanism is noise-resilient.

In the evolving landscape of Portfolio Management (PM), the fusion of advanced machine 1 learning techniques with traditional financial methodologies has opened new avenues for innovation. 2 Our study introduces a cutting-edge model combining Deep Reinforcement Learning (DRL) with 3 a Non-stationary Transformer architecture. This model is specifically designed to decode complex 4 patterns in financial time series data, enhancing portfolio management strategies with deeper insights 5 and robustness. It effectively tackles the challenges of data heterogeneity and market uncertainty, 6 key obstacles in PM. Our approach integrates key macroeconomic indicators and targeted news 7 sentiment analysis into its framework, capturing a comprehensive picture of market dynamics. This 8 amalgamation of varied data types addresses the multifaceted nature of financial markets, enhancing 9 the model’s ability to navigate through the complexities of asset management. Rigorous testing 10 demonstrates the model’s efficacy, highlighting the benefits of blending diverse data sources and 11 sophisticated algorithmic approaches in mastering the nuances of PM.12

This paper provides a proof of concept for a linear electric generator that can be used to harvest energy from various sources of linear motion, such as vibrations, free-piston engines and wave energy. The generator can be used to power small electronic devices, such as sensors, or charge household batteries. Literature was reviewed to develop an understanding about the applications, control methods, excitation methods and mechanics of rotating and linear electric machines. A bidirectional, two-sided linear machine was designed with two stator cores and a single mover core. The stator windings and mover winding can be independently excited, allowing for three modes: no mover excitation, DC excited mover, and AC excited mover. Simulations results showed that the magnetic flux generated by DC excited stator cores were concentrated in the centre of the mover core. The use of two stator cores eliminates lateral flux in the mover core when it is not excited, minimising attraction and repulsion forces. Parametric analysis showed that flux cutting occurred in all operation modes, verifying that the generator will produce power when operating. Hardware tests produced an output current when the machine is electrically and mechanically excited, verifying the proposed design.

Alectinib hydrochloride is an anticancer medication used for the first-line treatment of non-small cell lung cancer. Although it has been approved for medical use ten years ago, and three polymorphs of this substance were proposed based on X-ray diffraction patterns, their crystal structures remained unknown to date. The main problem was preparation of high quality single crystals due to very low solubility of the salt. Herein we report on molecular and crystal structure of form I of alectinib hydrochloride as obtained by powder X-ray diffraction data from laboratory source. The refinement revealed that the nitrogen atom of the morpholinyl ring is protonated. The chloride anion interacts with the H(N) atoms of the morpholine and benzo[b]carbazole rings to form infinite H-bonded chains.

Methane is a greenhouse gas and identified as a key driver of near-term climate change. Bottom-up approaches estimate annual methane loss from US natural gas production and transport at 6 Tg, but recent studies suggest this may be an underestimate. To investigate this possibility, an equipment-based emissions inventory, using EPA emission factors, was developed to calculate methane emissions from oil and gas operations in the Delaware Basin, USA. Emission factors and activity data were then updated using contemporary and region-specific measurement data. The original inventory estimated emissions at 315 Gg CH4 y-1 (gas production-normalized rate of 0.6% loss), while the updated inventory estimated emissions of 1,500 Gg CH4 y-1 (2.8% loss). The largest changes resulted from large fugitive emissions from oil production (+430 Gg CH4 y-1), updating maintenance activity emissions (+214 Gg CH4 y-1), considering flaring inefficiency (+174 Gg CH4 y-1), and the inclusion of associated gas venting (+136 Gg CH4 y-1). This study suggests that a systematic underestimate probably exists in current bottom-up inventories and identifies sources currently missing or may be incorrect. We also strongly recommend that emission factors should be validated through direct comparison against measurement campaigns that include long-tail distributions typical of oil and gas activities.

In the present study, the effect of melatonin and sodium nitroprusside treatments on the improvement of high temperature tolerance in California Wonder green bell pepper plant was studied. For this purpose, a factorial experiment was conducted in form of a completely randomized design in three replications at Zabol University's Faculty of Agriculture. In this research, green bell pepper plants of Wonder cultivar were exposed to different temperature treatments (25°C, 35°C and 40°C) for 24 hours after being sprayed (foliar application) with 0μM, 50μM and 100μM concentrations of melatonin and Sodium nitroprusside. Results showed that Instantaneous leaf water consumption efficiency in 40°C temperature treatment with 50μM and 100μM melatonin increased. Also in 40°C temperature with 100µM melatonin, fruit dry weight in compared to control (0uM) 13.15% increased. Also, melatonin and sodium nitroprusside treatments in compared to control treatment improved fruit characteristics and reduced Appearance Characteristics of Fruit Marketability (ACFM) number. Under 40°C temperature treatment in compared to control temperature (25°C), fruit total soluble solids (TSS) 18.50% decreased also under 100μM melatonin treatment, fruit TSS 20.69% was increased compared to control treatment (0μM). Also fruit fructose at 40°C temperature in compared to 25°C temperature treatment 5.52% reduced. Fruit calcium at 35°C and 40°C temperature in compared to control temperature 6.96% and 15.65% decreased respectively. At temperature 35°C under foliar application of 100uM sodium nitroprusside, fruit copper in compared to control 28.93% increased.

To meet the International Maritime Organization’s requirements for carbon dioxide emissions from ships, a cost-effective and practical ship decarbonization tower was autonomously designed based on the marine diesel engine model 6135G128ZCa. The tower is internally filled with structured packing, and a slot-type liquid distributor at the top of the packing evenly distributes the NaOH solution to absorb carbon dioxide from the ship’s exhaust gas. Experimental research was conducted to investigate the influence of factors such as liquid-to-gas ratio and NaOH concentration on the performance of the decarbonization tower. The results confirmed that the use of NaOH solution can effectively absorb CO2 from ship exhaust gas, reducing CO2 emissions by more than 81.59% and meeting the EEDI (Energy Efficiency Design Index) requirements specified by the IMO.

Mulberry (Morus alba L), a significant fruit tree crop, requires magnesium (Mg) for its optimal growth and productivity. Nonetheless, our understanding of the molecular basis underlying magnesium stress tolerance in mulberry plants remains unexplored. In our previous study, we identified several differential candidate genes associated with Mg homeostasis via transcriptome analysis including xyloglucan endotransglucosylase/hydrolase (XTH) gene family. The XTH gene family is crucial for plant cell wall reconstruction and stress responses. These genes have been identified and thoroughly investigated in various plant species. However, there is no research pertaining to XTH genes within the M. alba plant. This research systematically examined the M. alba XTH (MaXTH) gene family at the genomic level using a bioinformatic approach. In total, 22 MaXTH genes were discovered and contained the Glyco_hydro_16 and XET_C conserved domains. The MaXTH’s were categorized into five distinct groups by their phylogenetic relationships. The gene structure possesses four exons and three introns. Furthermore, the MaXTH gene promoter analysis reveals a plethora of cis-regulatory elements; mainly stress responsiveness, growth and development, light responsiveness, and phytohormone responsiveness. GO analysis indicated that MaXTHs encode proteins that exhibit xyloglucan xyloglucosyl transferase and hydrolase activities in addition cell wall biogenesis as well as xyloglucan and carbohydrate metabolic processes. Moreover, a synteny analysis unveiled an evolutionary relationship between the XTH genes in M. alba and those in three other species: A. thaliana, P. trichocarpa, and Zea mays. Expression profiles from RNA-Seq data displayed distinct expression patterns of XTH genes in M. alba leaf tissue during Mg treatments. Real-time quantitative PCR analysis confirmed the expression of the MaXTH genes in Mg stress response. Overall, this research enhances our understanding of the characteristics of MaXTH gene family members and lays the foundation for future functional genomic study in M. alba.

In recent years, Synthetic Aperture Radar (SAR) has received attention in the Automatic Target Recognition (ATR) field due to its ability to acquire images both day and night, combined with the superior resolution offered by the latest sensors. However, despite advances, there remains a deficiency in dataset quality and quantity. This has necessitated the use of simulated data to overcome challenges associated with obtaining large quantities of high-quality images of targets and their subsequent labeling. In this study, we trained a Convolutional Neural Network (CNN) using a simulated dataset, subsequently employing it to classify real images from the MSTAR dataset. Traditional training methodologies often struggle with generalization when confronted with domain disparities between simulated and real data. To mitigate this, we exploited Margin Disparity Discrepancy (MDD), a domain adaptation technique not previously explored in SAR context. Findings from this study showcased a 13% increase in classification accuracy and improved generalization using MDD. Through Explainable AI (XAI) techniques such as t-SNE and other metrics like Kullback Leibler Divergence (KLD), we revealed a more distinct alignment of feature spaces between the two domains after domain adaptation, emphasizing MDD’s role in enhancing model accuracy and generalization.

Few-shot fine-grained image classification (FSFGIC) methods refer to machine learning methods which aim to classify images (e.g., bird species, flowers, and airplanes) belonging to subordinate object categories of the same entry-level category with only a few samples. It is worth to note that feature representation learning is used not only to represent training samples, but also to construct classifiers for performing various FSFGIC tasks. In this paper, starting from the definition of FSFGIC, a taxonomy of feature representation learning for FSFGIC is proposed. According to this taxonomy, we discuss key issues on FSFGIC (including data augmentation, local or/and global deep feature representation learning, class representation learning, and task specific feature representation learning). The existing popular datasets and evaluation standards are introduced. Furthermore, a novel classification performance evaluation mechanism is designed with a 0.95 confidence interval for judging whether the classification accuracy obtained by a certain specified method is good or bad. Moreover, current challenges and future trends of feature representation learning on FSFGIC are elaborated.

Sphingolipids are ubiquitous in eukaryotes and certain prokaryotes, where they serve as vital components of biological membranes and as bioactive molecules. Chloroplasts have complex membrane structures that play crucial roles in photosynthesis, but their specific sphingolipidome remains unreported. In this study, we used high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS) to analyze the sphingolipidome of purified Arabidopsis thaliana chloroplasts. We detected 92 chloroplast sphingolipids. The chloroplast sphingolipidome differed from total leaf (TL) samples, having a higher content of free long-chain bases and hydroxyceramides, and a greater proportion of complex sphingolipids with 16C fatty acid (FA) forms. Notably, chloroplast glucosylceramides were predominantly the d18:1 h16:0 and t18:1 h16:0 forms, rather than the 24C FA form found in TL and other cellular structures. Comparing the sphingolipidomes of different cellular structures underscores the inhomogeneity of intracellular distribution of sphingolipids. This provides a robust reference for further elucidating the function of sphingolipids in plant cells.

Increasing the effectiveness of eradication therapy is an important task of gastroenterology. The aim was to evaluate the efficacy and safety of postbiotic (metabiotic) contained inactivated Limosilactobacillus reuteri DSM 17648 (Pylopass™; Helinorm™) as adjuvant treatment for Helicobacter pylori eradication in patients with functional dyspepsia (FD). This randomized, double-blind, placebo-controlled, multicenter, parallel study included H. pylori positive patients with FD. The postbiotic group received Pylopass 200 mg bid for 14 days in combination with the eradication therapy (esomeprazole 20 mg bid + amoxicillin 1000 mg bid + clarithromycin 500 mg bid for 14 days) and another 14 days after completion of eradication therapy. The eradication efficiency was 96.7% for the postbiotic group versus 86.0% for the placebo group (P=0.039). Both groups showed significant improvements in quality of life and reduction of most gastrointestinal symptoms with no significant differences between groups. The overall number of digestive adverse effects in the postbiotic group was lower than in the placebo group. Serious adverse effects were not registered. The postbiotic (metabiotic) contained inactivated L. reuteri DSM 17648 significantly improves the effectiveness of H. pylori eradication therapy in FD and decreases overall number of digestive adverse effects of this therapy.

Background: We previously reported that human keratinocytes express protease-activated receptor (PAR)-2 and play an important role in activated protein C (APC)-induced cutaneous wound healing. This study investigated the involvement of PAR-2 in the production of gelatinolytic matrix metalloproteinases (MMP)-2 and -9 by APC during cutaneous wound healing. Methods: Full-thickness excisional wounds were made on the dorsum of male C57BL/6 mice. Wounds were treated with APC on days 1, 2, and 3 post-wounding. Cultured neonatal foreskin keratinocytes were treated with APC with or without intact PAR-2 signalling to examine the effects on MMP-2 and MMP-9 production. Murine dermal fibroblasts from PAR2 knock-out (KO) mice were also assessed. MMP-2 and -9 were measured by gelatin zymography, fluorometric assay, and immunohistochemistry. Results: APC accelerated wound healing in WT mice, but had negligible effect in PAR-2 KO mice. APC stimulated murine cutaneous wound healing was associated with differential 0temporal production of MMP-2 and MMP-9, with the latter peaking on Day 1 and the former on Day 6. In-hibition of PAR-2 in human keratinocytes reduced APC-induced MMP-2 activity 25~50, but had little effect on MMP-9. Similarly, APC-induced MMP-2 activation was reduced by 40% in cultured dermal fibroblasts derived from PAR-2 KO mice. Conclusion: This study shows for the first time that PAR-2 is essential for APC-induced MMP-2 production. Considering the important role of MMP-2 in wound healing, this work helps explain the underlying mechanisms of action of APC to promote wound healing through PAR2.

The mechanistic influences of dopamine (DA) signaling and impact on motor function is nearly always interpreted from changes in nigrostriatal neuron terminals in striatum. This is a standard practice in studies of human Parkinson’s disease (PD) and aging, and related animal models of PD and aging-related parkinsonism. However, despite dozens of studies indicating an ambiguous relationship between changes in striatal DA signaling and motor phenotype, this perseverating focus on striatum continues. Although DA release in substantia nigra (SN) was first reported almost 50 years ago. assessment of nigral DA signaling changes in relation to motor function is rarely considered. Whereas DA signaling has been well-characterized in striatum at all 5 steps of neurotransmission (biosynthesis and turnover, storage, release, reuptake, and post-synaptic binding) in the nigrostriatal pathway, the depth of such interrogations in the SN, outside of cell counts, is sparse. However, there is sufficient evidence that these steps in DA neurotransmission in the SN are operational and regulated autonomously from striatum, and are present in human PD and aging, and related animal models. To complete our understanding of how nigrostriatal DA signaling affects motor function, it is past time to include interrogation of nigral DA signaling. This brief review highlights evidence that changes in nigral DA signaling at each step in DA neurotransmission are autonomous from those in striatum and changes in the SN alone can influence locomotor function. Accordingly, for full characterization of how nigrostriatal DA signaling affects locomotor activity, interrogation of DA signaling in SN is essential.

The objective was to estimate the correlation between VIs and grain yield and identify the optimal timing and VIs for precise corn grain yield estimation. Furthermore, the study aims to employ photographic quantification to measure corn ear traits and establish their correlation with corn grain yield. Ten corn hybrids were evaluated in CRB with three rep-lications at three locations. Vegetation indices and green leaf area were estimated throughout the cycle using an unmanned aerial vehicle (UAV) and subsequently corre-lated with grain productivity. In addition, photographs were taken of the corn ear to esti-mate their length, width and total number of kernels and compare these values with manual measurements. The experiments consistently demonstrated significant experi-mental quality across sites, with accuracy ranging from 79.07% to 95.94%. UAV flights carried out at the beginning of the crop cycle revealed a positive correlation between grain productivity and the evaluated indices (NGRDI, VARI, GLI). Regarding the phenotyping of corn ears, the regression coefficients for width, length and TNG were 0.92, 0.88 and 0.62, respectively, indicating an association with manual measurements. However, stage V5 in the localities of Lavras and Ijaci and stage V8 in the locality of Nazareno showed a posi-tive correlation with productivity. The use of images for ear phenotyping is promising as a method for measuring corn components.

Although hydrogen gas (H2) treated soil improves crop biomass, this approach appears difficult for the field application due to the flammability of H2 gas. In this report, we investigated whether and how H2 applied in hydrogen nanobubble water (HNW) improves the yield and quality of cherry tomato (Lycopersicon esculentum var. cerasiforme) with/without fertilizers. Two-year field trials showed that compared to corresponding controls, HNW without/with fertilizers improved cherry tomato yield per plant by 39.7% and 26.5% in 2021 (Shanghai), and 39.4% and 28.2% in 2023 (Nanjing). Compared to surface water (SW), HNW increased soil available nitrogen (N), phosphorus (P), and potassium (K) consumption regardless of fertilizer application, which may be attributed to the increased NPK transport related genes in roots (LeAMT2, LePT2, LePT5, and SlHKT1,1). Also, HNW-irrigated cherry tomato displayed higher sugar-acid ratio (8.6%) and lycopene content (22.3%) than SW-irrigated plants without fertilizers. Importantly, the beneficial effects of HNW without fertilizers on yield per plant (9.1%), sugar-acid ratio (31.1%), volatiles (20.0%) and lycopene contents (54.3%) were stronger than those achieved by fertilizers alone. In short, this study clearly indicated that HNW-supplied H2 not only exhibited fertilization effect on enhancing tomato yield, but also improved fruit quality with a lower carbon footprint.

Smart farm is a technology that greatly helps improve the productivity and quality of crops and is being actively introduced into indoor environments such as green houses. However, it has not yet been distributed to field-grown crops. The main reason is that, unlike indoor environments, it is very difficult to collect sensor data for monitoring the growth environment of crops in open fields where weather conditions change significantly and power supply is difficult. Additionally, because various sensors are used, the data formats of the devices are different, making it difficult to process. This paper presents a field crop growth environment monitoring and big data analysis system. The proposed system first solves power supply and data communication problems using solar power generation and LPWA technology. Additionally, based on the oneM2M architecture, data from various sensors is transmitted to the server using standardized technology. The transmitted data is stored and managed on the server as big data, and can be used to predict the production and quality of field-grown crops and take appropriate measures. The proposed system is expected to create an environment optimized for the growth of field crops and help prevent and manage diseases.

X-band marine radar captures signal reflected from the sea surface. Theoretical studies indicate that the initial unfiltered signal contains meaningful information including parameters related to wind waves. Traditional methods for estimating significant wave height (SWH) rely on the physical laws describing signal reflection from rough surfaces. However, recent studies suggest the feasibility of employing artificial neural networks (ANNs) for SWH approximation. Both classical and ANN-based approaches necessitate costly in situ data. In this study, we propose generating synthetic radar images with specified wind wave parameters using Fourier-based approach and Pierson–Moskowitz wave spectrum as a viable alternative. We generate synthetic images and use them for unsupervised learning approach to train a convolutional component of an ANN. After that, we train a regression ANN based on the previous convolutional component to obtain SWH back from synthetic images. Then, we apply preliminary trained weights for the same regression model to train SWH approximation on the dataset of real sea clutter images. In this study, we demonstrate the increase in the accuracy of SWH estimation from radar images with the preliminary training on synthetic data.

Schizophrenia is a spectrum of neuropsychiatric deformities, characterized by hallucination, delusion, mood disorders, speech pathology, and neurocognitive deficits. Among various clinical manifestations, hallucination has been recognized as a core psychotic symptom that occurs more frequently in schizophrenia. A significant number of subjects with neurocognitive disorders like Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and other neurological diseases like stroke and epileptic seizure also experience hallucinations. While aberrant neurotransmission has been linked to the neuropathogenic events of schizophrenia, the specific cellular mechanism contributing to hallucinations remains ambiguous. Neurodegeneration in the hippocampus of the brain has been identified as a predominant pathogenic determinant of dementia. While the scientific proof for the neurodegeneration in schizophrenia is limited, the occurrence of dementia in schizophrenia has become increasingly evident. To note, both neurodegenerative, neurodevelopmental, and neuropsychiatric disorders display impaired neurogenesis in the brain. Impaired neurogenesis in the hippocampus has been linked to dementia and mood disorders. Notably, the early phase of many neurodegenerative disorders has been characterized by reactive neuroblostosis and aberrant cell cycle activation in mature neurons leading to the fluctuation in neurogenic processes leading to abnormal synaptogenesis and neurotransmission in the brain. Thus, this article emphasizes a hypothesis that aberrant neurogenic processes could be an underlying mechanism of hallucination in schizophrenia and other neurological diseases.

This study mainly explores the effect of policy formalism, equipment insufficiency, covid-19 fear and job insecurity on construction workers’ insomnia during the epidemic. The main contribution of this article is to introduce policy formalism into the exploration of the causes of insomnia among construction site workers during the epidemic. This study collected 733 valid samples of construction site workers. We tested the established hypotheses using confirmatory factor analysis and structural equation modeling. The research results found that equipment insufficiency positively affects Covid-19 fear. Policy formalism and Covid-19 fear positively affect job burnout. Social support negatively affects job burnout. Job burnout and job insecurity positively affect insomnia.

The quality of cellular products used in biological research can directly impact the ability to ob-tain accurate results. Epstein-Barr virus (EBV) is a latent virus that spreads extensively world-wide, and cell lines used in experiments may carry EBV and pose an infection risk. The presence of EBV in a single cell line can contaminate other cell lines used in the same laboratory, affecting experimental results. We developed three EBV detection systems: (1) a polymerase chain reac-tion (PCR)-based detection system, (2) a recombinase polymerase amplification (RPA)-based de-tection system, and (3) a combined RPA-lateral flow assay (LFA) detection system. The mini-mum EBV detection limits were 1×103 copy numbers for the RPA-based and RPA-LFA systems and 1×104 copy numbers for the PCR-based system. Both the PCR and RPA detection systems were applied to 192 cell lines, and the results were consistent with those obtained by the EBV assay methods specified in the pharma-ceutical industry standards of the People's Republic of China. 10 EBV-positive cell lines were identified. The combined RPA-LFA system is simple to operate, allowing for rapid results visualization. This system can be implemented in laborato-ries and cell banks as part of a daily quality control strategy to ensure cell quality and experi-mental safety and may represent a potential new technique for the rapid detection of EBV in clinical samples.

Studies on the domestication, genetic differentiation, and crop evolution of foxtail millet are re-viewed. Several genetic studies were carried out to elucidate the genetic relationships among foxtail millet accessions originating mainly from Eurasia, based on intraspecific hybrid pollen semi-sterility, isozymes, DNA markers, and single-nucleotide polymorphisms. Most studies suggest that China is the center of diversity of foxtail millet, and landraces were categorized into geographical groups. Furthermore, the evolution of genes was reviewed (i.e., the Waxy gene conferring amylose content in the endosperm, the Si7PPO gene controlling polyphenol oxidase, the HD1 and SiPRR37 genes controlling heading time, the Sh1 and SvLes1 genes involved in grain shattering, and the C gene controlling leaf sheath pigmentation), and the variation and distribu-tion of these genes suggested complex patterns of evolution under human and/or natural selec-tion.

In any forest stand, the theoretically modelled output of sawlogs (the most valuable roundwood assortments) will differ from what is actually obtained. The aim of this study was to assess whether it is possible to characterize this difference by site properties or inventory parameters of forest element for birch, black alder and as-pen. The differences in yield of sawlogs varied among soil types, and was increasing with age and average diameter of the forest element. The theoretical model for assortment grading predicted a lower yield of saw-logs compared to actually obtained yield at a lower age and at a lower average diameter, but overestimated output of sawlogs as age and diameter increase. The results highlight necessity to consider decreasing wood quality with increasing age to account for damage, such as stem rot, in assortment tables.

Conflicts are frequent within the complex professional environment of the construction industry. If claims cannot be overcome amicably, they result in disputes that lead to litigation. Identification of the causes of these claims and their impact on the duration, cost, and quality of the final project is expected to facilitate the prevention of unsuccessful performance of construction contracts. The novelty of this study is that after codifying the most common causes of construction contract claims derived from the extant literature, they are further investigated in terms of their probability of occurrence and the perceived impact they have on the project completion time, its total cost, and quality. Based on calculated relative importance indices from expert opinion, this paper proposes probability and severity of impact values for 39 common causes of claims in the construction industry. These can be applied to calculate their risk values for stakeholders in public construction contracts to plan mitigation measures of contractual claims. The findings show that the top five riskiest causes of contractual claims in the Greek construction industry are changes in quantities, work, or scope, design quality deficiencies or errors, payment delays, delays in work progress, and the financial failure of the contractor.

Coronaviruses (CoVs) are enveloped positive-sense single-stranded RNA viruses with a genome 27-31kbases in length. Critical genes include the spike (S), envelope (E), membrane (M), nucleocapsid (N) and 9 accessory open reading frames encoding for non-structural proteins (NSPs) that have multiple roles in the replication cycle and immune evasion (1). There are 7 known human CoVs that most likely appeared after zoonotic transfer, the most recent being SARS-CoV-2 responsible for the Covid-19 pandemic. Antivirals that have been approved by the FDA for use against Covid-19 such as Paxlovid can potentially target and successfully inhibit the main protease (MPro) activity of multiple human CoVs, however alternative proteomes encoded by CoV genomes have a closer genetic similarity to each other, suggesting that antivirals could be developed now that target future CoVs. New zoonotic introductions of CoVs to humans are inevitable and unpredictable. Therefore, new antivirals are required to control not only the next human CoV outbreak, but also the 4 common human CoVs (229E, OC43, NL63, HKU1) that circulate frequently and to contain sporadic outbreaks of the severe human CoVs (SARS-CoV, MERS and SARS-CoV-2). The current study found that emerging antiviral drugs, such as Paxlovid, have only been proven to target SARS-CoV-2. Other drugs which have the potential to target other human CoVs, are still within clinical trial and are not yet available for public use. Monoclonal antibody (mAb) treatment and vaccines for SARS-CoV-2 can reduce mortality and hospitalisation rates, however they target the Spike protein whose sequence mutates frequently and drifts. Spike also is not applicable for targeting other HCoVs as these are not well conserved sequences among human CoVs. Thus, there is a need for readily available treatments globally that target all 7 human CoVs and improve the preparedness for inevitable future outbreaks. Here we discuss antiviral research contributing to the control of common and severe CoV replication and transmission, including the current SARS-CoV-2 outbreak. The aim was to identify common features of CoVs for antivirals, biologics and vaccines that could reduce the scientific, political, economic and public health strain caused by CoV outbreaks now and in the future.

Abstract: CRISPR/Cas systems are used for genome editing, both in basic science and in bio-technology. At the same time, CRISPR/Cas editors have several limitations, including insufficient specificity leading to "off-targets" and the dependence of activity on chromatin state. A number of highly specific Cas9 variants have been obtained now, but most of them are characterized by reduced activity on eukaryotic chromatin. We identified a spatial cluster of amino acid residues in the PAM-recognizing domain of Streptococcus pyogenes Cas9, whose mutations restore the activity of one of the highly specific forms of SpyCas9 without reducing its activity in Saccharomyces cere-visiae. In addition, one of these new mutations also increases the efficiency of SpyCas9-mediated editing of a site localized on the stable nucleosome. The improved Cas9 variants we obtained, which are capable of editing hard-to-reach regions of the yeast genome, may help in both basic research and yeast biotechnological applications.

When wastewater containing methylene blue, a cationic dye is improperly treated or immediately dumped into the environment, it can degrade the quality of water bodies and contaminate the ecological environment. The removal of methylene blue from the aqueous solution is critical and urgent. The present work focuses on the use of maize stalk agricultural waste to prepare an adsorbent for the removal of cationic dye (methylene blue) from an aqueous solution. The prepared maize stalk biochar (MSB) was modified with MnO2 and the modified maize stalk biochar was characterized using Fourier transform infrared (FTIR) spectroscopy. Taguchi technique was employed to determine the optimum conditions for the removal of methylene blue (MB) dye from aqueous solution by MMSB. The factors were adsorbent dose (0.5, 1.0 and 1.5 g/100 mL), initial dye concentration (100, 200 and 300 mg/L), contact time (30, 60, 90 minutes) and pH (4, 6 and 8). Among the investigated process factors, adsorption efficiency was influenced by adsorbent dose and initial MB dye concentration. The optimum parameters were 1 g/100 mL adsorbent dose, 100 mg/L initial dye concentration, 60 minutes contact time and a pH of 8.0. This yielded a removal efficiency of 87.49% with adsorption capacity of 206.53 mg/g. The adsorption kinetics agreed with the pseudo-second-order (R2 of 0.999968) model and fitted a Langmuir isotherm (R2 of 0.9795) representing homogeneous monolayer chemisorption.

Traditional methods of non-metallic materials joining for industrial uses are limited to lasers, or induction or conduction heating. The precision and efficiency for large-scale applications are difficult to be achieved at the same time. Here in this paper, we report a precise microwave welding method induced by localized surface plasmon of metallic macro-gaps under microwave excitation. The proposed method utilizes energy from well-developed commercial microwaver, and benefits from the precision of localized surface plasmon, an oscillating electron cloud excited by electromagnetic waves, which is currently under hot investigation only at micro- or nano-scales. The method is applied to the welding of polymers to solve the problems of complex processing equipment and high processing energy consumption in the traditional welding process, so as to cope with the growing energy and environmental crisis. Finite element numerical simulations are used to study the distribution characteristics of electric fields and temperature fields. The results show that the process is a high quality, efficient, low consumption and environmentally friendly green process compared to traditional polymer welding methods.

Background: Genome-wide association studies (GWAS) have shed light on various complex diseases and traits, by detecting more than 400,000 associated genetic loci. This number is expected to drastically increase because of the use of novel artificial intelligence methods offering new ways to study the effects of variants. Deep learning using artificial neural networks (ANN) is a sub-field of artificial intelligence, which simulates how the human brain learns. We aimed at assessing the potential of deep learning in human genetic studies of Alzheimer’s Disease (AD) and how these compare to the traditional statistical methods used in GWAS, by simultaneously testing the two approaches on the same dataset, while discovering new genetic loci associated to AD. Methods: To address this aim, phenotypic and genome-wide SNP data from the UK Biobank was analysed on a binary outcome, AD diagnosis, in two different data balance options, of one-to-one and one-to-two case-control datasets, using 2,764 cases vs 2,764 controls and 5,528 controls respectively matched on gender, age, ethnicity, PC1-20 and genotyping array. Genetic data handling and GWAS were performed using PLINK, whereas neural networks were trained using GenNet, a new ANN tool, with the same datasets, separated into training (60%), validation (20%) and test (20%) sets. Neural network layers were determined using biological knowledge, by annotating SNPs to genes and genes to AD related pathways, using ANNOVAR annotations followed by GeneSCF and KEGG. Results: Significant associations were detected between four SNPs linked to two different genes and AD for the 1 to 1 case-control study design and six SNPs linked to four different genes for the 1 to 2 case-control study design by using PLINK. All identified regions have been previously associated to AD. GenNet identified twelve SNPs on seven genes to be associated with AD, all with biological plausibility, achieving an AUC of 0.80 when using three biologically determined layers and 0.73 when using two layers at the neural networks. No common top SNPs were identified between the machine learning and GWAS models. Conclusion: This is one of the first studies attempting to compare the traditional GWAS to more sophisticated state-of-art methods for understanding the genetic architecture of complex phenotypes using the same dataset. More systematic comparisons with such approaches on real data are needed to enable best practises for machine learning in the analysis of genome-wide genetic data.

Aim. The analysis of incidence, clinical and morphological manifestations, and the prognosis of IgA nephropathy in the Northeastern Europe (NEE). Materials and methods. We enrolled cases with primary IgA nephropathy (IgAN) from 2005 to 2020 in the single-center retrospective study. Demographic and clinical parameters, histology data, and the treatment were analyzed. The outcomes were the occurrence of complete (PR) or partial remission (CR), renal replacement therapy (RRT), eGFR<15 ml/min/1.73m² and eGFR decrease ≥ 50%. The composite endpoint included all renal outcomes and was used to evaluate the risk of IgAN progression and associated factors. Results. The incidence of IgAN cases was 23.6% of all indication biopsies and 41.2% of primary immune glomerulopathies. At the time of the kidney biopsy, the proteinuria was 2.5 (1.5-5.0) g/24h, eGFR - 69 ± 32 ml/min/1.73 m². Proportions of cases with arterial hypertension and eGFR <60 ml / min / 1.73 m² were 75% and 36%, respectively. The prevalence of histological changes in accordance with the Oxford (MEST-C) classification was M1 - 40.5%, E1 -22.9%, S1-70.2%, T1-22%, T2 - 9%, C1-16.7%, C2 - 4.4%. In the follow-up of 27 (11; 61) months, 6 deaths occured (1.7%). The 10-year cumulative dialysis-free survival was 80% and progression-free survival was 56%. Remissions registered in 50% of cases, being more frequent in patients who received immunosuppression vs renoprotection (60% vs. 40%, p = 0.001). In multivariable Cox regression, the independent factors associated with the increased probability of IgAN progression were male gender, a younger age, higher blood pressure and hematuria, lower eGFR, interstitial fibrosis/ tubular atrophy (≥50%), the presence of peritubular capillaritis and any crescents. Compared to the cohorts of other ethnic or geographical origin, IgAN in the studied cohort were found to have more severe clinical and morphological presentations and faster progression rate. Conclusion. Being the most common glomerulopathy in the NEE population, IgAN has more pronounced clinical and morphological presentations. Considering the distinctive features of the disease, further research is warranted in order to establish approaches for enhancing risk stratification and treatment efficacy.

Mineral extraction is one of the most important for the economies of different countries, and Brazil is one of the world's primary iron ore producers. Unfortunately, the dams are still the main problem, mainly in Minas Gerais, especially after the Fundão Dam rupture happened in 2015. Additionally, there is still a massive presence of buildings produced on earth throughout the Minas Gerais mining region, built from the 18th century to today. Investigating the potential of IOT to be incorporated into traditional earthen construction techniques in regions affected by dam ruptures presents a relevant and innovative research approach. In addition, the local reuse of these sediments should be the priority. Thus, the main objective of this work was to collect, characterize, and analyze the possibilities of application of these tailings to produce RE. XRD, XRF, granulometry, porosity, specific density, toxicity, and MEV/EDS were performed on the samples collected at three points. To analyze the soil-IOT compatibility, representative mixtures of RE were produced, and the specific mass, compaction, and compressive strength were performed. It was observed that the IoT samples have a high silica content and that the mixtures of IOT-soil, even without cement, reached compressive strength values above the current international standards.

Cacao (Theobroma cacao L.) is an important woody species for production in Ecuador. Woody species have the potential to incorporate soil organic carbon (SOC) into the soil. The province of Manabí is known for its cocoa plantations and several cocoa species, which generates interest in their analysis. This study aimed to determine the concentration and accumulation of SOC in three cocoa species (EET95, EET103 and EET116) 15 years old, both under the tree and in the rows, at different depths 0-5, 5-10, 10-20 and 20-30 (cm). The results showed that soil SOC concentration was higher in the row for EET116 (2.51%) and significantly different under the tree for EET103 (1.57%) at 0-5 cm. No significant differences were found in the other sections. In the whole profile analyzed (0-30 cm), the EET116 species accumulated 41 t ha-1 and 54 t ha-1 under the tree and in the row, respectively. The species EET103 in the row presented 47 t ha-1 with no significant differences. Species EET95 and EET103 had the lowest values, but without significant differences between sampling locations, with 36 t ha-1, 37 t ha-1, and 47 t ha-1 under the tree and in the windrow, respectively. In conclusion, the species EET116 accumulated more organic carbon due to several factors, possibly a higher decomposing biomass, not only from the tree but also from other trees in the area, which contributes to the incorporation of organic matter throughout the soil profile.

Coastal resources in Lian, Batangas, have undergone significant degradation due to a combination of human activities and natural factors, including sea-level rise, changes in sea-surface temperature, fluctuations in rainfall, increased storm frequency, and sedimentation. This has led to adverse effects on key ecosystems such as corals, mangroves, and seagrasses. To assess the extent of climate change hazards on these coastal resources, a Vulnerability Assessment was conducted in the municipality of Lian, Batangas. The assessment employed a combination of the Coastal Integrity Vulnerability Assessment Toolkit (CIVAT) and Integrated Coastal Sensitivity, Exposure, and Adaptive Capacity to Climate Change (ICSEA-C) guidelines. Additionally, the analysis incorporated detailed mapping using Light Detection and Ranging (LiDAR) data for mangroves and LandSat 8 imagery for corals and seagrasses. The findings at the municipal scale revealed that a staggering 90% of mangroves in Lian, Batangas, were highly vulnerable. This vulnerability was attributed to a high score in intrinsic characteristics and a low adaptive capacity. Similarly, a majority of seagrasses in the area were classified as medium and highly vulnerable at the municipal level. Only 10–20% of seagrasses received low to medium vulnerability scores. Shockingly, almost 100% of the coral reefs in Lian, Batangas, were deemed highly vulnerable, indicating a critical threat to this crucial marine ecosystem.

Purpose To investigate secondary glaucoma resulting from uveitis in a patient infected with Human T-cell Leukemia Virus Type 1 (HTLV-1) pathologically and discuss the management of glaucoma with recurrent uveitis. Clinical course An octogenarian woman diagnosed as a carrier of HTLV-1 experienced recurrent uveitis and a sudden rise in intraocular pressure (IOP) in both eyes. Due to the uncontrolled IOP and severely damaged visual field in her left eye, a combined procedure of trabeculectomy and DGIS (Glaucoma drainage implant surgery, Baerveldt 350) was performed. The presence of HTLV-1 provirus was detected in the aqueous humor. Her trabeculectomy sample was processed for light microscopic observation. Following an irregular follow-up, she presented with a sudden decrease in vision and pain in her fellow eye, four years after the glaucoma surgeries. Her right eye exhibited a significant accumulation of mutton-fat-like keratic precipitates. Results Clinical manifestations revealed the presence of granulomatous uveitis. The combined glaucoma surgery, along with continuous topical corticosteroid medication post-surgery in her left eye, effectively suppressed the high IOP spikes and the recurrence of uveitis for 4 years. The pathological examination of the outflow pathways showed a range of damages in Schlemm's canal (SC), including SC endothelial loss, narrowing, and occlusion, as well as loss of trabecular meshwork (TM) cells and fused TM beams. Conclusion Combined GDIS and trabeculectomy represents a promising approach for managing such refractory cases of secondary glaucoma. The continuous topical corticosteroid medication is strongly recommended to prevent irreversible changes in SC and TM associated with granulomatous uveitis.

This work develops a new method for vector data augmentation. The proposed method applies the Principal Component Analysis (PCA) and determines the Eigenvectors of a set of training vectors for a Machine Learning (ML) method and uses them to generate the distilled vectors. The training and the PCA distilled vectors have the same dimension. The user chooses the number of vectors to be distilled and augmented to the set of training vectors. A statistical approach determines the lowest number of vectors to be distilled such that when augmented to the original vectors the extended set trains a ML classifier to achieve a required accuracy. Hence, the novelty of this study is, the distillation of vectors with the PCA method and their use to augment the original set of vectors. The advantage that comes from the novelty is that it increases the statistics of ML classifiers. To validate the advantage, we conducted experiments with four public databases and applied four classifiers: Neural Network, Logistic Regression, Support Vector Machine with linear and polynomial kernels. For the purpose of augmentation, we conduct several distillations including nested (double distillation). The latter notion means that new vectors were distilled from already distilled vectors. We train the classifiers with three sets of vectors: original vectors; original augmented with vectors distilled by PCA; original augmented with distilled and double distilled, by PCA, vectors. The experimental results are presented in the paper, and they confirm the advantage that the PCA distilled vectors increase the classification statistics of ML methods, if the distilled vectors augment the original training vectors.

PPR is a highly contagious viral disease and one of the deadliest affecting wild goats, sheep and small ruminants, however goats are generally more sensitive. The causative agent is the Peste des Petits Ruminants virus (PPRv) which is a single-stranded RNA virus of negative polarity belonging to the Paramyxoviridae family. In February 2020, an active outbreak of PPR was re-ported in a herd of a transhumant farmer in the village of Ngainth Pathé (department of Kounguel, Kaffrine region,Senegal). Of the ten (10) swabs collected from goats, eight (8) came back positive by real-time PCR. The most positive sample was amplified, purified and amplicon sent for sequencing. Phylogenetic analysis showed that the sequence of the PPR virus obtained belonged to lineage IV. These results confirm those found in the latter countries and reinforce the hypothesis of the importance of animal mobility between these neighboring countries. In perspective, following the discovery of this lineage IV in Senegal, a study on its dispersion is underway throughout the national territory. The results that will emerge from this study, asso-ciated with detailed data on animal movements and epidemiological data, will provide appro-priate and effective information to improve PPR surveillance and control strategies with a view to its eradication.

Abstract: To explore the interrelationship between groundwater microbial communities and hydrochemical characteristics in Mancheng district, Baoding city, samples from karst groundwater and porous groundwater were collected respectively. The hydrochemical analysis and 16s RNA gene V4-V5 region sequencing method were used to detail the water environmental characteristics and microbial community distribution pattern in the study area. The hydrochemical results reveal significant differences in major ion contents and other hydrochemical indices between karst water and porous water. Microbial community analysis demonstrates that Proteobacteria, Actinobacteria, and Firmicutes are the dominant taxa in both types of water bodies, but their proportions vary significantly in different water bodies. The difference in microbial communities between karst water and porous water reflects the influence of environmental factors such as ion concentration and pH on microbial distribution. Redundancy analysis (RDA) indicates significant associations between microbial community composition and environmental factors in both types of groundwater environments. Studying the distribution pattern of microbial communities and the connection with environmental factors in two types of groundwater provides important basis for interpreting the functional roles of microbes in groundwater ecosystems.

This work reports the isolation, purification, and anti-inflammatory evaluation of compounds from the plant species Ageratina pichinchesis. Using open-column chromatography, 11 known compounds were purified, whose chemical structures were elucidated by nuclear magnetic resonance techniques (1D and 2D). All compounds were evaluated in the in vitro model of RAW 264.7 mouse macrophage cells measuring nitric oxide inhibition as a determination of the anti-inflammatory effect. Outstandingly, the compound betuletol 3-O-β-glucoside (11) exhibited inhibition of nitric oxide with an IC50 of 75.08 ± 3.07% at 75 µM, additionally, it inhibited the secretion of interleukin 6 (IL-6) and activation of nuclear factor (NF-kβ). These results show that compound 11 could be considered as a potential anti-inflammatory agent in suppressing the expression of NF-kβ target genes such as the proinflammatory pathway and inducible nitric oxide synthase (iNOS). Consequently, the medicinal use attributed in traditional medicine to the A. pichinchensis species is due to the diversity of compounds that the plant biosynthesizes, demonstrating that its effect relates to anti-inflammatory effects associated with compound 11.

Sugarcane mosaic virus (SCMV) is a (genus, Potyvirus; family, Potyviridae) is a widespread, deleterious, and the most damaging pathogen of sugarcane ((Saccharum officinarum L. and Saccharum spp.) which causes a substantial barrier to producing high sugarcane earning. Sugarcane mosaic disease (SCMD) is caused by, single or compound infection of SCMV, disseminated by the several aphid vectors in a non-persistent manner. SCMV has flexuous filamentous particle of 700-750 nm long, which encapsidated a positive-sense, single-stranded RNA molecule of 9575 nucleotides. RNA interference (RNAi)-mediated antiviral innate immunity is an evolutionary conserved, key biological process in eukaryotes and has evolved as an antiviral defence system to interfere with viral genomes for controlling infections in plants. The current study aims to analyze sugarcane (Saccharum officinarum L. and Saccharum spp.) locus-derived microRNAs (sof-miRNAs/ssp-miRNAs) with predicted potential for targeting the SCMV +ssRNA-encoded mRNAs, using a predictive approach that involves five algorithms. The ultimate goal of this research is to mobilize the in silico- predicted endogenous sof-miRNAs/ssp-miRNAs to experimentally trigger the catalytic RNAi pathway and generate sugarcane cultivars to evaluate the potential antiviral resistance surveillance ability and capacity for SCMV. Experimentally validated mature sugarcane (S. officinarum, 2n = 8X = 80) and (S. spp., 2n = 100-120) sof-miRNA/ssp-miRNA sequences (n = 28) were downloaded from the miRBase database and aligned with the SCMV genome (KY548506). Among the 28 targeting mature locus-derived sof-miRNAs/ssp-miRNAs evaluated, one sugarcane miRNA homolog, sof-miR159c, was identified to have a predicted miRNA binding site, at nucleotide position 3847 of the SCMV genome targeting CI ORF. To verify the accuracy of the target prediction accuracy and to determine, whether the sugarcane sof-miRNA/ssp-miRNA could bind the predicted SCMV mRNA target(s), we constructed an integrated Circos plot. A genome-wide in-silico-predicted miRNA-mediated target gene regulatory network has implicated to validate interactions necessary to warrant in vivo analysis. The current work provides valuable computational evidence for the generation of SCMV-resistant sugarcane cultivars.

Sideritis royoi is described from limestone rocky habitats of the Port Massif (southern Catalonia, Spain). The species was first collected by the local botanist Lluís de Torres in the late part of the 20th century, but the specimens have remained unidentified positively in herbaria for over 40 years. Sideritis royoi likely belongs to section Sideritis subsection Hyssopifoliae and shows some morphological affinities with the relatively widespread South European species S. hyssopifolia L., but it much differs in having subspinescent upper leaves, main surfaces of the leaves glabrous or glabrescent, the main abaxial surface of the bracts without eglandular hairs, and shorter inflorescences. Weaker similarities were also observed with some species belonging to S. subsection Fruticulosae Obón &amp; D.Rivera. A description for the new orophilous species is provided, along with a detailed illustration, field photographs, and a comparison with closely related species. We include an assessment of its conservation status and a dichotomous key for identification of all the species of Sideritis subsection Hyssopifoliae.

Topologically non-trivial magnetization configurations in ferromagnetic materials on the nanoscale, such as hopfions, skyrmions, vortices, etc., attract considerable attention of researchers during the last years. In this article applying the theory of micromagnetism, I demonstrate that the toroidal hopfion magnetization configuration is a metastable state of a thick cylindrical ferromagnetic nanodot or a nanowire of a finite radius. Existence of this state is a result of the competition of the exchange, magnetostatic, and magnetic anisotropy energies. The Dzyaloshinskyi-Moriya exchange interaction and surface magnetic anisotropy are of the second importance for the hopfion stabilization. The toroidal hopfion metastable magnetization configuration may be reached in the process of the sample re-magnetizing by applying an external magnetic field along the cylindrical axis.

Viruses pose a great threat to people’s lives. Enterovirus A71 (EV-A71) infects children and infants all over the world with no FDA-approved treatment to date. Understanding the basic mechanisms of viral processes aids in selecting more efficient drug targets and designing more effective antivirals to thwart this virus. The 5´-untranslated region (5’-UTR) of the viral RNA genome is composed of a cloverleaf structure and an internal ribosome entry site (IRES). Cellular proteins that bind to the cloverleaf structure regulate viral replication, while those that bind to the IRES also known as IRES trans-acting factors (ITAFs) regulate viral translation. In this review, we survey the cellular proteins currently known to bind the 5’-UTR and influence viral translation and replication with emphasis on comparing proteins’ functions and localizations pre- and post-(EV-A71) infection. Comprehensive understanding of how the host cell’s machinery is hijacked and reprogrammed by the virus to facilitate its replication is crucial for developing effective antivirals.

Background and Objectives: Cardiovascular events are the leading cause of morbidity and mortal-ity in hemodialysis (HD) patients. Identifying risk factors can help in the effort to reduce cardi-ovascular risk and improve life expectancy. The purpose of this study was to evaluate the ability of CHA2DS2-VASc score [risk index of stroke in atrial fibrillation (AF)] in patients with end stage kidney disease, to predict stroke, major cardiovascular events and mortality. Materials and Meth-ods: CHA2DS2-VASc and HAS-BLED (bleeding risk from use of anticoagulation in AF) scores were calculated in 237 HD patients, [99 women, median age 76 (15) years], at time they com-menced on HD. The scores’ ability to predict long term cardiovascular morbidity and mortality was estimated, both in those with and without AF. Results: CHA2DS2-VASc score was signifi-cantly increased in AF (n=69) compared to non-AF (n=168) patients, 5 (2.5) versus 4 (2), p<0.0001, respectively. Increased CHA2DS2-VASc score was associated with heart failure (p=0.007, p=0.024), stroke (p<0.0001, p<0.0001) and risk of all-cause mortality (p<0.0001, p<0.0001), in both groups, respectively. C statistics indicated that CHA2DS2-VASc score performed modest discrimination in this HD population with and without AF for heart failure, stroke, and all-cause mortality, but for cardiovascular mortality in AF group only. Conclusions: Increased CHA2DS2-VASc score at time starting on HD, can predict stroke, heart failure and all-cause mortality in HD patients in-dependent of the presence of AF. Risk of cardiovascular mortality could be predicted only on patients with AF.

Background: Number of investigations, which are focused on arbuscular mycorrhiza fungi (AMF) biodiversity is still limited. The analysis of AMF taxa in the North Caucasus, a temperate biodiversity hotspot, used to be limited by the genera level. The aim of this study was to define AMF biodiversity at the species level in the North Caucasus biotopes. Methods: Molecular genetic identification of fungi was carried out with ITS1 and ITS2 regions as barcodes, sequencing by Illumina MiSeq, analysis of phylogenetic trees for individual genera, searching the operational taxonomic units (OTUs) with identification at the species level. Sequences from MaarjAM and NCBI GenBank were used as references. Results: We analyzed >10 million reads in soil samples for 3 biotopes to estimate fungal biodiversity. 50 AMF species from 20 genera were found. The valley biotopes (disturbed ecosystems, grasslands) were characterized by a greater AMF diversity, as well as the number of OTUs and species, in comparison with the forest biotopes. Conclusion: Our hypothesis is that high AMF biodiversity for the river valley can be associated with a higher percentage of annual plants in these biotopes, as well as with the active development of restorative successional processes.

Wendan (a type of pomelo), as a popular fruit in China, is less known in other countries. Its peel is widely used in food and traditional medicine. Four different origins of Wendan pomelo were selected, and crude extracts were obtained by the Soxhlet extraction method. The composition of Wendan peel was analyzed by high-performance liquid chromatography-mass spectrometry (HPLC-MS) in positive ion mode. The compounds were identified by searching the Metabolite Link (METLIN), Spectral Database for Organic Compounds (SDBS) and referring to literature reports. A total of 20 compounds were identified among the samples from the four origins, of which 8 compounds were common. The majority of the compounds belonged to the flavonoid and coumarin classes, Meranzin hydrate was identified for the first time in pomelo peel. In vitro antioxidant activity experiments showed that samples from Taizhou, Zhejiang exhibited the highest antioxidant activity in the DPPH, ABTS, and FRAP assays, with values of 0.59 mg/mL, 97.06 μmol TE/g, and 60.62 μmol Fe2+/g, respectively. Samples from Zhangzhou, Fujian showed antioxidant activity second only to the samples from Taizhou, Zhejiang. The sample from Zhangzhou, Fujian Province, showed excellent inhibitory activity in the α-glucosidase inhibition assay (IC50=7.99 mg/mL).

In implantology, among the key choices, to obtain predictable results, it’s essential to establish, using CBCT, the bone site where to insert the implants; these sites must be identified on the mucosa and secondarily, during the surgical phase, on the cortical bone. Surgical templates are a valid aid especially in complex cases which require sev-eral implants. In cases of single implant, on the other hand, surgical guides are rarely used and the implant is inserted freehand and the identification of the implant site on the oral mucosa is more difficult; for this reason the clinician uses the teeth in the arch as a ref-erence. This experimental study is based on the use of a thermo-printed mask, con-taining radiopaque references which, worn by edentulous patients during the execu-tion of the CBCT, helps to evaluate the identification of the implant sites on the patient when surgical guides are not used and the free hand technique is adopted. This method is certainly reserved for experienced operators capable of identifying the correct angle of the implants with the freehand technique. The level of experience remains fundamental in the clinician's decision whether or not to use surgical guides; in fact, doctors with little experience should use surgical guides even in the simplest cases to reduce the risk of error.

Postoperative headaches (POH) following retrosigmoid microsurgery for ves-tibular schwannoma (VS) can significantly diminish patients' perceived health benefits (PHB). A survey of 101 VS patients, utilizing the Rostock Headache Compendium, Glasgow Benefit Inventory, Ten-Item Personality Inventory (TI-PI-G), and German pain processing questionnaire (FESV), revealed that almost half experienced POH, leading to a markedly reduced overall PHB compared to those without POH. This decrease in PHB was linked to higher levels of pain-related helplessness, depression, anxiety, and anger. Positive associations were found between PHB and action-planning competence, cognitive restruc-turing, and experience of competence. Personality traits, particularly low emo-tional stability and openness, were associated with pain-related psychological impairment. Interestingly, hearing loss and facial paresis did not impact PHB. The study underscores the influence of pain-related mental interference and coping strategies on PHB in long-term POH patients, suggesting a role for personality traits in their pain management. The findings advocate for short-term psychological interventions, emphasizing adaptive coping mecha-nisms like cognitive restructuring, to enhance PHB in VS patients post-microsurgery.

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

The variability of the material properties of steel from different suppliers causes problems in achieving the required surface quality after turning. Therefore, the manufacturer needs to be able to estimate the resulting quality before starting production, especially if it is an expensive, small-batch production from stainless steel. Predictive models will make it possible to estimate the surface roughness from the mechanical properties of steel in the Mill Test Certificate (MTC) and thus support decision-making about the supplier selection or the acceptance of a material supply. Multivariate second-order polynomial model and feedforward back-propagation Artificial Neural Network (ANN) model were used to enhance the methodological robustness in formulating the decision if the predicted surface roughness is outside the required range, even before accepting the delivery. Both models can accurately predict surface roughness, while the ANN model is more accurate than the polynomial model; however, the predictive model is sensitive to the accuracy of the input data, and the model’s prediction is valid only under precisely defined conditions. The prediction model is used in a step-by-step decision-making procedure, which enables the trained staff to make a quick decision.

The cultivation of pea pods spans across various regions and climates with a global production of around 20 million tons. The pea peel wastes, which make up 30–40% of the total weight of the peas, are freely available in large quantities. The biomass used was characterized via ultimate, proximate, and structural analysis obtaining a 20,2 %w of cellulose and 17,4 %w of hemicellulose which via valorization processes can be transformed into platform chemicals. Hydrothermal valorization presents itself as a clean form of treatment of said wastes, ranging from 120 – 180 ºC (LHW) to 180 to 260 ºC (HTC). The use of LHW can lead to the production of sugars (up to 70%w yield) and levulinic acid (4 %w yield) while the use of HTC leads to Formic acid (40 %w) and levulinic acid (4 %w yield). The use of LHW for longer times favors the production of HMF and Furfural. The use of homogeneous catalysts (H2SO4, CH3COOH, KOH and NaHCO3) was implemented and their selectivity described. Solid fractions of LHW and HTC were characterized via FTIR and elemental analysis and the change in their structure described as they shift from biomass to biochar. Optimal conditions for each platform chemical were reported for best utilization of the peapod waste.

Reliable motor control design is a critical part of the electric vehicle application. Control techniques and motor control devices play an important role in achieving system reliability. In this study, motor control devices have been proposed to implement control techniques in sensorless PMSM. Motor control devices including PWM signal generation by FPGA, gate driver circuit (GDC), and power inverter were developed to be integrated into single-stage motor control devices for sensorless motor control applications. Improvements in GDC design have been the focus to increase the performance of the motor control devices used. Switching losses due to the parasitic elements caused ringing switching behavior of the power inverter circuit, which reduces the performance when the system is integrated. Moreover, in the implementation of sensorless motor control using observer techniques, these conditions affect the accuracy of rotor position estimation, which uses the current or voltage information of the power converter as a variable in the observer design model. Therefore, a GDC design using IR2110 with additional ringing suppression circuit configuration was proposed for the application of motor device control topology to improve the performance of sensorless motor control system. Furthermore, the reduction of switching losses, reduction of current distortion, and accuracy of rotor position information were confirmed by experimental results.