We explore 3D printing modules for self-assembling spacecraft and robots – structure, actuators, electrics. Self-assembly has long been viewed as a highly desirable capability for autonomous construction of large space structures. We review self-assembly in space which focusses on the self-assembly of modules that demonstrate the range of applications. However, self-assembly may be synergised with 3D printing to offer an automated capability from raw material to modules for assembling new spacecraft or habitats. One application of 3D printing is using space debris on-orbit as an in-situ resource - defunct spacecraft may be salvaged as raw material for in-situ construction on demand. The common features of all self-assembling modules are that the modules constitute a structure housing a computer-controlled actuator internally and a reversible latching mechanism externally. We have demonstrated a 3D printed DC electric motor in which the only components that were not 3D printed are the wire coils. We have married our 3D printed motor prototype as an actuated joint between two 3D printed trigon-type panels developed as part of a trigon self-assembling system concept. The trigon concept underlies a modular approach to self-assembling and self-deploying structures. The 3D printed motorised panel system demonstrates that the motor aspect and structural aspects of robotic self-assembling machines are amenable to 3D printing. This has implications for self-assembling systems into modular satellites as a solution to space debris.

The hard open problem of finding the lower bound for the Fokker Planck Kolmogorov’s time-dependent controller parameter probability density function is addressed for the first time in literature in this work. This revolutionary exposition will put control theory and other related inter-disciplinary fields to a higher level towards contemporary control theory. Notably, based on the influential role of control theory in both engineering and industry, this paper will be of great value to all engineering and industry professionals who seek to know more about advanced trends within control theory settings. On the other remit of the spectrum, Fokker Planck Kolmogorov(FPK) equations are of high importance to physicists as well as mathematicians, based on their multiple applicability to multi-interdisciplinary fields of human knowledge, and beyond. So, this by default adds more taste and credibility to this study. Concluding thoughts are included at the end of the work along with a difficult outstanding problem and the direction of future work.

Photovoltaic (PV) technologies are developing rapidly as a result of their ability to reduce energy consumption from conventional sources as well as the growth of global energy expenditures. Nevertheless, several impact categories during the PV panel life cycle occur and some principal sectors are affected. These impact categories are assessed by applying life cycle analysis (LCA) techniques through various software programs, databases and methods. Such analyses of energy technologies are crucial, as they can reveal the occurrence of hazardous emissions at distinct life cycle stages. In the present paper, a PV panel impact assessment through life cycle analysis is carried out. More precisely, a combination of PV technologies—monocrystalline, multicrystalline, copper indium gallium selenide (CIGS), amorphous, and ribbon—is studied. The impact categories of ozone depletion, human toxicity, particulate matter, ionising radiation, photochemical ozone formation, acidification, eutrophication, and ecotoxicity, as well as the sectors of human health, ecosystem quality, and environment, are assessed using the openLCA software, the ecoinvent database, the eco-indicator 99, IMPACT 2002+, ReCiPe, and TRACI methods.

We consider the Dirac field in polar formulation, showing that when torsion is taken in effective approximation the theory has the thermodynamic properties of a van der Waals gas, that when the limit of zero chiral angle is taken the theory reduces to that of a Weyssenhoff fluid, and that under the spinless condition it gives the Newtonian particle. This nesting of approximations will allow us to interpret the various objects pertaining to the spinor, with torsion providing a form of negative pressure, and the chiral angle being related to a type of internal dynamics.

This investigation mainly focused on the microstructural analysis of Al based hybrid metal matrix composites. Aluminium as base metal and reinforced with 2% B4 C, ZrO2 (2%,4%,6%), SiC (4%,8%,12%). Specimens were manufactured by using conventional stir casting technique. Hybrid metal matrix composites were examined by using Scanning electron microscope. This research explained XRD, EDS and Fractography analysis. This investigation shows the reinforced material has given better results in all aspects than pure Al6061 alloy and homogeneous dispersion of particles inside the matrix. Porosity, delamination, cracks, particle distributions were observed through microstructural analysis. Additionally determined the crystal size of pure Aluminium 6061 alloy and hybrid composites.

Antibacterial bone cements (ABCs), such as antibiotic-loaded bone cements (ALBCs), have been widely utilized in clinical treatments. Currently, bone cements loaded with vancomycin, gentamicin, tobramycin, or clindamycin are approved by the US Food and Drug Administration. However, traditional ALBCs exhibit drawbacks like burst release and bacterial resistance. Therefore, there is a demand for the development of antibacterial bone cements containing novel agents. In this review, we summarize the latest advancements in ABCs. More importantly, we provide an overview and outlook on novel antibacterial agent-loaded bone cements, including metallic antibacterial agents, pH-switchable antibacterial agents, cationic polymers, N-halamines, non-leaching acrylic monomers, antimicrobial peptides, etc. Offering novel perspectives for designing ABCs and treating bone infections.

Ophthalmological services face global inadequacies, especially in low- and middle-income countries, which are marked by a shortage of both practitioners and equipment. This study employed a portable slit-lamp microscope with video capabilities and cloud storage for a more equitable global diagnostic resource distribution. To enhance accessibility and quality of care, this study targeted corneal opacity, a global cause of blindness. To boost the online diagnosis efficiency, an AI pipeline was developed using anterior segment videos to detect corneal opacity. First, we extracted image frames from videos and learned them using a Convolutional Neural Network(CNN) model. Second, we manually annotated to extract only the corneal margins, adjusted the contrast with CLAHE, and learned using the CNN model. Finally, we performed semantic segmentation of the cornea using the annotated data. The results showed an accuracy of 0.8 for image frames, and 0.96 for corneal margins. Dice and IoU were 0.94 for semantic segmentation of corneal margins. While corneal opacity detection from video frames seemed challenging in the early stages of this study, manual annotation, corneal extraction, and CLAHE contrast adjustment significantly improved accuracy. Integrating manual annotation into the AI pipeline through semantic segmentation achieved a high accuracy in detecting corneal opacity.

Evidence suggests that COVID-19 infection can cause lasting health consequences. Multidiscipli-nary rehabilitation services have been recommended to reduce the sequalae. However, the effec-tiveness of physical exercise interventions remains insufficiently documented. The aim of this study was to develop and implement a specific and well tolerated protocol-based, intervention to reduce muscle weakness in older adults impacted by COVID-19. Forty six older adults random-ized into Intervention and Control Groups. Isometric and isokinetic strength assessments were conducted for selected muscle groups using JBA Staniak® torquemeter and Biodex System 3 dy-namometer. Functional abilities were evaluated with the Time Up and Go test and Chair Stand Tests. Men in intervention group demonstrated a significant improvement in static conditions for knee flexors (KF), trunk extensors (TE) and trunk flexors (TF) and in dynamic conditions for knee extensors (KE). Women in intervention group showed significant improvement in static condition for EF, KF, TE and TF and in dynamic for a KE and for a KF. The interaction GROUP x TESTING SESSION was significant for Chair test (s) and Chair test (n). Our results demonstrate the effec-tiveness of a well-tolerated, protocol-based approach that can be used to diminish long lasting functional deficits in post-covid survivors.

The wetlands in northeastern Bangladesh are home to some of the most prolific and valuable ecosystems on the planet, offering a wide range of benefits to both the local and wider communities. A limited number of research on this topic have been carried out in various wetlands in coastal hilsa, but none have been located in the northeastern region of Bangladesh. Thus, the goal of this study was to better understand the most commercially relevant species (anadromous hilsa) in northeastern Bangladesh as well as to assess the wetland ecosystem services more thoroughly with the contribution of their fish genetic resources. In addition to laboratory studies, fieldwork was done in the fishing settlements along the Surma River, Hakaluki Haor, and Tanguar Haor. The study classified 77 fish species into 21 subcategories of ecosystem services, of which 56% were not threatened, 17% were vulnerable, and 10% were highly endangered. The species Hilsa was found to be the most economically significant, accounting for 1% of the GDP overall. The study determined if the hilsa stock of the examined wetlands maintains a single or several subpopulations in comparison to other parts of the country when they migrate from saline water to northeastern Bangladesh (freshwater). But when compared to other groups, the FST score was between 0.000 to 0.017, indicating that the populations are identical and that there is genetic material sharing between them. Additionally, these statistics imply that the Sylhet haor and adjacent river systems contain a single stock of Hilsa fish. Nevertheless, a number of stresses and risks, including overexploitation, illicit and destructive fishing, siltation, and natural disasters like flash floods, have been linked to changes in the supply of hilsa and other ecosystem services. This study also emphasizes the necessity of policy attention and research to address the concerns.

The solvent extraction of gold(III) by undiluted 2-ethylhexanol or dissolved in toluene from HCl solution has been investigated. The numerical analysis of gold distribution data suggests the formation of HAuCl4·L and HAuCl4·2L (L= 2-ethylhexanol) species in the organic phase with formation constants K11= 38 and K12= 309, respectively. The results derived from gold(III) distribution have been implemented in a solid-supported liquid membrane system. The influence of several variables on gold transport was considered: feed and receiving phases stirring speeds, HCl and gold concentrations in the feed phase and carrier concentration in the membrane phase as well as the presence of base metals (Fe,Cu,Ni) and PGMs in the feed phase. Also, diffusional resistances to mass transfer are estimated. Gold is recovered as zero valent nanoparticles.

Magnetic levitation vibration isolation platform are essential for testing space telescopes. This paper presents the design of a Large Air-Gap Voice Coil Motor (LAG-VCM) specifically for use in such platforms. The electromagnetic design of the voice coil motor begins with the Equivalent Magnetic Circuit Method, followed by the analysis and optimization of the motor design using Finite Element Analysis (FEA). Subsequently, a prototype was constructed and tested. The test results confirm that the prototype meets the design specifications and requirements.

This research ventures into the critical public health challenge of childhood obesity by exploring the dynamic interplay between psychological well-being and Body Mass Index (BMI) throughout various developmental stages of childhood. It delves into how emotional regulation, attachment dynamics, and social relationships correlate with obesity from early childhood to adolescence. Highlighting key findings, such as the negative correlation between psychological resilience and higher BMI in young children, the impact of social relationships on obesity risk during pre-adolescence, and the link between adaptive emotional strategies and higher BMI in adolescents, this study brings to the fore the nuanced relationship between psychological factors and obesity. Utilizing the interdisciplinary process of bioinformatics, this research synergizes psychometric and biomedical data to unearth psychological markers critical for crafting targeted, age-appropriate interventions. This study advocates for a holistic healthcare approach, emphasizing the integration of psychological support within obesity prevention and management strategies, thereby underscoring the indispensable role of psychological factors in the fight against childhood obesity. The application of bioinformatics methods to analyze complex datasets demonstrates how collaboration across medical specialties can enrich our understanding and response to childhood obesity, contributing significantly to the development of comprehensive, bioinformatics-enhanced healthcare solutions.

The COVID-19 pandemic over the past years has shown a great need for rapid, low-cost and on-site detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this paper, an aptamer-based colloidal gold nanoparticle lateral flow test strip was well developed to realize the visual detection of wild type SARS-CoV-2 spike protein (SP) and multiple variants. Under the optimal reaction conditions, a low detection limit of SARS-CoV-2 S protein of 0.68 nM was acquired, and the actual detection recovery was 83.3% to 108.8% for real world samples. This suggests a potential tool for the prompt detection of SARS-CoV-2 with good sensitivity and accuracy, and a new method for the development of alternative antibody test strips for the detection of other viral targets.

This paper proposed a three-layer distributed simulation network architecture: 1. Distributed virtual simulation network, 2. Perception and control sub-network, and 3. Collaborative communication service network. This simulation architecture runs on a distributed platform. The platform can provide a unique virtual scenario and several simulation services to verify the individual robot system's basic perception, control, and planning algorithms and the distributed collaborative algorithms of heterogeneous multi-robot systems. Furthermore, we designed a simulation experiment scenario for classical heterogeneous robot systems such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). At last, We gave a rough estimation model of the bandwidth.

Heavy metal pollution poses a significant threat to ecosystems and human health worldwide. In response to this pressing issue, bioremediation has emerged as a promising approach for mitigating contamination. This paper adopts a bibliographic method to explore the key techniques and applications of bioremediation. Furthermore, we delve into the most prominent threats to human environmental health and the corresponding remediation methods. Additionally, we discuss the future trajectory of bioremediation research, with a particular focus on the integration of big data and machine learning technologies. These advanced methodologies hold great potential for enhancing the effectiveness and efficiency of environmental remediation efforts in the face of escalating pollution challenges.

Environmental monitoring programs rely on periodic spot sampling at specific locations, followed by laboratory analysis, aiming at the evaluation of water quality at a catchment scale. For this purpose, automatic telemetric stations for specific parameters have been installed by the Institute of Marine Biological Resources and Inland Waters of Hellenic Centre for Marine Research (IMBRIW-HCMR) within several Greek rivers and lakes, providing continuous and temporal monitoring possibilities. Nevertheless, the employment of unmanned surface vehicles (USV) equipped with integrated sensors represents a tool valuable to several monitoring strategies, offering enhanced temporal and spatial coverage over specific timeframes, allowing for targeted examination of sites or events of interest. The USVs have been deployed by Athens Water and Sewerage Company (EYDAP) as a cost-effective tool for the environmental monitoring of surface water bodies of interest, with emphasis on the spatial fluctuations of chlorophyllα, electrical conductivity, dissolved oxygen and pH, observed in Koumoundourou Lake and rivers Acheloos, Asopos and Kifissos. The effectiveness of an innovative heavy metal (HM) system installed in the USV for thein situ determination of copper and lead was also evaluated herewith. The results obtained demonstrate that both the timely detection of potential pollution from both anthropogenic activities and natural processes as well as the monitoring of inland waters are made feasible through the employment of USV.

Despite available global efforts and funding, Tuberculosis (TB) continues to affect a considerable number of patients worldwide. Policy makers and stakeholders set clear goals to reduce TB incidence and mortality, but the emergence of multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) complicate the reach of these goals. Drug-resistance TB needs to be diagnosed rapidly and accurately to effectively treat patients, prevent the transmission of MDR-TB, minimise mortality, reduce treatment costs and avoid unnecessary hospitalisations. In this narrative review, we provide a comprehensive overview of laboratory methods for detecting drug resistance in MTB, focusing on phenotypic, molecular and other drug susceptibility testing (DST) techniques. We found a large variety of methods used, with the BACTEC MGIT 960 being the most common phenotypic DST and the Xpert MTB/RIF being the most common molecular DST. We emphasise the importance of integrating phenotypic and molecular DST to address issues like resistance to new drugs, heteroresistance, mixed infections and low-level resistance mutations. Notably, most of the analysed studies adhered to the outdated definition of XDR-TB and did not consider the pre-XDR definition, thus posing challenges in aligning diagnostic methods with the current landscape of TB resistance.

Eutrophication, a global concern, impacts water quality, ecosystems, and human health. It’s crucial to monitor algal blooms in freshwater reservoirs, as they indicate the trophic condition of a waterbody through Chlorophyll-a (Chla) concentration. Traditional monitoring methods, however, are expen-sive and time-consuming. Addressing this hindrance, we developed models using remotely sensed data from the Sentinel-2 satellite for large-scale coverage, including its bands and spectral indexes, to estimate the Chla concentration on 149 freshwater reservoirs in Ceará, Brazil. Several machine learning models were trained and tested, including k-nearest neighbours, random forests, extreme gradient boosting, the least absolute shrinkage, group method of data handling (GMDH), and sup-port vector machine models. A stepwise approach determined the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH, achieving an R2 of 0.91, MAPE of 102.34%, and RMSE of 20.38 g/L, which are values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near infra-red bands.

In a densely populated country like Bangladesh, universities grapple with the challenge of efficiently addressing myriad queries from a large student body, leading to a heightened workload for university stakeholders. To tackle these challenges, we introduce PixieGPT, a tailor-made Generative Pre-Trained Transformer for Bangladeshi universities. PixieGPT significantly mitigates workload by adeptly handling common university-related queries, thereby enhancing user experience. The hierarchical structure plays a crucial role in managing diverse queries from thousands of students about the university system. The solution introduces a modular hierarchical knowledge base (KB) with simpler complexities, addressing the intricacies of efficiently managing large volumes of queries. PixieGPT is designed in a modular way so that the solution is also adaptable for the implementation of other universities worldwide based on the requirements of a particular administrative system. The modular nature facilitates easy adaptation with minor changes based on specific university requirements, ensuring a seamless integration process. This paper delves into the intricacies of PixieGPT's design, emphasizing its pivotal role in mitigating workload challenges for university stakeholders in Bangladesh. The incorporation of BERT for Natural Language Understanding(NLU) and GPT models for Natural Language Generation(NLG) enhances PixieGPT's capabilities, contributing to the scalability and efficiency of the system. The presented use case underscores the practical benefits of PixieGPT, positioning it as a promising solution for universities globally with similar operational frameworks.

Disk galaxies have a typical dimension of few tens of thousands of light years. It follows from the theory of general relativity that any signal originating from the galactic center will be noticed at the outskirts of the galaxy only tens of thousands of years later. This retardation effect is absent in modelling used to calculate rotation curves throughout the entire galaxy and its external gas. The considerable differences between Newtonian action at a distance predictions and observed velocities are currently removed either by assuming dark matter or by suggesting various modifications to the laws of gravity, MOND being a long standing alternative to Newtonian gravity. In previous papers we have shown that applying general relativity in a rigorous fashion without mindlessly neglecting retardation, one can explain the radial velocities of galactic matter without modifying gravity or adding unseen matter. Moreover, it was shown that dark matter effects as they appear in gravitational lensing, the Tully-Fisher relation, and mass estimations based on the virial theorem could also be explained as retarded-gravity effects. In the original main paper on the subject only one galaxy (M33) was analysed in detail. This was later amended somewhat with a published study of eleven galaxies. Here we give a more comprehensive retardation analysis of 143 galaxies of different types from the SPARC Galaxy collection. We show that in most cases we obtain an excellent fit without postulating dark matter or modifying general relativity.

In Taiwan, mountainous areas account for approximately two-thirds of the total area. The steep terrain and concentrated rainfall during typhoons cause landslides, which pose a considerable threat to mountain settlements. Therefore, models for analyzing rainfall-induced landslide hazards are urgently required to ensure adequate land use in mountainous areas. In this study, focusing on Pingtung County in southern Taiwan, we developed a landslide hazard index (IRL) to land use. Using FORMOSA-2 and SPOT-5 satellite images, data were collected before and after four typhoons (one in 2009 and three in 2013). The ArcGIS random tree classifier was used for interpreting satellite images to explore surface changes and disasters, which were used to analyze slope disturbances. The product of the maximum 3-h rolling rainfall intensity and effective accumulated rainfall was used as a rainfall trigger index (IRT). Considering environmental and slope disturbance factors, an index of slope environmental strength potential (ISESP) was developed through logistic regression (LR). Landslide hazard to land use was estimated using IRT and ISESP. The average coefficient of agreement (Kappa) was approximately 0.71 (medium to high accuracy); the overall accuracy of slope environmental strength potential analysis was approximately 80.4%. At a constant ISESP, IRT increased with the increasing hazard potential of rainfall-induced landslides. Furthermore, IRT and ISESP were positively correlated with landslide occurrence. When large ISESP values occur (e.g., fragile environment and high land development intensity), small IRT values may induce landslides.

Abscisic acid (ABA) is a conserved “stress hormone” in unicellular organisms, plants and animals. In mammals, ABA and its receptors LANCL1 and LANCL2 stimulate insulin-independent cell glucose uptake and oxidative metabolism: overexpression of LANCL1/2 increases, and their si-lencing conversely reduces, mitochondrial number, respiration and proton gradient dissipation in muscle cells and in brown adipocytes. We hypothesized that the ABA/LANCL hor-mone/receptors system could be involved in thermogenesis. Heat production by LANCL1/2-overexpressing vs. double-silenced cells was compared in rat H9c2 cardiomyocytes with two different methods: differential temperature measurements using sensitive thermistor probes, and differential isothermal calorimetry. Results indicate that overexpressing cells generate an approx. double amount of thermal power compared with double-silenced cells, and that addition of ABA further doubles heat production in overexpressing cells. With the temperature probes, we find a time-scale of approx. 4 min for thermogenesis to “turn on” after nutrient addition. These results identify the ABA/LANCL hormone receptors system as a hitherto unknown regu-lator of cell thermogenesis. Pharmacologic means of increasing metabolic energy dissipation are considered a promising strategy to reduce oxidative stress and lipid accumulation at a cellular and organismic level: from this study, LANCL1-2 proteins emerge as targetable controllers of ther-mogenesis through their natural ligand ABA.

This paper provides the first-time ever analytic modelling for Pointwise Stationary Fluid Flow Approximation(PSFFA) model of the non-stationary

Hair cosmetics are in fashion. Numerous products are commonly used in combination. Frequent allergens are present in hair cosmetics, which, added to almost daily use, increase the risk of sensitization. Familiarity with the clinical distribution of the eczematous rash is necessary to seriously consider patch testing. There are haptens on the market, although not all of them. It is necessary to know how to patch your own products. When avoidance of the allergen cannot be possible, new treatments are being evaluated to combat allergic contact dermatitis, mainly of the occupational type. In this manuscript, a complete and practical review of the main allergens and contact sources of hair cosmetic origin has been carried out.

Concrete being one of the most frequently used construction materials in the field of construction. The use of concrete has increased with the passage of time as the more and more construction activities are taking place around the globe. Different materials have been added in to the concrete mix to provide the concrete more strength and make it more durable and long lasting. This paper looks into a detailed comparison of concrete strength when casted with in the presence of carbon fibers, steel fibers and glass fibers. The compressive strength for cubes and cylinder with carbon fibers is the highest as compare to steel fibers as well as glass fibers. The compressive strength of concrete is measured on 3 day, 7 day, 14 day and 28 day strength. The compressive strength of cube on 28th day strength is 37.52 MPa for carbon fiber steel, for steel fiber it is 34.22 MPa and with glass fiber the strength is 29.335 MPa whereas the compressive strength of cylinder with carbon fibers the 34.88 MPa, with steel fiber it is 34.22 MPa and with glass fiber the strength is 27.447 MPa. For the cubes the maximum difference between different fibers inducted concrete sample is 28% approximately and for cylinder it is 27%, respectively. On the other hand, the tensile strength of concrete was also gained with the carbon fiber. It was determined that carbon fiber provided about 16% and 12.8% tensile strength higher than glass and steel fibers respectively.

Batch and transport experiments were used to investigate the remediation of a loamy sand soil contaminated with Cr(VI) using zero valent iron nanoparticles (nZVI) stabilized by the carboxy methyl cellulose (CMC-nZVI). The effect of pH, ionic strength (IS), and flow rate on the removal efficiency of Cr(VI) were investigated under equilibrium (uniform transport) and non-equilibrium (Two-site sorption) transport using the Hydrus-1D model. The overall removal efficiency ranged from 70 to over 90% based on the chemical characteristics of the CMC-nZVI suspension and the transport conditions. The concentration and pH of the CMC-nZVI suspension had the most significant effect on the removal efficiency and transport of Cr(VI) in the soil. The average removal efficiency of Cr(VI) was increased from 24.1 to 75.5% when the concentration of CMC-nZVI nanoparticles was increased from 10 to 250 mg L-1, mainly because of the increased total surface area at larger particle concentration. Batch experiments showed that removal efficiency of Cr(VI) was much larger under acidic conditions. The average removal efficiency of Cr(VI) reached 90.1 and 60.5 % at pH 5 and 7, respectively. The Two-site sorption model described quite well (r2 = 0.96-0.98) the transport of Cr(VI) in soil as compared to the uniform transport model (r2 = 0.81-0.98). The average retardation of Cr(VI) was 3.51 and 1.61 at pH 5 and 7, respectively, indicating earlier arrival for the breakthrough curves and shorter time to reach maximum relative concentration at lower pH. The Hydrus-1D model has proven to be an efficient tool for assessing the removal of Cr(VI) pollutants, offering innovative, cost-effective, and environmentally friendly remediation methodologies.

Climate change has become a global nightmare, and the awareness of the causes of carbon emissions have resulted in rigorous studies. These studies linked the increase in global warming with booming economic growth. Since global warming has become more apparent, researchers have explored ways to decouple economic activities from carbon growth. Economic and carbon growth must be decoupled to achieve a low-carbon economy to support the carbon growth plan or emission reduction strategy. The world is transitioning towards a carbon-neutral and green ecosystem, so finding ways to decouple carbon emissions from economic activities is an exciting topic to explore. The study reviews current information on the importance of decoupling energy from economic growth innovative techniques that thoroughly examine the challenges and constraints of low-carbon energy systems. This review revealed that decarbonization and dematerialization had been achieved without declining global economic growth. It also provides information on energy use and economic activities leading to global carbon emissions and alternative solutions to the global challenge of climate change. The decoupling methods commonly used to determine the impact of energy decarbonization on economic growth are explored. All suggested that economic growth is a primary mover of global carbon emission increase and must be separated to achieve a carbon environment.

Panax notoginseng is a traditional Chinese medicine, and saponins are its main active ingredients. The content of saponins in flower of P. notoginseng is the highest in the whole plant. Minor ginsenosides have stronger pharmacological activity than main ginsenosides, but its content in nature is very low. We can use extracted total saponins from P. notoginseng flower as the substrate to produce minor ginsenosides by biotransformation method. In this study, we used the fungus Talaromyces flavus and its crude enzymes to transform the extracted total saponins of P. notoginseng flower. Through the qualitative and quantitative analysis of the transformation substrates and products, it was found that minor ginsenosides Gyp-XVII, ginsenosides C-Mc1, C-Mx1, F2, Rg3, 20(R)-Rg3, Rk1 and Rg5 were found in the products and the conversion rate of the major saponins in P. notoginseng flower ranged from 44.16 to 55.22%, the yields of the minor ginsenosides ranged from 0.05 to 3.24%. This study provided a good experimental idea, greatly increased the utilization rate of total saponins in P. notoginseng flower, and laid a foundation for promoting the wide application of minor saponins.

Silicon Photomultipliers find applications across various fields. One potential Silicon Photomultiplier application domain is neutrino telescopes, where they may enhance the angular resolution. However, the elevated dark count rate associated with Silicon Photomultipliers represents a significant challenge to their widespread utilization. To address this issue, it is proposed to use Silicon Photomultipliers and Photomultiplier Tubes together. The Photomultiplier Tubes signals would serve as a trigger to mitigate the dark count rate, thereby preventing undue saturation of the available bandwidth. This paper presents an investigation into a fast and resource-efficient method for filtering Silicon Photomultiplier dark count rate. A low-resource and fast coincident filter has been developed, which removes the Silicon Photomultiplier dark count rate by using as trigger the Photomultiplier Tube input signals. The architecture of the coincidence filter, together with the first results obtained, which validate the effectiveness of this method are presented.

The antimicrobial quantitative structure–activity relationship of plant flavonoids against Gram-positive bacteria was established in our previous works, and the cell membrane was confirmed as a major site of action. To investigate whether plant flavonoids have similar antibacterial effects and mechanisms against both Gram-negative and Gram-positive bacteria, here, the minimum inhibitory concentrations (MICs) of 37 plant flavonoids against Escherichia coli were determined using the microdilution broth method, and then the correlation between their lipophilic parameter ACD/LogP or LogD_7.40 value and their MIC was analyzed. Simultaneously, the correlation between the ACD/LogP or LogD_7.40 value and the MIC of 46 plant flavonoids reported in literature against E. coli was also analyzed. Both sets of results showed that there is a significant correlation between the LogP value and the MIC of plant flavonoids against Gram-negative bacteria. However, it is difficult to effectively predict the MIC of plant flavonoids against Gram-negative bacteria from their lipophilic parameters. By comparing two regression curves derived from plant flavonoids against Gram-negative and Gram-positive bacteria, it was further discovered that the antibacterial activities of most plant flavonoids against Gram-negative bacteria are stronger than those against Gram-positive bacteria when their LogP values are less than about 3.0, but the opposite is true when their LogP values are more than about 3.6. Moreover, this comparison also suggests that unlike mainly acting on the cell membrane of Gram-positive bacteria, plant flavonoids have multiple mechanisms against Gram-negative species, while the cell membrane is also an important action site among them. Combined with the correlation analyses between the enzyme inhibitory activity and the LogP value of the reported flavonoids, it was further suggested that DNA gyrase is another important target of plant flavonoids against Gram-negative bacteria.

Precise individual tree or sample-based inventories derived from 3D point cloud data of mobile laser scanning can improve our comprehensive understanding of the structure, function, resilience, biodiversity, and ecosystem services of urban forests. This study assessed the performance of a hand-held laser scanning system for the extraction of tree position, diameter at breast height (DBH) and tree height (H) in an urban area. A total of 2083 trees of 13 species from 34 plots were analyzed. The results showed that the registration of tree positions using ground control points (GCPs) demonstrated high accuracy, with errors consistently below 0.4 m, except for a few instances. The extraction accuracy of DBH for all trees and individual species remained consistently high, with a total root mean square error (RMSE) of 2.06 cm (6.89%) and a bias of 0.62 cm (2.07%). Notably, broad-leaved trees outperformed coniferous trees, with RMSE and bias values of 1.86 cm (6%) and 0.76 cm (2.46%) compared to 2.54 cm (9.46%) and 0.23 cm (0.84%). Tree height extraction accuracy varied significantly among different species, with R2 values ranging from 0.65 to 0.92. Generally, both DBH and height were underestimated. Linear mixed effects models (LME) were applied to evaluate factors affecting the performance of HLS with the plot as a random factor. LME analysis revealed that plant type and terrain significantly influenced the accuracy of DBH and H derived from HLS data. Other fixed factors of plot area, tree density and trajectory length didn’t have significance. With a large sample size, we concluded that the HLS demonstrated sufficient accuracy in extracting individual tree parameters in urban forests.

Taeniid infections pose a significant threat to both animal and public health, as certain tapeworms within this group can also infect humans, potentially leading to severe health conditions. Therefore, this calls for preventive and control measures, such as regular deworming of dogs. While the efficacy of deworming has been established in developed countries, there is a paucity of reported deworming practices in rural areas of developing countries, such as Tanzania. This study aims to understand determinants for helminth treatment-seeking behavior towards control of cestodial taeniid infections in rural settings in northern Tanzania. A cross-sectional study was undertaken in agropastoral and pastoral areas of northern Tanzania. Comprehensive data from household surveys in selected sub-villages were collected to ascertain dog ownership, dog deworming practices, and the availability of dewormers. Analytical methods were employed to discern how various determinants influenced deworming practices among dog owners in these communities. Awareness of dewormers specifically formulated for dogs emerged as a pivotal factor affecting dog deworming practices. Dog owners who were informed about appropriate dog dewormers were nearly two times more likely to engage in deworming compared to those with limited knowledge (OR = 1.78, 95% CI 1.77 - 4.18, p < 0.001). A majority 32(51.6%, 95% CI: 38.7-64.3) acknowledged accessing dewormers for livestock within their wards. Praziquantel a potent dewormer on cestodes was least known to most dog owners 2(13%, 95% CI: 2.3-41.6). Praziquantel, a potent and common dewormer effective against cestodial taeniid infections, remains relatively unknown in rural areas of northern Tanzania. Therefore, more awareness on appropriate deworming agents against taeniids in dogs should be raised in rural dog-keeping communities.

We propose a theoretical method for the deterministic shaping of quantum light via photon number state selective interactions. Nonclassical states of light are an essential resource for high precision optical techniques that rely on photon correlations and noise reshaping. Notable techniques include quantum enhanced interferometry, ghost imaging, and generating fault tolerant codes for continuous variable optical quantum computing. We show that a class of nonlinear-optical resonators can transform many-photon wavefunctions to produce structured states of light with nonclassical noise statistics. The devices, based on parametric down conversion, utilize the Kerr effect to tune photon number dependent frequency matching, inducing photon number selective interactions. With a high amplitude coherent pump, the number selective interaction shapes the noise of a two-mode squeezed cavity state with minimal dephasing, illustrated with simulations. We specify the requisite material properties to build the device and highlight the remaining material degrees of freedom which offer flexible material design.

The Mexican Republic ranks fifth in the world in terms of amphibian diversity, and within Mexico, the state of Oaxaca has the greatest amphibian richness and endemism. Unfortunately, various factors, such as land use change and global warming, have caused a global crisis that threatens the conservation of this class. In the state of Oaxaca, few studies of amphibians have been carried out at the state level. Several research has shown that an analysis of beta diversity provides information that can be applied to conservation strategies. In this work, we analyzed the beta diversity at the species level and higher taxon level for the amphibians of Oaxaca and their replacement components and the differences in richness for anurans and caudates separately between physiographic subprovinces. Very high beta diversity was recorded, with higher diversity occurring among caudates than among anurans. Replacement was the component that most contributed to this result, and the subprovinces with substantial environmental differences had the most dissimilar amphibian communities. The results of this study show the need to implement conservation strategies in subprovinces with high amphibian richness and endemism levels, following the example of Sierra Madre de Oaxaca (SMO)

The African continent has the highest prevalence of hypertension globally, with South Africa reporting the highest prevalence in Southern Africa. While the influence of genetic variability in the pathogenesis of hypertension is well described internationally, limited reports are available for African populations. This study aimed to assess the association of genetic variants and essential hypertension in a cohort of two ethnic South African population groups. Two hundred and seventy-seven hypertensive and one hundred and seventy-six normotensive individuals were genotyped for 79 variants. Genotyping was performed using the Illumina GoldenGate Assay and allele-specific polymerase chain reaction. The association of variants was assessed using the Fisher Exact test under the additive and allelic genetic models, while multivariate logistic regression was used to predict the development of hypertension. Five variants (CYP11B2 rs179998, AGT rs5051 and rs699, AGTR1 rs5186 and ACE rs4646994) were significantly associated with essential hypertension in the cohort under study. Furthermore, AGTR1 rs5186 and AGT rs699 were identified as risk factors for the development of hypertension in both ethnic groups. In two ethnic South African populations an association was observed between Renin-Angiotensin-Aldosterone System (RAAS) related genes and the development of hypertension.

This study focuses on examining various approaches for Tensor Decompositions (TDs) and their potential applications in satellite imaging (SI) and deep learning (DL). The research highlights how these decompositions can contribute to the advancement of SI and DL, providing valuable insights for future utilization of TDs in research. It explores how these techniques contribute to the advancement of SI and DL methods, providing insights into their potential applications and suggesting future research directions. The study aims to enhance the use of TDs techniques to further advance research efforts in these fields. More importantly, the current investigation offers a thorough analysis of the potential advantages and reasons for employing TDs techniques in different domains, including satellite imaging and deep learning. The aim is to enhance research outcomes by utilizing TDs. The review also identifies unresolved issues and proposes future directions for further investigation in this field. Fundamentally, these proposed open problems will open new grounds to the research community to articulate, innovate, and provide more real-life applications to improve the current state of the art by delving into a wider vision for a higher-level performance of both satellite imaging (SI) and deep learning (DL). Looking at the bigger scenario, this also suggests that TDs could be potentially employed to revolutionize existing machine learning technologies as well as the current space AI industry.

The mushroom industry generates large amounts of stems, a co-product with valuable sensory and health characteristics. This work aimed to study the influence of particle size (L — &gt;0.510 mm; LI — 0.510-0.315 mm; SI—0.315-0.180 mm; S — &lt;0.180 mm) and species in the characterization and antioxidant activity of flours from stems of Agaricus bisporus (ABSF) and Pleurotus ostreatus (POSF). ABSF was rich in protein (14 g/100 g), calcium (428.23-700.77 mg/100 g), and sorbitol (22.57-26.60 g/100 g), while POSF in β-glucans (36.62-40.34 g/100 g) and linoleic acid (20.57-39.86 g/100 g of lipid). Both species were flush in amino acids with umami flavor. The highest hydration properties and oil holding capacity were exhibited in the S size of both species. ABSF showed elevated values for emulsifying activity than POSF. ABSF-S recorded the heightened antioxidant capacity in vitro, in consonance with the total phenolic compounds (0.91 mg/g). However, this antioxidant assays in POSF presented a positive correlation with β-glucans content. According to these results, there could be several food applications for these co-products, such as emulsifier stabilizer, sweetener or fortifier in the development of functional food.

In recent years, machine learning (ML) techniques have emerged as an efficient and effective technology within the petroleum industry. This paper focuses on the current application of ML in enhanced oil recovery (EOR) through CO2 injection, which exhibits promising economic and environmental benefits of climate-change mitigation strategy. Our comprehensive review explores the diverse use cases of ML techniques in CO2-EOR, including aspects such as minimum miscible pressure (MMP) prediction, well location optimization, oil production and recovery factor prediction, multi-objective optimization, Pressure-Volume-Temperature (PVT) properties estimation, Water Alternating Gas (WAG) analysis, and CO2-foam EOR, from 101 reviewed papers. In this comprehensive review, we catalog relative information, including the input parameters, objectives, data sources, train/test/validate information, results, evaluation, and rating score for each area based on criteria such as data quality, ML building process, and analysis of results. We also briefly summarized the benefits and limitations of ML methods in petroleum industry applications. Our detailed and extensive study could serve as an invaluable reference for employing ML techniques in the petroleum industry. Based on the review, we found that ML techniques offer great potential in solving problems in the majority of CO2-EOR areas involving prediction and regression. With the generation of massive amounts of data in the everyday oil and gas industry, machine learning techniques can provide efficient and reliable preliminary results for the industry.

Our goal was to determine the cellular immune response (CIR) among a random sample of the Borriana COVID-19 cohort (Spain) to identify its associated factors and their relationship with infection, reinfection and sequelae. We conducted a nested case-control study using a randomly selected sample of 225 individuals age 18 and older, including 36 individual naïve to SARS-CoV-2 infection, and 189 infected patients. We employed flow cytometry-based immunoassays for intracellular cytokine staining, utilizing Wuhan and BA.2 antigens and chemoluminescence microparticle immunoassay for detection SARS-CoV-2 antibodies. Logistic regression models were used. A total of 215 (95.6%) participants exhibited T-cell response (TCR) to at least one antigen. Positive responses of CD4+ and CD8+ T cells were 89.8% and 85.3% respectively. No difference in CIR was found for naïve and infected patients. Patients who experienced sequelae exhibited higher CIR than those without. A positive correlation was observed between TCR and Anti-Spike IgG levels. Factors positive associated with TCR included A-blood group, number of SARS-CoV-2 vaccine doses received, and Anti-N IgM; factors inversely related were the time elapsed since the last vaccine dose or infection, and B-blood group. These findings contribute valuable insights into the nuance immune landscape shaped by SARS-CoV-2 infection and vaccination.

This work is the first in literature to tackle the difficult open problem of determining the upper bound and threshold theorem for the TDCDP (time-dependent controller parameter) of the (Fokker Planck Kolmogorov) probability density function. This revolutionary exposition will put control theory and other related inter-disciplinary fields to a higher level towards contemporary control theory. Notably, based on the influential role of control theory in both engineering and industry, this paper will be of great value to all engineering and industry professionals who seek to know more about advanced trends within control theory settings. On the other remit of the spectrum, Fokker Planck Kolmogorov(FPK) equations are of high importance to physicists as well as mathematicians, based on their multiple applicability to information theory, graph theory, data science, finance, economics, and beyond. So, this by default adds more taste and credibility to this study. This leads by nature to introducing a different flavor to this ground-breaking research by highlighting the impact of Fokker Planck Kolmogorov(FPK) to revolutionize crypocurrency,which have received its name because it uses encryption to verify transactions, a new debatable digital payment system that doesn't rely on banks to verify transactions. It’s a peer-to-peer system that can enable anyone anywhere to send and receive payments. The paper ends with closing remarks combined with some challenging open problems and the next phase of research.

The generation of platform chemicals and hydrochar is of great interest because they reduce dependence on fossil resources and contribute to climate change mitigation by reducing carbon emissions. The main objective of this study was to evaluate the effect of biomass particle size and biomass water ratio in a hydrothermal conversion system for the generation of value-added products obtained from sugarcane bagasse. Biomass characterization was performed using proximal, elemental, and structural analysis; hydrothermal carbonization was carried out at 220 to 260 °C for one hour; and conversion was monitored using pH, conductivity, and IR spectroscopy. Platform chemicals were quantified by HPLC-IR. Hydrochars were characterized by scanning electron microscopy and energy dispersive spectroscopy. The results show that with a biomass:water ratio of 1:50 and a particle size of 212 μm at 220 °C, a yield of 31.07 % of platform chemicals was obtained on a dry basis. Likewise, the hydrochar with the highest carbon content and a higher porosity appearance was obtained with a biomass-to-water ratio of 1:50 and a particle size of 600 μm at 260 °C.

Plants can utilize the different forms of nitrogen (N), including inorganic and organic N nutrients, and show the great difference in the utilizing efficiency of N-form among species and genotypes within a species. In our previous study, we found that Tibetan wild barley (XZ16) was better in utilization of organic nitrogen in comparison with cultivated barley (Hua30). In this study, metabolite profiles of the two barley genotypes were comprehensively compared in their response to four N forms, including nitrate (NO3-), ammonium (NH4+), urea and glycine. Macro-and micro-nutrient concentrations in shoots were mostly found higher in nitrate and urea treatments than in ammonium and glycine in both the genotypes. XZ16 had higher concentrations of nutrient ions in glycine treatment, but Hua30 accumulated more nutrients in ammonium treatment. Among the total 163 differentially expressed metabolites, highest up-regulation and highest down-regulation was found in XZ16 in glycine and urea treatments respectively. Some important metabolites such as proline, glutamine, serine, asparagine, L-homoserine, aspartic acid, putrescine, ornithine, 4-aminobutyrate were up-regulated in glycine treatment in both the genotypes with higher fold change in XZ16 than in Hua30. Similarly, fructose-6-PO4, aconitic acid and isocitrate were only up-regulated in XZ16 in glycine treatment. Here, we concluded that the genotype XZ16 exhibited higher response to glycine treatment, while Hua30 showed higher response to NH4+ treatment which is attributed to their better utilization of glycine-N and NH4+-N, respectively. .

The ferrocell is an optical viewing device that interacts with a magnetic field and one or more light sources to produce a real-time visual light display. Although this device has been extensively studied by several researchers, a proper computer simulation replicating this optical effect has not yet been presented. Using numerical methods and the law of reflection, a computer simulation was developed that shows that the light patterns as seen in the ferrocell are primarily caused by light reflections off the ferrofluid nano-particles that self organize into needle-like filaments and align along magnetic flux lines in the presence of a magnetic field.

Background: The bacterium Capnocytophaga spp. is a part of the oral microbiome of dogs, which has zoonotic potential. This study aimed to assess factors that influence the prevalence of Capnocytophaga cynodegmi and Capnocytophaga canimorsus in Wrocaw’s dog population. Methods: Molecular detection (PCR) involved 105 swabs, examining parameters such as oral cavity condition, underlying health issues, recent antibiotics, age, medications, material collection, and dog chews. Results: The study highlighted significant associations between the presence of Capnocytophaga cynodegmi and oral cavity condition, material collection method, and use of dog’s chews. Conclusions: Correlations obtained in this study between the oral health of Wroclaw's dog population and the presence of Capnocytophaga cynodegmi suggest its significance in the oral microbiome of dogs. Limitations, including the small sample size, highlight the need for more research to comprehensively understand additional factors that affect the prevalence of Capnocytophaga spp. in dogs

The purpose of the study is to establish compliance of the design of the modernized car with the requirements for safe operation. An option has been proposed for upgrading a passenger wagon to a Mobile Diagnostic Complex for transporting a combined vehicle equipped with appropriate flaw detection equipment intended for rail diagnostics. The authors have established the most optimal option for locating the diagnostic vehicle on board the Mobile Diagnostic Complex (MDC) wagon, taking into account the additional load from the vehicle. To achieve this goal, the article developed a three-dimensional model of the supporting structure of the wagon body, proposed a methodology and presented the results of a study of the stress-strain state of the wagon body, taking into account the additional weight of the wagon. The article develops a methodology for studying the natural frequencies of vibrations of the wagon body and develops a three-dimensional model of the wagon for calculating dynamic indicators in the Universal Mechanism software package. Taking into account the previously carried out strength calculations, as well as in order to maintain the factory life cycle indicators of the supporting structure of the wagon body, it is recommended to use the model in the first version.

One of the most important unresolved mysteries in mathematics is the Riemann Hypothesis, which suggests a fundamental connection between the non-trivial zeros of the Riemann zeta function and the distribution of prime numbers. Here, we explore the fascinating union of trigonometric functions, prime numbers, and the Riemann zeta function through an examination of the zeros in the statement ln(sec(π·n log(n))). We demonstrate a strong mathematical connection between these components, providing information on the mysterious properties of prime numbers and their complex relationships to basic mathematical operations. Our thorough investigation adds to the current discussion of the Riemann Hypothesis by offering possible solutions and deepening our comprehension of the intricate relationship between number theory and analytic functions.

The main purpose of this article is to identify brain diseases such as Alzheimer's and Parkinson's in an intelligent way using Python software and processing the function of neurons in the neural network areas of the brain, which are classified into different components, and the data is based on the information obtained from FMRI images using algorithms artificial intelligence can intelligently diagnose the type of disease. Therefore, by using spatial clustering based on the deep learning method with noise algorithm (DBSCAN), fMRI brain image and fMRI brain surface complexity are classified, and then image preprocessing will be applied to fMRI images by using artificial intelligence neural networks (ANN) algorithm. became. Then, by using the ANN algorithm and Deep CNN topology of the brain neural network, the type of disease in the brain can be diagnosed by simulating the performance of an artificial neural network (ANN) in Python software. Deep machine learning-based methods, especially algorithms based on neural networks, have shown great success in medical image analysis for various tasks including brain tumor detection, segmentation, and classification, and Alzheimer's and epilepsy. In this research, we propose an integrated model based on a convolutional neural network for simultaneous recognition and classification of brain diseases for intelligent diagnosis of tumors and epilepsy. The average accuracy obtained from our proposed model is 97.89 percent accuracy and 91.66 percent specificity.

The swift progress in low-dimensional nanocarbons, specifically in the realm of the authentic one-dimensional carbon chain featuring sp¹ hybridization, has unveiled exciting prospects for integrating them into cutting-edge technologies across diverse industries. To fully harness their potential, precise control over the growth process is necessary to obtain desired nanostructure and functionality. However, optimizing properties through traditional approaches has been challenging due to complex interactions. To address this, we implement a focused data-driven strategy for nanocarbon inverse design by leveraging a state-of-the-art data-driven nanocarbon genome approach (NCGA). By uncovering relationships between growth parameters and resultant traits, this serves as an expedient catalyst in engineering nanostructures with tailored attributes. We introduce an extensive array of technological approaches aimed at precisely controlling the growth process. These methods encompass stimulating and precisely adjusting synergistic effects, coordinating atomic vibrations at a collective level through the implementation of multilayer nano-interfaces, harnessing the potential of active screen plasma surface engineering, utilizing nano-patterning and allotropic phase transformations, incorporating heteroatom doping, and effectively directing self-assembly. These aim to unlock nanomaterials' latent potential and reveal novel neural pathways within the data-driven NCGA, enhancing its predictive capabilities. Specifically, triggering self-organization during growth can potentially unlock previously unexplored neural pathways. The data-driven NCGA offers a paradigm shift, accelerating discovery and design of nanocarbons with optimized, application-specific properties.

This paper explores the imminent threat posed by climate change to the unique platypus species. It delves into the ecological repercussions of global temperature rise and altered climate patterns on platypus habitats. The study emphasises the multifaceted impacts, including changes in aquatic habitat quality, food sources, and breeding rituals. Climate-induced shifts in phytoplankton spring blooms and anthropogenic pressures further endanger the platypus's habitat. The article stresses the importance of understanding these effects for both species conservation and anticipating broader ecological and socio-economic consequences. Mitigating climate change impacts requires a multidimensional approach, involving habitat manipulation and restoration strategies.

Using density functional theory (DFT), the density of states of NH4V3O8 (NVO) was analyzed pre and post-oxygen defects (Od) formation. The findings revealed a reduced bandgap in NVO after Od introduction, emphasizing the role of Od in enhancing conductivity of the material, thus improving its electrochemical attributes. Through the water bath method, both NVO and its oxygen-deficient counterpart, (NH4)2V10O25⸱8H2O (NVOd), were synthesized as potential cathode materials for aqueous zinc-ion batteries (AZIBs). Experimental outcomes resonated with DFT predictions, highlighting the beneficial role of oxygen defects in boosting electrical conductivity. Notably, the refined material displayed a remarkable capacity of 479.3 mAh g-1 at 0.1 A g-1, underscoring its promise for advanced energy storage solutions.

Polymer electrolyte was used as a medium for testing the performance of microband electrodes under conditions of linear diffusion. Cyclic voltammetry and chronoamperometry experiments were performed in such viscous medium, where diffusion rates are much slower than in fluid solutions. Confirmation of the existence of such conditions from the log i vs. log v or log i vs. log t relationships in the current equation always yielded values lower than the expected value of 0.5. This could indicate impure linear diffusion profile, i. e. some contribution from radial diffusion (edge effect). However, performing these tests in monomeric solvents of similar viscosities, such as glycerol or propylene glycol, gave the desired value of 0.5. These results lead to the conclusion that current equations which are based on Fick’s laws may not be applicable for polymer electrolytes, where various obstruction to diffusion make it a more complicated process than in monomeric solvents.

Dirofilariosis is an infectious disease caused by species of the Dirofilaria genus. It is manifested by the appearance of a subcutaneous swelling, especially in the eye region. We present the case of a 29-year-old patient that accused facial asymmetry in the right genian region. Following clinical and paraclinical evaluations, the diagnosis of parasitic cyst was established in the context of dirofilariosis with Dirofilaria repens (D. repens). Treatment consisted of surgical excision of the formation associated with prophylactic antibiotic medication. Macroscopic analysis of the excision piece revealed a structure that contains a cystic cavity and a filamentous form with a length of approximately 10 mM and a diameter of 1 mM. This is the first case of dirofilariosis located in the genian region reported in Romania. The overview of this pathology is important to raise awareness among physicians about its presence and its clinical variations.

Rabbit hemorrhagic disease virus disease 2 (RHDV2) emerged in the United States in 2018 and has spread in both domestic and wild rabbits nationwide. The virus has a high mortality rate and can spread rapidly between infected individuals. Vaccination against RHDV2 provides the best protection against disease and should be considered by all rabbit owners. Here, we investigate the duration of immunity provided by vaccination with the Medgene Platform conditionally licensed commercial vaccine 6 months following the initial series. Rabbits received either the vaccination or a placebo and were challenged with RHDV2 6 months later. All vaccinated rabbits survived challenge whereas 18/19 non-vaccinated controls succumbed to infection within 10 or fewer days post-challenge. These results demonstrate lasting immunity following vaccination with the Medgene RHDV2 vaccine.

This review investigates the multifaceted role of the p66Shc adaptor protein and the gut microbiota in regulating mitochondrial function and oxidative stress, and their collective impact on the pathogenesis of chronic diseases. The study delves into molecular mechanisms by which p66Shc influences cellular stress responses through Rac1 activation, Forkhead-type transcription factors inactivation, and mitochondria-mediated apoptosis, alongside modulatory effects of gut microbiota-derived metabolites and endotoxins. Employing an integrative approach, the review synthesizes findings from a broad array of studies, including molecular biology techniques and analyses of microbial metabolites' impacts on host cellular pathways. The results underscore a complex interplay between microbial metabolites, p66Shc activation, and mitochondrial dysfunction, highlighting the significance of the gut microbiome in influencing disease outcomes through oxidative stress pathways. Conclusively, the review posits that targeting the gut microbiota-p66Shc-mitochondrial axis could offer novel therapeutic strategies for mitigating the development and progression of metabolic diseases. This underscores the potential of dietary interventions and microbiota modulation in managing oxidative stress and inflammation, pivotal factors in chronic disease etiology.

Spiking neural networks (SNNs) promise to perform tasks currently done by classical artificial neural networks (ANNs) faster, in smaller footprints and using less energy. Neuromorphic processors are set out to revolutionize computing at a large scale, but the move to edge-computing applications calls for finely-tuned custom implementations to keep pushing towards more efficient systems. To that end, we have examined the architectural design space for executing spiking neuron models on FPGA platforms, focusing on achieving ultra-low area and power consumption. This work presents an efficient clock-driven spiking neuron architecture used for the implementation of both fully-connected cores and 2D convolutional cores, which rely on deep pipelines for synaptic processing and distributed memory for weight and neuron states. With them, we have developed an accelerator for an SNN version of the LeNet-5 network trained on the MNIST dataset. At around 5.5 slices/neuron and only 348 mW, it is able to use 33% less area and 4 times as less power per neuron as current state-of-the-art implementations while keeping low simulation step times.

Agriculture is an important sector of European Union in societal , economic and environmental dimensions. Sustainability of the sector require improvements in its key operations. Weeding is one of the crucial operation having impact on farms’ productivity, safety for farmers, food security and environmental state. To achieve EU policy goals of sustainable agriculture there are needed new technical and organizational solutions. One of the advanced techniques are laser based weeding technique. It is important to understand well the impacts of introduction of these tech-niques to the markets and agricultural practice in sustainability context. For that reason Social Life Cycle Assessment was performed. The method was based on participatory approach. The as-sessment was carried in three perspectives: general society, farmers, and business perspective re-lated to agriculture. Expert interviews based on questionnaires and workshops were realized to get the opinions on the impacts of a new laser technique on particular aspects of its implementation. The results show generally positive impact in all perspectives, especially in the perspective of farmers.

Abstract Epithelial ovarian carcinoma (EOC) is the fifth-leading cause of cancer-related death in women, largely reflecting early, prediagnosis dissemination of malignant disease to the peritoneum. EOC is poorly responsive to immune check-point inhibitors due to immunologic features including limited tumor mutational burden (TMB), poor infiltration by immune cells and active immunosuppression. Thus, novel strategies are needed to overcome the frequent lack of preexisting immunity in patients with EOC. We developed and tested an autologous dendritic cell (DC)-based vaccine (DCVAC) which has recently been shown to be safe and to significantly improve progression-free survival (PFS) in two independent randomized phase II clinical trials enrolling patients with EOC (SOV01, NCT02107937; SOV02, NCT02107950). In addition, our exploratory data analyses suggest that the clinical benefits of DCVAC were more pronounced in patients with EOC with lower than-median TMB and scant CD8+ T cells infiltration. Thus, DC-based vaccine stands out as a promising clinical tool to jumpstart anticancer immunity in patients with immunologically „cold“ EOC. Our findings underscore the need for personalized immunotherapy and clinical relevance of potential tumor-related biomarkers within immunotherapy field. Additional clinical trials are needed to address these strategies as well as the potential value of TMB and immune infiltrate at baseline as biomarkers to guide the clinical management of EOC.

(1) Background: Among the chronic complications of type 2 diabetes mellitus, cancer became the leading cause of death in several countries. Our objective was the determination of whether prevalent type 2 diabetes mellitus is associated with a higher incidence of cancer. (2) Methods: This study is a nationwide analysis performed in Hungary. Study population was divided into a group with type 2 diabetes mellitus and into a non-diabetic group. The primary outcome was the risk related to overall cancer incidence. A key secondary outcome was the overall incidence of cancer in distinct study years. Further outcomes were the annual percent changes. (3) Results: The odds ratio related to the overall incidence of cancer was 2.50 (95% confidence interval: 2.46-2.55, P&lt;0.0001) in patients with diabetes as related to controls. The odds ratio was higher in males than in females. The highest risk was found in the 18-39-year-old subgroup. The annual cancer incidence rate declined in controls, but not in patients with diabetes. Several types of cancer showed a decreasing tendency in non-diabetic controls.; (4) Conclusions: type 2 diabetes is associated with a higher risk of cancer. High risk in young patients with type 2 diabetes suggests that these young individuals should be considered as a risk group.

Neuronal cells are believed to rely on autophagy to maintain cellular homeostasis. Nevertheless, autophagy may be a therapeutic target for neurodegenerative diseases because of the role it plays in neuronal cell death. Despite Beclin 1 association with autophagy and apoptosis, the molecular mechanisms that underlie Tau-induced vesicular pathology and Tau protein aggregates in humans remain poorly understood. Here, we set out to examine the role of three genes-ATG14L, Beclin 1 and VPS34- in autophagy nucleation failure-associated Tau disorders in fruit fly models. We found that either reduced ATG14L levels improve neuronal outcomes, or transgenic expression of ATG6/Beclin 1 greatly reduced age-dependent neurodegeneration and Tau pathology; whereas, the loss of Beclin 1 exacerbated these effects. Prior to p-Tau accumulation, the neuronal Beclin 1 network is critical for reducing p-Tau accumulation, indicating that the loss of Beclin 1 causes Tau pathology and neurodegeneration. As neurodegeneration progressed, glial cells showed an upregulation of vesicular Beclin 1 immunoreactivities. The reduced VPS34 also resulted in p-Tau accumulation, suggesting that neurons require both Beclin 1 and VPS34 to reduce p-Tau accumulation. Our results provide mechanistic insight into the molecular pathways that cause age-dependent Tau pathology and neurodegeneration due to an excess of autophagy nucleation processes and autophagy activity, and we derive novel inferences on the therapeutic possibilities of rebalanced Beclin 1 network by reducing autophagic specific nucleation component of ATG14L.

We present a case of a 47-year-old male who died unexpectedly from acute pulmonary hemorrhage 555 days after completing the BNT162b2 (Pfizer) COVID-19 vaccination primary series. Before death, he exhibited symptoms of a mild respiratory infection. Despite a healthy medical history and no medication use, the patient’s condition rapidly deteriorated and he experienced severe respiratory distress, followed by cardiopulmonary arrest with evidence of profuse pulmonary bleeding. Autopsy findings revealed massive lung congestion without embolism, normal heart size, moderate coronary atherosclerosis without myocardial infarction, and no evidence of other hemorrhagic events. The patient tested negative for COVID-19 and other respiratory pathogens at autopsy. Despite these findings, the medical examiner determined the cause of death was attributed to atherosclerotic and hypertensive cardiovascular disease, without considering the recent pulmonary hemorrhage and unremarkable medical history. Investigation into the vaccine batch indicated a higher-than-average number of serious adverse events, including fatalities. The patient's BNT162b2 batch was among the top 2.8% for reported deaths. Moreover, the autopsy failed to investigate potential contributions from the vaccine, such as the presence of the Spike protein or related antibodies. The evidence suggests that the pulmonary hemorrhage, exacerbated by a viral infection, was the immediate cause of death, with the COVID-19 vaccine potentially playing a role in the development of cardiopulmonary pathology and hemorrhage. We propose autopsy protocols for COVID-19 vaccine recipients to better investigate vaccine-related pathologies among those with one or more prior injections.

Growth environment greatly alters many facets of pathogen physiology, including pathogenesis and antimicrobial tolerance. The importance of host-mimicking environments for attaining an accurate picture of pathogen behaviour is widely recognised. Whilst this recognition has translated into the extensive development of artificial cystic fibrosis (CF) sputum medium, attempts to mimic the growth environment in other respiratory disease states have been completely neglected. The composition of the airway surface liquid (ASL) in different pulmonary diseases is far less well characterised than CF sputum, making it very difficult for researchers to model these infection environments. In this review we discuss the components of human ASL, how different lung pathologies affect ASL composition, and how different pathogens interact with these components. This will provide researchers interested in mimicking different respiratory environments with the information necessary to design a host-mimicking medium, allowing for better understanding of how to treat pathogens causing infection in these environments.

Layered halide perovskites have emerged as a promising contender in solid-state lighting thanks to their tunable bandgap, high luminescence quantum yield, and high color purity. However, the fabrication of perovskite light-emitting devices in laboratories usually experiences low de-vice-to-device reproducibility since perovskite crystallization is highly sensitive to ambient condi-tions, such as humidity and temperature. Although device processing inside gloveboxes is primarily used to reduce the influence of oxygen and moisture, several extraneous variables, including gas-eous pollution traces and thermal fluctuations in the inert atmosphere or contaminations from re-sidual solvents confined in the enclosed environment, can destabilize the crystallization process and alter the properties of the emissive layers. Here, we examine different experimental set-ups for spin-coating deposition of layered perovskites in inert atmospheres and their influences on the formation of polycrystalline thin films. Our results demonstrate that fluctuations in the glovebox properties (concentrations of residual O2 and H2O or solvent traces), even in very short timescales, can negatively impact the consistency of the perovskite film formation, while thermal variation plays a relatively minor role in this phenomenon. Furthermore, careful storage of chemical species inside the workstation is critical for reproducing high-quality perovskite layers. Consequently, when applying our most controlled environment for perovskite deposition, the photoluminescence life-time of perovskite thin films shows a standard deviation of only 3%, whereas the reference set-up yields a 15% standard deviation. Regarding complete perovskite light-emitting diodes, the uncer-tainties in statistical luminance and EQE data are significantly reduced from 230% and 140% to 38% and 42%, respectively.

Foodborne illnesses can be infectious and dangerous and most of them are caused by bacteria. Some types of common food-related bacteria exist widely in nature and pose a serious threat to both humans and animals, and can cause poisoning, diseases, disabilities and even death. Rapid, reliable and cost-effective methods for bacteria detection are of paramount importance in food-safety and environmental monitoring. Polymerase chain reaction (PCR), lateral flow immunochromatographic assay (LFIA) and electrochemical method have been widely used in food-safety and environmental monitoring. In this paper, the recent developments (2013-2023) covering PCR, LFIA and electrochemical method for various bacteria detection (Salmonella, Listeria, Campylobacter, Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli)) considering different foods types, analytical performances and the reported limit of detection (LOD) are discussed. It is found that the bacteria type and food sample type contribute significantly to the analytical performance and LOD. Detection by LFIA has higher average LOD (24 CFU/ml) than detection by electrochemical method (12 CFU/ml) and PCR (6 CFU/ml). Salmonella and E. coli in the Pseudomonadota domain usually have low LODs. LODs are usually lower for detection in fish and eggs. LFIA with gold and iron nanoparticles are prominent in the majority of articles of 26 CFU/ml and 12 CFU/ml respectively. Electrochemical method reveals that the average LOD is highest for cyclic voltammetry (CV) of 18 CFU/ml), followed by electrochemical impedance spectroscopy (EIS) of 12 CFU/ml and differential pulse voltammetry (DPV) of 8 CFU/ml. LOD usually decreases when replicate number increases until it remain unchanged. Exponential relations with (R²>0.95) between LOD of Listeria in milk by LFIA and electrochemical method with replicate number have been obtained. Finally, the review discusses the challenges and future perspectives (including the role of nanomaterials/advanced materials) to improve the analytical performance for bacterial detection.

A proposed low-temperature forging method aims to enhance stainless steel bearings by creating a martensitic subsurface layer, significantly boosting bearing fatigue life due to increased surface hardness. This technique induces beneficial residual stresses, particularly in axial bearings, streamlining their construction and improving machine elements. Challenges persist, especially with radial bearings, but simplicity in axial bearing forging promotes compact, resource-efficient facility construction. Future research will focus on applying this technique to axial bearing washers, potentially replicating success in other bearing components. Despite the energy ex-penditure for cooling during forging, the substantial increase in bearing fatigue life offsets this, enhancing overall longevity and reliability of critical machine components. Integration of this forging technique into bearing fabrication appears seamless, offering a promising trade-off be-tween energy use and enhanced performance.

Recent advances in artificial intelligence combined with behavioral sciences have led to the development of cutting-edge tools for recognizing human emotions based on text, video, audio, and physiological data. However, these data sources are expensive, intrusive, and regulated, unlike plants which have been shown to be sensitive to human steps and sounds. A methodology is proposed to use plants as human emotion detectors. Electrical signals from plants are tracked and labeled based on video data. Labeled data are then used for classification. MLP, biLSTM, MFCC-CNN, MFCC-ResNet, and additionally, Random Forests, 1-Dimensional CNN, and biLSTM without windowing models are set using a grid search algorithm with cross-validation. Finally, best-parameterized models are trained and used with the test set for classification. The performance of this methodology is measured with a case study with 54 participants, who were watching an emotionally charged video, as ground truth their facial emotions were simultaneously measured using face emotion analysis. The Random Forest model shows the best performance, particularly in recognizing high-arousal emotions, achieving an overall weighted accuracy of 55.2% and demonstrating high weighted recall in emotions such as fear 61.0% and happiness 60.4%. The MFCC-ResNet model offers decently balanced results, with Accuracy(MFCC-ResNet)=0.318 and Recall (MFCC-ResNet)=0.324. With the MFCC-ResNet model fear and anger are recognized with 75% and 50% recall respectively. Thus, using plants as an emotion recognition tool seems worth investigating, addressing both cost and privacy concerns.

Water plays a vital role in the healthy growth of pears. Early detection of water stress can play a significant role in the timely management of water deficiency for pear yield. Most current methods are labour-intensive, time-consuming, only provide point measurements, and could be destructive. In this research, a real-time non-destructive method using a push-broom hyperspectral system (400-1000nm) was used to collect hyperspectral image data and detect the water stress of the pear seedling leaves. To build a reliable prediction model, machine learning techniques were used. The Successive Projections Algorithm (SPA) was applied for optimal wavelength selection. In particular, CNN was applied to obtain the features of key wavelengths. Both the CNN features and key wavelengths were put into RR-MLR, BLR and ENN for analysis. The training accuracy of the three modellings all reach the accuracy above 70% after about 100 epochs, while combination of CNN features outperformed the mere main spectra analysis. This research demonstrated that hyperspectral imaging coupled with machine learning techniques could be applied to predict the water content of pear leaves predict non-destructively.

The SK model proposed by Sherrington and Kirkpatrick is an important mean field model for spin glasses. The dependence structure of Hamiltonians of the SK model is considered in this paper. We have investigated the covariance matrix of Hamiltonians of the SK model, and establish a relationship between the rank of the corresponding covariance matrix and the dimension of the space spanned by the Hamiltonians. Finally, we also give an upper bound for the rank of the covariance matrix, which might be the best.

Nanocrystalline silicon oxide (nc-SiOx:H) is a multipurpose material with its varied application in solar cells as a transparent front contact, intermediate reflector, back reflector layer and even tunnel layer for passivating contacts, owing to easy tailoring of its optical properties. In this work, we systematically investigate the influence of gas mixture (SiH4, CO2, PH3 and H2), RF power and process pressure on the optical, structural and passivation properties of thin n-type nc-SiOx:H films prepared in an industrial, high throughput, plasma-enhanced chemical vapor deposition (PECVD) reactor. We provide a detailed description of the n-type nc-SiOx:H material develop-ment using various characterization techniques (SEM, EDX, Raman spectroscopy, and spectro-scopic ellipsometry) with a focus on the relationship between the material properties and the pas-sivation they provide to n-type c-Si wafers characterized by their effective carrier lifetime (τeff). Further, we also outline the parameters to be kept in mind while developing different n-type nc-SiOx:H layers for different solar cell applications. We report a tunable optical gap (1.8 – 2.3 eV) for our n-type nc-SiOx:H films as well as excellent passivation properties with τeff up to 3.5 ms (iVoc ~ 715 mV) before annealing. Oxygen content plays an important role in determining the crystallinity and hence passivation quality of the deposited nanocrystalline silicon oxide films.

The emergence of SARS-CoV-2 mutations pose significant challenges to diagnostic tests as these mutations can reduce the sensitivity of commonly used RT-PCR assays. Therefore, there is a need to design diagnostic assays with multiple targets to enhance sensitivity. In this study, we identified a novel diagnostic target, nsp10 gene, using nanopore sequencing. Firstly, we determined the analytical sensitivity and specificity of our COVID-19-nsp10 assay. The COVID-19-nsp10 assay had the limit of detection of 74 copies/mL (95% confidence interval: 48-299 copies/mL) and did not show cross-reactivity with other respiratory viruses. Next, we determined the diagnostic performance of the COVID-19-nsp10 assay using 261 respiratory specimens, including 147 SARS-CoV-2-positive specimens belonging to ancestral strain and Alpha, Beta, Gamma, Delta, Mu, Eta, Kappa, Theta and Omicron lineages. Using the LightMix E-gene RT-PCR assay as the reference method, the diagnostic sensitivity and specificity of the COVID-19-nsp10 assay were 100%. The median Cp values for the LightMix E-gene RT-PCR and our COVID-19-nsp10 RT-PCR were 22.48 (range: 12.95-36.60) and 25.94 (range 16.37-36.87), respectively. The Cp values of the COVID-19-nsp10 RT-PCR assay correlated well with those of the LightMix E-gene RT-PCR assay (Spearman’s ρ = 0.968; P&lt;0.0001). In conclusion, nsp10 is a suitable target for SARS-CoV-2 RT-PCR assay.

This article studies electrostatic fields and potentials in the presence of conductors and point charges under the framework of solving Laplace’s equation with specifi ed boundary conditions. The results demonstrate that many problems posed and solved in elementary electrostatics through various heuristics such as the method of images, can be more rigorously treated under the solution framework of Laplace’s equation.

With the rapid development of intelligent transportation, Vehicle Ad hoc Networks (VANETs) is becoming increasingly important.As a critical component of intelligent transportation systems, the Conditional Privacy-Preserving Authentication (CPPA) scheme plays a significant role in ensuring drivers' privacy and security.However, current CPPA schemes still face limitations in terms of security and efficiency, with these schemes either imposing higher computational overhead for enhanced security or compromising some level of security to reduce overhead. Motivated by this challenge, we propose a CPPA scheme that strikes a better balance between efficiency and security.By leveraging the token mechanism to achieve multiple authentication and minimizing information leakage. Furthermore, our performance analysis demonstrates that our ECC-based algorithm can significantly reduce overhead, with pseudonymous generation requiring only 0.0039ms.

Solid-liquid interfacial phenomena play an essential role in our everyday lives and are often regarded as the outcome of interactions at the solid-liquid interface. However, the intricately intrinsic mechanism underlying interfacial interactions renders in-situ simulations and direct measurements challenging. As an effective analytic method for studying solid-liquid interfacial interactions, Microcalorimetry can provide the most basic thermodynamic information (including changes in enthalpy, entropy, and Gibbs free energy during solid-liquid binding/separation processes), which is extremely crucial for understanding interaction directionality and limitation. This review is dedicated to highlighting the pivotal role of microcalorimetry in studying solid-liquid immersion and adsorption processes. Specifically, we provide an overview of the commonly employed microcalorimetric methods, delve into the influence factors of enthalpy change, and finally discuss the specific applications of microcalorimetry in studying various solid-liquid binding processes. There remains a vast expanse of thermodynamic information regarding solid-liquid interactions that await exploration via calorimetry.

The changes in the properties of three biological events, that occur with cerebral aging, are discussed. These adverse changes already begin to develop early in mid-life and gradually become more pronounced with senescence. Essentially, they are reflections of the progressive decline in effectiveness of key processes, resulting in the deviation of essential biochemical trajectories to ineffective and ultimately harmful variants of these programs. The emphasis of this review is the major role played by mitochondria in such transition of these three important processes toward more deleterious variants as brain aging proceeds. 1. The immune system. The shift away from an efficient immune response to a more unfocused, continuing inflammatory condition. Such a state is both ineffective and harmful. 2. Reactive oxygen species are important intracellular signaling systems. Also, microglial phagocytic activity utilizing short lived reactive oxygen species contribute to the removal of aberrant or dead cells and bacteria. These processes are transformed into an excessive, untargeted and persistent generation of pro-oxidant free radicals (oxidative stress). 3. The normal efficient neural transmission is modified to a state of undirected, chronic low-level excitatory activity. Each of these changes is characterized by the occurrence of continuous activity that is inefficient and diffuse. The signal/noise ratio of several critical biological events is thus reduced as beneficial responses are gradually replaced by their impaired and deleterious variants.

Rebar cages form the skeletal framework of reinforced concrete structural members. They comprise longitudinal and transverse reinforcing bars, usually connected by tie wires. Past studies have identified tie-wire connections as the "weak links" in large, prefabricated rebar cages, leading to their failure during site handling and construction stages. Recent research has explored the use of mechanical U-bolt connectors to bolster stability and safety of rebar cages while reducing their vulnerability and risk of failure. This paper presents a comprehensive experimental and numerical investigation of a new type of mechanical connector, referred to as "Cage Clamp", to enhance rebar cage stability and safety. Component-level experimental tests are conducted to determine the force-displacement response of Cage Clamp connectors for crossbar connections in different degrees of freedom. The results highlight the remarkable strength and stiffness of Cage Clamp connectors, significantly outperforming traditional tie-wire connections. Furthermore, full-scale experimental tests are performed on a circular underground pile-shaft rebar cage with Cage Clamp connectors to assess its behavior under various site handling conditions. The data obtained from these experiments are used to construct and refine detailed 3D finite element models of the rebar cages, capable of capturing the complex behavior of rebar cages with Cage Clamp connectors. Using the numerical model, a sensitivity analysis is performed to assess the effect of the number of Cage Clamps on the deflection and stability of the rebar cage. The findings show that using cage clamps to connect pick-up rebar with lateral reinforcement at every 8 to 10 feet is sufficient to ensure the stability of the cage during typical site handling conditions. The results of this study serve as a basis for establishing analysis, design, fabrication, and handling guidelines for rebar cages, contributing to safe and efficient construction practices.

This study investigated the inhibitory effect of Siegesbeckia orientalis ethanolic extract (SOE) on growth and metastasis of hepatic tumors in C57BL/6JNarl mice, which is the model of tumor-igenesis induced by subcutaneous injection of Hepa1-6 murine hepatoma cells on the back of mice. Feeding 25 mg SOE/kg BW daily for 14 days effectively reduced the hepatoma weight in tu-mor-bearing mice by 59.7%. SOE intervention reduced the expression of anti-apoptotic Bcl-2 gene in tumor tissue by 48.5% and down-regulated the antioxidant system, by reducing the gene ex-pression of catalase (48.0%), glutathione peroxidase (46.3%), and superoxide dismutase (32.0%). In addition, SOE intervention increased the production of ROS by 21.4%, and the gene expression of pro-inflammatory cytokines TNF-α, IL-1β, and IL-6 by 135.3%, 179.3% and 91.7%, respectively, indicating that SOE intervention promoted the acute inflammatory response. Results of H&E staining analysis showed that SOE had the ability to inhibit angiogenesis in tumor tissue. Fur-thermore, SOE treatment inhibited the expression of migration-related genes, such as MMP-2, MMP-7, MMP-9 and β-catenin, and the inhibition rates were 24.8%, 27.4%, 10.4% and 21.9%, respectively. These results imply that SOE had the ability to suppress tumor metastasis. On the other hand, SOE intervention decreased the serum contents of inflammatory cytokines, such as TNF-α, IL-1β and IL-6 by 27.4%, 30.4%, and 30.7%, respectively, indicating that SOE intervention has an anti-inflammatory effect on normal tissues of mice. Since SOE intervention had no signif-icant effect on liver and kidney weights, and no mice died throughout the experiment, it shows that it is safe to feed SOE under this experimental condition. Based on the above results, SOE has a great potential to be used in the treatment of hepatoma.

An off-grid solar energy storage system (ESS) in National Pingtung University of Science and Technology (NPUST) was built and officially operated on Jun. 16th 2022. The system is installed in a 40' general container with PV panels of solar power 8250 W_p on top of the container. The ESS is made by repurposed lithium iron phosphate (LFP) batteries of 20 kWh capacity, where a battery management system (BMS) is adopted to ensured the safety of the battery system. An energy management system (EMS) is built to receive, process, analyse, store, and output the energy information. In this manuscript, a brief engineering report about the system is given.

Introduction: The trophoblast derived angiogenic factors are considered to play an important role in the pathophysiology of various complications of pregnancy. The aim of this study was to establish relationship between concentrations of soluble Human Leukocyte Antigen-G (sHLA-G) in maternal serum and amniotic fluid at 16-22 weeks of gestation and the sonographic measurements of the fetal and placental growth. Materials and methods: sHLA-G in serum and amniotic fluid, as well as fetal biometric data and placental volume and perfusion indices were determined in 41 uncomplicated singleton pregnancies. Results: The sHLA-G levels were unchanged both in amniotic fluid and in serum during mid-pregnancy. The sHLA-G level in serum correlated positively with amniotic sHLA-G level (β=0.63, p<0.01). Significant correlation was found between serum sHLA-G level and abdominal measurement (β=0.41, p<0.05) and estimated fetal weight (β=0.41, p<0.05). The amniotic sHLA-G level and placental perfusion (VI: β = -0.34, p<0.01 and VFI: β = -0.44, p<0.01, respectively) were negatively interrelated. A low amniotic sHLA-G level was significantly associated with nuchal translucency (r = -0.102, p<0.05). Conclusions: sHLA-G assayed in amniotic fluid might be a potential indicator for placental function, whereas the sHLA-G level in serum can be a prognostic modality for feto-placental insufficiency.

In this paper, a low-power Injection-Locked Clock and Data Recovery (ILCDR) using 28nm FDSOI technology is presented. The back-gate auto-biasing of UTBB-FDSOI transistors allows us to create a QRO (Quadrature Ring Oscillator) reducing both size and power consumption. By injecting a digital signal into this circuit, we realize an Injection-Locked Oscillator (ILO) with low jitter. Thanks to the good performance of this oscillator, we propose a low power ILCDR with fast locking time and low jitter for burst-mode applications. The main novelty consists in the implementation of a complementary QRO based on back-gate control using FDSOI technology to realize a simple and efficient ILCDR circuit. With a Pseudo-Random Binary Sequence (PRBS7) at 868Mbps, the recovered clock jitter is 26.7ps (2.3%UIp-p) and the recovered data jitter is 11.9ps (1%UIp-p). With a 0.6V power supply, the power consumption is 318μW. The estimated Chip area is around 6600μm².

IgA nephropathy (IgAN) is a kidney disease that is characterized by the deposition of IgA (immunoglobulin A) in the kidney tissue, leading to inflammation and damage. This disease is a common chronic glomerulopathy that affects people worldwide (1). Therefore, it is crucial to identify factors that worsen the disease, monitor its activity, and predict appropriate treatments. Complement component 4d (C4d) is a protein that is involved in the activation of the complement system, which is a part of the immune system.

Today, issues related to people’s mental health and well-being have been described as a challenge for society, globally as well as in Sweden. This calls for new approaches to mental health promotion. The aim was to evaluate the adequacy of the content and structure, describing experiences of study circles as a means of supporting participants' self-care and self-compassion. The overall design is a descriptive, QUAL+quan design, where quantitative and qualitative results are integrated. Five participants participated in a focus group interview, of whom four completed questionnaires. One individual interview was conducted with the study circle leader. Study circles can be an arena for mental health promotion as learning and sharing of experience contributes to a sense of coherence, as well as self-compassion and a genuine concern for one’s own and others' wellbeing, but not considered an alternative to psychiatric care, for those in need of professional services. Study circles can be a possible means to support self-care and thereby promote mental health in the general population, and a valuable contribution to public health. However, in addition to modifications of the content, further research is needed on the qualifications for study circle leaders as well as the dissemination of study circles.

Despite accumulating evidence that suggests a unique gut microbiota composition in athletes, a comprehensive understanding of this phenomenon is lacking. Furthermore, seasonal variation in the gut microbiota of athletes, particularly during the off-season, remains underexplored. This study aimed to compare the gut microbiotas between athletic subjects (AS) and non-athletic subjects (NS), and to investigate variations between athletic and off-season periods. The data were derived from an observational study involving Japanese male handball players. The results revealed a distinct gut microbiota composition in AS compared with NS, characterized by significantly higher alpha-diversity and a greater relative abundance of Faecalibacterium and Streptococcus. Moreover, a comparative analysis between athletic and off-season periods in AS demonstrated a significant change in alpha-diversity. Notably, AS exhibited significantly higher alpha-diversity than NS during the athletic season, but no significant difference was observed during the off-season. This study demonstrates the characteristics of the gut microbiota of Japanese handball players and highlights the potential for changes in alpha-diversity during the off-season. These findings contribute to our understanding of the dynamic nature of the gut microbiota of athletes throughout the season.

The industrial metaverse paradigm is generally a virtual environment that integrates various technologies such as augmented reality (AR) and mixed reality (MR) to enhance business operations and processes. It aims to streamline workflows, reduce error rates, improve efficiency, and provide a more engaging experience for employees. The promise of the industrial Metaverse to drive sustainability and resource efficiency is compelling. Utilizing advanced technologies like the Industrial Metaverse is essential for staying competitive in today’s rapidly evolving business environment. However, the environmental impact of the technologies underpinning the industrial metaverse, like data centres and network infrastructure, should not be overlooked. The ecological footprint of these technologies must be considered in the sustainability equation. Researchers have warned that by 2025, without sustainable AI practices, AI will consume more energy than the human workforce, significantly offsetting carbon-zero gains. As the metaverse persists in evolving and gaining momentum, it will be necessary for companies to prioritize sustainability and explore new ways to balance technological advancements with environmental stewardship. However, recent studies have conjectured that the Metaverse holds the potential to reduce carbon emissions, as digital replacements for physical goods become more prevalent and physical activities like mobility and construction are reduced. However, the specific extent to which this substitution can alleviate environmental concerns remains understudied, presenting a knowledge gap in understanding the real-world impact of digital replacements. Thus, the objective of this paper is to provide a comprehensive review of the industrial metaverse as well as explore the environmental impact of the industrial metaverse. The methodological approach is that of integrative literature review design wherein multiple sources from web science and databases such as ACM library, IEEE Library, and Google Scholar, were analysed to provide a comprehensive understanding of the developments in the industrial metaverse. Specifically, firstly by considering the Industrial metaverse architecture we elucidate the concept of the "industrial metaverse" and the associated enabling technologies. Secondly, an exploration through discussion, of the prevalent use cases and deployment of the emerging industrial metaverse. Thirdly, exploration of the impact of the industrial metaverse on the environment. In the fourth instance, we address novel security and privacy risks as well as upcoming research challenges, keeping in mind that the industrial metaverse is based on a strong data fabric. Results point to the Industrial Metaverse as having both positive and negative environmental effects by energy consumption, e-waste, and pollution. Overall research, however, indicates that most industrial metaverse applications have a positive environmental impact and subsequently trend toward sustainability. Ultimately for sustainability in the industrial metaverse, enterprises may consider utilizing renewable energy sources and cloud services. Furthermore, examining the effects of products on the environment as well as creating a circular economy.

Computational simulations of three-dimensional flow around a NACA 0018 wing with an aspect ratio of AR=5 are carried out using the Unsteady Reynolds-Averaged Navier-Stokes (URANS) equations with the Shear-Stress Transport turbulence model closure. Simulations were performed to capture the aerodynamic stall hysteresis using a developed Pseudo-Transient Continuation (PTC) method based on a dual time step approach in the CFD code OpenFOAM. The flow was characterized by an incompressible Mach number of M=0.12 and a moderate Reynolds number Re=0.67×10^6. The results obtained indicate the presence of a noticeable aerodynamic hysteresis in the static dependencies of the force and moment coefficients, as well as the manifestation of bi-stable flow separation patterns, accompanied by the development of asymmetry in the stall zone. The URANS simulation results are in good agreement with the experimental data obtained for the NACA 0018 finite aspect ratio wing from the low-speed wind tunnel under the same test conditions. A new phenomenological bifurcation model of aerodynamic stall hysteresis under static and dynamic conditions is formulated, which is proven to be able to closely match the experimental data.

Powdery mildew is a severe disease causing substantial yield losses in wheat production worldwide. The Chinese wheat landrace Changanhongmai (CAHM) exhibits high resistance to the physiological race E09 of powdery mildew. This study aims to characterize the powdery mildew resistance gene in CAHM, and develop molecular markers for marker-assisted selection of wheat. To investigate the genetic characteristics of this resistant gene, we developed F1 plants, F2 generation population, and F2:3 families from a cross between CAHM and SY225. Genetic analysis demonstrated that all F1 plants were resistant to the disease, while the ratio of resistant to susceptible plants was 3: 1 in both the F2 population and F2:3 families, indicating that CAHM is inherited in a manner of single dominant powdery mildew resistance gene, which was tentatively designated as PmCAHM. By using bulk segregation analysis, we constructed a genetic map encompassing Xgwm273, Xwmc626, Xgwm11, Xgwm18, Xgdm28, Xgpw7812, Xgpw5195, Xwmc694 and PmCAHM. Among these markers, Xgpw7812 and Xgpw5195 were flanking markers tightly linked to PmCAHM at a genetic distance of 2.5 cM and 8.4 cM, respectively. Furthermore, nullisomic-tetrasomic analysis revealed that PmCAHM is located on chromosome 1B. These results indicate that PmCAHM differs from the internationally recognized powdery mildew resistance genes in both location and source. In addition, a new germplasm/line NW1748 with large seeds and powdery mildew resistance was developed from the cross and back cross populations of Fengyou 1718 (FY1718)/CAHM//FY 1718. Therefore, PmCAHM can serve as a novel powdery mildew resistance source for breeding of wheat by using NW1748 as the donor in the future.

In February 2004, the Food and Drug Administration (FDA) was the first to approve the combination of cisplatin (CDDP) and pemetrexed (PEM) as standard first-line chemotherapy for untreated, unresectable malignant pleural mesothelioma (MPM). However, after that approval, no progress was made in the standard first-line treatment of MPM for almost 15 years. Positive results from a phase 3 study (Mesothelioma Avastin Cisplatin Pemetrexed Study: MAPS) verifying the effect of bevacizumab, an anti-angiogenesis agent added to CDDP/PEM for unresectable MPM, were published in The Lancet in December 2015; however, this did not lead to approval by national drug regulatory agencies. Furthermore, no second-line treatment was established for cases refractory to CDDP/PEM. In August 2018, the Pharmaceuticals and Medical Devices Agency of Japan was the first in the world to approve monotherapy with nivolumab, an immune checkpoint inhibitor (ICI), for previously unresectable, advanced, or recurrent MPM. Following Japan, in October 2020, the FDA approved the combination of nivolumab and ipilimumab for the treatment of previously untreated, unresectable MPM. In this article, we review the transition of drug treatment for MPM, in light of the historical background, focusing on the benefits of ICIs.

The goal of the study presented in this article is to investigate interconnection issues of the Intelligent Electrical Networks (IENs) with the existing ones, as well as the deployment of the appropriate load forecasting demand techniques. The concept of IENs has recently emerged as a potential solution to the global energy problem, due to the more flexible dispersed production units that are involved. In the same context, additional issues, such as the reduction of production-transfer-distribution costs as well as power losses are analyzed as well, via the penetration of Renewable Energy Sources (RESs). To this end, it is necessary to use appropriate advanced infrastructure, where in combination with modern telecommunication networks will make possible the full exploitation of IENs. Moreover, to make the electricity system more efficient, to avoid voltage and frequency imbalance issues and to implement optimal production and consumption planning, the Electric Load Forecast (ELF) is a vital process and plays a key role in the planning as well as in the management of electricity systems. Therefore, recent state of the art approaches in ELF methods are discussed as well.

The theory of water transport in trees, according to which the main driving force of water movement is the suction created by the evaporation of water by the meniscus (the curved surface of the capillary liquid column) on the evaporating elements, supported from below by root pressure, is flawed. The capillary effect in nature is around one meter. In the case of open-air gaps, the leaf cannot suck in the water against gravity because, in this case, the plant would not be sucking in water, but air through the open-air gap. If not, how do trees transport water? When evaporation occurs, there is no suction towards the canopy, but pressure is due to a reduction in the cross-section of the water pipe caused by heat loss through evaporation. At night, when evaporation stops, a thermal equilibration process is triggered, which restores the original cross-section of the pipe. This generates suction and draws water from the soil. As the hydrostatic pressure in the pipe is high for tall trees, the pipe is cut off. To prove my theory, I have given several examples. Water transport is not based on physical mechanical laws alone. There may be complex physiological, biochemical, and biophysical processes behind the operation of the pipe system.

The background of this work is related to the scheduling of household appliances, taking into account variations in energy costs during the day, due to official Brazilian domestic tariffs: constant and white. The white tariff can reach an average price of around 17\% lower than the constant, but charges twice its value at peak hours. In addition to cost reduction, we propose a methodology to reduce user discomfort due to time shifting of controllable devices, presenting a balanced solution through the analytical analysis of a new method here called tariff space, derived from white tariff posts. To achieve this goal, we explore the geometric properties of the movement of devices through the tariff space (geometric locus of the load), over which we can define a limited region in which the cost of a load under the white tariff will be equal to or less than the constant tariff. As a trial for the efficiency of this new methodology, we collected some benchmarks (such as execution time and memory usage) against a classic multi-objective algorithm (Hierarchical) available in the language portfolio in which the project has been executed (Julia language). As a result, both methodologies achieve similar results, but the one presented in this article shows a significant reduction in processing time and memory usage, which could lead to the future implementation of the solution in a simple, low-cost embedded system like an ARM cortex M.

Human endogenous retroviruses (HERV) are remnants of ancient retroviral infections that, over millions of years, became integrated into the human genome. While normally inactive, environmental stimuli such as infections have contributed to the transcriptional reactivation of HERV promoting pathological conditions including the development of autoimmunity, neurodegenerative disease and cancer. What infections then trigger HERV activation? Mycobacterium avium subspecies paratuberculosis (MAP) is a pluripotent driver of human disease. Aside from granulomatous diseases Crohn’s, sarcoidosis and Blau syndrome, MAP is associated with autoimmune disease: type one diabetes (T1D), multiple sclerosis (MS), rheumatoid arthritis and autoimmune thyroiditis. MAP is also associated with Parkinson’s disease. Autoimmune diabetes, multiple sclerosis and rheumatoid arthritis are the diseases with the strongest MAP/HERV association. There are several other diseases associated with HERV activation including diseases whose epidemiology and/or pathology would prompt speculation for a causal role of MAP. These include non-solar uveal melanoma, colon cancer, glioblastoma and amyotrophic lateral sclerosis (ALS). This article further points to MAP infection as a contributor to autoimmunity, neurodegenerative disease and cancer via unsilencing of HERV. We examine the link between the ever-increasing number of MAP- associated diseases, the MAP/HERV intersection with these diverse medical conditions and propose treatment opportunities based upon this association.

Aim: To analyze a single center's experience with Short Bowel Syndrome/ Chronic Intestinal Failure (SBS/CIF) in adult patients treated with Home Parenteral Nutrition (HPN). Materials and methods: In the last thirty years, 13 patients with SBS/CIF were included in an HPN program. Results: Between 1992 and 2023, 13 patients were included in an HPN program. The primary underlying pathology was acute mesenteric ischemia. The median age of the starting HPN was 44 years old. Most are submitted to several surgeries of extensive intestinal resection with posterior intestinal reconstruction. Five of the 13 patients have died while on HPN for a median of 42 months. The causes of death HPN-related were catheter sepsis, endocarditis with cardiac failure, or hepatic failure. One patient died due to underlying pathology: pelvic abscesses and bleeding related to radiotherapy. Eight patients remain alive with a median time of HPN of 173 months. During the HPN support, the most frequent complications were venous catheter infection and venous territory thrombosis. None of the eight patients alive have hepatic failure. Two patients recently started teduglutide, with good tolerance, and need reduction of HPN support. All these eight patients have a satisfactory quality of life (parenteral support needs range between five to two nutrition bags per week). Conclusion: HPN remains the gold standard of SBS/CIF treatment, although Teduglutide may reduce HPN needs and HPN complications and provide a better quality of life. An HPN multidisciplinary healthcare group support is essential to ensure these patients' survival and quality of life. Keywords Short bowel Syndrome, Chronic Intestinal failure, Home Parenteral Nutrition, Teduglutide, Complications of Parenteral Support.

Generative artificial intelligence tools are flourishing across various sciences, particularly in the educational domain. ChatGPT, with its versions 3.5 and 4.0, stands out for its multilingual capabilities, catering to both students and educators. This research aimed to evaluate the performance differences between these versions, and importantly, to compare the accuracy of ChatGPT's responses in English versus Arabic. In our study, we presented ChatGPT with 39 chemistry problems related to 6th-7th grade curriculum, including twenty-five open questions and fourteen multiple-choice questions. Each response was categorized as accurate, partially accurate, or inaccurate. Our analysis, focused on trends across versions and languages, revealed significant enhancements in version 4.0, particularly in its ability to process Arabic. However, despite these improvements, our findings indicate that responses in English consistently outperformed those in Arabic in terms of accuracy. In light of this discrepancy, we suggest that ChatGPT either incorporates additional Arabic databases into its training regimen or develops a code that adopts the following strategy to ensure more accurate responses in science education: questions should first be translated from Arabic to English, addressed, and then retranslated into Arabic before being presented to the user. This approach takes advantage of the greater accuracy of responses in English to benefit Arabic-speaking users, and it may also improve outcomes for speakers of other languages who experience less precise answers in the fields of chemistry or science education.

Daridorexant, as the first Dual Orexin Receptor Antagonist (DORA) marketed in Europe, offers a novel therapeutic approach to insomnia. However, data regarding its real-world safety are scarce. Thus, the aim of this study was to assess its safety profile using a large-scale pharma-covigilance database. Daridorexant-related Adverse Drug Reaction (ADR) reports from the FDA Adverse Event Reporting System were scrutinized, and ADRs were selected using reporting odds ratio (ROR) as a measure of disproportionality. Frequencies of events related to daridorexant were compared to all other drugs (reference group, RG1) and only to other DORAs (RG2). Only significant disproportionalities to both RGs were evaluated in-depth. A total of 845 dari-dorexant-related reports were selected; nightmares (n = 146; Dari vs. RG1: ROR = 113.74; 95%CI[95.13, 136]; Dari vs. RG2: ROR = 2.35; 95CI%[1.93, 2.85]), depression (n = 22; Dari vs. RG1: 2.13; [1.39, 3.25]; Dari vs. RG2: ROR = 2.31; 95CI%[1.45, 3.67]), and hangover (n = 20; Dari vs. RG1: ROR = 127.92; 95CI%[81.98, 199.62]; Dari vs. RG2: 3.38; [2.04, 5.61]) were considered as safety signals. These data provide valuable insights into the real-world safety profile of dari-dorexant, supporting the existence of safety signals related to nightmares, depression, and hang-over.

Background: To compare photodynamic therapy 41 and the use of probiotics in reducing halitosis assessed through gas chromatography and microbiome analysis. Methods: Participants aged from 18 to 25 years, showing sulfide (SH2) ≥ 112 ppb on gas chromatography, were selected. They were divided into 4 treatment groups: Group 1 - Tongue Scraping; Group 2- Antimicrobial Photodynamic Therapy (aPDT); Group 3- Probiotics; Group 4- Antimicrobial Photodynamic Therapy (aPDT) and Probiotics. The halimetry process was performed before, immediately after the treatments, 7 days, 14 days and 30 days after the initial collection. The collections for later microbiological analysis were made along with the halimetry, for the microbiome analysis. Results: Treatment with aPDT or probiotics under these experimental conditions was not able to change the bacteria present in the biofilm of the tongue. Conclusions: More research is needed to know the behavior of the oral microbiome in the presence of halitosis and the effectiveness of new treatments.

Bioinformatics is a rapidly developing field enabling scientific experiments through computer models and simulations. Considering the vast databases of biological data available, it is ex-tremely important to develop efficient methods and algorithms for their processing. Sequence comparison is the best method for studying the evolutionary interaction between genes. It is based on alignment – the process of arranging two or more sequences to achieve the maximum level of identity and degree of similarity. The paper presents a new version of the algorithm for pairwise DNA sequences alignment, based on a new method called CAT, where a dependency with a previous match and the closes neighbor are taken in consideration to increase uniqueness of the CAT profile and to reduce possible collisions, i.e. two or more sequence having same CAT profiles. This makes proposed algorithm suitable for finding exact match of a concrete DNA se-quence in a big set of DNA data faster. The generation of CAT profiles is made once before data has been uploaded to the database, allowing the profiles to be used as metadata for the sequenc-es. It consists of an algorithm to calculate a CAT profile against the selected reference sequences and an algorithm to compare two sequences based on the calculated CAT profiles. Improve-ments in generation of the CAT profiles, are detailed described in the paper. Block scheme, pseudo code tables and figures were updated according to the proposed new version and ex-perimental results. Experiments have been carried out with the new version of the method and different datasets to align DNA sequences based on the CAT method. New experimental results in terms of collisions, speed, and efficiency of the proposed solutions are presented. Experiments related to the performance comparison with Needleman-Wunsch were re-executed with the new version of the algorithm to confirm that we have the same performance. An analysis of the per-formance of the proposed CAT based algorithm against Knuth–Morris–Pratt algorithm, which has a complexity of O(n) and is one of the most widely used for searching biological data, was performed. The impact of prior matching dependencies on uniqueness for generated CAT pro-files is investigated. The analysis of the experimental results obtained by sequence alignment shows a small deviation of the proposed algorithm based on the CAT method, which can be ig-nored if this deviation is acceptable at the expense of performance. The time efficiency of the CAT algorithm remains constant, regardless of the length of the sequences. Therefore, the ad-vantage of the proposed method is its fast processing in the alignment of large sequences, for which the execution of the exact algorithms takes a long time. The example code realization of the CAT Method, under the protection of the GNU General Public License v3.0, can be accessed on GitHub at: https://github.com/HristoS/CATSequenceAnalysis.

Rapid increase in urbanization and industrialization due to increase in human population has led to increase in global warming resulting to climate change that affects the ecosystem. Unusual natural disasters such as cyclones, storms, floods, weather fluctuations, rising seal levels, transitional ecosystems and coastal disruptions are major adverse effects of climate change. There are huge worrisome concerns and uncertainties about the future of the planet earth due to climate change caused by high carbon emissions. Global aggressive policies in control of Carbon-dioxide (CO2) emissions from several combustion mechanisms have paved way for alternative/ clean energy sources. Green hydrogen is a promising means of clean energy sources for commercial power generation stations and other crucial systems such as hydrogen fuel cell vehicles (HFCVs) , motors, drives, backup power for buildings, and also serving as a source of electrical energy for remote locations. The goal of this study is to review the current level of Sub-Saharan African nations’ preparedness to adopt green hydrogen as a way to mitigate local energy supply issues and strengthen their individual economies.

The use of privileged scaffolds as starting point for the construction of libraries of bioactive compounds is a widely used strategy in drug discovery and development. Scaffold decoration, morphing and hopping are additional techniques that allow to modify the privileged framework chosen and better explore the chemical space around it. In this study, two series of highly functionalized pyrimidine and pyridine derivatives were synthesized by scaffold morphing approach of triazine compounds obtained previously as antiviral agents. Newly synthesized azines were evaluated against lymphoma, hepatocarcinoma, and colon epithelial carcinoma cells showing in five cases acceptable to good anticancer activity associated to low cytotoxicity on healthy fibroblasts. Finally, ADME in vitro studies were conducted on the best derivatives of the two series showing good passive permeability and resistance to metabolic degradation.

China's cotton production has shown a concentration to the advantageous regions in recent years. Based on the relevant data of cotton production in China, the study measures the cotton production agglomeration levels of 16 major cotton-producing provinces from 1978 to 2020, summarizes the spatiotemporal evolution characteristics of cotton production agglomeration, and analyzes the factors influencing cotton production agglomeration using spatial econometric models. The results indicate that: Firstly, since the opening-up and reform, China's cotton production agglomeration levels have shown an overall upward trend, exhibiting periodic dynamic changes over time, and the spatial pattern reveals a dynamic regional gradient. Secondly, there is spatial correlation among the production scales of cotton in different provinces, with a trend resembling a "U" shape, and the agglomeration form shows a low-level convergence. Thirdly, technological level, policies, substitute industrial products and foreign trade are identified as important factors. The cotton production in each province is spatially correlated through these factors, and there is also a presence of spillover effects. In conclusion, the government should prioritize considerations of various influencing factors to formulate tailored support policies, promote large-scale production to enhance technological levels, and focus on policies aimed at improving the quality of cotton.

The Work Ability Index (WAI) is the most widely used questionnaire for the self-assessment of working ability. Because of its different applications, shorter versions, and widespread use in healthcare activities, it is worthwhile assessing its characteristics. The WAI was distributed online among the workers of a healthcare company; the results were compared with data contained in the workers' personal health records and with absence registers. 340 out of 575 workers (59.1%) participated; 6.5% of them reported poor work ability. Exploratory factor analysis indicated that the one-factor version best described the characteristics of the WAI. The scores of the complete WAI, the shorter form without the list of diseases, and the minimal one-item version (WAS) had equal distribution and were significantly correlated. The WAI score was inversely related to age and significantly lower in women than in men, but it was higher in night workers than in colleagues due to the probable effect of selective factors. The WAI score was also correlated with absenteeism, but no differences were found between males and females in the average number of absences, suggesting that cultural or emotional factors influence the self-rating of the WAI. Workers tended to overreport illnesses in the online survey compared to data collected during occupational health checks. Musculoskeletal disorders were the most frequently reported illnesses (53%). Psychiatric illnesses affected 21% of workers and had the greatest impact on work ability. Multilevel ergonomic and human factor intervention seems to be needed to recover the working capacity of healthcare workers.

Post-ovulatory aging (POA) is an inevitable factor during some assisted reproduction technology procedures, and results in poor fertilization rates and impaired embryo development. This study used RNA sequencing analysis and experimental validation to study the similarities and differences between in vivo- and in vitro-matured porcine oocytes before and after POA. Differentially expressed genes (DEGs) between fresh in vivo-matured oocyte (F_vivo) and aged in vivo-matured oocyte(A_vivo) and DEGs between fresh in vitro-matured oocyte (F_vitro) and aged in vitro-matured oocyte(A_vitro) were intersected to explore the co-effects of POA. It was found that "organelles", especially "mitochondria", were significantly enriched GO terms. The expression of genes related to the “electron transport chain” and “cell redox homeostasis” pathways related to mitochondrial function significantly showed low expression patterns in both A_vivo and A_vitro groups. WGCNA analysis to explore gene expression modules specific to A_vivo. Trait-modules association analysis showed that the red modules were most associated with in vivo aging. There are a total of 959 genes in the red module, which are mainly enriched in “RNA binding”, “mRNA metabolic process”, etc. GO terms, and “spliceosome” and “nucleotide excision repair” pathways. DNAJC7, IK, and DDX18 were at the hub of the gene regulatory network. And these genes were all expressed higher in A_vivo. In summary, POA affects the organelle function of oocytes. A_vivo oocytes have some unique gene expression patterns. These genes may be potential anti-aging targets. This study helps us understand the detailed mechanism of POA and provides strategies for researching oocyte aging.

Alzheimer's disease (AD) is a neurodegenerative olfactory disorder affecting millions of people worldwide. Alterations in the hexosamine or glucose-related pathways have been described through AD progression. Specifically, an alteration in glucosamine 6 phosphate isomerase 2 (GNPDA2) protein levels has been observed in olfactory areas from AD subjects. However, the biological role of GNPDA2 in neurodegeneration remains unknown. Using mass-spectrometry, multiple GNPDA2 interactors were identified in human nasal epithelial cells (NECs) mainly involved in intraciliary transport. Moreover, GNPDA2 overexpression induced an increment in NEC proliferation rates, accompanied by transcriptomic alterations in Type II interferon signalling or cellular stress responses. In contrast, the presence of beta-amyloid or mutated Tau-P301L in GNPDA2-overexpressing NECs induced a slow-down in the proliferative capacity in parallel with a disruption in protein processing. The proteomic characterization of Tau-P301L transgenic zebrafish embryos demonstrated that GNPDA2 overexpression interfered with collagen biosynthesis and RNA/protein processing, without inducing additional changes in axonal outgrowth defects nor neuronal cell death. In humans, a significant increase in serum GNPDA2 levels was observed across multiple neurological proteinopathies (AD, Lewy body dementia, progressive supranuclear palsy, mixed-dementia, and amyotrophic lateral sclerosis) (n=187). These data shed new light on GNPDA2-dependent mechanisms associated to the neurodegenerative process beyond the hexosamine route.