Abstract: COVID-19 in 2020 brought challenges to the Brazilian public health system with an emerging virus with respiratory contagion called SARS-CoV-2. There are few studies in Brazil and in some countries, on the increased incidence of certain viral respiratory infections, includ-ing H1N1 and coronavirus and their association with low levels of vitamin D, zinc and iron. The aim of this study was to demonstrate that the deficit of vitamin D, zinc and iron has an impact on the infectious process of patients with COVID-19 and to establish new forms of prevention for the worsening of COVID-19 in the human body. Data were collected from medical records and test results from patients being followed up during the treatment period for COVID-19. Patients with low blood levels of vitamin D, zinc and iron during the treatment period of COVID-19 had a higher percentage of worsening and complications requiring hospitalization in intensive care beds. The ingestion of vitamin D, zinc and iron in the treatment period of patients with COVID-19 in addition to being an immunological protector against SARS-CoV-2 and alleviating the process of worsening the disease can also act as a biomarker in cases of this disease.

Chronic diseases are characterised by cell’s autophagy and proteins disarrangement resulting in sarcopenia, hypoalbuminemia and hypo-haemoglobinaemia. Hypo-haemoglobinaemia couses worse prognosis independentely of the principal disease. Currently, the cornerstone of therapy of anaemia is iron supplementation, with or without erythropoietin for the stimulation of hematopoiesis. However, treatment strategies should incorporate the addition of heme, the principal biochemical constituent of haemoglobin. Heme synthesis follows a complex biochemical pathway. The limiting step of heme synthesis is D-ALA availability which, for its synthesis, requires Glycine and Succinil-CoA. Consequently, treatment of anaemia should not be based only on iron availability, but also on the availability of the molecules fundamental for heme synthesis. Therefore, an adequate clinical therapeutic strategy should integrate the standard iron infusion and the supply of essential amino acids and vitamins involved in the heme synthesis. We report preliminary data in selected elderly anaemic patients with congestive heart failure (CHF) and catabolic disarrangement, who, in addition to standard iron therapy, received personalized therapy with essential-AAs and vitamins involved in the maintenance of heme. Notably, such individualized therapy resulted in a significant increase in the serum concentration of haemoglobin after 30 days of treatment compared to standard iron therapy.

Streptococcus suis is a zoonotic pathogen causing serious infections in both swine and humans. Although metals are essential for life, excess amounts of metals they are toxic to bacteria. when accumulated in excess amounts. Except for zinc, Transcriptome-level data of the mechanisms for resistance to metal-induced toxicity in S. suis are available for no metals other than zinc. have not been investigated from the transcriptome level in S. suis. Herein, we explored the transcriptome-level changes of in S. suis in response to ferrous iron and cobalt toxicity by RNA sequencing. Many A lot of genes were differentially expressed in the presence of excess ferrous iron and cobalt. Most of the genes in response to cobalt toxicity showed the same expression trends as those were expressed in the same trend in response to ferrous iron toxicity. qRT-PCR analysis of the selected genes confirmed the accuracy of RNA sequencing results. Bioinformatics analysis of the differentially expressed genes indicated that ferrous iron and cobalt have similar impacts effects on the cellular processes of S. suis. Treatment with ferrous Ferrous iron treatment resulted in down-regulation of several oxidative stress tolerance-related genes involved in oxidative stress tolerance and up-regulation of the genes in an amino acid ABC transporter operon. Expression of the several genes in the arginine deiminase system was down-regulated in the presence of after ferrous iron and cobalt treatment. Collectively, our results suggested that S. suis alters the expression of a lot of multiple genes to respond to ferrous iron and cobalt toxicity.

The evolution of metals micro/nano-structure upon severe plastic deformation (SPD) is still far to be theoretically explained, while experimental datasets are persistently growing for several decades. Major problem associated with understanding of SPD is related to a fact that the latter is a synergetic product of several competing physical effects which alter the material micro/nano-structure. In attempt to find deformational boundaries, where predominantly one mechanism determines the micro/nano-structure, in this paper we propose a continuous piecewise model for the analysis of experiments on material hardness vs strain of SPD processed materials. The novelty of this approach lies in its ability to find, as free-fitting parameters, the strain breakpoints which separate different micro/nano-structure modes generated upon SPD process. The model is applied to analyse experimental data for polycrystalline samples of pure iron and two distinctive strain breakpoints are revealed with good accuracies. This finding is in a good agreement with our earlier results on TEM microscopy studies on pure iron polycrystals after SPD treatment.

The current study investigated the impacts of light quality and different levels of fertility on mineral nutrient concentrations in shoot and root tissues of Chinese kale (Brassica oleracea var. alboglabra). ‘Green Lance’ Chinese kale were grown under: 1) fluorescent/incandescent light; 2) 10% blue (447 ± 5 nm) / 90% red (627 ± 5 nm) LED light; 3) 20% blue / 80% red LED light; and 4) 40% blue / 60% red LED light as sole-source lighting at two different levels of fertility. All plants were harvested 30 d after seeding, and shoot and root tissues were analyzed for mineral nutrients. Lighting and fertility interacted to influence kale shoot and root mineral nutrient concentrations. Results indicate sole-source LED lighting used in production can impact mineral nutritional values of baby leafy greens now popular for the packaged market.

The cause-effect relationship between iron deficiency anemia (IDA) and osteoporosis has not been established in the general population. Thus, the current longitudinal study determined the role of IDA as a risk factor for osteoporosis by analyzing a large nationwide population-based sample. In a sample of 1,000,000 randomly sampled individuals from the 1998-2012 Taiwan National Health Insurance Research Database, patients with IDA (case group [n=35,751]) and individuals without IDA (control group [n=178,755]) were compared. Patients who were <20 years of age and who had pre-existing osteoporosis prior to the diagnosis of IDA were excluded. Each patient with IDA was age- and gender-matched to 5 individuals without IDA. The diagnoses of IDA and osteoporosis (coded using ICD-9CM) were further confirmed with blood test results and X-ray bone densitometry to ensure the accuracy of the diagnoses. Osteoporosis occurred more often among patients with IDA compared to individuals without IDA (2.27% vs. 1.32%, p<0.001). Cox proportional hazard analysis revealed that the risk for osteoporosis was significantly higher in the case than the control group (hazard ratio [HR]=1.74; 95% CI=1.61-1.88) and remained similar after adjustment for covariates (adjusted HR=1.81; 95% CI=1.67-1.97). Compared with individuals without IDA, the risk for osteoporosis was even higher for patients with IDA who received intravenous ferrum therapy (adjusted HR=2.21; 95% CI=1.85-2.63). In contrast, the risk for osteoporosis was reduced for patients with IDA who received a blood transfusion (adjusted HR=1.47; 95% CI=1.20-1.80). As a predictor, prior IDA is a significant and independent risk factor for development of osteoporosis. In contrast to blood transfusion treatment, the use of intravenous ferrum may further increase the risk for future osteoporosis.

Two pilot scale desalination system employing carbon modified nano-sized, zero valent metals (n-ZVM) were maufactured and tested to determine (1) the degree to which high salt water (20 to 130 mS) could be desalinated and (2) if this degree of desalination could be maintained throughout an extended treatment period. Two pilot systems were tested (referred to as Generation 1 and Generation 2) consisting of parallel lines of 4 individual reactors in series, a settling tank and an activated carbon cell at the end of each reactor line. The system capacity was 300 gal in Generation 1 and 600 gal in Generation 2 total with a total hydraulic residence time of 6 hours per reactor line (one hour per cell/tank). A slurry of n-ZVM was introduced in the first reactor on each line to yield approximately 5 to 45 grams of nano-metal per 100 L of influent salt water. The n-ZVM slurry was made from mixing tea extract with metal salts (eg ferrous sulfate, ferric chloride) to produce the carbon modified metal slurry. Initial runs focused on dosing required to achieve maximum salt removal at each of three influent salt contents, 28 mS, 44 mS and 123 mS. Once dosing was set, continuous runs (14 days, 23 days and 9 days) were carried out to (1) examine the ability of the pilot to maintain the removal efficiency over an extended period, (2) to determine the weaknesses inherent in the pilot systems, and (3) to begin to develop CAPEX and OPEX estimates for the pilot and pre-design full scale mobile desalination unit (MDU) system. Results demonstrated that maximum removal occurred with 10 g/100L of salt for the 30 mS salt solution,16 g/100 L of salt for the 40 mS influent water and 40 g/100 L for the 130 mS influent. Salt removal (expressed as Na and Cl removed) approached 78% for the 30 mS influent and 41 mS influent respectively while removal for the highest salt influent (130 mS) approached 81 %. Addition of greater amounts of carbon modified slurry than those described resulted in a pH less than 6 which inhibits continued salt removal and would require addition of calcium or magnesium oxides/hydroxides or bicarbonate solutions at the influent side to achieve greater salt removal and of course greater cost per gallon. Continuous operation over the extended time-period showed no significant decrease in salt removal with typical day to day variation of no more than 10% suggesting that this approach to desalination could rapidly provide usable water from saline aquifers, seawater or even produced water. Treatment of saline waters at 30 mS or less yields effluent water in the range necessary for irrigation use of many crops while treatment of seawater or produced water levels of salt (40 mS or higher) yields effluent in the range of 8 to 10 mS a range ideal for RO influent that would prevent the production of large volumes of brine. Pilot testing continues with seawater extracted from Puget Sound, Seattle WA and higher levels of salt to better represent produced water.

The radiative heat transfer in arc plasma models is considered from the point of view of its description in terms of a net emission coefficient, the method of spherical harmonics in its lowest order, and the discrete ordinate method. Net emission coefficients are computed applying approximate and numerical approaches, the multi-band representation of the spectral absorption coefficient with three kinds of its averaging and two datasets. The self-consistent access to the radiative heat transfer is applied to a two-dimensional axisymmetric model of free-burning arc in argon at atmospheric pressure. The results obtained from the models employing the net emission coefficient, the method of spherical harmonics, and the discrete ordinate method are compared.

(1) Background: Learning and training in variable conditions favors adapting to unstable or changing environments. The aim of the study was to test the effect of variable practice on the accuracy of the forehand net approach shot in tennis.; (2) Methods: Thirty (n=30) first-class national players (12.9 ± 1.1 years old) participated, divided into three groups: i) induced variability training (n=10) (varying court surfaces and balls), ii) specific training (n=10) and iii) usual training -control group- (n=10). All groups trained for a month: 12 sessions of 20 minutes -3 per week-. The accuracy of the shots were analyzed through a 2D capture and digitization process of the ball bounce on the court; (3) Results: The variability group presented better accuracy values after the period without practice than the stable training group (p = 0.041; ES= 0.51).; (4) Conclusions: the application of variability in the game conditions during tennis training seems to have a favorable effect on the retention of accuracy in the forehand down the line approach to the net.

Owing to the growing concerns due to dwindling fossil fuel reserves, increasing energy demand and climate emergency, it is imperative to develop and deploy sustainable energy technologies to ensure future energy supply as well as transition to the net zero world. In this context, there is great potential in the biorefinery concept for supplying drop in biofuels in the form of biodiesel. Biodiesel as a fuel can certainly bridge the gap where electrification or the use of hydrogen is not feasible, for instance heavy vehicles, farm and marine transportation sector. However, biodiesel industry also leads to formation of large amount of crude glycerol as the byproduct. Crude glycerol due to presence of several impurities may not be a suitable feedstock for all high value products derived from glycerol but fits well with glycerol esterification for producing glycerol acetins, which have numerous applications. This review critically looks at the processes using nanostructured solid/liquid acid catalysts for glycerol esterification including the economic viability of the scale up.

Rapid screening of botanical extracts for the discovery of bioactive natural products was performed using a fractionation approach in conjunction with flow-injection high-resolution mass spectrometry for obtaining chemical fingerprints of each fraction, enabling the correlation of the relative abundance of molecular features (representing individual phytochemicals) with the read-outs of bioassays. We applied this strategy for discovering and identifying constituents of Centella asiatica (C. asiatica) that protect against Aβ cytotoxicity in vitro. C. asiatica has been associated with improving mental health and cognitive function, with potential use in Alzheimer’s disease. Human neuroblastoma MC65 cells were exposed to subfractions of an aqueous extract of C. asiatica to evaluate the protective benefit derived from these subfractions against amyloid β-cytotoxicity. The % viability score of the cells exposed to each subfraction was used in conjunction with the intensity of the molecular features in two computational models, namely Elastic Net and selectivity ratio, to determine the relationship of the peak intensity of molecular features and % viability. Finally, the correlation of mass spectral features with MC65 protection and their abundance in different sub-fractions were visualized using GNPS molecular networking. Both computational methods unequivocally identified dicaffeoylquinic acids as providing strong protection against Aβ-toxicity in MC65 cells, in agreement with the protective effects observed for these compounds in previous preclinical model studies.

Crack inspection in railway sleepers is crucial for ensuring rail safety and avoiding deadly accidents. Traditional methods for detecting cracks on railway sleepers are very time-consuming and lack efficiency. Therefore nowadays, researchers are paying attention to the vision-based algorithm, especially Deep Learning algorithms. In this work, we adopted the U-net for the first time for detecting cracks on a railway sleeper and proposed a modified U-net architecture named Dense U-net for segmenting the cracks. In the Dense U-net structure, we established several short connections between the encoder and decoder blocks, which enabled the architecture to obtain better pixel information flow. Thus, the model extracted the necessary information in more detail to predict the cracks. We collected images from railway sleepers, processed them in a dataset, and finally trained the model with the images. The model achieved an overall F1-score, precision, Recall, and IoU of 86.5% 88.53%, 84.63%, and 76.31% respectively. We compared our suggested model with the original U-net, and the results demonstrate that our model outperformed the U-net in both quantitative and qualitative results. Moreover, we considered the necessity of crack severity analysis and measured a few parameters of the cracks (e.g., length, maximum width, area, density). The engineers must know the severity of the cracks to have an idea about the most severe locations and take the necessary steps to repair the badly affected sleepers.

Abstract: This study observes the characteristics and influencing factors of carbon fluxes of the Momoge salt marsh ecosystem over four years, which behaves as a CO2 sink. The daily, seasonal, and interannual variations of CO2 fluxes in the Momoge salt marshes were observed using the eddy covariance method and compared with various environmental factors. An overall daily “U” shaped distribution was observed, with uptake during the day (negative values) and release at night (positive values). Annually, the carbon fluxes in the study area roughly exhibit a “V” shape. Carbon fluxes during the non-growing season predominantly showed positive values, indicating the release of CO2 into the atmosphere. Photosynthetically active radiation was the primary influencing factor affecting hourly and daytime variations in net ecosystem exchange (NEE) during the growing season, while temperature was the main factor influencing nighttime NEE dynamics. Air temperature, soil temperature, photosynthetically active radiation, precipitation, and water level all had significant impacts on daily net CO2 exchange. At the monthly scale, larger values of soil temperature, air temperature, photosynthetically active radiation, and aboveground biomass correspond to a stronger carbon absorption capacity of the ecosystem. Overall, temperature remains the primary factor for carbon fluxes in the Momoge wetlands.

The operating theatre represents a key contributor to healthcare-derived emissions. Here, we outline the carbon footprint of a lumbar microdiscectomy at our regional unit in Greater Manchester, with the use of AI technology. In doing so, we aim to define the extent of the environmental impact and identify carbon hotspots for sustainable change. Our unit of analysis covered the start of the patient journey into hospital through to the final outpatient appointment. Publicly available and locally sourced data was used. An inventory analysis of two lumbar microdiscectomies was conducted. Each item was categorised reconciled against our AI-powered ‘CO2 analysis’ database. Kilograms of carbon dioxide equivalent (kg CO2e) was chosen as the primary measure of impact. An average lumbar microdiscectomy at our trust generates 477.73kg CO2e. The operation (173.89kg CO2e, 36.40%) and the inpatient stay (144.67kg CO2e, 30.28%) constituted the biggest contributors. Single-use intraoperative equipment contributed to over three-quarters of the total emissions from the operation. By defining the environmental burden of elective spinal surgery, we have identified carbon hotspots to target for sustainable change. Substituting polluting elements for carbon-friendly alternatives and challenging the single-use culture, modern healthcare systems can help pave the way to a ‘net zero’ healthcare system.

The implementation of residential distributed generation (DG) in South America has shown significant growth in the last decade, driven by increased research on photovoltaics and renewable energy sources. Regulatory policies and business models have been crucial in fostering the adoption of DG in the region, with residential self-supply being the most receptive sector for implementing the concept. This article presents a literature review on DG as applied to residential self-supply in South America by comprehensively analyzing documents published between 2013 and 2023, applying the PRISMA methodology. In total, 37 literature resources have been systematically selected and reviewed to contribute to the identification of trends and developments in the field of DG in the residential sector, as well as highlighting areas that require further research and attention from stakeholders, including policymakers, investors, technology providers and consumers in the South American context.

Bone Scan Index (BSI) is an image biomarker for quantification on bone metastasis of cancers. To compute BSI, not only the hotspots (metastasis) but also the bones have to be segmented. Most related researches focus on binary classification on bone scintigraphy: having metastasis or none. Rare studies focus on pixelwise segmentation. In this study we compare three advanced convolutional neural network (CNN) based models to explore the bone segmentation on dataset in house. The best model is Mask R-CNN, which reaches the precision, sensitivity and F1-score: 0.93, 0.87, 0.90 for prostate cancer patients and 0.92, 0.86, 0.88 for breast cancer patients, respectively. The results are the average of 10-fold cross-validation, which reveals the reliability for clinical use on bone segmentation.

A real-time monitoring system for the operational status of fishing vessels is an essential element for the modernization of the fishing industry. The operational status of fishing vessels can be identified by using onboard cameras to detect the deck crew and the use of fishing nets. Due to the typically limited processing capacity of shipboard equipment and the significant memory consumption of detection models, however, general target detection models are unable to perform real-time image detection to identify the operational status of fishing vessels. In this paper, we propose a lightweight real-time deck crew and the use of fishing nets detection method, YOLOv5s-SGC. It is based on the YOLOv5s model, which uses surveillance cameras to obtain video of fishing vessels operating at sea and enhances the dataset. YOLOv5s-SGC replaces YOLOv5s’s backbone and the ordinary convolutional blocks in the feature fusion network with ShufflNetV2 and Ghost module.

In recent years, with the development of social media, people are more and more inclined to upload text, pictures and videos on the platform to express their personal emotions, thus the number of short videos is increasing and becoming the first choice for people to socialize. Unlike the traditional way, people can convey their personal emotions and opinions through media other than words, such as video images, etc. for external information. Therefore, the expression and analysis of emotions is not only through text, but also through the analysis of emotional needs in images and videos, and the research scholars have customized products for individual users. Compared with pure text content, video information can more intuitively express users' happiness, anger and sorrow, thus short video-related applications have gained more and more popularity among Internet users in recent years. However, not all short videos on social networking sites can accurately express users' emotions, and related text information can more accurately assist sentiment analysis and thus improve accuracy. However, short video sentiment analysis based on video frame images is inaccurate in some scenarios, such as when expressing tears of joy, the sentiment expressed by the user's facial expression and voice are different, which will cause errors in the analysis of sentiment. As a result, researchers began to consider multimodal sentiment analysis to reduce the impact of the above scenarios on short video sentiment analysis. This paper focuses on proposing a sentiment analysis method for short videos. We first propose a residual attention model to make full use of the information in audio to classify the emotions contained in them. Then the text information in the dataset is classified by feature extraction. The key to extract features from text information is not only to retain the semantic information of the text, but also to explore the potential emotional information in the text, so as to ensure the integrity of the text information features. The experiments show that the sentiment analysis model proposed in this paper is more superior than the baselines.

Despite a rapid rise in NBS development in recent years, the methods for evaluating NBS still have certain gaps. We propose an approach based on a combination of remote sensing data and meteorological variables to reconstruct the spatio-temporal variation of net ecosystem exchange from eddy-covariance stations. Lagrangian particle dispersion model was used for upscaling of satellite images and flux towers. We trained data-driven models based on kernel methods separately for each selected land cover class. The results suggest that the proposed approach to quantifying carbon exchange on a medium-to-large scale by blending eddy covariance flux data with moderate resolution satellite and weather data provides a set of key advantages over previously deployed methods: (1) scalability, achieved via the validation design based on a separate set of eddy covariance stations; (2) high spatial and temporal resolution due to use of the Landsat imagery; (3) robust and accurate predictions due to improved data quality control, advanced machine learning techniques, and rigorous validation. The machine learning models yielded high cross-validation results. Overall we present here globally scaled technology for the land sector based on high resolution remote sensing imagery, meteorological variables, and direct carbon flux measurements of eddy covariance flux stations.

Agricultural mechanization is an important factor to improve the green total factor productivity of planting industry, which is the key way to realize the sustainable development and high-quality development of agriculture. Based on the panel data of 30 provinces in China from 2001 to 2019, this paper uses the stochastic frontier analysis method of output oriented distance function to measure the green total factor productivity of China’s planting industry based on net carbon sink, and empirically studies the impact of agricultural mechanization on the green total factor productivity in China’s planting industry. The empirical analysis finds that mechanization can significantly promote the planting green total factor productivity, and this basic conclusion is still robust after using instrumental variables, sub sample regression. Further research found that the path of mechanization on planting green total factor productivity is mainly reflected in technology progress and spatial spillover. The mechanism of operation scale expansion, factor allocation optimization and technical efficiency change is not significant. Given these findings, the paper adds considerable value to the empirical literature and also provides various policy- and practical implications.

Nowadays, and with the mechanization of life, speech processing has become so crucial for the interaction between humans and machines. Deep neural networks require a database with enough data for training. The more features are extracted from the speech signal, the more samples are needed to train these networks. Adequate training of these networks can be ensured when there is access to sufficient and varied data in each class. If there is not enough data; it is possible to use data augmentation methods to obtain a database with enough samples. One of the obstacles to developing speech emotion recognition systems is the Data sparsity problem in each class for neural network training. The current study has focused on making a cycle generative adversarial network for data augmentation in a system for speech emotion recognition. For each of the five emotions employed, an adversarial generating network is designed to generate data that is very similar to the main data in that class, as well as differentiate the emotions of the other classes. These networks are taught in an adversarial way to produce feature vectors like each class in the space of the main feature, and then they add to the training sets existing in the database to train the classifier network. Instead of using the common cross-entropy error to train generative adversarial networks and to remove the vanishing gradient problem, Wasserstein Divergence has been used to produce high-quality artificial samples. The suggested network has been tested to be applied for speech emotion recognition using EMODB as training, testing, and evaluating sets, and the quality of artificial data evaluated using two Support Vector Machine (SVM) and Deep Neural Network (DNN) classifiers. Moreover, it has been revealed that extracting and reproducing high-level features from acoustic features, speech emotion recognition with separating five primary emotions has been done with acceptable accuracy.

Modern portfolio theory claims that diversification into non-correlated or negatively correlated activities reduces the overall risk of a portfolio. Considering the total income of a bank as a portfolio of interest income and non-interest income, this paper investigates how the variability of interest income and non- interest income, and covariance between interest income and non-interest income influence the various risk factors of banks. We set out a study in the Indian context. We have extracted data for the period 2005-2017 and employed an extended version of Ridge, Lasso and Elastics Net regression to take care of multi-collinearly in our data. We have considered 10-fold cross-validation techniques to get optimal values of tuning parameters for Ridge, Lasso, and Elastics Net regression (which is a convex combination of ridge and the LASSO). We have compared different regression techniques by comparing RMSE and R2. We observe that non-interest income is positively correlated with interest income in the Indian context, but it does stabilize variance, idiosyncratic risk & market risk (Beta) of Indian Banks.

Background: In response to inflammation, the absorption of nutritional iron is restricted. Multiple inflammation-related signaling pathways can lead to ferroptosis, an iron-dependent mechanism of programmed cell death. Since the pathophysiological significance of the presence and uptake of iron in chronic inflammation is still unknown, we tested the effect of a low iron diet on the clinical course of arthritis in the mouse model of collagen-induced arthritis (CIA). Methods: Six- to eight-week-old male DBA/1 mice were fed either a normal or a low iron diet starting three weeks before arthritis induction. From day four after the collagen-booster made on day 25, the development of arthritis was regularly monitored until the end of the experiment (day 34), using a standard clinical arthritis score. The complete blood count was determined as well as Fe2+, Fe3+, oxidized and reduced glutathione (GSH and GSSG) and malondialdehyde (MDA) in whole paw tissue by ELISA. Quantitative PCR was performed from whole paw tissue for glutathione peroxidase 4 and other key regulator genes of iron metabolism and ferroptosis. We used nonparametric tests to com-pare markers of iron metabolism and ferroptosis as well as nonlinear regression models for longitu-dinal data for signs of arthritis. Results: Mice fed a low iron diet showed a significantly less severe course of arthritis compared to mice fed a normal iron diet (p

Tungsten carbide (WC) was well known as a wear-resistant material. Iron-based pow-der was suitable for remanufacturing of cast iron engine parts. To improve the wear resistance of the remanufactured iron-based cast iron engine parts, WC-Co was intro-duced into iron-based powders to prepare composite coatings for cast iron engine components. In this work, FeAlC-x(WC-Co) composite coatings were prepared by plasma transfer arc on compacted graphite cast iron. The influences of WC-Co con-tents in the Fe-AlC-x(WC-Co) composite coatings on the microstructure and properties of the composite coatings were investigated. The composite coatings contained γ-Fe, α-Fe, WC and Fe3W3C mainly. The microhardness of FeAlC-x(WC-Co) composite coatings had the highest hardness of 814 HV0.2, the-lowest coefficient of 0.5 and the lest wear mass loss of 1.3mg when the weight content of WC-Co in the composite coating was 20wt%. As compared with the FeAlC coating, FeAlC-x(WC-Co) composite coating achieved the effect of reducing friction and wear resistance simultaneously, which resulted from the self-lubricating effect of graphite and high wear-resistance of carbides in the claddings.

Stable half-sandwich iron(II) dihalido complexes of the type [Fe(5-Cp’)X2] are extremely scarce, being limited to congeners containing the bulky C5H2-1,2,4-tBu3 ligand. We have extended this to homologues [Fe(5-Cp*)X2] (X = Cl – I) containing the particularly popular C5Me5 (Cp*) ligand. Corresponding ionic compounds ER4[Fe(5-Cp*)X2] are easily accessible from FeX2, MCp* (M = Li, K) and a suitable halide source R4EX (E = N, P) in THF. The new compounds NnPr4[Fe(5-Cp*)X2] (X = Cl, Br), NnPr4[Fe(5-Cp*)BrCl] and PPh4[Fe(5-Cp*)X2] (X = Cl, Br, I) have been structurally characterised by single-crystal X-ray diffraction. NnPr4[Fe(5-Cp*)Cl2] reacts readily with CO to afford [Fe(5-Cp*)Cl(CO)2], indicating the synthetic potential of ER4[Fe(5-Cp*)X2] in FeCp* half-sandwich chemistry.

Brassicaceae are well known as non-arbuscular mycorrhizal (AM) host plants. This study was aimed to examine effects of two Brassica; cabbage (Brassica oleracea L. cv. cabitata) and mustard (B. juncea Coss.) on three AM species (Acaulospora, Entrophospora and Glomus) and to examine effect of root incorporation into soil on root AM infectivity in maize growing in iron toxicity soil. Experiment 1, cabbage and mustard crop reduced spore density of Acaulospora, but there was no effect on spore density of Entrophospora. While, mustard crop had more effect on Glomus spore density than cabbage crop. Experiment 2, AM spore from two conditions; without and with 6 weeks of root residues (cabbage, mustard and maize) incorporation was examined for its infectivity in maize roots. At D21, without root incorporated, the infectivity of Acaulospora, Entrophospora and Glomus were not different. By contrast, cabbage and mustard root incorporation depressed infec-tivity of Acaulospora but, there was no effect on Entrophospora. Whereas, cabbage root incorporation only depressed infectivity of Glomus. However, AM infectivity was recovered at D42. These results suggested that Brassica root incorporated into soil was the actual effect on the viability of AM spore resulting in reduction of AM infectivity at the early stage.

Transferrin (TF) is a key iron carrier protein. Studies in human cohorts and in cultured adipocytes suggested that decreased transferrin expression and intracellular iron in adipocytes leads to impaired insulin responsiveness and causes differential expression of genes involved in metabolism, insulin action, and obesity. While suggestive, these studies did not definitively prove in an intact organism that altering TF expression in adipocytes is sufficient to affect glucose homeostasis and insulin resistance. In this study we created a mouse model of decreased transferrin expression only in adipocytes (Trf+/-Cre+/-) to test the hypothesis that iron trafficking in adipose tissue is sufficient to affect insulin sensitivity. Area under the glucose curve (AUCg) after intraperitoneal glucose administration was increased in the Trf+/-Cre+/- mice compared to wild type (WT) mice on a normal chow diet, and the mice were slightly heavier (p=0.02 for both). Homeostasis Model of Insulin Resistance (HOMA-IR) values were also significantly higher in the Trf+/- Cre+/- mice (p<0.05). Thus, supporting our hypothesis, analysis of mice with decreased expression of TF restricted to adipocytes showed that a decrease in adipocyte TF is sufficient to affect insulin resistance, weight, and glucose homeostasis.

Arsenic (As) is the most poisonous metalloid with negative effects on the plant and ecosystem. In the current study, observed that iron oxide nanoparticles (NPs) affected the molecular features of rice under arsenic (As) stress. The result showed that 1-D electrophoresis (SDS-PAGE) demonstrated variable expression of protein profiles. The polypeptide pattern was altered in the SDS-PAGE under arsenic stress. The findings also showed that low molecular weight polypeptides (MTs) expression was maximum under arsenic stress, while iron oxide NPs decreased the expression of these polypeptides and reduced the arsenic stress. RT-PCR was used to analyse the expression of the MYB transcriptional factor, WRKY, and OsGF14 genes in the rice plant. The results showed that these genes were up-regulated in response to arsenic stress, iron oxide NPs reduced arsenic stress, and Myeloblastosis (MYB), WRKY, and OsGF14 genes had very low expression levels. Our work showed that iron oxide nanoparticles (NPs) not only reduced the effects of arsenic stress but also decreased the expression of stress-related genes and metallothioneins (MTs).

Iron oxide nanoparticles have been investigated due to their suitable characteristics for diverse applications in the fields of biomedicine, electronics, water/wastewater treatment and sensors. Maghemite, magnetite and hematite are the most widely studied iron oxide particles and have ferrimagnetic characteristics. When very small, however, these particles have superparamagnetic properties and are called superparamagnetic iron oxide nanoparticles (SPIONs). Several methods are used for the production of these particles, such as coprecipitation, thermal decomposition and microemulsion. However, the variables of the different types of synthesis must be assessed to achieve greater control over the particles produced. In some studies, it is possible to compare the influence of variations in the factors for production with each of these methods. Thus, researchers use different adaptations of synthesis based on each objective and type of application. With coprecipitation, it is possible to obtain smaller, more uniform particles with adjustments in temperature, pH and the types of reagents used in the process. With thermal decomposition, greater control is needed over time, temperature as well as the proportion of surfactants and organic and aqueous phases in order to produce smaller particles and a narrower size distribution. With the microemulsion process, the control of the confinement of the micelles formed during synthesis through the proportions of surfactant and oil makes the final particles smaller and less dispersed. These nanoparticles can be used as additives for the creation of new materials, such as magnetic bacterial cellulose, which has different innovative applications. Composites that have SPIONs, which are produced with greater rigour with regards to size and distribution, have superparamagnetic properties and can be used in medical applications, whereas materials containing larger particles have ferromagnetic applications. To arrive at a particular particle with specific characteristics, researchers must be attentive to both the mechanism selected and the production variables to ensure greater quality and control of the materials produced.

Nowadays, there is evidence that bacteria can contribute to cancer formation and interfere with therapy by mediating its carcinogenesis and related infection. Moreover, it is acknowledged that microbial infections and antibiotic resistance represent severe economic and health risks to society. These facts have led to the developing of several new techniques for impeding crucial biological processes in microbial cells. One of these techniques centers on using metal-chelating agents, which can disrupt the microorganism's vital metal metabolism by obstructing metal uptake and bioavailability for critical reactions. Additionally, nanotechnology has made a wide range of nanomaterials available for possible uses in the antibacterial industry. This complex field is shaped by antimicrobial nanoparticles, also investigated as therapeutic and drug-delivery tools. Halloysite nanotubes (HNTs) are naturally occurring tubular clay nanomaterials consisting of aluminosilicate kaolin sheets rolled up several times. The aluminon and siloxane groups on the surface of HNTs facilitate the formation of hydrogen bonds with biomaterials on their surface. These properties make HNTs crucial in a wide range of applications, such as in environmental sciences, wastewater treatment, dye removal, nanoelectronics and nanocomposite fabrication, catalytic studies, coatings for glass or anti-corrosive coatings, cosmetics, flame retardants, stimulus-response, and in forensic sciences. This work aimed to produce an antibacterial material by combining the properties of halloysite nanotubes with the ability of kojic acid to chelate iron. Starting from kojic acid, a simple nucleophilic substitution involving the hydroxyl groups on the surface of the nanotubes was performed. The obtained material was characterized by IR and SEM, and its ability to chelate iron was evaluated. Finally, the capacity to load drugs such as resveratrol and curcumin was also evaluated by UV analysis. In this way, a new bio-based material that can be used as a drug carrier and antimicrobial was produced.

This study presents a tri-layer broadband metamaterial absorber which operates in the GHz range. The absorber was composed of a polyhedral iron-cobalt alloy/graphite nanosheet material arranged in a flat sheet with two punched-in rings for the top layer, a continuous FR-4 layer at the middle, and a continuous copper layer at the bottom. For the normal incidence of electromagnetic wave, the proposed absorber demonstrated an exceptional broadband absorption in a frequency range of 7.9-14.6 GHz, revealing an absorption exceeding 90%. The absorption magnitude remains to be above 90% in a frequency range of 8-11.1 GHz for transverse-electric-polarized waves at incident angles up to 55°. For both transverse-magnetic- and electric-polarized waves, the absorption exceeds 90% in a frequency range of 9.5-14.6 GHz. The physical mechanism behind the absorption properties is analyzed thoroughly through the electric- and magnetic-field distributions. The obtained results could contribute potentially to the development of microwave applications based on metamaterial absorbers, such as radar-stealth technology, electromagnetic shielding for health safety and reduced electromagnetic interferences for high-performance communications and electronic devices.

In eutrophic waters, such as Mikawa Bay, Japan, anoxic bottom water develops in summer. This causes sulfide release into seawater by sulfate reduction in the sediment, leading to environmental problems. The addition of iron to sediments is a method used to improve the sediment environment, which was devised from a natural phenomenon. However, this method has not yet been quantitatively evaluated. In this study, we aim to quantitatively evaluate the suppressive effect of iron on sulfide release. First, we develop a sediment model that focuses on S and Fe. We then attempted to reproduce the observations and experiments on sulfide dynamics using the model. Consequently, the proposed model was able to reproduce field sulfur and Fe dynamics in sediments. Additionally, the model described the characteristics of sulfide release considering the effects of additive iron materials. Finally, we conducted predictive calculations and quantitatively evaluated the effects of adding iron materials to the sediments in terms of sulfur and iron cycles.

This study investigated the impact of boron and copper on the microstructure and mechanical properties of Spheroidal graphite Cast Iron (SCI). The addition of boron increased the ferrite content, while copper increased the stability of pearlite. The interaction between boron and copper significantly increased the ferrite content. Differential Scanning Calorimetry (DSC) revealed that boron decreased the enthalpy change of the α + Fe3C -> γ transformation and increased the enthalpy change of the α -> γ transformation. The addition of boron and copper reduced the tensile and yield strength but increased elongation and impact energy. The findings provide valuable insights into the effect of boron and copper on SCI, facilitating the design and development of high-performance SCI materials.

Children under two years old are at risk for anemia because young children have an increased need for iron for their physical growth and brain development. The purpose of this study was designed to evaluate the effects of a caregiver potential support program on anemia prevention in children six months to two years old attending the subdistrict health-promoting hospital in Thasala District, Nakhon Si Thammarat Province. This study was quasi-experimental. The sample included children aged six months to two years old and their caregivers, who were selected by random sampling and allocated to either the experimental or control group, with 40 pairs per group. The experimental group received a potential support program, while the control group received regular care. Both groups were followed for 12 weeks. The instruments used were the potential caregiver assessment, children’s anemia assessment, and a program to support the potential of primary caregivers. Descriptive statistics, chi-square, and t-tests were used to analyze the data. The results revealed that 11.4% of the children had anemia, and a hematocrit count of less than 33% (range = 30-40, M = 34.89, SD = 1.97). The mean scores of knowledge about anemia and iron supplementation after using the program in the experimental group and control group were significantly different (p < 0.001). The mean scores of knowledge about anemia in the experimental and control group were 15.75, SD = 0.54, and 13.28, SD = 1.43 respectively. The mean scores of knowledge about iron supplementation in the experimental and control group were 10.75, SD = 0.49, and 8.15, SD = 1.54 respectively. It was found that the experimental group had a higher mean score on food care behaviors than the control group for 6-11 months and 1-2 years, with statistical significance. The experimental group also had a higher mean score of care behaviors concerning children’s consumption of medicine than the control group (M = 58.20, SD = 4.05; M = 45.78, SD = 9.66, respectively), (p < 0.001). The mean score of the hematocrit level for the experimental group was found to be higher than the control group after receiving the program (M = 35.80, SD = 1.55; M = 34.83, SD = 2.14, respectively), (p < 0.05). Therefore, healthcare providers should support caregivers' capacity to provide continued care for children to prevent anemia.

The development and improvement of methods for the synthesis of environmentally friendly catalysts based on base metals is currently an urgent and promising task of modern catalysis. Catalysts based on nanoscale magnetite and maghemite have fast adsorption-desorption kinetics and high chemical activity. The purpose of this work was to obtain magnetic composites, determine their physicochemical characteristics and verify their activity in the process of liquid-phase oxidation of phenol with oxygen. Magnetic nanocomposites were obtained by chemical co-deposition of salts of ferrous and trivalent iron. The synthesized magnetic composites were studied by X-ray diffractometry, energy dispersive X-ray fluorescence and Mössbauer spectroscopy, IR-Fourier spectroscopy, elemental analysis. To increase the catalytic activity in oxidative processes, the magnetite surfaces were modified using cobalt nitrate salt. Further, CoFe2O4 was stabilized by adding polyethylenimine (PEI) as a surfactant. Preliminary studies of the oxidation of phenol with oxygen, as the most typical environmental pollutant were carried out on the obtained Fe3O4, CuFe2O4, CoFe2O4/PEI catalysts. The spectrum of the reaction product shows the presence of CH in the aromatic ring and double C=C bonds, stretching vibrations of the C=O groups of carbonyl compounds; the band at 3059 cm–1 corresponds to the presence of double C=C bonds, the band at 3424 cm–1 hydroquinone compounds. The band at 1678 cm–1 and the intense band at 1646 cm–1 refer to vibrations of the С=О bonds of the carbonyl group of benzoquinone. Peaks at 1366 cm–1 and 1310 cm–1 can be related to the vibrations of C–H and C–C bonds of the quinone ring. Thus it was demonstrated that produced magnetic composites based on iron oxide are quite effective in the oxidation of phenol with oxygen.

The transport of iron(III) from Fe(III)-Mn(II)-HCl mixed solutions through a flat-sheet supported liquid membrane is investigated, being the carrier phase of Cyanex 923 (commercially available phosphine oxide extractant) dissolved in Solvesso 100 (commercially available diluent), as a function of hydrodynamic conditions, concentration of manganese and HCl in the feed phase, and carrier concentration in the membrane phase. A transport model is derived that describes the transport mechanism, consisting of diffusion through a feed aqueous diffusion layer, a fast interfacial chemical reaction, and diffusion of the Fe(III)-Cyanex 923 complex across the membrane phase. The membrane diffusional resistance (Δm) and feed diffusional resistance (Δf) are calculated from the model, and their values are 145 s/cm and 361 s/cm, respectively. It is apparent that the transport of iron(III) is mainly controlled by diffusion through the aqueous feed boundary layer, being the thickness of this layer calculated as 2.9x10-3 cm. Since Mn(II) is not transported through the membrane phase, the present system allows to the purification of this manganese-bearing solutions.

All over the world birds’ eggs are an important and valuable component of the human diet. The study aimed to compare the content of lipid components and their nutritional value as well as iron and zinc levels in chicken and quail eggs commonly available on the market. In egg lipids, unsaturated acids were dominated, especially oleic acid, the content of which was about 40% of total fatty acids (TFA). Linoleic acid was the major polyunsaturated fatty acid. Compared to other products of animal origin, eggs were characterized by favourable values of lipid quality indices, especially index of atherogenicity, thrombogenicity and hypocholesterolemic to hypercholesterolemic ratio. In the present study, no differences in the content of tested nutrients between eggs from different production methods (organic, free-range, barn, cages), as well as inter-breed differences were noticed. Cluster analysis showed that eggs enriched in n3 PUFA (according to producers’ declaration) differ from other groups of chicken eggs. However, only in eggs from one producer, the amount of EPA and DHA exceed 80 mg per 100 g, entitling to use the nutrition claim on the package. Quail eggs differed from chicken eggs in FA profile; they also had much higher iron and cholesterol levels.

This research evaluates the inhibitory effect of L-amino acids (AAs) with different side chain lengths on Fe surface implementing Monte Carlo (MC) simulation. A quantitative and qualitative description of the adsorption behavior of AAs on the iron surface has been carried out. Calculations have shown that the absolute values of the adsorption energy of L-amino acids increase with side chain prolongation; they are also determined by the presence of heteroatoms. AAs from nonpolar and basic groups have the best adsorption ability to the iron surface, which indicates their highest inhibitory efficiency according to the results of MC simulation.

Thalassemia is genetic blood disease cause by absence or decrease of one or more of the globin chain synthesis. Beta thalassemia is characterized by one or more mutations in beta globin gene. Absence or reduced amount the of beta globin chains cause ineffective erythropoiesis which leads to anemia. Beta thalassemia has been further divided into three main forms: Thalassemia minor/silent carrier, major and intermedia. More severe form is thalassemia major in which patients depend upon blood transfusion for survival and high level of iron occur as a consequence of consistent blood transfusion. Over loaded iron invokes the synthesis of reactive oxygen species that are toxic in redundancy and triggering the impairment to vascular, endocrine and hepatic system. Thalassemia can be diagnosed and detected through various laboratory tests such as blood smear, prenatal testing (genetic testing of amniotic fluid), DNA analysis (genetic testing) and complete blood count. Treatment of thalassemia intermedia is symptomatic but it can also be managed by splenectomy and folic supplementation. While thalassemia major can be treated by transplantation of bone marrow, regular transfusion of blood and iron chelation treatment, stimulation of fetal hemoglobin production, hematopoietic stem cell transplantation and gene therapy.

Considering the manufacturing of automotive components, there exists a dilemma around the substitution of traditional Cast Iron (CI) with lighter metals. Nowadays, aluminium alloys, being lighter compared to traditional materials, are considered as a more environmentally friendly solution. However, the energy required for the extraction of the primary materials and manufacturing of components is usually not taken into account in this debate. In this study, an extensive literature review has been performed to estimate the overall energy required for the manufacturing of an engine cylinder block using (a) cast iron and (b) aluminium alloys. Moreover, data from over 100 automotive companies, ranging from mining companies to consultancy firms, have been collected in order to support the soundness of this investigation. The environmental impact of the manufacturing of engine blocks made of these materials is presented with respect to the energy burden; the “cradle-to-grave approach” has been implemented to take into account the energy input of each stage of the component lifecycle starting from the resource extraction and reaching to the end-of-life processing stage. Our results indicate that although aluminium components contribute towards reduced fuel consumption during their use phase, the vehicle distance needed to be covered in order to compensate for the up-front energy consumption related to the primary material production and manufacturing phases is very high. Thus, the substitution of traditional materials with lightweight ones in the automotive industry should be very thoughtfully evaluated.

Gray cast iron is one of the most important engineering materials that has many applications in various industries including automotive and machinery manufacturing due to its mechanical properties, wear resistance, machining potentials and low price. In this research effect of adding aluminum and silicon to composition of gray cast iron on microstructure and wear resistance was studied. Moreover, it was investigated the role of formation of Fe-Al-Si intermetallic compound in final properties of the alloy. For studying wear resistance of samples pin-on-disc method was carried out. The results showed that addition of aluminum to gray cast iron causes formation of ferrite matrix, which leads to a decrease in hardness value. Increasing silicon content up to 2 wt. % in cast iron with 4 wt. % aluminum intensifies the formation of ferrite matrix, while further increase to 3 wt. % causes emerging a Fe-Al-Si intermetallic phase. Improvement in hardness value was achieved by increasing silicon content from 3 wt. % to 4 wt. % due to the increased percentage of intermetallic phase. Effect of intermetallic phase on decreasing wear rate was showed by studying microstructure and hardness values, however the lowest wear resistance was observed in aluminum bearing cast iron containing 2 wt. % silicon.

Gray cast iron is one of the most important engineering materials that has many applications in various industries including automotive and machinery manufacturing due to its mechanical properties, wear resistance, machining potentials and low price. In this research effect of adding aluminum and silicon to composition of gray cast iron on microstructure and wear resistance was studied. Moreover, it was investigated the role of formation of Fe-Al-Si intermetallic compound in final properties of the alloy. For studying wear resistance of samples pin-on-disc method was carried out. The results showed that addition of aluminum to gray cast iron causes formation of ferrite matrix, which leads to a decrease in hardness value. Increasing silicon content up to 2 wt. % in cast iron with 4 wt. % aluminum intensifies the formation of ferrite matrix, while further increase to 3 wt. % causes emerging a Fe-Al-Si intermetallic phase. Improvement in hardness value was achieved by increasing silicon content from 3 wt. % to 4 wt. % due to the increased percentage of intermetallic phase. Effect of intermetallic phase on decreasing wear rate was showed by studying microstructure and hardness values, however the lowest wear resistance was observed in aluminum bearing cast iron containing 2 wt. % silicon.

Iron oxide nanoparticles are widely used as contrast agent for MRI and may be used as therapeutic agent by magnetic hyperthermia if they display a high magnetic anisotropy. Considering the effect of the nanoparticles shape on anisotropy, the reproducible shape control of nanoparticles is currently a challenge of synthesis methods. By investigating reaction parameters which are the iron precursor structure, the water content and the amount of the surfactant, sodium oleate, reported to trigger the cubic shape, iron oxide nanoparticles with different shape and composition were observed to form. In particular, iron oxide nanoplates have been thus synthesized. The effect of the surfactant coming from precursor was taking into account by using in house iron stearates bearing either two or three stearate chains and the negative effect of water on shape was confirmed by considering these precursors after their dehydration. Nanocubes with straight faces and a FeO@Fe3-xO4 composition were obtained only with dehydrated precursors and 50% of sodium oleate in the oleic acid and sodium oleate surfactant mixtures. When iron stearates with three chains led mainly to nanocubes in presence of soduim oleate, Iron stearates with two chains led to the formation of nanoplates with 80% of sodium oleate. The original flat shape of the plates was confirmed with 3D TEM tomography. The investigation of the synthesis mechanisms confirmed the major role of deprotonated carboxylic acid and of the heating rate to drive the cubic shape of nanoparticles and showed that the nanoplate formation would depend mainly on the nucleation step and possibly on the presence of a given ratio of oleic acid and deprotonated carboxylic acid.

Two types of heterometallic (Fe(III),Na) silsesquioxanes [Ph₅Si₅O₁₀]₂[Ph₁₀Si₁₀O₂₁]Fe₆(O^2‒)₂Na₇(H₃O⁺)(MeOH)₂(MeCN)_4.5.1.25(MeCN), I, and [Ph₅Si₅O₁₀]₂[Ph₄Si₄O₈]₂Fe₆Na₆(O^2‒)₃(MeCN)_8.5(H₂O)_8.44, II, were obtained and characterized. X-Ray studies established distinctive structures of both products, with pair of Fe(III)-O-based triangles surrounded by siloxanolate ligands, giving fascinating cage architectures. Complex II proved to be catalytically active in the formation of amides from alcohols and amines, thus becoming a rare example of metallasilsesquioxanes performing homogeneous catalysis. Benzene, cyclohexane and other alkanes, as well as alcohols, can be oxidized in acetonitrile solution to phenol, the corresponding alkyl hydroperoxides and ketones, respectively, by hydrogen peroxide in air in the presence of catalytic amounts of complex II and trifluoroacetic acid. Thus, the cyclohexane oxidation at 20 °C gave oxygenates in very high for alkanes yield (48% based on alkane). The kinetic behaviour of the system indicates that the mechanism includes the formation of hydroxyl radicals generated from hydrogen peroxide in its interaction with diiron species. The latter are formed via monomerization of starting hexairon complex with further dimerization of the monomers.

The expression "Follow the water" is used in order to recognize inside the universe, life as it exists on Earth. It is shown here that the expression "Follow the water in its high-subcritical state" can be used in order to recognize the components of life which form prior to the emergence of life. This specific state of water leaves signatures inside the minerals which are produced during high-subcritical water/rock interaction.

Neuroendocrine neoplasms (NENs) comprise a heterogeneous group of rare malignancies mainly originated from hormones secreting cells, which are widespread in human tissues. The identification of mutations in ATRX/DAXX genes in sporadic NENs, as well as the high burden of mutations scattered throughout MEN-1 gene in both sporadic and inherited syndromes, provided new insights into the molecular biology of tumour development. Other molecular mechanisms, such as the NOTCH signaling pathway, have shown to play an important role in the pathogenesis of NENs. NOTCH receptors are expressed on neuroendocrine cells and generally, act as tumour suppressor proteins, but in some contexts can function as oncogenes. The biological heterogeneity of NENs suggests that to fully understand the roles and the potential therapeutic implications of gene mutations and NOTCH signaling in NENs, a comprehensive analysis of genetic alterations, NOTCH expression patterns and their potential roles across all NEN subtypes is required.

Rapidly and accurately extracting tobacco plant information can facilitate tobacco planting man-agement, precise fertilization and yield prediction. In karst plateau of southern China, tobacco plant identification is affected by large ground undulations, fragmented planting areas, complex and diverse habitats and uneven plant growth. This study took a tobacco planting area in Gui-zhou Province as the research object and used DJI UAVs to collect UAV visible light images. Considering plot fragmentation, plant size, presence of weeds and shadow masking, this area was classified into eight habitats. The datasets of different habitats were constructed to train the U-Net model. The results show that (1) the overall precision, recall, F1-score and IOU of tobacco plant information extraction were 0.68, 0.85, 0.75 and 0.60, respectively. (2) The precision was the highest for the subsurface-fragmented and weed-free habitat and the lowest for the smooth-tectonics and weed-infested habitat. (3) The weed-infested habitat with smaller tobacco plants can cause blurred images, reducing the plant identification accuracy. This study verified the feasibility of the U-Net model for tobacco single-plant identification in complex habitats. Decomposing complex habitats to establish the sample set method is a new attempt to improve crop identification in complex habitats in karst mountainous areas.

Rising demand and urbanization have led to the global increase and intensification of livestock production. Intensive production has harmful effects on the environment, public health (due to the emergence of zoonotic diseases), and animal welfare. In order to better understand how livestock production intensifies as countries develop and incomes rise, we examine the relationship between income per capita and intensification of poultry and cattle, using the states of India as a study region. We also replicate this analysis at the district level to test whether the relationship holds at different spatial scales. We identify outlying states and districts to see if there are policies that can keep agriculture production largely extensive, even as economic growth occurs. The results support the relationship of income being positively correlated with the proportion of livestock in intensive conditions. However, there are notable outlier states which have grown wealthy while still maintaining largely extensive production, indicating that there may be some cases where economic growth can occur without intensification.

It is time-consuming for acquiring complete data by fully phase encoding in two orthogonal directions along with one frequency encoding direction. Undersampling in the 3D k-space is promising in accelerating such 3D MRI process. Though 3D undersampling can be conducted according to predefined probability density, the density based method is not optimal. Because of the large amount of 3D data and computational cost, it is challenging to perform data-driven and learning-based 3D undersampling and subsequent 3D reconstruction. To tackle this challenge, this paper proposes a deep neural network called EEUR-Net, realized by optimizing specific undersampling patterns for the fully-sampled 3D k-space data. Innovatively, our undersampling algorithm employs an end-to-end deep learning approach to optimize phase encoding patterns and uses a 3D U-Net for image reconstruction of undersampled data. Through end-to-end training, we obtain an optimized 3D undersampling pattern, which significantly enhances the quality of the reconstructed image under the same acceleration factor. A series of experiments on a knee MRI dataset demonstrated that, in comparison to standard random uniform, radial, Poisson and equispaced Cartesian undersampling schemes, our end-to-end learned undersampling pattern considerably improves the reconstruction quality of undersampled MRI images.

Long-lasting insecticidal nets (LLINs) have been the main contributor to the reduction of malaria in the past two decades in sub-Saharan Africa. Development of pyrethroid insecticide resistance threatens the effectiveness of these LLINs, especially when nets become holed and the insecticide decays. Three classes of dual active ingredient (AI) LLINs, have been assessed for their physical durability as follows: 1) Royal Guard®, combining pyriproxyfen, which is known to disrupt female reproduction and fertility of eggs, and a pyrethroid alpha-cypermethrin; 2) Interceptor® G2, two adulticides with differing modes of action; chlorfenapyr and alpha-cypermethrin; 3) OlysetTM Plus incorporates permethrin (pyrethroid) and a synergist, piperonyl butoxide, to enhance the potency of pyrethroid insecticides; all nets were compared to standard pyrethroid only net (Interceptor®). About 40,000 nets of each type were distributed in February 2019 to different villages in Misungwi. A total of 3072 LLINs were followed at 6, 12, 24, 30 and 36 months to assess survivorship and fabric integrity in a community setting. The median functional survival were less than three years with Interceptor®, Interceptor® G2 and Royal Guard® having 1.9 year each and 0.9 years for OlysetTM Plus . After 36 months, 90% of OlysetTM Plus and Royal Guard® and 87% of Interceptor® G2 were no longer present (thrown away) in the households due to wear and tear, compared to 79% for standard Interceptor®. Short life spans of all assessed LLINs were driven by material of the net, rather than social economic status and housing material. All dual AI LLINs have a poor textile durability with OlysetTM Plus being the worst of the three.

In Holter monitoring, the precise detection of standard heartbeats and Ventricular Premature Contractions (PVCs) is paramount for accurate cardiac rhythm assessment. This study introduces a novel application of the 1D U-Net neural network architecture aimed at enhancing PVC detection in Holter recordings. Training data comprised the Icentia11k, INCART DB, and our custom dataset. The model’s efficacy was subsequently validated against traditional Holter analysis methodologies across multiple databases, including AHA DB, MIT 11 DB, NST, and another custom dataset encompassing challenging real-world examples. The results underscored the 1D U-Net model’s prowess in QRS complex detection, achieving near-perfect balanced accuracy scores across all databases. PVC detection exhibited variability, with balanced accuracy scores ranging from 0.909 to 0.986. Despite some databases, like the AHA DB, showcasing lower sensitivity metrics, their robust balanced accuracy accentuates the model’s equitable performance in discerning both false positives and negatives. In conclusion, while the 1D U-Net architecture is a formidable tool for QRS detection, there’s a clear avenue for further refinement in its PVC detection capability, given the inherent complexities and noise challenges in real-world PVC occurrences.

Quantification of the trade-offs among greenhouse gas (GHG) emissions, yield and farmers’ incomes is essential for proposing economic and environment nitrogen (N) management strategies for optimizing agricultural production. A four-year (2017–2020) field experiment (including four treatments: control experiment (CK), suitable utilization of fertilization (SU), emission reduction treatment (ER), and high fertilization (HF)) was conducted on maize (Zea mays L.) in the North China Plain. The Life Cycle Assessment (LCA) method was used in this study to quantify the GHG emissions and farmers’ incomes during the whole maize production process. The total GHG emissions of CK, SU, ER and HF treatments in the process of maize production are 10,755.2, 12,908.7, 11,950.1, and 14,274.5 kg CO2-eq ha−1 respectively, of which the direct emissions account for 84.8%, 76.8%, 74.9%, and 71.0%, respectively. Adding inhibitor significantly reduced direct GHG emissions, and the N2O and CO2 emissions from the maize fields in the ER treatment decreased by 30.0% and 7.9% compared to those in the SU treatment. Insignificant differences in yield were found between the SU and ER treatments, indicating that adding fertilizer inhibitors did not affect farmers’ incomes while reducing GHG emissions. The yield for SU, ER and HF treatments all significantly increased by 12.9%–24.0%, 10.0%–20.7% and 2.1%–17.4% compared to CK, respectively. In comparison with CK, both SU and ER significantly promoted agricultural net profit (ANP) by 16.6% and 12.2% with the mean ANP values of 3,101.0 USD ha-1 and 2,980.0 USD ha-1, respectively. Due to the high agricultural inputs, the ANP values in the HF treatment was 11.2%, 16.6% and 12.4% lower than those in the SU treatment in 2018–2020. In conclusion, the combination of N fertilizer and inhibitors was proved to be an environmentally friendly, high-profit and low-emissions production technology while sustaining or even increasing maize yields in the North China Plain, which was conducive to achieving agricultural sustainability.

Vein segmentation and projection correction constitute the core algorithms of auxiliary venipuncture device, responding to the accurate venous positioning to assist puncture and reduce the number of punctures and pain of patients. This paper proposes an improved U-Net for segmenting vein and a coaxial correction for image alignment in the self-build vein projection system. The proposed U-Net is embedded by Gabor convolution kernels in the shallow layers to enhance segmentation accuracy. Additionally, to mitigate the semantic information loss caused by channel reduction, the network model is lightweighted by the mean of replacing conventional convolutions with inverted residual blocks. During the visualization process, a method that combining coaxial correction and homography matrix is proposed to address the non-planarity of the dorsal hand in this paper. First, use a hot mirror to adjust the light paths of both projector and camera to be coaxial, and then align the projected image with dorsal hand using a homography matrix. Using this approach, the device requires only a single calibration before use. With the implementation of the improved segmentation method, an accuracy rate of 95.12% is achieved by the dataset. The intersection over union ratio between the segmented and original images is reached at 90.07%. The entire segmentation process is completed in a 0.09 second, and the largest distance error of vein projection onto dorsal hand is 0.53mm. Experiments show that the device has reached practical accuracy and has the value of research and application.

We investigated whether cigarette smoking and dietary acid load (DAL) are associated with the risk of chronic obstructive pulmonary disease (COPD) in middle-aged healthy Korean men. Healthy men without diagnosed chronic disease (40-64 yrs) from the KNHANES-VI (2013–2015) included in the analysis (n=774) were subdivided by smoking status and DAL levels as estimated by the net endogenous acid production (NEAP) quartile. Current smokers tended to have a higher risk of COPD than non-smokers before and after the adjustment. When divided by the DAL quartile, the Q4 tended to have a higher risk of COPD than the Q1. Additionally, current smokers with lower (Q2), modest (Q3), and highest NEAP scores (Q4) showed more than 4-fold higher risks of COPD than the non-smokers with the lowest NEAP scores (Q1). Ex–smokers with higher NEAP scores (Q3 and Q4) showed more than 4-fold higher risks of COPD than the Q1. Interestingly, the risk of COPD was also more than 6-fold higher in non-smokers with the highest NEAP scores compared to the Q1. NEAP scores and smoking status synergistically increased the risk of COPD in middle-aged healthy Korean men. It suggests that DAL levels are an important factor for the prevention and management of COPD.

Compressed Sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically Generative Adversarial Networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning recon-struction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study we present a novel GAN-based model that delivers superior performance without escalating model complexity. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network's receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby fo-cusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies af-firmed the efficacy of the modified modules within the network. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent sta-bility but also outperforming other networks in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.

Transitioning towards net zero emissions is critical in the chemical and process industries to combat climate change and ensure environmental sustainability. Key focus areas of this review include the reduction of carbon emissions, the efficient utilization of energy resources, and the adoption of sustainable practices. Cutting-edge technologies such as biomass utilization, biotechnology applications, and waste management strategies are crucial in achieving this transition. Industries, including cement production, encounter unique challenges in their quest for sustainability and must actively seek innovative solutions to mitigate their carbon footprint effectively. The role of hydrogen as a clean fuel and its potential to revolutionize the chemical and process sectors is also discussed. The European Green Deal and Sustainable Development Goals (SDGs) are significant for the chemical industry. They provide a clear roadmap and framework for promoting sustainability, driving innovation, and reducing the industry's environmental impact. By aligning with these initiatives, the chemical industry can enhance its competitiveness, contribute to societal well-being, and foster collaboration across sectors to achieve shared sustainability objectives.

Abstract: Establishing carbon neutrality (or Net Zero) in cities appears to be an ambitious challenge, which drives urban transformation efforts around the world to enhance people-centric living. Cities launch corresponding strategies focusing mainly on the United Nations 2030 goals to form their climate-neutral future. This paper provides practical guidance on city digital transformation to achieve carbon net zero. These guidelines illustrate actionable recommendations to reduce their carbon footprint using digital technologies. Following a multi-method research approach, findings from a bibliometric literature and a standards’ gap analysis were combined with practical evidence and circulated with focus group experts to conclude to these guidelines, which are the foundation for further standardization. The outcomes provide concrete guidelines to policymakers, planners, and other stakeholders for city digital transformation to establish carbon neutrality, via emphasizing the need for digital technologies to enhance efficiency in several areas and via leveraging green ICTs for city economic growth while also transforming citizen behavior.

In neuroendocrine neoplasms (NENs), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance approaches, and introduced specific and personalized radiation therapies. Nuclear medicine has therefore acquired a crucial role in the management of NENs patients by improving their risk stratification and personalized therapies. Artificial intelligence (AI) and radiomics can enable physicians to further improve the overall efficiency and accuracy of the use of these tools in both di-agnostic and therapeutic settings by improving the prediction of tumor grade, differential diagnosis from other malignancies, assessment of tumor behavior and aggressiveness, and prediction of treatment response. This systematic review aims to describe the state-of-the-art on AI and radiomics applications in molecular imaging of NENs.

The relation between CO2 emissions and atmospheric CO2 concentration has traditionally been treated with more or less complex models with several boxes. Our approach is motivated by the question of how much CO2 must necessarily be absorbed by sinks. Observations lead to the model assumption, that carbon sinks like oceans or biosphere are linearly dependent on CO2 concentration on a decadal scale. In particular this implies the falsifiable hypothesis that oceanic and biological CO2 buffers have not significantly changed in the past 50 years and are not saturated in the forseeable future. The simple model with 2 parameters explains very well the CO2 emission and historical CO2 concentration data. The model gives estimates of the natural emissions, the pre-industrial CO2 equilibrium concentration levels, the half-life time of an emission pulse, and the future CO2 concentration level from a given emission scenario. This is validated by an ex-post forecast of the last 20 years. The important result is that with the stated polices scenario of the IEA future CO2 concentrations will not rise above 475 ppm. The model is compared with the Carbon modul of the Bern model, mapping their complex IRFs to a single time variant parameter.

Many corporations and governments aspire to become Net Zero Carbon Dioxide by 2030-2050. Achieving this goal requires understanding where energy is produced and consumed, the magnitude of CO₂ generation, and the Carbon Cycle. Many prior proposed solutions focus on reducing future CO₂ emissions from continued use of fossil fuels. Examination of these technologies exposes their limitations and shows that none offer a complete solution. For example, bioethanol is shown to be both carbon and energy inefficient. Direct Air Capture technologies are needed to reduce CO₂ already in the air. The most natural form of Direct Air Capture involves letting nature do the work of creating biomass via photosynthesis. However, it is necessary to break the Carbon Cycle by permanently sequestering that biomass carbon in “landfills” modified to discourage decomposition to CO₂ and methane. Tree leaves and biomass grown on-purpose, such as high yield switchgrass, are proposed as good biomass sources for this purpose. Left unsequestered, leaves decompose with a short Carbon Cycle time constant releasing CO₂ back to the atmosphere. While in any given year, leaves represent a small fraction of a tree’s above ground biomass, leaves can represent a substantial fraction of the total biomass generated by a tree when integrated over a tree’s lifetime. Understanding the chemistry of the distinct phases landfills undergo is the key to minimizing or eliminating decomposition. First, the compact cross-linked structure of cellulose and keeping water out will make it difficult for initial depolymerization to release sugars. Air ingress should be minimized to minimize Phase I aerobic decomposition. pH manipulation can discourage acid formation during Phase II. Lignocellulose is low in nutrients needed for anaerobic decomposition. Inhibitors can be added if needed. The goal is to move quickly to the dormant phase where decomposition stops. The cost for Carbon Capture and Storage (CCS) for growing and sequestering high yield switchgrass is estimated to be lower than CCS for steam reforming of methane hydrogen plants (SRM) and supercritical or combined cycle coal power plants. Thus, sequestration of biomass is a natural, carbon efficient, and low-cost method of Direct Capture. Biomass sequestration can provide CO₂ removal on giga tonnes per year scale and can be implemented in the needed timeframe (2030-2050).

We have an unprecedented ability to analyze and map the Earth’s surface, as deep learning technologies are applied to an abundance of Earth observation systems collecting images of the planet daily. In order to realize the potential of these data to improve conservation outcomes, simple, free, and effective methods are needed to enable a wide variety of stakeholders to derive actionable insights from these tools. In this paper we demonstrate simple methods and workflows using free, open computing resources to train well-studied convolutional neural networks and use these to delineate objects of interest in publicly available Earth observation images. With limited training datasets (<1000 observations), we used Google Earth Engine and Tensorflow to process Sentinel-2 and National Agricultural Imaging Program data, and use these to train U-Net and DeepLab models that delineate ground mounted solar arrays and parking lots in satellite imagery. The trained models achieved 81.5% intersection over union between predictions and ground-truth observations in validation images. These images were generated at different times and from different places from those upon which they were trained, indicating the ability of models to generalize outside of data on which they were trained. The two case studies we present illustrate how these methods can be used to inform and improve the development of renewable energy in a manner that is consistent with wildlife conservation.

The continuous and precise mapping of glacier calving fronts is essential for monitoring and understanding rapid glacier changes in Antarctica and Greenland, which have the potential for significant sea level rise within the current century. This effort has been mostly restricted to the slow and painstaking manual digitalization of the calving front positions in thousands of satellite imagery products. Here, we have developed a machine learning toolkit to robustly and automatically detect glacier calving front margins in satellite imagery. The toolkit is based on semantic image segmentation using Convolutional Neural Networks (CNN) with a modified U-Net architecture to isolate the calving fronts from satellite images after having been trained with a dataset of images and their corresponding manually-determined calving fronts. As a case study we train our neural network on a varied set Landsat images with lowered resolutions from Jakobshavn, Sverdrup, and Kangerlussuaq glaciers, Greenland and test the results on novel images from Helheim glacier, Greenland to evaluate the performance of the approach. The neural network is able to identify the calving front in new images with a mean deviation of 96.3 m from the true fronts, equivalent to 1.97 pixels on average, while the corresponding error for manually-determined fronts on the same resolution images is 92.5 m. We find that the trained neural network significantly outperforms common edge detection techniques, and can be used to continuously map out calving-ice fronts with a variety of data products.

Viruses rely on host cells to replicate their genomes and assemble new viral particles. Thus, they have evolved intricate mechanisms to exploit host factors. Host cells, in turn, have developed strategies to inhibit viruses, resulting in a nuanced interplay of co-evolution between virus and host. This dynamic often involves competition for resources crucial for both host cell survival and virus replication. Iron and iron-containing cofactors, including iron-sulfur clusters, are known to be a heavily battled resource during bacterial infections where control over iron can tug the war in favor of the pathogen or the host. It is logical to assume that viruses also engage in this competition. Surprisingly, our knowledge about how viruses utilize iron (Fe) and iron-sulfur (FeS) clusters remains limited. The handful of reviews on this topic primarily emphasize the significance of iron in supporting the host immune response against viral infections. The aim of this review, however, is to organize our current understanding of how viral proteins utilize FeS clusters, to give perspectives on what questions to ask next and to propose important avenues for future investigations.

Pseudomonas aeruginosa is an opportunistic pathogen that requires iron to survive in the host; however, the host immune system limits the availability of iron. Pyochelin (PCH) is a major siderophore produced by P. aeruginosa during infection, which can help P. aeruginosa survive in an iron-restricted environment and cause infection. The infection activity of P. aeruginosa is regulated by the Pseudomonas quinolone signal (PQS) quorum-sensing system. The system uses 2-heptyl-3-hydroxy-4-quinolone (PQS) or its precursor, 2-heptyl-4-quinolone (HHQ), as the signal molecule. PQS can control specific life processes such as mediating quorum sensing, cytotoxicity, and iron acquisition. This review summarizes the biosynthesis of PCH and PQS, the shared transport system of PCH and PQS, and the regulatory relationship between PCH and PQS. The correlation between the PQS and PCH is emphasized to provide a new direction for future research.

Integrating nanomaterials like carbon nanotubes (CNTs) and iron oxide (Fe₃O₄) into epoxy composites has significant interest due to their potential to enhance mechanical properties. This study evaluates the impact of dispersion quality on the mechanical performance of CNT/Fe₃O₄/Epoxy composites, comparing stirring and sonication methods at three different loadings: 0.1, 0.3, and 0.5 wt.%. Tensile testing revealed that sonicated composites consistently outperformed stirred composites, with a significant increase in elastic modulus and ultimate tensile strength (UTS). However, fracture strain decreased in both composite types compared to pure epoxy, with sonicated composites experiencing a more significant reduction than stirred composites. These results underscore the importance of high-quality dispersion for optimizing mechanical properties.

Phosphorus (P) and iron (Fe) deficiency are major limiting factors for plant productivity worldwide. White lupin (Lupinus albus L.) has become a model plant for understanding plant adaptations to P and Fe deficiency, because of its ability to form cluster roots, bottle-brush-like root structures that play an important role in the uptake of P and Fe from soil. However, little is known about the signaling pathways involved in sensing and responding to P and Fe deficiency. Sucrose, sent in increased concentrations from the shoot to the root, has been identified as a long-distance signal of both P and Fe deficiency. To unravel responses to sucrose as signal, we performed Oxford Nanopore cDNA sequencing of white lupin roots treated with sucrose for 10 min, 15 min, and 20 min, compared to untreated controls. We identified a set of 17 genes, including two bHLH transcription factors, that were up-regulated at all three time points of sucrose treatment. GO (gene ontology) analysis revealed enrichment of auxin- and gibberellin-responses as early as 10 min after sucrose addition, and the emerging of ethanol-responses at 20 min of sucrose treatment, indicating a sequential involvement of these hormones in plant responses to sucrose.

Iron tailings are solid waste generated after the mining of iron mines, which have caused serious damage to the surrounding ecology and environment and have attracted widespread attention. By analyzing the formation causes, main components, and environmental effect mechanisms of iron tailings, the article systematically elaborates on the degree and mechanism of the harm of iron tailings to water bodies, atmosphere, soil, and ecosystems, and evaluates its impact on human health and sustainable development of society. On this basis, specific prevention and control measures have been proposed to reduce iron tailings emissions, realize resource recycling, and restore ecosystems. Research shows that iron tailings containing heavy metals can widely pollute the environment through dissolution and migration, and have significant biological toxicity effects. The research results can provide a theoretical reference for decision-making departments to formulate scientific and reasonable iron tailings disposal and ecological protection policies. It is recommended to strengthen specific research on the infiltration of toxic and harmful components in tailings in the future, and develop efficient and stable solidification technology, and vegetation-microbial joint restoration models to further reduce the ecological and environmental risks of iron tailings.

Nanocelluloses have gained significant attention in recent years due to singular properties (good biocompatibility, high optical transparency and mechanical strength, large specific surface area and good film-forming ability) and wide-ranging applications (paper, food packaging, textiles, electronics and biomedical). This article is a comprehensive review of nanocelluloses from the preparation methods to their applications in conservation and restoration of historical paper documents. The novelty lies in the information collected about nanocelluloses as renewable, environmentally friendly and sustainable materials in the field of preservation of cultural heritage as an alternative to conventional methods. Several studies have demonstrated that nanocelluloses, with or without other particles, impart to the paper documents excellent optical and mechanical properties, very good stability against temperature and humidity aging, stronger antibacterial and antifungal activity, high protection to UV light and do not require additional adhesive.

Using nanoparticles (NPs) in drug delivery has exhibited promising therapeutic potential 13 in various cancer types. Nevertheless, several challenges must be addressed, including the 14 formation of the protein corona, reduced targeting efficiency and specificity, potential immune 15 responses, and issues related to NP penetration and distribution within 3-dimensional tissues. 16 To tackle these challenges, we have successfully integrated iron oxide nanoparticles into 17 neuroblastoma-derived extracellular vesicles (EVs) using the parental labeling method. We first de- 18 veloped a tissue-engineered (TE) neuroblastoma model, confirming the viability and proliferation 19 of neuroblastoma cells for at least 12 days, supporting its utility for EV isolation. Importantly, EVs 20 from long-term cultures exhibited no differences compared to short-term cultures. Concurrently, 21 we designed Rhodamine (Rh), Polyacrylic acid (PAA)-functionalized magnetite nanoparticles 22 (Fe3O4@PAA-Rh) with high crystallinity, purity, and superparamagnetic properties (average size: 23 9.2 ± 2.5 nm). We then investigated the internalization of Fe3O4@PAA-Rh nanoparticles within 24 neuroblastoma cells within the TE model. Maximum accumulation was observed overnight while 25 ensuring robust cell viability. However, nanoparticle internalization was low. Taking advantage of 26 the enhanced glucose metabolism exhibited by cancer cells, glucose (Glc)-functionalized nanoparti- 27 cles (Fe3O4@PAA-Rh-Glc) were synthesized, showing superior cell uptake within the 3D model 28 without inducing toxicity. These glucose-modified nanoparticles were selected for parental labeling 29 of the TE models, showing effective NPs encapsulation into EVs. 30 Our research introduces innovative approaches to advance NPs delivery. By partially addressing 31 the challenges associated with 3D systems, optimizing internalization, and enhancing NPs stability 32 and specificity through EV-based carriers. Also, our findings hold the promise of more precise and 33 effective cancer therapies while minimizing potential side effects.

Transplanted rice cultivation has caused groundwater depletion in several regions globally. Direct-seeded rice under aerobic conditions is a water-saving alternative. However, under aerobic conditions, iron in the soil is oxidized from ferrous to ferric, which is not easily available for rice, resulting in iron deficiency induced chlorosis (IDIC) causing significant reduction in yield. Cultivated rice genotypes have limited variations for IDIC tolerance, while wild Oryza germplasm could be a potential source for IDIC tolerance. In this study, 313 Oryza accessions were evaluated for IDIC tolerance at the tillering stage under aerobic conditions and twenty IDIC tolerant lines were identified. The twenty lines showed no signs of chlorosis and had high levels of iron content and SPAD values, while eight cultivated controls exhibited varying degrees of chlorosis symptoms and low levels of SPAD and iron content. To confirm their tolerance, the selected lines were evaluated again in a subsequent year, and they showed comparable levels of tolerance indicating these lines were efficient in iron uptake and utilization resulted maintained high chlorophyll and leaf area index. These accessions may be useful for developing IDIC-tolerant cultivars for aerobic rice cultivation and future study of molecular basis of IDIC tolerance.

Mimosa acutistipula is endemic to Brazil and grows in ferruginous outcrops (canga) in Serra dos Carajás, eastern Amazon, where one of the largest iron ore deposits in the world is located. Plants that develop in these ecosystems are subject to severe environmental conditions and must have adaptive mechanisms to grow and thrive in cangas. Mimosa acutistipula is a native species used to restore biodiversity in post-mining areas in canga. Understanding the molecular mechanisms involved in the adaptation of M. acutistipula in canga is essential to deduce the ability of native species to adapt to possible stressors in rehabilitating minelands over time. In this study, the root proteomic profiles of M. acutistipula grown in a native canga ecosystem and rehabilitating minelands were compared to identify essential proteins involved in the adaptation of this species in its native environment and that should enable its establishment in rehabilitating minelands. The results showed differentially abundant proteins, where 436 proteins with significant values (p < 0.05) and fold change ≥ 2 were more abundant in canga and 145 in roots from the rehabilitating minelands. Among them, a representative amount and diversity of proteins were related to responses to water deficit, heat, and responses to metal ions. Other identified proteins are involved in biocontrol activity against phytopathogens and symbiosis. This research provides insights into proteins involved in M. acutistipula responses to environmental stimuli, suggesting critical mechanisms to support the establishment of native canga plants in rehabilitating minelands over time.

Reactions of Fe(II) with the tris-(pyridin-2-yl)ethoxymethane (py3C-OEt) tripodal ligand in presence of the pseudohalide ancillary NCE- (E = S, Se, BH3) ligands led to a series of three mononuclear complexes formulated as [Fe(py3C-OEt)2][Fe(py3C-OEt)(NCE)3]2·2CH3CN, with E = S (1), BH3 (2) and Se (3). Single crystal characterizations (complexes 1-2) and X-ray powder diffraction (complexes 1-3) reveal monomeric isomorph structures formed by the spin crossover (SCO) anionic [Fe(py3C-OEt)(NCE)3] complex, associated with the low spin (LS) cationic [Fe(py3C-OEt)2]2+ complex and two solvent acetonitrile molecules. In the [Fe(py3C-OEt)2]2+ cation, the Fe(II) is coordinated by two py3C-OEt tridentate ligands, while the [Fe(py3C-OEt)(NCE)3] anion displays a hexacoordinated environment involving three N-donor atoms of one py3C-OEt ligand and three nitrogen atoms arising from of the three (NCE) coligands. The magnetic studies show the presence of gradual SCO behavior for the three complexes: a one-step transition around 205 K for 1 and two step-transitions for compounds 2 and 3, centred at 245 K and 380 K for 2, and at 170 K and 298 K for 3. The magnetic behaviors of complexes 1 and 2 remain unchanged when heating up to 500 K, while complex 3 shows significant changes which are caused by the crystallisation solvent loss above room temperature.

Acinetobacter baumannii is a nosocomial pathogen capable of causing serious infections associated with high rates of morbidity and mortality. Due to its antimicrobial drug resistance profile, A. baumannii is categorized as an urgent priority pathogen by the Centers for Disease Control and Prevention in the United States and priority group 1 critical microorganism by the World Health Organization. Understanding how A. baumannii adapts to different host environments may provide critical insights into strategically targeting this pathogen with novel antimicrobial and biological therapeutics. Exposure to human fluids was previously shown to alter the gene expression profile of a highly drug susceptible A. baumannii strain A118 leading to persistence and survival of this pathogen. Herein, we explore the impact of human pleural fluid (HPF) and human serum albumin (HSA) on the gene expression profile of a highly multi-drug resistant strain of A. baumannii AB5075. Differential expression was observed for ~30 genes, whose products are involved in quorum sensing, quorum quenching, iron acquisition, fatty acid metabolism, biofilm formation, secretion systems and type IV pilus formation. Phenotypic and further transcriptomic analysis using quantitative RT-PCR confirmed RNA-seq data and pointed out a distinctive role of HSA as the molecule involved in A. baumannii response.

Maghemite (γ-Fe2O3) is a mineral formed from magnetite oxidation at low temperatures, an intermediate metastable term of the magnetite to hematite oxidation and could be mixed with both. It has magnetic susceptibility similar to magnetite, crystal structure close to magnetite with which it forms a solid solution, while compositionally it equals hematite. Maghemite is thus easily misidentified as magnetite by X-ray diffraction and/or as hematite by spot chemical analysis in iron ore characterization routines. Nonstoichiometric magnetite could be quantified in samples of Brazilian soils and iron ores by the Rietveld method using a constrained refinement of the X-ray patterns. The results were confirmed by reflected light microscopy and Raman spectroscopy, thus qualitatively validating the method. X-ray diffraction with the refinement of the isomorphic substitution of Fe2+ by Fe3+ along the magnetite-maghemite solid solution could help to suitably characterize maghemite in iron ores, allowing for the evaluation of its ultimate influence on mineral processing, by affecting its surface and breakage properties.

Ferric uptake regulation protein is a repressor protein which binds an AT rich region of DNA (the iron box). Fur binds as a dimer in a helix turn helix mode and it is activated by iron(II) and other divalent transition metal ions at elevated concentrations in a process to regulate the ion uptake. Each transition metal ion induces certain conformational changes to aid the Fur binding, both the N-terminal and C-terminal domains take part in binding to DNA in addition to His 88 and His 86 residues. The process is discussed in view of experimental reports. Fe(II), Mn(II) and Co(II) activate Fur to bind DNA experimentally but Zinc plays a structural role and does not activate Fur to bind DNA.

Currently, the use of sustainable chemistry as an ecological alternative for the generation of products or processes, free of polluting substance has assumed a preponderant role. The aim of this work is propose a bioinspired, facile, at low cost, non-toxic and environmentally friendly alternative to obtaining magnetic nanoparticles whit a majority phase of magnetite (Fe₃O₄). Is important to empathize that the synthesis was based on the chemical reduction through the Cnicus Benedictus extract, whose use as reducing agent has not been reported in the synthesis of iron oxides nanoparticles. In addition, the Cnicus Benedictus is abundant endemic plant in Mexico, with several medicinal properties and a large number of natural antioxidants. The obtained nanoparticles exhibited significant magnetic and antibacterial properties and an enhanced photocatalytic activity. The crystallite size of the Fe₃O₄ nanoparticles (Fe₃O₄ NP’s) was calculated by Williamson-Hall method. The photocatalytic properties of the Fe₃O₄ NP´s were studied by kinetics absorptions models in the Congo red (CR) degradation. Finally the antibacterial effect of the Fe3O4 NP´s were evaluated mediated the Kirby-Bauer method against E. coli and S. aureus bacteria. This route offers a green alternative to obtain Fe3O4 NP´s with remarkable magnetic, photocatalytic and antibacterial properties.

Iron powders having average particle sizes of ~40µm are mechanically mixed thoroughly with aluminum powders ranging from 1 to 10 in wt.% with an average particle size of ~10µm. Two different powder metallurgical techniques cold and hot pressing are used to study the effect of the additive element powder on the mechanical properties, wear properties and the microstructure of the iron based alloys. The hot pressing technique was performed at a temperature up to 500°C at 445.6 MPa. The cold pressing technique was performed at 909 MPa in room temperature. By increasing the Al content to 10 wt. % in the base Fe-based matrix, the hardness was decreased from 780 to 690 MPa and the radial strength was decreased from 380 to 202 MPa with reductions of 11.5% and 40%, respectively. Improvement of the wear resistance with the increase addition of the Al powder to the Fe matrix up to 5 times was achieved compared to the alloy without Al addition for different wear parameters namely; wear time and sliding speed.

The carbonyl iron particles were dispersed in a polychloroprene rubber (CR) matrix under an external magnetic field for practical application as microwave absorption composites film. The film prepared under external magnetic field with a thickness of only 0.54 mm showed least reflection loss of -15.98 dB and the reflection loss value less than -10.0 dB over the frequency range of 11.4~14.8 GHz. In comparison with the microwave absorption properties of calculation by transmission line theory based on the tested relative complex permittivity and permeability and film prepared by general route without external magnetic field, the film made with external magnetic field exhibited more excellent microwave absorption properties, strongly depending on the increment of anisotropy and rearrangement of magnetic particles. The results indicated the composite film made under external magnetic field have excellent microwave absorption properties, which suggest that the composites thin film could be used as a thinner and lighter microwave absorber.

Accurate detection of forest restoration sites using deep learning is crucial for effective ecosystem management, optimizing reforestation efforts, and ensuring environmental sustainability. While various deep learning-based systems have been developed for this purpose, the influence of different neural network architectures on model performance during the identification of forest restoration sites remains underexplored. This study aimed to address this gap by exploring the optimal methodology for extracting and classifying candidate sites for forest restoration based on Convolutional Neural Networks (CNNs), specialized for image recognition. Four categories—arable land, road and barren, quarry, and forest—were defined as candidate sites for forest restoration. A dataset comprising 17,043 samples was divided into training (11,929) and validation (5,114) sets at a ratio of 7:3 for model learning. Model accuracy was evaluated using pixel accuracy (PA) and mean intersection over union (mean IoU). The Resnet152-U-Net model demonstrated a pixel accuracy of 95.2% and a mean IoU of 61.3%, indicating excellent performance in extracting candidate sites for forest restoration. This approach offers spatial and temporal advantages over conventional field surveys or aerial image-based assessments, potentially serving as valuable data for selecting future forest restoration sites.

The energy efficiency of existing buildings is a crucial factor in addressing Europe’s energy con-sumption challenges, accounting for nearly 40% of the total energy usage. The ongoing energy crisis, driven by the reliance on fossil fuels and increasing prices, has underscored the urgency of adopting sustainable and energy efficient practices especially to the existing building stock. Cyprus is par-ticularly vulnerable due to its heavy dependence on imported fuels for electricity generation and faces significant challenges in transforming its existing building stock into energy-efficient and sustainable structures. Extensive focus has been made by the government on the energy-efficient retrofit of non-residential public buildings. This study examines an existing cultural hall. The case study of the Cultural Hall represents the building stock before 2010 which is lacking thermal in-sulation, double glazing and any energy efficient element. It then offers an ideal case study to ex-plore sustainable solutions which can be easily adopted from the construction industry and from the building owners. The primary objective of this research is twofold: first, to develop a comprehensive retrofit plan to transform the cultural hall into a net-zero energy building; and second, to explore how this transformation can give value back to the local community. After running two simulation scenarios and providing two energy performance certificates, one with at the existing state and one with the intended retrofit measures, it is clear that the building can be a net zero energy consump-tion building. Some interesting results derived, present also how this building can provide value back to the community i.e. through solar carports for EV charging.

Partial differential equations (PDEs) are used to describe a wide range of phenomena, such as heat transfer, fluid dynamics, and quantum mechanics. By solving PDEs, we can ob- tain insights into the behavior of the system and make predictions about its future evolution. Conventional numerical methods for obtaining the approximate solutions of PDEs may re- quire extensive computational resources and time, especially for complex PDEs and large- scale problems. The recently developed physics-informed neural network (PINN) has shown promise in a variety of scientific and engineering fields by incorporating physical laws into the loss functions of the neural network (NN). In addition, the training of PINN does not require ground truth data, but it has poor generalization ability to unseen domains. On the other hand, a convolutional neural network (CNN) has fast inference and better generalization ability, but it requires a large amount of training data. Taking the advantages of PINN and CNN by using Legendre multiwavelets (LMWs) as basis functions, we introduce a new method to approach the PDEs in this paper, namely Physics-Informed Legendre Multiwavelets CNN (PiLMWs-CNN), in order to continuously approximate a grid-based state representation that can be handled by a CNN. PiLMWs-CNN enable us to train our models using only physics-informed loss functions without any pre- computed training data, simultaneously providing fast and continuous solutions that gener- alize to previously unknown domains. In particular, the LMWs can simultaneously possess compact support, orthogonality, symmetry, high smoothness, and high approximation order. Compared to orthonormal polynomial (OP) bases, the approximation accuracy can be greatly increased and computation costs can be significantly reduced by using LMWs. We applied PiLMWs-CNN to approximate the damped wave equation, incompressible Navier-Stokes (N- S) equation, and two-dimensional heat conduction equation. The experimental results show that this method provides more accurate, efficient, and fast convergence with better stability when approximating the solution of PDEs.

The Colony-Forming Unit (CFU) counting problem remains a complex issue without a universal solution in biomedical and food safety domains. A multitude of sophisticated heuristics and segmentation-driven approaches have been proposed by numerous researchers. Among those, U-Net is the most frequently cited and popular Deep Learning method. The latter approach provides a segmentation output map and requires an additional counting procedure which accounts for unique segmented regions and detected microbial colonies. However, because of pixel-based targets it tends to generate irrelevant artifacts or errant pixels, leading to inaccurate and mixed post-processing results. In response to these challenges, we propose a novel hybrid counting approach, incorporating a multi-loss U-Net reformulation and a post-processing Petri dish localization algorithm. First of all, our unique innovation lies in the multi-loss U-Net reformulation. We introduce an additional loss term at the bottleneck U-Net layer, focusing on delivering an auxiliary signal indicating where to look for distinct CFUs. Second, our novel localization algorithm accurately incorporates an agar plate and its bezel into the CFU counting routines. Finally, our proposition is further enhanced by the integration of a fully automated solution. This comprises a specially designed uniform Petri dish illumination system and a counting web application. The latter application is capable of directly receiving images from the camera, which are subsequently processed, and the segmentation results are sent back to the user. This feature provides an opportunity to correct the CFU counts, offering a feedback loop that contributes to the continued development of the Deep Learning model. Through extensive experimentation, we have found that all probed multi-loss U-Net architectures incorporated in our hybrid approach consistently outperform their single-loss counterparts which utilize exclusively the combination of Tversky and Cross-Entropy training losses at the output U-Net layer. We report further significant improvements by the means of our novel localization algorithm. This reaffirms the effectiveness of our proposed hybrid solution in addressing contemporary challenges of the precise in-vitro CFU counting.

The automatic diagnosis of cardiovascular diseases has received much attention in the deep learning field. In this context, the segmentation of the left ventricle endocardium constitutes a major task in diagnosing heart conditions such as health failure and hypertrophic cardiomyopathy. The objective of this paper is to propose a "deep convolution network" for segmenting the internal cavity of the left ventricle (endocardium) using MRI images. In particular, we design an improved UNET model which handles additional inception modules for efficiently segmenting the internal cavity of the left ventricle. Our approach has been validated on the Sunnybrook Cardiac Data (SCD) dataset and has showed promising results in terms of precision. More specifically, the improved UNET largely outperforms the baseline UNET model and many existing state-of-the-art methods.

Using the World Bank Global Findex Database for 91 countries in 2014, 2017, and 2021, we examine whether fintech levels influence bank performance and whether fintech’s interaction with GDP per capita causes differential effects on bank performance globally. Since fintech levels were already very high for rich countries when the World Bank started providing fintech development statistics in 2014, we estimate AbFintech by regressing fintech levels on GDP per capita by year. AbFintech is the difference between the fintech level and its fitted values. Then, using multiple regression analyses, we investigate the impact of AbFintech on bank performance worldwide, focusing on the differential effects of AbFintech and GDP levels on bank performance. We find AbFintech significantly increases bank performance, primarily in less developed countries. Specifically, AbFintech increases banks’ ROA in the least developed countries and net interest margin in the 75th percentile countries. Also, AbFintech decreases the cost-to-income ratio in the 75th percentile countries, while it increases the ratio in the most developed countries. AbFintech does not affect the ratio of noninterest income to total income, regardless of the level of economic development.

Rooftop solar (RTS) projects have the potential to contribute to climate change mitigation and sustainable development objectives. In India, grid-connected RTS capacity has been targeted at 40 GW by the end of the year 2022 however, the installed capacity is limited to 8.2 GW only. As a result, the country missed its 2022 target of installing 100 GW of solar power capacity, largely due to inadequate uptake of RTS. The prevailing practices of RTS implementation is observed to be complex, repetitive, tedious, and time-consuming for potential consumers and there is a lack of coordination among implementing agencies. Therefore, even with vast potential, supportive measures, dedicated policies, and regulations the penetration of RTS systems is far below their respective potential. The introduction of information technology (IT) intervention in the RTS sector would integrate key stakeholders at a single platform so as to reduce the administrative burden of implementing agencies, optimization of processing time, handle multiple consumers simultaneously, and connect consumers with authentic vendors, etc. This would help them to understand and perform their shared roles and responsibilities and enhance the RTS implementation across the state. In this study, the scope for IT interventions in the existing RTS implementation process of the Odisha Renewable Energy Development Agency (OREDA) has been assessed and a simplistic approach has been developed for the execution of the RTS projects in relatively lesser time with the least human interface as well as resources. This approach has been successfully demonstrated with OREDA and a cumulative capacity of around 5 MWp (individual project of 1-2 kWp) residential grid-connected RTS projects implemented through the web platform. Currently, other state nodal agencies are also adopting the model for RTS execution and consumer proliferation through IT interventions.

In order to explore the grassland ecosystem productivity and landscape ecological patterns of main pastoral grasslands in China, it provides a theoretical basis for the efficient implementation of ecological engineering and rational management of grassland resources in the region. This study analyzed the changes in grassland area, landscape index (LSI), and net primary productivity (NPP) in seven major pastoral areas in China in 1985, 1995, 2005, and 2015. Results showed that (1) the sizes of the grassland study area in 1985, 1995, 2005, and 2015 were 248.34, 243.93, 245.80, and 244.660 km2 respectively. (2) The dominance of grassland in the landscape pattern increased from 2005 to 2015 as compared with that in 1985–1995 and 1995–2005. The degrees of spatial heterogeneity were reduced. (3) The grassland NPP showed spatial and temporal differences. The average NPP of grassland increased by 21.30%, 16.47%, and 36.17% during 1985–1995, 2005–2015, and 1985–2015, respectively. From 1995 to 2005, the average NPP decreased by 5.05 gC/m2, which is equivalent to -3.61% of the average NPP in 1995. The total amount of grassland NPP in the study area was the greatest in 2015, showing increments of 36.37% and 16.61% compared with those in 1985 and 2005, respectively.

To date, theoretical analyses have provided several useful methods/algorithms for studying transition states and non-covalent interactions. Potential Energy Scan (PES) is one such method that has found wide application in the physicochemical community. Analyzing the PES profiles of the catalytic system of FeBr3 in Electrophilic Aromatic Substitution (EAS) with molecular halogens, one can notice an obvious difference from the modern textbook mechanisms proposed in this area of study. Moreover, the newly presented Graphic Methodology (GM) allows a simple and reliable transition state determination even for very weakly bound charge transfer complexes using second-order derivatives as an efficient tool in the graphical analysis. Relative errors that can be reduced to a few parts per thousand in transition state estimation make this method a potentially very useful tool in further graphical studies of non-covalent interactions. Studies performed using high-precision semiempirical methods suggest that the process of halonium ion quenching proceeds along a completely different pathway, suggesting a possible novel transition metal-nonmetal catalytic system involved in the EAS electrophilic quenching step. The results presented here strongly suggest a future GM application that is widely used and the introduction of pseudo and pre-transition states as new terms in the description of non-covalent interactions.

The dynamic equilibrium of iron homeostasis has an important role in sustaining metabolic and neurological functions during human life. Iron excretion is modest and unregulated hence this overall equilibrium is tightly regulated in normal conditions by increasing deficient or limiting excessive iron assimilation. Here we focus on neural tissue overload, dyshomeostasis that is preceded by excessive assimilation, disturbances in internal homeostasis or both. Normal steps in iron homeostasis include its uptake across the apical membrane of the enterocytes in a mildly acidic duodenal milieu by the major importer of ferrous iron, Divalent Metal Transporter 1 (DMT1) (Nramp2/DCT1/SLC11A2, solute carrier family 11 member 2) [1, 2], responsible for the uptake of Non-Transferrin Bound Iron (NTBI) and divalent metals [3], passage through enterocytes bound to a chaperone and crossing the basal membrane via the only known cellular ferrous iron exporter, ferroportin [4] with the iron bound to transferrin (Tf) as it exits. Cells in peripheral tissues largely utilize circulating iron after uptake by receptor-mediated endocytosis of Tf-bound Iron [5]. Derangement of duodenal NTBI absorption of iron and heavy metals is influenced by systemic iron metabolism associated with inflammation that leads to iron accumulation into peripheral tissues and potential development of neurodegenerative diseases, involving distribution and fine regulation of DMT1 also in the central nervous system, where inflammatory-related acidosis influences DMT1 uptake, via its action as a proton cotransporter. Pharmacological strategies to inhibit NTBI transport selectively will be illustrated in relationship to protection, particularly neuroprotection, against iron overload associated with acidosis, inflammation, neuroinflammation and subsequent neurodegeneration.

This study aimed to develop multifunctional nanoplatforms for both cancer imaging and therapy using superparamagnetic iron oxide nanoparticles (SPIONs). Two distinct synthetic methods, reduction-precipitation (MR/P) and co-precipitation at controlled pH (MpH), were explored, including the assessment of coating’s influence, namely dextran and gold, on their magnetic properties. These SPIONs were further functionalized with gadolinium to act as dual T1/T2 contrast agents for magnetic resonance imaging (MRI). Parameters such as size, stability, morphology, and magnetic behavior were evaluated by a detailed characterization analysis. To assess their efficacy in imaging and therapy, relaxivity and hyperthermia experiments were performed, respectively. The results revealed that both synthetic methods lead to SPIONs with similar average, 9 nm. Mössbauer spectroscopy indicated that samples obtained from MR/P consist of approximately 11-13% of Fe present in magnetite, while samples obtained from MpH have higher content, of 33%-45%. Despite coating and functionalization, all samples exhibited superparamagnetic behavior at room temperature. Hyperthermia experiments showed increased SAR values with higher magnetic field intensity and frequency. Moreover, the relaxivity studies suggested potential dual T1/T2 contrast agent capabilities for the coated SPpH-Dx-Au-Gd sample, thus demonstrating its potential in cancer diagnosis.

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.

Children with history of prematurity are at higher risk of complications, comorbidities and iron deficiency. In this study we assess the prevalence of restless sleep disorder, as well as that of periodic leg movements during sleep (PLMS) in these children. Retrospective chart review of sleep studies in children aged 1-18 years, with history of prematurity. Only diagnostic studies in children without diagnosis of a genetic syndrome or airway surgery or tracheostomy were included. Three groups were compared, children with PLMS index&gt;5, children with restless sleep disorder (RSD), and children with neither elevated PLMS index nor RSD. During the study, 2,577 sleep studies were conducted. Ninety-two studies fit our criteria and were included in analysis. Median birth age was 31 weeks, interquartile range (IQR) 27-34 weeks. Thirty-two (34.8%) children were referred for restless sleep and 55 (59.8%) for snoring; after polysomnography 18% were found to have PLMS index &gt;5/hour, 14% fit the criteria for RSD. There were no statistically significant differences in polysomnographic parameters between the children with RSD, PLMS and the remaining group, except for lower obstructive apnea/hypopnea index (Kruskal-Wallis ANOVA 8.621, p=0.0135) in the RSD group (median 0.7, IQR 0.3-0.9) than in the PLMS (median 1.7, IQR 0.7-3.5), or than in the nonRSD/nonPLMS group (median 2.0, IQR 0.8-4.5). There was elevated frequency of RSD and elevated PLMS in children with history of prematurity that might be linked to the increased risk of iron deficiency in premature infants. These new results add new knowledge on the prevalence of RSD in these children.

The reference method for cefiderocol antimicrobial susceptibility testing is broth microdilution (BMD) with iron-depleted-Mueller-Hinton (ID-MH) medium, whereas breakpoints recommended for disk diffusion (DD) are based on MH-agar plates. We aimed to compare the performance of the commercial BMD tests ComASP (Liofilchem) and UMIC (Bruker), DD and E-test using MH- and ID-MH-agar plates with the reference BMD method using 100 carbapenem-resistant-A. baumannii isolates. Standard BMD was performed according to the EUCAST guidelines, DD and E-test were carried out using two commercial MH-agar plates (bioMérieux and Liofilchem) and an in-house ID-MH-agar plate, while ComASP and UMIC were performed according to the manufacturer’s guidelines. DD performed with the ID-MH-agar plates led to a higher categorical agreement (CA, 95.1%) with standard BMD and fewer categorization errors compared to the commercial MH-agar plates (CA bioMérieux 91.1%, Liofilchem 89.2%). E-test on ID-MH-agar plates exhibited a significantly higher essential agreement (EA, 75%) with standard BMD compared to the two MH-agar plates (EA bioMérieux 57%, Liofilchem 44%), and showed a higher performance in detecting high-level resistance than ComASP and UMIC (mean log2 difference with standard BMD for resistant isolates of 0.5, 2.83 and 2.08, respectively). In conclusion, DD and E-test on ID-MH-agar plates exhibit a higher diagnostic performance than on MH-agar plates and the commercial BMD methods. Therefore, we recommend using ID-MH-agar plates for cefiderocol susceptibility testing of A. baumannii.

Mn-promoted bulk iron catalysts with high specific surface area (82-211 m2.g-1) were synthesized by coprecipitation followed by drying under supercritical conditions. The catalysts were tested in the CO2 hydrogenation reaction. The Mn-promoted iron catalysts showed better textural properties than the bare Fe2O3 catalyst, allowing better dispersion of the active phase, easier reduction of iron oxide, better carburization of iron oxides and higher catalytic activity than the bare Fe2O3 catalyst. The best activity results were obtained by catalyst promotion with a very low amount of Mn (Mn/Fe ratio of 0.05). Upon steady-state conditions (T=340 ºC, total pressure of 20 bar and H2/CO2=3), this catalyst showed high CO2 conversion (44.2%) and selectivity to C2-C4 hydrocarbons (68%, olefin to paraffin ratio of 0.54), while the selectivity to C5+ hydrocarbons, CH4 and CO was about 3.2, 38.5 and 5%, respectively. A close correlation was found between catalyst textural properties and CO2 conversion. The most active MnFe-0.05 catalyst shows high stability during 72 h of reaction related to low amount of soft coke formation and catalyst activation through the formation of χ-Fe5C2 phase during the on-stream reaction.

All living organisms require some metal ions for their energy production as well as metabolic and biosynthetic processes. Within cells, metal ions are involved in the formation of adducts interact with metabolites and macromolecules (proteins and nucleic acids). The proteins that require binding to one or more metal ions to be able to carry out their physiological function are called metalloproteins. About one third of all protein structures in the Protein Data Bank involve metalloproteins. Over the past few years there has been a tremendous progress in the number of computational tools and techniques making use of 3D structural information to support the investigation of metalloproteins. This trend has been boosted also by the successful applications of neural networks and deep learning approaches in molecular and structural biology at large. In this review, we discuss recent advances in the development and availability of resources dealing with metalloproteins from a structure-based perspective. We start by addressing tools for the prediction of metal-binding sites (MBSs) using structural information on apo-proteins. Then, we provide an overview of methods for and lessons learned from the structural comparison of MBSs in a fold-independent manner. We then move to describing databases of metalloprotein/MBS structures. Finally, we summarize recent DL applications enhancing the functional interpretation of metalloprotein structures.

The use of cobalt-iron (VaCoFe) core is investigated as an alternative to silicon-iron (FeSi) on the design of interior permanent magnet synchronous motors (IPMSM). A spoke-type IPMSM geometry is optimized considering FeSi and VaCoFe cores for a torque range up to 40 N.m, providing a general comparative analysis between materials, considering the application of a 4-motor competition vehicle’s powertrain. A genetic optimization algorithm is applied over a hybrid analytical/finite-element model of the motor to provide sufficiently accurate electromagnetic and thermal results within a feasible time. VaCoFe can result in an estimated increase of up to 5 % in efficiency for the same torque, or up to 64 % torque increase for the same efficiency level. After optimization, and using a detailed time-dependent model, a potential 3.2 % increase in efficiency, a core weight reduction of 4.1 %, and a decrease of 9.6 % in the motor’s core volume was found for the VaCoFe at 20 Nm. In addition, for the same motor volume, the VaCoFe allows an increase of 51.9 % of torque with an increase of 1.1 % of efficiency, when compared with FeSi.

(200 words) Imidazolium ionic liquids containing acetylacetone, thenoyltrifluoroacetone, or 8-hydroxyquinoline, respectively, were used as the extracting agents for the separation of traces of iron (III) from its aqueous solutions with or without citric and oxalic acids. The results show that 8-hydroxyquinoline in imidazolium ionic liquids extract iron quantitatively from all the tested solutions including complexing ones, regardless indications of unexpected iron behavior/speciation.

Assuming that climate warming in the WSL will lead to a northward shift of the forest and permafrost boundaries, a “substituting space for time” approach predicts an increase in concentration of DIC and labile major and trace elements and a decrease of the transport of DOC and low soluble trace metals in the form of colloids in the main stem of the Ob River. However, an unknown factor is the change in hydrochemistry of the largest southern tributary, the Irtysh River, which is impacted by permafrost-free steppe and forest-steppe zone. Overall, seasonally-resolved transect studies of large riverine systems of western Siberia are needed to assess the hydrochemical response of this environmentally-important territory to on-going climate change.

There is a marked momentum towards the use of clean hydrogen energy as an alternative for fossil fuels. Renewable energies such as solar and wind are being used to generate hydrogen from the water hydrolysis process. Indeed, this approach stores renewable energies in the form of combustible hydrogen for other energy uses. The other alternative that could be economically more cost-effective at the current technology stage is to explore the natural “Hydrogen System” where the natural hydrogen is generated and accumulated within the earth system, the same that stands for a “Petroleum System”. The Discovery of a large accumulation of relatively pure natural hydrogen (H2) in Mali has triggered the opportunity of searching for natural hydrogen accumulations in other countries. The generation of hydrogen from a circular depression in Mali and some other countries is linked to the presence of geologically very old iron-rich basement rocks. Solid-liquid redox reactions between iron-rich minerals and groundwater that split water are a possible source of H2 in deep basement rocks. It is believed that the hydrogen degassing may be detected by surface topographic circular to sub-circular shallow depressions. Chemical processes such as dissolution by hydrogen are considered to play the main role in the formation of the circular depressions through preferential vertical hydrogen migration channel. Archean iron-rich Yilgarn Craton that covers a vast area of Western Australia (WA) contains abundant iron-rich mafic-ultramafic rocks. The craton reveals many surficial circular depressions visible through satellite images. The area has abundant fault systems and is blanketed with Eocene sedimentary rocks containing high-quality reservoir rocks. All these characteristics seem to provide most of the required elements, such as hydrogen source, migration pathway, and reservoir rock for a “Hydrogen System” in this area.

Background: Besides anemia, iron deficiency may cause more subtle symptoms including those of the restless legs syndrome (RLS), the chronic fatigue syndrome (CFS) or sleeping disorders. Objective: The aim of this pre-planned secondary analysis was to compare the frequency and severity of symptoms associated with iron deficiency before and after (intravenous or oral) iron supplementation in iron deficient blood donors. Methods/Design: Prospective, randomized, controlled, single centre trial. (ClinicalTrials.gov: NCT01787526). Setting: Tertiary care center in Graz, Austria Participants: 138 female and 38 male whole blood and platelet apheresis donors aged ≥18 and ≤65 years with iron deficiency (ferritin ≤30ng/ml at the time of blood donation). Interventions: Intravenous iron (1 g ferric carboxymaltose, n=86) or oral iron supplementation (10 g iron fumarate, 100 capsules, n=90). Measurements: Clinical symptoms were evaluated by a survey before iron therapy (visit 0, V0) and after 8-12 weeks (visit 1, V1) including questions about symptoms of RLS, CFS, sleeping disorders, quality of life and symptoms like headaches, dyspnoea, dizziness, palpitations, pica and trophic changes of fingernails or hair. Results: We found a significant improvement in the severity of symptoms for RLS, fatigue and sleep quality (p<0.001). Furthermore, a significant decrease of headaches, dyspnoea, dizziness and palpitations was reported (p<0.05). There was no difference between the type of iron supplementation (intravenous versus oral) and clinical outcome data. Conclusion: Iron supplementation in iron deficient blood donors may be an effective strategy to improve symptoms related to iron deficiency and the wellbeing of blood donors.

Magnetic iron oxide particles are used for in vitro diagnostics for nearly 40 years. Due to their unique physical, chemical, thermal and mechanical properties, as well as biocompatibility and low toxicity in the human body, iron oxide nanoparticles have been used in many biomedical applications, such as contrast agents for magnetic resonance imaging, carriers for controlled drug delivery and immunoassays, but also in magnetic hyperthermia. Our aim is to investigate the effect of pressure and temperature on the structural, thermal and magnetic properties of iron oxide nanomaterials prepared by hydrothermal synthesis. Iron oxide nanoparticles were synthesized at temperatures of 100-200°C and pressures of 20-1000 bar. It has been found that pressure influences the type of iron oxide crystalline phase. Thus, for lower pressure values (< 100 bar), iron oxide is predominantly formed as hematite, while at pressures > 100 bar, the major crystalline phase is goethite. The complex thermal analysis revealed the polymorphic changes of iron oxides at different temperatures. The existence of specific magnetite and hematite phases in all thermally treated samples are evidenced through the specific Verwey and Morin transitions highlighted by ZFC-FC (Zero Field Cooled-Field Cooled) measurements, whereas their relative content is precisely provided by Mössbauer spectroscopy.