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Seasonal variations result in hydrophytes and undrained hydric soil changes in wetland areas, which lead to a dynamic environment that makes wetland classification challenging. This study aims to explore the applicability of multi-seasonal Gray-Level Co-Occurrence Matrix (GLCM) texture-derived features for object-based wetland classification over large-extent for the first time. We attempted to enhance the performance of the random forest classifier by incorporating multi-source remote sensing data, including Sentinel-2, Sentinel-1, Alos-Palsar, and topographic features. A total of 47 features were extracted from multi-source remote sensing data. In this context, we assessed the applicability of multi- versus mono-seasonal derived features for the wetland's classes with low within-class separability. We investigated the mean decrease in the Gini impurity index for each GLCM feature. We observed that including GLCM features enhanced overall accuracy by 7.38% when using imagery from one season and 4.21% for multi-season imagery. The multi-season scenario that included GLCM measures (93.49%) attained the highest overall accuracy. For this scenario, the means of decrease in Gini impurity index suggested that Soil Adjusted Vegetation Index, Modified Normalized Difference Water Index, slope, correlation in summer (GLCM feature), and Sentinel-1 VH are the most important features in increasing the random forest's classifier performance. In looking at the GLCM features, the separability analysis suggested that Entropy, Sum of Average, and Variance calculated from the summer imagery improve the classifier's performance while other textural features from spring imagery better contributed to classifier accuracy.

Although economic growth and development in different countries has increased economic prosperity, but also environmental pollution threatens human life. The purpose of this study is to investigate the factors affecting air pollution by emphasizing the emission of carbon dioxide in among the countries of the ECO region from 2000 to 2018 with an emphasis on financial development. To investigate the effect of financial development on environmental pollution, the Panel Data econometric method has been used and the model coefficients have been estimated with Eviews 10 software. The dependent variable of the research is the amount of carbon dioxide emissions in terms of tons per year and the independent variable is the combined index of financial development. Two control variables including GDP and energy consumption are also included in the model. In the long run, the effect of financial development on carbon dioxide emissions in the ECO countries is positive and significant. Also, the effects of GDP and energy consumption on carbon dioxide emissions are positive and significant. The coefficient of determination of the model is equal to 98%, which indicates the high descriptive power of the model. Although increasing the level of economic activity increases welfare in different societies, but given that increasing the level of economic activity, especially industrial activity, increases the emission of carbon dioxide and biodegradation. یEnvironment in ECO member countries, economic policy makers of these countries should work together to implement the necessary policies and strategies to control and reduce emissions of carbon dioxide and other environmental pollutants.

Feedback on cognitive workload may reduce decision-making mistakes. Machine learning-based models can produce feedback from physiological data such as electroencephalography (EEG) and electrocardiography (ECG). Supervised machine learning requires large training data sets that are (1) relevant and decontaminated and (2) carefully labeled for accurate approximation, a costly and tedious procedure. Commercial over-the-counter devices are low-cost resolutions for the real-time collection of physiological modalities. However, they produce significant artifacts when employed outside of laboratory settings, compromising machine learning accuracies. Additionally, the physiological modalities that most successfully machine-approximate cognitive workload in everyday settings are unknown. To address these challenges, a first-ever hybrid implementation of feature selection and self-supervised machine learning techniques is introduced. This model is employed on data collected outside controlled laboratory settings to (1) identify relevant physiological modalities to machine approximate six levels of cognitive-physical workloads from a seven-modality repository and (2) postulate limited labeling experiments and machine approximate mental-physical workloads using self-supervised learning techniques. [ABSTRACT FROM AUTHOR]

Background: Pediatric cow milk allergies (CMA) can occur in immunoglobulin (Ig) E and non-IgE-mediated forms. Unlike IgE-mediated allergies, the mechanisms of disease pathogenesis in non-IgE-mediated food allergy and an association with microbiome has not been well established. Previous studies have identified the presence of altered humoral responses to gut bacteria in IgE mediated allergies. Here, we analyzed IgA, IgE and IgG responses to gut bacteria in subjects with either IgE or non-IgE mediated CMA to identify relative proportions of Ig-coated bacteria and characterize unique disease specific microbial signatures. Methods: Multi-parametric flow cytometry analysis was used to identify IgA, IgE and IgG responses to gut bacteria in CMA patients. Cell sorting of Ig coated gut bacteria was subsequently performed followed by high throughput 16S rRNA gene sequencing and specific patterns of humoral responses to gut bacteria assessed in each study group. Results: We identified significant alterations in IgA and IgG gut bacterial coating patterns in CMA subjects. Proportions of IgA-coated bacteria were decreased in IgE mediated CMA subjects without atopic dermatitis (ALL) and non-IgE mediated CMA subjects (ENP), compared to healthy controls (CON). In comparison, IgG-coated bacteria was significantly elevated in CMA subjects with atopic dermatitis (AD). Alpha and beta diversities displayed significant differences in IgA-, IgE-, and IgG-coated bacteria in AD and ENP groups. Significant differences in bacteria coated by IgA, IgE and IgG were detected at Phyla, Genus and Species levels and associated bacterial dysbiosis in IgE and non-IgE mediated allergies were identified. Linear discriminant analysis (LDA) effect size (LEFse) revealed unique disease associated bacterial signatures, including several pathogenic bacteria namely Bacteroides fragilis, Ruminococcus gnavus, Eubacterium dolichum, Fusobacterium, Clostridium neonatale and Robinsoniella peoriensis. Receiver operating characteristic curve analysis confirmed the efficiency of using the bacterial signatures identified as biomarkers for disease. Conclusions: Altered IgA and IgG responses to gut bacteria were identified in CMA subjects. The disease-specific responses were associated with alterations in bacterial diversity and concomitant dysbiosis of Ig-coated bacteria in IgE-mediated and non-IgE-mediated CMA pediatric subjects. The identification of pathogenic bacteria uniquely associated with different classes of allergic disease indicates a role of these bacteria in driving disease-specific pathological phenotypes. [ABSTRACT FROM AUTHOR]

Dementia develops as a result of multiple factors, including cerebrovascular disease which is called vascular dementia (VD). Histone‐3 lysine‐9 dimethylation (H3K9me2) broadly increases during VD and inhibits neuroprotective gene expressions. So, we aimed to determine how H3K9me2 inhibitor (BIX01294) affects neuronal damage in VD. An in vivo model of VD was used followed by BIX01294 treatment. Behavioral tests, hematoxylin, and eosin (H&E), Congo red, and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining were carried out. Hippocampal phosphorylated cyclic‐AMP responsive element binding protein (p‐CREB), c‐fos, brain‐derived neurotrophic factor (BDNF), and H3K9me2, were detected by western blot analysis technique. Neurological deficit and anxiety‐related behavior significantly reduced in the treatment group compared to the VD group (p < 0.05). BIX01294 improved spatial and passive avoidance memory (p < 0.01 and p < 0.05, respectively) compared to the VD group. Treatment with BIX01294 restored the level of p‐CREB/CREB ratio (p < 0.05), cfos (p < 0.01), BDNF (p < 0.01), and suppressed H3K9me2 (p < 0.001) when compared to the VD group. BIX01294 microinjection reduced the apoptosis level in TUNEL staining (p < 0.05), and raised neural cell count in H&E staining (p < 0.01); amyloid beta accumulation significantly decreased in the treatment group (p < 0.05) compared to the VD group. In conclusion, long‐term treatment with a low dose of BIX01294 can prevent the progression of neuronal loss in VD model by raising the expression of neurotrophic factors, and reducing the apoptosis level. [ABSTRACT FROM AUTHOR]

This research investigated the physicochemical properties and biological activities of green-synthesized copper oxide nanoparticles (CuO NPs) via Moringa peregrina extract, graphene oxide (GO), and their composite (CuO–GO). SEM revealed the morphology and structure, indicating polygonal CuO NPs, thin wrinkled sheets of GO, and a combination of CuO NPs and GO in the nanocomposite. EDS confirmed the elemental composition and distribution. XRD analysis confirmed the crystalline monoclinic structure of CuO NPs and GO, as well as their composite, CuO–GO, with characteristic peaks. DLS analysis exhibited distinct size distributions, with CuO NPs showing the narrowest range. BET surface area analysis revealed mesoporous structures for all materials, with the nanocomposite showing enhanced surface area and pore volume. Anticancer assays on MCF-7 and normal NIH/3T3 cells demonstrated CuO–GO's superior cytotoxicity against cancer cells, with minimal effects on normal cells, suggesting selective cytotoxicity. Moreover, antibacterial assays against Pseudomonas aeruginosa and Staphylococcus aureus indicated CuO–GO's potent inhibitory activity. The composite's synergistic effects were evidenced by its lower minimum inhibitory concentration (MIC) compared to individual components. In conclusion, this study elucidated the promising biomedical applications of CuO NPs, GO, and their nanocomposite, particularly in cancer treatment and antibacterial therapies, showcasing their potential as multifunctional nanomaterials. [ABSTRACT FROM AUTHOR]

This research presents a comprehensive optimization of green ammonia synthesis in Morocco by leveraging advanced deep learning techniques to maximize the utilization of the country's abundant renewable energy resources. By employing deep learning models such as LSTM and LSTM_adv, we forecast ammonia production over the next decade, improving strategies for production and energy storage. Our findings confirmed the viability of sustainable ammonia production in Dakhla and highlighted its transformative potential for global sustainability goals. Under the optimal scenario of 20 % photovoltaic and 80 % wind energy, the production cost was US$575 per tNH 3 , delivering an energy output of 8916.64 GWh and a daily ammonia production of 2503.36 tNH 3. Shifting to 100 % wind energy further enhances the potential, increasing daily ammonia production to a maximum of 3090 tNH 3 while reducing the production cost to US$376 per tNH 3. These results demonstrate the significant economic and environmental benefits of using renewable energy sources for ammonia production in Morocco. By leveraging a country's abundant wind and solar resources, we can contribute to a sustainable and energy-independent future. [Display omitted] • Optimizes ammonia synthesis using renewable energy sources for a greener future. • Projects daily ammonia output of up to 3090 tNH 3 in various energy scenarios. • Achieves 54% efficiency with competitive costs of $376-$575 per tNH 3. • Forecasts ammonia production over 10 years using deep learning models. • Provides insights for sustainable development and energy use in Morocco. [ABSTRACT FROM AUTHOR]

One of the most prevalent cancers affecting women globally is breast cancer. Consequently, the development of cost‐effective and low‐risk treatment options remains a critical pursuit. This study describes the synthesis via water‐in‐oil‐in‐water (W/O/W) of a pH‐responsive nanocarrier for curcumin delivery, a promising anticancer drug. The nanocarrier comprises carboxymethyl starch (CMS), polyethylene glycol (PEG), and gamma alumina (γ‐Al2O3) nanoparticles. The molecular interactions between the nanocomposite components, its crystalline structure, surface morphology, size distribution, and surface charge were assessed via Fourier‐transformed infrared (FTIR) spectroscopy, field emission‐scanning electron microscopy (FE‐SEM), x‐ray diffraction (XRD), dynamic light scattering (DLS) and zeta potential, respectively. The nanocarrier showed a size ranging from 150 to 280 nm, zeta potential of +35.4 mV, drug loading of 47% and an encapsulation efficiency of 87%, which are among the maximum values reported to date for curcumin nanocarriers. A gradual drug release was observed, with 51% and 90% released after 72 h at pH 7.4 and 5.4, respectively, which corroborates the pH‐sensitivity of the nanocarrier. The cytotoxic effects of the curcumin‐loaded nanocomposite on MCF‐7 breast cancer cells and normal cells were investigated using methylthiazolyldiphenyl‐tetrazolium bromide (MTT) assay and flow cytometry. The results demonstrated that loading curcumin onto the hydrogel significantly enhances its penetration into MCF‐7 cells. Overall, this novel nanocomposite offers a promising approach for curcumin delivery in breast cancer treatment. [ABSTRACT FROM AUTHOR]

In this study, the reduction kinetics of ilmenite concentrate from a domestic mine (Kahnuj, Kerman, Iran) in pure hydrogen in the temperature range of 500 °C to 1100 °C was investigated. From the non-isothermal reduction results corresponding kinetic parameters for the as-received and pre-oxidized concentrates were calculated using the Coats-Redfern method. The reduction process in both raw and pre-oxidized ilmenite, at 800 °C was controlled by diffusion through the product layer. The kinetic analysis for ilmenite pre-oxidized at 1000 °C indicated that the reduction process followed a chemical reaction and nucleation and growth mechanism. The samples pre-oxidized at 800 °C and 1000 °C exhibited higher mass loss values and reduction degrees compared to the raw ilmenite. The promoting effect of pre-oxidation on the reduction of ilmenite is attributed to the phase changes in pre-oxidized ilmenite and the porous structure created during the reduction process after the pre-oxidation process. X-ray diffraction (XRD) patterns confirmed the presence of pseudorutile, rutile, and hematite after oxidation at 800 °C, and pseudobrookite and rutile were stable phases after oxidation at 1000 °C. [ABSTRACT FROM AUTHOR]

Postprandial dyslipidemia is commonly present in people with type 2 diabetes and obesity and is characterized by overproduction of apolipoprotein B48-containing chylomicron particles from the intestine. Peripheral serotonin is emerging as a regulator of energy homeostasis with profound implications for obesity; however, its role in dietary fat absorption and chylomicron production is unknown. Chylomicron production was assessed in Syrian golden hamsters by administering an olive oil gavage and IP poloxamer to inhibit lipoprotein clearance. Administration of serotonin or selective serotonin reuptake inhibitor, fluoxetine, increased postprandial plasma triglyceride (TG) and TG-rich lipoproteins. Conversely, inhibiting serotonin synthesis pharmacologically by p-chlorophenylalanine (PCPA) led to a reduction in both the size and number of TG-rich lipoprotein particles, resulting in lower plasma TG and apolipoprotein B48 levels. The effects of PCPA occurred independently of gastric emptying and vagal afferent signaling. Inhibiting serotonin synthesis by PCPA led to increased TG within the intestinal lumen and elevated levels of TG and cholesterol in the stool when exposed to a high-fat/high-cholesterol diet. These findings imply compromised fat absorption, as evidenced by reduced lipase activity in the duodenum and lower levels of serum bile acids, which are indicative of intestinal bile acids. During the postprandial state, mRNA levels for serotonin receptors (5-HTRs) were upregulated in the proximal intestine. Administration of cisapride, a 5-HT4 receptor agonist, alleviated reductions in postprandial lipemia caused by serotonin synthesis inhibition, indicating that serotonin controls dietary fat absorption and chylomicron secretion via 5-HT4 receptor. [ABSTRACT FROM AUTHOR]