Miracle-dz/UniFAR-FTUs huggingface dataset:
Batch Reverse Osmosis Desalination Modeling under a Time-Dependent Pressure Profile

As world demand for clean water increases, reverse osmosis (RO) desalination has emerged as an attractive solution. Continuous RO is the most used desalination technology today. However, a new generation of configurations, working in unsteady-state feed concentration and pressure, have gained more attention recently, including the batch RO process. Our work presents a mathematical modeling for batch RO that offers the possibility of monitoring all variables of the process, including specific energy consumption, as a function of time and the recovery ratio. Validation is achieved by comparison with data from the experimental set-up and an existing model in the literature. Energetic comparison with continuous RO processes confirms that batch RO can be more energy efficient than can continuous RO, especially at a higher recovery ratio. It used, at recovery, 31% less energy for seawater and 19% less energy for brackish water. Modeling also proves that the batch RO process does not have to function under constant flux to deliver good energetic performance. In fact, under a linear pressure profile, batch RO can still deliver better energetic performance than can a continuous configuration. The parameters analysis shows that salinity, pump and energy recovery devices efficiencies are directly linked to the energy demand. While increasing feed volume has a limited effect after a certain volume due to dilution, it also shows, interestingly, a recovery ratio interval in which feed volume does not affect specific energy consumption.
Repeated but Not Single Administration of Ketamine Prolongs Increases of the Firing Activity of Norepinephrine and Dopamine Neurons

Abstract Background Clinical studies have shown that the rapid antidepressant effect of the glutamate N-methyl-D-aspartate receptor antagonist ketamine generally disappears within 1 week but can be maintained by repeated administration. Preclinical studies showed that a single ketamine injection immediately increases the firing and burst activity of norepinephrine (NE) neurons, but not that of serotonin (5-HT) neurons. It also enhances the population activity of dopamine (DA) neurons. In the present study, we investigated whether such alterations of monoamine neuronal firing are still present 1 day after a single injection, and whether they can be maintained by repeated injections. Methods Rats received a single ketamine injection or 6 over 2 weeks and the firing activity of dorsal raphe nucleus 5-HT, locus coeruleus NE, and ventral tegmental area DA neurons was assessed. Results One day following a single injection of ketamine, there was no change in the firing activity of 5-HT, NE, or DA neurons. One day after repeated ketamine administration, however, there was a robust increase of the firing activity of NE neurons and an enhancement of burst and population activities of DA neurons, but still no change in firing parameters of 5-HT neurons. The increased activity of NE neurons was no longer present 3 days after the last injection, whereas that of DA neurons was still present. DA neurons were firing normally 7 days after repeated injections. Conclusion These results imply that the enhanced activity of NE and DA neurons may play a significant role in the maintenance of the antidepressant action of ketamine.
Raman Spectra of Possible Corrosion Products of Iron

Raman spectra of FeO, Fe3O4, α-Fe2O3, α-FeOOH, and γ-FeOOH, the common products of iron oxidation, have been measured. The spectra of FeO and Fe3O4 appear to be identical. Armco iron oxidized in air at 250°C was examined by Raman spectroscopy after varying periods of exposure and the surface film was found to contain Fe3O4 and α-Fe2O3.
Non-Coding RNAs in Retinoic Acid as Differentiation and Disease Drivers

All-trans retinoic acid (RA) is the most active metabolite of vitamin A. Several studies have described a pivotal role for RA signalling in different biological processes such as cell growth and differentiation, embryonic development and organogenesis. Since RA signalling is highly dose-dependent, a fine-tuning regulatory mechanism is required. Thus, RA signalling deregulation has a major impact, both in development and disease, related in many cases to oncogenic processes. In this review, we focus on the impact of ncRNA post-transcriptional regulatory mechanisms, especially those of microRNAs and lncRNAs, in RA signalling pathways during differentiation and disease.
Ultrafast Dynamics of meso-Tetraphenylmetalloporphyrins: The Role of Dark States.

Studying the relaxation pathways of porphyrins and related structures upon light absorption is crucial to understand the fundamental processes of light harvesting in biosystems and many applications. Herein, we show by means of transient absorption studies, following Q- and Soret-band excitation, and ab initio calculations on meso-tetraphenylporphyrinato magnesium(II) (MgTPP) and meso-tetraphenylporphyrinato cadmium(II) (CdTPP) that electronic relaxation following Soret-band excitation of porphyrins with a heavy central atom is mediated by a hitherto disregarded dark state. This accounts for an increased rate of internal conversion. The dark state originates from an orbital localized at the central nitrogen atoms and its energy continuously decreases along the series from magnesium to zinc to cadmium to below 2.75 eV for CdTPP dissolved in tetrahydrofuran. Furthermore, we are able to directly trace fast intersystem crossing in the cadmium derivative, which takes place within (110±20) ps.
Liver and serum adiponectin levels in non‐alcoholic fatty liver disease

Adiponectin is an adipokine that has anti‐steatotic, anti‐inflammatory and anti‐fibrotic effects. The impact of these different activities impact on the development and progression of non‐alcoholic fatty liver disease (NAFLD) is not well understood. The aim of this study was to evaluate both liver and serum adiponectin levels in patients with and without NAFLD and determine any clinical correlations.
Towards Conformation-Sensitive Inhibition of Gyrase: Implications of Mechanistic Insight for the Identification and Improvement of Inhibitors

Gyrase is a bacterial type IIA topoisomerase that catalyzes negative supercoiling of DNA. The enzyme is essential in bacteria and is a validated drug target in the treatment of bacterial infections. Inhibition of gyrase activity is achieved by competitive inhibitors that interfere with ATP- or DNA-binding, or by gyrase poisons that stabilize cleavage complexes of gyrase covalently bound to the DNA, leading to double-strand breaks and cell death. Many of the current inhibitors suffer from severe side effects, while others rapidly lose their antibiotic activity due to resistance mutations, generating an unmet medical need for novel, improved gyrase inhibitors. DNA supercoiling by gyrase is associated with a series of nucleotide- and DNA-induced conformational changes, yet the full potential of interfering with these conformational changes as a strategy to identify novel, improved gyrase inhibitors has not been explored so far. This review highlights recent insights into the mechanism of DNA supercoiling by gyrase and illustrates the implications for the identification and development of conformation-sensitive and allosteric inhibitors.
Synthetic, Natural, and Semisynthetic Polymer Carriers for Controlled Nitric Oxide Release in Dermal Applications: A Review

Nitric oxide (NO•) is a free radical gas, produced in the human body to regulate physiological processes, such as inflammatory and immune responses. It is required for skin health; therefore, a lack of NO• is known to cause or worsen skin conditions related to three biomedical applications— infection treatment, injury healing, and blood circulation. Therefore, research on its topical release has been increasing for the last two decades. The storage and delivery of nitric oxide in physiological conditions to compensate for its deficiency is achieved through pharmacological compounds called NO-donors. These are further incorporated into scaffolds to enhance therapeutic treatment. A wide range of polymeric scaffolds has been developed and tested for this purpose. Hence, this review aims to give a detailed overview of the natural, synthetic, and semisynthetic polymeric matrices that have been evaluated for antimicrobial, wound healing, and circulatory dermal applications. These matrices have already set a solid foundation in nitric oxide release and their future perspective is headed toward an enhanced controlled release by novel functionalized semisynthetic polymer carriers and co-delivery synergetic platforms. Finally, further clinical tests on patients with the targeted condition will hopefully enable the eventual commercialization of these systems.
Assessing the Impact of Climate Change on the Distribution of Lime (16srii-B) and Alfalfa (16srii-D) Phytoplasma Disease Using MaxEnt

Witches’ broom disease has led to major losses in lime and alfalfa production in Oman. This paper identifies bioclimatic variables that contribute to the prediction of distribution of witches’ broom disease in current and future climatic scenarios. It also explores the expansion, reduction, or shift in the climatic niche of the distribution of the disease across the different geographical areas of the entire country (309,501 km²). The maximum entropy model (MaxEnt) and geographical information system were used to investigate the potential suitability of habitats for the phytoplasma disease. This study used current (1970–2000) and future projected climatic scenarios (2021–2040, 2041–2060, 2061–2080, and 2081–2100) to model the distribution of phytoplasma for lime trees and alfalfa in Oman. Bioclimatic variables were downloaded from WorldClim with ± 60 occurrence points for lime trees and alfalfa. The area under the curve (AUC) was used to evaluate the model’s performance. Quantitatively, the results showed that the mean of the AUC values for lime (16SrII-B) and alfalfa (16SrII-D) future distribution for the periods of 2021–2040, 2041–2060, 2061–2080, and 2081–2100 were rated as “excellent”, with the values for the specified time periods being 0.859, 0.900, 0.931, and 0.913 for 16SrII-B; and 0.826, 0.837, 08.58, and 0.894 for 16SrII-D respectively. In addition, this study identified the hotspots and proportions of the areas that are vulnerable under the projected climate-change scenarios. The area of current (2021–2040) highly suitable distribution within the entire country for 16SrII-D was 19474.2 km2 (7.1%), while for 16SrII-B, an area of 8835 km2 (3.2%) was also highly suitable for the disease distribution. The proportions of these suitable areas are very significant from the available arable land standpoint. Therefore, the results from this study will be of immense benefit and will also bring significant contributions in mapping the areas of witches’ broom diseases in Oman. The results will equally aid the development of new strategies and the formulation of agricultural policies and practices in controlling the spread of the disease across Oman.
Artificial Intelligence to Get Insights of Multi-Drug Resistance Risk Factors during the First 48 Hours from ICU Admission

Multi-drug resistance (MDR) is one of the most current and greatest threats to the global health system nowadays. This situation is especially relevant in Intensive Care Units (ICUs), where the critical health status of these patients makes them more vulnerable. Since MDR confirmation by the microbiology laboratory usually takes 48 h, we propose several artificial intelligence approaches to get insights of MDR risk factors during the first 48 h from the ICU admission. We considered clinical and demographic features, mechanical ventilation and the antibiotics taken by the patients during this time interval. Three feature selection strategies were applied to identify statistically significant differences between MDR and non-MDR patient episodes, ending up in 24 selected features. Among them, SAPS III and Apache II scores, the age and the department of origin were identified. Considering these features, we analyzed the potential of machine learning methods for predicting whether a patient will develop a MDR germ during the first 48 h from the ICU admission. Though the results presented here are just a first incursion into this problem, artificial intelligence approaches have a great impact in this scenario, especially when enriching the set of features from the electronic health records.
S100A7 promotes the development of human endometriosis by activating NF‐κB signaling pathway in endometrial stromal cells

Endometriosis (EM) is a chronic inflammatory disease affecting women aged between 23 and 42 years with a prevalence of 6%–10%. S100A7, a member of the S100 protein family, has been implicated in promoting inflammation. However, the role of S100A7 in EM and its underlying mechanism remain to be elucidated. S100A7 was silenced or overexpressed in primary endometrial stromal cells (ESCs). Cell proliferation was determined using a Cell Counting Kit‐8. Cell cycle/apoptosis was monitored using a flow cytometer. Cell invasion was studied by a Transwell assay. Quantitative RT‐PCR and Western blot analyses were used to evaluate gene expression. S100A7 and NF‐κB expression is increased in both endometriotic tissue and ESCs from women with EM. The expression of S100A7 is correlated with the expression of NF‐κB. S100A7 knockdown inhibits ESCs proliferation, cell cycle progression, cell invasion, and inflammation, but promotes cell apoptosis in an NF‐κB dependent manner. In contrast, S100A7 overexpression demonstrated an inverse effect. S100A7 is increased in both endometriotic tissue and ESCs from women with EM. S100A7 overexpression contributes to EM through increasing ESCs proliferation, cell cycle progression, cell invasion, and inflammation, and inhibiting cell apoptosis in the NF‐κB dependent manner. These findings highlight the importance of S100A7/NF‐κB signaling in EM and provide new insights into therapeutic strategies for EM.
Effect of Selected Anionic and Cationic Drugs Affecting the Central Nervous System on Electrical Properties of Phosphatidylcholine Liposomes: Experiment and Theory

Interactions between phospholipid membranes and selected drugs affecting the central nervous system (CNS) were investigated. Small, unilamellar liposomes were used as biomimetic cell membrane models. Microelectrophoretic experiments on two-component liposomes were performed using the electrophoretic light scattering technique (ELS). The effect of both positively (perphenazine, PF) and negatively (barbituric acid, BA) charged drugs on zwitterionic L-α-phosphatidylcholine (PC) membranes were analyzed. Experimental membrane surface charge density (δ) data were determined as a function of pH. Quantitative descriptions of the adsorption equilibria formed due to the binding of solution ions to analyzed two-component membranes are presented. Binding constants of the solution ions with perphenazine and barbituric acid-modified membranes were determined. The results of our research show that both charged drugs change surface charge density values of phosphatidylcholine membranes. It can be concluded that perphenazine and barbituric acid are located near the membrane surface, interacting electrostatically with phosphatidylcholine polar heads.
DISTRIBUTION OF CARBON DIOXIDE AND OXYGEN IN THE GAS PHASE OF AEROBIC SOILS

Summary It is shown that, under certain conditions, the increase in carbon dioxide partial pressure is proportional to the drop in oxygen partial pressure in the gas phase in fallow soils. The principal assumptions are that respiration is unaffected by lowering of oxygen partial pressure until very low levels are reached, and that the respiration quotient is constant. Previously reported measurements of the composition of gases extracted from soil are consistent with the theory.
Atypical advanced interatrial block due to giant atrial lipoma

1 Department of Cardiology, Hospital of Tortosa “Verge de la Cinta,”, Tortosa, Tarragona, Spain 2 Division of Cardiology, Kingston Health Sciences Center, Kingston, Ontario, Canada 3 Cardiovascular Research Foundation. Cardiovascular ICCCProgram, Research Institute Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau, Barcelona, Spain 4 Unidad de Imagen Cardiaca, Servicio de Cardiología, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain 5 Cardiology Department, Hospital General Universitario GregorioMarañón, Instituto Investigación Biomédica GregorioMarañón, CIBERCV, Universidad Complutense, Universidad Europea, Madrid, Spain 6 Cardiovascular Epidemiology and Genetics Research Group, IMIM (Hospital delMarMedical Research Institute), Barcelona, Spain 7 CIBERCardiovascular Diseases (CIBERCV), Barcelona, Spain 8 Faculty ofMedicine, University of Vic-Central University of Catalonia (UVic-UCC) Vic, Barcelona, Spain 9 Heart Institute (iCor), University Hospital Germans Trias i Pujol, Badalona, Spain
A Comparison of the In Vitro Effects of 2’Fucosyllactose and Lactose on the Composition and Activity of Gut Microbiota from Infants and Toddlers

Because of the recognized health benefits of breast milk, it is recommended as the sole nutrition source during the first 6 months of life. Among the bioactive components are human milk oligosaccharides (HMOs) that exert part of their activity via the gut microbiota. Here, we investigated the gut microbiota fermentation of HMO 2’fucosyllactose (2’-FL), using two in vitro models (48 h fecal incubations and the long-term mucosal simulator of the human intestinal microbial ecosystem [M-SHIME®]) with fecal samples from 3-month-old breastfed (BF) infants as well as 2–3 year old toddlers. The short-term model allowed the screening of five donors for each group and provided supportive data for the M-SHIME® study. A key finding was the strong and immediate increase in the relative abundance of Bifidobacteriaceae following 2’-FL fermentation by both the BF infant and toddler microbiota in the M-SHIME®. At the metabolic level, while decreasing branched-chain fatty acids, 2’-FL strongly increased acetate production together with increases in the health-related propionate and butyrate whilst gas production only mildly increased. Notably, consistently lower gas production was observed with 2’-FL fermentation as compared to lactose, suggesting that reduced discomfort during the dynamic microbiome establishment in early life may be an advantage along with the bifidogenic effect observed.
Syndecans and Pancreatic Ductal Adenocarcinoma

Pancreatic Ductal Adenocarcinoma (PDAC) is a fatal disease with poor prognosis because patients rarely express symptoms in initial stages, which prevents early detection and diagnosis. Syndecans, a subfamily of proteoglycans, are involved in many physiological processes including cell proliferation, adhesion, and migration. Syndecans are physiologically found in many cell types and their interactions with other macromolecules enhance many pathways. In particular, extracellular matrix components, growth factors, and integrins collect the majority of syndecans associations acting as biochemical, physical, and mechanical transducers. Syndecans are transmembrane glycoproteins, but occasionally their extracellular domain can be released from the cell surface by the action of matrix metalloproteinases, converting them into soluble molecules that are capable of binding distant molecules such as extracellular matrix (ECM) components, growth factor receptors, and integrins from other cells. In this review, we explore the role of syndecans in tumorigenesis as well as their potential as therapeutic targets. Finally, this work reviews the contribution of syndecan-1 and syndecan-2 in PDAC progression and illustrates its potential to be targeted in future treatments for this devastating disease.
Experiments of Image Classification Using Dissimilarity Spaces Built with Siamese Networks

Traditionally, classifiers are trained to predict patterns within a feature space. The image classification system presented here trains classifiers to predict patterns within a vector space by combining the dissimilarity spaces generated by a large set of Siamese Neural Networks (SNNs). A set of centroids from the patterns in the training data sets is calculated with supervised k-means clustering. The centroids are used to generate the dissimilarity space via the Siamese networks. The vector space descriptors are extracted by projecting patterns onto the similarity spaces, and SVMs classify an image by its dissimilarity vector. The versatility of the proposed approach in image classification is demonstrated by evaluating the system on different types of images across two domains: two medical data sets and two animal audio data sets with vocalizations represented as images (spectrograms). Results show that the proposed system’s performance competes competitively against the best-performing methods in the literature, obtaining state-of-the-art performance on one of the medical data sets, and does so without ad-hoc optimization of the clustering methods on the tested data sets.
Promoting Self-Care in Nursing Encounters with Persons Affected by Long-Term Conditions—A Proposed Model to Guide Clinical Care

Background: Nursing interventions for persons affected by long-term conditions should focus on providing support to enhance the ability to manage disease in everyday life. Many clinical nurses feel they have inadequate training or experience to provide self-management support in a beneficial and structured way. This study explores the process towards independent self-care and management of disease in persons affected by Parkinson’s disease and the support required from healthcare to achieve this. It presents a nursing model to guide nurses in providing self-management support in the clinical care encounter. Methods: The results from three previously published articles investigating a self-management support program for persons with Parkinson’s disease were combined to form a new data set, and analyzed using qualitative thematic analysis. Results: Three separate, but interrelated, themes were identified, which described the process towards self-management of disease as expressed by the participants of the self-management program. Themes describe the factors important for developing and improving self-management abilities and actions. The results were applied to Orem’s Self-care deficit theory to suggest a model of self-management support in the clinical nursing encounter. Conclusion: This study investigated factors important for self-management and highlighted the unique contribution and focus of nursing support to promote independent self-care.