Search Results (217)

Background: Chronic relapsing colitis involves immune dysregulation and oxidative stress, making monotherapies often insufficient. This study investigates a therapeutic strategy combining doxycycline (Dox), an immunomodulatory antibiotic, with Arthrospira platensis extracts to enhance anti-inflammatory and antioxidant effects, improving remission and controlling relapse. Methods: Ethanolic (ES) and aqueous (AS) extracts of A. platensis were chemically characterized by GC-MS after derivatization. Colitis was induced in mice using two intrarectal DNBS administrations spaced 7 days apart, with oral treatments (Dox, ES, AS, or combinations) given daily between doses. Disease progression was evaluated through clinical monitoring, histological scoring, and biochemical analysis, including MPO and CAT activities, as well as NO, MDA, and GSH levels. Results: GC-MS identified 16 bioactive compounds in each extract. ES contained mainly fatty acids and amino acids, whereas AS was rich in polysaccharides and phytol. Combined doxycycline and A. platensis extracts significantly enhanced recovery in reactivated DNBS colitis compared to monotherapies. Each treatment alone reduced disease severity, but their combination showed synergistic effects, significantly reducing disease activity index (p < 0.001), restoring mucosal integrity, and modulating inflammatory and oxidative markers (p < 0.001). Conclusion: Doxycycline potentiates the anti-colitic effects of A. platensis extracts via complementary mechanisms, offering a promising combination for managing relapsing colitis. Full article

Two fluorescent ureido-dihomooxacalix[4]arene derivatives containing naphthyl residues at the lower rim (1 and 2) were studied for the detection of nitroaromatic compounds (NACs) in solution and in vapour phases. Their affinity in solution was determined by UV-Vis absorption, fluorescence and NMR spectroscopy. For NAC vapour sensing, calixarenes were dispersed in a polytetrafluoroethylene (PTFE) matrix. Four new solvated crystals of dihomooxacalix[4]arene 2 were obtained and the solvent’s influence on its structural characteristics was investigated. The solvent-dependent structural variations observed in the crystal structures highlight the intrinsic flexibility of the calixarene framework. Such conformational adaptability, evident in the disruption and reorganization of hydrogen bonding and π–π interactions, is directly relevant to nitroaromatic sensing, where a rapid and reversible host response is crucial for effective detection. Theoretical calculations were also performed to provide further insights on the binding process. The corrected Stern–Volmer constants (K_SV) obtained showed that both receptors present selectivity for TNP and follow the same quenching order (TNP > NT > NB > DNT > TNT > DNB). Factors other than electron density distribution should dominate the quenching extent and therefore the values of the SV constants, which will be greatly overestimated if no correction to the inner filter effect is applied. Detection of NB and NT and vapours by both calixarenes produced a complete, very fast (2 to 5 s), and reversible quenching, indicating the potential use of this porous PTFE–calixarene matrix for the sensing of volatile NACs. Full article

Nighttime sea fog exhibits high frequency and prolonged duration, posing significant risks to maritime navigation safety. Current detection methods primarily rely on the dual-infrared channel brightness temperature difference technique, which faces challenges such as threshold selection difficulties and a tendency toward overestimation. In contrast, the VIIRS Day/Night Band (DNB) offers exceptional nighttime visible-like cloud imaging capabilities, offering a new solution to alleviate the overestimation issues inherent in infrared detection algorithms. Recent advances in artificial intelligence have further addressed the threshold selection problem in traditional detection methods. Leveraging these developments, this study proposes a novel generative adversarial network model incorporating attention mechanisms (SEGAN) to achieve accurate nighttime sea fog detection using DNB data. Experimental results demonstrate that SEGAN achieves satisfactory performance, with probability of detection, false alarm rate, and critical success index reaching 0.8708, 0.0266, and 0.7395, respectively. Compared with the operational infrared detection algorithm, these metrics show improvements of 0.0632, 0.0287, and 0.1587. Notably, SEGAN excels at detecting sea fog obscured by thin cloud cover, a scenario where conventional infrared detection algorithms typically fail. SEGAN emphasizes semantic consistency in its output, endowing it with enhanced robustness across varying sea fog concentrations. Full article

Inflammatory bowel diseases (IBDs) are complex disorders involving interconnected immune, oxidative, and microbial dysregulations. Arthrospira platensis (Spirulina) is a rich source of bioactive compounds with antioxidant, anti-inflammatory, and immunomodulatory properties. This study investigates the pharmacological efficacy of its aqueous extract (APA) in mitigating 2,4-Dinitrobenzene Sulfonic Acid (DNBS)-induced sub-chronic colitis with a focus on restoring redox balance and modulating gut microbiota composition. APA’s antioxidant capacity was assessed in vitro by 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azino-bis (3-ethylbenzthiazoline-6-sulphonic) acid (ABTS) radical scavenging, and metal chelation assays. In vivo, BALB/c mice received two DNBS inductions to establish sub-chronic colitis and were treated with APA (50, 100, and 200 mg/kg). Therapeutic efficacy was assessed through clinical scoring, histopathological assessment, biochemical analysis, and gut microbiota profiling based on 16S rRNA gene sequencing. APA exhibited strong antioxidant activity and significantly attenuated colitis severity, as evidenced by reduced Disease Activity Index (DAI) scores, decreased colon inflammation, suppression of Myeloperoxidase (MPO)-mediated neutrophil infiltration, and modulation of redox biomarkers. Moreover, metagenomic profiling revealed APA-induced modulation of the gut microbiota, mainly through a decreased abundance of pathogenic genera such as Staphylococcus and Enterobacteriaceae. APA demonstrates potent antioxidant, anti-inflammatory, and microbiota-modulating activities, supporting its potential as a complementary therapy for IBDs and encouraging further clinical studies. Full article

Inflammatory bowel diseases (IBD), such as Crohn’s disease (CD) and ulcerative colitis (UC), are chronic, progressive conditions with increasing prevalence worldwide. The aim of this study was to evaluate the usefulness of a porcine model of colitis induced by 2,4-dinitrobenzenesulfonic acid (DNBS) as a translational model of IBD. Sixteen Polish White pigs were divided into a control group and colitis group. Colitis was induced by rectal administration of DNBS (80 mg/kg in 50% ethanol). Clinical status, hematological and biochemical parameters, fecal calprotectin levels, cytokine plasma concentrations, and histopathological changes in the gastrointestinal tract were evaluated. DNBS administration resulted in persistent diarrhea and mild abdominal pain without general deterioration of health. Significant increases in fecal calprotectin levels and aspartate aminotransferase (AST) activity were observed. Histopathological changes in the colon were limited to the mucosa, which is similar to human UC, while the mild changes observed in the ileum indicate similarity to CD. This model is characterized by moderate inflammation, high reproducibility, and low mortality, making it valuable model in translational research on IBD. Full article

Background/Objectives: This study investigates the efficacy of working memory training (WMT) using the dual N-back (DNB) task on cognitive performance in young adults with Attention-Deficit/Hyperactivity Disorder (ADHD). Methods: Over the course of at least 18 daily sessions conducted within one month, 106 participants (33 non-medicated ADHD, 42 medicated ADHD, and 45 controls) were randomly assigned to either a fixed dual 1-back (FD1B) training condition or an adaptive DNB condition, wherein the N-back level increased following successful completion of each trial block. Cognitive performance was assessed pre- and post-intervention using the Wechsler Adult Intelligence Scale–Fourth Edition (WAIS-IV) Working Memory Index (WMI) and the Corsi Block-Tapping Task. Results: A mixed-design ANOVA revealed significant improvements in DNB performance across all groups, with the adaptive training condition producing larger gains (e.g., a 204.6% improvement in controls, Cohen’s $d=1.85$). WAIS-IV WMI scores—particularly the Digit Span Backward subtest—also improved significantly post-training, with greater effect sizes in the adaptive condition ($d=0.46$) than in FD1B ($d=0.27$). Corsi performance showed very modest gains, showing a surprising tendency to be more associated with the FD1B condition than the adaptive condition. Control participants outperformed the medicated ADHD group on WAIS-IV subtests, although no significant differences emerged between medicated and non-medicated ADHD participants. Correlational analyses indicated task-specific training effects, with adaptive training enhancing associations between DNB and Corsi performance in both controls ($r=0.60$) and medicated ADHD participants ($r=0.51$). Conclusions: This study demonstrates that dual N-back training improves verbal working memory in young adults with ADHD, specifically in a sample without psychiatric comorbidities. Transfer benefit to visuospatial domains appears limited and may not generalize to adolescents, older adults, or individuals with complex clinical profiles. The results underscore the importance of tailoring training protocols to maximize cognitive outcomes across different domains. Full article

Broadcasting in Mobile Ad Hoc Networks (MANETs) is significantly challenged by dynamic network topologies. Traditional fuzzy logic-based schemes that often rely on static fuzzy tables and fixed membership functions are limiting their ability to adapt to evolving network conditions. To address these limitations, in this paper, we conduct a comparative study of two innovative broadcasting schemes that enhance adaptability through dynamic fuzzy logic membership functions for the broadcasting problem. The first approach (Model A) dynamically adjusts membership functions based on changing network parameters and fine-tunes the broadcast (BC) versus do-not-broadcast (DNB) ratio. Model B, on the other hand, introduces a multi-profile switching mechanism that selects among distinct fuzzy parameter sets optimized for various macro-level scenarios, such as energy constraints or node density, without altering the broadcasting ratio. Reinforcement learning (RL) is employed in both models: in Model A for BC/DNB ratio optimization, and in Model B for action decisions within selected profiles. Unlike prior fuzzy logic or reinforcement learning approaches that rely on fixed profiles or static parameter sets, our work introduces adaptability at both the membership function and profile selection levels, significantly improving broadcasting efficiency and flexibility across diverse MANET conditions. Comprehensive simulations demonstrate that both proposed schemes significantly reduce redundant broadcasts and collisions, leading to lower network overhead and improved message delivery reliability compared to traditional static methods. Specifically, our models achieve consistent packet delivery ratios (PDRs), reduce end-to-end Delay by approximately 23–27%, and lower Redundancy and Overhead by 40–60% and 40–50%, respectively, in high-density and high-mobility scenarios. Furthermore, this comparative analysis highlights the strengths and trade-offs between reinforcement learning-driven broadcasting ratio optimization (Model A) and parameter-based dynamic membership function adaptation (Model B), providing valuable insights for optimizing broadcasting strategies. Full article

Solar lentigines, commonly caused by prolonged ultraviolet exposure, raise the risk of skin disorders and remain challenging to manage due to their complex mechanisms. Understanding the molecular mechanisms driving the progression of solar lentigines is crucial for developing effective protective strategies. In this study, we introduced a novel method, Dynamic Network Driver (DND), which identifies upstream regulators that drive disease progression by integrating the Dynamic Network Biomarker (DNB) approach with network control theory. By applying DND to multi-omics data from solar lentigines subjects, we (1) identified the key drivers associated with solar lentigo progression, with their functions involved in differentiation and dermal–epidermal junction; and (2) highlighted ARNT2 and TBX2 as significant master factors supported by in vitro validation in melanocytes and pigmented 3D living skin equivalent models. These results demonstrate the potency of DND for uncovering the molecular mechanisms behind solar lentigines and informing therapeutic strategies. In summary, the DND approach identified novel drivers of solar lentigo progression, acting as new markers for spot mitigation in 3D spot mimic models. Full article

The bis-thiourea chiral solvating agent (CSA) BTDA enables the NMR-based determination of absolute configuration in N-3,5-dinitrobenzoyl (DNB) amino acid derivatives without requiring covalent derivatization. A reliable trend of the sense of nonequivalence and absolute configuration is found in both ¹H and ¹³C NMR spectra. A dual-enantiomer approach, using (R,R)- and (S,S)-BTDA, generates diastereomeric complexes with the enantiopure substrate, and distinct spatial arrangements are reflected in consistent and interpretable Δδ values. The observed chemical shift differences correlate reliably with the stereochemistry of the chiral center and are further supported by ROESY (Rotating-frame Overhauser Enhancement SpectroscopY) experiments and binding constants’ measurements, confirming the formation of stereoselective non-covalent complexes. This methodology extends the logic of Mosher’s analysis to solvating agents and remains effective even in samples containing single pure enantiomers of the amino acid derivative. The BTDA-based dual-CSA system thus represents a robust, non-derivatizing strategy for stereochemical assignment by NMR, combining operational simplicity with broad applicability to DNB derivatives of amino acids with free carboxyl function. Full article

Hurricanes can cause massive power outages and pose significant disruptions to society. Accurately monitoring hurricane power outages will improve predictive models and guide disaster emergency management. However, many challenges exist in obtaining high-quality data on hurricane power outages. We systematically evaluated machine learning (ML) approaches to reconstruct historical hurricane power outages based on high-resolution (1 km) satellite night light observations from the Defense Meteorological Satellite Program (DMSP) and other ancillary information. We found that the two-step hybrid model significantly improved model prediction performance by capturing a substantial portion of the uncertainty in the zero-inflated data. In general, the classification and regression tree-based machine learning models (XGBoost and random forest) demonstrated better performance than the logistic and CNN models in both binary classification and regression models. For example, the xgb+xgb model has 14% less RMSE than the log+cnn model, and the R-squared value is 25 times larger. The Interpretable ML (SHAP value) identified geographic locations, population, and stable and hurricane night light values as important variables in the XGBoost power outage model. These variables also exhibit meaningful physical relationships with power outages. Our study lays the groundwork for monitoring power outages caused by natural disasters using satellite data and machine learning (ML) approaches. Future work should aim to improve the accuracy of power outage estimations and incorporate more hurricanes from the recently available Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data. Full article

Arthrospira platensis, a filamentous photosynthetic cyanobacterium, is widely recognized for its high nutritional value, broad spectrum of bioactive compounds, and excellent safety profile, making it a promising natural source for health-promoting applications. This study aimed to profile the phenolic constituents of an ethanolic extract of A. platensis (EAP) using HPLC-DAD-ESI-MS and to investigate its pharmacological effects in attenuating acute and sub-chronic experimental colitis, as well as its antioxidant and antifungal properties. Colitis was induced in BALB/c mice by intrarectal administration of 2,4-dinitrobenzenesulfonic acid (DNBS), followed by oral administration of EAP at doses of 50, 100, and 200 mg/kg. Phenolic profiling revealed eight major compounds, with a cumulative content of 6.777 mg/g of extract, with Pyrogallol, Ferulic acid, and Chlorogenic acid being the most abundant. In vivo, EAP treatment significantly reduced the Disease Activity Index (DAI), alleviated macroscopic colonic damage, and preserved colonic mucosal integrity in both inflammatory phases. Biochemical analyses revealed significant reductions in myeloperoxidase (MPO) activity, nitric oxide (NO), and malondialdehyde (MDA) levels, accompanied by increased reduced glutathione (GSH) content and catalase activity. In vitro, EAP demonstrated notable antioxidant effects, including 56% DPPH and 47% ABTS radical scavenging activities, and an 81% ferrous ion-chelating capacity. Furthermore, it exhibited antifungal activity, with inhibition zones of 20 mm against Candida albicans and 15 mm against Aspergillus flavus, respectively. These findings highlight the multitarget bioactivity of EAP and support its potential as a natural agent for managing intestinal inflammation and oxidative stress across both acute and sub-chronic phases. Full article

DNA nanoball sequencing (DNBSEQ) is one of the most rapidly developing sequencing technologies and is widely applied in genomic and transcriptomic investigations. Recently, a new PE300 sequencing option primarily recommended for amplicon analysis was released for DNBSEQ-G99 and G400 devices. Given their unprecedentedly high data yield per flow cell, the new PE300 kits could be a great choice for various sequencing tasks, but we found that combining different types of DNA libraries in a single run could lead to undesired artifacts in the data. In this study, we investigate the occasional read cross-contamination that we first observed in our DNBSEQ PE300 run. The phenomenon, which we refer to as “software contamination”, is not actual contamination but primarily manifests as improper forward/reverse read pairing, improper demultiplexing, or as “digital chimeric” reads. Although rare, these artifacts were found in all runs we have analyzed, including several MGI demo datasets (both PE100 and PE150). In this study, we demonstrate that these artifacts arise primarily from the incorrect resolution of sequencing signals produced by neighboring DNA nanoballs, leading to mixing out forward and reverse reads or improper demultiplexing. The artifacts occur most frequently with read pairs where the length of insert sequence is shorter than the read length. Based on a few external NA12878 human exome sequencing data, we conclude that the total improper pairing rate in DNBSEQ data is comparable to Illumina ones. Overall, the problem only affects the analysis results when simultaneously sequenced libraries have markedly different insert size distribution or flow cell loading. Additionally, we demonstrate here that raw DNBSEQ data might contain ~2% optical duplicates, resulting from the same effect of close neighboring of DNB-sites in the flow cell. Full article

Dothistroma needle blight (DNB) is one of the most significant diseases of conifers, causing premature defoliation, growth reduction, and, in extreme cases, mortality. Histological analysis was undertaken on inoculated seedlings of three different seed sources of Pinus sylvestris L. to investigate the process of infection and degradation of needle tissue on this host species. Seedlings were inoculated using a single spore isolate of Dothistroma septosporum (Doroguine) M. Morelet (D636) from northern Scotland. Mesophyll degradation in the needles occurred by four weeks after inoculation; collapse of mesophyll, bundle sheath tissues, and tracheids by five weeks; and eruption of fruiting bodies in near proximity to stomatal openings by six weeks. Significantly greater collapse of mesophyll during the early stages of infection occurred in the Austrian provenance compared with the United Kingdom provenance, although in the later stages of infection, this difference disappeared. Furthermore, disease severity, assessed as the proportion of needles with D. septosporum conidiomata on each tree, was not significantly different between seed sources. Full article

The extensive use of nitro compounds in agriculture, industry, armaments, and pharmaceuticals, along with their toxic effects on living organisms, necessitates efficient and environmentally sustainable analytical methods. Traditional extraction techniques often involve practices that are not eco-friendly, such as the use of large volumes of solvents, toxic chemicals, and the generation of significant waste; therefore, the single-drop microextraction technique was involved in overcoming these limitations. This study shows an environmentally friendly method for nitro compound analysis focusing on NB (Nitrobenzene), 2-NT (2-Nitrotoluene), 3-NT (3-Nitrotoluene), 4-NT (4-Nitrotoluene), 1,3-DNB (1,3-Dinitrobenzene), 1,2-DNB (1,2-Dinitrobenzene), 2,4-DNT (2,4-Dinitrotoluene), and TNT (Trinitrotoluene). To separate and to detect selected nitro compounds, gas chromatography with an electron capture detector was utilized, which is highly selective for analytes containing nitro groups. To determine optimal experimental conditions, extraction parameters were studied, including the impact of salt addition, temperature, and pH on extraction efficiency. Key performance parameters, such as limit of detection (LOD), limit of quantification (LOQ), repeatability, extraction recoveries, calibration range, and matrix effects, were assessed. The LOD values ranged from 0.01 to 0.09 μg/L in deionized water, 0.01 to 0.06 μg/L in tap water, 0.01 to 0.03 μg/L in seawater, and 0.03 to 0.11 μg/L in model forensic rinse water. The optimized method was successfully applied to the determination of nitro compounds in real environmental water samples and forensic rinse water samples. The environmental sustainability and greenness of the proposed method was evaluated with the AGREE, AGREEprep, and AESA techniques. Full article

The long-term monitoring of nighttime lights is essential for understanding sources of light pollution. Nighttime lights observed in space are affected by atmospheric conditions as they transmit from the Earth surface through clouds and aerosols to the top of the atmosphere. In this study, based on the monthly cloud-free VIIRS/DNB products, we analyzed the long-term nighttime lights in Hong Kong (2012–2020). We found that the monthly variations in nighttime lights were large, especially in bright regions. The 12-month average of nighttime lights ranged from 13.0 to 18.9 nWcm⁻²sr⁻¹. Public transportation facilities, such as port facilities and the airport, were the brightest, twice as bright as other urban areas. Public residential areas were slightly brighter than private ones. These urban areas were at least four times brighter than undeveloped regions, showing a significant alteration in light at night due to artificial facilities. Further, we used an unsupervised clustering method to identify specific patterns. While nighttime lights were stable in most regions, increasing trends were found at construction sites of a new artificial island and the airport expansion. Abnormal patterns, such as wildfires, were also recognized. We found that the background nighttime lights were brighter in wet months (e.g., April) and dimmer in dry months (e.g., January). The amount of water in the atmosphere affects nighttime light scattering, with a linear correlation (R = 0.68) between humidity and the occurrence of bright nighttime lights each month. The diverse sources and variations in nighttime lights call for continuous monitoring and advanced analytical methods to better understand their environmental and societal impacts. Full article