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Prevalence of anhedonia in women with deep endometriosis
Unveiling nature’s antidiabetic potential in Berberis orthobotrys seeds for alloxan-induced diabetic rats
Glycosylation gene expression profiles enable prognosis prediction for colorectal cancer
ATRX loss inhibits DDR to strengthen radio-sensitization in p53-deficent HCT116 cells
Analysis of full length transcriptome and resistance characteristics of Atraphaxis bracteata under drought
AbstractDrought is one of the main environmental factors affecting plant survival and growth. Atraphaxis bracteata is a common desert plant mainly utilized in afforestation and desertification control. This study analyzed the morphological, physiological and molecular regulatory characteristics of different organs of A. bracteata under drought stress. The results showed that with the intensification of drought stress, the height, root length and leaf area of A. bracteata seedlings decreased, while the content of osmotic substances and antioxidant enzyme activity increased. Furthermore, a total of 63 907 non-redundant transcript sequences, of which 55 574 transcripts were annotated, 248 178 FLNC sequences, 107 112 high-quality consistent sequences and 291 314 CCSs were obtained from Iso-Seq. Meanwhile, a total of 2 039 AS events, 22 919 SSR, 40 404 CDS and 5 902 lncRNA were also obtained. The RNA-Seq analysis results revealed that a total of 2 821, 3 907 and 5 532 DETs were identified from roots, stems and leaves, respectively, and which had significantly enrichment in “circadian rhythm-plant” and “starch and sucrose metabolism” pathway. These results would be great significance for further research on the stress resistance of A. bracteata and these DETs function.
Enhancement of quantum synchronization in triple-cavity system
Enhancing security and efficiency in Mobile Ad Hoc Networks using a hybrid deep learning model for flooding attack detection
Effects of urban sprawl due to migration on spatiotemporal land use-land cover change: a case study of Bartın in Türkiye
Unveiling the radiation shielding efficacy of diorite, granodiorite, tonalite, and granite: experimental and simulation study
Abstract For the purpose of this study, four natural rock samples-namely, diorite, granodiorite, tonalite, and granite-are being investigated about their radiation attenuation. The elemental composition of the rocks was obtained through Energy dispersive X-ray spectroscopy (EDX) which examines the microstructural and localized area elemental analyses of the four rock samples. A Monte Carlo simulation (MCNP) was used to determine and evaluate the investigated samples. Additionally, the samples were validated by Phy-X software (within the energy range of 0.015 to 15 MeV), and experimental measurements were achieved through the utilization of an HPGe detector (0.060, 0.662, 1.173, and 1.332 MeV). The investigation was carried out using various parameters such as linear attenuation (µ) and others. Furthermore, the Fast Neutrons Removal Cross Sections (FNRCS) were calculated using theoretical methods. In the case of granodiorite, tonalite, diorite, and granite, the values of µ were found to range from 7.931 to 0.049, 31.922 to 0.061, 17.267 to 0.060, and 23.860 to 0.056 cm−1, respectively. The samples of tonalite and diorite have the highest µ values due to the presence of heavy elements and the high densities of these samples. Granodiorite is the substance that possesses the highest value of FCS (0.108 cm−1) due to the high content of light elements (O = 0.6802%, and C = 0.2286% wt). The results of the study demonstrated that the investigated natural rocks possessed a substantial potential for shielding γ-rays and neutrons from radiation and could be suitable for use in radiological protection applications.
Natural sampling and aliasing of marine geochemical signals
AbstractIt is well known that the sedimentary rock record is both incomplete and biased by spatially highly variable rates of sedimentation. Without absolute age constraints of sufficient resolution, the temporal correlation of spatially disjunct records is therefore problematic and uncertain, but these effects have rarely been analysed quantitatively using signal processing methods. Here we use a computational process model to illustrate and analyse how spatial and temporal geochemical records can be biased by the inherent, heterogenous processes of marine sedimentation and preservation. This confirms that sedimentary hiatuses can span a substantial proportion of geological time, caused by inherent geological processes. Moreover, even in marine geochemical records that are essentially spatially continuous and complete, the signal is irreversibly disguised in time as lower frequency signals by an aliasing effect. We demonstrate that Nyquist’s theorem correctly predicts these biased signatures, proving that aliasing can be caused by cyclical and multiscale relative sea-level changes - a natural sampling effect. Our combined results show that deeper marine records are significantly more likely to provide unaliased environmental signatures. Also, some proxy residence times will be sufficiently long that they average over aliased frequencies, cancelling these in the geochemical record. And field observations of changes in sedimentation rate (such as hiatuses, condensation, or lateral expansion) can be used to infer possible aliasing. Where aliasing by natural sampling occurs, this cannot be undone simply by increasing sample resolution (density): aliasing is caused by an absence of sedimentary record, which by definition cannot be sampled at all. To overcome these issues, we propose that spatially separated aliased records may still be correctly correlated in age, and true geochemical cycles inferred, if a paired-sampling strategy informed by local stratigraphy is adopted. In this, two (or more) closely-spaced samples are analysed at each sampling point instead of only one, after which aliased cycles are inferred from geochemical gradients.
Integrated approach for estimating climate change impacts on CO2 sink capacity of inland waterbodies using hydrodynamic modelling and GIS analysis
AbstractAs one of their key regulatory ecosystem functions, inland lakes serve as CO2 sinks. The CO2 sink capacity of inland lakes depends on their water temperature and salinity as well as their water volume which are all highly sensitive to climate conditions. This paper aims to quantitatively estimate the change in the CO2 sink capacity of Wadi El-Rayan Lakes under climate change scenarios. For this purpose, an integrated approach combining CO2 solubility modelling, hydrodynamic simulations (Delft3D-FLOW) and GIS analysis was employed. According to the developed approach, CO2 solubility under variable temperature and salinity is mathematically modelled and this model is further used with the developed hydrodynamic model data for Wadi El-Rayan Lakes (temperature, salinity and water depth) to estimate their CO2 sink capacities. CO2 sink capacity is estimated for 2014 and 2050 under two Representative Concentration Pathways (RCPs) 2.6 and 8.5. Afterwards, the alteration in CO2 sink capacities due to climate change is determined using the modified hydrodynamic model. The results revealed that by 2050, the lakes would lose about 23–25% of their capacities compared to that of 2014 according to RCP 2.6 and 8.5, respectively.
Serial biomarker measurements may be helpful to predict the successful application of high flow nasal cannula in COVID-19 pneumonia patients
Age but not vitamin D is related to sarcopenia in vitamin D sufficient male elderly in rural China
Treatment patterns for chronic obstructive pulmonary disease under the tiered medical system
Preparation of Ag-Metal organic frameworks-loaded Sodium Alginate Hydrogel for the treatment of periodontitis
Optimizing optical, dielectric, and electrical properties of polyvinyl alcohol/polyvinyl pyrrolidone/poly(3,4-ethylene dioxythiophene) polystyrene sulfonate/NiO-based polymeric nanocomposites for optoelectronic applications
AbstractAn electro- and optically favorable quaternary nanocomposite film was produced by solution-casting nickel oxide nanoparticles (NiO NPs) into polyvinyl alcohol (PVA), polyvinyl pyrrolidone (PVP), and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT/PSS). Based on transmission electron microscopy (TEM) and X-ray diffraction (XRD) observations, the synthesized NiO NPs have a cubic phase and a diameter between 10 and 45 nm. The complexity and interactions observed through XRD patterns, UV–visible spectra, and FTIR measurements suggest that the NPs are not just dispersed within the polymer matrix, but are interacting with it, leading to enhanced dielectric properties and AC electrical conductivity. From 9 × 103 to 3.22 × 103 Ω, NiO NPs concentrations reduce bulk resistance Rb, indicating more linked conductive channels. The dielectric tests showed that polarized nanoparticles increased polarizability under electric field conditions. The incorporation of NiO NPs boosted DC conductivity from 1.25 × 10–6 to 5.64 × 10–5 S m−1. The mobility of NiO NPs boosts DC conductivity linearly with field frequency. These interactions can lead to improved electrical conductivity, energy storage capabilities, and overall efficiency of the nanocomposite, making it a promising material for various applications.
Unsupervised self-organising map classification of Raman spectra from prostate cell lines uncovers substratified prostate cancer disease states
AbstractProstate cancer is a disease which poses an interesting clinical question: Should it be treated? Only a small subset of prostate cancers are aggressive and require removal and treatment to prevent metastatic spread. However, conventional diagnostics remain challenged to risk-stratify such patients; hence, new methods of approach to biomolecularly sub-classify the disease are needed. Here we use an unsupervised self-organising map approach to analyse live-cell Raman spectroscopy data obtained from prostate cell-lines; our aim is to exemplify this method to sub-stratify, at the single-cell-level, the cancer disease state using high-dimensional datasets with minimal preprocessing. The results demonstrate a new sub-clustering of the prostate cancer cell-line into two groups—protein-rich and lipid-rich sub-cellular components—which we believe to be mechanistically linked. This finding shows the potential for unsupervised machine learning to discover distinct disease-state features for more accurate characterisation of highly heterogeneous prostate cancer. Applications may lead to more targeted diagnoses, prognoses and clinical treatment decisions via molecularly-informed stratification that would benefit patients. A method that could discover distinct disease-state features that are mechanistically linked could also assist in the development of more effective broad-spectrum treatments that simultaneously target linked disease-state processes.