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Gap solitons in moiré optical lattice with quartic dispersion
A tight binding study of electron transport in notched graphene nanoribbons
Abstract Edge corrugated, notched graphene nanoribbons (GNRs) exhibit intriguing electronic properties distinct from their straight counterparts, thereby offering suitable candidates for the exploration of electron transport in future carbon-based nanoelectronic devices. Here, we utilize the tight binding (TB) method to investigate the electronic structure and quantum transport in gulf- and chevron-type notched GNRs. Consistent with earlier TB calculations, we reaffirm that the third-nearest neighbour hopping parameter is responsible for the electronic braiding effect and zero-energy conductance channels in straight zigzag GNRs (ZGNRs), here demonstrated for 2ZGNR and 5ZGNR. However, for notched gulf- or chevron-type GNRs, generated by selectively eliminating carbon atoms at either one or both ZGNR edges, the electronic band structures can be radically changed from semiconductor to metallic, with near-Fermi dispersive or flat bands. For the explored asymmetrically notched chevron-type GNRs hosting a metallic flat band at the Fermi energy, unlike straight ZGNRs, the electronic transport was found to depend primarily on the second-nearest neighbour, exhibiting a sharp conductance peak (of 1 unit conductance) at the Fermi energy. This result is found to be generic for all asymmetric chevron-type GNRs, irrespective of the nanoribbon width, and also for edge-notched armchair GNRs hosting similarly metallic flat bands. For the metallic symmetrically notched chevron-type GNR, however, the near-Fermi dispersive bands lead to multiple conductance channels around the Fermi energy, with fine structure dependence on the number of hopping parameters utilized. These results are analyzed with respect to the spatial distribution of the metallic states and how they transverse across the ZGNR leads. The present study should have large implications on the exploration of electronic transport in carbon-based nanoelectronic devices.
Exopolysaccharide produced from Lactiplantibacillus plantarum HAN99 and its nanoparticle formulations in agricultural applications
Abstract In this study, Lactiplantibacillus plantarum HAN99, isolated from sediment samples collected along the Alexandria Mediterranean Seacoast in Egypt, was evaluated for its ability to produce polysaccharides. To optimize polysaccharide production, statistical techniques were used, and the extracted polysaccharides were purified for further characterization. High-Performance Liquid Chromatography (HPLC) analysis identified glucose and galactose as the primary components of the polysaccharide. These polysaccharides were then loaded onto chitosan-based nanoparticles, which were characterized using Fourier Transform-Infrared Spectroscopy (FT-IR) and scanning electron microscopy (SEM). The study further investigated the potential agricultural applications of the polysaccharide-loaded nanoparticles by assessing their effects on plant growth. The results revealed that the nanoparticles enhanced the growth of Mentha (mint) leaves, reducing leaf loss compared to the control group. Additionally, the EPS chitosan-based nanoparticles exhibited strong antioxidant activity, as demonstrated by a DPPH assay (∼75.6–80.3%). These findings highlight the potential of microbial polysaccharides as sustainable, eco-friendly alternatives for agricultural enhancement and the development of green agricultural practices.
The presence of neutralizing antibodies against omicron subvariants among a vaccinated cohort at one year after the first dose of vaccination in Malaysia
Abstract This study assesses the neutralizing antibodies response to Omicron subvariants and examines factors associated with seropositivity in a vaccinated Malaysian cohort. It is a prospective cohort study, conducted between June 2021 and October 2022. Descriptive and binary logistic regression analyses were performed on 1,117 adults aged 18 and above. Among the subvariants, seropositivity rates were: BA.2 (81.9%), BA.1 (68.4%), and BA.4/5 (64.2%). Non-Malaysians had significantly higher odds of testing positive for BA.2 compared to Malaysians (OR: 8.009; 95% CI: 1.273–50.402). Recipients of AstraZeneca (OR: 3.955; 95% CI: 2.414–6.482) and CanSino (OR: 1.980; 95% CI: 1.047–3.743) vaccines had higher odds of BA.2 seropositivity compared to Pfizer recipients. For BA.4/5, individuals aged 60 and above had greater odds of seropositivity (OR: 1.751; 95% CI: 1.029–2.979) compared to those aged 18–39. Chinese ethnicity was associated with lower odds of seropositivity than Malay ethnicity across BA.1 (OR: 0.508; 95% CI: 0.350–0.736), BA.2 (OR: 0.570; 95% CI: 0.377–0.861), and BA.4/5 (OR: 0.671; 95% CI: 0.467–0.963). This study highlights that completing primary vaccination and booster doses remains critical to reducing severe COVID-19 outcomes, underscoring the need for ongoing surveillance and targeted strategies for vulnerable demographic and socio-environmental groups.
Impact of carbon nanotubes on chloride diffusion in cement mortar under temperature gradient conditions
Identification and validation of inflammatory response genes linking chronic kidney disease with coronary artery disease based on bioinformatics and machine learning
Amylin takes another shot at the obesity prize
Correlation of optic nerve hemoglobin levels with structural and functional parameters in glaucoma
Is the FDA’s plan to phase out animal toxicity testing realistic?
Point cloud registration based on surface feature extraction and an improved Grey Wolf Optimization algorithm
Abstract This study introduces an innovative feature point extraction method combined with an improved Grey Wolf Optimizer (GWO)-based coarse registration approach to address common challenges of low registration accuracy and slow processing speed in point cloud registration. The feature extraction design method begins by projecting the point cloud onto a uniformly segmented sphere. Principal component analysis (PCA) is then employed to compute the curvature change rate of the point set within each patch area. Subsequently, sampling weights are assigned nonlinearly based on the calculated change rates, facilitating effective feature point extraction. The extracted feature points serve as the initial values for the improved gray wolf optimization algorithm, which is employed to refine the registration results. Experimental comparisons conducted on three public datasets demonstrate that the feature extraction method proposed in this study achieves improved accuracy and efficiency. Furthermore, the registration results substantiate that our method outperforms other algorithms with respect to both accuracy and computational efficiency.
Deep learning driven interpretable and informed decision making model for brain tumour prediction using explainable AI
FDA approves a rare novel–novel oncology combination that pairs a first-in-class FAK inhibitor with a MEK–RAF glue inhibitor
Effect of lead zinc mineralization area on heavy metals accumulation and geochemical fractions of agricultural soils in Southwest China
Multi-omics for unveiling potential antidiabetic markers from red, green and black mung beans using NIR-UPLC-MS/MS multiplex approach
Abstract Inspired by the nutritional and biological attributes of mung beans, the current work aims to monitor metabolome patterns of different mung species and their entanglements on antidiabetic potential using NIR-UPLC-MS/MS multiplex approach combined with chemometrics. In this regard, a total of 71 chromatographic peaks spanning sugars, amino acids, flavonoids, fatty acids and their lipid derivatives, and phytosterols were chemically profiled. Coincidently, OPLS-DA underscored an obvious discrimination among the green, red and black mung species suggesting their chemical discrepancies where eriodictyol-O-glucoside, caffeic acid, formononetin-O-glucoside, viniferal and genistin serve as focal discriminators of green mung beans while lysoPC 18:2, lanosterol, gallocatechin, tyramine, petunidin 3-O-glucoside, biochanin A, vigvexin A, vignatic acid B, lysoPC 16:0 and phaseollin were the determining metabolites of red ones. Successively, the differential markers enriched in black mung samples included 10-formyltetrahydrofolate, stearidonic acid, hydroxylinoleic acid, vignatic acid A, campestrol, arachidonic acid and PG (18:2/18:1). Experimentally speaking, all mung samples exerted noteworthy dose-dependent inhibitory potential towards α-amylase and α-glucosidase enzymes. OPLS coefficient plots highlighted gamma-aminobutyric acid (GABA), gallic acid and beta-sitosterol as possible efficacy metabolites harmoniously mediated antidiabetic potential. Equally important, NIR spectroscopic analysis coupled with PLS-R model quantitively predicted the bio-efficient markers from various mung bean samples with a significant level of experimental reliability. These findings pursue concept of nutritional therapy and provide a fresh perspective to probe into mung beans bioactive molecules which might serve as referenced templates for mitigating diabetes. However, future work should be explored to uncover muti-target mechanisms of mung beans-derived compounds and strengthen their relevance.