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Oral delivery of therapeutic proteins by engineered bacterial type zero secretion system

Author Correction: Fully addressable designer superstructures assembled from one single modular DNA origami

Polymeric ionic liquid promotes acidic electrocatalytic CO2 conversion to multicarbon products with ampere level current on Cu

A comprehensive multicriteria decision analysis framework for shallow geothermal energy suitability evaluation in Yinchuan Area, China

Association between gallbladder disease and colorectal neoplasia: a meta-analysis

Association between current relative fat mass and history of female infertility based on the NHANES survey

Exploring the determinants of mathematics teachers’ willingness to implement STEAM education using structural equation modeling

Multiple color images security by SPN over the residue classes of Gaussian integer $$\:Z{\left[i\right]}_{h}$$

SiO2 nanoparticles as disruptors of endogenous resolution mechanisms of inflammatory responses that exacerbate pneumonia

Low-frequency tremor-like episodes before the 2023 MW 7.8 Türkiye earthquake linked to cement quarrying

Abstract Recent advances in artificial intelligence have enhanced the detection and identification of transient low-amplitude signals across the entire frequency spectrum, shedding light on deformation processes preceding natural hazards. This study investigates low-frequency, low-amplitude signals preceding the 2023 M W 7.8 Kahramanmaraş earthquake in Türkiye. Using a deep neural network, we extract key features from the spectrograms of continuous seismic signals and employ unsupervised clustering to reveal distinct transient patterns. We identify an increased occurrence of low-frequency tremor-like signals during the six months preceding the mainshock. However, the location of these signals suggests that their origin is not tectonic, but rather related to anthropogenic activities at cement plants along the Narlı Fault, where the M W 7.8 mainshock nucleated. Such findings highlight the importance of understanding the origin of patterns detected by machine-learning methods and the large variety of seismic signals due to anthropogenic activities. Furthermore, the search for the origin of the tremor-like signals motivated an investigation into the local seismicity around the Narlı Fault. The resulting extended seismicity catalog suggests that seismicity in this area arises from a combination of tectonic and anthropogenic processes.

Impact of using oral spherical carbon adsorbent in predialysis chronic kidney disease period on cardiovascular outcome after dialysis therapy

A novel lightweight multi-scale feature fusion segmentation algorithm for real-time cervical lesion screening

Optimizing spatial normalization of multisubject inner ear MRI: comparison of different geometry-preserving co-registration approaches

Abstract Spatial normalization of multisubject inner ear imaging data is challenging, due to both substantial intraindividual differences and the small size of the organ compared to other intracranial structures. Automatic whole brain co-registration to standard space can only roughly co-align the peripheral vestibular endorgan, and complemental manual registration is highly time-consuming. Here, we compared the accuracy of four geometry-maintaining co-registration methods (one semi-manual method and three automatic methods). High-resolution structural T2-MRI of 153 inner ears from patients and healthy participants were co-registered to an inner-ear atlas. The semi-manual method used a three-point landmark-based approach (3P), two automatic methods were based on unassisted standard algorithms (Advanced Normalization Tools (ANTs), Elastix (EL)), while the fourth automatic method utilized a volumetrically dilated, atlas-based mask (thick inner ear, TIE) for probabilistic inner ear masking. Registration accuracy was evaluated by neurotologists blinded to the respective registration paradigm, and the resulting median volumes were quantified using colocalization analyses. The mask-aided automatic approach showed the best ratings, followed by the semi-manual three-point landmark-based registration (mean ratings (lower: better) TIE 2.21 ± 1.15; 3P 2.58 ± 0.61; EL 3.42 ± 1.06; ANTs 3.49 ± 1.26). The semi-manual method had the lowest rate of insufficient registrations, followed by TIE (3P: 3.70%; TIE: 8.28%; EL: 22.66%; ANTs: 27.02%). TIE showed the highest colocalization metrics with the atlas. Only TIE and 3P allowed for sufficient semicircular canal visualization in method-wise average volumes. Overall, geometry-preserving spatial normalization of multisubject inner ear imaging data is possible and could allow groupwise examinations of the bony labyrinth or temporal bone morphology in the future.

Human fetal lung mesenchymal stem cells ameliorate lung injury in an animal model

Influence of behavior of a coupled dynamic system on an energy harvester

Root uptake, translocation and persistence of EAB-specific dsRNA in ash seedlings

Synthesis of graphitic biocarbons from lignin fostered by concentrated solar energy

Abstract The approach aiming at replacing fossil-based carbons by graphitic biocarbon has gained momentum in applications from environmental remediation to battery electrodes and supercapacitors, reducing their environmental impact. To address biocarbon high production temperature and energy consumption, this work uses lignin, a renewable feedstock, and concentrated solar as a sustainable energy source. New insights into lignin’s graphitization mechanism using solar energy are provided. Graphene layers stacking appears as early as 1000 °C in solar carbonization. The structuration and reduction of amorphous carbon was further highlighted at 1400 °C and 1800 °C. At 2000 °C, high graphitic (L a(XRD)  ≈ 9.1 nm, d 002 = 0.3386 nm, 110 stacked layers) and turbostratic (d 002 = 0.3593 nm, 5.5 stacked layers) phases are obtained, showing the structural heterogeneity of solar biocarbon. Contrariwise, conventional biocarbon from electrical heating was homogeneous with limited carbonization at 1800 °C (L a(XRD)  ≈ 3.8 nm, d 002 = 0.3600 nm, 4.4 stacked layers). Textural analysis of solar biocarbons showed aligned graphene layers whereas only random texture was observed on conventional samples. This work established that solar carbonization triggers and enhances graphene layers stacking and growth at lower temperatures whereas conventional carbonization allows the progressive apparition of short graphene layers before stacking and growth.

Evaluating the impact of mine tailings wastes on the development of sustainable Ultra High Performance Fiber Reinforced concrete

Changes in the spectrum of ocular disease during the COVID-19 pandemic in late 2022 in the Hubei Province

Assessment of environmental and socioeconomic drivers of urban stormwater microplastics using machine learning