Mosquitoes, notorious as the deadliest animals to humans due to their capacity to transmit diseases, pose a persistent challenge to public health. The primary prevention strategy currently in use involves chemical repellents, which often prove ineffective as mosquitoes rapidly develop resistance. Consequently, the invention of new preventive methods is crucial. Such development hinges on a thorough understanding of mosquito biting behaviors, necessitating an experimental setup that accurately replicates actual biting scenarios with controllable testing parameters and quantitative measurements. To bridge this gap, a bio-hybrid atomic force microscopy (AFM) probe was engineered, featuring a biological stinger - specifically, a mosquito labrum - as its tip. This bio-hybrid probe, compatible with standard AFM systems, enables a near-authentic simulation of mosquito penetration behaviors. This method marks a step forward in the quantitative study of biting mechanisms, potentially leading to the creation of effective barriers against vector-borne diseases (VBDs) and opening new avenues in the fight against mosquito-transmitted illnesses.
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http://dx.doi.org/10.3791/66675 | DOI Listing |
J Phys Chem A
January 2025
Computer Modelling Group, 3710 33 St NW, Calgary, Alberta T2L 2M1, Canada.
Coarse-grained molecular dynamics simulation is widely accepted for assessment of a large complex biological system, but it may also lead to a misleading conclusion. The challenge is to simulate protein structural dynamics (such as folding-unfolding behavior) due to the lack of a necessary backbone flexibility. This study developed a standard coarse-grained model directly from the protein atomic structure and amino acid coarse-grained FF (such as MARTINI FF v2.
View Article and Find Full Text PDFSci Rep
January 2025
Jilin Key Laboratory of Solid-State Laser Technology and Application, School of Science, Changchun, 130022, Jilin, China.
The response mechanism of a Four-Quadrant Photodetector (QPD) in an experimental setting was studied by irradiating a single QPD cell with a millisecond-pulsed laser. The response signal of the irradiated QPD cell varied with energy flux, pulse width, and applied bias, and comprised four main stages: an initial stage, decreasing barrier stage, holding stage, and recovery stage. Not only was the response signal of the irradiated cell affected by laser irradiation, but also the responses of the other three cells.
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January 2025
Department of Graphic Arts and Photophysics, Faculty of Chemical Technology, University of Pardubice, Studentská 573, Pardubice, 532 10, Czech Republic.
Radio frequency magnetron co-sputtering method employing GeTe and Sc targets was exploited for the deposition of Sc doped GeTe thin films. Different characterization techniques (scanning electron microscopy with energy-dispersive X-ray analysis, X-ray diffraction, atomic force microscopy, sheet resistance temperature-dependent measurements, variable angle spectroscopic ellipsometry, and laser ablation time-of-flight mass spectrometry) were used to evaluate the properties of as-deposited (amorphous) and annealed (crystalline) Ge-Te-Sc thin films. Prepared amorphous thin films have GeTe, GeTeSc, GeTeSc, GeTeSc and GeTeSc chemical composition.
View Article and Find Full Text PDFLangmuir
January 2025
Univ. Rouen Normandie, Normandie Univ., SMS, UR 3233, F-76000 Rouen, France.
It has been shown that depositing ketoprofen as thin films on glass substrates has a stabilizing effect on the amorphous state of ketoprofen. Polyethylene glycol ( = 6000 g/mol) was mixed with ketoprofen in a wide range of concentrations. Amorphous thin films were prepared by spin coating and subjected to storage conditions with different levels of relative humidity.
View Article and Find Full Text PDFJ Phys Chem A
January 2025
Liaoning Key Laboratory of Manufacturing System and Logistics Optimization, Shenyang 110819, China.
Artificial intelligence technology has introduced a new research paradigm into the fields of quantum chemistry and materials science, leading to numerous studies that utilize machine learning methods to predict molecular properties. We contend that an exemplary deep learning model should not only achieve high-precision predictions of molecular properties but also incorporate guidance from physical mechanisms. Here, we propose a framework for predicting molecular properties based on data-driven electron density images, referred to as D3-ImgNet.
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