In the example of alcohol/water, the computation time is less, and it can be applied to continuous column monitoring.The electrocatalytic reduction of CO2 into useful fuels, exploiting rationally designed, inexpensive, active, and selective catalysts, produced through easy, quick, and scalable routes, represents a promising approach to face today’s climate challenges and energy crisis. This work presents a facile strategy for the preparation of doped SnO2 as an efficient electrocatalyst for the CO2 reduction reaction to formic acid and carbon monoxide. Zn or Ti doping was introduced into a mesoporous SnO2 matrix via wet impregnation and atomic layer deposition. It was found that doping of SnO2 generates an increased amount of oxygen vacancies, which are believed to contribute to the CO2 conversion efficiency, and among others, Zn wet impregnation resulted the most efficient process, as confirmed by X-ray photoelectron spectroscopy analysis. Electrochemical characterization and active surface area evaluation show an increase of availability of surface active sites. In particular, the introduction of Zn elemental doping results in enhanced performance for formic acid formation, in comparison to un-doped SnO2 and other doped SnO2 catalysts. At -0.99 V versus reversible hydrogen electrode, the total faradaic efficiency for CO2 conversion reaches 80%, while the partial current density is 10.3 mA cm-2. These represent a 10% and a threefold increases for faradaic efficiency and current density, respectively, with respect to the reference un-doped sample. The enhancement of these characteristics relates to the improved charge transfer and conductivity with respect to bare SnO2.The brain encompasses a complex network of neurons with exceptionally elaborated morphologies of their axonal (signal-sending) and dendritic (signal-receiving) parts. De novo actin filament formation is one of the major driving and steering forces for the development and plasticity of the neuronal arbor. Actin filament assembly and dynamics thus require tight temporal and spatial control. Such control is particularly effective at the level of regulating actin nucleation-promoting factors, as these are key components for filament formation. Arginine methylation represents an important post-translational regulatory mechanism that had previously been mainly associated with controlling nuclear processes. We will review and discuss emerging evidence from inhibitor studies and loss-of-function models for protein arginine methyltransferases (PRMTs), both in cells and whole organisms, that unveil that protein arginine methylation mediated by PRMTs represents an important regulatory mechanism in neuritic arbor formation, as well as in dendritic spine induction, maturation and plasticity. Recent results furthermore demonstrated that arginine methylation regulates actin cytosolic cytoskeletal components not only as indirect targets through additional signaling cascades, but can also directly control an actin nucleation-promoting factor shaping neuronal cells-a key process for the formation of neuronal networks in vertebrate brains.The mechanistic target of rapamycin (mTOR) is a central regulator of cellular homeostasis that integrates environmental and nutrient signals to control cell growth and survival. Over the past two decades, extensive studies of mTOR have implicated the importance of this protein complex in regulating a broad range of metabolic functions, as well as its role in the progression of various human diseases. Recently, mTOR has emerged as a key signaling molecule in regulating animal entry into a hypometabolic state as a survival strategy in response to environmental stress. Here, we review current knowledge of the role that mTOR plays in contributing to natural hypometabolic states such as hibernation, estivation, hypoxia/anoxia tolerance, and dauer diapause. Studies across a diverse range of animal species reveal that mTOR exhibits unique regulatory patterns in an environmental stressor-dependent manner. We discuss how key signaling proteins within the mTOR signaling pathways are regulated in different animal models of stress, and describe how each of these regulations uniquely contribute to promoting animal survival in a hypometabolic state.The world population is growing by 1 billion people every 10 years. There will come a time when there will be more people to feed but less land to grow food. Greenhouses can be the solution to this problem because they provide the highest production yield per m2 and also use less water, provide food safety, and offer high quality. Photosynthetic active radiation (PAR) favors vegetable growth with a specific blue and red light ratio. Thus, increasing the amount of red light improves chlorophyll absorption and photosynthetic efficiency. In this article, we present a hybrid system that combines luminescent materials and photonic crystals for better management of the light reaching the greenhouse. The luminescent dyes considered herein are combined ensuring a Förster resonance energy transfer (FRET) nonradiative mechanism to enhance the absorption range. The designed photonic crystal maximizes reflections in the Near-Infrared (NIR) range, and therefore, thermal losses are minimized. Thus, by converting harmful or ineffective radiation for plant growth to the PAR region, we aim to demonstrate growth-condition enhancement for the different vegetables that have been used as a model.Accurate mandible segmentation is significant in the field of maxillofacial surgery to guide clinical diagnosis and treatment and develop appropriate surgical plans. In particular, cone-beam computed tomography (CBCT) images with metal parts, such as those used in oral and maxillofacial surgery (OMFS), often have susceptibilities when metal artifacts are present such as weak and blurred boundaries caused by a high-attenuation material and a low radiation dose in image acquisition. To overcome this problem, this paper proposes a novel deep learning-based approach (SASeg) for automated mandible segmentation that perceives overall mandible anatomical knowledge. SASeg utilizes a prior shape feature extractor (PSFE) module based on a mean mandible shape, and recurrent connections maintain the continuity structure of the mandible. The effectiveness of the proposed network is substantiated on a dental CBCT dataset from orthodontic treatment containing 59 patients. Selleck Compound 19 inhibitor The experiments show that the proposed SASeg can be easily used to improve the prediction accuracy in a dental CBCT dataset corrupted by metal artifacts.