The effects of γ-aminobutyric acid (GABA) on the biomass and astaxanthin and lipids production in Haematococcus pluvialis under combined salinity stress and high-light stresses were investigated. read more The results showed that the highest biomass (1.65 g L-1), astaxanthin production (3.86 mg L-1 d-1) and lipids content (55.11%) in H. pluvialis LUGU were observed under the 0.25 mM GABA treatment. Moreover, compared with salinity and high-light stress, GABA treatment also increased the transcript levels of biosynthesis genes, the contents of endogenous GABA and carbohydrates but decreased reactive oxygen species (ROS) levels. Further evidence revealed that intracellular GABA could regulate cell growth, astaxanthin production and lipids synthesis by mediating carotenogenesis, lipogenesis and ROS signalling. Collectively, this study provides a combined strategy for promoting the coproduction of astaxanthin and lipids and sheds light on the regulatory mechanism through which GABA affects cell growth, astaxanthin production and lipids biosynthesis in H. pluvialis under unfavourable conditions.Hydrothermal carbonization (HTC) of yard waste (YW) and food waste (FW) was performed in landfill leachate (LL) to overcome the unnecessary exploitation of our limited natural resources. The physicochemical properties and combustion behavior of the resulting hydrochars were compared with those obtained using distilled water (DW) as reaction medium. Although performing HTC in LL led to lower hydrochar mass yields (43% YWH and 36% FWH) than DW (47.1% YWH and 41.5% FWH), it had minimal impact on the fuel characteristics of the hydrochars. Notably, the higher heating value of the hydrochars prepared in LL (22.8 MJ kg-1 for YWH and 30.2 MJ kg-1 for FWH) is comparable to that of conventional solid fuels, and may, therefore, be considered as inexpensive alternatives to fossil fuels. Overall, the results of this study conclusively suggest that the use of LL as an alternative moisture source can significantly improve the sustainability of HTC technology.

[

F]THK-5351 was originally developed as a positron emission tomography (PET) imaging tracer for the detection of accumulated tau proteins, the pathological hallmark of Alzheimer’s disease (AD). However, clinical studies of [

F]THK-5351 revealed the existence of off-target binding to monoamine oxidase-B (MAO-B). To overcome this off-target binding, in this work, we synthesized and evaluated 2-pyrrolopyridinylquinoline (PPQ) derivatives as selective tau PET imaging tracers.

The core structure of PPQ derivatives was synthesized mainly using the Buchwald-Hartwig amination coupling reaction. All derivatives were evaluated for binding affinity towards tau and MAO-B by in vitro competitive binding assay. Radiosynthesis of PPQ derivatives was performed by

F-radiolabeling of their tosylate precursors with activated [

F]KF/Kryptofix222 complex in dimethylsulfoxide by heating at 110°C for 10min. The biological properties of these [

F]PPQ derivatives were characterized by in vitro autoradiography of postmortem derivatives improved binding affinity and selectivity for tau aggregates in AD. Further structural optimization to improve pharmacokinetics for potent tau PET imaging tracers is required.Endoscopic images are used to observe the internal structure of the human body. Specular reflection (SR) images are mostly a consequence of the strong light and bright regions appearing on endoscopic images, which affects the performance of minimally invasive surgery. In this study, we propose a novel method for automatic SR detection based on intrinsic image layer separation (IILS). The proposed method consists of three steps. Initially, it involves the normalization of the image followed by the extraction of high gradient area, and the separation of SR is done on the basis of the color model. The image melding technique is utilized to reconstruct the reflected pixels. The experiments were conducted on 912 endoscopic images from CVC-EndoSceneStill. The results of accuracy, sensitivity, specificity, precision, Jaccard index, Dice coefficient, standard deviation, and pixel count difference show that the detection performance of the proposed method outperforms that of other state-of-the-art methods. The evaluation of the proposed IILS-based SR detection demonstrates that our method obtains better qualitative and quantitative assessments compared with other methods, which can be used as a promising preprocessing step for further analysis of endoscopic images.Common properties of dermatological diseases are mostly lesions with abnormal pattern and skin color (usually redness). Therefore, dermatology is one of the most appropriate areas in medicine for automated diagnosis from images using pattern recognition techniques to provide accurate, objective, early diagnosis and interventions. Also, automated techniques provide diagnosis without depending on location and time. In addition, the number of patients in dermatology departments and costs of dermatologist visits can be reduced. Therefore, in this work, an automated method is proposed to classify dermatological diseases from color digital photographs. Efficiency of the proposed approach is provided by 2 stages. In the 1st stage, lesions are detected and extracted by using a variational level set technique after noise reduction and intensity normalization steps. In the 2nd stage, lesions are classified using a pre-trained DenseNet201 architecture with an efficient loss function. In this study, five common facial dermatological diseases are handled since they also cause anxiety, depression and even suicide death. The main contributions provided by this work can be identified as follows (i) A comprehensive survey about the state-of-the-art works on classifications of dermatological diseases using deep learning; (ii) A new fully automated lesion detection and segmentation based on level sets; (iii) A new adaptive, hybrid and non-symmetric loss function; (iv) Using a pre-trained DenseNet201 structure with the new loss function to classify skin lesions; (v) Comparative evaluations of ten convolutional networks for skin lesion classification. Experimental results indicate that the proposed approach can classify lesions with high performance (95.24% accuracy).