Collagen films with proton conduction are a candidate of next generation of fuel-cell electrolyte. To clarify a relation between proton conductivity and formation of water networks in the collagen film originating from a tilapia's scale, we systematically measured the ac conductivity, infrared absorption spectrum, and weight change as a function of relative humidity (RH) at room temperature. The integrated absorbance concerning an O-H stretching mode of water molecules increases above 60% RH in accordance with the weight change. The dc conductivity varies in the vicinity of 60 and 83% RH. From those results, we have determined the dc conductivity vs. https://www.selleckchem.com/products/pkc-theta-inhibitor.html hydration number (N) per unit (Gly-X-Y). The proton conduction is negligible in the collagen molecule itself, but dominated by the hydration shell, the development of which is characterized with three regions. For 0 less then N less then 2, the conductivity is extremely small, because the water molecule in the primary hydration shell has a little hydrogen bonded with each other. For 2 less then N less then 4, a quasi-one-dimensional proton conduction occurs through intra-water bridges in the helix. For 4 less then N, the water molecule fills the helix, and inter-water bridges are formed in between the adjacent helices, so that a proton-conducting network is extended three dimensional.Relying on large scale labeled datasets, deep learning has achieved good performance in image classification tasks. In agricultural and biological engineering, image annotation is time-consuming and expensive. It also requires annotators to have technical skills in specific areas. Obtaining the ground truth is difficult because natural images are expensive. In addition, images in these areas are usually stored as multichannel images, such as computed tomography (CT) images, magnetic resonance images (MRI), and hyperspectral images (HSI). In this paper, we present a framework using active learning and deep learning for multichannel image classification. We use three active learning algorithms, including least confidence, margin sampling, and entropy, as the selection criteria. Based on this framework, we further introduce an "image pool" to make full advantage of images generated by data augmentation. To prove the availability of the proposed framework, we present a case study on agricultural hyperspectral image classification. The results show that the proposed framework achieves better performance compared with the deep learning model. Manual annotation of all the training sets achieves an encouraging accuracy. In comparison, using active learning algorithm of entropy and image pool achieves a similar accuracy with only part of the whole training set manually annotated. In practical application, the proposed framework can remarkably reduce labeling effort during the model development and upadting processes, and can be applied to multichannel image classification in agricultural and biological engineering.Alveolar-ridge augmentation, anterior aesthetics, and digital technologies are probably the most popular topics in the dental-implant field. The aim of this report is to present a clinical case of severe atrophy of the anterior maxilla in a younger female patient, treated with a titanium membrane customized with computer-aided design/computer-aided manufacturing (CAD/CAM), simultaneous guided implant placement, and a fully digital workflow. A young female patient with a history of maxillary trauma was treated and followed-up for 1 year after implant placement. A narrow implant was inserted in a prosthetically driven position with the aid of computer-guided surgery. In the same surgical section, a customized implantable titanium mesh was applied. The scaffold was designed according to the contralateral maxillary outline in order to recreate a favorable maxillary bone volume. Finally, highly aesthetic, CAD/CAM, metal-free restorations were delivered using novel digital technologies.Considering the global health threat posed by kidney disease burden, a search for new nephroprotective drugs from our local flora could prove a powerful strategy to respond to this health threat. In this study we investigated the antihyperuricemic and nephroprotective potential of RA-3, a plant-derived lanosteryl triterpene. The antihyperuricemic and nephroprotective effect of RA-3 was investigated using the adenine and gentamicin induced hyperuricemic and nephrotoxicity rat model. Following the induction of hyperuricemia and nephrotoxicity, the experimental model rats (Sprague Dawley) were orally administered with RA-3 at 50 and 100 mg/kg body weight, respectively, daily for 14 days. Treatment of the experimental rats with RA-3, especially at 100 mg/kg, effectively lowered the serum renal dysfunction (blood urea nitrogen and creatinine) and hyperuricemic (uric acid and xanthine oxidase) biomarkers. These were accompanied by increased antioxidant status with decrease in malondialdehyde content. A much improved histomorphological structure of the kidney tissues was also observed in the triterpene treated groups when compared to the model control group. It is evident that RA-3 possesses the antihyperuricemic and nephroprotective properties, which could be vital for prevention and amelioration of kidney disease.The isomers 8-prenylnaringenin and 6-prenylnaringenin, both secondary metabolites occurring in hops, show interesting biological effects, like estrogen-like, cytotoxic, or neuro regenerative activities. Accordingly, abundant sources for this special flavonoids are needed. Extraction is not recommended due to the very low amounts present in plants and different synthesis approaches are characterized by modest yields, multiple steps, the use of expensive chemicals, or an elaborate synthesis. An easy synthesis strategy is the demethylation of xanthohumol, which is available due to hop extraction industry, using lithium chloride and dimethylformamide, but byproducts and low yield did not make this feasible until now. In this study, the demethylation of xanthohumol to 8-prenylnaringenin and 6-prenylnaringenin is described the first time and this reaction was optimized using Design of Experiment and microwave irradiation. With the optimized conditions-temperature 198 °C, 55 eq. lithium chloride, and a reaction time of 9 min, a final yield of 76% of both prenylated flavonoids is reached.