Hen egg white lysozyme fibrillation: a deep‐UV resonance Raman spectroscopic study

Amyloid fibrils are associated with numerous degenerative diseases. The molecular mechanism of the structural transformation of native protein to the highly ordered cross‐β structure, the key feature of amyloid fibrils, is under active investigation. Conventional biophysical methods have limited application in addressing the problem because of the heterogeneous nature of the system. In this study, we demonstrated that deep‐UV resonance Raman (DUVRR) spectroscopy in combination with circular dichroism (CD) and intrinsic tryptophan fluorescence allowed for quantitative characterization of protein structural evolution at all stages of hen egg white lysozyme fibrillation in vitro. DUVRR spectroscopy was found to be complimentary to the far‐UV CD because it is (i) more sensitive to β ‐sheet than to α ‐helix, and (ii) capable of characterizing quantitatively inhomogeneous and highly light‐scattering samples. In addition, phenylalanine, a natural DUVRR spectroscopic biomarker of protein structural rearrangements, exhibited substantial changes in the Raman cross section of the 1000‐cm^–1 band at various stages of fibrillation. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

High-Throughput Cryo-EM Enabled by User-Free Preprocessing Routines

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Historical demography and climate driven distributional changes in a widespread Neotropical freshwater species with high economic importance

The Neotropical region exhibits the greatest worldwide diversity and the diversification history of several clades is related to the puzzling geomorphologic and climatic history of this region. The freshwater Amazon ecoregion contains the main hydrographic basins of the Neotropical region that are highly dendritic and ecologically diverse. It contains a rich and endemic fish fauna, including one of its most iconic and economically important representatives, the bony-tongue Arapaima gigas (Teleostei, Osteoglossiformes). Here, we evaluated the projected distribution of the genus in different historical periods (Present, Last Glacial Maximum, Last Interglacial Maximum and Near Future) and interpreted these results in light of the genomic diversity and modeled historical demography. For that, we combined species distribution models, population genetic analysis using SNPs and deep learning model selection. We analyzed a representative sample of the genus from the two basins where it naturally occurs, four localities in the Amazon (Am) and three in the Tocantins-Araguaia (To-Ar) basin, as well as individuals from three fish farms. We inferred a potentially smaller distribution in the glacial period, with a possible refuge in central Am. Our genetic data agrees with this result, suggesting a higher level of genetic diversity in the Am basin, compared to that observed in To-Ar. Our deep learning model comparison indicated that the To-Ar basin was colonized by the population from the Am basin. Considering a global warming scenario in the near future, A. gigas could reach an even larger range, especially if anthropogenic related dispersal occurs, potentially invading new areas and impacting their communities.     

肝素C5异构酶的表达优化及分子改造

肝素和硫酸乙酰肝素是一类应用于临床抗凝血的糖胺聚糖。肝素葡萄糖醛酸C5异构酶(Heparosan-N-sulfate-glucuronate 5-epimerase,C5,EC 5.1.3.17) 是肝素和硫酸乙酰肝素合成过程中重要的修饰酶,催化N-硫酸化肝素前体 (N-sulfoheparosan) 的D-葡萄糖醛酸 (D-GlcA) 上5号位羧基翻转生成L-艾杜糖醛酸 (L-iduronic acid,L-IdoA)。文中以大肠杆菌Escherichia coli为宿主对斑马鱼来源的肝素葡萄糖醛酸C5异构酶基因Glce进行重组表达优化与分子改造。比较了3种不同的表达载体pET20b(+)、pET28a(+) 和pCold Ⅲ对C5表达的差异情况,其中以嗜冷启动型载体pCold Ⅲ表达酶活最高,达到(1 873.61±5.42) U/L。为了进一步提高C5的可溶表达量,在N端融合促溶标签SET2后,可溶蛋白表达量比对照提高了50%,酶活达到 (2 409.25±6.43) U/L。在此基础上,通过理性设计对底物结合口袋进行定点突变,获得最优突变体 (V153R) 的酶活和比酶活分别为 (5 804.32±5.63) U/L和(145.14±2.33) U/mg,是原始酶的2.41倍和2.28倍。肝素C5异构酶改造与表达优化为酶法催化合成肝素奠定了基础。     

Three critical factors affecting automated image species recognition performance for camera traps

Ecological camera traps are increasingly used by wildlife biologists to unobtrusively monitor an ecosystems animal population. However, manual inspection of the images produced is expensive, laborious, and time‐consuming. The success of deep learning systems using camera trap images has been previously explored in preliminary stages. These studies, however, are lacking in their practicality. They are primarily focused on extremely large datasets, often millions of images, and there is little to no focus on performance when tasked with species identification in new locations not seen during training. Our goal was to test the capabilities of deep learning systems trained on camera trap images using modestly sized training data, compare performance when considering unseen background locations, and quantify the gradient of lower bound performance to provide a guideline of data requirements in correspondence to performance expectations. We use a dataset provided by Parks Canada containing 47,279 images collected from 36 unique geographic locations across multiple environments. Images represent 55 animal species and human activity with high‐class imbalance. We trained, tested, and compared the capabilities of six deep learning computer vision networks using transfer learning and image augmentation: DenseNet201, Inception‐ResNet‐V3, InceptionV3, NASNetMobile, MobileNetV2, and Xception. We compare overall performance on “trained” locations where DenseNet201 performed best with 95.6% top‐1 accuracy showing promise for deep learning methods for smaller scale research efforts. Using trained locations, classifications with <500 images had low and highly variable recall of 0.750 ± 0.329, while classifications with over 1,000 images had a high and stable recall of 0.971 ± 0.0137. Models tasked with classifying species from untrained locations were less accurate, with DenseNet201 performing best with 68.7% top‐1 accuracy. Finally, we provide an open repository where ecologists can insert their image data to train and test custom species detection models for their desired ecological domain.     

基于植保大数据的病虫害移动智能采集新设备

为提高农作物重大病虫害发生信息自动化、智能化采集能力,全面提升监测预警水平,笔者基于大数据、人工智能和深度学习技术,研发了一款农作物病虫害移动智能采集设备——智宝,主要实现了3个方面的功能:一是病虫害发生信息自动采集上报.通过该产品进行人工拍照,可实现对田间农作物重大病虫害发生图像、发生位置、发生数量、微环境因子等数据的实时采集和上报.二是自动识别计数.基于植保大数据与人工智能技术,通过构建病虫害自动识别系统,可实现重大病虫害精准识别与分析,只要拍摄照片,即可快速、精确地识别病虫害种类,并自动计数、上报到指定的测报系统.三是自动分析判别分级.针对拍摄采集上报的重大病虫害发生信息,系统可在自动识别和计数的基础上,进一步对病虫害发生严重程度进行智能判别分级,甚至根据相关预测模型,对病虫害的发生趋势进行辅助分析预测,提出预测建议.通过2016—2019年组织多地植保机构进行试验改进,该技术产品日趋成熟,有望在未来的农作物病虫害发生信息采集和预测预报工作中推广使用.