Exploration of GH94 Sequence Space for Enzyme Discovery Reveals a Novel Glucosylgalactose Phosphorylase Specificity

The substantial increase in DNA sequencing efforts has led to a rapid expansion of available sequences in glycoside hydrolase families. The ever-increasing sequence space presents considerable opportunities for the search for enzymes with novel functionalities. In this work, the sequence-function space of glycoside hydrolase family 94 (GH94) was explored in detail, using a combined approach of phylogenetic analysis and sequence similarity networks. The identification and experimental screening of unknown clusters led to the discovery of an enzyme from the soil bacterium Paenibacillus polymyxa that acts as a 4-O-β-d -glucosyl-d -galactose phosphorylase (GGalP), a specificity that has not been reported to date. Detailed characterization of GGalP revealed that its kinetic parameters were consistent with those of other known phosphorylases. Furthermore, the enzyme could be used for production of the rare disaccharides 4-O-β-d -glucosyl-d -galactose and 4-O-β-d -glucosyl-l -arabinose. Our current work highlights the power of rational sequence space exploration in the search for novel enzyme specificities, as well as the potential of phosphorylases for rare disaccharide synthesis.     

Non‐geostationary satellite orbit communications satellite constellations history

This special issue of the journal on ‘constellations’ comes at a critical time in their development as a second wave of such non‐geostationary satellite orbit (NGSO) systems is being planned and deployed. These mega‐constellations as they have become known are, with a few exceptions, very much larger than those in the first wave and are focused on broadband and 5G applications rather than speech and narrow band data as those deployed in the first wave during the 1990s. However, as we explain in this editorial, there are many similarities in the design and business plans to the first wave and, perhaps, many similar lessons to be learned.     

基于改进 abcd 模型的黄河源区径流变化与归因

为改进模型对高寒地区融雪径流模拟不足的缺陷，将融雪模块耦合到传统 abcd 模型。利用 1980—2018 年 逐月实测的径流数据和通过 AnuSpline 方法插值的格网气象要素，驱动改进后的abcd 模型，分析三江源生态保护 措施实施前后（1980—1999 年和 2000—2018 年）黄河源区径流的动态变化，并量化关键气象因素与人类活动对 径流变化的影响程度，即相对贡献。结果表明：耦合融雪模块的 abcd-snow 模型完善了高寒地区水文过程的模拟， 提高对径流的模拟性能，在黄河源区表现出较好的适用性；整个研究时段黄河源区的实测径流呈不显著减少趋势 （?0.80?mm/a，p>0.05），但 2000 年前径流则呈现显著下降趋势（?4.12?mm/a，p<0.05），2000 年后径流则呈显著增加 趋势（3.16?mm/a，p<0.05）；?归因分析表明气候变化是源区径流变化的主导因素。2000 年前，气候变化对径流减少 的相对贡献率为 62.8%，人类活动对径流的贡献为 37.2%；2000 年后，气候变化对径流增加的贡献率达到 120.0?%， 人类活动对径流的贡献为?20.0%。其中：降水的变化是决定径流变化主导因素；其他气候因素的相对贡献较小； 以人类活动为主的生态恢复可显著降低河川径流。本研究有助于理解气候变化和下垫面变化对黄河源区水资源 变化的系统驱动机理，并为流域水资源合理配置提供科学参考依据。     

丹尼尔斯大厦 加拿大多伦多

正多伦多大学丹尼尔斯建筑、景观和设计学院的设计采用了分期的方法,包括修复和扩建以前称为"知识学院"的标志性建筑,将现有建筑物改造成与教学和需求更相关的框架体系。这个十九世纪遗址的总体规划是通过对预期用途模式和场地生态的分析而制定的,目的是重新定位校园西南角与安大略湖的轴线,并为学院创造新的特性。丹尼尔斯建筑、景观和设计学院需要一个可持续发展的新型工     

Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture

Agricultural robots rely on semantic segmentation for distinguishing between crops and weeds to perform selective treatments and increase yield and crop health while reducing the amount of chemicals used. Deep‐learning approaches have recently achieved both excellent classification performance and real‐time execution. However, these techniques also rely on a large amount of training data, requiring a substantial labeling effort, both of which are scarce in precision agriculture. Additional design efforts are required to achieve commercially viable performance levels under varying environmental conditions and crop growth stages. In this paper, we explore the role of knowledge transfer between deep‐learning‐based classifiers for different crop types, with the goal of reducing the retraining time and labeling efforts required for a new crop. We examine the classification performance on three datasets with different crop types and containing a variety of weeds and compare the performance and retraining efforts required when using data labeled at pixel level with partially labeled data obtained through a less time‐consuming procedure of annotating the segmentation output. We show that transfer learning between different crop types is possible and reduces training times for up to 80%. Furthermore, we show that even when the data used for retraining are imperfectly annotated, the classification performance is within 2% of that of networks trained with laboriously annotated pixel‐precision data.     

Sulfonylguanidine Derivatives as Potential Antimelanoma Agents

Sulfonylguanidines are interesting bioactive compounds with a broad range of applications in the treatment of different pathologies. 2-Aminobenzazole-based structures are well employed in the development of new anticancer drugs. Two series of novel N-benzazol-2-yl-N′-sulfonyl guanidine derivatives were synthesized with the sulfonylguanidine in either an extra- or intracyclic frame. They were evaluated for their antiproliferative activity against malignant melanoma tumor cells, thus allowing structure-activity relationships to be defined. Additionally, NCI-60 screening was performed for the best analogue to study its efficiency against a panel of other cancer cell lines. The stability profile of this promising compound was then validated. During the synthetic process, an unexpected new deamidination of the sulfonylguanidine towards sulfonamide function was also identified.     

A Dual Inhibitor of DYRK1A and GSK3β for β-Cell Proliferation: Aminopyrazine Derivative GNF4877

Loss of β-cell mass and function can lead to insufficient insulin levels and ultimately to hyperglycemia and diabetes mellitus. The mainstream treatment approach involves regulation of insulin levels; however, approaches intended to increase β-cell mass are less developed. Promoting β-cell proliferation with low-molecular-weight inhibitors of dual-specificity tyrosine-regulated kinase 1A (DYRK1A) offers the potential to treat diabetes with oral therapies by restoring β-cell mass, insulin content and glycemic control. GNF4877, a potent dual inhibitor of DYRK1A and glycogen synthase kinase 3β (GSK3β) was previously reported to induce primary human β-cell proliferation in vitro and in vivo. Herein, we describe the lead optimization that lead to the identification of GNF4877 from an aminopyrazine hit identified in a phenotypic high-throughput screening campaign measuring β-cell proliferation.