The theoretical basis and practical application of relationships between different disease intensity measurements in plants

The literature concerning relationships among different measures of plant disease intensity is reviewed. Some previous confusion over the definition of the terms “incidence” and “severity” is noted and clarified. The review highlights the common features of relationships between incidence and severity, incidence and disease density, and incidence at pairs of scales in a spatial hierarchy. These relationships often show a similar saturation curve form that can frequently be described empirically using the complementary log‐log transformation. A catalogue of alternative functional forms is provided. Practical applications in varietal evaluation are discussed.     

Identifying climatic niche shifts using coarse‐grained occurrence data: a test with non‐native freshwater fish

Aim We tested whether coarse‐grained occurrence data can be used to detect climatic niche shifts between native and non‐native ranges for a set of widely introduced freshwater fishes. Location World‐wide. Methods We used a global database of freshwater fish occurrences at the river basin scale to identify native and non‐native ranges for 18 of the most widely introduced fish species. We also examined climatic conditions within each river basin using fine‐grained climate data. We combined this information to test whether climatic niche shifts have occurred between native and non‐native ranges. We defined climatic niche shifts as instances where the ranges of a climatic variable within native and non‐native basins exhibit zero overlap. Results We detected at least one climatic niche shift for each of the 18 studied species. However, we did not detect common patterns in the thermal preference or biogeographic origin of the non‐native fish, hence suggesting a species‐specific response. Main conclusions Coarse‐grained occurrence data can be used to detect climatic niche shifts. They also enable the identification of the species experiencing niche shifts, although the mechanisms responsible for these shifts (e.g. local adaptation, dispersal limitation or physiological constraints) have yet to be determined. Furthermore, the coarse‐grained approach, which highlights regions where climatic niche shifts have occurred, can be used to select specific river basins for more detailed, fine‐grained studies.     

海南岛不同地形上某些土壤化学性质的多样性分析

多样性指数长时间以来在生态学领域被用于分析不同生态体的发生规律．将类似手段用于某些土壤化学性质(土壤pH值、表层阳离子代换量和表层有机质含量)多样性的分析中,讨论了基于海南岛土壤一地体数字化数据库(SOTER),通过对不同地形上土壤pH值、表层阳离子代换量和表层有机质含量类别SOTER单元的丰富度指数(S)、多样性指数(Shannon指数H’)和均匀度指数(Pielou E)的计算,建立了类别多度分布模型,并在地理信息系统环境中实现了海南岛不同地形上土壤化学性质多样性分析的数字化表达．研究表明,上述研究路线具有可操作性。几种多样性指数和多度分布模型的建立可以较好地反映不同地形上土壤化学性质类别的空间变异特点．因此,SOTER单元作为土壤性质多样性的测度单元是可行的．     

法夫酵母分批发酵虾青素动力学研究

通过三联30L全自动发酵罐对虾青素产生菌法夫酵母的分批发酵动力学进行了研究,结果表明,法夫酵母的生长与限制性基质葡萄糖浓度之间符合Logistic方程,建立了细胞生长、产物合成和基质消耗随时间变化的数学模型。应用MATLAB软件对发酵动力学模型进行最优参数估计和非线性拟和,获得最大比生长速率（umax）和产物得率（Yp/x）分别为0.1829/h、0.1524g/g,虾青素分批发酵中细胞生长与产物合成属于偶联型,模型模拟计算结果和实验值能较好地吻合,动力学研究结果表明该模型能较好地反映细胞的生长、底物消耗和产物合成过程机制。     

Quantitative structure–activity relationship analysis using deep learning based on a novel molecular image input technique

Quantitative structure–activity relationship (QSAR) analysis uses structural, quantum chemical, and physicochemical features calculated from molecular geometry as explanatory variables predicting physiological activity. Recently, deep learning based on advanced artificial neural networks has demonstrated excellent performance in the discipline of QSAR research. While it has properties of feature representation learning that directly calculate feature values from molecular structure, the use of this potential function is limited in QSAR modeling. The present study applied this function of feature representation learning to QSAR analysis by incorporating 360° images of molecular conformations into deep learning. Accordingly, I successfully constructed a highly versatile identification model for chemical compounds that induce mitochondrial membrane potential disruption with the external validation area under the receiver operating characteristic curve of ≥0.9.     

Three-dimensional in vitro tumor models for cancer research and drug evaluation

Cancer occurs when cells acquire genomic instability and inflammation, produce abnormal levels of epigenetic factors/proteins and tumor suppressors, reprogram the energy metabolism and evade immune destruction, leading to the disruption of cell cycle/normal growth. An early event in carcinogenesis is loss of polarity and detachment from the natural basement membrane, allowing cells to form distinct three-dimensional (3D) structures that interact with each other and with the surrounding microenvironment. Although valuable information has been accumulated from traditional in vitro studies in which cells are grown on flat and hard plastic surfaces (2D culture), this culture condition does not reflect the essential features of tumor tissues. Further, fundamental understanding of cancer metastasis cannot be obtained readily from 2D studies because they lack the complex and dynamic cell–cell communications and cell–matrix interactions that occur during cancer metastasis. These shortcomings, along with lack of spatial depth and cell connectivity, limit the applicability of 2D cultures to accurate testing of pharmacologically active compounds, free or sequestered in nanoparticles. To recapitulate features of native tumor microenvironments, various biomimetic 3D tumor models have been developed to incorporate cancer and stromal cells, relevant matrix components, and biochemical and biophysical cues, into one spatially and temporally integrated system. In this article, we review recent advances in creating 3D tumor models employing tissue engineering principles. We then evaluate the utilities of these novel models for the testing of anticancer drugs and their delivery systems. We highlight the profound differences in responses from 3D in vitro tumors and conventional monolayer cultures. Overall, strategic integration of biological principles and engineering approaches will both improve understanding of tumor progression and invasion and support discovery of more personalized first line treatments for cancer patients.     

甲状腺髓样癌遗传学研究进展

甲状腺髓样癌(medullary thyroid carcinoma,MTC)是起源于甲状腺C细胞或滤泡旁细胞的恶性肿瘤,分遗传型髓样癌和散发型髓样癌两种.MTC主要由RET原癌基因突变引起,对患者进行基因测序分析能在基因水平诊断MTC,从而为患者早期行预防性手术治疗提供依据.从甲状腺髓样癌临床分型、分子遗传基础及动物模型不同层面进行综述,有利于进一步了解疾病致病机制和开展药物实验性治疗研究.     

Prediction of potential drivers connecting different dysfunctional levels in lung adenocarcinoma via a protein–protein interaction network

Lung cancer is a serious disease that threatens an affected individual's life. Its pathogenesis has not yet to be fully described, thereby impeding the development of effective treatments and preventive measures. “Cancer driver” theory considers that tumor initiation can be associated with a number of specific mutations in genes called cancer driver genes. Four omics levels, namely, (1) methylation, (2) microRNA, (3) mutation, and (4) mRNA levels, are utilized to cluster cancer driver genes. In this study, the known dysfunctional genes of these four levels were used to identify novel driver genes of lung adenocarcinoma, a subtype of lung cancer. These genes could contribute to the initiation and progression of lung adenocarcinoma in at least two levels. First, random walk with restart algorithm was performed on a protein–protein interaction (PPI) network constructed with PPI information in STRING by using known dysfunctional genes as seed nodes for each level, thereby yielding four groups of possible genes. Second, these genes were further evaluated in a test strategy to exclude false positives and select the most important ones. Finally, after conducting an intersection operation in any two groups of genes, we obtained several inferred driver genes that contributed to the initiation of lung adenocarcinoma in at least two omics levels. Several genes from these groups could be confirmed according to recently published studies. The inferred genes reported in this study were also different from those described in a previous study, suggesting that they can be used as essential supplementary data for investigations on the initiation of lung adenocarcinoma. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.     

甘蓝类无蜡粉亮叶性状遗传规律及其利用的研究

我们于1987年从普通结球甘蓝“迎春”品种自交二代群体中,发现了无蜡粉亮叶甘蓝突变株, 经过多年对其遗传规律进行的研究,认为这一无蜡粉亮叶性状是由一对隐性纯合基因控制。利用这一性状可培育结球甘蓝及其它甘蓝类具有这同一性状的新类型、新品种,提高其品质,更可作一代杂种利用的标记性状,充分发挥一代杂种的优势。     

Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is a key to meeting the pressing needs of science and society in the 21st century.