“dendRoAnalyst”: A tool for processing and analysing dendrometer data

Dendrometers are vital tools for studying the response of trees to intra-annual environmental changes in different temporal resolutions, ranging from hourly, daily to weekly time resolution. Dendrometers are increasingly used in forest management and tree physiological studies. Besides the data analysis, data processing is also challenging, time-consuming and potentially error-prone due to the immense number of measurements generated by self-registering electronic dendrometers. We present the package ‘dendRoAnalyst’ based on R statistical software to process and analyse dendrometer data using various approaches. This package offers algorithms for handling and pre-cleaning of dendrometer data before the application of subsequent data analytical steps. This includes identifying and erasing artefacts in dendrometer datasets not related to actual stem circumference change, identifying data gaps within records, and the possibility of change(s) in temporal resolution. Furthermore, the package can calculate different daily statistics of dendrometer data, including the daily amplitude of tree growth. The package dendRoAnalyst is therefore intended to facilitate researchers with a collection of functions for handling and analysing dendrometer data.     

Adjusting for Nonignorable Missingness When Estimating Generalized Additive Models

Generalized additive models (GAMs) have been widely used for flexible modeling of various types of outcomes. When the outcome in a GAM is subject to missing, practical analyses often assume that missingness is missing at random (MAR). This assumption can be of suspicion when the missingness is not by design. Evaluating the potential effects of alternative nonignorable missing data mechanism on the MAR inference from a GAM can be important but often challenging due to the complicatedness of alternative nonignorable models. We apply the index approach to local sensitivity (Troxel, Ma, and Heitjan 2004 (2004). Statistica Sinica 14 , 1221–1237) to evaluate the potential changes of the GAM estimates in the neighborhood of the MAR model. The approach avoids fitting any complicated nonignorable GAM. Only MAR estimates are required to calculate the resulting sensitivity index and adjust the GAM estimates to account for nonignorable missingness. Thus the proposed approach is considerably simpler to conduct, as compared with the alternative methods. The simulation study shows that the index provides valid assessment of the local sensitivity of the GAM estimates to nonignorable missingness. We then illustrate the method using a rheumatoid arthritis clinical trial data set.     

Translating Cancer Molecular Variability into Personalized Information Using Bulk and Single Cell Approaches

Cancer research is striving toward new frontiers of assigning the correct personalized drug(s) to a given patient. However, extensive tumor heterogeneity poses a major obstacle. Tumors of the same type often respond differently to therapy, due to patient‐specific molecular aberrations and/or untargeted tumor subpopulations. It is frequently not possible to determine a priori which patients will respond to a certain therapy or how an efficient patient‐specific combined therapy should be designed. Large‐scale datasets have been growing at an accelerated pace and various technologies and analytical tools for single cell and bulk level analyses are being developed to extract significant individualized signals from such heterogeneous data. However, personalized therapies that dramatically alter the course of the disease remain scarce, and most tumors still respond poorly to medical care. In this review, the basic concepts of bulk and single cell approaches are discussed, as well as their emerging role in individualized designs of drug therapies, including the advantages and limitations of their applications in personalized medicine.     

PDMS Microwell Stencil Based Multiplexed Single‐Cell Secretion Analysis

Multiplexed single‐cell protein secretion analysis provides an in‐depth understanding of cellular heterogeneity in intercellular communications mediated by secreted proteins in both fundamental and clinical research. However, it has been challenging to increase the proteomic parameters co‐profiled from every single cell in a facile way. Herein, a simple method to improve the multiplexed proteomic parameters of PDMS microwell based single‐cell secretion analysis platform by sandwiching PDMS stencil in between two antibody‐coated glass slides is introduced. Two different antibody panels can be immobilized easily by static coating, without using sophisticated fluid handling or bulky equipment. 5‐plexed, 3‐fluorescence color single‐cell secretion assay is demonstrated with this platform to investigate human monocytic U937 cells in response to lipopolysaccharide and phorbol myristate acetate stimulation, which identified the existence of functional subsets dictated by different cytokine profiles. The technology introduced here is simple, easy to operate, which holds great potential to become a powerful tool for profiling multiplexed single‐cell cytokine secretion at high throughput to dissect cellular heterogeneity in secretome signatures.     

微生物燃料电池发展态势分析

随着世界经济的高速发展和人口的不断增长,能源短缺和环境污染问题日益成为制约发展的瓶颈。微生物燃料电池(microbial fuel cell,MFC)能将污染物中蕴含的化学能直接转化为电能,实现同步污水处理和电能回收,是一种极具前景的可持续污水处理技术。同时,MFC在污泥处理、生物修复、环境监测、海水淡化等方面也展示了诱人的前景。基于科睿唯安Web of Science数据库和德温特专利检索分析平台(Derwent Innovation, DI),对MFC领域1990~2018年的论文和专利数据进行统计分析,得出全球MFC领域的发展趋势、国际分布、研发热点和技术格局。在此基础上,对未来MFC领域的发展做出了展望,对中国MFC产业化发展提出了思考和建议。     

全球冠状病毒疫苗专利分析

冠状病毒是一类可感染人类和动物的RNA病毒,可引起严重急性呼吸综合征(SARS)和中东呼吸综合征(MERS)等严重疾病。新型冠状病毒是以前从未在人体中发现的冠状病毒新毒株,其人际传播迅速,引起了各国政府的高度重视并积极寻求疫苗防控对策。基于冠状病毒疫苗领域全景专利,在综合对比分析该领域的全部专利的发展趋势、主要国家和主要机构的专利产出的同时,重点揭示了其中的人用相关疫苗的发展与分布情况以及重点分析了人用疫苗产品的研发现状,以期为我国冠状病毒疫苗领域的科研工作者和管理决策者提供参考数据。     

栽培条件对灵芝子实体中四种重金属含量的影响

以不同灵芝品种、不同栽培基质、不同栽培方式、不同生长时期获得的菌草灵芝和木屑灵芝为原料,对栽培基质及灵芝子实体中铅、砷、汞、镉4种重金属的含量参照国际标准进行检测,结果表明虽然菌草栽培基质中重金属含量高于木屑栽培基质,但菌草灵芝对重金属富集率远低于木屑灵芝,成熟期的菌草灵芝与木屑灵芝中的重金属含量均低于农业行业标准和国家标准。因此菌草可以替代木屑用作灵芝栽培的营养来源。