Unlocking the potential of survival data for model organisms through a new database and online analysis platform: SurvCurv

Lifespan measurements, also called survival records, are a key phenotype in research on aging. If external hazards are excluded, aging alone determines the mortality in a population of model organisms. Understanding the biology of aging is highly desirable because of the benefits for the wide range of aging‐related diseases. However, it is also extremely challenging because of the underlying complexity. Here, we describe SurvCurv, a new database and online resource focused on model organisms collating survival data for storage and analysis. All data in SurvCurv are manually curated and annotated. The database, available at www.ebi.ac.uk/thornton-srv/databases/SurvCurv/ , offers various functions including plotting, Cox proportional hazards analysis, mathematical mortality models and statistical tests. It facilitates reanalysis and allows users to analyse their own data and compare it with the largest repository of model‐organism data from published experiments, thus unlocking the potential of survival data and demographics in model organisms.     

Managing data locally to answer questions globally: The role of collaborative science in ecology

Ecologists are increasingly asking large‐scale and/or broad‐scope questions that require vast datasets. In response, various top‐down efforts and incentives have been implemented to encourage data sharing and integration. However, despite general consensus on the critical need for more open ecological data, several roadblocks still discourage compliance and participation in these projects; as a result, ecological data remain largely unavailable. Grassroots initiatives (i.e. efforts initiated and led by cohesive groups of scientists focused on specific goals) have thus far been overlooked as a powerful means to meet these challenges. These bottom‐up collaborative data integration projects can play a crucial role in making high quality datasets available because they tackle the heterogeneity of ecological data at a scale where it is still manageable, all the while offering the support and structure to do so. These initiatives foster best practices in data management and provide tangible rewards to researchers who choose to invest time in sound data stewardship. By maintaining proximity between data generators and data users, grassroots initiatives improve data interpretation and ensure high‐quality data integration while providing fair acknowledgement to data generators. We encourage researchers to formalize existing collaborations and to engage in local activities that improve the availability and distribution of ecological data. By fostering communication and interaction among scientists, we are convinced that grassroots initiatives can significantly support the development of global‐scale data repositories. In doing so, these projects help address important ecological questions and support policy decisions.     

Evaluating online sampling probes for substrate concentration and protein production by a Design of Experiments screening approach

A polypropylene sampling probe and ceramic sampling probe were tested for the online measurement of substrate and protein concentrations in fed batch cultivations of a recombinant Pichia pastoris strain overexpressing the enzyme HRP. Although small substrate molecules could be determined precisely under process conditions, online and offline data for enzyme activity and protein content showed offsets for both sampling probes. An easy‐to‐do multivariate Design of Experiments (DoE) screening approach revealed the limitations for both sampling probes. Online and offline determined data for enzymatic activity were fitted to a suitable exponential equation. A direct correlation of these equations showed a linear relation between online and offline data for the polypropylene probe and a quadratic relation for the ceramic probe. Using the resulting formulas, observed offsets could be compensated for by mathematical transformation, which consequently could allow the use of online calculator tools to determine the enzymatic activity and the protein content in quasi real‐time. This study demonstrates the usefulness of a DoE approach to evaluate online sampling probes in a short time and introduces a strategy to obtain data with two different online sampling probes also for large target molecules despite observed offsets.     

Approaches for Addressing Life Cycle Assessment Data Gaps for Bio‐based Products

There is an increasing need for life cycle data for bio‐based products, which becomes particularly evident with the recent drive for greenhouse gas reporting and carbon footprinting studies. Meeting this need is challenging given that many bio‐products have not yet been studied by life cycle assessment (LCA), and those that have are specific and limited to certain geographic regions. In an attempt to bridge data gaps for bio‐based products, LCA practitioners can use either proxy data sets (e.g., use existing environmental data for apples to represent pears) or extrapolated data (e.g., derive new data for pears by modifying data for apples considering pear‐specific production characteristics). This article explores the challenges and consequences of using these two approaches. Several case studies are used to illustrate the trade‐offs between uncertainty and the ease of application, with carbon footprinting as an example. As shown, the use of proxy data sets is the quickest and easiest solution for bridging data gaps but also has the highest uncertainty. In contrast, data extrapolation methods may require extensive expert knowledge and are thus harder to use but give more robust results in bridging data gaps. They can also provide a sound basis for understanding variability in bio‐based product data. If resources (time, budget, and expertise) are limited, the use of averaged proxy data may be an acceptable compromise for initial or screening assessments. Overall, the article highlights the need for further research on the development and validation of different approaches to bridging data gaps for bio‐based products.     

To share or not to share: Incentivizing data sharing in life science communities

Most scientists recognize the importance of sharing data online in an open fashion. Nonetheless, many studies have documented the concerns that accompany data sharing activities, including loss of credit or IP, misuse and the time needed to curate interoperable data. To this end, discussions around data sharing often identify incentives that could potentially ameliorate these disincentivising concerns. Nonetheless, current Open Data discussions often rely on evidence‐based studies to identify the disincentives to overcome. This results in highly specific and directed interventions. In contrast, this paper offers a different interpretation of these concerns. To do so, it makes use of the Thomas Theorem which suggests that: “If men define situations as real, they are real in their consequences”. Using empirical evidence from sub‐Saharan African (bio)chemistry laboratories, this paper illustrates how individual perceptions of research environments – whether associated with evidence or not – are highly influential in shaping data sharing practices. It concludes with the suggestion that discussion on incentivising data sharing amongst scientific communities need to take a broader set of concerns into account and offer a more creative approach to ameliorating environmental disincentives.     

基于网络点评数据的园林展会后利用评价研究

随着互联网的快速发展和大数据技术的应用,网络点评数据成为获取游客游览景区后体验感知的重要手段。从使用者的角度出发,构建基于网络点评数据的园林展会后利用评价体系,利用网络点评数据对园林展会后利用的各项因素进行分析和评价,从中总结出目前园林展会后利用面临的突出问题,并针对这些问题提出相关建议,以期为中国未来园林展会后利用提供可持续发展的新思路。     

Directional genetic differentiation and relative migration

Understanding the population structure and patterns of gene flow within species is of fundamental importance to the study of evolution. In the fields of population and evolutionary genetics, measures of genetic differentiation are commonly used to gather this information. One potential caveat is that these measures assume gene flow to be symmetric. However, asymmetric gene flow is common in nature, especially in systems driven by physical processes such as wind or water currents. As information about levels of asymmetric gene flow among populations is essential for the correct interpretation of the distribution of contemporary genetic diversity within species, this should not be overlooked. To obtain information on asymmetric migration patterns from genetic data, complex models based on maximum‐likelihood or Bayesian approaches generally need to be employed, often at great computational cost. Here, a new simpler and more efficient approach for understanding gene flow patterns is presented. This approach allows the estimation of directional components of genetic divergence between pairs of populations at low computational effort, using any of the classical or modern measures of genetic differentiation. These directional measures of genetic differentiation can further be used to calculate directional relative migration and to detect asymmetries in gene flow patterns. This can be done in a user‐friendly web application called divMigrate‐online introduced in this study. Using simulated data sets with known gene flow regimes, we demonstrate that the method is capable of resolving complex migration patterns under a range of study designs.     

Non‐invasive online detection of microbial lysine formation in stirred tank bioreactors by using calorespirometry

Non‐invasive methods for online monitoring of biotechnological processes without compromising the integrity of the reactor system are very important to generate continuous data. Even though calorimetry has been used in conventional biochemical analysis for decades, it has not yet been specifically applied for online detection of product formation at technical scale. Thus, this article demonstrates a calorespirometric method for online detection of microbial lysine formation in stirred tank bioreactors. The respective heat generation of two bacterial strains, Corynebacterium glutamicum ATCC 13032 (wild‐type) and C. glutamicum DM1730 (lysine producer), was compared with the O₂‐consumption in order to determine whether lysine was formed. As validation of the proposed calorespirometric method, the online results agreed well with the offline measured data. This study has proven that calorespirometry is a viable non‐invasive technique to detect product formation at any time point. Biotechnol. Bioeng. 2013; 110: 1386–1395. © 2012 Wiley Periodicals, Inc.     

Online but not remote: Adapting field‐based ecology laboratories for online learning

Teaching ecology effectively and experientially has become more challenging for at least two reasons today. Most experiences of our students are urban, and we now face the near immediate and continuing need to deliver courses (either partially or wholly) online because of COVID‐19. Therefore, providing a learning experience that connects students to their environment within an ecological framework remains crucial and perhaps therapeutic to mental health. Here, we describe how prior to the pandemic we adapted our field‐based laboratories to include data collection, analysis, and interpretation, along with the development of a citizen‐science approach for online delivery. This design is simple to implement, does not require extensive work, and maintains the veracity of original learning outcomes. Collaboration online following field data collection in ecology courses within the context of cities offers further options to adapt to student experience levels, resource availability, and accessibility, as well as bringing instructors and students together to build an open well‐curated data set that can be used in ecology courses where no laboratories are available. Finally, it promotes an open collaboration among ecology instructors that can drive lasting conversations about ecology curriculum.     

Transformer‐4 version 2.0.1, a free multi‐platform software to quickly reformat genotype matrices of any marker type,and archive them in the Demiurge information system

Transformer‐4 version 2.0.1 (T4) is a multi‐platform freeware programmed in java that can transform a genotype matrix in Excel or XML format into the input formats of one or several of the most commonly used population genetic software, for any possible combination of the populations that the matrix contains. T4 also allows the users to (i) draw allozyme gel interpretations for any number of diploid individuals, and then generate a genotype matrix ready to be used by T4; and (ii) produce basic reports about the data in the matrices. Furthermore, T4 is the only way to optionally submit ‘genetic diversity digests’ for publication in the Demiurge online information system ( http://www.demiurge-project.org ). Each such digest undergoes peer‐review, and it consists of a geo‐referenced data matrix in the tfm4 format plus any ancillary document or hyperlink that the digest authors see fit to include. The complementarity between T4 and Demiurge facilitates a free, safe, permanent, and standardized data archival and analysis system for researchers, and may also be a convenient resource for scientific journals, public administrations, or higher educators. T4 and its converters are freely available (at, respectively, http://www.demiurge-project.org/download_t4 and http://www.demiurge-project.org/converterstore ) upon registration in the Demiurge information system ( http://demiurge-project.org/register ). Users have to click on the link provided on an account validation email, and accept Demiurge's terms of use (see http://www.demiurge-project.org/termsofuse ). A thorough user's guide is available within T4. A 3‐min promotional video about T4 and Demiurge can be seen at http://vimeo.com/29828406 .     

Pattern Detection in Intensive Care Monitoring Time Series with Autoregressive Models: Influence of the Model Order

The detection of patterns in monitoring data of vital signs is of great importance for adequate bedside decision support in critical care. Currently used alarm systems, which are based on fixed thresholds and independency assumptions, are not satisfactory in clinical practice. Time series techniques such as AR‐models consider autocorrelations within the series, which can be used for pattern recognition in the data. For practical applications in intensive care the data analysis has to be automated. An important issue is the suitable choice of the model order which is difficult to accomplish online. In a comparative case‐study we analyzed 34564 univariate time series of hemodynamic variables in critically ill patients by autoregressive models of different orders and compared the results of pattern detection. AR(2)‐models seem to be most suitable for the detection of clinically relevant patterns, thus affirming that treating the data as independent leads to false alarms. Moreover, using AR(2)‐models affords only short estimation periods. These findings for pattern detection in intensive care data are of medical importance as they justify a preselection of a model order, easing further automated statistical online analysis.     

A database application for long‐term ecological field experiments

Problem: A series of long‐term field experiments is described, with particular reference to monitoring and quality control. This paper addresses problems in data‐management of particular importance for long‐term studies, including data manipulation, archiving, quality assessment, and flexible retrieval for analysis Method: The problems were addressed using a purpose‐built database system, using commercial software and running under Microsoft Windows. Conclusion: The database system brings many advantages compared to available software, including significantly improved quality checking and access. The query system allows for easy access to data sets thus improving the efficiency of analysis. Quality assessments of the initial dataset demonstrated that the database system can also provide general insight into types and magnitudes of error in data‐sets. Finally, the system can be generalised to include data from a number of different projects, thus simplifying data manipulation for meta‐analysis.     

Multiset sparse redundancy analysis for high‐dimensional omics data

Redundancy Analysis (RDA) is a well‐known method used to describe the directional relationship between related data sets. Recently, we proposed sparse Redundancy Analysis (sRDA) for high‐dimensional genomic data analysis to find explanatory variables that explain the most variance of the response variables. As more and more biomolecular data become available from different biological levels, such as genotypic and phenotypic data from different omics domains, a natural research direction is to apply an integrated analysis approach in order to explore the underlying biological mechanism of certain phenotypes of the given organism. We show that the multiset sparse Redundancy Analysis (multi‐sRDA) framework is a prominent candidate for high‐dimensional omics data analysis since it accounts for the directional information transfer between omics sets, and, through its sparse solutions, the interpretability of the result is improved. In this paper, we also describe a software implementation for multi‐sRDA, based on the Partial Least Squares Path Modeling algorithm. We test our method through simulation and real omics data analysis with data sets of 364,134 methylation markers, 18,424 gene expression markers, and 47 cytokine markers measured on 37 patients with Marfan syndrome.     

Extensive gaps and biases in our knowledge of a well‐known fauna: implications for integrating biological traits into macroecology

Aim Ecologists seeking to describe patterns at ever larger scales require compilations of data on the global abundance and distribution of species. Comparable compilations of biological data are needed to elucidate the mechanisms behind these patterns, but have received far less attention. We assess the availability of biological data across an entire assemblage: the well‐documented demersal marine fauna of the United Kingdom. We also test whether data availability for a species depends on its taxonomic group, maximum body size, the number of times it has been recorded in a global biogeographic database, or its commercial and conservation importance. Location Seas of the United Kingdom. Methods We defined a demersal marine fauna of 973 species from 15 phyla and 40 classes using five extensive surveys around the British Isles. We then quantified the availability of data on eight key biological traits (termed biological knowledge) for each species from online databases. Relationships between biological knowledge and our predictors were tested with generalized linear models. Results Full data on eight fundamental biological traits exist for only 9% (n= 88) of the UK demersal marine fauna, and 20% of species completely lack data. Clear trends in our knowledge exist: fish (median biological knowledge score = six traits) are much better known than invertebrates (one trait). Biological knowledge increases with biogeographic knowledge and (to a lesser extent) with body size, and is greater in species that are commercially exploited or of conservation concern. Main conclusions Our analysis reveals deep ignorance of the basic biology of a well‐studied fauna, highlighting the need for far greater efforts to compile biological trait data. Clear biases in our knowledge, relating to how well sampled or ‘important’ species are suggests that caution is required in extrapolating small subsets of biologically well‐known species to ecosystem‐level studies.     

Analysing censored data in agricultural research: A review with examples and software tips

Metric data are usually assessed on a continuous scale with good precision, but sometimes agricultural researchers cannot obtain precise measurements of a variable. Values of such a variable cannot then be expressed as real numbers (e.g., 1.51 or 2.56), but often can be represented by intervals into which the values fall (e.g., from 1 to 2 or from 2 to 3). In this situation, statisticians talk about censoring and censored data, as opposed to missing data, where no information is available at all. Traditionally, in agriculture and biology, three methods have been used to analyse such data: (a) when intervals are narrow, some form of imputation (e.g., mid‐point imputation) is used to replace the interval and traditional methods for continuous data are employed (such as analyses of variance [ANOVA] and regression); (b) for time‐to‐event data, the cumulative proportions of individuals that experienced the event of interest are analysed, instead of the individual observed times‐to‐event; (c) when intervals are wide and many individuals are collected, non‐parametric methods of data analysis are favoured, where counts are considered instead of the individual observed value for each sample element. In this paper, we show that these methods may be suboptimal: The first one does not respect the process of data collection, the second leads to unreliable standard errors (SEs), while the third does not make full use of all the available information. As an alternative, methods of survival analysis for censored data can be useful, leading to reliable inferences and sound hypotheses testing. These methods are illustrated using three examples from plant and crop sciences.     

Advances in tracking small migratory birds: a technical review of light‐level geolocation

Light‐level geolocation data loggers, or geologgers, have recently been miniaturized to the extent that they can be deployed on small songbirds, allowing us to determine many previously unknown migration routes, breeding locations, and wintering sites. Use of geologgers on small birds has great potential to help address major research and conservation questions, but the method is not without its shortcomings. Among these shortcomings are the need to recapture birds after they have carried a device throughout a migration cycle and the potential for the devices to affect survival and behavior. We examined return rates of birds with geologgers in published and unpublished studies and found no evidence of a general negative effect of geologgers on survival, although there were a few individual studies where such an effect was evident. From these same studies, we found that most currently used harness materials are equivalent in terms of failure rates, and the most reliable geologgers are those made by the British Antarctic Survey (although these were also the largest geologgers used in the studies we examined). With regard to analysis methods, we believe there is much room for improvement. Use of online archiving of both data and analysis parameters would greatly improve the repeatability and transparency of geologger research.