A latent class model to multiply impute missing treatment indicators in observational studies when inferences of the treatment effect are made using propensity score matching

Analysts often estimate treatment effects in observational studies using propensity score matching techniques. When there are missing covariate values, analysts can multiply impute the missing data to create m completed data sets. Analysts can then estimate propensity scores on each of the completed data sets, and use these to estimate treatment effects. However, there has been relatively little attention on developing imputation models to deal with the additional problem of missing treatment indicators, perhaps due to the consequences of generating implausible imputations. However, simply ignoring the missing treatment values, akin to a complete case analysis, could also lead to problems when estimating treatment effects. We propose a latent class model to multiply impute missing treatment indicators. We illustrate its performance through simulations and with data taken from a study on determinants of children's cognitive development. This approach is seen to obtain treatment effect estimates closer to the true treatment effect than when employing conventional imputation procedures as well as compared to a complete case analysis.     

Using real-world data to predict health outcomes—The prediction design: Application and sample size planning

The gold standard for investigating the efficacy of a new therapy is a (pragmatic) randomized controlled trial (RCT). This approach is costly, time-consuming, and not always practicable. At the same time, huge quantities of available patient-level control condition data in analyzable format of (former) RCTs or real-world data (RWD) are neglected. Therefore, alternative study designs are desirable. The design presented here consists of setting up a prediction model for determining treatment effects under the control condition for future patients. When a new treatment is intended to be tested against a control treatment, a single-arm trial for the new therapy is conducted. The treatment effect is then evaluated by comparing the outcomes of the single-arm trial against the predicted outcomes under the control condition. While there are obvious advantages of this design compared to classical RCTs (increased efficiency, lower cost, alleviating participants’ fear of being on control treatment), there are several sources of bias. Our aim is to investigate whether and how such a design—the prediction design—may be used to provide information on treatment effects by leveraging external data sources. For this purpose, we investigated under what assumptions linear prediction models could be used to predict the counterfactual of patients precisely enough to construct a test and an appropriate sample size formula for evaluating the average treatment effect in the population of a new study. A user-friendly R Shiny application (available at: https://web.imbi.uni-heidelberg.de/PredictionDesignR/ ) facilitates the application of the proposed methods, while a real-world application example illustrates them.     

一种感知视觉运动信息的简化脑模型

视觉运动信息的感知过程,包括从局域运动检测到对模式整体运动的感知过程.我们以蝇视觉系统的图形-背景相对运动分辨的神经回路网络为基本框架,采用初级运动检测器的六角形阵列作为输入层,构造了一种感知视觉运动信息的简化脑模型,模拟了运动信息应该神经计算模型各个层次上的处理.该模型对差分行为实验结果作出了正确预测.本文并对空间生理整合的神经机制作了讨论.     

Toward a hypothesis-free understanding of how phosphorylation dynamically impacts protein turnover

The turnover measurement of proteins and proteoforms has been largely facilitated by workflows coupling metabolic labeling with mass spectrometry (MS), including dynamic stable isotope labeling by amino acids in cell culture (dynamic SILAC) or pulsed SILAC (pSILAC). Very recent studies including ours have integrated themeasurement of post-translational modifications (PTMs) at the proteome level (i.e., phosphoproteomics) with pSILAC experiments in steady state systems, exploring the link between PTMs and turnover at the proteome-scale. An open question in the field is how to exactly interpret these complex datasets in a biological perspective. Here, we present a novel pSILAC phosphoproteomic dataset which was obtained during a dynamic process of cell starvation using data-independent acquisition MS (DIA-MS). To provide an unbiased “hypothesis-free” analysis framework, we developed a strategy to interrogate how phosphorylation dynamically impacts protein turnover across the time series data. With this strategy, we discovered a complex relationship between phosphorylation and protein turnover that was previously underexplored. Our results further revealed a link between phosphorylation stoichiometry with the turnover of phosphorylated peptidoforms. Moreover, our results suggested that phosphoproteomic turnover diversity cannot directly explain the abundance regulation of phosphorylation during cell starvation, underscoring the importance of future studies addressing PTM site-resolved protein turnover.     

FAIR research data management as community approach in bioengineering

Research data management (RDM) requires standards, policies, and guidelines. Findable, accessible, interoperable, and reusable (FAIR) data management is critical for sustainable research. Therefore, collaborative approaches for managing FAIR-structured data are becoming increasingly important for long-term, sustainable RDM. However, they are rather hesitantly applied in bioengineering. One of the reasons may be found in the interdisciplinary character of the research field. In addition, bioengineering as application of principles of biology and tools of process engineering, often have to meet different criteria. In consequence, RDM is complicated by the fact that researchers from different scientific institutions must meet the criteria of their home institution, which can lead to additional conflicts. Therefore, centrally provided general repositories implementing a collaborative approach that enables data storage from the outset In a biotechnology research network with over 20 tandem projects, it was demonstrated how FAIR-RDM can be implemented through a collaborative approach and the use of a data structure. In addition, the importance of a structure within a repository was demonstrated to keep biotechnology research data available throughout the entire data lifecycle. Furthermore, the biotechnology research network highlighted the importance of a structure within a repository to keep research data available throughout the entire data lifecycle.     

Sodium and potassium relations of Spergularia marina following N and P deprivation: results of short-term growth studies

In this, we consider the coordination of plant growth and ion acquisition, reporting the short-term adjustments of growth and K⁺ and Na⁺ relations which follow when plants are subject to a sudden deprivation of N and P. The plant used for the experiments, Spergularia marina (L.) Grieseb., is a small coastal halophyte, and the growth medium was 0.2 × modified seawater. By considering nutrients whose availability has not been changed, we report on an aspect of organismal integration which has received little attention either experimentally or in mathematical models. The studies are limited to the first 60 h after N and P deprivation in order to consider changes that, if they are not primary responses, are not temporally remote, passive adjustments. For growth analyses, plants were used approximately 30 days after germination and 16 days after transfer to solution culture. Random harvests were made at hourly invervals, and after 12 h, one-half of the plants were transferred to cultures without N or P. Tissue analyses were used to calculate relative growth rates, relative accumulation rates and net uptake rates. For comparison, isotope uptake studies using ⁴²K⁺ and ²²Na⁺ were conducted at 12, 36 and 60 h after deprivation. The effects on growth and biomass allocation were very rapid, detectable within 13 h. K⁺ transport also responded quickly, and from the beginning of the study, there was essentially no net translocation of K⁺ to the shoot. Isotope studies confirmed the responsiveness, with translocation reduced 33 and 90% after 12 and 36 h, respectively. Though Na⁺ adjustments were slower, they were coordinated with growth such that tissue concentrations in the N and P-deprived plants were comparable to those in the controls. We conclude that N and C are insufficient elements on which to build mathematical models useful to environmental physiologists. At a minimum, the incorporation of K⁺ relations in growth models would both allow the development of the osmotic potential needed to drive cell expansion, and provide a means to probe –experimentally as well as mathematically – the coordinating mechanisms of plant growth and resource management.     

Resource and hardware options for microarray-based experimentation.

DNA microarray technology permits the study of biological systems and processes on a genome-wide scale. Arrays based on cDNA clones, oligonucleotides and genomic clones have been developed for investigations of gene expression, genetic analysis and genomic changes associated with disease. Over the past 3-4 years, microarrays have become more widely available to the research community. This has occurred through increased commercial availability of custom and generic arrays and the development of robotic equipment that has enabled array printing and analysis facilities to be established in academic research institutions. This brief review examines the public and commercial resources, the microarray fabrication and data capture and analysis equipment currently available to the user.     

Sectoriality and physiological organisation in herbaceous plants: an overview

The physiological organisation of plants is considered in relation to the carbon economy of plant parts. Although assimilate is partitioned according to the relative strength of sinks, in many species there is also a very close relationship between partitioning and shoot phyllotaxy, giving rise to sectorial patterns of allocation whereby only certain sinks are supported by any source leaf. Essentially these sinks are in the same orthostichy as the source leaf. This constraint of the vascular architecture on assimilate distribution to developing sinks such as leaves, flowers and fruits is not always absolute, as following the loss of their principal source leaves these sinks can in many cases be supplied with assimilate by other leaves via new inter-orthostichy pathways. The supply of assimilate to major sinks such as developing fruits becomes more and more localised with time so that a fruit in an axillary position becomes largely supported by its subtending leaf; the reproductive node—a metamer-can thus be regarded as a relatively autonomous unit of the plant (an IPU). Similary, once established after a developmental phase of assimilate import, tiller ramets and branches in unitary plants tend to become physiologically autonomous modules. However, the functional autonomy of tillers is reversed following defoliation or shading as they are then sustained by the import of assimilate, subject to its availability, from unaffected tillers. Consequently the plant becomes physiologically integrated by the flow of assimilate from one part to another. The mainly autonomous ramets of many stoloniferous and rhizomatous species display a similar pattern of physiological integration in response to source manipulation, but in some species the ramets appear to maintain their independent functioning as a normal feature of the carbon allocation within the clone. In other clonal species, as the clone develops and becomes more structurally complex, vascular constraints start to restrict the movement of resources, and the clone becomes composed of a number of semi-autonomous IPUs. In unitary plants branches appear to remain very physiologically isolated in terms of their carbon economy once they become established, irrespective of a range of source-sink manipulations.These different patterns of physiological integration and organisation are discussed in relation to different strategies of assimilate utilisation and conservation.     

The intrinsic electrophysiological characteristics of fly lobula plate tangential cells: I. Passive membrane properties

The passive membrane properties of the tangential cells in the fly lobula plate (CH, HS, and VS cells, Fig. 1) were determined by combining compartmental modeling and current injection experiments. As a prerequisite, we built a digital base of the cells by 3D-reconstructing individual tangential cells from cobalt-stained material including both CH cells (VCH and DCH cells), all three HS cells (HSN, HSE, and HSS cells) and most members of the VS cell family (Figs. 2, 3). In a first series of experiments, hyperpolarizing and depolarizing currents were injected to determine steady-state I-V curves (Fig. 4). At potentials more negative than resting, a linear relationship holds, whereas at potentials more positive than resting, an outward rectification is observed. Therefore, in all subsequent experiments, when a sinusoidal current of variable frequency was injected, a negative DC current was superimposed to keep the neurons in a hyperpolarized state. The resulting amplitude and phase spectra revealed an average steady-state input resistance of 4 to 5 M and a cut-off frequency between 40 and 80 Hz (Fig. 5). To determine the passive membrane parameters R _m (specific membrane resistance), R _i (specific internal resistivity), and C _m (specific membrane capacitance), the experiments were repeated in computer simulations on compartmental models of the cells (Fig. 6). Good fits between experimental and simulation data were obtained for the following values: R _m = 2.5 kcm², R _i = 60 cm, and C _m = 1.5 F/cm² for CH cells; R _m = 2.0 kcm², R _i = 40 cm, and C _m = 0.9 F/cm² for HS cells; R _m = 2.0 kcm², R _i = 40 cm, and C _m = 0.8 F/cm² for VS cells. An error analysis of the fitting procedure revealed an area of confidence in the R _m -R _i plane within which the R _m -R _i value pairs are still compatible with the experimental data given the statistical fluctuations inherent in the experiments (Figs. 7, 8). We also investigated whether there exist characteristic differences between different members of the same cell class and how much the exact placement of the electrode (within ±100 m along the axon) influences the result of the simulation (Fig. 9). The membrane parameters were further examined by injection of a hyperpolarizing current pulse (Fig. 10). The resulting compartmental models (Fig. 11) based on the passive membrane parameters determined in this way form the basis of forthcoming studies on dendritic integration and signal propagation in the fly tangential cells (Haag et al., 1997; Haag and Borst, 1997).