Bias-reduced estimators of conditional odds ratios in matched case-control studies with unmatched confounding

We study bias-reduced estimators of exponentially transformed parameters in general linear models (GLMs) and show how they can be used to obtain bias-reduced conditional (or unconditional) odds ratios in matched case-control studies. Two options are considered and compared: the explicit approach and the implicit approach. The implicit approach is based on the modified score function where bias-reduced estimates are obtained by using iterative procedures to solve the modified score equations. The explicit approach is shown to be a one-step approximation of this iterative procedure. To apply these approaches for the conditional analysis of matched case-control studies, with potentially unmatched confounding and with several exposures, we utilize the relation between the conditional likelihood and the likelihood of the unconditional logit binomial GLM for matched pairs and Cox partial likelihood for matched sets with appropriately setup data. The properties of the estimators are evaluated by using a large Monte Carlo simulation study and an illustration of a real dataset is shown. Researchers reporting the results on the exponentiated scale should use bias-reduced estimators since otherwise the effects can be under or overestimated, where the magnitude of the bias is especially large in studies with smaller sample sizes.     

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.     

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.     

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.     

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).     

Phase labeling of C−H and C−C spin-system topologies: Application in PFG-HACANH and PFG-HACA(CO)NH triple-resonance experiments for determining backbone resonance assignments in proteins

Summary Triple-resonance experiments can be designed to provide useful information on spin-system topologies. In this paper we demonstrate optimized proton and carbon versions of PFG-CT-HACANH and PFG-CT-HACA(CO)NH straight-through triple-resonance experiments that allow rapid and almost complete assignments of backbone H, ¹³C, ¹⁵N and H^N resonances in small proteins. This work provides a practical guide to using these experiments for determining resonance assignments in proteins, and for identifying both intraresidue and sequential connections involving glycine residues. Two types of delay tunings within these pulse sequences provide phase discrimination of backbone Gly C and H resonances: (i) C–H phase discrimination by tuning of the refocusing period _{a_f}; (ii) C–C phase discrimination by tuning of the ¹³C constant-time evolution period 2T_c. For small proteins, C–C phase tuning provides better S/N ratios in PFG-CT-HACANH experiments while C–H phase tuning provides better S/N ratios in PFG-CT-HACA(CO)NH. These same principles can also be applied to triple-resonance experiments utilizing ¹³C-¹³C COSY and TOCSY transfer from peripheral side-chain atoms with detection of backbone amide protons for classification of side-chain spin-system topologies. Such data are valuable in algorithms for automated analysis of resonance assignments in proteins.     

不健全资料的参数关联分析法

首先建立相关变量取值随时序变化的数学模型,而后,利用模型中能够反映变量几何形状的适宜参数,提出了参数关联度的概念．从而解决了观测值非一一对应、且不齐的相关变量关联分析问题．同时给出一种较高拟合精度的非等间隔序列的灰色建模方法．     

Analysis of vertical ozone and nitrogen oxides profiles in aPrunus cerasifera canopy

An automatic system was installed for continuous analyses of ozone, sulphur dioxide, nitrogen monoxide and nitrogen dioxide in an experimental orchard with a canopy ofPrunus cerasifera plants in summer 1993. Air samples from three elevations (0.8 m, 1.6 m and 3 m above ground) were sequentially analyzed. Ozone concentrations above the canopy were usually higher than within the canopy; their relationships with stomatal resistance have been investigated. Sulphur dioxide levels were negligible. Nitrogen oxides showed a complex profile, with no particular trend, likely due to a reciprocal exchange between the atmosphere and the ground surface.