Explore wide dynamic range, low distortion radio equipment, the use of direct conversion and phasing methods, and digital signal processing. Use the models and discussion to design, build and measure equipment at both the circuit and the system level. Laced with new unpublished projects and illustrated with CW and SSB gear.
In medical informatics, the quasi-experimental, sometimes called the pre-post intervention, design often is used to evaluate the benefits of specific interventions. The increasing capacity of health care institutions to collect routine clinical data has led to the growing use of quasi-experimental study designs in the field of medical informatics as well as in other medical disciplines. However, little is written about these study designs in the medical literature or in traditional epidemiology textbooks.1,2,3 In contrast, the social sciences literature is replete with examples of ways to implement and improve quasi-experimental studies.4,5,6
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Key advantages and disadvantages of quasi-experimental studies, as they pertain to the study of medical informatics, were identified. The potential methodological flaws of quasi-experimental medical informatics studies, which have the potential to introduce bias, were also identified. In addition, a summary table outlining a relative hierarchy and nomenclature of quasi-experimental study designs is described. In general, the higher the design is in the hierarchy, the greater the internal validity that the study traditionally possesses because the evidence of the potential causation between the intervention and the outcome is strengthened.4
Using this basic definition, it is evident that many published studies in medical informatics utilize the quasi-experimental design. Although the randomized controlled trial is generally considered to have the highest level of credibility with regard to assessing causality, in medical informatics, researchers often choose not to randomize the intervention for one or more reasons: (1) ethical considerations, (2) difficulty of randomizing subjects, (3) difficulty to randomize by locations (e.g., by wards), (4) small available sample size. Each of these reasons is discussed below.
Here, X is the intervention and O is the outcome variable (this notation is continued throughout the article). In this study design, an intervention (X) is implemented and a posttest observation (O1) is taken. For example, X could be the introduction of a pharmacy order-entry intervention and O1 could be the pharmacy costs following the intervention. This design is the weakest of the quasi-experimental designs that are discussed in this article. Without any pretest observations or a control group, there are multiple threats to internal validity. Unfortunately, this study design is often used in medical informatics when new software is introduced since it may be difficult to have pretest measurements due to time, technical, or cost constraints.
Indeed, to develop an optical integrated system for mid-IR sensors which requires precise opto-geometric characteristics of the chalcogenide layers10, it is essential to adequately determine the influence of deposition parameters. By the role discrimination of each factor and their possible correlation, it will be possible to control thin film characteristics such as thickness, refractive index, roughness, or chemical composition. The approach based on the experimental design is undoubtedly a way to be explored allowing fast optimization of chalcogenide glasses deposition by means of Physical Vapor Deposition (PVD) processes that are used for a thin films growth onto the appropriate substrate.
In order to determine the optimal conditions for the film deposition parameters, an experimental design study was carried out. This approach proposes to realize the most significant experiments to obtain the maximum information on the characteristics of the thin films obtained from Ge28.1Sb6.3Se65.6 and Ge12.5Sb25Se62.5 glass targets.
V.N. planned and managed the project, E.B., PhD student, performed the experiments with the help of V.N. and P.N., E.B., P.N. and V.N. analyzed the data with great help of C.C. (a specialist of XPS and physics of cold plasma) and M.S. who has developed the experimental design approach of this study. L.J. performed SEM and EDS analysis at CMEBA-SCANMAT platform at University of Rennes 1. E.B., M.S., P.N. and V.N. wrote the manuscript with the help of B.B., K.M. and E.R. concerning the final application of these chalcogenide materials devoted to environmental sensors.
Communication antennas have been studied for decades. However, power-harvesting antennas are currently in the developmental stage. At first, antenna classification was based on design characteristics and applications. It originally included wire antennas, aperture antennas, printed planar antennas, and reflector antennas. An illustration of some examples of antennas is shown in Fig. 4. To date, the growth in technology has paved the way for a variety of antenna design and fabrication methods for making it more compact and mature.
The experimental methods described herein focus on primary neuronal cultures, but can be performed in most cellular models. Decisions on specific cellular models and experimental design depend on the research question and on the available equipment, reagents, and expertise. Regardless, conditions should remain as similar as possible for all experiments within a study, and matched experiments (control and treated, or wild-type and transgenic littermates) should always be performed, ideally on the same day. Maintaining consistency both within a study and across platforms will facilitate inter-experiment comparison.
Smaller, single protein-based Ca2+ indicators may localise more easily to mitochondria [98]. Aequorin, a jellyfish protein that bioluminesces in a Ca2+-dependent manner, can be localised to neuronal mitochondria (mtAEQ) [123, 124], but its low luminescence intensity limits its utility in single-cell measurements [118]. Other single FP-based Ca2+ indicators that have been successfully imaged in neuronal mitochondria (Table 5) include the cpYFP-based Pericam [125] and multiple variants of GCaMP and RCaMP [112, 126,127,128]. GECO Ca2+ reporters [129], variants of GCaMP3, have been used extensively to measure mitochondrial Ca2+ in neurons [53, 102, 119, 130]. CEPIA probes are GCaMP2 variants that can be localised to mitochondria [131]. Luminescence-based methods (BRET) are also promising [132]. Mitochondria-targeted reporters can be co-expressed with fluorescent Ca2+ indicators designed for other subcellular locations (e.g. endoplasmic reticulum or cytoplasm) to simultaneously monitor Ca2+ flux between cellular compartments [119, 130].
We strongly stress that current methods for measuring mitochondrial ROS, particularly redox-sensitive fluorophores, are associated with significant difficulties and limitations that must be carefully considered, both when interpreting the existing literature and during experiment design. These difficulties are associated with, among others, signal specificity, identifying the intracellular origin of ROS, and the rapid turnover of ROS within the cell. If performing experiments, stringent control measures must be employed, all findings should be interpreted with caution, and results should be confirmed using alternative techniques, e.g. [159]. Many of these issues have been discussed previously [148, 149, 160, 161], and technical specifications, use guidelines and limitations of several redox-sensitive fluorophores and FPs can be found in [157, 162, 163].
Images generated by a microscope are never a perfect representation of the biological specimen. Microscopes and specimen preparation methods are prone to error and can impart images with unintended attributes that might be misconstrued as belonging to the biological specimen. In addition, our brains are wired to quickly interpret what we see, and with an unconscious bias toward that which makes the most sense to us based on our current understanding. Unaddressed errors in microscopy images combined with the bias we bring to visual interpretation of images can lead to false conclusions and irreproducible imaging data. Here we review important aspects of designing a rigorous light microscopy experiment: validation of methods used to prepare samples and of imaging system performance, identification and correction of errors, and strategies for avoiding bias in the acquisition and analysis of images.
It cannot be assumed that an antibody, organic dye, or fluorescent protein (FP) will perform as an inert, specific label. All methods of labeling biological specimens with fluorophores have the potential for nonspecificity and for perturbation of the localization or function of the labeled component or associated structures, binding partners, etc. (Couchman, 2009; Burry, 2011; Bosch et al., 2014; Ganini et al., 2017). Immunofluorescence and FP conjugates have become so ubiquitous in cell biology that validation of binding specificity and that the biology is not affected by the label are frequently omitted (Freedman et al., 2016). In our experience, these issues are often more problematic than realized, and there are many published examples of commonly used probes introducing artifacts under specific experimental conditions (Allison and Sattenstall, 2007; Couchman, 2009; Costantini et al., 2012; Landgraf et al., 2012; Schnell et al., 2012; Bosch et al., 2014; Norris et al., 2015; Ganini et al., 2017).
Imaging system validation generally consists of the following: (1) collecting images of known samples (Table 2); (2) identifying systematic errors present in images of known samples (Fig. 2); (3) correcting for errors, either computationally or by adjusting specimens, optics, or acquisition parameters (Figs. 2 and 3); and (4) testing correction methods (Figs. 2, 3, and 4). There is no one known sample or correction method that will reveal and correct all possible errors. Instead, researchers should assess whether a particular error may affect interpretation of their experimental results, and test error correction methods. It is critical to validate correction methods; performing inaccurate corrections can make matters even worse (Fig. 4). Errors vary from one microscope to the next, and when different optics or filters are used in the same microscope. Neglecting to correct systematic errors in images can therefore result in inaccurate and irreproducible data (Fig. 1, A and B). Common sources of error in microscopy images are discussed thoroughly elsewhere (Stelzer, 1998; Hibbs et al., 2006; North, 2006; Zucker, 2006; Waters, 2009; Wolf et al., 2013). Here, we discuss examples of validation of detection, illumination, and optics important to consider in many experiments. 2ff7e9595c
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