GuPPy, a Python toolbox for the analysis of fiber photometry data

Fiber photometry (FP) is an adaptable method for recording in vivo neural activity in freely behaving animals. It has become a popular tool in neuroscience due to its ease of use, low cost, the ability to combine FP with freely moving behavior, among other advantages. However, analysis of FP data can be challenging for new users, especially those with a limited programming background. Here, we present Guided Photometry Analysis in Python (GuPPy), a free and open-source FP analysis tool. GuPPy is designed to operate across computing platforms and can accept data from a variety of FP data acquisition systems. The program presents users with a set of graphic user interfaces (GUIs) to load data and provide input parameters. Graphs are produced that can be easily exported for integration into scientific figures. As an open-source tool, GuPPy can be modified by users with knowledge of Python to fit their specific needs.

GuPPy, a Python toolbox for the analysis of fiber photometry data

Fiber photometry (FP) is an adaptable method for recording in vivo neural activity in freely behaving animals. It has become a popular tool in neuroscience due to its ease of use, low cost, the ability to combine FP with freely moving behavior, among other advantages. However, analysis of FP data can be a challenge for new users, especially those with a limited programming background. Here, we present Guided Photometry Analysis in Python (GuPPy), a free and open-source FP analysis tool. GuPPy is provided as a Jupyter notebook, a well-commented interactive development environment (IDE) designed to operate across platforms. GuPPy presents the user with a set of graphic user interfaces (GUIs) to load data and provide input parameters. Graphs produced by GuPPy can be exported into various image formats for integration into scientific figures. As an open-source tool, GuPPy can be modified by users with knowledge of Python to fit their specific needs.

The Animated Document: Animation’s Dual Indexicality in Mixed Realities

Animation has become ubiquitous within digital visual culture and fundamental to knowledge production. As such, its status as potentially reliable imagery should be clarified. This article examines how animation’s indexicality (both as trace and deixis) changes in mixed realities where the physical and the virtual converge, and how this contributes to the research of animation as documentary and/or non-fiction imagery. In digital culture, animation is used widely to depict both physical and virtual events, and actions. As a result, animation is no longer an interpretive visual language. Instead, animation in virtual culture acts as real-time visualization of computer-mediated actions, their capture and documentation. Now that animation includes both captured and generated imagery, not only do its definitions change but its link to the realities depicted and the documentary value of animated representations requires rethinking. This article begins with definitions of animation and their relation to the perception of animation’s validity as documentary imagery; thereafter it examines indexicality and the strength of indexical visualizations, introducing a continuum of strong and weak indices to theorize the hybrid and complex forms of indexicality in animation, ranging from graphic user interfaces (GUI) to data visualization. The article concludes by examining four indexical connections in relation to physical and virtual reality, offering a theoretical framework with which to conceptualize animation’s indexing abilities in today’s mixed realities.

Ergonomic qualities of graphic user interfaces (GUI): state and evolution

More workers are involved into interaction with graphic user interfaces most part of the working shift. However, low ergonomic qualities or incorrect usage of graphic user interface could result in risk of unfavorable influence on workers’ health. The authors revealed and classified typical scenarios of graphic user interface usage. Various types of graphic user interface and operator occupations are characterized by various parameters of exertion, both biomechanical and psycho-physiological. Among main elements of graphic user interface are presence or absence of mouse or joystick, intuitive clearness, balanced palette, fixed position of graphic elements, comfort level, etc. Review of various graphic user interface and analysis of their characteristics demonstrated possibility of various occupational risk factors. Some disclosed ergonomic problems are connected with incorporation of graphic user interface into various information technologies and systems. The authors presented a role of ergonomic characteristics of graphic user interface for safe and effective work of operators, gave examples of algorithms to visualize large information volumes for easier comprehension and analysis. Correct usage of interactive means of computer visualization with competent design and observing ergonomic principles will optimize mental work in innovative activity and preserve operators’ health. Prospective issues in this sphere are ergonomic interfaces developed with consideration of information hygiene principles, big data analysis technology and automatically generated cognitive graphics.

The theoretical molecular weight of NaYF4:RE upconversion nanoparticles

Upconversion nanoparticles (UCNPs) are utilized extensively for biomedical imaging, sensing, and therapeutic applications, yet the molecular weight of UCNPs has not previously been reported. We present a theory based upon the crystal structure of UCNPs to estimate the molecular weight of UCNPs: enabling insight into UCNP molecular weight for the first time. We estimate the theoretical molecular weight of various UCNPs reported in the literature, predicting that spherical NaYF4 UCNPs ~ 10 nm in diameter will be ~1 MDa (i.e. 106 g/mol), whereas UCNPs ~ 45 nm in diameter will be ~100 MDa (i.e. 108 g/mol). We also predict that hexagonal crystal phase UCNPs will be of greater molecular weight than cubic crystal phase UCNPs. Additionally we find that a Gaussian UCNP diameter distribution will correspond to a lognormal UCNP molecular weight distribution. Our approach could potentially be generalised to predict the molecular weight of other arbitrary crystalline nanoparticles: as such, we provide standalone graphic user interfaces to calculate the molecular weight both UCNPs and arbitrary crystalline nanoparticles. We expect knowledge of UCNP molecular weight to be of wide utility in biomedical applications where reporting UCNP quantity in absolute numbers or molarity will be beneficial for inter-study comparison and repeatability.