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Pathogen identification is a field that can contribute largely to the prevention of the spreading of illness and disease. In the past, pathogen identification has been a long and arduous process due to the time-consuming processes and steps that requires technician’s time and effort. With new technologies emerging however, screening of bacteria colonies can be done in a quick and high-throughput way. The problem is that using the current methods, bacteria cannot be transferred to petri dishes fast enough to keep up with the new screening methods. The current study focuses on exploring different methods to create an ergonomic device that can dispense and inoculate bacteria cells onto petri dishes in a fast, repeatable, and high-throughput manner. The testing of bacteria in liquid allows for the most versatility because bacteria already suspended in liquid could be tested or bacteria could be suspended in liquid from a solid if needed. Different methods of dispensing liquid were tested such as solenoid valves, and different methods of dispenser movements in the X-Y plane around the surface of the petri dishes were tested such as a five-bar mechanism controlled by two rotary motors. It was found that a small solenoid valve in combination with either a five-bar mechanism with two motors or a simple XY stage were both ergonomic and able to provide high-throughput dispensing of bacteria colonies. Based on the devices performance, it can dispense 86 microliter droplets with 8 millimeters of spacing in 69 seconds (1.25 drops per second).

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Managing HIV/AIDS presents challenges to public health policymakers, frontline workers, and researchers worldwide. A key strategy in the disease management is early diagnosis and rapid treatment initiation. While the technological field of point‐of‐care HIV/AIDS diagnostics has advanced significantly in the past two decades, several critical issues remain that hinder the deployment of point‐of‐care testing devices in resource‐deprived settings. In this policy brief, we discuss these issues, including technological specifics of point‐of‐care CD4+ T‐cell counting approaches and requirements of deploying them. We also discuss cultural and religious concerns on the deployment. At the end of the brief, we propose a roadmap for the efficient and cost‐effective deployment and call for action to assemble multidisciplinary teams for the undertaking. We argue that joint effort must be taken to conduct research and development for low‐cost portable point‐of‐care testing and for tailoring the technology deployment and care delivery support system design for specific cohorts.

High-throughput biological imaging promises to be a powerful tool for modern biological research. These imaging technologies are used to monitor the behavior of large populations of cells in response to different experimental conditions. The resulting knowledge can help in drug discovery and in understanding biological phenomena. Different imaging modalities are used for high-throughput imaging. These include 2D fluorescence images, 3D confocal datasets, time-lapse image sequences, and multispectral images. Integrated analysis of these multi-modality spatial, temporal, and spectral data sets for extracting quantitative knowledge is challenging and requires new modeling and data processing tools. This paper presents a multi-layered architecture and spatio-temporal models for analysis of such data. The analysis is divided into low-level and high-level processing. At the lower level, issues like segmentation, tracking and object recognition are addressed, and at the high level, finite state machine- and Petri-net–based models are used for spatio-temporal event recognition. This approach provides a mechanism for extracting high-level spatio-temporal knowledge, and improves searching and retrieval of high-throughput biological imaging data by means of semantic and conceptual queries.

High-throughput biological imaging uses automated imaging devices to collect a large number of microscopic images for analysis of biological systems and validation of scientific hypotheses. Efficient manipulation of these data sets for knowledge discovery requires high performance computational resources, efficient storage, and automated tools for extracting and sharing such knowledge among different research sites. Newly emerging grid technologies provide powerful means for exploiting the full potential of these imaging techniques. Efficient utilization of grid resources requires the development of knowledge-based tools and services that combine domain knowledge with analysis algorithms. In this paper we first investigate how grid infrastructure can facilitate high-throughput biological imaging research, and present an architecture for providing knowledge-based grid services for this field. We identify two levels of knowledge-based services. The first level services provide tools for extracting spatio-temporal knowledge from image sets and the second level provides high-level knowledge management and reasoning services. We then present cellular imaging markup language (CIML), an XML-based language for modeling of biological images and representation of spatio-temporal knowledge. This scheme can be used for spatio-temporal event composition, matching, and automated knowledge extraction and representation for large biological imaging data sets. We demonstrate the expressive power of this formalism by means of different examples and experimental results.

Atomic force microscopy applications extend across a number of fields; however, limitations have reduced its effectiveness in live cell analysis. This report discusses the use of AFM to evaluate the three-dimensional (3-D) architecture of living chick dorsal root ganglia and sympathetic ganglia. These data sets were compared to similar images acquired with confocal laser scanning microscopy of identical cells. For this comparison we made use of visualization techniques which were applicable to both sets of data and identified several issues when coupling these technologies. These direct comparisons offer quantitative validation and confirmation of the character of novel images acquired by AFM. This paper is one in a series emphasizing various new applications of AFM in neurobiology.