I read this book the same weekend that the Packers took on the Rams, and the experience of the latter event, obviously, colored my judgment. Although I abhor anything that smacks of being a handbook (like, \"How to Earn a Merit Badge in Neurosurgery\") because too many volumes in biomedical science already evince a boyscout-like approach, I must confess that parts of this volume are fast, scholarly, and significant, with certain reservations. I like parts of this well-illustrated book because Dr. Sj6strand, without so stating, develops certain subjects on technique in relation to the acquisition of judgment and sophistication. And this is important! So, given that the author (like all of us) is somewhat deficient in some areas, and biased in others, the book is still valuable if the uninitiated reader swallows it in a general fashion, realizing full well that what will be required from the reader is a modulation to fit his vision, propreception, adaptation and response, and the kind of problem he is undertaking. A major deficiency of this book is revealed by comparison of its use of physics and of chemistry to provide understanding and background for the application of high resolution electron microscopy to problems in biology. Since the volume is keyed to high resolution electron microscopy, which is a sophisticated form of structural analysis, but really morphology in a modern guise, the physical and mechanical background of The instrument and its ancillary tools are simply and well presented. The potential use of chemical or cytochemical information as it relates to biological fine structure , however, is quite deficient. I wonder when even sophisticated morphol-ogists will consider fixation a reaction and not a technique; only then will the fundamentals become self-evident and predictable and this sine qua flon will become less mystical. Staining reactions (the most inadequate chapter) ought to be something more than a technique to selectively enhance contrast of morphological elements; it ought to give the structural addresses of some of the chemical residents of cell components. Is it pertinent that auto-radiography gets singled out for more complete coverage than other significant aspects of cytochemistry by a high resolution microscopist, when it has a built-in minimal error of 1,000 A in standard practice? I don't mean to blind-side (in strict football terminology) Dr. Sj6strand's efforts for what is \"routinely used in our laboratory\"; what is done is usually well done. It's just that …

Methods in Enzymology.

Ground vehicles equipped with monocular vision systems are a valuable source of high-resolution image data for precision agriculture applications in orchards This paper presents an image processing framework for fruit detection and counting using orchard image data A general-purpose image segmentation approach is used, including two feature learning algorithms; multiscale multilayered perceptrons (MLP) and convolutional neural networks (CNN) These networks were extended by including contextual information about how the image data was captured (metadata), which correlates with some of the appearance variations and/or class distributions observed in the data The pixel-wise fruit segmentation output is processed using the watershed segmentation (WS) and circular Hough transform (CHT) algorithms to detect and count individual fruits Experiments were conducted in a commercial apple orchard near Melbourne, Australia The results show an improvement in fruit segmentation performance with the inclusion of metadata on the previously benchmarked MLP network We extend this work with CNNs, bringing agrovision closer to the state-of-the-art in computer vision, where although metadata had negligible influence, the best pixel-wise F1-score of 0791 was achieved The WS algorithm produced the best apple detection and counting results, with a detection F1-score of 0861 As a final step, image fruit counts were accumulated over multiple rows at the orchard and compared against the post-harvest fruit counts that were obtained from a grading and counting machine The count estimates using CNN and WS resulted in the best performance for this data set, with a squared correlation coefficient of r2=0826

Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

Digital microfluidics (DMF) has recently emerged as a popular technology for a wide range of applications. In DMF, nanoliter to microliter droplets containing samples and reagents can be manipulated to carry out a range of discrete fluidic operations simply by applying a series of electrical potentials to an array of patterned electrodes coated with a hydrophobic insulator. DMF is distinct from microchannel-based fluidics as it allows for precise control over multiple reagent phases (liquids and solids) in heterogeneous systems with no need for complex networks of connections, microvalves, or pumps. In this review, we discuss the most recent developments in this technology with particular attention to the potential benefits and outstanding challenges for applications in chemistry, biology, and medicine.

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Digital microfluidics: a versatile tool for applications in chemistry, biology and medicine.

Novel applications of micro total analysis systems (µTAS) are addressing fundamental biological questions, creating new biomedical reagents, and developing innovative cell and biochemical assays. These efforts impact progress in all areas of µTAS from materials to fluidic handling as well as detection and external control systems. Three areas show the greatest current and potential impact on the biomedical sciences: improvements in device fabrication and operation, development of enabling technologies, and advancements at the interface with biology (Figure 1). The range of materials from which devices can be fabricated has expanded considerably and now includes paper, fabric and thread, and a multitude of polymers as well as more conventional materials. Thus device substrates and component materials suitable for nearly all biological applications are readily available. Devices are also becoming increasingly integrated with advancements in sample handling and preparation, key first steps in any biological analysis. Another growing area focuses on modular components that can be mixed and matched on-demand and applied to many different assays, so-called programmable microfluidics. This development should enhance the rate at which new bioassays are generated as well as customize existing experimental protocols. A second area of rapid advancement has been the development new technologies that enable assays that cannot be efficiently performed by any method except µTAS. Novel analyses of single cells are enabled due to effective manipulation of picoliter-scale volumes. Synthesis and screening of large-scale libraries has become increasingly feasible due to the fast processing speeds and combinatorial mixing of reagents provided by lab-on-chip systems. Increased automation within a completely contained system has now begun to provide some of the first true µTAS diagnostic devices for clinical medicine. The third area in which µTAS has begun to yield high dividends is the interfacing of living entities with microdevices to create biological communities, including tissues and organs on-chip. Control of cell placement in multiple dimensions has produced biological systems midway between the conventional tissue-culture dish and an intact animal. Thus the complexities of living constructs can be recreated in a controlled experimental environment permitting groundbreaking biological questions to be addressed. Application of µTAS in all of these areas continues to be highly interdisciplinary, utilizing techniques and strategies from almost every scientific field. This multidisciplinary focus insures continued relevance to the biological community as well as a bright future.



Figure 1

We highlight recent contributions to µTAS in three interlocking areas: fabrication & operation, enabling technologies, and interfacing with biology.



Due to the rapid progress of µTAS or “lab-on-a-chip” systems, this review focuses on advances impacting cell biology and biochemistry and covers the time span from March 2010 through August 2011. The material for the review was compiled using several strategies: reviews of high impact journals such as Analytical Chemistry, Lab on a Chip, Science, Nature, and PNAS; extensive key word searches in databases such as PubMed, SciFinder, Web of Science, and Google Scholar; and screens of other recent topical reviews. Although several thousand papers were identified and over a thousand papers received a detailed examination, we focused on the most novel and exciting methods, devices, and applications in the areas of cell biology and biochemistry. We also endeavored to cover the most prominent work from a range of labs and countries. Ultimately we were limited by space constraints and our desire to craft a readable commentary on the state of the field. We apologize in advance for omitted papers and welcome feedback regarding any oversights on our part.

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Micro total analysis systems for cell biology and biochemical assays.

Ground vehicles equipped with monocular vision systems are a valuable source of high resolution image data for precision agriculture applications in orchards. This paper presents an image processing framework for fruit detection and counting using orchard image data. A general purpose image segmentation approach is used, including two feature learning algorithms; multi-scale Multi-Layered Perceptrons (MLP) and Convolutional Neural Networks (CNN). These networks were extended by including contextual information about how the image data was captured (metadata), which correlates with some of the appearance variations and/or class distributions observed in the data. The pixel-wise fruit segmentation output is processed using the Watershed Segmentation (WS) and Circular Hough Transform (CHT) algorithms to detect and count individual fruits. Experiments were conducted in a commercial apple orchard near Melbourne, Australia. The results show an improvement in fruit segmentation performance with the inclusion of metadata on the previously benchmarked MLP network. We extend this work with CNNs, bringing agrovision closer to the state-of-the-art in computer vision, where although metadata had negligible influence, the best pixel-wise F1-score of $0.791$ was achieved. The WS algorithm produced the best apple detection and counting results, with a detection F1-score of $0.858$. As a final step, image fruit counts were accumulated over multiple rows at the orchard and compared against the post-harvest fruit counts that were obtained from a grading and counting machine. The count estimates using CNN and WS resulted in the best performance for this dataset, with a squared correlation coefficient of $r^2=0.826$.

This paper demonstrates a novel drop-to-drop liquid−liquid micro-extraction (DTD-LLME) device, which is based on an electrowetting on dielectric (EWOD) digital microfluidic chip. Droplets of two immiscible liquids, one of which is an ionic liquid, are formed in nanoliter volumes, driven along electrodes, merged and mixed for extraction, and finally separated upon the completion of the extraction process. All the steps are carried out on a microfluidic chip using combined electrowetting and dielectrophoretic forces, which act on the droplet upon the application of electric potential. Specially, the phase separation of two immiscible nanoliter-scale liquid drops was achieved for the first time on an EWOD digital microfluidic chip. To study the on-chip extraction kinetics, an image-based concentration measurement technique with suitable color parameters was studied and compared with the typical UV absorption based technique. Finally, the effect of applied ac voltage frequency on the extraction kinetics was s...

On-chip drop-to-drop liquid microextraction coupled with real-time concentration monitoring technique

For tissue engineering and regenerative medicine, cryopreservation, a technique for preserving biomaterials in the frozen state with cryoprotective agents (CPAs), is critically important for preserving engineered tissues (ETs) as well as cells necessary to create ETs. As more diverse ETs are produced using various cell types, CPAs and corresponding freeze/thaw (F/T) protocols need to be developed cell/tissue-type specifically. This is because CPAs and F/T protocols that have been successful for one cell/tissue type have proven to be difficult to adapt to other cell/tissue types. The most critical barrier to address this challenge is the inability to screen and identify CPA or CPA mixtures efficiently. In this paper, we developed an “electro-wetting-on-dielectic” (EWOD) based digital microfluidic platform to characterize and screen CPA mixtures cell-type specifically. The feasibility of the EWOD platform was demonstrated by characterizing and optimizing a mixture of dimethlysulfoxide (DMSO) and PBS for human breast cancer cell line as model CPA mixture and cell line. The developed platform multiplexed droplets of DMSO and PBS to create an array of DMSO-PBS mixtures, and mapped the phase change diagram of the mixture. After loading cell suspensions on the platform, the mixture was further screened on-chip for toxicity and cryoprotection. The results were discussed to illustrate the capabilities and limitations of the EWOD platform for cell and tissue-type specific optimization of CPA mixtures and F/T protocols.

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On-chip characterization of cryoprotective agent mixtures using an EWOD-based digital microfluidic device

This Paper investigates the use of digital image analysis techniques for developing an automated kiwifruit counting system. Three simple counting methods followed by a minimum distance classifier based segmentation technique in L*a*b colour space were studied. Images were taken prior to harvesting at a New Zealand kiwifruit orchard. Accurate counting of kiwifruit in several sample regions of the orchard is required in order to estimate the fruit harvest. At present, the counting is manually done by hired employees. Manual counting has several issues, such as low accuracy, long duration and higher costs. Automated counting technique facilitates a fast, low cost and potentially more accurate way of counting kiwifruit. Several approaches were trialled and validated on different sets of images. Above 90% accuracy on gold image data and above 60% accuracy on green image data were obtained, showing the potential of using the approach for counting kiwifruit for the harvest estimation purpose. The results, limitations and ongoing research in developing a more robust and consistent technique will be discussed.

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Digital image analysis based automated kiwifruit counting technique

Aqueous two-phase systems (ATPSs) allow an advantageous aqueous two-phase extraction process (ATPE), a special type of liquid–liquid extraction. Compared with conventional liquid–liquid extraction using aqueous/organic extraction media, ATPE is known to provide relatively easy mass transfer and a gentle environment for biological separation applications. Considering the recent interest in microscale ATPE, we aimed to study (i) the potential of preparing ATPS droplets on a digital microfluidic device, and (ii) the influence of the fluidic dynamics created during the formation of ATPS, with the goal of enhancing on-chip ATPE process. On-chip ATPS formation was evaluated by preparing a series of ATPSs on electrowetting on dielectric digital microfluidic chips and comparing their characteristics with the same ATPSs prepared at macroscale using conventional procedures. An enhanced on-chip drop-to-drop ATPE process was achieved by incorporating a self-mixing condition created during ATPSformation. Results indicate a successful on-chip ATPS preparation as well as enhanced extraction performance by self-mixing in the absence of forced mixing. Findings of this research suggest an alternative, simple, yet adequate technique to provide mixing for on-chip applications, such as sample preparation in portable microfluidics, for which it is unfavorable to implement complicated mixing sequences or complex device geometries.

https://iopscience.iop.org/article/10.1088/0960-1317/25/9/094002/pdf

On-chip aqueous two-phase system (ATPS) formation, consequential self-mixing, and their influence on drop-to-drop aqueous two-phase extraction kinetics

Developing robust computer vision algorithms to detect fruit in trees is challenging due to less controllable conditions, including variation in illumination within an image as well as between image sets. There are two classes of techniques: local-feature-based techniques and shape-based techniques, which have been used extensively in this application domain. Out of the two classes, the local-feature-based techniques have shown higher accuracies over shape-based techniques, but are less desirable due to the requirement of repeated calibration. In this paper, we investigate the potential of developing a generalized colour pixel classifier that can be employed to detect kiwifruit on vines, under variable fruit maturity levels and imaging conditions. First, we observed the colour data patterns of fruit and nonfruit regions from different image sets. With consistant data patterns it was found that a suitable normalization could produce an invariant colour descriptor. Then, a neural network Self-Organizing Map (SOM) model, which has a hierarchical clustering ability was used to investigate the potential of developing a generalized neural network model to classify pixels under variable conditions. Models were built for colour features extracted in CIELab space for both absolute colour values and relative colour descriptors. The paper presents the positive results of the preliminary investigations. The conditions for a successful application of the approach as well as the potential for extending it for automatic calibration will also be discussed.

/pdf/towards-a-generalized-colour-image-segmentation-for-2dujp5ks0d.pdf

Pavithra A. L. Wijethunga

Papers

On-chip drop-to-drop liquid microextraction coupled with real-time concentration monitoring technique

On-chip characterization of cryoprotective agent mixtures using an EWOD-based digital microfluidic device

Digital image analysis based automated kiwifruit counting technique

On-chip aqueous two-phase system (ATPS) formation, consequential self-mixing, and their influence on drop-to-drop aqueous two-phase extraction kinetics

Towards a generalized colour image segmentation for kiwifruit detection