Lensless wide-field fluorescent imaging on a chip using compressive decoding of sparse objects.

We demonstrate the use of a compressive sampling algorithm for on-chip fluorescent imaging of sparse objects over an ultra-large field-of-view (>8 cm(2)) without the need for any lenses or mechanical scanning. In this lensfree imaging technique, fluorescent samples placed on a chip are excited through a prism interface, where the pump light is filtered out by total internal reflection after exciting the entire sample volume. The emitted fluorescent light from the specimen is collected through an on-chip fiber-optic faceplate and is delivered to a wide field-of-view opto-electronic sensor array for lensless recording of fluorescent spots corresponding to the samples. A compressive sampling based optimization algorithm is then used to rapidly reconstruct the sparse distribution of fluorescent sources to achieve approximately 10 microm spatial resolution over the entire active region of the sensor-array, i.e., over an imaging field-of-view of >8 cm(2). Such a wide-field lensless fluorescent imaging platform could especially be significant for high-throughput imaging cytometry, rare cell analysis, as well as for micro-array research

Profitable working capital management in industrial maintenance companies

Purpose – The purpose of this paper is to analyze the impact of working capital management on profitability in industrial maintenance service companies. Design/methodology/approach – Analytical modeling has been used as the research method. Finnish industrial maintenance companies have been analyzed on the basis of their financial statements. Findings – We reveal a significant negative correlation between the cycle times of operational working capital and the return on investment of industrial maintenance companies. Light fixed assets and good profitability of the maintenance sector emphasize the importance of working capital management. Large maintenance service companies seem to achieve competitive advantage over small and medium sized maintenance service providers through both fixed assets- and working capital-related economies of scale, and through the fact that large maintenance service providers often focus on providing services mostly for their host companies. Research limitations – The scarcity of large enterprises in the market in question precludes the use of a more extensive sample in the analysis. Practical implications – In the industrial maintenance service business, more attention should be paid to active management of working capital. We can conclude that this holds true especially in large industrial maintenance service enterprises. Originality/value – We contribute to the unexplored perspective of industrial maintenance companies. Previous studies of industrial maintenance companies have not addressed working capital management, which gains more and more attention under the volatile economic circumstances of the present day. Keywords - Industrial maintenance services, Profitability, Working capital, Flexible asset management model Paper type - Research pape

More Homogeneous Capillary Flow and Oxygenation in Deeper Cortical Layers Correlate with Increased Oxygen Extraction

Our understanding of how capillary blood flow and oxygen distribute across cortical layers to meet the local metabolic demand is incomplete. We addressed this question by using two-photon imaging of resting-state microvascular oxygen partial pressure (PO2) and flow in the whisker barrel cortex in awake mice. Our measurements in layers I-V show that the capillary red-blood-cell flux and oxygenation heterogeneity, and the intracapillary resistance to oxygen delivery, all decrease with depth, reaching a minimum around layer IV, while the depth-dependent oxygen extraction fraction is increased in layer IV, where oxygen demand is presumably the highest. Our findings suggest that more homogeneous distribution of the physiological observables relevant to oxygen transport to tissue is an important part of the microvascular network adaptation to local brain metabolism. These results will inform the biophysical models of layer-specific cerebral oxygen delivery and consumption and improve our understanding of the diseases that affect cerebral microcirculation

Lensfree Fluorescent On-Chip Imaging of Transgenic Caenorhabditis elegans Over an Ultra-Wide Field-of-View

We demonstrate lensfree on-chip fluorescent imaging of transgenic Caenorhabditis elegans (C. elegans) over an ultra-wide field-of-view (FOV) of e.g., >2–8 cm2 with a spatial resolution of ∼10µm. This is the first time that a lensfree on-chip platform has successfully imaged fluorescent C. elegans samples. In our wide-field lensfree imaging platform, the transgenic samples are excited using a prism interface from the side, where the pump light is rejected through total internal reflection occurring at the bottom facet of the substrate. The emitted fluorescent signal from C. elegans samples is then recorded on a large area opto-electronic sensor-array over an FOV of e.g., >2–8 cm2, without the use of any lenses, thin-film interference filters or mechanical scanners. Because fluorescent emission rapidly diverges, such lensfree fluorescent images recorded on a chip look blurred due to broad point-spread-function of our platform. To combat this resolution challenge, we use a compressive sampling algorithm to uniquely decode the recorded lensfree fluorescent patterns into higher resolution images, demonstrating ∼10 µm resolution. We tested the efficacy of this compressive decoding approach with different types of opto-electronic sensors to achieve a similar resolution level, independent of the imaging chip. We further demonstrate that this wide FOV lensfree fluorescent imaging platform can also perform sequential bright-field imaging of the same samples using partially-coherent lensfree digital in-line holography that is coupled from the top facet of the same prism used in fluorescent excitation. This unique combination permits ultra-wide field dual-mode imaging of C. elegans on a chip which could especially provide a useful tool for high-throughput screening applications in biomedical research

Lensfree Computational Microscopy Tools and their Biomedical Applications

Conventional microscopy has been a revolutionary tool for biomedical applications since its invention several centuries ago. Ability to non-destructively observe very fine details of biological objects in real time enabled to answer many important questions about their structures and functions. Unfortunately, most of these advance microscopes are complex, bulky, expensive, and/or hard to operate, so they could not reach beyond the walls of well-equipped laboratories. Recent improvements in optoelectronic components and computational methods allow creating imaging systems that better fulfill the specific needs of clinics or research related biomedical applications. In this respect, lensfree computational microscopy aims to replace bulky and expensive optical components with compact and cost-effective alternatives through the use of computation, which can be particularly useful for lab-on-a-chip platforms as well as imaging applications in low-resource settings. Several high-throughput on-chip platforms are built with this approach for applications including, but not limited to, cytometry, micro-array imaging, rare cell analysis, telemedicine, and water quality screening.[1]-[6]The lack of optical complexity in these lensfree on-chip imaging platforms is compensated by using computational techniques. These computational methods are utilized for various purposes in coherent, incoherent and fluorescent on-chip imaging platforms e.g. improving the spatial resolution, to undo the light diffraction without using lenses, localization of objects in a large volume and retrieval of the phase or the color/spectral content of the objects.[3], [5] For instance, pixel super resolution approaches based on source shifting are used in lensfree imaging platforms to prevent under sampling, Bayer pattern, and aliasing artifacts. Another method, iterative phase retrieval, is utilized to compensate the lack of lenses by undoing the diffraction and removing the twin image noise of in-line holograms. This technique enables recovering the complex optical field from its intensity measurement(s) by using additional constraints in iterations, such as spatial boundaries and other known properties of objects. Another computational tool employed in lensfree imaging is compressive sensing (or decoding), which is a novel method taking advantage of the fact that natural signals/objects are mostly sparse or compressible in known bases.[7] This inherent property of objects enables better signal recovery when the number of measurement is low, even below the Nyquist rate[8], and increases the additive noise immunity of the system

Lensfree color imaging on a nanostructured chip using compressive decoding

We demonstrate subpixel level color imaging capability on a lensfree incoherent on-chip microscopy platform. By using a nanostructured substrate, the incoherent emission from the object plane is modulated to create a unique far-field diffraction pattern corresponding to each point at the object plane. These lensfree diffraction patterns are then sampled in the far-field using a color sensor-array, where the pixels have three different types of color filters at red, green, and blue (RGB) wavelengths. The recorded RGB diffraction patterns (for each point on the structured substrate) form a basis that can be used to rapidly reconstruct any arbitrary multicolor incoherent object distribution at subpixel resolution, using a compressive sampling algorithm. This lensfree computational imaging platform could be quite useful to create a compact fluorescent on-chip microscope that has color imaging capability

Spectral demultiplexing in holographic and fluorescent on-chip microscopy.

Lensfree on-chip imaging and sensing platforms provide compact and cost-effective designs for various telemedicine and lab-on-a-chip applications. In this work, we demonstrate computational solutions for some of the challenges associated with (i) the use of broadband, partially-coherent illumination sources for on-chip holographic imaging, and (ii) multicolor detection for lensfree fluorescent on-chip microscopy. Specifically, we introduce spectral demultiplexing approaches that aim to digitally narrow the spectral content of broadband illumination sources (such as wide-band light emitting diodes or even sunlight) to improve spatial resolution in holographic on-chip microscopy. We also demonstrate the application of such spectral demultiplexing approaches for wide-field imaging of multicolor fluorescent objects on a chip. These computational approaches can be used to replace e.g., thin-film interference filters, gratings or other optical components used for spectral multiplexing/demultiplexing, which can form a desirable solution for cost-effective and compact wide-field microscopy and sensing needs on a chip

Lensless wide-field fluorescent imaging on a chip using compressive decoding of sparse objects.

We demonstrate the use of a compressive sampling algorithm for on-chip fluorescent imaging of sparse objects over an ultra-large field-of-view (>8 cm(2)) without the need for any lenses or mechanical scanning. In this lensfree imaging technique, fluorescent samples placed on a chip are excited through a prism interface, where the pump light is filtered out by total internal reflection after exciting the entire sample volume. The emitted fluorescent light from the specimen is collected through an on-chip fiber-optic faceplate and is delivered to a wide field-of-view opto-electronic sensor array for lensless recording of fluorescent spots corresponding to the samples. A compressive sampling based optimization algorithm is then used to rapidly reconstruct the sparse distribution of fluorescent sources to achieve approximately 10 microm spatial resolution over the entire active region of the sensor-array, i.e., over an imaging field-of-view of >8 cm(2). Such a wide-field lensless fluorescent imaging platform could especially be significant for high-throughput imaging cytometry, rare cell analysis, as well as for micro-array research

Lensfree fluorescent on-chip imaging of transgenic Caenorhabditis elegans over an ultra-wide field-of-view.

We demonstrate lensfree on-chip fluorescent imaging of transgenic Caenorhabditis elegans (C. elegans) over an ultra-wide field-of-view (FOV) of e.g., >2-8 cm(2) with a spatial resolution of ∼10 µm. This is the first time that a lensfree on-chip platform has successfully imaged fluorescent C. elegans samples. In our wide-field lensfree imaging platform, the transgenic samples are excited using a prism interface from the side, where the pump light is rejected through total internal reflection occurring at the bottom facet of the substrate. The emitted fluorescent signal from C. elegans samples is then recorded on a large area opto-electronic sensor-array over an FOV of e.g., >2-8 cm(2), without the use of any lenses, thin-film interference filters or mechanical scanners. Because fluorescent emission rapidly diverges, such lensfree fluorescent images recorded on a chip look blurred due to broad point-spread-function of our platform. To combat this resolution challenge, we use a compressive sampling algorithm to uniquely decode the recorded lensfree fluorescent patterns into higher resolution images, demonstrating ∼10 µm resolution. We tested the efficacy of this compressive decoding approach with different types of opto-electronic sensors to achieve a similar resolution level, independent of the imaging chip. We further demonstrate that this wide FOV lensfree fluorescent imaging platform can also perform sequential bright-field imaging of the same samples using partially-coherent lensfree digital in-line holography that is coupled from the top facet of the same prism used in fluorescent excitation. This unique combination permits ultra-wide field dual-mode imaging of C. elegans on a chip which could especially provide a useful tool for high-throughput screening applications in biomedical research