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This paper presents the Vega robot, which is a small, low cost, potentially disposable ground robot designed for nuclear decommissioning. Vega has been developed specifically to support characterization and inspection operations, such as 2D and 3D mapping, radiation scans and sample retrieval. The design and construction methodology that was followed to develop the robot is described and its capabilities detailed. Vega was designed to provide flexibility, both in software and hardware, is controlled via tele-operation, although it can be extended to semi and full autonomy, and can be used in either tethered or untethered configurations. A version of the tethered robot was designed for extreme radiation tolerance, utilizing relay electronics and removing active electronic systems. Vega can be outfitted with a multitude of sensors and actuators, including gamma spectrometers, alpha/beta radiation sensors, LiDARs and robotic arms. To demonstrate its flexibility, a 5 degree-of-freedom manipulator has been successfully integrated onto Vega, facilitating deployments where handling is required. To assess the tolerance of Vega to the levels of ionizing radiation that may be found in decommissioning environments, its individual components were irradiated, allowing estimates to be made of the length of time Vega would be able to continue to operate in nuclear environments. Vega has been successfully deployed in an active environment at the Dounreay nuclear site in the UK, deployed in nonactive environments at the Atomic Weapons Establishment, and demonstrated to many other organizations in the UK nuclear industry including Sellafield Ltd, with the goal of moving to active deployments in the future.

Ground robotic vehicles are often deployed to inspect areas where radioactive floor contamination is a prominent risk. However, the accuracy of detection could be adversely affected by enhanced radiation signal through self-contamination of the robot occurring over the course of the inspection. In this work, it was hypothesised that a six-legged robot could offer advantages over the more conventional ground robotic devices such as wheeled and tracked rovers. To investigate this, experimental contamination testing and computational Monte Carlo simulation techniques (GEANT4) were employed to understand how radioactive contamination pick-up on three different robotic vehicles would affect their detection accuracy. Two robotic vehicles were selected for comparison with the hexapod robot based on their type of locomotion; a wheeled rover and a tracked rover. With the aid of a non-toxic fluorescent tracer dust, the contamination received by the all three vehicles when traversing a contaminated area was initially compared through physical inspection using high definition cameras. The parametric results from these tests where used in the computational study carried out in GEANT4. A cadmium zinc telluride detector was simulated at heights ranging from 10 to 50 cm above each contaminated vehicle, as if it were mounted on a plinth. Assuming a uniform activity of 60 Bq cm−2 on all contaminated surfaces, the results suggested that due to the hexapod’s small ground-contacting surface area and geometry, radiation detection rates using an uncollimated detector are likely to be overestimated by between only 0.07%–0.12%, compared with 3.95%–8.43% and 1.75%–14.53% for the wheeled and tracked robot alternatives, respectively.

ObjectiveTo report a case of idiopathic hypertrophic pachymeningitis with recurrence in a new region of the brain after years of quiescence.BackgroundIdiopathic hypertrophic pachymeningitis (IHP) is a rare condition defined by thickening of the dural layer secondary to inflammation without discernible cause. Common symptoms include headache, cranial neuropathies, visual loss, mastoiditis and hearing loss. We present a case of a woman with two discrete episodes of headache and vision changes associated with dural thickening and parenchymal edema in separate locations, eventually with biopsy-supported diagnosis of IHP. A 41-year-old woman presented to our hospital with days of persistent temporal headache, blurred vision and confusion. MRI of the brain with contrast demonstrated left temporal lobe edema and overlying dural thickening, initially concerning for mastoiditis versus malignancy. Bloodwork revealed mildly elevated CRP and chronic untreated hepatitis C (HCV). Lumbar puncture was unrevealing, including cell counts, flow cytometry, cytology, cultures, CSF RPR and herpes simplex. Additional infectious workup, including for tuberculosis and fungi, was negative. IgG4 levels were normal, and ANCA screening was negative. CT of the chest revealed lung and liver nodules with non-specific inflammation on biopsy. Mastoidectomy with myringotomy showed no infection. PET scan was unremarkable. Ultimately, biopsy of dural thickening showed chronic inflammation, predominantly CD-163+ histiocytes without granulomas or malignancy. Seven years prior, the patient suffered a similar episode, with MRI showing extensive bilateral frontal dural thickening with associated edema. Symptoms resolved after course of corticosteroids with taper, though minor right frontopolar gliosis persisted. IHP suspected after similar workup, but no biopsy performed.Design/MethodsNA.ResultsNA.ConclusionsIHP is clinically well-described, but data on course and recurrence patterns over time is scarce. This case demonstrates that recurrence is not restricted to original affected areas and can happen after years of quiescence. Given course and positive HCV, further longitudinal follow-up and studies are warranted.

Thyroglobulin is a glycoiodoprotein that is produced by thyroid follicular cells; it is stored in follicles in structures known as colloids. The main function of this protein is to stock the hormones triiodothyronine (T3) and thyroxine (T4) until the body requires them. This study aims to demonstrate that infrared spectral imaging with appropriate multivariate analysis can reveal biochemical changes in this glycoprotein. The results achieved herein point out biochemical differences in the colloid samples obtained from normal and goiter patients including glycosylation and changes in the secondary conformational structure. We have presented the first spectral histopathology-based method to detect biochemical differences in thyroid colloids, such as TG iodination, glycosylation, and changes in the secondary structure in normal and goiter patients. The observed changes in the colloids were mainly due to the alterations in amide I and amide II (secondary conformation of proteins) and there is a correlation with different glycosylation between normal and goiter tissues.

The suitability of using an upstream deflector to improve the efficiency of a vertical axis wind turbine is presented in this study. A two-dimensional vertical axis wind turbine (VAWT) was modelled and simulated using ANSYS Fluent 14.0 computational fluid dynamics (CFD) software to solve the k-epsilon (RNG) turbulence model. Firstly, the open rotor design was optimised by varying orientation and pitch angle, prior to analysing the effect increasing wind speed had on the turbine performance. A maximum efficiency of 19.101% was achieved and was used as the open rotor design. A series of curved upstream deflectors were then evaluated in terms of efficiency improvements against the original open rotor design. Installation of the deflector resulted in a redirection of the fluid flow from the returning turbine blade, therefore reducing the negative torque induced on the system. Additionally, deflector width angles of 45 o and 36 o were found to improve the turbine performance by 1.266%. Finally, a scale model of the wind turbine was constructed and experimentally tested using a wind tunnel. No correlation between the CFD and experimental results was found due to variations in the wind speed tested by both methods. However, the VAWT design operated at a reasonably efficient level during the experimental testing, even under suboptimal conditions.

Raman spectroscopy can be used to measure the chemical composition of a sample, which can in turn be used to extract biological information. Many materials have characteristic Raman spectra, which means that Raman spectroscopy has proven to be an effective analytical approach in geology, semiconductor, materials and polymer science fields. The application of Raman spectroscopy and microscopy within biology is rapidly increasing because it can provide chemical and compositional information, but it does not typically suffer from interference from water molecules. Analysis does not conventionally require extensive sample preparation; biochemical and structural information can usually be obtained without labeling. In this protocol, we aim to standardize and bring together multiple experimental approaches from key leaders in the field for obtaining Raman spectra using a microspectrometer. As examples of the range of biological samples that can be analyzed, we provide instructions for acquiring Raman spectra, maps and images for fresh plant tissue, formalin-fixed and fresh frozen mammalian tissue, fixed cells and biofluids. We explore a robust approach for sample preparation, instrumentation, acquisition parameters and data processing. By using this approach, we expect that a typical Raman experiment can be performed by a nonspecialist user to generate high-quality data for biological materials analysis.

Mobile robots are playing a significant role in Higher Education science and engineering teaching, as they offer a flexible platform to explore and teach a wide-range of topics such as mechanics, electronics and software. Unfortunately the widespread adoption is limited by their high cost and the complexity of user interfaces and programming tools. To overcome these issues, a new affordable, adaptable and easy-to-use robotic platform is proposed. Mona is a low-cost, open-source and open-hardware mobile robot, which has been developed to be compatible with a number of standard programming environments. The robot has been successfully used for both education and research at The University of Manchester, UK.

The field of infrared spectral imaging and microscopy is advancing rapidly due in large measure to the recent commercialization of the first high-throughput, high-spatial-definition quantum cascade laser (QCL) microscope. Having speed, resolution and noise performance advantages while also eliminating the need for cryogenic cooling, its introduction has established a clear path to translating the well-established diagnostic capability of infrared spectroscopy into clinical and pre-clinical histology, cytology and hematology workflows. Demand for even higher throughput while maintaining high-spectral fidelity and low-noise performance continues to drive innovation in QCL-based spectral imaging instrumentation. In this talk, we will present for the first time, recent technological advances in tunable QCL photonics which have led to an additional 10X enhancement in spectral image data collection speed while preserving the high spectral fidelity and SNR exhibited by the first generation of QCL microscopes. This new approach continues to leverage the benefits of uncooled microbolometer focal plane array cameras, which we find to be essential for ensuring both reproducibility of data across instruments and achieving the high-reliability needed in clinical applications. We will discuss the physics underlying these technological advancements as well as the new biomedical applications these advancements are enabling, including automated whole-slide infrared chemical imaging on clinically relevant timescales.

Quantum Cascade Laser (QCL) spectroscopic imaging is a novel technique with many potential applications to histopathology. Like traditional Fourier Transform Infrared (FT-IR) imaging, QCL spectroscopic imaging derives biochemical data coupled to the spatial information of a tissue sample, and can be used to improve the diagnostic and prognostic value of assessment of a tissue biopsy. This technique also offers advantages over traditional FT-IR imaging, specifically the capacity for discrete frequency and real-time imaging. In this work we present applications of QCL spectroscopic imaging to tissue samples, including discrete frequency imaging, to compare with FT-IR and its potential value to pathology.

In this paper we propose and demonstrate a novel void characterisation algorithm which is able to distinguish between internal and external voids that are present in point clouds of both manifold and non-manifold objects and 3D scenes. We demonstrate the capabilities of our algorithm using several point clouds representing both scenes and objects. Our algorithm is shown in both a descriptive overview format as well as pseudocode. We also compare a variety of different void detection algorithms and then present a novel refinement to the best performing of these algorithms. Our refinement allows for voids in point clouds to be detected more efficiently, with fewer false positives and with over an order of magnitude improvement in terms of run time. We show our run time performance and compare it to results obtained using alternative algorithms, when tested using popular single board computers. This comparison is important as our work is intended for online robotics applications, where hardware is typically of low computational power. The target application for this work is 3D scene reconstruction to aid in the decommissioning of nuclear facilities.

Infrared microscopy has become one of the key techniques in the biomedical research field for interrogating tissue. In partnership with multivariate analysis and machine learning techniques, it has become widely accepted as a method which can distinguish between normal and cancerous tissue with both high sensitivity and high specificity1, 2. While spectral histopathology (SHP) is highly promising for improved clinical diagnosis, several practical barriers currently exist, which need to be addressed before successful implementation in the clinic. Sample throughput and speed of acquisition are key barriers and have been driven by the high volume of samples awaiting histopathological examination. FTIR chemical imaging utilising FPA technology is currently state–of-the-art for infrared chemical imaging, and recent advances in its technology have dramatically reduced acquisition times. Despite this, infrared microscopy measurements on a tissue micro array (TMA), often encompassing several million spectra takes several hours to acquire. The problem lies with the vast quantities of data which FTIR collects, each pixel in a chemical image is derived from a full infrared spectrum, itself composed of thousands of individual data points. Furthermore data management is quickly becoming a barrier to clinical translation and poses the question of how to store these incessantly growing data sets. Recently3 doubts have been raised as to whether the full spectral range is actually required for accurate disease diagnosis using SHP. These studies suggest that once spectral biomarkers have been pre-determined it may be possible to diagnose disease based on a limited number of discrete spectral features. In this current study, we explore the possibility of utilising discrete frequency chemical imaging for acquiring high-throughput, high resolution chemical images. Utilising a Quantum Cascade Laser imaging microscope with discrete frequency collection at key diagnostic wavelengths, we demonstrate that we can diagnose prostate cancer with high sensitivity and specificity. Finally we extend the study to a large patient data set utilising tissue micro arrays and show that high sensitivity and specificity can be achieved using high-throughput, rapid data collection, thereby paving the way for practical implementation in the clinic.

Accurate early diagnosis is critical to patient survival, management and quality of life. Biofluids are key to early diagnosis due to their ease of collection and intimate involvement in human function. Large-scale mid-IR imaging of dried fluid deposits offers a high-throughput molecular analysis paradigm for the biomedical laboratory. The exciting advent of tuneable quantum cascade lasers allows for the collection of discrete frequency infrared data enabling clinically relevant timescales. By scanning targeted frequencies spectral quality, reproducibility and diagnostic potential can be maintained while significantly reducing acquisition time and processing requirements, sampling 16 serum spots with 0.6, 5.1 and 15% relative standard deviation (RSD) for 199, 14 and 9 discrete frequencies respectively. We use this reproducible methodology to show proof of concept rapid diagnostics; 40 unique dried liquid biopsies from brain, breast, lung and skin cancer patients were classified in 2.4 cumulative seconds against 10 non-cancer controls with accuracies of up to 90%.

Spectral Cytopathology (SCP) is a novel approach for disease diagnosis that utilizes infrared spectroscopy to interrogate the biochemical components of cellular samples and multivariate statistical methods, such as principal component analysis, to analyze and diagnose spectra. SCP has taken vast strides in its application for disease diagnosis over the past decade; however, fixation induced changes and sample handling methods are still not systematically understood. Conversely, fixation and staining methods in conventional cytopathology, typically involving protocols to maintain the morphology of cells, have been documented and widely accepted for nearly a century. For SCP, fixation procedures must preserve the biochemical composition of samples so that spectral changes significant to disease diagnosis are not masked. We report efforts to study the effects of fixation protocols commonly used in traditional cytopathology and SCP including fixed and unfixed methods applied to exfoliated oral (buccal) mucosa cells. Data suggest that the length of time in fixative and duration of sample storage via desiccation contribute to minor spectral changes where spectra are nearly super-imposable. These findings illustrate that changes influenced by fixation are negligible in comparison to changes induced by disease.

This paper summarizes the progress achieved over the past fifteen years in applying vibrational (Raman and IR) spectroscopy to problems of medical diagnostics and cellular biology. During this time, a number of research groups have verified the enormous information content of vibrational spectra; in fact, genomic, proteomic, and metabolomic information can be deduced by decoding the observed vibrational spectra. This decoding process is aided enormously by the availability of high-power computer workstations and advanced algorithms for data analysis. Furthermore, commercial instrumentation for the fast collection of both Raman and infrared microspectral data has rendered practical the collection of images based solely on spectral data. The progress in the field has been manifested by a steady increase in the number and quality of publications submitted by established and new research groups in vibrational biological and biomedical arenas.

The detection of micro-metastases and individual metastatic cells in lymph node tissue by spectral methods is summarized. These methods are based on instrument-based acquisition of thousands of infrared spectra of individual tissue pixels from the tissue section, and analysis of the resulting spectral hypercube by multivariate algorithms. The method of infrared image acquisition, followed by multivariate analysis, is henceforth referred to as Spectral Histopathology (SHP). SHP produces pseudo-color images of tissue sections which reveal details that compare very favorably with images collected from hematoxylin/eosin (H & E) stained tissues in that the same tissue structures are detected. However, the infrared results are based on objective and reproducible measurements and do not depend on subjective interpretation. One of the major topics of this paper is the comparison of spectral patterns observed for the same cancer type from different patients. While this is easy in some tissue types, we found it to be difficult in tissues of very different cellularity, or tissue sections that exhibit high levels of inflammatory response. In both cases, spectral quality will be compromised due to confounding effects resulting from scattering effects. The correction of these effects now permits the direct comparison of different patient samples, and paves the way for diagnostic algorithms for cancer detection to be developed.

In this paper we describe the advantages of collecting infrared microspectral data in imaging mode opposed to point mode. Imaging data are processed using the PapMap algorithm, which co-adds pixel spectra that have been scrutinized for R-Mie scattering effects as well as other constraints. The signal-to-noise quality of PapMap spectra will be compared to point spectra for oral mucosa cells deposited onto low-e slides. Also the effects of software atmospheric correction will be discussed. Combined with the PapMap algorithm, data collection in imaging mode proves to be a superior method for spectral cytopathology.

In this manuscript, we report the application of EMSC to correct infrared micro-spectral data recorded from tissue that describe resonant Mie scattering contributions. Small breast micro-metastases previously undetectable using the raw measured spectra were provided clear contrast from the surrounding tissue after signal correction. The technique also proved transferrable, successfully correcting imaging data sets recorded from multiple patients. It is envisaged more robust methods of supervised analysis can now be constructed to automatically classify and diagnose tissue spectra. (© 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Recently Fourier Transform Infrared (FTIR) spectroscopic imaging has been used as a tool to detect the changes in cellular composition that may reflect the onset of a disease. This approach has been investigated as a mean of monitoring the change of the biochemical composition of cells and providing a diagnostic tool for various human cancers and other diseases. The discrimination between different types of tissue based upon spectroscopic data is often achieved using various multivariate clustering techniques. However, the number of clusters is a common unknown feature for the clustering methods, such as hierarchical cluster analysis, k-means and fuzzy c-means. In this study, we apply a FCM based clustering algorithm to obtain the best number of clusters as given by the minimum validity index value. This often results in an excessive number of clusters being created due to the complexity of this biochemical system. A novel method to automatically merge clusters was developed to try to address this problem. Three lymph node tissue sections were examined to evaluate our new method. These results showed that this approach can merge the clusters which have similar biochemistry. Consequently, the overall algorithm automatically identifies clusters that accurately match the main tissue types that are independently determined by the clinician.

This article examines the way in which a number of US fiction writers have responded to the political challenge of 9/11 in ways that counter the militaristic response of their government and insist on the necessity for a process of mourning and self-examination that military action is intended to displace. The fictions I examine emphasize the need for the contemporary US to sharpen its sense of history, in particular, to develop greater understanding of the prehistory of 9/11 and reconsider the past in light of the Al-Qaeda attacks. They insist on the need for reflection on the body, both as a site of trauma and as a palimpsest-like history of the human subject, which may be examined for clues to the interrelation of past and present. Moreover, they insist on the need to consider the close connection between American corporations and violence, especially that which is abetted or provoked by corporations in the wider culture. I include discussion of four prose works: ‘Extremely Loud and Incredibly Close’, by Jonathan Safran Foer; ‘The Good Life’, by Jay McInerney; ‘Cosmopolis’, by Don DeLillo, and ‘The Suffering Channel’, by David Foster Wallace, a novella-length piece from his last collection of stories ‘Oblivion’.

Instrumental advances in infrared micro-spectroscopy have made possible the observation of individual human cells and even subcellular structures. The observed spectra represent a snapshot of the biochemical composition of a cell; this composition varies subtly but reproducibly with cellular effects such as progression through the cell cycle, cell maturation and differentiation, and disease. The aim of this summary is to provide a synopsis of the progress achieved in infrared spectral cytopathology (SCP) – the combination of infrared micro-spectroscopy and multivariate methods of analysis – for the detection of abnormalities in exfoliated human cells of the upper respiratory and digestive tract, namely the oral and nasopharyngeal cavities, and the esophagus.

Imagining the king’s death: figurative treason, fantasies of regicide (1793–1796) (2000), John Barrell examines in detail the paranoid reaction to political dissent that the French Revolution provoked among supporters of George III. Through close attention to the treason trials of that period he demonstrates that the suspicions of the monarchy extended deep into the imaginative life of the king’s subjects and, in treason cases, led to intense scrutiny of the private thoughts and fantasies of defendants. In doing so, Barrell draws attention to the increasingly pejorative connotations that attached to the term ‘imagination’ in the English law courts of the1790s. He shows that by the middle of the decade the fears of the ruling class were so intense that the mere act of ‘imagining’ the death of the king was treated with the same severity as more concrete and genuinely threatening acts of treason. Yet, anxiety over purely imaginative acts of ‘treason’ in England did not begin with the revolution of 1789: rather, as I seek to show, such fears have a notable literary history. As E.P. Thompson has argued, ‘Too often events in England in the 1790s are seen only as a reflected glow from the storming of the Bastille’, but ‘the elements precipitated by the French example-the Dissenting and libertarian traditions-reach far back into English history’. According to Thompson, the effect of these schools of thought, particularly in the period after 1780, was to produce ‘a new notion of democracy, which cast aside ancient inhibitions and trusted to self-activating and selforganizing processes among the common people’ (24). In line with Thompson’s view, in this article I reach further ‘back’ than some commentators in finding the emergence of a self-grounding, proto-democratic subject in key canonical texts of the 1760s. This ‘selfactivating’ subject, to use Thompson’s term, finds it increasingly difficult to disguise its suspicion of monarchy and the extent to which this central, hereditary power constrains its personal liberty and freedom of expression. Yet, the corollary of the concern with independence and autonomy that it demonstrates is an anxious questioning of its own desires and, in particular, a disturbing suspicion that its wish for greater liberty is inherently treasonous and illegitimate. It would seem that, at this pivotal moment in modern history, the conceptual apparatus was lacking to conceive of a democratic subject without attributing to it the absolute rights and power of monarchy and, hence, verging on treason, by ‘imagining’ the death of the existing king. The average citizen therefore experienced considerable inner turmoil in making the transition between what Thompson calls ‘an anti-absolutist’, ‘with few affirmative rights’ (87), to ‘the free-born citizen challenging King and Ministers and claiming Benjamin Bird

(2006). Don DeLillo's Americana: From Third-to First-Person Consciousness. Critique: Studies in Contemporary Fiction: Vol. 47, No. 2, pp. 185-200.

We report results of a study utilizing a novel tissue classification method, based on label-free spectral techniques, for the classification of lung cancer histopathological samples on a tissue microarray. The spectral diagnostic method allows reproducible and objective classification of unstained tissue sections. This is accomplished by acquiring infrared data sets containing thousands of spectra, each collected from tissue pixels ∼6 μm on edge; these pixel spectra contain an encoded snapshot of the entire biochemical composition of the pixel area. The hyperspectral data sets are subsequently decoded by methods of multivariate analysis that reveal changes in the biochemical composition between tissue types, and between various stages and states of disease. In this study, a detailed comparison between classical and spectral histopathology is presented, suggesting that spectral histopathology can achieve levels of diagnostic accuracy that is comparable to that of multipanel immunohistochemistry.

Technological advances, namely the integration of quantum cascade lasers (QCLs) within an infrared (IR) microscope, are enabling the development of valuable label-free biomedical-imaging tools capable of targeting and detecting salient chemical species within practical clinical timeframes.

This paper presents a short review on the improvements in data processing for spectral cytopathology, the diagnostic method developed for large scale diagnostic analysis of spectral data of individual dried and fixed cells. This review is followed by the analysis of the confounding effects introduced by utilizing reflecting “low-emissivity” (low-e) slides as sample substrates in infrared micro-spectroscopy of biological samples such as individual dried cells or tissue sections. The artifact introduced by these substrates, referred to as the “standing electromagnetic wave” artifact, indeed, distorts the spectra noticeably, as postulated recently by several research groups. An analysis of the standing wave effect reveals that careful data pre-processing can reduce the spurious effects to a level where they are not creating a major problem for spectral cytopathology and spectral histopathology.

Recent results suggest that infrared microspectroscopy, coupled to methods of multivariate analysis, can distinguish normal from virally infected cells. This was established for infection of the oral cavity by the herpes simplex virus, which is associated with the outbreak of cold sores, and for infection of the cervix uteri by the human papillomavirus (HPV). In both cases, subtle (but different) spectral changes are observed that could be used for fast and inexpensive viral screening. Keywords: spectral cytopathology; infrared microspectroscopy; principal component analysis; epithelial cells; herpes simplex virus; human papillomavirus

Spectral cytopathology (SCP) is a robust and reproducible diagnostic technique that employs infrared spectroscopy and multivariate statistical methods, such as principal component analysis to interrogate unstained cellular samples and discriminate changes on the biochemical level. In the past decade, SCP has taken considerable strides in its application for disease diagnosis. Cultured cell lines have proven to be useful model systems to provide detailed biological information to this field; however, the effects of sample fixation and storage of cultured cells are still not entirely understood in SCP. Conventional cytopathology utilizes fixation and staining methods that have been established and widely accepted for nearly a century and are focused on maintaining the morphology of a cell. Conversely, SCP practices must implement fixation protocols that preserve the sample's biochemical composition and maintain its spectral integrity so not to introduce spectral changes that may mask variance significant to disease. It is not only necessary to evaluate the effects on fixed exfoliated cells but also fixed cultured cells because although they are similar systems, they exhibit distinct differences. We report efforts to study the effects of fixation methodologies commonly used in traditional cytopathology and SCP including both fixed and unfixed routines applied to cultured HeLa cells, an adherent cervical cancer cell line. Data suggest parallel results to findings in Part 1 of this series for exfoliated cells, where the exposure time in fixative and duration of sample storage via desiccation contribute to minor spectral changes only. The results presented here reinforce observations from Part 1 indicating that changes induced by disease are much greater than changes observed as a result of alternate fixation methodologies. Principal component analysis of HeLa cells fixed via the same conditions and protocols as exfoliated cells (Part 1) yield nearly identical results. More importantly, the overall conclusion is that it is necessary that all samples subjected to comparative analysis should be prepared identically because although changes are minute, they are present.

The first study interpreting B-lymphocyte activation in normal lymph nodes using vibrational micro-spectral imaging is reported. Lymphocyte activation indicates the presence and response against a pathogen, regardless of the inciting pathogen's etiology, whether a benign, reactive or malignant process. Understanding the biochemical makeup of lymphocyte activation during early stages of disease and immune response may offer significant aid in determining a tumor's origin without the presence of malignant metastatic cells but within lymph nodes that are reactive and displaying regions of hyperplasia. Infrared and Raman data scrutinized via unsupervised multivariate methods may provide a physical and reproducible method to determine the biochemical components and variances therein of activated lymph nodes with distinguishing characteristics depending on the malignancy present in the region or elsewhere in the body. The results reported here provide a proof-of-concept study that reveal a potential to screen lymph nodes for disease without the presence of metastatic cells. (© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

We report results of a study utilizing a recently developed tissue diagnostic method, based on label-free spectral techniques, for the classification of lung cancer histopathological samples from a tissue microarray. The spectral diagnostic method allows reproducible and objective diagnosis of unstained tissue sections. This is accomplished by acquiring infrared hyperspectral data sets containing thousands of spectra, each collected from tissue pixels about 6 μm on edge; these pixel spectra contain an encoded snapshot of the entire biochemical composition of the pixel area. The hyperspectral data sets are subsequently decoded by methods of multivariate analysis, which reveal changes in the biochemical composition between tissue types, and between various stages and states of disease. In this study, a detailed comparison between classical and spectral histopathology (SHP) is presented, which suggests SHP can achieve levels of diagnostic accuracy that is comparable to that of multi-panel immunohistochemistry.

Aim Spectral Cytopathology (SCP) is a novel spectroscopic method for objective and unsupervised classification of individual exfoliated cells. The limitations of conventional cytopathology are well-recognized within the pathology community. In SCP, cellular differentiation is made by observing molecular changes in the nucleus and the cytoplasm, which may or may not produce morphological changes detectable by conventional cytopathology. This proof of concept study demonstrates SCP’s potential as an enhancing tool for cytopathologists by aiding in the accurate and reproducible diagnosis of cells in all states of disease. Method Infrared spectra are collected from cervical cells deposited onto reflectively coated glass slides. Each cell has a corresponding infrared spectrum that describes its unique biochemical composition. Spectral data are processed and analyzed by an unsupervised chemometric algorithm, Principal Component Analysis (PCA). Results In this blind study, cervical samples are classified by analyzing the spectra of morphologically normal looking squamous cells from normal samples and samples diagnosed by conventional cytopathology with low grade squamous intraepithelial lesions (LSIL). SCP discriminated cytopathological diagnoses amongst twelve different cervical samples with a high degree of specificity and sensitivity. SCP also correlated two samples with abnormal spectral changes: these samples had a normal cytopathological diagnosis but had a history of abnormal cervical cytology. The spectral changes observed in the morphologically normal looking cells are most likely due to an infection with human papillomavirus, HPV. HPV DNA testing was conducted on five additional samples, and SCP accurately differentiated these samples by their HPV status. Conclusions SCP tracks biochemical variations in cells that are consistent with the onset of disease. HPV has been implicated as the cause of these changes detected spectroscopically. SCP does not depend on identifying the sparse number of morphologically abnormal cells within a large sample in order to make an accurate classification, as does conventional cytopathology. These findings suggest that the detection of cellular biochemical variations by SCP can serve as a new enhancing screening method that can identify earlier stages of disease.

Spectral cytopathology (SCP) is a novel approach for diagnostic differentiation of disease in individual exfoliated cells. SCP is carried out by collecting information on each cell's biochemical composition through an infrared micro-spectral measurement, followed by multivariate data analysis. Deviations from a cell's natural composition produce specific spectral patterns that are exclusive to the cause of the deviation or disease. These unique spectral patterns are reproducible and can be identified and used through multivariate statistical methods to detect cells compromised at the molecular level by dysplasia, neoplasia, or viral infection. In this proof of concept study, a benchmark for the sensitivity of SCP is established by classifying healthy oral squamous cells according to their anatomical origin in the oral cavity. Classification is achieved by spectrally detecting cells with unique protein expressions: for example, the squamous cells of the tongue are the only cell type in the oral cavity that have significant amounts of intracytoplasmic keratin, which allows them to be spectrally differentiated from other oral mucosa cells. Furthermore, thousands of cells from a number of clinical specimens were examined, among them were squamous cell carcinoma, malignancy-associated changes including reactive atypia, and infection by the herpes simplex virus. Owing to its sensitivity to molecular changes, SCP often can detect the onset of disease earlier than is currently possible by cytopathology visualization. As SCP is based on automated instrumentation and unsupervised software, it constitutes a diagnostic workup of medical samples devoid of bias and inconsistency. Therefore, SCP shows potential as a complementary tool in medical cytopathology.

We report microscopically collected infrared spectra of cells found in human urine in an effort to develop automatic methods for bladder cancer screening. Unsupervised multivariate analysis of the observed spectral patterns reveals distinct spectral classes, which correlated very well with visual cytology. Therefore, we believe that spectral analysis of individual cells can aid cytology in rendering reliable diagnoses based on objective measurements and discriminant algorithms.

We report the ability of infrared micro-spectral imaging, coupled with completely unsupervised methods of multivariate statistical analysis, to accurately reproduce the histological architecture of axillary lymph nodes and detect metastatic breast cancer cells. The acquisition of spectral data from tissue embedded in paraffin provided spectra free of dispersive artefacts that may be observed for infrared microscopic measurements using a ‘reflection/absorption’ methodology. As a consequence, superior tissue classification and identification of cellular abnormality unattainable for deparaffinised tissue was achieved.

The first detection of breast cancer micrometastases in lymph nodes by infrared spectral imaging and methods of multivariate analysis is reported. Micrometastases are indicators of early spread of cancer from the organ originally affected by disease, and their detection is of prime importance for the staging and treatment of cancer. Infrared spectral imaging, at a spatial resolution of ca. 10–12 μm, can detect small metastases down to the level of a few cancerous cells. The results presented here add to a rapidly growing database of infrared spectral imaging results for cancer diagnostics. Microscopic image of a stained lymph node section, and a 5-cluster pseudo-color infrared image obtained from same section. The blue areas co-localize with the micro-metastases apparent in left Panel.

Background Histopathologic evaluation of surgical specimens is a well established technique for disease identification, and has remained relatively unchanged since its clinical introduction. Although it is essential for clinical investigation, histopathologic identification of tissues remains a time consuming and subjective technique, with unsatisfactory levels of inter- and intra-observer discrepancy. A novel approach for histological recognition is to use Fourier Transform Infrared (FT-IR) micro-spectroscopy. This non-destructive optical technique can provide a rapid measurement of sample biochemistry and identify variations that occur between healthy and diseased tissues. The advantage of this method is that it is objective and provides reproducible diagnosis, independent of fatigue, experience and inter-observer variability. Methods We report a method for analysing excised lymph nodes that is based on spectral pathology. In spectral pathology, an unstained (fixed or snap frozen) tissue section is interrogated by a beam of infrared light that samples pixels of 25 μm × 25 μm in size. This beam is rastered over the sample, and up to 100,000 complete infrared spectra are acquired for a given tissue sample. These spectra are subsequently analysed by a diagnostic computer algorithm that is trained by correlating spectral and histopathological features. Results We illustrate the ability of infrared micro-spectral imaging, coupled with completely unsupervised methods of multivariate statistical analysis, to accurately reproduce the histological architecture of axillary lymph nodes. By correlating spectral and histopathological features, a diagnostic algorithm was trained that allowed both accurate and rapid classification of benign and malignant tissues composed within different lymph nodes. This approach was successfully applied to both deparaffinised and frozen tissues and indicates that both intra-operative and more conventional surgical specimens can be diagnosed by this technique. Conclusion This paper provides strong evidence that automated diagnosis by means of infrared micro-spectral imaging is possible. Recent investigations within the author's laboratory upon lymph nodes have also revealed that cancers from different primary tumours provide distinctly different spectral signatures. Thus poorly differentiated and hard-to-determine cases of metastatic invasion, such as micrometastases, may additionally be identified by this technique. Finally, we differentiate benign and malignant tissues composed within axillary lymph nodes by completely automated methods of spectral analysis.

This chapter presents novel microscopic methods to monitor cell biological processes of live or fixed cells without the use of any dye, stains, or other contrast agent. These methods are based on spectral techniques that detect inherent spectroscopic properties of biochemical constituents of cells, or parts thereof. Two different modalities have been developed for this task. One of them is infrared micro‐spectroscopy, in which an average snapshot of a cell's biochemical composition is collected at a spatial resolution of typically 25 μm. This technique, which is extremely sensitive and can collect such a snapshot in fractions of a second, is particularly suited for studying gross biochemical changes. The other technique, Raman microscopy (also known as Raman micro‐spectroscopy), is ideally suited to study variations of cellular composition on the scale of subcellular organelles, since its spatial resolution is as good as that of fluorescence microscopy. Both techniques exhibit the fingerprint sensitivity of vibrational spectroscopy toward biochemical composition, and can be used to follow a variety of cellular processes.