Information Preserving Component Analysis: Data Projections for Flow Cytometry Analysis

Flow cytometry is often used to characterize the malignant cells in leukemia and lymphoma patients, traced to the level of the individual cell. Typically, flow cytometric data analysis is performed through a series of 2-dimensional projections onto the axes of the data set. Through the years, clinicians have determined combinations of different fluorescent markers which generate relatively known expression patterns for specific subtypes of leukemia and lymphoma -- cancers of the hematopoietic system. By only viewing a series of 2-dimensional projections, the high-dimensional nature of the data is rarely exploited. In this paper we present a means of determining a low-dimensional projection which maintains the high-dimensional relationships (i.e. information) between differing oncological data sets. By using machine learning techniques, we allow clinicians to visualize data in a low dimension defined by a linear combination of all of the available markers, rather than just 2 at a time. This provides an aid in diagnosing similar forms of cancer, as well as a means for variable selection in exploratory flow cytometric research. We refer to our method as Information Preserving Component Analysis (IPCA).Comment: 26 page

Interleukin-33 contributes to both M1 and M2 chemokine marker expression in human macrophages

Abstract Background Interleukin-33 is a member of the IL-1 cytokine family whose functions are mediated and modulated by the ST2 receptor. IL-33-ST2 expression and interactions have been explored in mouse macrophages but little is known about the effect of IL-33 on human macrophages. The expression of ST2 transcript and protein levels, and IL-33-mediated effects on M1 (i.e. classical activation) and M2 (i.e. alternative activation) chemokine marker expression in human bone marrow-derived macrophages were examined. Results Human macrophages constitutively expressed the membrane-associated (i.e. ST2L) and the soluble (i.e. sST2) ST2 receptors. M2 (IL-4 + IL-13) skewing stimuli markedly increased the expression of ST2L, but neither polarizing cytokine treatment promoted the release of sST2 from these cells. When added to naïve macrophages alone, IL-33 directly enhanced the expression of CCL3. In combination with LPS, IL-33 blocked the expression of the M2 chemokine marker CCL18, but did not alter CCL3 expression in these naive cells. The addition of IL-33 to M1 macrophages markedly increased the expression of CCL18 above that detected in untreated M1 macrophages. Similarly, alternatively activated human macrophages treated with IL-33 exhibited enhanced expression of CCL18 and the M2 marker mannose receptor above that detected in M2 macrophages alone. Conclusions Together, these data suggest that primary responses to IL-33 in bone marrow derived human macrophages favors M1 chemokine generation while its addition to polarized human macrophages promotes or amplifies M2 chemokine expression.http://deepblue.lib.umich.edu/bitstream/2027.42/78250/1/1471-2172-11-52.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78250/2/1471-2172-11-52.pdfPeer Reviewe

Using coupled micropillar compression and micro-Laue diffraction to investigate deformation mechanisms in a complex metallic alloy Al13Co4

In this investigation, we have used in-situ micro-Laue diffraction combined with micropillar compression of focused ion beam milled Al13Co4 complex metallic alloy to study the evolution of deformation in Al13Co4. Streaking of the Laue spots showed that the onset of plastic flow occured at stresses as low as 0.8 GPa, although macroscopic yield only becomes apparent at 2 GPa. The measured misorientations, obtained from peak splitting, enabled the geometrically necessary dislocation density to be estimated as 1.1 x 1013 m-2

LISA, binary stars, and the mass of the graviton

We extend and improve earlier estimates of the ability of the proposed LISA (Laser Interferometer Space Antenna) gravitational wave detector to place upper bounds on the graviton mass, m_g, by comparing the arrival times of gravitational and electromagnetic signals from binary star systems. We show that the best possible limit on m_g obtainable this way is ~ 50 times better than the current limit set by Solar System measurements. Among currently known, well-understood binaries, 4U1820-30 is the best for this purpose; LISA observations of 4U1820-30 should yield a limit ~ 3-4 times better than the present Solar System bound. AM CVn-type binaries offer the prospect of improving the limit by a factor of 10, if such systems can be better understood by the time of the LISA mission. We briefly discuss the likelihood that radio and optical searches during the next decade will yield binaries that more closely approach the best possible case.Comment: ReVTeX 4, 6 pages, 1 figure, submitted to Phys Rev

First NuSTAR Limits on Quiet Sun Hard X-Ray Transient Events

We present the first results of a search for transient hard X-ray (HXR) emission in the quiet solar corona with the \textit{Nuclear Spectroscopic Telescope Array} (\textit{NuSTAR}) satellite. While \textit{NuSTAR} was designed as an astrophysics mission, it can observe the Sun above 2~keV with unprecedented sensitivity due to its pioneering use of focusing optics. \textit{NuSTAR} first observed quiet Sun regions on 2014 November 1, although out-of-view active regions contributed a notable amount of background in the form of single-bounce (unfocused) X-rays. We conducted a search for quiet Sun transient brightenings on time scales of 100 s and set upper limits on emission in two energy bands. We set 2.5--4~keV limits on brightenings with time scales of 100 s, expressed as the temperature T and emission measure EM of a thermal plasma. We also set 10--20~keV limits on brightenings with time scales of 30, 60, and 100 s, expressed as model-independent photon fluxes. The limits in both bands are well below previous HXR microflare detections, though not low enough to detect events of equivalent T and EM as quiet Sun brightenings seen in soft X-ray observations. We expect future observations during solar minimum to increase the \textit{NuSTAR} sensitivity by over two orders of magnitude due to higher instrument livetime and reduced solar background.Comment: 11 pages, 7 figures; accepted for publication in The Astrophysical Journa

Spring 1969

Design Features the Golfer Wants by Geoffrey S. Cornish & William G. Robinson (page 3) How Water Moves in the Soil by Walter H. Gardner (6) Course Care in Winter (8) Spray-on Protein Boosters by S. K. Ries (9) 1969 Turf Conference Program (12) How to Get More Benefits from Our Next Conference (13) Budget Control by Ken Voorhies (15) Turf Analysis Test by Dale Kern (18) Tolerance of Various Turfgrasses to Foliar Applications of Road Salt by W.E. Cordukes (22) He \u27Fitz\u27 the Bill by Gerry Finn (24

The First Focused Hard X-ray Images of the Sun with NuSTAR

We present results from the the first campaign of dedicated solar observations undertaken by the \textit{Nuclear Spectroscopic Telescope ARray} ({\em NuSTAR}) hard X-ray telescope. Designed as an astrophysics mission, {\em NuSTAR} nonetheless has the capability of directly imaging the Sun at hard X-ray energies ($>$3~keV) with an increase in sensitivity of at least two magnitude compared to current non-focusing telescopes. In this paper we describe the scientific areas where \textit{NuSTAR} will make major improvements on existing solar measurements. We report on the techniques used to observe the Sun with \textit{NuSTAR}, their limitations and complications, and the procedures developed to optimize solar data quality derived from our experience with the initial solar observations. These first observations are briefly described, including the measurement of the Fe K-shell lines in a decaying X-class flare, hard X-ray emission from high in the solar corona, and full-disk hard X-ray images of the Sun.Comment: 11 pages, accepted to Ap

Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP - RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization.Comment: 19 pages, 8 tables, 18 figure

Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient’s quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP - RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization