Daniel R. Feldman

Achieving Climate Change Absolute Accuracy in Orbit

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission will provide a calibration laboratory in orbit for the purpose of accurately measuring and attributing climate change. CLARREO measurements establish new climate change benchmarks with high absolute radiometric accuracy and high statistical confidence across a wide range of essential climate variables. CLARREOs inherently high absolute accuracy will be verified and traceable on orbit to Systeme Internationale (SI) units. The benchmarks established by CLARREO will be critical for assessing changes in the Earth system and climate model predictive capabilities for decades into the future as society works to meet the challenge of optimizing strategies for mitigating and adapting to climate change. The CLARREO benchmarks are derived from measurements of the Earths thermal infrared spectrum (5–50 μm), the spectrum of solar radiation reflected by the Earth and its atmosphere (320–2300 nm), and radio occultation refractivity from which...

Intraoperative mass spectrometry mapping of an onco-metabolite to guide brain tumor surgery

Significance The diagnosis of tumors during surgery still relies principally on an approach developed over 150 y ago: frozen section microscopy. We show that a validated molecular marker—2-hydroxyglutarate generated from isocitrate dehydrogenase 1 mutant gliomas—can be rapidly detected from tumors using a form of ambient MS that does not require sample preparation. We use the Advanced Multimodality Image Guided Operating Suite at Brigham and Women’s Hospital to demonstrate that desorption electrospray ionization MS could be used to detect residual tumor that would have been left behind in the patient. The approach paves the way for the clinical testing of MS-based intraoperative monitoring of tumor metabolites, an advance that could revolutionize the care of surgical oncology patients. For many intraoperative decisions surgeons depend on frozen section pathology, a technique developed over 150 y ago. Technical innovations that permit rapid molecular characterization of tissue samples at the time of surgery are needed. Here, using desorption electrospray ionization (DESI) MS, we rapidly detect the tumor metabolite 2-hydroxyglutarate (2-HG) from tissue sections of surgically resected gliomas, under ambient conditions and without complex or time-consuming preparation. With DESI MS, we identify isocitrate dehydrogenase 1-mutant tumors with both high sensitivity and specificity within minutes, immediately providing critical diagnostic, prognostic, and predictive information. Imaging tissue sections with DESI MS shows that the 2-HG signal overlaps with areas of tumor and that 2-HG levels correlate with tumor content, thereby indicating tumor margins. Mapping the 2-HG signal onto 3D MRI reconstructions of tumors allows the integration of molecular and radiologic information for enhanced clinical decision making. We also validate the methodology and its deployment in the operating room: We have installed a mass spectrometer in our Advanced Multimodality Image Guided Operating (AMIGO) suite and demonstrate the molecular analysis of surgical tissue during brain surgery. This work indicates that metabolite-imaging MS could transform many aspects of surgical care.

Mass Spectrometry Imaging as a Tool for Surgical Decision-Making

Despite significant advances in image-guided therapy, surgeons are still too often left with uncertainty when deciding to remove tissue. This binary decision between removing and leaving tissue during surgery implies that the surgeon should be able to distinguish tumor from healthy tissue. In neurosurgery, current image-guidance approaches such as magnetic resonance imaging (MRI) combined with neuronavigation offer a map as to where the tumor should be, but the only definitive method to characterize the tissue at stake is histopathology. Although extremely valuable information is derived from this gold standard approach, it is limited to very few samples during surgery and is not practically used for the delineation of tumor margins. The development and implementation of faster, comprehensive, and complementary approaches for tissue characterization are required to support surgical decision-making--an incremental and iterative process with tumor removed in multiple and often minute biopsies. The development of atmospheric pressure ionization sources makes it possible to analyze tissue specimens with little to no sample preparation. Here, we highlight the value of desorption electrospray ionization as one of many available approaches for the analysis of surgical tissue. Twelve surgical samples resected from a patient during surgery were analyzed and diagnosed as glioblastoma tumor or necrotic tissue by standard histopathology, and mass spectrometry results were further correlated to histopathology for critical validation of the approach. The use of a robust statistical approach reiterated results from the qualitative detection of potential biomarkers of these tissue types. The correlation of the mass spectrometry and histopathology results to MRI brings significant insight into tumor presentation that could not only serve to guide tumor resection, but that is also worthy of more detailed studies on our understanding of tumor presentation on MRI.

Far-infrared surface emissivity and climate

Significance We find that many of the Earths climate variables, including surface temperature, outgoing longwave radiation, cooling rates, and frozen surface extent, are sensitive to far-IR surface emissivity, a largely unconstrained, temporally and spatially heterogeneous scaling factor for the blackbody radiation from the surface at wavelengths between 15 μm and 100 μm. We also describe a previously unidentified mechanism that amplifies high-latitude and high-altitude warming in finding significantly lower values of far-IR emissivity for ocean and desert surfaces than for sea ice and snow. This leads to a decrease in surface emission at far-IR wavelengths, reduced cooling to space, and warmer radiative surface temperatures. Far-IR emissivity can be measured from spectrally resolved observations, but such measurements have not yet been made. Presently, there are no global measurement constraints on the surface emissivity at wavelengths longer than 15 μm, even though this surface property in this far-IR region has a direct impact on the outgoing longwave radiation (OLR) and infrared cooling rates where the column precipitable water vapor (PWV) is less than 1 mm. Such dry conditions are common for high-altitude and high-latitude locations, with the potential for modeled climate to be impacted by uncertain surface characteristics. This paper explores the sensitivity of instantaneous OLR and cooling rates to changes in far-IR surface emissivity and how this unconstrained property impacts climate model projections. At high latitudes and altitudes, a 0.05 change in emissivity due to mineralogy and snow grain size can cause a 1.8–2.0 W m−2 difference in the instantaneous clear-sky OLR. A variety of radiative transfer techniques have been used to model the far-IR spectral emissivities of surface types defined by the International Geosphere-Biosphere Program. Incorporating these far-IR surface emissivities into the Representative Concentration Pathway (RCP) 8.5 scenario of the Community Earth System Model leads to discernible changes in the spatial patterns of surface temperature, OLR, and frozen surface extent. The model results differ at high latitudes by as much as 2°K, 10 W m−2, and 15%, respectively, after only 25 y of integration. Additionally, the calculated difference in far-IR emissivity between ocean and sea ice of between 0.1 and 0.2, suggests the potential for a far-IR positive feedback for polar climate change.

MALDI mass spectrometry imaging analysis of pituitary adenomas for near-real-time tumor delineation

Significance This study presents the use of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to detect and delineate pituitary tumors. Using MALDI MSI, it is possible to determine the peptide and protein hormone composition of pituitary tumor resection samples in fewer than 30 min. Surgeons could therefore have access to critical information for surgical decision-making in a near-real-time manner and be able to localize and discriminate pituitary tumor from nonpathological pituitary gland. This study supports the inclusion of MALDI MSI in the clinical workflow for the surgical resection of pituitary tumors, potentially allowing for improved surgical precision and patient outcomes. We present a proof of concept study designed to support the clinical development of mass spectrometry imaging (MSI) for the detection of pituitary tumors during surgery. We analyzed by matrix-assisted laser desorption/ionization (MALDI) MSI six nonpathological (NP) human pituitary glands and 45 hormone secreting and nonsecreting (NS) human pituitary adenomas. We show that the distribution of pituitary hormones such as prolactin (PRL), growth hormone (GH), adrenocorticotropic hormone (ACTH), and thyroid stimulating hormone (TSH) in both normal and tumor tissues can be assessed by using this approach. The presence of most of the pituitary hormones was confirmed by using MS/MS and pseudo-MS/MS methods, and subtyping of pituitary adenomas was performed by using principal component analysis (PCA) and support vector machine (SVM). Our proof of concept study demonstrates that MALDI MSI could be used to directly detect excessive hormonal production from functional pituitary adenomas and generally classify pituitary adenomas by using statistical and machine learning analyses. The tissue characterization can be completed in fewer than 30 min and could therefore be applied for the near-real-time detection and delineation of pituitary tumors for intraoperative surgical decision-making.

Profiling of adrenocorticotropic hormone and arginine vasopressin in human pituitary gland and tumor thin tissue sections using droplet-based liquid-microjunction surface-sampling-HPLC–ESI-MS–MS

AbstractDescribed here are the results from the profiling of the proteins arginine vasopressin (AVP) and adrenocorticotropic hormone (ACTH) from normal human pituitary gland and pituitary adenoma tissue sections, using a fully automated droplet-based liquid-microjunction surface-sampling-HPLC–ESI-MS–MS system for spatially resolved sampling, HPLC separation, and mass spectrometric detection. Excellent correlation was found between the protein distribution data obtained with this method and data obtained with matrix-assisted laser desorption/ionization (MALDI) chemical imaging analyses of serial sections of the same tissue. The protein distributions correlated with the visible anatomic pattern of the pituitary gland. AVP was most abundant in the posterior pituitary gland region (neurohypophysis), and ATCH was dominant in the anterior pituitary gland region (adenohypophysis). The relative amounts of AVP and ACTH sampled from a series of ACTH-secreting and non-secreting pituitary adenomas correlated with histopathological evaluation. ACTH was readily detected at significantly higher levels in regions of ACTH-secreting adenomas and in normal anterior adenohypophysis compared with non-secreting adenoma and neurohypophysis. AVP was mostly detected in normal neurohypophysis, as expected. This work reveals that a fully automated droplet-based liquid-microjunction surface-sampling system coupled to HPLC–ESI-MS–MS can be readily used for spatially resolved sampling, separation, detection, and semi-quantitation of physiologically-relevant peptide and protein hormones, including AVP and ACTH, directly from human tissue. In addition, the relative simplicity, rapidity, and specificity of this method support the potential of this basic technology, with further advancement, for assisting surgical decision-making. Graphical AbstractMass spectrometry based profiling of hormones in human pituitary gland and tumor thin tissue sections

The spectroscopic foundation of radiative forcing of climate by carbon dioxide

Abstract The radiative forcing (RF) of carbon dioxide (CO2) is the leading contribution to climate change from anthropogenic activities. Calculating CO2 RF requires detailed knowledge of spectral line parameters for thousands of infrared absorption lines. A reliable spectroscopic characterization of CO2 forcing is critical to scientific and policy assessments of present climate and climate change. Our results show that CO2 RF in a variety of atmospheres is remarkably insensitive to known uncertainties in the three main CO2 spectroscopic parameters: the line shapes, line strengths, and half widths. We specifically examine uncertainty in RF due to line mixing as this process is critical in determining line shapes in the far wings of CO2 absorption lines. RF computed with a Voigt line shape is also examined. Overall, the spectroscopic uncertainty in present‐day CO2 RF is less than 1%, indicating a robust foundation in our understanding of how rising CO2 warms the climate system.

Porting Existing Radiation Code for GPU Acceleration

Graphics processing units (GPUs) have proven very robust architectures for performing intensive scientific calculations, resulting in speedups as high as several hundred times. In this paper, the GPU acceleration of a radiation code for use in creating simulated satellite observations of predicted climate change scenarios is explored, particularly the prospect of porting an already existing and widely used radiation transport code to a GPU version that fully exploits the parallel nature of GPUs. The porting process is attempted with a simple radiation code, revealing that this process centers on creating many copies of variables and inlining function/subroutine calls. A resulting speedup of about 25x is reached. This is less than the speedup achieved from a radiation code built for CUDA from scratch, but it was achieved with an already existing radiation code using the PGI Accelerator to automatically generate CUDA kernels, and this demonstrates a possible strategy to speed up other existing models like MODTRAN and LBLRTM.

Temporal variability of observed and simulated hyperspectral reflectance

Multivariate analysis techniques were used to quantify and compare the spectral and temporal variability of observed and simulated shortwave hyperspectral Earth reflectance. The observed reflectances were measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instrument between 2002 and 2010. The simulated reflectances were calculated using climate Observing System Simulation Experiments (OSSEs), which used two Intergovernmental Panel on Climate Change AR4 scenarios (constant CO2 and A2 emission) to drive Moderate Resolution Atmospheric Transmission simulations. Principal component (PC) spectral shapes and time series exhibited evidence of physical variables including cloud reflectance, vegetation and desert albedo, and water vapor absorption. Comparing the temporal variability of the OSSE-simulated and SCIAMACHY-measured hyperspectral reflectance showed that their Intertropical Convergence Zone-like Southern Hemisphere (SH) tropical PC1 ocean time series had a 90° phase difference. The observed and simulated PC intersection quantified their similarity and directly compared their temporal variability. The intersection showed that despite the similar spectral variability, the temporal variability of the dominant PCs differed as in, for example, the 90° phase difference between the SH tropical intersection PC1s. Principal component analysis of OSSE reflectance demonstrated that the spectral and centennial variability of the two cases differed. The A2 PC time series, unlike the constant CO2 time series, exhibited centennial secular trends. Singular spectrum analysis isolated the A2 secular trends. The A2 OSSE PC1 and PC4 secular trends matched those in aerosol optical depth and total column precipitable water, respectively. This illustrates that time series of hyperspectral reflectance may be used to identify and attribute secular climate trends with a sufficiently long measurement record and high instrument accuracy.

Improved Representation of Surface Spectral Emissivity in a Global Climate Model and Its Impact on Simulated Climate

AbstractSurface longwave emissivity can be less than unity and vary significantly with frequency. However, most climate models still assume a blackbody surface in the longwave (LW) radiation scheme of their atmosphere models. This study incorporates realistic surface spectral emissivity into the atmospheric component of the Community Earth System Model (CESM), version 1.1.1, and evaluates its impact on simulated climate. By ensuring consistency of the broadband surface longwave flux across different components of the CESM, the top-of-the-atmosphere (TOA) energy balance in the modified model can be attained without retuning the model. Inclusion of surface spectral emissivity, however, leads to a decrease of net upward longwave flux at the surface and a comparable increase of latent heat flux. Global-mean surface temperature difference between the modified and standard CESM simulation is 0.20 K for the fully coupled run and 0.45 K for the slab-ocean run. Noticeable surface temperature differences between th...