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This study evaluates a new generation of satellite imaging spectrometers to measure point source methane emissions from anthropogenic sources. We used the Airborne Visible and Infrared Imaging Spectrometer Next Generation(AVIRIS-NG) images with known methane plumes to create two simulated satellite products. One simulation had a 30 m spatial resolution with ~200 Signal-to-Noise Ratio (SNR) in the Shortwave Infrared (SWIR) and the other had a 60 m spatial resolution with ~400 SNR in the SWIR; both products had a 7.5 nm spectral spacing. We applied a linear matched filter with a sparsity prior and an albedo correction to detect and quantify the methane emission in the original AVIRIS-NG images and in both satellite simulations. We also calculated an emission flux for all images. We found that all methane plumes were detectable in all satellite simulations. The flux calculations for the simulated satellite images correlated well with the calculated flux for the original AVIRIS-NG images. We also found that coarsening spatial resolution had the largest impact on the sensitivity of the results. These results suggest that methane detection and quantification of point sources will be possible with the next generation of satellite imaging spectrometers.

Image analysis provides powerful tools for extracting meaningful and actionable information from varied image modalities. Applications of image analysis are incredibly diverse across multidisciplinary sciences. This dissertation investigates three applications of image analysis that impact human health on a global scale. First, the quantification of point-source emission plumes of greenhouse gases through image analysis of spectrometer data is improved using a novel processing algorithm and improved detection criteria. The developed algorithms improve processing speed, enabling faster delivery of actionable information from collected images and improving the accuracy of carbon dioxide concentration estimates. Second, effective radiation therapy for lung cancers relies on understanding the tumor motion. Image analysis of computed tomography volumes collected before treatment allows learning patient-specific respiratory motion patterns to provide more accurate tumor targeting. These motion tracking algorithms improve the accuracy and latency of treatment. Third, a scaleable method for dynamic radiotherapy dose tracking is investigated, initially for head and neck cancer patients. Head and neck cancer patients are especially prone to weight loss and a dynamic soft tissue presentation. A rigid consideration of tumor and surrounding organs limits the accuracy and applicability of dose tracking. Deformable image registration enables a dose tracking method to demonstrate the consideration of dynamic soft tissue presentation for these patients. These applications improve the image analysis capabilities and availability of actionable information for climate scientists and cancer care clinicians. Image analysis within this dissertation extracts valuable insights from images across domains to address global-scale issues directly impacting world health.

Motion estimation of organs in a sequence of images is important in numerous medical imaging applications. The focus of this paper is the analysis of 4D Respiratory Correlated Computed Tomography (RCCT) Imaging. It is hypothesized that the quasi-periodic breathing induced motion of organs in the thorax can be represented by deformations spanning a very low dimension subspace of the full infinite dimensional space of diffeomorphic transformations. This paper presents a novel motion estimation algorithm that includes the constraint for low-rank motion between the different phases of the RCCT images. Low-rank deformation solutions are necessary for the efficient statistical analysis and improved treatment planning and delivery. Although the application focus of this paper is RCCT the algorithm is quite general and applicable to various motion estimation problems in medical imaging.

Radiation therapy presents a need for dynamic tracking of a target tumor volume. Fiducial markers such as implanted gold seeds have been used to gate radiation delivery but the markers are invasive and gating significantly increases treatment time. Pretreatment acquisition of a respiratory correlated 4DCT allows for determination of accurate motion tracking which is useful in treatment planning. We design a patient-specific motion subspace and a deep convolutional neural network to recover anatomical positions from a single fluoroscopic projection in real-time. We use this deep network to approximate the nonlinear inverse of a diffeomorphic deformation composed with radiographic projection. This network recovers subspace coordinates to define the patient-specific deformation of the lungs from a baseline anatomic position. The geometric accuracy of the subspace deformations on real patient data is similar to accuracy attained by original image registration between individual respiratory-phase image volumes.

Noninvasive MR-guided focused ultrasound (MRgFUS) treatments are promising alternatives to the surgical removal of malignant tumors. A significant challenge is assessing the viability of treated tissue during and immediately after MRgFUS procedures. Current clinical assessment uses the nonperfused volume (NPV) biomarker immediately after treatment from contrast-enhanced MRI. The NPV has variable accuracy, and the use of contrast agent prevents continuing MRgFUS treatment if tumor coverage is inadequate. This work presents a novel, noncontrast, learned multiparametric MR biomarker that can be used during treatment for intratreatment assessment, validated in a VX2 rabbit tumor model. A deep convolutional neural network was trained on noncontrast multiparametric MR images using the NPV biomarker from follow-up MR imaging (3-5 days after MRgFUS treatment) as the accurate label of nonviable tissue. A novel volume-conserving registration algorithm yielded a voxel-wise correlation between treatment and follow-up NPV, providing a rigorous validation of the biomarker. The learned noncontrast multiparametric MR biomarker predicted the follow-up NPV with an average DICE coefficient of 0.71, substantially outperforming the current clinical standard (DICE coefficient = 0.53). Noncontrast multiparametric MR imaging integrated with a deep convolutional neural network provides a more accurate prediction of MRgFUS treatment outcome than current contrast-based techniques.

Matched filter (MF) techniques have been widely used for retrieval of greenhouse gas enhancements (enh.) from imaging spectroscopy datasets. While multiple algorithmic techniques and refinements have been proposed, the greenhouse gas target spectrum used for concentration enh. estimation has remained largely unaltered since the introduction of quantitative MF retrievals. The magnitude of retrieved methane and carbon dioxide enh., and thereby integrated mass enh. (IME) and estimated flux of point-source emitters, is heavily dependent on this target spectrum. Current standard use of molecular absorption coefficients to create unit enh. target spectra does not account for absorption by background concentrations of greenhouse gases, solar and sensor geometry, or atmospheric water vapor absorption. We introduce geometric and atmospheric parameters into the generation of scene-specific (SS) unit enh. spectra to provide target spectra that are compatible with all greenhouse gas retrieval MF techniques. For methane plumes, IME resulting from use of standard, generic enh. spectra varied from -22 to +28.7% compared to SS enh. spectra. Due to differences in spectral shape between the generic and SS enh. spectra, differences in methane plume IME were linked to surface spectral characteristics in addition to geometric and atmospheric parameters. IME differences for carbon dioxide plumes, with generic enh. spectra producing integrated mass enh. -76.1 to -48.1% compared to SS enh. spectra. Fluxes calculated from these integrated enh. would vary by the same %s, assuming equivalent wind conditions. Methane and carbon dioxide IME were most sensitive to changes in solar zenith angle and ground elevation. SS target spectra can improve confidence in greenhouse gas retrievals and flux estimates across collections of scenes with diverse geometric and atmospheric conditions.

The strong radiative forcing by atmospheric methane has stimulated interest in identifying natural and anthropogenic sources of this potent greenhouse gas. Point sources are important targets for quantification, and anthropogenic targets have potential for emissions reduction. Methane point source plume detection and concentration retrieval have been previously demonstrated using data from the Airborne Visible InfraRed Imaging Spectrometer Next Generation (AVIRIS-NG). Current quantitative methods have tradeoffs between computational requirements and retrieval accuracy, creating obstacles for processing real-time data or large datasets from flight campaigns. We present a new computationally efficient algorithm that applies sparsity and an albedo correction to matched filter retrieval of trace gas concentration-pathlength. The new algorithm was tested using AVIRIS-NG data acquired over several point source plumes in Ahmedabad, India. The algorithm was validated using simulated AVIRIS-NG data including synthetic plumes of known methane concentration. Sparsity and albedo correction together reduced the root mean squared error of retrieved methane concentration-pathlength enhancement by 60.7% compared with a previous robust matched filter method. Background noise was reduced by a factor of 2.64. The new algorithm was able to process the entire 300 flightline 2016 AVIRIS-NG India campaign in just over 8 hours on a desktop computer with GPU acceleration.

Key Points Speciation is a central and fundamental process in evolution that concerns the origin of reproductive isolation. The latest generation of genomic approaches provide remarkable opportunities to describe speciation and to learn about its underlying mechanisms. Genome scans, which can now be carried out in a truly genome-wide scale and at base-pair resolution, reveal substantial genomic divergence among incipient species even in the face of gene flow and show that there is extensive genomic heterogeneity in the extent of differentiation, especially at early stages of speciation, both in sympatry and in allopatry. The sources of this heterogeneity remain incompletely understood. The combination of genome scans with sophisticated population genetic modelling, quantitative trait locus mapping, admixture analyses and ecology has the potential to distinguish the influence of selection from demographic, historical and structural effects and to link these sources of genomic divergence to phenotypes and to reproductive isolation. Available empirical data suggest that differentiation between parapatric populations can be restricted to few genomic islands, whereas incipient species that coexist in sympatry show differentiation that is widely distributed across the genome. This suggests that genomically widespread selection is required to permit the maintenance and perhaps the build-up of genetic differentiation in sympatry. Recent genomic studies reveal that the genetic basis of reproductive isolation is often complex. The effects of pleiotropy, genetic correlations and patterns of recombination need to be considered alongside effects of ecological and sexual selection as well as genomic conflict. A surprising recent discovery is the re-use of ancient genetic variants in speciation, which are acquired either from standing genetic variation or by introgressive hybridization. In this Review, we propose a 'roadmap' for the development of speciation genomics towards answering classical and emerging questions in speciation research. Genomic approaches are an increasingly important aspect of speciation research. The authors review recent insights from speciation genomics and propose a roadmap for this field, which is aimed at addressing both long-standing and emerging questions about speciation. Speciation is a fundamental evolutionary process, the knowledge of which is crucial for understanding the origins of biodiversity. Genomic approaches are an increasingly important aspect of this research field. We review current understanding of genome-wide effects of accumulating reproductive isolation and of genomic properties that influence the process of speciation. Building on this work, we identify emergent trends and gaps in our understanding, propose new approaches to more fully integrate genomics into speciation research, translate speciation theory into hypotheses that are testable using genomic tools and provide an integrative definition of the field of speciation genomics.

Human Campylobacter jejuni infections are rising globally. Since antibiotics are usually not indicated in acute campylobacteriosis, antibiotic-independent intervention measures are desirable. The phenolic compound carvacrol constitutes a promising candidate molecule given its antimicrobial and immune-modulatory features. To test the disease-alleviating effects of oral carvacrol treatment in acute murine campylobacteriosis, IL-10−/− mice harboring a human gut microbiota were perorally infected with C. jejuni and treated with carvacrol via the drinking water. Whereas C. jejuni stably established in the gastrointestinal tract of mice from the placebo cohort, carvacrol treatment resulted in lower pathogen loads in the small intestines on day 6 post infection. When compared to placebo, carvacrol ameliorated pathogen-induced symptoms including bloody diarrhea that was accompanied by less distinct histopathological and apoptotic cell responses in the colon. Furthermore, innate and adaptive immune cell numbers were lower in the colon of carvacrol- versus placebo-treated mice. Notably, carvacrol application dampened C. jejuni-induced secretion of pro-inflammatory mediators in intestinal, extra-intestinal and systemic organs to naive levels and furthermore, resulted in distinct shifts in the fecal microbiota composition. In conclusion, our preclinical placebo-controlled intervention study provides evidence that therapeutic carvacrol application constitutes a promising option to alleviate campylobacteriosis in the infected vertebrate host.

Given that human Campylobacter jejuni infections are rising globally and antibiotic treatment is not recommended, infected patients would substantially benefit from alternative therapeutic strategies. Short-chain fatty acids such as butyrate are known for their health benefits, including anti-microbial and anti-inflammatory effects. This prompted us to investigate potential disease-alleviating properties of butyrate treatment during acute murine C. jejuni-induced enterocolitis. Therefore, following gut microbiota depletion IL-10−/− mice were challenged with 109 viable C. jejuni cells by oral gavage and treated with butyrate via the drinking water (22 g/L) starting on day 2 post-infection. As early as day 3 post-infection, butyrate reduced diarrheal severity and frequency in treated mice, whereas on day 6 post-infection, gastrointestinal C. jejuni burdens and the overall clinical outcomes were comparable in butyrate- and placebo-treated cohorts. Most importantly, butyrate treatment dampened intestinal pro-inflammatory immune responses given lower colonic numbers of apoptotic cells and neutrophils, less distinct TNF-α secretion in mesenteric lymph nodes and lower IL-6 and MCP-1 concentrations in the ileum. In conclusion, results of our preclinical intervention study provide evidence that butyrate represents a promising candidate molecule for the treatment of acute campylobacteriosis.