Quantitative Hyperspectral Imaging Pipeline to Recover Surface Images from CRISM Radiance Data

Hyperspectral data are important for remote applications such as mineralogy, geology, agriculture and surveillance sensing. A general pipeline converting measured hyperspectral radiance to the surface reflectance image can provide planetary scientists with clean, robust and repeatable products to work on. In this dissertation, the surface single scattering albedos (SSAs), the ratios of scattering eciency to scattering plus absorption eciences of a single particle, are selected to describe the reflectance. Moreover, the IOF, the ratio of measured spectral radiance (in the unit of watts per squared-meter and micrometer) to the solar spectral radiance (in the unit of watts per squared-meter and micrometer) at the observed time, is used to indicate the measurements. This pipeline includes two main parts: retrieving SSAs from IOF and reconstructing the SSA images from the SSA cube. The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on the Mars Reconnaissance Orbiter (MRO) helps scientists identify locations on Mars that may have exhibit hydrated mineral phases. This dissertation mainly focuses on developing the pipeline for CRISM data. One should notice that pipelines for other hyperspectral spectrometers can also be developed based on almost the same idea. Retrieving surface kinetic temperatures and SSA values from IOF data is challenging because the problem is under-determined and ill-posed, including modulating effects of atmospheric aerosols and gases, and surface scattering and emission properties. We introduce a general framework called STANN (Separating Temperature and Albedo using Neural Networks) to solve this kind of problem. STANN takes the hyperspectral IOF cube as inputs and outputs the retrieved temperature mapping and the corresponding SSA cube. Our STANN is derived using the Discrete Ordinates Radiative Transfer function to describe the forward model from SSA and temperature to IOF. In the STANN, we have a generator to generate more training samples based on limited library spectra and a neural network to approximate the inverse function based on enough generated training samples. This framework has been implemented for the Compact Imaging Spectrometer for Mars in a detailed manner. SSA can be computed from IOF directly by modeling the thermal and solar reflectance together, based on retrieved temperatures. Because accurate retrieved temperature directly leads to accurate SSA, we compare the accuracy of retrieved temperatures from STANN. The retrieved temperature has only 4 K error by one point validation (242 K) using the Curiosity Rover\u27s thermal radiometer data. Our STANN temperature map is compared with a temperature map generated independently from a theoretical thermal model. The theoretical thermal model describes the relationship between Lambert albedo at the wavelength 1.0 µm, thermal inertia and the surface temperature. However, because the thermal inertia has pixel size larger than 100 m/pixel, the generated temperature also has the same pixel size. Our STANN temperature is projected into the same pixel size (100 m/pixel) by the classic projection method. The two temperature maps have consistent global patterns. Retrieved from an IOF cube, a noisy hyperspectral SSA cube needs to be denoised and reconstructed onto the Mars surface. We propose a new algorithm, hypothesis-based estimation with regularization (HyBER), to reconstruct and denoise hyperspectral image data without extra statistical assumptions. The hypothesis test selects the best statistical model approximating measurements based on the data only. Gaussian and Poisson distributions are common respectively for continuous and integer random variables, due to the law of large numbers. Hyperspectral IOF data result from converting discrete photon counting data to continuous electrical signals after calibration. Thus, so far, Gaussian and Poisson are candidate distributions for our hypothesis tests. A regularized maximum log-likelihood estimation method is derived based on the selected model. A spatially dependent weighting on the regularization penalty is presented, substantially eliminating row artifacts that are due to non-uniform sampling. A new spectral weighting penalty is introduced to suppress varying detector-related noise. HyBER generates reconstructions with sharpened images and spectra in which the noise is suppressed, whereas fine-scale mineral absorptions are preserved. The performance is quantitatively analyzed for simulations with relative error 0.002%, which is better than the traditional non-statistical methods (baselines) and statistical methods with improper assumptions. When applied to the Mars Reconnaissance Orbiter\u27s Compact Reconnaissance Imaging Spectrometer for Mars data, the spatial resolution and contrast are about 2 times better as compared to map projecting data without the use of HyBER. So far, part of our results has enabled planetary scientists to identify minerals and understand the forming history of Mars craters. Some of these findings are verified by the Opportunity Rover\u27s measurements. In the future, results from this pipeline for CRISM are promising to play more and more critical roles in the planetary science

Nonparametric Learning of Two-Layer ReLU Residual Units

We describe an algorithm that learns two-layer residual units with rectified linear unit (ReLU) activation: suppose the input $\mathbf{x}$ is from a distribution with support space $\mathbb{R}^d$ and the ground-truth generative model is such a residual unit, given by $$\mathbf{y}= \boldsymbol{B}^\ast\left[\left(\boldsymbol{A}^\ast\mathbf{x}\right)^+ + \mathbf{x}\right]\text{,}$$ where ground-truth network parameters $\boldsymbol{A}^\ast \in \mathbb{R}^{d\times d}$ is a nonnegative full-rank matrix and $\boldsymbol{B}^\ast \in \mathbb{R}^{m\times d}$ is full-rank with $m \geq d$ and for $\mathbf{c} \in \mathbb{R}^d$, $[\mathbf{c}^{+}]_i = \max\{0, c_i\}$. We design layer-wise objectives as functionals whose analytic minimizers express the exact ground-truth network in terms of its parameters and nonlinearities. Following this objective landscape, learning residual units from finite samples can be formulated using convex optimization of a nonparametric function: for each layer, we first formulate the corresponding empirical risk minimization (ERM) as a positive semi-definite quadratic program (QP), then we show the solution space of the QP can be equivalently determined by a set of linear inequalities, which can then be efficiently solved by linear programming (LP). We further prove the statistical strong consistency of our algorithm, and demonstrate the robustness and sample efficiency of our algorithm by experiments

Immunogenicity and safety of an inactivated enterovirus 71 vaccine coadministered with trivalent split-virion inactivated influenza vaccine: A phase 4, multicenter, randomized, controlled trial in China

BackgroundFew data exist on the immunogenicity and safety of an inactivated enterovirus 71 vaccine (EV71 vaccine) coadministered with trivalent split-virion inactivated influenza vaccine (IIV3) in infants.MethodsThis trial was a phase 4, randomized, controlled trial. Infants aged 6-11 months were eligible, with no history of hand, foot and mouth disease (HFMD) and no history of EV71 vaccine or any influenza vaccine. Eligible infants were randomly assigned to EV71+IIV3 group, EV71 group or IIV3 group. Blood samples were collected on day 0 and 56.ResultsBetween September 2019 and June 2020, 1151 infants met eligibility criteria and 1134 infants were enrolled. 1045 infants were included in the per-protocol population, including 347 in the EV71+IIV3 group, 343 in the EV71 group, and 355 in the IIV3 group. The seroconversion rate (98.56% vs 98.54%; seroconversion rates difference of 0.02% [95% CI: 0.70-0.98]) and GMT (419.05 vs 503.72; GMT ratio of 0.83 [95% CI 0.70 - 0.98]) of EV71 neutralizing antibodies in the EV71+IIV3 group was not inferior to those in the EV71 group. The non-inferiority results for influenza virus antibodies (A/H1N1, A/H3N2 and B) showed that the seroconversion rates and GMTs of the EV71+IIV3 group were non-inferiority to those of the IIV3 group. Systemic and local adverse event rates were similar between groups. None of serious adverse events (SAEs) were related to vaccination.ConclusionsCoadministration of the EV71 vaccine with IIV3 was safe and did not interfere with immunogenicity. These findings support a viable immunization strategy for infants with the EV71 vaccine coadministered with IIV3 in China. This trial is registered with ClinicalTrials.gov, number NCT04091880

Quetiapine, an atypical antipsychotic, is protective against autoimmune-mediated demyelination by inhibiting effector T cell proliferation.

Quetiapine (Que), a commonly used atypical antipsychotic drug (APD), can prevent myelin from breakdown without immune attack. Multiple sclerosis (MS), an autoimmune reactive inflammation demyelinating disease, is triggered by activated myelin-specific T lymphocytes (T cells). In this study, we investigated the potential efficacy of Que as an immune-modulating therapeutic agent for experimental autoimmune encephalomyelitis (EAE), a mouse model for MS. Que treatment was initiated on the onset of MOG(35-55) peptide induced EAE mice and the efficacy of Que on modulating the immune response was determined by Flow Cytometry through analyzing CD4(+)/CD8(+) populations and the proliferation of effector T cells (CD4(+)CD25(-)) in peripheral immune organs. Our results show that Que dramatically attenuates the severity of EAE symptoms. Que treatment decreases the extent of CD4(+)/CD8(+) T cell infiltration into the spinal cord and suppresses local glial activation, thereby diminishing the loss of mature oligodendrocytes and myelin breakdown in the spinal cord of EAE mice. Our results further demonstrate that Que treatment decreases the CD4(+)/CD8(+) T cell populations in lymph nodes and spleens of EAE mice and inhibits either MOG(35-55) or anti-CD3 induced proliferation as well as IL-2 production of effector T cells (CD4(+)CD25(-)) isolated from EAE mice spleen. Together, these findings suggest that Que displays an immune-modulating role during the course of EAE, and thus may be a promising candidate for treatment of MS

Association of variants in MMEL1 and CTLA4 with rheumatoid arthritis in the Han Chinese population

Background The genome-wide association study era has made great progress in identifying susceptibility genes and genetic loci for rheumatoid arthritis ( RA) in populations of White European ancestry. However, few studies have tried to dissect disease aetiopathogenesis in other ethnic populations