Image Reconstruction from Undersampled Confocal Microscopy Data using Multiresolution Based Maximum Entropy Regularization

We consider the problem of reconstructing 2D images from randomly under-sampled confocal microscopy samples. The well known and widely celebrated total variation regularization, which is the L1 norm of derivatives, turns out to be unsuitable for this problem; it is unable to handle both noise and under-sampling together. This issue is linked with the notion of phase transition phenomenon observed in compressive sensing research, which is essentially the break-down of total variation methods, when sampling density gets lower than certain threshold. The severity of this breakdown is determined by the so-called mutual incoherence between the derivative operators and measurement operator. In our problem, the mutual incoherence is low, and hence the total variation regularization gives serious artifacts in the presence of noise even when the sampling density is not very low. There has been very few attempts in developing regularization methods that perform better than total variation regularization for this problem. We develop a multi-resolution based regularization method that is adaptive to image structure. In our approach, the desired reconstruction is formulated as a series of coarse-to-fine multi-resolution reconstructions; for reconstruction at each level, the regularization is constructed to be adaptive to the image structure, where the information for adaption is obtained from the reconstruction obtained at coarser resolution level. This adaptation is achieved by using maximum entropy principle, where the required adaptive regularization is determined as the maximizer of entropy subject to the information extracted from the coarse reconstruction as constraints. We demonstrate the superiority of the proposed regularization method over existing ones using several reconstruction examples

Cosmology and thermodynamics of FRW universe with bulk viscous stiff fluid

We consider a cosmological model dominated by stiff fluid with a constant bulk viscosity. We classify all the possible cases of the universe predicted by the model and analyzing the scale factor, density as well as the curvature scalar. We find that when the dimensionless constant bulk viscous parameter is in the range $0 < \bar\zeta <6$ the model began with a Big Bang, and make a transition form the decelerating expansion epoch to an accelerating epoch, then tends to the de Sitter phase as $t\to \infty$. The transition into the accelerating epoch would be in the recent past, when $4<\bar\zeta<6.$ For $\bar\zeta>6$ the model doesn't have a Big Bang and suffered an increase in the fluid density and scalar curvature as the universe expands, which are eventually saturates as the scale factor $a \to \infty$ in the future. We have analyzed the model with statefinder diagnostics and find that the model is different from $\Lambda$CDM model but approaches $\Lambda$CDM point as $a \to \infty.$ We have also analyzed the status of the generalized second law of thermodynamics with apparent horizon as the boundary of the universe and found that the law is generally satisfied when $0 \leq \bar\zeta <6$ and for $\bar\zeta >6$ the law is satisfied when the scale factor is larger than a minimum value

Role of Defects in the Band Gap Tailoring of Carbon Black

With the rise in the need for cost-effective production of graphene-like systems, Carbon Black (CB) is found to be a potential candidate. This report presents the structural modification of Carbon Black (CB) subjected to hydrothermal exfoliation at various temperatures. X-ray diffraction (XRD) revealed the graphitic structure with a broad peak, indicating the amorphous nature regardless of the variation in temperatures. Raman spectroscopy revealed that defect intensity increased with the increase in temperature. The band-gaps are found to be 4eV, 2.95eV, 2.86eV and 2.21eV at the exfoliation temperatures 160⁰C,180⁰C,200⁰C and 220⁰C respectively exhibiting a lowering with rise in temperature.&nbsp; &nbsp

Do younger women with non-metastatic and non-inflammatory breast carcinoma have poor prognosis?

BACKGROUND: Controversy abounds over whether breast cancer in younger women is more aggressive than those in older. The aim of the study was to assess the influence of age on long-term survival of women with breast carcinoma. MATERIALS AND METHODS: Patients with non-metastatic and non-inflammatory invasive breast carcinoma treated at the Regional Cancer Centre, Trivandrum, Kerala, India during 1990–93 were divided into 4 age groups as < 40 years, 40–49, 50–59, and > 60 years. The overall survival (OS) for each age group was estimated using the Kaplan-Meier method in relation to the primary tumor (T) and the axillary node status (N). The OS of the various age groups were compared using the log-rank test. Hazard ratio and 95% confidence interval for each age group was estimated using Cox-regression model after adjusting for T and N. RESULTS: Between 1990–1993, 1701 women (26%, <40 years) reported with non-metastatic and non-inflammatory invasive breast carcinoma. Overall survival (OS) of all the women was 52.6 % (standard error 1%) at 10 years. The OS for women with age < 40 years and with T3 and T4 disease status was 36.6% and 10.4% respectively and for those in 40–49 age group was 41.9% and 33.5%. The 10-year OS for women with node positive (N1) disease was 24.6% in < 40 years and 45.2% in the 40–49 age group (p = 0.0006). After adjusting for tumor and node stage the relative risk for death was 24% lower for women in 40–49 age group as compared to women <40 years of age. CONCLUSION: Women under 40 years with T3/ T4 breast lesions and/or positive axillary nodes were found to have a significantly poorer survival

Lipoprotein associated phospholipase A2 enzyme; possible new roles and inhibition for therapeutic intervention

Lipoprotein-Associated Phospholipase A2 (Lp-PLA2) is a 45-kDa protein of 441 amino acids encoded by the pla2g7 gene in the humans. In the blood it is associated mainly with Low Density Lipoprotein (LDL) and less than 20% is associated with High Density Lipoprotein (HDL). This enzyme is characterized by its ability to specifically hydrolyze PAF as well as glycerophospholipids containing short, truncated, and/or oxidized fatty acyl groups at the sn-2 position of the glycerol backbone. Genetic studies conducted in humans harboring an inactivating mutation at this locus suggest that loss of Lp-PLA2 function is a risk factor for inflammatory and vascular conditions. Consistently, overexpression of Lp-PLA2 has anti-inflammatory or pro-inflammatory actions and anti-atherogenic properties in animal models. This article discusses two simple techniques to estimate Lp-PLA2 activity. New therapeutic agents inhibiting the activity of Lp-PLA2 are being investigated for curative purpose

Elevated plasma free fatty acids increase cardiovascular risk by inducing plasma biomarkers of endothelial activation, myeloperoxidase and PAI-1 in healthy subjects

<p>Abstract</p> <p>Background</p> <p>CVD in obesity and T2DM are associated with endothelial activation, elevated plasma vascular inflammation markers and a prothrombotic state. We examined the contribution of FFA to these abnormalities following a 48-hour <it>physiological </it>increase in plasma FFA to levels of obesity and diabetes in a group of healthy subjects.</p> <p>Methods</p> <p>40 non-diabetic subjects (age = 38 ± 3 yr, BMI = 28 ± 1 kg/m², FPG = 95 ± 1 mg/dl, HbA_1c= 5.3 ± 0.1%) were admitted twice and received a 48-hour infusion of normal saline or low-dose lipid. Plasma was drawn for intracellular (ICAM-1) and vascular (VCAM-1) adhesion molecules-1, E-selectin (sE-S), myeloperoxidase (MPO) and total plasminogen inhibitor-1 (tPAI-1). Insulin sensitivity was measured by a hyperglycemic clamp (M/I).</p> <p>Results</p> <p>Lipid infusion increased plasma FFA to levels observed in obesity and T2DM and reduced insulin sensitivity by 27% (p = 0.01). Elevated plasma FFA increased plasma markers of endothelial activation ICAM-1 (138 ± 10 vs. 186 ± 25 ng/ml), VCAM-1 (1066 ± 67 vs. 1204 ± 65 ng/ml) and sE-S (20 ± 1 vs. 24 ± 1 ng/ml) between 13-35% and by ≥ 2-fold plasma levels of myeloperoxidase (7.5 ± 0.9 to 15 ± 25 ng/ml), an inflammatory marker of future CVD, and tPAI-1 (9.7 ± 0.6 to 22.5 ± 1.5 ng/ml), an indicator of a prothrombotic state (all p ≤ 0.01). The FFA-induced increase was independent from the degree of adiposity, being of similar magnitude in lean, overweight and obese subjects.</p> <p>Conclusions</p> <p>An increase in plasma FFA within the physiological range observed in obesity and T2DM induces markers of endothelial activation, vascular inflammation and thrombosis in healthy subjects. This suggests that even transient (48-hour) and modest increases in plasma FFA may initiate early vascular abnormalities that promote atherosclerosis and CVD.</p

The Application of Markov Chain Monte Carlo Techniques in Non-Linear Parameter Estimation for Chemical Engineering Models

Modeling of chemical engineering systems often necessitates using non-linear models. These models can range in complexity, from a simple analytical equation to a system of differential equations. Regardless of what type of model is being utilized, determining parameter estimates is essential in everyday chemical engineering practice. One promising approach to non-linear regression is a technique called Markov Chain Monte Carlo (MCMC).This method produces reliable parameter estimates and generates joint confidence regions (JCRs) with correct shape and correct probability content. Despite these advantages, its application in chemical engineering literature has been limited. Therefore, in this project, MCMC methods were applied to a variety of chemical engineering models. The objectives of this research is to (1) illustrate how to implement MCMC methods in complex non-linear models (2) show the advantages of using MCMC techniques over classical regression approaches and (3) provide practical guidelines on how to reduce the computational time. MCMC methods were first applied to the biological oxygen demand (BOD) problem. In this case study, an implementation procedure was outlined using specific examples from the BOD problem. The results from the study illustrated the importance of estimating the pure error variance as a parameter rather than fixing its value based on the mean square error. In addition, a comparison was carried out between the MCMC results and the results obtained from using classical regression approaches. The findings show that although similar point estimates are obtained, JCRs generated from approximation methods cannot model the parameter uncertainty adequately. Markov Chain Monte Carlo techniques were then applied in estimating reactivity ratios in the Mayo-Lewis model, Meyer-Lowry model, the direct numerical integration model and the triad fraction multiresponse model. The implementation steps for each of these models were discussed in detail and the results from this research were once again compared to previously used approximation methods. Once again, the conclusion drawn from this work showed that MCMC methods must be employed in order to obtain JCRs with the correct shape and correct probability content. MCMC methods were also applied in estimating kinetic parameter used in the solid oxide fuel cell study. More specifically, the kinetics of the water-gas shift reaction, which is used in generating hydrogen for the fuel cell, was studied. The results from this case study showed how the MCMC output can be analyzed in order to diagnose parameter observability and correlation. A significant portion of the model needed to be reduced due to these issues of observability and correlation. Point estimates and JCRs were then generated using the reduced model and diagnostic checks were carried out in order to ensure the model was able to capture the data adequately. A few select parameters in the Waterloo Polymer Simulator were estimated using the MCMC algorithm. Previous studies have shown that accurate parameter estimates and JCRs could not be obtained using classical regression approaches. However, when MCMC techniques were applied to the same problem, reliable parameter estimates and correct shape and correct probability content confidence regions were observed. This case study offers a strong argument as to why classical regression approaches should be replaced by MCMC techniques. Finally, a very brief overview of the computational times for each non-linear model used in this research was provided. In addition, a serial farming approach was proposed and a significant decrease in computational time was observed when this procedure was implemented

Modeling and State Estimation of Lithium-Ion Battery Packs for Application in Battery Management Systems

As lithium-ion (Li-Ion) battery packs grow in popularity, so do the concerns of its safety, reliability, and cost. An efficient and robust battery management system (BMS) can help ease these concerns. By measuring the voltage, temperature, and current for each cell, the BMS can balance the battery pack, and ensure it is operating within the safety limits. In addition, these measurements can be used to estimate the remaining charge in the battery (state-of-charge (SOC)) and determine the health of the battery (state-of-health (SOH)). Accurate estimation of these battery and system variables can help improve the safety and reliability of the energy storage system (ESS). This research aims to develop high-fidelity battery models and robust SOC and SOH algorithms that have low computational cost and require minimal training data. More specifically, this work will focus on SOC and SOH estimation at the pack-level, as well as modeling and simulation of a battery pack. An accurate and computationally efficient Li-Ion battery model can be highly beneficial when developing SOC and SOH algorithms on the BMS. These models allow for software-in-the-loop (SIL) and hardware-in-the-loop (HIL) testing, where the battery pack is simulated in software. However, development of these battery models can be time-consuming, especially when trying to model the effect of temperature and SOC on the equivalent circuit model (ECM) parameters. Estimation of this relationship is often accomplished by carrying out a large number of experiments, which can be too costly for many BMS manufacturers. Therefore, the first contribution of this research is to develop a comprehensive battery model, where the ECM parameter surface is generated using a set of carefully designed experiments. This technique is compared with existing approaches from literature, and it is shown that by using the proposed method, the same degree of accuracy can be obtained while requiring significantly less experimental runs. This can be advantageous for BMS manufacturers that require a high-fidelity model but cannot afford to carry out a large number of experiments. Once a comprehensive model has been developed for SIL and HIL testing, research was carried out in advancing SOH and SOC algorithms. With respect to SOH, research was conducted in developing a steady and reliable SOH metric that can be determined at the cell level and is stable at different battery operating conditions. To meet these requirements, a moving window direct resistance estimation (DRE) algorithm is utilized, where the resistance is estimated only when the battery experiences rapid current transients. The DRE approach is then compared with more advanced resistance estimation techniques such as extended Kalman filter (EKF) and recursive least squares (RLS). It is shown that by using the proposed algorithm, the same degree of accuracy can be achieved as the more advanced methods. The DRE algorithm does, however, have a much lower computational complexity and therefore, can be implemented on a battery pack composed of hundreds of cells. Research has also been conducted in converting these raw resistance values into a stable SOH metric. First, an outlier removal technique is proposed for removing any outliers in the resistance estimates; specifically, outliers that are an artifact of the sampling rate. The technique involves using an adaptive control chart, where the bounds on the control chart change as the internal resistance of the battery varies during operation. An exponentially weighted moving average (EWMA) is then applied to filter out the noise present in the raw estimates. Finally, the resistance values are filtered once more based on temperature and battery SOC. This additional filtering ensures that the SOH value is independent of the battery operating conditions. The proposed SOH framework was validated over a 27-day period for a lithium iron phosphate (LFP) battery. The results show an accurate estimation of battery resistance over time with a mean error of 1.1% as well as a stable SOH metric. The findings are significant for BMS developers who have limited computational resources but still require a robust and reliable SOH algorithm. Concerning SOC, most publications in literature examine SOC estimation at the cell level. Determining the SOC for a battery pack can be challenging, especially an estimate that behaves logically to the battery user. This work proposes a three-level approach, where the final output from the algorithm is a well-behaved pack SOC estimate. The first level utilizes an EKF for estimating SOC while an RLS approach is used to adapt the model parameters. To reduce computational time, both algorithms will be executed on two specific cells: the first cell to charge to full and the first cell to discharge to empty. The second level consists of using the SOC estimates from these two cells and estimating a pack SOC value. Finally, a novel adaptive coulomb counting approach is proposed to ensure the pack SOC estimate behaves logically. The accuracy of the algorithm is tested using a 40 Ah Li-Ion battery. The results show that the algorithm produces accurate and stable SOC estimates. Finally, this work extends the developed comprehensive battery model to examine the effect of replacing damaged cells in a battery pack with new ones. The cells within the battery pack vary stochastically, and the performance of the entire pack is evaluated under different conditions. The results show that by changing out cells in the battery pack, the SOH of the pack can be maintained indefinitely above a specific threshold value. In situations where the cells are checked for replacement at discrete intervals, referred to as maintenance event intervals, it is found that the length of the interval is dependent on the mean time to failure of the individual cells. The simulation framework, as well as the results from this paper, can be utilized to better optimize Li-ion battery pack design in electric vehicles (EVs) and make long-term deployment of EVs more economically feasible

Properties and occurrence rates of KeplerKepler exoplanet candidates as a function of host star metallicity from the DR25 catalog

Correlations between the occurrence rate of exoplanets and their host star properties provide important clues about the planet formation processes. We studied the dependence of the observed properties of exoplanets (radius, mass, and orbital period) as a function of their host star metallicity. We analyzed the planetary radii and orbital periods of over 2800 $Kepler$ candidates from the latest $Kepler$ data release DR25 (Q1-Q17) with revised planetary radii based on $Gaia$~DR2 as a function of host star metallicity (from the Q1-Q17 (DR25) stellar and planet catalog). With a much larger sample and improved radius measurements, we are able to reconfirm previous results in the literature. We show that the average metallicity of the host star increases as the radius of the planet increases. We demonstrate this by first calculating the average host star metallicity for different radius bins and then supplementing these results by calculating the occurrence rate as a function of planetary radius and host star metallicity. We find a similar trend between host star metallicity and planet mass: the average host star metallicity increases with increasing planet mass. This trend, however, reverses for masses $> 4.0\, M_\mathrm{J}$: host star metallicity drops with increasing planetary mass. We further examined the correlation between the host star metallicity and the orbital period of the planet. We find that for planets with orbital periods less than 10 days, the average metallicity of the host star is higher than that for planets with periods greater than 10 days.Comment: 14 pages, 13 Figures, Accepted for publication in The Astronomical Journa