Positive Kurtosis Research Articles

Large Field of View Scanning Electron Microscopy of the Paraventricular Nucleus of the Hypothalamus During Diet‐Induced Obesity

The paraventricular nucleus of the hypothalamus (PVN) is critical in autonomic and endocrine control. Previous work has indicated that PVN dysregulation is associated with a variety of cardiometabolic diseases including those related to obesity. However, investigations have focused primarily on molecular alterations. Importantly, changes in the anatomical structure of brain networks also leads to functional alterations. While electron microscopy (EM) can provide nanometer resolution of normal/abnormal brain structures, an inherent limitation of traditional transmission EM is the random sampling of an object within a large region of interest (i.e. PVN). To overcome the inherent limitations of EM, we introduce an approach that utilizes large field of view high-resolution focused ion beam scanning electron microscopy (SEM) of the PVN. Six week old male C57Bl/6J mice were fed a high fat diet (HFD) or normal chow for 10 weeks (n=4/group), and brains prepared for SEM imaging. Due to the COVID-19 pandemic, which required social distancing, SEM imaging and analysis was conducted remotely (i.e. at home) using a virtual network computing approach. We first acquired interactive, zoomable maps of the PVN at a low magnification. This image was then used to navigate precisely to the PVN and acquire a high-resolution image encompassing ~30-40% of the nucleus (Figure). With this approach, we could rapidly scan for any visually apparent anatomical alterations. This initial evaluation revealed more electron dense regions within PVN neuronal nuclei, potentially indicative of heterochromatin clusters, in HFD mice relative to normal chow counterparts. Thus, we performed subsequent analysis on 80-120 neurons from each diet group by outlining neuronal nuclear envelopes and quantifying pixel distribution. This provided an indirect estimate of heterochromatin (i.e. dark pixels) and euchromatin (lighter pixels), with a shift towards darker pixels suggestive of increased chromatin “clustering”. The area and perimeter of analyzed PVN neuronal nuclei between groups was similar (e.g. area: 4.0±2.9 vs 3.7±1.4 µm2x106, normal chow vs HFD, p<0.05). However, histogram analysis of pixel intensities revealed positive kurtosis, indicating that HFD resulted in a redistribution of nuclear pixels to dark and light pixel intensities (-0.05±0.03 vs 0.12±0.03 a.u., normal chow vs HFD, p<0.05). Measures of skew, an indicator of curve symmetry, further revealed a leftward pixel shift (i.e. more negative skew toward dark pixels) in diet-induced obese mice (-0.48±0.01 vs -0.64±0.01 a.u., normal chow vs HFD, p<0.05). Finally, we created a PVN “map” to examine where the most altered neurons were located. In short, following HFD feeding, “altered” neurons were distributed throughout the entire PVN and not located in discrete PVN sub-nuclei. Collectively, these findings present an approach that allows for the precise placement of PVN cells within the overall region (i.e. locating a house within a city). Moreover, they suggest that HFD feeding may disrupt PVN neuronal chromatin structure, which may be reflective of epigenetic modifications and/or transcriptional alterations.

T2* placental MRI in pregnancies complicated with fetal congenital heart disease

BackgroundCongenital heart disease (CHD) is one of the most important and common group of congenital malformations in humans. Concurrent development and close functional links between the fetal heart and placenta emphasise the importance of understanding placental function and its influence in pregnancy outcomes. The aim of this study was to evaluate placental oxygenation by relaxometry (T2*) to assess differences in placental phenotype and function in CHD. MethodsIn this prospective cross-sectional observational study, 69 women with a fetus affected with CHD and 37 controls, whole placental T2* was acquired using a 1.5-Tesla MRI scanner. Gaussian Process Regression was used to assess differences in placental phenotype in CHD cohorts compared to our controls. ResultsPlacental T2* maps demonstrated significant differences in CHD compared to controls at equivalent gestational age. Mean T2* values over the entire placental volume were lowest compared to predicted normal in right sided obstructive lesions (RSOL) (Z-Score 2.30). This cohort also showed highest lacunarity indices (Z-score −1.7), as a marker of lobule size. Distribution patterns of T2* values over the entire placental volume were positively skewed in RSOL (Z-score −4.69) and suspected, not confirmed coarctation of the aorta (CoA-) (Z-score −3.83). Deviations were also reflected in positive kurtosis in RSOL (Z-score −3.47) and CoA- (Z-score −2.86). ConclusionPlacental structure and function appear to deviate from normal development in pregnancies with fetal CHD. Specific patterns of altered placental function assessed by T2* deliver crucial complementary information to antenatal assessments in the presence of fetal CHD.

Diagnostic accuracy of MRI textural analysis in the classification of breast tumors

ObjectiveTo investigate whether textural analysis (TA) of MRI heterogeneity may play a role in the clinical assessment and classification of breast tumors. Materials and methodsFor this retrospective study, patients with breast masses ≥1 cm on contrast-enhanced MRI were obtained in 69 women (mean age: 51 years; range 21–78 years) with 77 masses (38 benign, 39 malignant) from 2006 to 2018. The selected single slice sagittal peak post-contrast T1-weighted image was analyzed with commercially available TA software [TexRAD Ltd., UK]. Eight histogram TA parameters were evaluated at various spatial scaling factors (SSF) including mean pixel intensity, standard deviation of the pixel histogram (SD), entropy, mean of the positive pixels (MPP), skewness, kurtosis, sigma, and Tx_sigma. Additional statistical tests were used to determine their predictiveness. ResultsEntropy showed a significant difference between benign and malignant tumors at all textural scales (p < 0.0001) and kurtosis was significant at SSF = 0–5 (p = 0.0026–0.0241). The single best predictor was entropy at SSF = 4 with AUC = 0.80, giving a sensitivity of 95% and specificity of 53%. An AUC of 0.91 was found using a model combining entropy with sigma, which yielded better performance with a sensitivity of 92% and specificity of 79%. ConclusionTA of breast masses has the potential to assist radiologists in categorizing tumors as benign or malignant on MRI. Measurements of entropy, kurtosis, and entropy combined with sigma may provide the best predictability.

Computed tomography texture-based radiomics analysis in gallbladder cancer: initial experience.

Aim of the studyTo investigate computed tomography (CT) texture parameters in suspected gallbladder cancer (GBC) and assess its utility in predicting histopathological grade and overall survival.Material and methodsThis retrospective pilot study included consecutive patients with clinically suspected GBC. CT images, clinical, and histological or cytological data were retrieved from the database. CT images were reviewed by two radiologists. A single axial CT section in the portal venous phase was selected for texture analysis. Radiomic feature extraction was done using commercially available research software.ResultsThirty-eight patients (31 females, mean age 53.1 years) were included. Malignancy was confirmed in 29 patients in histopathology or cytology analysis, and the rest had no features of malignancy. Exophytic gallbladder mass with associated gallbladder wall thickening was present in 22 (58%) patients. Lymph nodal, liver, and omental metastases were present in 10, 1, and 3 patients, respectively. The mean overall survival was 9.7 months. There were significant differences in mean and kurtosis at medium texture scales to differentiate moderately differentiated and poorly differentiated adenocarcinoma (p < 0.05). The only texture parameter that was significantly associated with survival was kurtosis (p = 0.020) at medium texture scales. In multivariate analysis, factors found to be significantly associated with length of overall survival were mean number of positive pixels (p = 0.02), skewness (p = –0.046), kurtosis (0.018), and standard deviation (p = 0.045).ConclusionsOur preliminary results highlight the potential utility of CT texture-based radiomics analysis in patients with GBC. Medium texture scale parameters including both mean and kurtosis, or kurtosis alone, may help predict the histological grade and survival, respectively.

ESTIMATION OF GENETIC VARIANCE COMPONENTS AND IDENTIFICATION OF TRANSGRESSIVE SEGREGANTS FOR TWO INTRASPECIFIC EXTRA-LONG STAPLE COTTON CROSSES

The available genetic variability in the studied germplasm may be used for direct selection or hybridization program. The experimental material comprised of F1 and F2 populations along with their respective parents of two intra-specific cotton crosses belonging to G. barbadense L., to estimate heritability, genetic advance as percent of mean and transgressive segregation for boll weight, seed cotton yield per plant, lint yield per plant, lint percentage, fiber length, fiber strength, fiber fineness and uniformity ratio. Variance of parents was lowest for all the studied traits suggesting thereby homogeneity within parents. But it abruptly increased in F2 population due to release of segregation variability. Most of the studied traits showed high heritability coupled with low genetic advance as percent of mean which indicated the predominant role of non-additive gene action in the expression of these traits. Both skewness and kurtosis had lower values and differ in the two crosses for studied traits. Lint yield / plant and lint % showed negative skewness sign and micronaire value had positive kurtosis sign for the two studied crosses. The two crosses had thrown maximum number of transgressive segregants over better parent for fiber length followed by micronaire value and boll weight, while, uniformity ratio had lowest number of transgressive segregants. The most promising transgressive segregants selected in F2 generation also to breakdown the negative correlation between high yield and fiber quality traits. The cotton breeder could be used indirect selection to improve these traits. These desirable selected plants had higher threshold value for the eight studied traits. A track on these transgressive segregants should be maintained and forwarded to further generations up to reach to homozygosity. Most promising one could be used in further breeding programme.

On approximations of value at risk and expected shortfall involving kurtosis

We derive new approximations for the Value at Risk and the Expected Shortfall at high levels of loss distributions with positive skewness and excess kurtosis, and we describe their precisions for notable ones such as for exponential, Pareto type I, lognormal and compound (Poisson) distributions. Our approximations are motivated by that kind of extensions of the so-called Normal Power Approximation, used for approximating the cumulative distribution function of a random variable, which incorporate not only the skewness but the kurtosis of the random variable in question as well. We show the performance of our approximations in numerical examples and we also give comparisons with some known ones in the literature.

Изменчивость высоты дуба черешчатого (Quercus robur L.) и клена остролистного (Acer platanoides L.) в полезащитной полосе

The organizing framework of adaptive landscape agriculture is protective afforestation. The study of the shelterbelt state is necessary for detection of their protective height due to the dramatically changing climatic conditions. The research purpose is to carry out the distribution of trees of English oak (Quercus robur L.) and Norway maple (Acer platanoides L.) with height for the stand structure evaluation, as well as to propose the measures for the condition improvement of English oak. The shelterbelt, the spatial structure of which was studied, is located in the vicinity of the village of Zolotarevka (65 km from the city of Lugansk). Archival data on the creation time and age of the studied forest shelterbelt were not revealed. Shelterbelts in the Lugansk region were created in accordance with the “Great Plan for the Transformation of Nature”, which was designed for 1949–1965. Sampling areas were laid out in accordance with the industrial standard OST 56-69–83. The composition of the shelterbelt, the diameter and average height of the trees were determined based on the results of enumerative valuation. The coefficients of skewness and kurtosis of stands of the studied species were calculated by the standard methods of biometrics. The distribution of Norway maple and English oak trees with height was checked with the normal distribution of trees in the plantation according to the Shapiro-Wilk test. The authors revealed that arithmetic and valuation mean heights of the stands of English oak and Norway maple on two permanent sampling areas differ slightly. It was found that the stand of Norway maple on the first and second sampling areas is characterized by positive right-side symmetry and positive kurtosis. Analyzing the stand condition, it is arguable that the coefficient of skewness of the English Oak stand on both areas is not the same in sign and numerical value: on the first sampling area – leftside negative (As = –2.026) and on the second – right-side positive (As = 0.973). The authors pay particular attention to the fact that the value of the coefficient of kurtosis of the English Oak stands is the highest on the first sampling area – 3.044. On the basis of the Shapiro-Wilk test it is shown that the curve of distribution of the Norway maple stands with height on the first and second sampling areas does not correspond to the normal distribution curve for the plantations. While the curve of distribution of the English Oak stands with height on the second sampling area is close to the indicator of the Shapiro-Wilk test for normal stands and is 0.823 (for p = 0.05, W = 0.842, n = 10).

Forecasting VIX Using Filtered Historical Simulation

AbstractWe propose a new VIX forecast method using Generalized Autoregressive Conditional Heteroscedasticity models based on the filtered historical simulation put forward in Barone-Adesi, Engle, and Mancini (2008). The flexible change of measure accommodates for non-normalities such as negative skewness and positive excess kurtosis. We present an application for four well-established volatility indices (VIX9D, VIX, VIX3M, and VIX6M). Our results show that our proposed estimation method outperforms the Normal-VIX model of Hao and Zhang (2013) both in-sample and out-of-sample. Furthermore, the use of volatility indices reduces the computational burden significantly compared to the options-based pricing method.

Higher-order statistical moments to analyse Arctic sea-ice drift patterns

AbstractGeophysical systems are often assumed to follow Gaussian probability density functions; however, deviations from Gaussian behaviour can shed light on the underlying dynamics. For the large-scale motion of the Arctic sea ice, such deviations have been interpreted as signatures of structure in dynamic flow fields. In this study, we use higher-order moments (skewness and kurtosis) to identify spatiotemporal changes in the Beaufort Gyre (BG) and the Transpolar Drift (TPD) sea-ice drift patterns. Higher-order moments of satellite-derived ice drift speeds are examined over the winter period of 2006–2017 to describe the persistence of features like the BG and TPD, and their variation over time. Index patterns indicate that the periphery of the BG can be identified by a combination of high positive skewness and high kurtosis in the ice drift time series on an annual basis.

Trend analysis of long-term rainfall and temperature data for Ethiopia

ABSTRACT Trend analysis for the long-term average temperature and rainfall of Ethiopia during 1901 to 2015 has been performed to understand the pattern of these important meteorological features under climate change. The rainfall characterizations viz., precipitation concentration index (PCI), seasonality index (SI), rainfall anomaly index (RAI) and departure analysis of rainfall (DAR) have been calculated and interpreted. The annual rainfall recorded for the whole period was 816.3 ± 90.82 mm/year. During kiremt (long rainy) season, maximum seasonal rainfall of 453.2 mm were recorded while, its minimum value (39.6 mm) were received in bega (dry) season, respectively. The analysis of probability distribution for the time series data showed slightly positive skewness and kurtosis in monthly, seasonal and annual rainfall from the normal distribution. The results of Modified Mann–Kendall trend analysis for average temperature revealed a significant increasing trend from 1961 to 2015. Conversely, dry season (bega) rainfall, showed an increasing trend while kiremt season rainfall with decreasing trend for the period of 1901–2015. However, annual and other seasonal rainfall did not show any statistically significant trend. Study concludes that there is an increase in the average temperature in Ethiopia but, it constitutes a high degree of stability in rainfall rate and distribution.

Mixing and scalar dissipation rate in a decaying jet

The temporal development of the mixing field in a decaying jet (Re = 50,000) was quantified by measuring mole fraction and scalar dissipation rate (SDR) in a decaying, isothermal, turbulent gaseous jet. The 2D scalar field was measured by using planar laser induced fluorescence of acetone and, with appropriate image processing, this allowed estimation of the SDR using the two in-plane components within 16% error. The instantaneous and averaged distributions of the mole fraction are reported for downstream dimensionless distances up to 7 nozzle exit diameters and 35 exit flow timescales after end of injection. With advection of the last uniform exit concentration (UEC) profile core away from the nozzle exit, a region of weak concentration arises at the decaying jet's trailing edge. Estimates made in a Lagrangian frame of reference show that the trailing edge of the jet becomes leaner, after the end of injection (AEI), faster than in the steady state, confirming the existence of an ‘entrainment wave’. The normalised probability density functions of the 2D SDR at various stations and times AEI differ from a lognormal distribution at both low and high SDR values with negative skewness and positive excess kurtosis. A pseudo 3D SDR, made by including an estimate for the out of plane component, showed reduced departure from lognormal. The departure may be attributed to the disappearance of the strong shear layer associated with the absence of nozzle momentum AEI. To the authors’ knowledge, this study provides the first measurements of the SDR in a decaying, isothermal turbulent jet.

СТРУКТУРА, ХОД РОСТА ДРЕВОСТОЕВ ЛИПЫ МЕЛКОЛИСТНОЙ В УСЛОВИЯХ БУРЗЯНСКОГО ЛЕСНИЧЕСТВА

The article presents the results of assessing the structure and growth of smallleaved Linden stands in the conditions of the burzyansk forest area. Regularities of small-leaved Linden renewal in the studied territory are revealed. The distribution curves of Linden trees by diameter have positive asymmetry and kurtosis. The difference in the arithmetic mean, mode, and median values, as well as the true average diameter, indicate that the stand is of different ages. Analysis of the growth of model trees indicates good growth conditions in the conditions of the design object, but in recent years there has been a decrease in growth in all indicators, which indicates the need for thinning of the stand.

Near-Gaussian distributions for modelling discrete stellar velocity data with heteroskedastic uncertainties

ABSTRACT The velocity distributions of stellar tracers in general exhibit weak non-Gaussianity encoding information on the orbital composition of a galaxy and the underlying potential. The standard solution for measuring non-Gaussianity involves constructing a series expansion (e.g. the Gauss–Hermite series) that can produce regions of negative probability density. This is a significant issue for the modelling of discrete data with heteroskedastic uncertainties. Here, we introduce a method to construct positive-definite probability distributions by the convolution of a given kernel with a Gaussian distribution. Further convolutions by observational uncertainties are trivial. The statistics (moments and cumulants) of the resulting distributions are governed by the kernel distribution. Two kernels (uniform and Laplace) offer simple drop-in replacements for a Gauss–Hermite series for negative and positive excess kurtosis distributions with the option of skewness. We demonstrate the power of our method by an application to real and mock line-of-sight velocity data sets on dwarf spheroidal galaxies, where kurtosis is indicative of orbital anisotropy and hence a route to breaking the mass–anisotropy degeneracy for the identification of cusped versus cored dark matter profiles. Data on the Fornax dwarf spheroidal galaxy indicate positive excess kurtosis and hence favour a cored dark matter profile. Although designed for discrete data, the analytic Fourier transforms of the new models also make them appropriate for spectral fitting, which could improve the fits of high-quality data by avoiding unphysical negative wings in the line-of-sight velocity distribution.

Retrospective CT/MRI Texture Analysis of Rapidly Progressive Hepatocellular Carcinoma.

Rapidly progressive hepatocellular carcinoma (RPHCC) is a subset of hepatocellular carcinoma that demonstrates accelerated growth, and the radiographic features of RPHCC versus non-RPHCC have not been determined. The purpose of this retrospective study was to use baseline radiologic features and texture analysis for the accurate detection of RPHCC and subsequent improvement of clinical outcomes. We conducted a qualitative visual analysis and texture analysis, which selectively extracted and enhanced imaging features of different sizes and intensity variation including mean gray-level intensity (mean), standard deviation (SD), entropy, mean of the positive pixels (MPP), skewness, and kurtosis at each spatial scaling factor (SSF) value of RPHCC and non-RPHCC tumors in a computed tomography (CT) cohort of n = 11 RPHCC and n = 11 non-RPHCC and a magnetic resonance imaging (MRI) cohort of n = 13 RPHCC and n = 10 non-RPHCC. There was a statistically significant difference across visual CT irregular margins p = 0.030 and CT texture features in SSF between RPHCC and non-RPHCC for SSF-6, coarse-texture scale, mean p = 0.023, SD p = 0.053, MPP p = 0.023. A composite score of mean SSF-6 binarized + SD SSF-6 binarized + MPP SSF-6 binarized + irregular margins was significantly different between RPHCC and non-RPHCC (p = 0.001). A composite score ≥3 identified RPHCC with a sensitivity of 81.8% and specificity of 81.8% (AUC = 0.884, p = 0.002). CT coarse-texture-scale features in combination with visually detected irregular margins were able to statistically differentiate between RPHCC and non-RPHCC. By developing an image-based, non-invasive diagnostic criterion, we created a composite score that can identify RPHCC patients at their early stages when they are still eligible for transplantation, improving the clinical course of patient care.

The performance of the Indian stock market during COVID-19

The current empirical study attempts to analyze the impact of COVID-19 on the performance of the Indian stock market concerning two composite indices (BSE 500 and BSE Sensex) and eight sectoral indices of Bombay Stock Exchange (BSE) (Auto, Bankex, Consumer Durables, Capital Goods, Fast Moving Consumer Goods, Health Care, Information Technology, and Realty) of India, and compare the composite indices of India with three global indexes S&amp;amp;amp;P 500, Nikkei 225, and FTSE 100. The daily data from January 2019 to May 2020 have been considered in this study. GLS regression has been applied to assess the impact of COVID-19 on the multiple measures of volatility, namely standard deviation, skewness, and kurtosis of all indices. All indices’ key findings show lower mean daily return than specific, negative returns in the crisis period compared to the pre-crisis period. The standard deviation of all the indices has gone up, the skewness has become negative, and the kurtosis values are exceptionally large. The relation between indices has increased during the crisis period. The Indian stock market depicts roughly the same standard deviation as the global markets but has higher negative skewness and higher positive kurtosis of returns, making the market seem more volatile.

Tumour texture features from preoperative CT predict high-risk disease in endometrial cancer

To enable more individualised treatment of endometrial cancer, improved methods for preoperative tumour characterization are warranted. Texture analysis is a method for quantification of heterogeneity in images, increasingly reported as a promising diagnostic tool in oncological imaging, but largely unexplored in endometrial cancer AIM: To explore whether tumour texture features from preoperative computed tomography (CT) are related to known prognostic histopathological features and to outcome in endometrial cancer patients. Preoperative pelvic contrast-enhanced CT was performed in 155 patients with histologically confirmed endometrial cancer. Tumour ROIs were manually drawn on the section displaying the largest cross-sectional tumour area, using dedicated texture analysis software. Using the filtration-histogram technique, the following texture features were calculated: mean, standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis. These imaging markers were evaluated as predictors of histopathological high-risk features and recurrence- and progression-free survival using multivariable logistic regression and Cox regression analysis, including models adjusting for high-risk status based on preoperative biopsy, magnetic resonance imaging (MRI) findings, and age. High tumour entropy independently predicted deep myometrial invasion (odds ratio [OR] 3.7, p=0.008) and cervical stroma invasion (OR 3.9, p=0.02). High value of MPP (MPP5 >24.2) independently predicted high-risk histological subtype (OR 3.7, p=0.01). Furthermore, high tumour kurtosis tended to independently predict reduced recurrence- and progression-free survival (HR 1.1, p=0.06). CT texture analysis yields promising imaging markers in endometrial cancer and may supplement other imaging techniques in providing a more refined preoperative risk assessment that may ultimately enable better tailored treatment strategies.

Decoding incidental ovarian lesions: use of texture analysis and machine learning for characterization and detection of malignancy.

To compare CT texture features of benign and malignant ovarian lesions and to build a machine learning model to detect malignancy in incidental ovarian lesions. In this IRB-approved, HIPAA-compliant, retrospective study, 427 consecutive patients with incidental ovarian lesions detected on contrast-enhanced CT (348, 81.5% benign and 79, 18.5% malignant) were included. The following CT texture features were analyzed using commercially available software (TexRAD, Feedback Plc, Cambridge, UK): total pixel, mean, standard deviation (SD), entropy, mean value of positive pixels (MPP), skewness, kurtosis and entropy. Three machine learning models were created by combining texture features and patients' age, and performance of these models was assessed using tenfold cross-validation. Receiver operating characteristics (ROC) were constructed to assess sensitivity and specificity. The cutoff value was picked using a cost-weighted method. Total pixels, mean, SD, entropy, MPP, and skewness were significantly different between benign and malignant groups (p < 0.05). With a selected 10 as a cost factor to optimize cutoff value selection, sensitivity 92%, specificity 60% in the random forest (RF) model, sensitivity 91%, specificity 69% in SVM model, and sensitivity 92%, specificity 61% in the logistic regression, respectively. CT texture analysis could provide objective imaging analysis of incidental ovarian lesions and ML models using CT texture features and age demonstrated high sensitivity and moderate specificity for detection of malignant lesions.

Impact of nonlinear overdensity statistics on primordial black hole abundance

It has been recently established that, if the nonlinear relationship between the overdensity perturbations and the curvature perturbations are taken into account, non-Gaussianity is introduced in the overdensity statistics, which alters the expected primordial black hole abundance. This is explored by using the nonlinear relationship between the overdensities and curvature perturbations up to second order, where a negative skewness and positive kurtosis aim at lowering and increasing the abundance, while an abundance comparable to Gaussian perturbations is obtained by adjusting the amplitude of the curvature power spectrum. The effects of the nonvanishing skewness and kurtosis are studied using a toy model Dirac delta and log-normal curvature power spectra as well as one obtained from an $\ensuremath{\alpha}$-attractor model capable of primordial black hole production. Finally, the nonlinear calculations using Press-Schechter are compared with peaks theory.

Feasibility of computed tomography texture analysis of hepatic fibrosis using dual-energy spectral detector computed tomography.

To evaluate feasibility of computer tomography texture analysis (CTTA) at different energy level using dual-energy spectral detector CT for liver fibrosis. Eighty-seven patients who underwent a spectral CT examination and had a reference standard of liver fibrosis (histopathologic findings, n = 61, or clinical findings for normal, n = 26) were included. Mean gray-level intensity, mean number of positive pixels (MPP), entropy, skewness, and kurtosis using commercially available software (TexRAD) were compared at different energy levels. Optimal CTTA parameter cutoffs to diagnose liver fibrosis were evaluated. CTTA parameters at different energy levels correlated with liver fibrosis. The association of CTTA parameters with energy level was evaluated. Mean gray-level intensity, skewness, kurtosis, and entropy showed significant differences between patients with and without clinically significant hepatic fibrosis (P < 0.05). Mean gray-level intensity at 50keV was significantly positively correlated with liver fibrosis (ρ = 0.502, P < 0.001). To diagnose stages F2-F4, entropy and mean gray-level intensity at low keV level showed the largest area under the curve (AUC; 0.79 and 0.79). Estimated marginal means (EMMs) of mean gray-level intensity showed prominent differences at low energy levels. CTTA parameters from different keV levels demonstrated meaningful accuracy for diagnosis of liver fibrosis or clinically significant hepatic fibrosis.

Quantum work statistics close to equilibrium

We study the statistics of work, dissipation, and entropy production of a quantum quasi-isothermal process, where the system remains close to the thermal equilibrium along the transformation. We derive a general analytic expression for the work distribution and the cumulant generating function. All work cumulants split into a classical (non-coherent) and quantum (coherent) term, implying that close to equilibrium there are two independent channels of dissipation at all levels of the statistics. For non-coherent or commuting protocols, only the first two cumulants survive, leading to a Gaussian distribution with its first two moments related through the classical fluctuation-dissipation relation. On the other hand, quantum coherence leads to positive skewness and excess kurtosis in the distribution, and we demonstrate that these non-Gaussian effects are a manifestation of asymmetry in relation to the resource theory of thermodynamics. Furthermore, we also show that the non-coherent and coherent contributions satisfy independently the Evans-Searles fluctuation theorem, which sets strong bounds on the statistics, with negative values of the dissipation being exponentially suppressed. Our findings are illustrated in a driven two-level system and an Ising chain, where quantum signatures of the work distribution in the macroscopic limit are discussed.