The aim of this study was to investigate characteristic CT manifestations of the group of ovarian thecoma-fibroma. 24 patients (26 lesions) presenting with the ovarian thecoma-fibroma were analyzed retrospectively, and the diagnosis were confirmed by pathology after surgery. Our findings included: 22 patients were unilateral, while 2 were bilateral; 12 lesions were located in the right side of ovary, while 14 lesions were in the left side. Of the 26 lesions, there were ovarian thecoma (16 lesions), fibrothecoma (6 lesions), and fibroma (4 lesions). The largest diameters of tumor ranged from 37 to 231 mm with the mean value of 100 ± 44.29 mm. 14 patients were accompanied by ascites. All the tumors had well-defined borders. The shape of 22 lesions appeared round or oval, and 4 lesions were irregular. The tumors were solid in 19 lesions, cystic in 2 lesions, and mixed in 5 lesions. Most of the tumors were of heterogeneous density. There were no (20 lesions) or slight enhancement (6 lesions) after injection of the contrast medium. CT values of plain scan, arterial phase and venous among three groups had no significant difference. The enhancement were in the range of 0-5 HU in 10 lesions, and 6-17 HU in 16 lesions. In conclusion, the characteristic CT manifestations of the group of ovarian thecoma-fibroma were: often unilateral solid mass with the shape of oval and well defined border; no enhancement or slight enhancement; accompanied by small amount of ascites.
Colloidal quantum dots (QDs) are promising candidates for the next generation of photovoltaic (PV) technologies. Much of the progress in QD PVs is based on using PbS QDs, partly because they are stable under ambient conditions. There is considerable interest in extending this work to PbSe QDs, which have shown an enhanced photocurrent due to multiple exciton generation (MEG). One problem complicating such device-based studies is a poor stability of PbSe QDs toward exposure to ambient air. Here we develop a direct cation exchange synthesis to produce PbSe QDs with a large range of sizes and with in situ chloride and cadmium passivation. The synthesized QDs have excellent air stability, maintaining their photoluminescence quantum yield under ambient conditions for more than 30 days. Using these QDs, we fabricate high-performance solar cells without any protection and demonstrate a power conversion efficiency exceeding 6%, which is a current record for PbSe QD solar cells.
Primitive neuroectodermal tumors (PNETs) constitute a rare type of malignant neuroectodermal tumors that have chromosomal translocations identical to Ewing's sarcoma (ES), and the characteristics of this disease remain unclear.
To assess the image quality of monochromatic imaging from spectral CT in patients with Budd-Chiari syndrome (BCS), fifty patients with BCS underwent spectral CT to generate conventional 140 kVp polychromatic images (group A) and monochromatic images, with energy levels from 40 to 80, 40 + 70, and 50 + 70 keV fusion images (group B) during the portal venous phase (PVP) and the hepatic venous phase (HVP). Two-sample t tests compared vessel-to-liver contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) for the portal vein (PV), hepatic vein (HV), inferior vena cava. Readers' subjective evaluations of the image quality were recorded. The highest SNR values in group B were distributed at 50 keV; the highest CNR values in group B were distributed at 40 keV. The higher CNR values and SNR values were obtained though PVP of PV (SNR 18.39 ± 6.13 vs. 10.56 ± 3.31, CNR 7.81 ± 3.40 vs. 3.58 ± 1.31) and HVP of HV (3.89 ± 2.08 vs. 1.27 ± 1.55) in the group B; the lower image noise for group B was at 70 keV and 50 + 70 keV (15.54 ± 8.39 vs. 18.40 ± 4.97, P = 0.0004 and 18.97 ± 7.61 vs. 18.40 ± 4.97, P = 0.0691); the results show that the 50 + 70 keV fusion image quality was better than that in group A. Monochromatic energy levels of 40-70, 40 + 70, and 50 + 70 keV fusion image can increase vascular contrast and that will be helpful for the diagnosis of BCS, we select the 50 + 70 keV fusion image to acquire the best BCS images.
We developed a simple non-hot-injection synthetic route that achieves in situ halide-passivated PbS and PbSe quantum dots (QDs) and simplifies the fabrication of Pb-chalcogenide QD solar cells. The synthesis mechanism follows a temperature-dependent diffusion growth model leading to strategies that can achieve narrow size distributions for a range of sizes. We show that PbS QDs can be produced with a diameter as small as 2.2 nm, corresponding to a 1.7 eV band gap, while the resulting size distribution (6-7%) is comparable to that of hot-injection syntheses. The in situ chloride surface passivation is demonstrated by X-ray photoelectron spectroscopy and an improved photostability of both PbS and PbSe QDs when stored under air. Additionally, the photoluminescence quantum yield of the PbS QDs is ?30% higher compared to the traditional synthesis. We show that PbS QD solar cells with 6.5% power conversion efficiency (PCE) can be constructed. Finally, we fabricated PbSe QD solar cells in air (rather than in inert atmosphere), achieving a PCE of 2.65% using relatively large QDs with a corresponding band gap of 0.89 eV.
The improvement of power conversion efficiency, especially current density (Jsc), for nanocrystal quantum dot based heterojunction solar cells was realized by employing a trenched ZnO film fabricated using nanoimprint techniques. For an optimization of ZnO patterns, various patterned ZnO films were investigated using electrical and optical analysis methods by varying the line width, interpattern distance, pattern height, and residual layer. Analyzing the features of patterned ZnO films allowed us to simultaneously optimize both the pronounced electrical effects as well as optical properties. Consequently, we achieved an enhancement in Jsc from 7.82 to 12.5 mA cm(-2) by adopting the patterned ZnO with optimized trenched shape.
To individually optimize contrast medium protocol for high-pitch prospective ECG-triggering coronary CT angiography using body weight. Ninety patients undergoing high-pitch coronary CT angiography were randomly assigned to 3 contrast medium injection protocols with bolus tracking technique: Group A, 0.7 ml CM per kg patient weight (ml/kg); Group B, 0.6 ml/kg; Group C, 0.5 ml/kg. Each group had 30 patients. The CT values of superior vena cava (SVC), pulmonary artery (PA), ascending aorta (AA), left atrium (LA), left ventricle (LV), left main artery (LM) and proximal segment of right coronary artery (RCA) were measured. The image quality of coronary artery was evaluated on per-segment basis using a 4-point scale (1-excellent, 4-non-diagnosis). The CT value was not significantly different on AA (p = 0.735), LM (p = 0.764), and proximal segment of RCA (p = 0.991). The CT value was significantly different on SVC, PA, LA and LV (all p < 0.05). The mean image quality score was 1.6 ± 0.1, 1.6 ± 0.1 and 1.6 ± 0.1 (p = 0.217). The volume of CM was 47 ± 8, 44 ± 8 and 36 ± 6 ml for 3 groups (p < 0.001). The effective radiation dose was 0.88 ± 0.04, 0.87 ± 0.06, and 0.85 ± 0.07 mSv for 3 groups. Contrast medium could be reduced to 0.5 ml/kg for high-pitch coronary CT angiography without compromising diagnostic image quality, which associated ~50 % reduction of total contrast volume compared with standard contrast protocol with test bolus technique.
Epilepsy is a relatively common brain disorder which may be very debilitating. Currently, determination of epileptic seizures often involves tedious, time-consuming visual inspection of electroencephalography (EEG) data by medical experts. To better monitor seizures and make medications more effective, we propose a recurrence time based approach to characterize brain electrical activity. Recurrence times have a number of distinguished properties that make it very effective for forewarning epileptic seizures as well as studying propagation of seizures: (1) recurrence times amount to periods of periodic signals, (2) recurrence times are closely related to information dimension, Lyapunov exponent, and Kolmogorov entropy of chaotic signals, (3) recurrence times embody Shannon and Renyi entropies of random fields, and (4) recurrence times can readily detect bifurcation-like transitions in dynamical systems. In particular, property (4) dictates that unlike many other non-linear methods, recurrence time method does not require the EEG data be chaotic and/or stationary. Moreover, the method only contains a few parameters that are largely signal-independent, and hence, is very easy to use. The method is also very fast-it is fast enough to on-line process multi-channel EEG data with a typical PC. Therefore, it has the potential to be an excellent candidate for real-time monitoring of epileptic seizures in a clinical setting.
Understanding the causal relation between neural inputs and movements is very important for the success of brain-machine interfaces (BMIs). In this study, we analyze 104 neurons firings using statistical, information theoretic, and fractal analysis. The latter include Fano factor analysis, multifractal adaptive fractal analysis (MF-AFA), and wavelet multifractal analysis. We find neuronal firings are highly non-stationary, and Fano factor analysis always indicates long-range correlations in neuronal firings, irrespective of whether those firings are correlated with movement trajectory or not, and thus does not reveal any actual correlations between neural inputs and movements. On the other hand, MF-AFA and wavelet multifractal analysis clearly indicate that when neuronal firings are not well correlated with movement trajectory, they do not have or only have weak temporal correlations. When neuronal firings are well correlated with movements, they are characterized by very strong temporal correlations, up to a time scale comparable to the average time between two successive reaching tasks. This suggests that neurons well correlated with hand trajectory experienced a "re-setting" effect at the start of each reaching task, in the sense that within the movement correlated neurons the spike trains long-range dependences persisted about the length of time the monkey used to switch between task executions. A new task execution re-sets their activity, making them only weakly correlated with their prior activities on longer time scales. We further discuss the significance of the coalition of those important neurons in executing cortical control of prostheses.
Heart rate variability (HRV) is highly non-stationary, even if no perturbing influences can be identified during the recording of the data. The non-stationarity becomes more profound when HRV data are measured in intrinsically non-stationary environments, such as social stress. In general, HRV data measured in such situations are more difficult to analyze than those measured in constant environments. In this paper, we analyze HRV data measured during a social stress test using two multiscale approaches, the adaptive fractal analysis (AFA) and scale-dependent Lyapunov exponent (SDLE), for the purpose of uncovering differences in HRV between chronic fatigue syndrome (CFS) patients and their matched-controls. CFS is a debilitating, heterogeneous illness with no known biomarker. HRV has shown some promise recently as a non-invasive measure of subtle physiological disturbances and trauma that are otherwise difficult to assess. If the HRV in persons with CFS are significantly different from their healthy controls, then certain cardiac irregularities may constitute good candidate biomarkers for CFS. Our multiscale analyses show that there are notable differences in HRV between CFS and their matched controls before a social stress test, but these differences seem to diminish during the test. These analyses illustrate that the two employed multiscale approaches could be useful for the analysis of HRV measured in various environments, both stationary and non-stationary.
Multiple exciton generation (MEG) is a process that can occur in semiconductor nanocrystals, or quantum dots (QDs), whereby absorption of a photon bearing at least twice the bandgap energy produces two or more electron-hole pairs. Here, we report on photocurrent enhancement arising from MEG in lead selenide (PbSe) QD-based solar cells, as manifested by an external quantum efficiency (the spectrally resolved ratio of collected charge carriers to incident photons) that peaked at 114 ± 1% in the best device measured. The associated internal quantum efficiency (corrected for reflection and absorption losses) was 130%. We compare our results with transient absorption measurements of MEG in isolated PbSe QDs and find reasonable agreement. Our findings demonstrate that MEG charge carriers can be collected in suitably designed QD solar cells, providing ample incentive to better understand MEG within isolated and coupled QDs as a research path to enhancing the efficiency of solar light harvesting technologies.
Physiological signals often are highly non-stationary (i.e., mean and variance change with time) and multiscaled (i.e., dependent on the spatial or temporal interval lengths). They may exhibit different behaviors, such as non-linearity, sensitive dependence on small disturbances, long memory, and extreme variations. Such data have been accumulating in all areas of health sciences and rapid analysis can serve quality testing, physician assessment, and patient diagnosis. To support patient care, it is very desirable to characterize the different signal behaviors on a wide range of scales simultaneously. The Scale-Dependent Lyapunov Exponent (SDLE) is capable of such a fundamental task. In particular, SDLE can readily characterize all known types of signal data, including deterministic chaos, noisy chaos, random 1/f(?) processes, stochastic limit cycles, among others. SDLE also has some unique capabilities that are not shared by other methods, such as detecting fractal structures from non-stationary data and detecting intermittent chaos. In this article, we describe SDLE in such a way that it can be readily understood and implemented by non-mathematically oriented researchers, develop a SDLE-based consistent, unifying theory for the multiscale analysis, and demonstrate the power of SDLE on analysis of heart-rate variability (HRV) data to detect congestive heart failure and analysis of electroencephalography (EEG) data to detect seizures.
Chloride channels (ClC) are involved in normal physiological processes and pathology of various diseases. Although it is recognized that suppression of ClC inhibits cell proliferation in different types of cells, the potential function of ClC in cell migration in ovarian cancer is still unclear. In this study, we investigated the effect of the ClC inhibitor, 5-nitro-2-(3-phenylpropylamino)-benzoate (NPPB), on cell migration in the human ovarian cancer cell line SKOV-3 as well as the related signaling pathway involved in this action.
Limb ischemic preconditioning (LIPC) induced by prior brief periods of ischemia-reperfusion (I/R) to a limb is a simple and convenient strategy to protect the heart from I/R injury. However, the optimal strategy is unknown. Therefore, the present study was conducted to test the most effective method of LIPC for clinical applications.
Health monitoring of world economy is an important issue, especially in a time of profound economic difficulty world-wide. The most important aspect of health monitoring is to accurately predict economic downturns. To gain insights into how economic crises develop, we present two metrics, positive and negative income entropy and distribution analysis, to analyze the collective "spatial" and temporal dynamics of companies in nine sectors of the world economy over a 19 year period from 1990-2008. These metrics provide accurate predictive skill with a very low false-positive rate in predicting downturns. The new metrics also provide evidence of phase transition-like behavior prior to the onset of recessions. Such a transition occurs when negative pretax incomes prior to or during economic recessions transition from a thin-tailed exponential distribution to the higher entropy Pareto distribution, and develop even heavier tails than those of the positive pretax incomes. These features propagate from the crisis initiating sector of the economy to other sectors.
Chaos and random fractal theories are among the most important for fully characterizing nonlinear dynamics of complicated multiscale biosignals. Chaos analysis requires that signals be relatively noise-free and stationary, while fractal analysis demands signals to be non-rhythmic and scale-free.
Mild Traumatic Brain Injury (mTBI) has been identified as a major public and military health concern both in the United States and worldwide. Characterizing the effects of mTBI on postural sway could be an important tool for assessing recovery from the injury.
The n-type transition metal oxides (TMO) consisting of molybdenum oxide (MoO(x)) and vanadium oxide (V(2)O(x)) are used as an efficient hole extraction layer (HEL) in heterojunction ZnO/PbS quantum dot solar cells (QDSC). A 4.4% NREL-certified device based on the MoO(x) HEL is reported with Al as the back contact material, representing a more than 65% efficiency improvement compared with the case of Au contacting the PbS quantum dot (QD) layer directly. We find the acting mechanism of the hole extraction layer to be a dipole formed at the MoO(x) and PbS interface enhancing band bending to allow efficient hole extraction from the valence band of the PbS layer by MoO(x). The carrier transport to the metal anode is likely enhanced through shallow gap states in the MoO(x) layer.
Detecting chaos and estimating the limit of prediction time in heavy-noise environments is an important and challenging task in many areas of science and engineering. An important first step toward this goal is to reduce noise in the signals. Two major types of methods for reducing noise in chaotic signals are chaos-based approaches and wavelet shrinkage. When noise is strong, chaos-based approaches are not very effective, due to failure to accurately approximate the local chaotic dynamics. Here, we propose a nonlinear adaptive algorithm to recover continuous-time chaotic signals in heavy-noise environments. We show that it is more effective than both chaos-based approaches and wavelet shrinkage. Furthermore, we apply our algorithm to study two important issues in geophysics. One is whether chaos exists in river flow dynamics. The other is the limit of prediction time for the Madden-Julian oscillation (MJO), which is one of the most dominant modes of low-frequency variability in the tropical troposphere and affects a wide range of weather and climate systems. Using the adaptive filter, we show that river flow dynamics can indeed be chaotic. We also show that the MJO is weakly chaotic with the prediction time around 50 days, which is considerably longer than the prediction times determined by other approaches.
The current-voltage (J-V) characteristics of ZnO/PbS quantum dot (QD) solar cells show a QD size-dependent behavior resulting from a Schottky junction that forms at the back metal electrode opposing the desirable diode formed between the ZnO and PbS QD layers. We study a QD size-dependent roll-over effect that refers to the saturation of photocurrent in forward bias and crossover effect which occurs when the light and dark J-V curves intersect. We model the J-V characteristics with a main diode formed between the n-type ZnO nanocrystal (NC) layer and p-type PbS QD layer in series with a leaky Schottky-diode formed between PbS QD layer and metal contact. We show how the characteristics of the two diodes depend on QD size, metal work function, and PbS QD layer thickness, and we discuss how the presence of the back diode complicates finding an optimal layer thickness. Finally, we present Kelvin probe measurements to determine the Fermi level of the QD layers and discuss band alignment, Fermi-level pinning, and the V(oc) within these devices.
To understand the nature of brain dynamics as well as to develop novel methods for the diagnosis of brain pathologies, recently, a number of complexity measures from information theory, chaos theory, and random fractal theory have been applied to analyze the EEG data. These measures are crucial in quantifying the key notions of neurodynamics, including determinism, stochasticity, causation, and correlations. Finding and understanding the relations among these complexity measures is thus an important issue. However, this is a difficult task, since the foundations of information theory, chaos theory, and random fractal theory are very different. To gain significant insights into this issue, we carry out a comprehensive comparison study of major complexity measures for EEG signals. We find that the variations of commonly used complexity measures with time are either similar or reciprocal. While many of these relations are difficult to explain intuitively, all of them can be readily understood by relating these measures to the values of a multiscale complexity measure, the scale-dependent Lyapunov exponent, at specific scales. We further discuss how better indicators for epileptic seizures can be constructed.
Previous studies on heart rate variability (HRV) using chaos theory, fractal scaling analysis, and many other methods, while fruitful in many aspects, have produced much confusion in the literature. Especially the issue of whether normal HRV is chaotic or stochastic remains highly controversial. Here, we employ a new multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE), to characterize HRV. SDLE has been shown to readily characterize major models of complex time series including deterministic chaos, noisy chaos, stochastic oscillations, random 1/f processes, random Levy processes, and complex time series with multiple scaling behaviors. Here we use SDLE to characterize the relative importance of nonlinear, chaotic, and stochastic dynamics in HRV of healthy, congestive heart failure, and atrial fibrillation subjects. We show that while HRV data of all these three types are mostly stochastic, the stochasticity is different among the three groups.
The synthesis of pentacene-based dendrimers has been achieved via esterification of 1,3,5-benzenetricarboxylic acid and unsymmetrical pentacene 4 possessing a hydroxy group. Dendrimers 1 (C(183)H(204)O(9)Si(9), 2800 g mol(-1)) and 2 (C(540)H(570)O(30)Si(24), 8214 g mol(-1)) are characterized by (1)H and (13)C NMR, IR, UV-vis, and fluorescence spectroscopy, as well as mass spectrometry. These branched oligomeric materials are benchtop stable and soluble in common organic solvents, allowing for solution cast formation of thin films. Photocurrent and photocurrent yield measurements of these films reveal improved efficiency in photogenerated conduction for dendrimers in comparison to linearly connected pentacene-based polymers.
Heart rate variability (HRV) is an important dynamical variable of the cardiovascular function. There have been numerous efforts to determine whether HRV dynamics are chaotic or random, and whether certain complexity measures are capable of distinguishing healthy subjects from patients with certain cardiac disease. In this study, we employ a new multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE), to characterize the relative importance of nonlinear, chaotic, and stochastic dynamics in HRV of healthy, congestive heart failure (CHF), and atrial fibrillation subjects. We show that while HRV data of all these three types are mostly stochastic, the stochasticity is different among the three groups. Furthermore, we show that for the purpose of distinguishing healthy subjects from patients with CHF, features derived from SDLE are more effective than other complexity measures such as the Hurst parameter, the sample entropy, and the multiscale entropy.
The authors present a tutorial description of adaptive fractal analysis (AFA). AFA utilizes an adaptive detrending algorithm to extract globally smooth trend signals from the data and then analyzes the scaling of the residuals to the fit as a function of the time scale at which the fit is computed. The authors present applications to synthetic mathematical signals to verify the accuracy of AFA and demonstrate the basic steps of the analysis. The authors then present results from applying AFA to time series from a cognitive psychology experiment on repeated estimation of durations of time to illustrate some of the complexities of real-world data. AFA shows promise in dealing with many types of signals, but like any fractal analysis method there are special challenges and considerations to take into account, such as determining the presence of linear scaling regions.
Fractal time series analysis methods are commonly used for analyzing center of pressure (COP) signals with the goal of revealing the underlying neuromuscular processes for upright stance control. The use of fractal methods is often coupled with the assumption that the COP is an instance of fractional Gaussian noise (fGn) or fractional Brownian motion (fBm). Our purpose was to evaluate the applicability of the fGn-fBm framework to the COP in light of several characteristics of COP signals revealed by a new method, adaptive fractal analysis (AFA). AFA quantifies how the variance of the residuals to fits of a globally smooth trend signal scales with the time scale at which the fits are performed. Application of AFA to COP signals revealed that there are potentially three fractal scaling regions in the COP as opposed to one as expected from a pure fGn or fBm process. The scaling region at the fastest scale was anti-persistent and spanned ~30-90 ms, the intermediate was persistent and spanned ~200 ms-1.9 s, and the slowest was anti-persistent and spanned ~5-40 s. The intermediate fractal scaling region was the most clearly defined, but it only contributed around 11% of the total spectral energy of the COP signal, indicating that other features of the COP signal contribute more importantly to the overall dynamics. Also, more than half of the Hurst exponents estimated for the intermediate region were greater than the theoretically expected range [0,1] for fGn-fBm processes. These results suggest the fGn-fBm framework is not appropriate for modeling COP signals. ON-OFF intermittency might provide a better modeling framework for the COP, and multiscale approaches may be more appropriate for analyzing COP data.
Analysis of photoluminescence (PL) from chemically treated lead sulfide (PbS) quantum dot (QD) films versus temperature reveals the effects of QD size and ligand binding on the motion of carriers between bright and dark trap states. For strongly coupled QDs, the PL exhibits temperature-dependent quenching and shifting consistent with charges residing in a shallow exponential tail of quasi-localized states below the band gap. The depth of the tail varies from 15 to 40 meV, similar to or smaller than exponential band tail widths measured for polycrystalline Si. The trap state distribution can be manipulated with QD size and surface treatment, and its characterization should provide a clearer picture of charge separation and percolation in disordered QD films than what currently exists.
Culturomics was recently introduced as the application of high-throughput data collection and analysis to the study of human culture. Here, we make use of these data by investigating fluctuations in yearly usage frequencies of specific words that describe social and natural phenomena, as derived from books that were published over the course of the past two centuries. We show that the determination of the Hurst parameter by means of fractal analysis provides fundamental insights into the nature of long-range correlations contained in the culturomic trajectories, and by doing so offers new interpretations as to what might be the main driving forces behind the examined phenomena. Quite remarkably, we find that social and natural phenomena are governed by fundamentally different processes. While natural phenomena have properties that are typical for processes with persistent long-range correlations, social phenomena are better described as non-stationary, on-off intermittent or Lévy walk processes.
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