Search

Sentinel-2 satellite, launched by the European Space Agency, plays a critical role in various Earth observation missions. However, the spatial resolutions of Sentinel-2 images are different across its spectral bands, including four bands with a resolution of 10 m, six bands with a resolution of 20 m, and three bands with a resolution of 60 m. To facilitate the effectiveness of analyzing these images, super-resolving of the low-/medium-resolution bands to a higher resolution is desired. As in any image restoration inverse problems, we exploit image self-similarity, a commonly observed property in natural images, which underlies the state-of-the-art techniques, e.g., in image denoising. However, the design of self-similarity priors in nondiagonal inverse problems is challenging; often, a denoiser based on self-similarity is plugged into the iterations of an algorithm, without a guarantee of convergence in general. In this article, for the first time, we introduce a convex and scene-adapted regularizer built explicitly on a self-similarity graph directly learned from the Sentinel-2 images. We then develop a fast algorithm, termed Sentinel-2 super-resolution via scene-adapted self-similarity (SSSS). We experimentally show the superiority of SSSS over four commonly observed scenes, indicating the potential usage of our convex self-similarity regularization in other imaging inverse problems. [ABSTRACT FROM AUTHOR]

Linear hyperspectral unmixing (HU) aims at factoring the observation matrix into an endmember matrix and an abundance matrix. Linear HU via variational minimum volume (MV) regularization has recently received considerable attention in the remote sensing and machine learning areas, mainly owing to its robustness against the absence of pure pixels. We put some popular linear HU formulations under a unifying framework, which involves a data-fitting term and an MV-based regularization term, and collectively solve it via a nonconvex optimization. As the former and the latter terms tend, respectively, to expand (reducing the data-fitting errors) and to shrink the simplex enclosing the measured spectra, it is critical to strike a balance between those two terms. To the best of our knowledge, the existing methods find such balance by tuning a regularization parameter manually, which has little value in unsupervised scenarios. In this paper, we aim at selecting the regularization parameter automatically by exploiting the fact that a too large parameter overshrinks the volume of the simplex defined by the endmembers, making many data points be left outside of the simplex and hence inducing a large data-fitting error, while a sufficiently small parameter yields a large simplex making data-fitting error very small. Roughly speaking, the transition point happens when the simplex still encloses the data cloud but there are data points on all its facets. These observations are systematically formulated to find the transition point that, in turn, yields a good parameter. The competitiveness of the proposed selection criterion is illustrated with simulated and real data. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing (HU) is a crucial signal processing procedure to identify the underlying materials (or endmembers) and their corresponding proportions (or abundances) from an observed hyperspectral scene. A well-known blind HU criterion, advocated by Craig during the early 1990s, considers the vertices of the minimum-volume enclosing simplex of the data cloud as good endmember estimates, and it has been empirically and theoretically found effective even in the scenario of no pure pixels. However, such kinds of algorithms may suffer from heavy simplex volume computations in numerical optimization, etc. In this paper, without involving any simplex volume computations, by exploiting a convex geometry fact that a simplest simplex of N vertices can be defined by N associated hyperplanes, we propose a fast blind HU algorithm, for which each of the N hyperplanes associated with the Craig’s simplex of N vertices is constructed from N-1 affinely independent data pixels, together with an endmember identifiability analysis for its performance support. Without resorting to numerical optimization, the devised algorithm searches for the N(N-1) active data pixels via simple linear algebraic computations, accounting for its computational efficiency. Monte Carlo simulations and real data experiments are provided to demonstrate its superior efficacy over some benchmark Craig-criterion-based algorithms in both computational efficiency and estimation accuracy. [ABSTRACT FROM PUBLISHER]

In blind hyperspectral unmixing (HU), the pure-pixel assumption is well known to be powerful in enabling simple and effective blind HU solutions. However, the pure-pixel assumption is not always satisfied in an exact sense, especially for scenarios where pixels are heavily mixed. In the no-pure-pixel case, a good blind HU approach to consider is the minimum volume enclosing simplex (MVES). Empirical experience has suggested that MVES algorithms can perform well without pure pixels, although it was not totally clear why this is true from a theoretical viewpoint. This paper aims to address the latter issue. We develop an analysis framework wherein the perfect endmember identifiability of MVES is studied under the noiseless case. We prove that MVES is indeed robust against lack of pure pixels, as long as the pixels do not get too heavily mixed and too asymmetrically spread. The theoretical results are supported by numerical simulation results. [ABSTRACT FROM PUBLISHER]

An adaptive reference control (ARC) technique is proposed for minimizing overshoot/undershoot voltage and settling time of low-dropout regulators. Linear operation provided by the ARC technique can dynamically and smoothly adjust the reference voltage so as to increase the slew rate of error amplifier thus forcing the output voltage back to its steady-state value rapidly. The amount of transient revision is proportional to transient state output voltage variation and load condition. In addition, a dynamic push-pull technique is used to enhance transient response. Experimental results demonstrate that the undershoot voltage, settling time, and load regulation are improved by 31%, 68.5%, and 70%, respectively, when load current changes between 1 and 100 mA. [ABSTRACT FROM AUTHOR]

Motivation Complex biological tissues are often a heterogeneous mixture of several molecularly distinct cell subtypes. Both subtype compositions and subtype-specific (STS) expressions can vary across biological conditions. Computational deconvolution aims to dissect patterns of bulk tissue data into subtype compositions and STS expressions. Existing deconvolution methods can only estimate averaged STS expressions in a population, while many downstream analyses such as inferring co-expression networks in particular subtypes require subtype expression estimates in individual samples. However, individual-level deconvolution is a mathematically underdetermined problem because there are more variables than observations. Results We report a sample-wise Convex Analysis of Mixtures (swCAM) method that can estimate subtype proportions and STS expressions in individual samples from bulk tissue transcriptomes. We extend our previous CAM framework to include a new term accounting for between-sample variations and formulate swCAM as a nuclear-norm and ℓ2,1-norm regularized matrix factorization problem. We determine hyperparameter values using cross-validation with random entry exclusion and obtain a swCAM solution using an efficient alternating direction method of multipliers. Experimental results on realistic simulation data show that swCAM can accurately estimate STS expressions in individual samples and successfully extract co-expression networks in particular subtypes that are otherwise unobtainable using bulk data. In two real-world applications, swCAM analysis of bulk RNASeq data from brain tissue of cases and controls with bipolar disorder or Alzheimer's disease identified significant changes in cell proportion, expression pattern and co-expression module in patient neurons. Comparative evaluation of swCAM versus peer methods is also provided. Availability and implementation The R Scripts of swCAM are freely available at https://github.com/Lululuella/swCAM. A user's guide and a vignette are provided. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

A 44-year-old man underwent percutaneous nephrolithotomy for a left renal staghorn stone. During the procedure, the tip of the ultrasound lithotripter dislodged from the probe rod when the renal stone was being disintegrated. The tip immediately disappeared from the field of nephroscopic vision. Despite a thorough search, the tip could not be found. Postoperative chest radiography revealed a foreign body, shaped like a lithotripter probe tip, located in the lingular branch of the left pulmonary artery. Eventually, left thoracotomy was performed to remove the foreign body. To our knowledge, this rare complication has never been reported before. [Copyright &y& Elsevier]

We present the first case of complete documentation of total cancer ablation in a previously cryoablated human renal tumor. A 61-year-old patient with metachronous bilateral renal cell carcinoma was treated with open partial nephrectomy and subsequently laparoscopic cryoablation. His renal function deteriorated, prompting bilateral radical nephrectomy before renal transplantation. Detailed histopathologic examination of the specimen did not reveal any evidence of malignancy in two cryoablated sites 36 months (left kidney) and 19 months (right kidney) after the respective cryoablations. [Copyright &y& Elsevier]

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a powerful imaging modality to study the pharmacokinetics in a suspected cancer/tumor tissue. The pharmacokinetic (PK) analysis of prostate cancer includes the estimation of time activity curves (TACs), and thereby, the corresponding kinetic parameters (KPs), and plays a pivotal role in diagnosis and prognosis of prostate cancer. In this paper, we endeavor to develop a blind source separation algorithm, namely convex-optimization-based KPs estimation (COKE) algorithm for PK analysis based on compartmental modeling of DCE-MRI data, for effective prostate tumor detection and its quantification. The COKE algorithm first identifies the best three representative pixels in the DCE-MRI data, corresponding to the plasma, fast-flow, and slow-flow TACs, respectively. The estimation accuracy of the flux rate constants (FRCs) of the fast-flow and slow-flow TACs directly affects the estimation accuracy of the KPs that provide the cancer and normal tissue distribution maps in the prostate region. The COKE algorithm wisely exploits the matrix structure (Toeplitz, lower triangular, and exponential decay) of the original nonconvex FRCs estimation problem, and reformulates it into two convex optimization problems that can reliably estimate the FRCs. After estimation of the FRCs, the KPs can be effectively estimated by solving a pixel-wise constrained curve-fitting (convex) problem. Simulation results demonstrate the efficacy of the proposed COKE algorithm. The COKE algorithm is also evaluated with DCE-MRI data of four different patients with prostate cancer and the obtained results are consistent with clinical observations. [ABSTRACT FROM AUTHOR]

By using a mouse NIH3T3 derivate designed 7-4 harboring the inducible Ha-ras oncogene, we demonstrated the close relationship between Ha-ras expression level and sensitization of 5-flurouracil (5-FU)-treated cells. Further studies revealed that the cells susceptible to 5-FU treatment died of apoptosis, which was demonstrated by caspase-3 activation, loss of mitochondria membrane potential (MMP), and DNA fragmentation. The 7-4 cells coexpressing dominant negative Ras (RasAsn17), dominant negative Raf-1 (Raf-1CB4), Bcl-2, or active form of phosphatidylinositol 3-kinase (PI3K) became resistant to 5-FU, and apoptosis was prevented. In contrast, the cells coexpressing dominant negative Rac 1 (Rac1Asn17) or dominant negative Rho A (RhoAAsn19) showed no change of sensitivity to 5-FU. These results indicate that Ras, Bcl-2, as well as Raf-1 and PI3K pathways play pivotal roles in 5-FU-induced apoptosis under Ha-ras-overexpressed condition. Aberrant levels of cyclin E and p21Cip/WAF-1 expression as well as Cdc 2 phosphorylation at Tyrosine 15 suggest that perturbation of G1/S and G2/M transitions in cell cycle might be responsible for 5-FU triggered apoptosis. Sensitization of Ha-ras-related cells to 5-FU was also demonstrated in human bladder cancer cells. Through understanding the mechanism of 5-FU induced apoptosis in tumor cells, a new direction toward the treatment of Ha-ras oncogene-related cancers with 5-FU at more optimal dosages is possible and combinational therapy with other drugs that suppress PI3K and Bcl-2 activities can also be considered. [Copyright &y& Elsevier]

: ObjectivesTo report our experience with laparoscopic partial nephrectomy for renal tumor with concomitant adrenalectomy. An upper pole renal tumor may contiguously involve the adrenal gland, requiring concomitant adrenalectomy. Although commonly performed in the setting of laparoscopic radical nephrectomy, concomitant adrenalectomy has not been described during laparoscopic partial nephrectomy.: MethodsFour patients with an upper pole renal tumor and suspected adrenal involvement underwent laparoscopic partial nephrectomy with concomitant ipsilateral adrenalectomy. Preoperative three-dimensional computed tomography revealed the renal tumor to be closely abutting the adrenal gland in 3 patients and a 4-cm adrenal mass in 1 patient. The mean renal tumor size was 3.2 cm (range 1.4 to 6.6). To maintain oncologic principles, our transperitoneal laparoscopic technique excises the adrenal gland en bloc with the renal tumor. As such, adrenalectomy is performed first, followed by partial nephrectomy, incorporating hilar control, tumor excision, and sutured renal reconstruction.: ResultsAll four procedures were performed without open conversion or intraoperative complications. The mean renal warm ischemia time was 36 minutes, estimated blood loss 169 mL, total operating time 3.9 hours, and hospital stay 3.2 days. One patient developed a transient urinary leak postoperatively. Pathologic examination of the renal tumor revealed renal cell carcinoma (n = 1), dystrophic calcification with ectopic bone formation (n = 1), adult mesoblastic nephroma (n = 1), and subcapsular heterotopic adrenal cortex with cyst (n = 1), all with negative surgical margins. Pathologic examination of the adrenal gland revealed adenoma in 1 case and a normal adrenal gland without malignant involvement in 3 cases. All patients were disease free at last follow-up (mean 6.2 months, range 2 to 12).: ConclusionsIn patients with an upper pole renal tumor and radiologically suspected adrenal involvement, laparoscopic partial nephrectomy with concomitant adrenalectomy can be performed efficaciously respecting oncologic principles. [Copyright &y& Elsevier]