swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution.

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.

Automatic Inverse Design of High-Performance Beam-Steering Metasurfaces via Genetic-type Tree Optimization.

We introduce a genetic-type tree search (GTTS) algorithm combined with unsupervised clustering for the automatic inverse design of high-performance metasurfaces. With the proposed method, we realize highly directive beam-steering metasurfaces via the cooptimization of the amplitude and phase. In comparison with previous topology optimization approaches, the developed GTTS algorithm optimizes the organization of subwavelength nanoantennas and, thus, is applicable to the design of both passive and active metasurfaces. The optimized beam-steering metasurface specifically exhibits a nearly constant directivity when the steering angle varies from 5° to 30°. Furthermore, the optimized nonintuitive reflectance and phase profiles assist in achieving highly directive beam steering when the phase modulation range is <180°, which was previously challenging. Our approach can diminish the requirements of scattering light properties with substantially enhanced angular resolution of beam-steering metasurfaces, which enables the realization of high-performance metasurfaces that will be promising for a wide range of advanced nanophotonic applications.

Metasurface-empowered snapshot hyperspectral imaging with convex/deep (CODE) small-data learning theory.

Hyperspectral imaging is vital for material identification but traditional systems are bulky, hindering the development of compact systems. While previous metasurfaces address volume issues, the requirements of complicated fabrication processes and significant footprint still limit their applications. This work reports a compact snapshot hyperspectral imager by incorporating the meta-optics with a small-data convex/deep (CODE) deep learning theory. Our snapshot hyperspectral imager comprises only one single multi-wavelength metasurface chip working in the visible window (500-650 nm), significantly reducing the device area. To demonstrate the high performance of our hyperspectral imager, a 4-band multispectral imaging dataset is used as the input. Through the CODE-driven imaging system, it efficiently generates an 18-band hyperspectral data cube with high fidelity using only 18 training data points. We expect the elegant integration of multi-resonant metasurfaces with small-data learning theory will enable low-profile advanced instruments for fundamental science studies and real-world applications.

Hyperspectral Tensor Completion Using Low-Rank Modeling and Convex Functional Analysis.

Hyperspectral tensor completion (HTC) for remote sensing, critical for advancing space exploration and other satellite imaging technologies, has drawn considerable attention from recent machine learning community. Hyperspectral image (HSI) contains a wide range of narrowly spaced spectral bands hence forming unique electrical magnetic signatures for distinct materials, and thus plays an irreplaceable role in remote material identification. Nevertheless, remotely acquired HSIs are of low data purity and quite often incompletely observed or corrupted during transmission. Therefore, completing the 3-D hyperspectral tensor, involving two spatial dimensions and one spectral dimension, is a crucial signal processing task for facilitating the subsequent applications. Benchmark HTC methods rely on either supervised learning or nonconvex optimization. As reported in recent machine learning literature, John ellipsoid (JE) in functional analysis is a fundamental topology for effective hyperspectral analysis. We therefore attempt to adopt this key topology in this work, but this induces a dilemma that the computation of JE requires the complete information of the entire HSI tensor that is, however, unavailable under the HTC problem setting. We resolve the dilemma, decouple HTC into convex subproblems ensuring computational efficiency, and show state-of-the-art HTC performances of our algorithm. We also demonstrate that our method has improved the subsequent land cover classification accuracy on the recovered hyperspectral tensor.

The Risk Factors for Radiolucent Nephrolithiasis among Workers in High-Temperature Workplaces in the Steel Industry.

Workers in high-temperature workplaces with inadequate water supply may exhibit symptoms of chronic dehydration and have increased risk of nephrolithiasis. The aim of this study was to investigate the risk of radiolucent stone formation among workers in a high-temperature workplace and the related risk factors associated with the condition. We collected data from 1681 workers in a steel factory in Southern Taiwan who underwent regular health examinations. Radiolucent stones were defined as positive findings on ultrasound with negative radiographic images. The prevalences of nephrolithiasis and radiolucent stones in this study were 12.0% and 5.1%, respectively. Heat exposure and age were two major risk factors influencing the probability of radiolucent stones. We combined the age and heat exposure into four groups (over and under 35 years of age with and without heat exposure) in a logistic regression. For workers younger than 35 years, the odds ratio of radiolucent stones was 2.695 (95% confidence interval: 1.201-6.049) in workers with heat exposure compared to workers without. Our investigation further demonstrated that heat exposure was a main risk factor for radiolucent stone formation. In conclusion, our identification of heat exposure as an independent factor for radiolucent stone development in steel workers highlights the need for attention to be paid to those working in similar environments.

Evaluation of in vitro and in vivo depigmenting activity of raspberry ketone from Rheum officinale.

Melanogenesis inhibition by raspberry ketone (RK) from Rheum officinale was investigated both in vitro in cultivated murine B16 melanoma cells and in vivo in zebrafish and mice. In B16 cells, RK inhibited melanogenesis through a post-transcriptional regulation of tyrosinase gene expression, which resulted in down regulation of both cellular tyrosinase activity and the amount of tyrosinase protein, while the level of tyrosinase mRNA transcription was not affected. In zebrafish, RK also inhibited melanogenesis by reduction of tyrosinase activity. In mice, application of a 0.2% or 2% gel preparation of RK applied to mouse skin significantly increased the degree of skin whitening within one week of treatment. In contrast to the widely used flavoring properties of RK in perfumery and cosmetics, the skin-whitening potency of RK has been demonstrated in the present study. Based on our findings reported here, RK would appear to have high potential for use in the cosmetics industry.

Nonnegative Blind Source Separation for Ill-Conditioned Mixtures via John Ellipsoid.

Nonnegative blind source separation (nBSS) is often a challenging inverse problem, namely, when the mixing system is ill-conditioned. In this work, we focus on an important nBSS instance, known as hyperspectral unmixing (HU) in remote sensing. HU is a matrix factorization problem aimed at factoring the so-called endmember matrix, holding the material hyperspectral signatures, and the abundance matrix, holding the material fractions at each image pixel. The hyperspectral signatures are usually highly correlated, leading to a fast decay of the singular values (and, hence, high condition number) of the endmember matrix, so HU often introduces an ill-conditioned nBSS scenario. We introduce a new theoretical framework to attack such tough scenarios via the John ellipsoid (JE) in functional analysis. The idea is to identify the maximum volume ellipsoid inscribed in the data convex hull, followed by affinely mapping such ellipsoid into a Euclidean ball. By applying the same affine mapping to the data mixtures, we prove that the endmember matrix associated with the mapped data has condition number 1, the lowest possible, and that these (preconditioned) endmembers form a regular simplex. Exploiting this regular structure, we design a novel nBSS criterion with a provable identifiability guarantee and devise an algorithm to realize the criterion. Moreover, for the first time, the optimization problem for computing JE is exactly solved for a large-scale instance; our solver employs a split augmented Lagrangian shrinkage algorithm with all proximal operators solved by closed-form solutions. The competitiveness of the proposed method is illustrated by numerical simulations and real data experiments.

Detection of Sources in Non-Negative Blind Source Separation by Minimum Description Length Criterion.

While non-negative blind source separation (nBSS) has found many successful applications in science and engineering, model order selection, determining the number of sources, remains a critical yet unresolved problem. Various model order selection methods have been proposed and applied to real-world data sets but with limited success, with both order over- and under-estimation reported. By studying existing schemes, we have found that the unsatisfactory results are mainly due to invalid assumptions, model oversimplification, subjective thresholding, and/or to assumptions made solely for mathematical convenience. Building on our earlier work that reformulated model order selection for nBSS with more realistic assumptions and models, we report a newly and formally revised model order selection criterion rooted in the minimum description length (MDL) principle. Adopting widely invoked assumptions for achieving a unique nBSS solution, we consider the mixing matrix as consisting of deterministic unknowns, with the source signals following a multivariate Dirichlet distribution. We derive a computationally efficient, stochastic algorithm to obtain approximate maximum-likelihood estimates of model parameters and apply Monte Carlo integration to determine the description length. Our modeling and estimation strategy exploits the characteristic geometry of the data simplex in nBSS. We validate our nBSS-MDL criterion through extensive simulation studies and on four real-world data sets, demonstrating its strong performance and general applicability to nBSS. The proposed nBSS-MDL criterion consistently detects the true number of sources, in all of our case studies.

Convex-Optimization-Based Compartmental Pharmacokinetic Analysis for Prostate Tumor Characterization Using DCE-MRI.

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.

Histopathologic confirmation of complete cancer-cell kill in excised specimens after renal cryotherapy.

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.

Internal bladder retractor for laparoscopic cystectomy in the female patient.

PURPOSE: To overcome difficult dissection of the vascular pedicles during laparoscopic radical cystectomy we used a novel technique of internal bladder retraction. MATERIALS AND METHODS: An internal bladder retractor was used to facilitate dissection of the vascular pedicles during laparoscopic radical cystectomy in a female patient. RESULTS: The application of the retractor is easy and it allows more precise control of the vascular pedicles and ureters. It also helps to overcome the space limitation when dissecting the perivesical structures. Laparoscopic cystectomy with hysterectomy was accomplished in 2.5 hours. CONCLUSIONS: We successfully used a novel internal bladder retractor to facilitate laparoscopic radical cystectomy in a female patient.

Migration of a dislodged tip of an ultrasound lithotripter probe to the pulmonary artery: a rare complication of percutaneous nephrolithotomy.

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.

Laparoscopic live donor nephrectomy in the presence of circumaortic or retroaortic left renal vein.

PURPOSE: Laparoscopic live donor nephrectomy is now an accepted alternative to open surgery in donors with normal renal vasculature. However, the suitability of laparoscopy for donors with anomalous vasculature is less well known. We compared the donor and recipient outcome data of 16 patients with circumaortic or retroaortic left renal vein to 20 recent patients with normal left renal venous anatomy undergoing laparoscopic donor left nephrectomy. MATERIALS AND METHODS: Of 170 patients undergoing laparoscopic donor nephrectomy at our institution from October 1997 to October 2002, 18 (10.6%) had either a circumaortic or retroaortic left renal vein (group 1). Demographic and perioperative parameters of these donors and their recipients were retrospectively compared to a contemporary cohort of 20 recent patients with a normal single left renal vein (group 2). RESULTS: All laparoscopic procedures were completed successfully without open conversion. Groups 1 and 2 were similar in regard to operative time (199 vs 226 minutes, p = 0.90), blood loss (125 vs 100 cc, p = 0.45), warm ischemia time (3.4 vs 3.9 minutes, p = 0.14) and hospital stay (2.9 vs 3.2 days, p = 0.45). Length of allograft renal artery and vein was similar between groups. Cold ischemia and revascularization times were also comparable between groups. Recipient serum creatinine was comparable at 5 days (1.7 vs 1.6 mg/dl), 3 months (1.5 vs 1.4 mg/dl) and 1 year (1.5 vs 1.5 mg/dl). CONCLUSIONS: Presence of a circumaortic or retroaortic renal vein is not a contraindication to laparoscopic live donor left nephrectomy. A left kidney with vasculature anatomically adequate for transplantation can be achieved with excellent donor and recipient outcomes.

Ha-ras overexpression mediated cell apoptosis in the presence of 5-fluorouracil.

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 (Ras(Asn17)), dominant negative Raf-1 (Raf-1(CB4)), 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 (Rac1(Asn17)) or dominant negative Rho A (RhoA(Asn19)) 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 p21(Cip/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.

Laparoscopic upper pole partial nephrectomy with concomitant en bloc adrenalectomy.

OBJECTIVES: To 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. METHODS: Four 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. RESULTS: All 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). CONCLUSIONS: In 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.