Schwannomas of the Left Adrenal Gland and Posterior Mediastinum

Schwannoma is a rare tumor of neural crest cell origin. Most schwannomas occur in the head, neck, stomach or limbs, with a few cases occurring in the retroperitoneal space. A 30-year-old Taiwanese woman presented with a 1-week history of left anterior chest discomfort and left flank pain. The laboratory findings and endocrine studies were all within normal limits. Chest X-ray revealed masses in the posterior mediastinum. Chest computed tomography and magnetic resonance imaging showed several masses in the left paraspinal region and in the left adrenal region. The patient underwent total excision of the left paraspinal tumors and laparoscopic left adrenalectomy. Pathologic studies showed a picture of benign schwannoma. In conclusion, preoperative differentiation of benign schwannoma from malignant peripheral nerve sheath tumor or other tumors is important for good prognosis. Total excision of benign schwannoma is associated with favorable outcome in patients

Qubit Mapping Toward Quantum Advantage

Qubit Mapping is a pivotal stage in quantum compilation flow. Its goal is to convert logical circuits into physical circuits so that a quantum algorithm can be executed on real-world non-fully connected quantum devices. Qubit Mapping techniques nowadays still lack the key to quantum advantage, scalability. Several studies have proved that at least thousands of logical qubits are required to achieve quantum computational advantage. However, to our best knowledge, there is no previous research with the ability to solve the qubit mapping problem with the necessary number of qubits for quantum advantage in a reasonable time. In this work, we provide the first qubit mapping framework with the scalability to achieve quantum advantage while accomplishing a fairly good performance. The framework also boasts its flexibility for quantum circuits of different characteristics. Experimental results show that the proposed mapping method outperforms the state-of-the-art methods on quantum circuit benchmarks by improving over 5% of the cost complexity in one-tenth of the program running time. Moreover, we demonstrate the scalability of our method by accomplishing mapping of an 11,969-qubit Quantum Fourier Transform within five hours

Learning Discriminative Shrinkage Deep Networks for Image Deconvolution

Most existing methods usually formulate the non-blind deconvolution problem into a maximum-a-posteriori framework and address it by manually designing kinds of regularization terms and data terms of the latent clear images. However, explicitly designing these two terms is quite challenging and usually leads to complex optimization problems which are difficult to solve. In this paper, we propose an effective non-blind deconvolution approach by learning discriminative shrinkage functions to implicitly model these terms. In contrast to most existing methods that use deep convolutional neural networks (CNNs) or radial basis functions to simply learn the regularization term, we formulate both the data term and regularization term and split the deconvolution model into data-related and regularization-related sub-problems according to the alternating direction method of multipliers. We explore the properties of the Maxout function and develop a deep CNN model with a Maxout layer to learn discriminative shrinkage functions to directly approximate the solutions of these two sub-problems. Moreover, given the fast-Fourier-transform-based image restoration usually leads to ringing artifacts while conjugate-gradient-based approach is time-consuming, we develop the Conjugate Gradient Network to restore the latent clear images effectively and efficiently. Experimental results show that the proposed method performs favorably against the state-of-the-art ones in terms of efficiency and accuracy

Integrating SPC and EPC for Multivariate Autocorrelated Process

Statistical process control (SPC) is a widely employed quality control method in industry. SPC is mainly designed for monitoring single quality characteristic. However, as the design of a product/process becomes complex, a process usually has multiple quality characteristics related to it. These characteristics must be monitored by multivariate SPC. When the autocorrelation is present in the process data, the traditional SPC may mislead the results. Hence, the autocorrelated data must be treated to eliminate the autocorrelation effect before employing SPC to detect the assignable causes. Besides, chance causes also have impact on the processes. When the process is out of control but no assignable cause is found, it can be adjusted by employing engineering process control (EPC). However, only using EPC to adjust the process may make inappropriate adjustments due to external disturbances or assignable causes. This study presents an integrated SPC and EPC procedure for multivariate autocorrelated process. The SPC procedure constructs a predicting model using group method of data handling (GMDH), which can transfer the autocorrelated data into uncorrelated data. Then, the Hotelling’s T2 and multivariate cumulative sum control charts are constructed to monitor the process. The EPC procedure constructs a controller utilizing data mining technique to adjust the multiple quality characteristics to their target values. Industry can employ this procedure to monitor and adjust the multivariate autocorrelated process

Seismo-Traveling Ionospheric Disturbances Triggered by the 12 May 2008 M 8.0 Wenchuan Earthquake

A network of 6 ground-based GPS receivers in East Asia was employed to study seismo-traveling ionospheric disturbances (STIDs) triggered by an M 8.0 earthquake which occurred at Wenchuan on 12 May 2008. The network detected 5 STIDs on the south side of the epicenter area. A study on the distances of the detected STIDs to the epicenter versus their associated traveling times shows that the horizontal speed is about 600 m s-1. Applying the circle method, we find that the 5 circles intercept at a point right above the epicenter when the horizontal speed of 600 m s-1 is given. Global searches of the ray-tracing and the beam-forming techniques confirm that the STIDs are induced by vertical motions in the Earth¡¦s surface during the Wenchuan Earthquake

Liquid biopsy genotyping in lung cancer: ready for clinical utility?

Liquid biopsy is a blood test that detects evidence of cancer cells or tumor DNA in the circulation. Despite complicated collection methods and the requirement for technique-dependent platforms, it has generated substantial interest due, in part, to its potential to detect driver oncogenes such as epidermal growth factor receptor (EGFR) mutants in lung cancer. This technology is advancing rapidly and is being incorporated into numerous EGFR tyrosine kinase inhibitor (EGFR-TKI) development programs. It appears ready for integration into clinical care. Recent studies have demonstrated that biological fluids such as saliva and urine can also be used for detecting EGFR mutant DNA through application other user-friendly techniques. This review focuses on the clinical application of liquid biopsies to lung cancer genotyping, including EGFR and other targets of genotype-directed therapy and compares multiple platforms used for liquid biopsy

Utilization of statins and aspirin among patients with diabetes and hyperlipidemia: Taiwan, 1998–2006

AbstractBackgroundThe proper use of statins and aspirin decrease the risk of coronary heart disease (CHD) among patients with diabetes (DM) and hyperlipidemia. The purpose of this study was to analyze the time trends and determinants of prescribing statins and aspirin among patients with DM and hyperlipidemia in medical practice in Taiwan.MethodsA cohort of 21,667 patients with DM and hyperlipidemia during the period from 1998 to 2006 was identified by using data of ambulatory care claims from Taiwan's National Health Insurance Database. The dataset was categorized into two equal calendar periods: Period 1 (September 1998–June 2002) and Period 2 (July 2002–April 2006). Multivariate logistic regression analyses were used to determine the independent determinants associated with receipt of lipid-lowering agents and aspirin among these patients.ResultsThere were significant increases in the prescribing of statins (OR 1.78; 95% CI 1.66−1.91) and aspirin (OR 1.47, 95% CI 1.50−1.59) in Period 2 as compared with Period 1. Nevertheless, 30% of patients with coexisting CHD neither received statins nor aspirin. Only 15% to 25% of DM patients with hyperlipidemia and CHD received the combined treatment with aspirin and statin. In multivariate logistic regression, we found that women received aspirin less frequently than men. Old patients (>45 years) with concomitant CHD were more likely to receive statins and aspirin.ConclusionDespite the increasing trend in the use of statins and aspirin in DM patients with hyperlipidemia in Taiwan, the improvements were at best modest, particularly for secondary prevention. Our data indicate the need for continued efforts to improve the utilization of these drugs in daily practice