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Objectives: To evaluate the comparative effectiveness of treatments, a randomized clinical trial remains the gold standard but can be challenged by a high cost, a limited sample size, an inability to fully reflect the real world, and feasibility concerns. The objective is to showcase a big data approach that takes advantage of large electronic medical record (EMR) data to emulate clinical trials. To overcome the limitations of regression analysis, a deep learning-based analysis pipeline was developed. Study Design and Setting: Lumpectomy (breast-conserving surgery) and mastectomy are the two most commonly used surgical procedures for early-stage female breast cancer patients. An emulation trial was designed using the Surveillance, Epidemiology, and End Results (SEER)-Medicare data to evaluate their relative effectiveness in overall survival. The analysis pipeline consisted of a propensity score step, a weighted survival analysis step, and a bootstrap inference step. Results: A total of 65,997 subjects were enrolled in the emulated trial, with 50,704 and 15,293 in the lumpectomy and mastectomy arms, respectively. The two surgery procedures had comparable effects in terms of overall survival (survival year change = 0.08, 95% confidence interval (CI): -0.08, 0.25) for the elderly SEER-Medicare early-stage female breast cancer patients. Conclusion: This study demonstrated the power of "mining large EMR data + deep learning-based analysis," and the proposed analysis strategy and technique can be potentially broadly applicable. It provided convincing evidence of the comparative effectiveness of lumpectomy and mastectomy.

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OBJECTIVE: Deep neural network (DNN) techniques have demonstrated significant advantages over regression and some other techniques. In recent studies, DNN-based analysis has been conducted on data with high-dimensional input such as omics measurements. In such analysis, regularization, in particular penalization, has been applied to regularize estimation and distinguish relevant input variables from irrelevant ones. A unique challenge arises from the "lack of information" attributable to high dimensionality of input and limited size of training data. For many data/studies, there exist other data/studies that may be relevant and can potentially provide additional information to boost performance. METHODS: In this study, we conduct integrative analysis of multiple independent datasets/studies, with the goal of borrowing information across each other and improving overall performance. Significantly different from regression-based integrative analysis (where alignment can be easily achieved based on covariates), alignment across multiple DNNs can be nontrivial. We develop ANNI, an Aligned DNN technique for Integrative analysis with high-dimensional input. Penalization is applied for regularized estimation, selection of important input variables, and, equally importantly, information borrowing across multiple DNNs. An effective computational algorithm is developed. RESULTS: Extensive simulations demonstrate competitive performance of the proposed technique. The analysis of cancer omics data further establishes its practical utility.

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A novel method of Lorentz distribution solution (LDS) from overlapped absorbance profile in time domain (incomplete absorbance profile in frequency domain) based on the direct absorption spectroscopy method (DAS) was experimentally demonstrated. It utilized the ratio of the integral in a certain interval on the lower horizontal axis of the Lorentzian profile to the integral in the entire interval on the horizontal axis has a certain relationship and can be expressed by a formula. This method effectively solves the difficulties of extracting gas concentration from incomplete absorbance profile. Formulation and detection procedure were presented, experiments were carried out to prove the method on the extraction of gas concentration from different overlapped absorbance profile and different concentration. Compared with the conventional DAS (C-DAS), the maximum relative errors on the concentration extraction are minimized from 25.55% to 2.64% at different concentration and absorbance profile. Meanwhile, the experimental results show that the obtained gas concentration by LDS presents a good linear relationship while those measured by C-DAS are significantly different.

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Optical vortex arrays have been achieved in an end-pumped Nd:YVO4 laser pumped by an annular beam. Spontaneous transverse mode locking of Laguerre-Gaussian modes in different frequency-degenerate families has been obtained by merely adjusting the pump power. A maximum output power of 0.88 W and optical conversion efficiency of 13.6% are achieved for optical vortex arrays. Optical vortex arrays formed in different frequency-degenerate families of Laguerre-Gaussian modes can be actively controlled by the position of the axicon. This work provides a way to research transverse mode locking of Laguerre-Gaussian modes in different frequency-degenerate families based on annular beam pumping.

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Hemoglobins are a group of respiratory proteins principally functioning in transport of oxygen and carbon dioxide in red blood cells of all vertebrates and some invertebrates. The blood clam T. granosa is one of the few invertebrates that have hemoglobin-containing red hemocytes. In the present research, the peroxidase activity of T. granosa hemoglobins (Tg-Hbs) was characterized and the associated mechanism of action was deciphered via structural comparison with other known peroxidases. We detected that purified Tg-Hbs catalyzed the oxidation of phenolic compounds in the presence of exogenous H2O2. Tg-Hbs peroxidase activity reached the maximum at pH 5 and 35°C and was inhibited by Fe2+, Cu2+, SDS, urea, and sodium azide. Tg-Hbs shared few similarities in amino acid sequence and overall structural characteristics with known peroxidases. However, the predicted structure at their heme pocket was highly similar to that of horseradish peroxidase (HRP) and myeloperoxidase (MPO). This research represented the first systemic characterization of hemoglobin as a peroxidase.

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