Risk Factors for Prognosis after the Maze IV Procedure in Patients with Atrial Fibrillation Undergoing Valve Surgery

The present study evaluated risk factors related to persistent atrial fibrillation (AF) at discharge (AF-d) and recurrent atrial fibrillation (rAF) and all-cause death after the maze IV procedure. Two hundred nineteen patients (63 female, aged 52.5±8.8 years) with valve disease and persistent AF undergoing valve surgery and the maze IV procedure in our center between 2015 and 2016 were included. Baseline demographic and clinical data were obtained by review of medical records. The median follow-up period was 27 months (interquartile range 21–34 months) in our patient cohort. The primary end point was all-cause death. The secondary end point was AF-d or rAF. rAF is defined as AF recurrence at 3 months or later after the procedure. Twenty-eight patients (12.8%) died during follow-up. Multiple logistic regression analysis showed that thrombocytopenia, elevated serum total bilirubin level, a larger right atrium, AF-d, and rAF were independent determinants for all-cause death after the maze IV procedure after adjustment for age, sex, and clinical covariates, including New York Heart Association class III/IV disease, hypertension, and aortic regurgitation, while valvular disease duration and left atrial diameter greater than 80.5 mm were independent determinants for AF-d, and thrombocytopenia, elevated serum total bilirubin level, higher mean pulmonary artery pressure, and AF-d were independent predictors for rAF. In conclusion, thrombocytopenia, elevated serum total bilirubin level, an enlarged right atrium, AF-d, and rAF are independent predictors of all-cause death in patients undergoing the maze IV procedure. </p

Photometry of Variable Stars from Dome A, Antarctica

Dome A on the Antarctic plateau is likely one of the best observing sites on Earth thanks to the excellent atmospheric conditions present at the site during the long polar winter night. We present high-cadence time-series aperture photometry of 10,000 stars with i<14.5 mag located in a 23 square-degree region centered on the south celestial pole. The photometry was obtained with one of the CSTAR telescopes during 128 days of the 2008 Antarctic winter. We used this photometric data set to derive site statistics for Dome A and to search for variable stars. Thanks to the nearly-uninterrupted synoptic coverage, we find 6 times as many variables as previous surveys with similar magnitude limits. We detected 157 variable stars, of which 55% are unclassified, 27% are likely binaries and 17% are likely pulsating stars. The latter category includes delta Scuti, gamma Doradus and RR Lyrae variables. One variable may be a transiting exoplanet.Comment: Accepted for publication in the Astronomical Journal. PDF version with high-resolution figures available at http://faculty.physics.tamu.edu/lmacri/papers/wang11.pd

Photometric Variability in the CSTAR Field: Results From the 2008 Data Set

The Chinese Small Telescope ARray (CSTAR) is the first telescope facility built at Dome A, Antarctica. During the 2008 observing season, the installation provided long-baseline and high-cadence photometric observations in the i-band for 18,145 targets within 20 deg2 CSTAR field around the South Celestial Pole for the purpose of monitoring the astronomical observing quality of Dome A and detecting various types of photometric variability. Using sensitive and robust detection methods, we discover 274 potential variables from this data set, 83 of which are new discoveries. We characterize most of them, providing the periods, amplitudes and classes of variability. The catalog of all these variables is presented along with the discussion of their statistical properties.Comment: 38 pages, 11 figures, 4 tables; Accepted for publication in ApJ

Eclipsing Binaries From the CSTAR Project at Dome A, Antarctica

The Chinese Small Telescope ARray (CSTAR) has observed an area around the Celestial South Pole at Dome A since 2008. About $20,000$ light curves in the i band were obtained lasting from March to July, 2008. The photometric precision achieves about 4 mmag at i = 7.5 and 20 mmag at i = 12 within a 30 s exposure time. These light curves are analyzed using Lomb--Scargle, Phase Dispersion Minimization, and Box Least Squares methods to search for periodic signals. False positives may appear as a variable signature caused by contaminating stars and the observation mode of CSTAR. Therefore the period and position of each variable candidate are checked to eliminate false positives. Eclipsing binaries are removed by visual inspection, frequency spectrum analysis and locally linear embedding technique. We identify 53 eclipsing binaries in the field of view of CSTAR, containing 24 detached binaries, 8 semi-detached binaries, 18 contact binaries, and 3 ellipsoidal variables. To derive the parameters of these binaries, we use the Eclipsing Binaries via Artificial Intelligence (EBAI) method. The primary and the secondary eclipse timing variations (ETVs) for semi-detached and contact systems are analyzed. Correlated primary and secondary ETVs confirmed by false alarm tests may indicate an unseen perturbing companion. Through ETV analysis, we identify two triple systems (CSTAR J084612.64-883342.9 and CSTAR J220502.55-895206.7). The orbital parameters of the third body in CSTAR J220502.55-895206.7 are derived using a simple dynamical model.Comment: 41 pages, 12 figures; published online in ApJ

Homozygous mutation in DNAAF4 causes primary ciliary dyskinesia in a Chinese family

Primary ciliary dyskinesia (PCD) is a rare autosomal recessive disorder that affects the structure and function of motile cilia, leading to classic clinical phenotypes, such as situs inversus, chronic sinusitis, bronchiectasis, repeated pneumonia and infertility. In this study, we diagnosed a female patient with PCD who was born in a consanguineous family through classic clinical manifestations, transmission electron microscopy and immunofluorescence staining. A novel DNAAF4 variant NM_130810: c.1118G&gt;A (p. G373E) was filtered through Whole-exome sequencing. Subsequently, we explored the effect of the mutation on DNAAF4 protein from three aspects: protein expression, stability and interaction with downstream DNAAF2 protein through a series of experiments, such as transfection of plasmids and Co-immunoprecipitation. Finally, we confirmed that the mutation of DNAAF4 lead to PCD by reducing the stability of DNAAF4 protein, but the expression and function of DNAAF4 protein were not affected

Emissive Platinum(II) Cages with Reverse Fluorescence Resonance Energy Transfer for Multiple Sensing

It is quite challenging to realize fluorescence resonance energy transfer (FRET) between two chromophores with specific positions and directions. Herein, through the self-assembly of two carefully selected fluorescent ligands via metal-coordination interactions, we prepared two tetragonal prismatic platinum(II) cages with a reverse FRET process between their faces and pillars. Bearing different responses to external stimuli, these two emissive ligands are able to tune the FRET process, thus making the cages sensitive to solvents, pressure, and temperature. First, these cages could distinguish structurally similar alcohols such as n-butanol, t-butanol, and i-butanol. Furthermore, they showed decreased emission with bathochromic shifts under high pressure. Finally, they exhibited a remarkable ratiometric response to temperature over a wide range (223–353 K) with high sensitivity. For example, by plotting the ratio of the maximum emission (I600/I480) of metallacage 4b against the temperature, the slope reaches 0.072, which is among the highest values for ratiometric fluorescent thermometers reported so far. This work not only offers a strategy to manipulate the FRET efficiency in emissive supramolecular coordination complexes but also paves the way for the future design and preparation of smart emissive materials with external stimuli responsiveness

Application of the Doppler weather radar in real-time quality control of hourly gauge precipitation in eastern China

AbstractThe current real-time operational quality control method for hourly rain gauge records at meteorological stations of China is primarily based on a comparison with historical extreme records, and the spatial and temporal consistencies of rain records. However, this method might make erroneous judgments for heavy precipitation because of its remarkable inhomogeneous features. In this study, we develop a Radar Supported Operational Real-time Quality Control (RS_ORQC) method to improve hourly gauge precipitation records in eastern China by using Doppler weather radar data and national automatic rain-gauge network in JJA (i.e., June, July and August) between 2010 and 2011. According to the probability density function (PDF) and cumulative probability density function (CDF), we establish the statistic relationships between NSN precipitation records under 7 radar coverage and radar quantitative precipitation estimation (QPE). The other NSN records under 5 radar coverage are used for the verification. The results show that the correct rate of this radar-supported new method in judging gauge precipitation is close to 99.95% when the hourly rainfall rate is below 10mmh−1 and is 96.21% when the rainfall intensity is above 10mmh−1. Moreover, the improved quality control method is also applied to evaluate the quality of provincial station network (PSN) precipitation records over eastern China. The correct rate of PSN precipitation records is 99.92% when the hourly rainfall rate is below 10mmh−1, and it is 93.33% when the hourly rainfall rate is above 10mmh−1. Case studies also exhibit that the radar-supported method can make correct judgments for extreme heavy rainfall

Closed-Loop Modeling to Evaluate the Performance of a Scaled-Up Lithium–Sulfur Battery in Electric Vehicle Applications

A closed-loop modeling method was established here to evaluate the performance of new battery technology from lab research to scaled-up developed electric vehicle (EV) applications. As an emerging energy-storage device, the lithium–sulfur battery (LSB) is a very promising candidate for the next generation of rechargeable batteries. However, it has been difficult to commercialize the LSB up to now. In this work, we designed and built a battery, EV, and driver system loop model to study the key performance parameters of LSB operation in EVs, in which the tested data from the lab were introduced into the model followed by simulating driving cycles and fast charging. A comparison with the lithium-ion batteries used in real vehicles verified the high reliability of the model. Meanwhile, the simulation results showed that the LSB needs more improvements for EV application; in particular, developments are still highly needed to overcome the high power and energy loss and sharp voltage vibration for practical applications. The novelty of this work relies on the created closed-loop modeling method to simulate lab research results for evaluating new battery technology in scaled-up EV applications in order to not only vividly predict EV operation performance and commercialization feasibility, but also thoughtfully guide researchers and developers for further optimization and problem solutions. Therefore, this method holds great promise as a powerful tool for both lab research and the industrial development of new batteries for EV applications

Prediction Model of End-Point Phosphorus Content in EAF Steelmaking Based on BP Neural Network with Periodical Data Optimization

The phosphorus (P) content of molten steel is of great importance for the quality of steel products in the electric arc furnace (EAF) steelmaking process. At present, the initial conditions of smelting process in the prediction of end-point P content are still the core part. However, few studies focus on the influence between process data and end-point P content. In this research, the relationships between process data and end-point P content are explored by a BP neural network. Based on the theoretical analysis, influencing factors with high correlation were selected. The prediction model of P content coupled with process data and end-point P content is established. On this basis, the model is optimized with process data of oxygen supply and the time of the first addition of lime. Compared with the practical production data, the results indicate that the hit rate of the model optimized is 87.78% and 75.56% when prediction errors are within ±0.004 and ±0.003 of P content. The model established has achieved the effective prediction for end-point P content, and provided a reference for the control of P content in practical production