Recent Trends in Hospitalization for Acute Myocardial Infarction in Beijing: Increasing Overall Burden and a Transition From ST-Segment Elevation to Non-ST-Segment Elevation Myocardial Infarction in a Population-Based Study

Comparable data on trends of hospitalization rates for ST-segment elevation myocardial infarction (STEMI) and non-STEMI (NSTEMI) remain unavailable in representative Asian populations.To examine the temporal trends of hospitalization for acute myocardial infarction (AMI) and its subtypes in Beijing.Patients hospitalized for AMI in Beijing from January 1, 2007 to December 31, 2012 were identified from the validated Hospital Discharge Information System. Trends in hospitalization rates, in-hospital mortality, length of stay (LOS), and hospitalization costs were analyzed by regression models for total AMI and for STEMI and NSTEMI separately. In total, 77,943 patients were admitted for AMI in Beijing during the 6 years, among whom 67.5% were males and 62.4% had STEMI. During the period, the rate of AMI hospitalization per 100,000 population increased by 31.2% (from 55.8 to 73.3 per 100,000 population) after age standardization, with a slight decrease in STEMI but a 3-fold increase in NSTEMI. The ratio of STEMI to NSTEMI decreased dramatically from 6.5:1.0 to 1.3:1.0. The age-standardized in-hospital mortality decreased from 11.2% to 8.6%, with a significant decreasing trend evident for STEMI in males and females (P < 0.001) and for NSTEMI in males (P = 0.02). The rate of percutaneous coronary intervention increased from 28.7% to 55.6% among STEMI patients. The total cost for AMI hospitalization increased by 56.8% after adjusting for inflation, although the LOS decreased by 1 day.The hospitalization burden for AMI has been increasing in Beijing with a transition from STEMI to NSTEMI. Diverse temporal trends in AMI subtypes from the unselected "real-world" data in Beijing may help to guide the management of AMI in China and other developing countries

High-sensitivity microfluidic calorimeters for biological and chemical applications

High-sensitivity microfluidic calorimeters raise the prospect of achieving high-throughput biochemical measurements with minimal sample consumption. However, it has been challenging to realize microchip-based calorimeters possessing both high sensitivity and precise sample-manipulation capabilities. Here, we report chip-based microfluidic calorimeters capable of characterizing the heat of reaction of 3.5-nL samples with 4.2-nW resolution. Our approach, based on a combination of hard- and soft-polymer microfluidics, provides both exceptional thermal response and the physical strength necessary to construct high-sensitivity calorimeters that can be scaled to automated, highly multiplexed array architectures. Polydimethylsiloxane microfluidic valves and pumps are interfaced to parylene channels and reaction chambers to automate the injection of analyte at 1 nL and below. We attained excellent thermal resolution via on-chip vacuum encapsulation, which provides unprecedented thermal isolation of the minute microfluidic reaction chambers. We demonstrate performance of these calorimeters by resolving measurements of the heat of reaction of urea hydrolysis and the enthalpy of mixing of water with methanol. The device structure can be adapted easily to enable a wide variety of other standard calorimeter operations; one example, a flow calorimeter, is described

General Linearized Polynomial Interpolation and Its Applications

In this paper, we first propose a general interpolation algorithm in a free module of a linearized polynomial ring, and then apply this algorithm to decode several important families of codes, Gabidulin codes, KK codes and MV codes. Our decoding algorithm for Gabidulin codes is different from the polynomial reconstruction algorithm by Loidreau. When applied to decode KK codes, our interpolation algorithm is equivalent to the Sudan-style list-1 decoding algorithm proposed by K/"otter and Kschischang for KK codes. The general interpolation approach is also capable of solving the interpolation problem for the list decoding of MV codes proposed by Mahdavifar and Vardy, and has a lower complexity than solving linear equations

Spintronic signatures of Klein tunneling in topological insulators

Klein tunneling, the perfect transmission of normally incident Dirac electrons across a potential barrier, has been widely studied in graphene and explored to design switches, albeit indirectly. We show that Klein tunneling maybe easier to detect for spin-momentum locked electrons crossing a PN junction along a three-dimensional topological insulator surface. In these topological insulator PN junctions (TIPNJs), the spin texture and momentum distribution of transmitted electrons can be measured electrically using a ferromagnetic probe for varying gate voltages and angles of current injection. Based on transport models across a TIPNJ, we show that the asymmetry in the potentiometric signal between PP and PN junctions and its overall angular dependence serve as a direct signature of Klein tunneling

Two-stage hybrid feature selection algorithms for diagnosing erythemato-squamous diseases

This paper proposes two-stage hybrid feature selection algorithms to build the stable and efficient diagnostic models where a new accuracy measure is introduced to assess the models. The two-stage hybrid algorithms adopt Support Vector Machines (SVM) as a classification tool, and the extended Sequential Forward Search (SFS), Sequential Forward Floating Search (SFFS), and Sequential Backward Floating Search (SBFS), respectively, as search strategies, and the generalized F-score (GF) to evaluate the importance of each feature. The new accuracy measure is used as the criterion to evaluated the performance of a temporary SVM to direct the feature selection algorithms. These hybrid methods combine the advantages of filters and wrappers to select the optimal feature subset from the original feature set to build the stable and efficient classifiers. To get the stable, statistical and optimal classifiers, we conduct 10-fold cross validation experiments in the first stage; then we merge the 10 selected feature subsets of the 10-cross validation experiments, respectively, as the new full feature set to do feature selection in the second stage for each algorithm. We repeat the each hybrid feature selection algorithm in the second stage on the one fold that has got the best result in the first stage. Experimental results show that our proposed two-stage hybrid feature selection algorithms can construct efficient diagnostic models which have got better accuracy than that built by the corresponding hybrid feature selection algorithms without the second stage feature selection procedures. Furthermore our methods have got better classification accuracy when compared with the available algorithms for diagnosing erythemato-squamous diseases

Money, moral transgressions, and blame

Two experiments tested participants' attributions for others' immoral behaviors when conducted for more versus less money. We hypothesized and found that observers would blame wrongdoers more when seeing a transgression enacted for little rather than a lot of money, and that this would be evident in observers' hand-washing behavior. Experiment 1 used a cognitive dissonance paradigm. Participants (N = 160) observed a confederate lie in exchange for either a relatively large or a small monetary payment. Participants blamed the liar more in the small (versus large) money condition. Participants (N = 184) in Experiment 2 saw images of someone knocking over another to obtain a small, medium, or large monetary sum. In the small (versus large) money condition, participants blamed the perpetrator (money) more. Hence, participants assigned less blame to moral wrong-doers, if the latter enacted their deed to obtain relatively large sums of money. Small amounts of money accentuate the immorality of others' transgressions