In vivo purging with high-dose cytarabine followed by high-dose chemoradiotherapy and reinfusion of unpurged bone marrow for adult acute myelogenous leukemia in first complete remission

PURPOSE: To evaluate in a prospective study the efficacy of autologous bone marrow transplantation (BMT) in adult patients with acute myelogenous leukemia (AML) in first remission, using a single course of high-dose Cytarabine (HD Ara-C) consolidation therapy as in vivo purging. PATIENTS AND METHODS: Sixty consecutive adult patients with AML in first complete remission (CR) were treated with HD Ara-C consolidation therapy as a method of in vivo purging before marrow collection. High-dose therapy consisted of fractionated total-body irradiation (FTBI) 12 Gy, intravenous etoposide 60 mg/kg, and cyclophosphamide 75 mg/kg, followed by reinfusion of cryopreserved marrow. RESULTS: Sixty patients underwent consolidation treatment with HD Ara-C with the intent to treat with autologous BMT. Sixteen patients were unable to proceed to autologous BMT (10 patients relapsed, one died of sepsis, one developed cerebellar toxicity, two had inadequate blood counts, and two refused). Forty-four patients underwent autologous BMT and have a median follow-up time of 37 months (range, 14.7 to 68.7) for patients who are alive with no relapse. The cumulative probability of disease-free survival (DFS) at 24 months in the intent-to-treat group is 49% (95% confidence interval [CI], 37% to 62%) and in those who actually underwent autologous BMT is 61% (95% CI, 46% to 74%). The probability of relapse was 44% (95% CI, 31% to 58%) and 33% (95% CI, 20% to 49%) for the intent-to-treat and autologous BMT patients, respectively. CONCLUSION: This approach offers a relatively high DFS rate to adult patients with AML in first CR. The results of this study are similar to those achieved with allogeneic BMT.     

ZH150重油钢包烘烤装置的研制与应用

ＺＨ１５０重油钢包烘烤装置具有点火方便，雾化好，燃烧稳定，燃烧效率高，火焰刚性好，调节性能好等优点，适合于钢包的烘烤，也可应用在基他工业炉窑中，该装置用蒸汽或压缩空气作为雾化介质，能燃用柴油，原油，重油，渣油等液体燃料，介绍了该装置的基本结构，工作原理，工作特点及设计对策，以及在生产中的应用情况。     

全数字高清晰度电视中8—VSB传输系统

分析了全数字高清晰度电视（ＨＤＴＶ）中地面广播信道的干扰及抑制措施，给出了抑制同频干扰的梳状滤波器的设计方法和ＨＤＴＶ中８－ＶＳＢ基本原理及传输方案，并基于传信率１／Ｔ＝２３．５４８Ｍｂｐｓ的８－ＶＳＢ作了计算机模拟。     

Image cipher using image filtering with 3D DNA-based confusion and diffusion strategy

Neural Computing and Applications - In this paper, an image cipher is presented based on DNA sequence operations, image filtering and memrisitve chaotic system. Firstly, plain image is preprocessed...     

Exploiting 2D compressed sensing and information entropy for secure color image compression and encryption

Neural Computing and Applications - Compared to 1D compressed sensing (CS), 2D CS is more efficient for compressing the plaintext image from two directions, but security level of current 2D...     

An iterative gradient convolutional neural network and its application in endoscopic photoacoustic image formation from incomplete acoustic measurement

Neural Computing and Applications - Endoscopic photoacoustic tomography (EPAT) is a rapidly developing catheter-based imaging technique to provide cross-sectional images of anatomical, functional...     

基于LabVIEW的惯性测量单元信号采集及处理系统设计

利用LabVIEW设计了惯性测量单元信号采集及处理软件,主要包括串口通信、数据分离及信号处理等模块;串口通信模块采用VISA方式编程,建立了惯性测量单元与上位机之间的通信;根据惯性测量单元输出数据的格式,利用LabVIEW中的匹配模式函数分离出沿三轴向的加速度信号和角速率信号;设计了一种用于去除加速度信号中低频成份的数字滤波器;在建立的陀螺漂移模型基础上,分别进行小波基、小波分解层数、阈值函数及阈值估计方法等小波参数的选取.     

Simple culling methods for continuous collision detection of deforming triangles

We present a simple and efficient approach for continuous collision detection of deforming triangles based on conservative advancement. The efficiency of our approach is due to a sequence of simple collision-free conditions for deforming triangles. In our experiment, we show that our CCD algorithm achieves 2-30 times performance improvement over existing algorithms for triangle primitives.     

Adaptive load balancing algorithm for multiple homing mobile nodes

In places where mobile users can access multiple wireless networks simultaneously, a multipath scheduling algorithm can benefit the performance of wireless networks and improve the experience of mobile users. However, existing literature shows that it may not be the case, especially for TCP flows. According to early investigations, there are mainly two reasons that result in bad performance of TCP flows in wireless networks. One is the occurrence of out-of-order packets due to different delays in multiple paths. The other is the packet loss which is resulted from the limited bandwidth of wireless networks. To better exploit multipath scheduling for TCP flows, this paper presents a new scheduling algorithm named Adaptive Load Balancing Algorithm (ALBAM) to split traffic across multiple wireless links within the ISP infrastructure. Targeting at solving the two adverse impacts on TCP flows, ALBAM develops two techniques. Firstly, ALBAM takes advantage of the bursty nature of TCP flows and performs scheduling at the flowlet granularity where the packet interval is large enough to compensate for the different path delays. Secondly, ALBAM develops a Packet Number Estimation Algorithm (PNEA) to predict the buffer usage in each path. With PNEA, ALBAM can prevent buffer overflow and schedule the TCP flow to a less congested path before it suffers packet loss. Simulations show that ALBAM can provide better performance to TCP connections than its other counterparts.     

Selective ensemble extreme learning machine modeling of effluent quality in wastewater treatment plants

Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.