This study compared 6-year follow-up data from patients undergoing reduced-intensity conditioning (RIC) transplantation with an HLA-matched related donor (MRD), an HLA-matched unrelated donor (MUD), or an HLA-haploidentical donor (HID) for leukemia. Four hundred and twenty-seven patients from the China RIC Cooperative Group were enrolled, including 301 in the MRD, 79 in the HID, and 47 in the MUD groups. The conditioning regimen involved fludarabine combined with anti-lymphocyte globulin and cyclophosphamide. Graft-versus-host disease (GVHD) prophylaxis was administered using cyclosporin A (CsA) and mycophenolate mofetil (MMF). Four hundred and nineteen patients achieved stable donor chimerism. The incidence of stage II-IV acute GVHD in the HID group was 44.3 %, significantly higher than that in the MRD (23.6 %) and MUD (19.1 %) groups. The 1-year transplantation-related mortality (TRM) rates were 44.3, 17.6, and 21.3, respectively. Event-free survival (EFS) at 6 years in the HID group was 36.7 %, significantly lower than that of the MRD and MUD groups (59.1 and 66.0 %, P < 0.001 and P = 0.001, respectively). For advanced leukemia, the relapse rate of the HID group was 18.5 %, lower than that of the MRD group (37.5 %, P = 0.05), but the EFS at 6 years was 31.7 and 30.4 % (P > 0.05), respectively. RIC transplantation with MRD and MUD had similar outcome in leukemia which is better than that with HID. RIC transplantation with HID had lower relapsed with higher TRM and GVHD rate, particularly in advanced leukemias. RIC transplantation with MRD and MUD had similar outcomes in leukemia and they were better than those with HID. RIC transplantation with HID had a lower relapse rate but higher TRM and GVHD rates, particularly in cases of advanced leukemia.

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This paper introduces a novel wireless collaborated hybrid data center architecture called RF-HYBRID that could optimize the effect of wireless transmission while reduce the complexity of wired network. RF-HYBRID improves throughput and packet delivery latency through flexible wireless detours and shortcuts, with a comprehensive routing and congestion control method.

Data summarization, i.e., selecting representative subsets of manageable size out of massive data, is often modeled as a submodular optimization problem. Although there exist extensive algorithms for submodular optimization, many of them incur large computational overheads and hence are not suitable for mining big data. In this work, we consider the fundamental problem of (non-monotone) submodular function maximization with a knapsack constraint, and propose simple yet effective and efficient algorithms for it. Specifically, we propose a deterministic algorithm with approximation ratio 6 and a randomized algorithm with approximation ratio 4, and show that both of them can be accelerated to achieve nearly linear running time at the cost of weakening the approximation ratio by an additive factor of ε. We then consider a more restrictive setting without full access to the whole dataset, and propose streaming algorithms with approximation ratios of 8+ε and 6+ε that make one pass and two passes over the data stream, respectively. As a by-product, we also propose a two-pass streaming algorithm with an approximation ratio of 2+ε when the considered submodular function is monotone. To the best of our knowledge, our algorithms achieve the best performance bounds compared to the state-of-the-art approximation algorithms with efficient implementation for the same problem. Finally, we evaluate our algorithms in two concrete submodular data summarization applications for revenue maximization in social networks and image summarization, and the empirical results show that our algorithms outperform the existing ones in terms of both effectiveness and efficiency.

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