No abstract

The most effective way to reduce the costs of PAC for stroke patients is to minimize the duration of their hospital stay before transfer to rehabilitative PAC. Because it substantially reduces medical costs, rehabilitative PAC should be considered standard care for stroke patients.

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Bone and bone marrow serve as an imperative ecosystem to various types of cells participating in critical body functions. The chemokine Cxcl12, also known as stromal cell-derived factor 1 (Sdf1), is one of the communication factors in the marrow microenvironment that regulates hematopoietic stem/progenitor cell homeostasis. However, the function of Cxcl12 in other bone marrow cells in vivo is yet to be discovered. Here we report a novel function of Cxcl12 in postnatal bone development and homeostasis. Targeted deletion of Cxcl12 in Paired related homeobox 1 (Prx1)-expressing or osterix (Osx)-expressing mesenchymal stem/progenitor cells (MSPCs), but not in mature osteoblasts, resulted in marrow adiposity and reduced trabecular bone content. In vivo lineage tracing analysis revealed biased differentiation of MSPCs toward adipocytes. In contrast, adult-stage deletion of Cxcl12 in Osx-expressing cells led to reduced bone content but not adiposity. Targeting the receptor Cxcr4 in the Prx1-expressing cells also resulted in reduced trabecular bone content but not adiposity. Our study reveals a previously unidentified role of the MSPC-secreting Cxcl12 that regulates its osteogenesis and adipogenesis through the cell-autonomous and non-autonomous mechanism, respectively; which could further influence the homeostatic control of the hematopoietic system. © 2017 American Society for Bone and Mineral Research.

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Plant homeodomain finger gene 6 (PHF6) encodes a 365-amino-acid protein containing 2 plant homology domain fingers. Germline mutations of human PHF6 cause Börjeson-Forssman-Lehmann syndrome, a congenital neurodevelopmental disorder. Loss-of-function mutations of PHF6 are detected in patients with acute leukemia, mainly of T-cell lineage and in a small proportion of myeloid lineage. The functions of PHF6 in physiological hematopoiesis and leukemogenesis remain incompletely defined. To address this question, we generated a conditional Phf6 knockout mouse model and investigated the impact of Phf6 loss on the hematopoietic system. We found that Phf6 knockout mice at 8 weeks of age had reduced numbers of CD4+ and CD8+ T cells in the peripheral blood compared with the wild-type littermates. There were decreased granulocyte-monocytic progenitors but increased Lin–c-Kit+Sca-1+ cells in the marrow of young Phf6 knockout mice. Functional studies, including competitive repopulation unit and serial transplantation assays, revealed an enhanced reconstitution and self-renewal capacity in Phf6 knockout hematopoietic stem cells (HSCs). Aged Phf6 knockout mice had myelodysplasia-like presentations, including decreased platelet counts, megakaryocyte dysplasia, and enlarged spleen related to extramedullary hematopoiesis. Moreover, we found that Phf6 loss lowered the threshold of NOTCH1-induced leukemic transformation at least partially through increased leukemia-initiating cells. Transcriptome analysis on the restrictive rare HSC subpopulations revealed upregulated cell cycling and oncogenic functions, with alteration of key gene expression in those pathways. In summary, our studies show the in vivo crucial roles of Phf6 in physiological and malignant hematopoiesis.

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Accurate forecasts of hourly water levels during typhoons are crucial to disaster emergency response. To mitigate flood damage, the development of a water-level forecasting model has played an essential role. We propose a model based on a dilated causal convolutional neural network (DCCNN) that can yield water-level forecasts with lead times of 1-to 6-h. A DCCNN model can efficiently exploit a broad-range history. Residual and skip connections are also applied throughout the network to enable training of deeper networks and to accelerate convergence. To demonstrate the superiority of the proposed forecasting technique, we applied it to a dataset of 16 typhoon events that occurred during the years 2012-2017 in the Yilan River basin in Taiwan. In order to examine the efficiency of the improved methodology, we also compared the proposed model with two existing models that were based on the multilayer perceptron (MLP) and the support vector machine (SVM). The results indicate that a DCCNNbased model is superior to both the SVM and MLP models, especially for modeling peak water levels. Much of the performance improvement of the proposed model is due to its ability to provide water-level forecasts with a long lead time. The proposed model is expected to be particularly useful in support of disaster warning systems.