Image1_A four-lncRNA risk signature for prognostic prediction of osteosarcoma.JPEG

Aim: Osteosarcoma is the most common primary malignant tumor of bone. However, our understanding of the prognostic indicators and the genetic mechanisms of the disease progression are still incomplete. The aim of this study was to identify a long noncoding RNA (lncRNA) risk signature for osteosarcoma survival prediction.Methods: RNA sequencing data and relevant clinical information of osteosarcoma patients were downloaded from the database of Therapeutically Applicable Research to Generate Effective Treatments (TARGET). We analyzed the differentially expressed lncRNAs between deceased and living patients by univariate and multivariate Cox regression analysis to identify a risk signature. We calculated a prognostic risk score for each sample according to this prognosis signature, and divided patients into high-risk and low-risk groups according to the median value of the risk score (0.975). Kaplan–Meier analysis and receiver operating characteristic (ROC) curve statistics were used to evaluate the performance of the signature. Next, we analyzed the signature’s potential function through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene-set enrichment analysis (GSEA). Lastly, qRT-PCR was used to validate the expression levels of the four lncRNAs in clinical samples.Results: Twenty-six differentially expressed lncRNAs were identified between deceased and living patients. Four of these lncRNAs (CTB-4E7.1, RP11-553A10.1, RP11-24N18.1, and PVRL3-AS1) were identified as independent prognostic factors, and a risk signature of these four lncRNAs for osteosarcoma survival prediction was constructed. Kaplan–Meier analysis showed that the five-year survival time in high-risk and low-risk groups was 33.1% and 82.5%, and the area under the curve (AUC) of the ROC was 0.784, which demonstrated that the prognostic signature was reliable and had the potential to predict the survival of patients with osteosarcoma. The expression level of the four lncRNAs in osteosarcoma tissues and cells was determined by qRT-PCR. Functional enrichment analysis suggested that the signature might be related to osteosarcoma through regulation of the MAPK signaling pathway, the PI3K-Akt signaling pathway, and the extracellular matrix and also provided new insights into the study of osteosarcoma, including the role of papillomavirus infection, olfactory receptor activity, and olfactory transduction in osteosarcoma.Conclusion: We constructed a novel lncRNA risk signature that served as an independent biomarker for predicting the prognosis of osteosarcoma patients.</p

DataSheet1_A four-lncRNA risk signature for prognostic prediction of osteosarcoma.ZIP

Aim: Osteosarcoma is the most common primary malignant tumor of bone. However, our understanding of the prognostic indicators and the genetic mechanisms of the disease progression are still incomplete. The aim of this study was to identify a long noncoding RNA (lncRNA) risk signature for osteosarcoma survival prediction.Methods: RNA sequencing data and relevant clinical information of osteosarcoma patients were downloaded from the database of Therapeutically Applicable Research to Generate Effective Treatments (TARGET). We analyzed the differentially expressed lncRNAs between deceased and living patients by univariate and multivariate Cox regression analysis to identify a risk signature. We calculated a prognostic risk score for each sample according to this prognosis signature, and divided patients into high-risk and low-risk groups according to the median value of the risk score (0.975). Kaplan–Meier analysis and receiver operating characteristic (ROC) curve statistics were used to evaluate the performance of the signature. Next, we analyzed the signature’s potential function through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene-set enrichment analysis (GSEA). Lastly, qRT-PCR was used to validate the expression levels of the four lncRNAs in clinical samples.Results: Twenty-six differentially expressed lncRNAs were identified between deceased and living patients. Four of these lncRNAs (CTB-4E7.1, RP11-553A10.1, RP11-24N18.1, and PVRL3-AS1) were identified as independent prognostic factors, and a risk signature of these four lncRNAs for osteosarcoma survival prediction was constructed. Kaplan–Meier analysis showed that the five-year survival time in high-risk and low-risk groups was 33.1% and 82.5%, and the area under the curve (AUC) of the ROC was 0.784, which demonstrated that the prognostic signature was reliable and had the potential to predict the survival of patients with osteosarcoma. The expression level of the four lncRNAs in osteosarcoma tissues and cells was determined by qRT-PCR. Functional enrichment analysis suggested that the signature might be related to osteosarcoma through regulation of the MAPK signaling pathway, the PI3K-Akt signaling pathway, and the extracellular matrix and also provided new insights into the study of osteosarcoma, including the role of papillomavirus infection, olfactory receptor activity, and olfactory transduction in osteosarcoma.Conclusion: We constructed a novel lncRNA risk signature that served as an independent biomarker for predicting the prognosis of osteosarcoma patients.</p

Approximate Circuits in Low-Power Image and Video Processing: The Approximate Median Filter

Low power image and video processing circuits are crucial in many applications of computer vision. Traditional techniques used to reduce power consumption in these applications have recently been accompanied by circuit approximation methods which exploit the fact that these applications are highly error resilient and, hence, the quality of image processing can be traded for power consumption. On the basis of a literature survey, we identified the components whose implementations are the most frequently approximated and the methods used for obtaining these approximations. One of the components is the median image filter. We propose, evaluate and compare two approximation strategies based on Cartesian genetic programming applied to approximate various common implementations of the median filter. For filters developed using these approximation strategies, trade-offs between the quality of filtering and power consumption are investigated. Under conditions of our experiments we conclude that better trade-offs are achieved when the image filter is evolved from scratch rather than a conventional filter is approximated

Diseño de una política óptima de inventario para una empresa distribuidora de llantas en la ciudad de Guayaquil

La empresa objetivo es una comercializadora de llantas, la misma que no cuenta con una política de inventarios, es decir realizan su aprovisionamiento de forma empírica. Así mismo la bodega donde ubican sus productos no tiene un orden establecido, tomando en cuenta estos dos factores la empresa incurre en altos costos de almacenamiento y se generan retrasos en los tiempos de despacho de mercadería. Para resolver el problema de inventario se va a implementar una política de inventario que consta de tres fases.GuayaquilINGENIERO EN LOGÍSTICA Y TRANSPORT

Additional file 9: Table S5. of Genome-Wide analysis of the AAAP gene family in moso bamboo (Phyllostachys edulis)

Primers used for qRT-PCR of 16 selected genes. (DOCX 14 kb

Additional file 3: Figure S2. of Genome-Wide analysis of the AAAP gene family in moso bamboo (Phyllostachys edulis)

Sliding-window analysis of Pe-Pe. (TIF 92 kb

Hierarchical Hollow Microspheres Constructed by Carbon Skeleton Supported TiO_2–<i>x</i> Few-Layer Nanosheets Enable High Rate Capability and Excellent Cycling Stability for Lithium Storage

Rational design and facile synthesis of TiO₂ based hybrid electrodes with hierarchical microstructure have great advantages for exploration of advanced electrodes for lithium-ion batteries (LIBs). We design and synthesize hierarchical hollow microspheres with inner carbon skeleton supported outer TiO_2–<i>x</i> few-layer nanosheets (C@TiO_2–<i>x</i>). The “core–shell” C@TiO_2–<i>x</i> microspheres exhibit relatively high specific surface area and a remarkable electric conductivity (0.264 μS cm^–1). The lithium kinetics of C@TiO_2–<i>x</i> microspheres is significantly improved due to synergistic effects of few-layer TiO_2–<i>x</i> nanosheets and conductive carbon skeleton. The C@TiO_2–<i>x</i> microspheres manifest an excellent reversible capacity of 323 mAh g^–1, together with an ultralong cycling lifetime that the capacity shows ∼220 mAh g^–1 after 1000 cycles at 1.0 C. The C@TiO_2–<i>x</i> microspheres also deliver a relatively high performance in rate capacity (108 mAh g^–1 at 20 C). When they are assembled into a hybrid lithium-ion capacitor, relatively high capacitance of 58 F g^–1 is achieved so that high power density reaches 14 kW kg^–1