Principles of microRNA regulation of a human cellular signaling network

MicroRNAs (miRNAs) are endogenous 22-nucleotide RNAs, which suppress gene expression by selectively binding to the 3-noncoding region of specific message RNAs through base-pairing. Given the diversity and abundance of miRNA targets, miRNAs appear to functionally interact with various components of many cellular networks. By analyzing the interactions between miRNAs and a human cellular signaling network, we found that miRNAs predominantly target positive regulatory motifs, highly connected scaffolds and most downstream network components such as signaling transcription factors, but less frequently target negative regulatory motifs, common components of basic cellular machines and most upstream network components such as ligands. In addition, when an adaptor has potential to recruit more downstream components, these components are more frequently targeted by miRNAs. This work uncovers the principles of miRNA regulation of signal transduction networks and implies a potential function of miRNAs for facilitating robust transitions of cellular response to extracellular signals and maintaining cellular homeostasis

Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems

Hybrid massive MIMO structures with lower hardware complexity and power consumption have been considered as a potential candidate for millimeter wave (mmWave) communications. Channel covariance information can be used for designing transmitter precoders, receiver combiners, channel estimators, etc. However, hybrid structures allow only a lower-dimensional signal to be observed, which adds difficulties for channel covariance matrix estimation. In this paper, we formulate the channel covariance estimation as a structured low-rank matrix sensing problem via Kronecker product expansion and use a low-complexity algorithm to solve this problem. Numerical results with uniform linear arrays (ULA) and uniform squared planar arrays (USPA) are provided to demonstrate the effectiveness of our proposed method

A Novel Deep Learning Framework for Internal Gross Target Volume Definition from 4D Computed Tomography of Lung Cancer Patients

In this paper, we study the reliability of a novel deep learning framework for internal gross target volume (IGTV) delineation from four-dimensional computed tomography (4DCT), which is applied to patients with lung cancer treated by Stereotactic Body Radiation Therapy (SBRT). 77 patients who underwent SBRT followed by 4DCT scans were incorporated in a retrospective study. The IGTV_DL was delineated using a novel deep machine learning algorithm with a linear exhaustive optimal combination framework, for the purpose of comparison, three other IGTVs base on common methods was also delineated, we compared the relative volume difference (RVI), matching index (MI) and encompassment index (EI) for the above IGTVs. Then, multiple parameter regression analysis assesses the tumor volume and motion range as clinical influencing factors in the MI variation. Experimental results demonstrated that the deep learning algorithm with linear exhaustive optimal combination framework has a higher probability of achieving optimal MI compared with other currently widely used methods. For patients after simple breathing training by keeping the respiratory frequency in 10 BMP, the four phase combinations of 0%, 30%, 50% and 90% can be considered as a potential candidate for an optimal combination to synthesis IGTV in all respiration amplitudes

Stabilizing the Oxygen Lattice and Reversible Oxygen Redox Chemistry through Structural Dimensionality in Lithium-Rich Cathode Oxides.

Lattice-oxygen redox (l-OR) has become an essential companion to the traditional transition-metal (TM) redox charge compensation to achieve high capacity in Li-rich cathode oxides. However, the understanding of l-OR chemistry remains elusive, and a critical question is the structural effect on the stability of l-OR reactions. Herein, the coupling between l-OR and structure dimensionality is studied. We reveal that the evolution of the oxygen-lattice structure upon l-OR in Li-rich TM oxides which have a three-dimensional (3D)-disordered cation framework is relatively stable, which is in direct contrast to the clearly distorted oxygen-lattice framework in Li-rich oxides which have a two-dimensional (2D)/3D-ordered cation structure. Our results highlight the role of structure dimensionality in stabilizing the oxygen lattice in reversible l-OR, which broadens the horizon for designing high-energy-density Li-rich cathode oxides with stable l-OR chemistry

Patch-level based vegetation change and environmental drivers in Tarim River drainage area of West China

Information on vegetation-related land cover change and the principle drivers is critical for environmental management and assessment of desertification processes in arid environments. In this study, we investigated patch-level based changes in vegetation and other major land cover types in lower Tarim River drainage area in Xinjiang, West China, and examined the impacts of environmental factors on those changes. Patterns of land cover change were analyzed for the time sequence of 1987-1999-2004 based on satellite-derived land classification maps, and their relationships with environmental factors were determined using Redundancy Analysis (RDA). Environmental variables used in the analysis included altitude, slope, aspect, patch shape index (fractal dimension), patch area, distance to water body, distance to settlements, and distance to main roads. We found that during the study period, 26% of the land experienced cover changes, much of which were the types from the natural riparian and upland vegetation to other land covers. The natural riparian and upland vegetation patches were transformed mostly to desert and some to farmlands, indicating expanding desertification processes of the region. A significant fraction of the natural riparian and upland vegetation experienced a phase of alkalinity before becoming desert, suggesting that drought is not the exclusive environmental driver of desertification in the study area. Overall, only a small proportion of the variance in vegetation-related land cover change is explainable by environmental variables included in this study, especially during 1987-1999, indicating that patch-level based vegetation change in this region is partly attributable to environmental perturbations. The apparent transformation from the natural riparian and upland vegetation to desert indicates an on-going process of desertification in the region