17,000 acres of the famous Warren Ranch : located nine to fifteen miles northeast of Muleshoe, in the shallow water belt.

28 pages (24 pdf pages) : illustrations ; 21 cmItem advertises lands for sale in Bailey, Lamb, Parmer and Castro counties.9 pages missing between pages 11 and 19.TEX 53 F198 B62

Healing Length and Bubble Formation in DNA

We have recently suggested that the probability for the formation of thermally activated DNA bubbles is, to a very good approximation, proportional to the number of soft AT pairs over a length L(n) that depend on the size $n$ of the bubble and on the temperature of the DNA. Here we clarify the physical interpretation of this length by relating it to the (healing) length that is required for the effect of a base-pair defect to become neligible. This provides a simple criteria to calculate L(n) for bubbles of arbitrary size and for any temperature of the DNA. We verify our findings by exact calculations of the equilibrium statistical properties of the Peyrard-Bishop-Dauxois model. Our method permits calculations of equilibrium thermal openings with several order of magnitude less numerical expense as compared with direct evaluations

Factors influencing bilateral deficit and inter-limb asymmetry of maximal and explosive strength: motor task, outcome measure and muscle group

Purpose The purpose of the present study was to investigate the influence of strength outcome (maximal voluntary contraction (MVC) torque vs. rate of torque development (RTD)), motor task (unilateral vs. bilateral) and muscle group (knee extensors vs. flexors) on the magnitude of bilateral deficits and inter-limb asymmetries in a large heterogeneous group of athletes. Methods 259 professional/semi-professional athletes from different sports (86 women aged 21 ± 6 years and 173 men aged 20 ± 5 years) performed unilateral and bilateral “fast and hard” isometric maximal voluntary contractions of the knee extensors and flexors on a double-sensor dynamometer. Inter-limb asymmetries and bilateral deficits were compared across strength outcomes (MVC torque and multiple RTD measures), motor tasks and muscle groups. Results Most RTD outcomes showed greater bilateral deficits than MVC torque for knee extensors, but not for knee flexors. Most RTD outcomes, not MVC torque, showed higher bilateral deficits for knee extensors compared to knee flexors. For both muscle groups, all RTD measures resulted in higher inter-limb asymmetries than MVC torque, and most RTD measures resulted in greater inter-limb asymmetries during unilateral compared to bilateral motor tasks. Conclusions The results of the present study highlight the importance of outcome measure, motor task and muscle group when assessing bilateral deficits and inter-limb asymmetries of maximal and explosive strength. Compared to MVC torque and bilateral tasks, RTD measures and unilateral tasks could be considered more sensitive for the assessment of bilateral deficits and inter-limb asymmetries in healthy professional/semi-professional athletes

Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods

Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods