ABSTRACT
This research was carried out to determine the effect of a specific single nucleotide polymorphism (SNP) region in exon 10 of the growth hormone receptor (GHR) gene on milk production traits in Jersey and Holstein cows raised in Turkey. Milk samples were recorded as a test day milk yield (TDMY) and an adjusted based 305â¯d milk yield (305-DMY). Also, milk component traits were detected. Based on the scope of this study, a total of 748 dairy cows, including 305 Holsteins raised in the Marmara Region and 163 Holstein and 280 Jersey raised in the Black Sea Region, were genotyped for the GHR gene using the RFLP-PCR technique. Jersey cows carrying the GG genotype (5.24â¯%) were associated with higher fat content ( P < 0.05 ). Jersey cows with GG and AG also had a higher protein content (3.44â¯% and 3.38â¯%, respectively) ( P < 0.05 ). Similarly, the protein content was the highest in Holstein cows with the GG genotype (3.46â¯%) ( P < 0.01 ), whereas Holstein cows having AA genotypes displayed higher TDMY (24.64â¯kg/d) ( P < 0.05 ) and 305-DMY (8472.4â¯kg) ( P < 0.01 ). The estimated increase in milk protein and fat contents due to the G allele was 0.07â¯% and 0.22â¯% in the Jersey breed, respectively. On the other hand, allele A was highly related to an increase in protein yield and 305-DMY of 0.04 and about 675â¯kg in the Holstein breed, respectively. The GHR gene should be considered as a potential candidate gene in marker-assisted selection programs to improve the performance of milk and related traits in Turkey dairy cattle populations.
Subject(s)
Heart Valve Prosthesis Implantation , Mitral Valve Insufficiency/diagnostic imaging , Mitral Valve Insufficiency/surgery , Severity of Illness Index , Aged, 80 and over , Female , Heart Valve Prosthesis Implantation/methods , Humans , Mitral Valve Insufficiency/complications , Pulmonary Edema/complications , Pulmonary Edema/diagnostic imaging , Pulmonary Edema/surgery , Treatment Outcome , UltrasonographyABSTRACT
We report two cases of postobstructif pulmonary oedema occurring after extubation at the recovery of general anaesthesia and presenting some similarities. The young age of the patient and the occurrence of upper airway obstruction after extubation, as well as the radiologic nondependent distribution of alveolar pattern are typical. The underlying pathophysiology seems to be the mechanical disruption of the alveolar-capillary membrane.
Subject(s)
Airway Obstruction/complications , Anesthesia, General , Intubation, Intratracheal/adverse effects , Postoperative Complications/etiology , Postoperative Complications/therapy , Pulmonary Edema/etiology , Pulmonary Edema/therapy , Adult , Anesthesia Recovery Period , Appendicitis/surgery , Cardiomegaly/etiology , Echocardiography , Electrocardiography , Humans , Male , Pulmonary Alveoli/diagnostic imaging , Pulmonary Alveoli/pathology , Pulmonary Edema/diagnostic imaging , Radiography , Rectal Fistula/surgeryABSTRACT
Many standard image processing operations can be implemented using quadtrees as a simple tree traversal where, at each terminal node, a computation is performed involving some of that node's neighbors. Most of this work has involved the use of bottom-up neighbor-finding techniques which search for a nearest common ancestor. Recently, top-down techniques have been proposed which make use of a neighbor vector as the tree is traversed. A simplified version of the top-down method for a quadtree in the context of a general-purpose tree traversal algorithm is presented. It differs, in part, from prior work in its ability to compute diagonally adjacent neighbors rather than just horizontally and vertically adjacent neighbors. It builds a neighbor vector for each node using a minimal amount of information. Analysis of the algorithm shows that its execution time is directly proportional to the number of nodes in the tree. However, it does require some extra storage. Use of the algorithm leads to lower execution time bounds for some common quadtree image processing operations such as connected component labeling.
ABSTRACT
The region quadtree is a hierarchical data structure that finds use in applications such as image processing, computer graphics, pattern recognition, robotics, and cartography. In order to save space, a number of pointerless quadtree representations (termed linear quadtrees) have been proposed. One representation maintains the nodes in a list ordered according to a preorder traversal of the quadtree. Using such an image representation and a graph definition of a quadtree, a general algorithm to compute geometric image properties such as the perimeter, the Euler number, and the connected components of an image is developed and analyzed. The algorithm differs from the conventional approaches to images represented by quadtrees in that it does not make use of neighbor finding methods that require the location of a nearest common ancestor. Instead, it makes use of a staircase-like data structure to represent the blocks that have been already processed. The worst-case execution time of the algorithm, when used to compute the perimeter, is proportional to the number of leaf nodes in the quadtree, which is optimal. For an image of size 2n × 2n, the perimeter algorithm requires only four arrays of 2n positions each for working storage. This makes it well suited to processing linear quadtrees residing in secondary storage. Implementation experience has confirmed its superiority to existing approaches to computing geometric properties for images represented by quadtrees.
ABSTRACT
A natural byproduct of the tree-like nature of the quadtree is that many basic image processing operations can be implemented as tree traversals which differ in the nature of the computation that is performed at each node. Some of these computations involve the inspection of a node's adjacent neighbors (termed neighbor finding). A new model is developed for images represented by quadtrees, and it is used to analyze various neighbor-finding techniques. The model's predicted costs for neighbor finding correlate very closely with empirical results and it is superior to the model that was used previously.
ABSTRACT
The ``line quadtree'' data structure hierarchically represents a region and its boundary. Based on the standard quadtree, each node in this structure stores adjacency information as well. While line quadtrees use the same amount of space as standard qaudtrees, they facilitate several region processing algorithms. In particular, we describe efficient algorithms for boundary and superimposing two maps encoded as line quadtrees.
ABSTRACT
The concept of distance used in binary array representations of images is adapted to a quadtree representation. The chessboard distance metric is shown to be particularly suitable for the quadtree. A chessboard distance transform for a quadtree is defined as the minimum distance in the plane from each BLACK node to the border of a WHiTE node. An algorithm is presented which computes this transform by only examining the BLACK node's adjacent and abutting neighbors and their progeny. However, unlike prior work with quadtrees, computation of the distance transform requires a capability of finding neighbors in the diagonal direction rather than merely in the horizontal and vertical directions. The algorithm's average execution time is proportional to the number of leaf nodes in the quadtree.
ABSTRACT
An algorithm is presented for constructing a quadtree for a binary image given its row-by-row description. The algorithm processes the image one row at a time and merges identically colored sons as soon as possible, so that a minimal size quadtree exists after processing each pixel. This method is spacewise superior to one which reads in an entire array and then attempts to build the quadtree.
ABSTRACT
An algorithm is presented for computing the total perimeter of a region in a binary image represented by a quadtree. The algorithm explores each segment of the boundary of the region once and only once. Analysis of the algorithm shows that its average execution time is proportional to the number of leaf nodes in the quadtree.