Enhanced vascular contractility in alpha1-adrenergic receptor-deficient mice.

AIM: Alpha1-adrenergic receptors (alpha1-ARs) are classified into three subtypes: alpha1A-AR, alpha1B-AR, and alpha1D-AR. Triple disruption of alpha1A-AR, alpha1B-AR, and alpha1D-AR genes results in hypotension and produces no contractile response of the thoracic aorta to noradrenalin. Presently, we characterized vascular contractility against other vasoconstrictors, such as potassium, prostaglandin F2alpha (PGF(2alpha)) and 5-hydroxytryptamine (5-HT), in alpha1A-AR, alpha1B-AR, and alpha1D-AR triple knockout (alpha1-AR triple KO) mice. MAIN METHODS: The contractile responses to the stimulation with vasoconstrictors were studied using isolated thoracic aorta. KEY FINDINGS: As a result, the phasic and tonic contraction induced by a high concentration of potassium (20 mM) was enhanced in the isolated thoracic aorta of alpha1-AR triple KO mice compared with that of wild-type (WT) mice. In addition, vascular responses to PGF(2alpha) and 5-HT were also enhanced in the isolated thoracic aorta of alpha1-AR triple KO mice compared with WT mice. Similar to in vitro findings with isolated thoracic aorta, in vivo pressor responses to PGF(2alpha) were enhanced in alpha1-AR triple KO mice. Real-time reverse transcription-polymerase chain reaction analysis and western blot analysis indicate that gene expression of the 5-hydroxytryptamine 2A (5-HT(2A)) receptor was up-regulated in the thoracic aorta of alpha1-AR triple KO mice while the prostaglandin F2alpha receptor (FP) was unchanged. SIGNIFICANCE: These results suggest that loss of alpha1-ARs can lead to enhancement of vascular responsiveness to the vasoconstrictors and may imply that alpha1-ARs and the subsequent signaling regulate the vascular responsiveness to other stimulations such as depolarization, 5-HT and PGF(2alpha).

Selecting Wavelet Filter for Compression of Spectra from Multispectral Images

The problem of selecting anappropriate wavelet filter is always present in signal compression based on thewavelet transform. In this report, we propose a method to select a wavelet filter from a predefined set of filters for the compression of spectra from a multispectral image. The wavelet filter selection is based on the Learning Vector Quantization (LVQ). In the training phase for the test images, the best wavelet filter for each spectrum has been found by a careful compression-decompression evaluation. Certain spectral features are used in characterizing the pixel spectra. The LVQ is used to form the best wavelet filter class for different types of spectra from multispectral images. When a new image is to be compressed, a set of spectra from that image is selected, the spectra are classified by the trained LVQand the filter associated to the largest class is selected for the compression of every spectrum from the multispectral image. The results show, that almost inevery case our method finds the most suitable wavelet filter from the pre-defined set for the compression.

Lossless compression of hyperspectral images

Technological progress has made a huge amount of data available at increasing spatial and spectral resolutions. Therefore, the compression of hyperspectral data is an area of active research. In somefields, the original quality of a hyperspectral image cannot be compromised andin these cases, lossless compression is mandatory. The main goal of this thesisis to provide improved methods for the lossless compression of hyperspectral images. Both prediction- and transform-based methods are studied. Two kinds of prediction based methods are being studied. In the first method the spectra of a hyperspectral image are first clustered and and an optimized linear predictor is calculated for each cluster. In the second prediction method linear prediction coefficients are not fixed but are recalculated for each pixel. A parallel implementation of the above-mentioned linear prediction method is also presented. Also,two transform-based methods are being presented. Vector Quantization (VQ) was used together with a new coding of the residual image. In addition we have developed a new back end for a compression method utilizing Principal Component Analysis (PCA) and Integer Wavelet Transform (IWT). The performance of the compressionmethods are compared to that of other compression methods. The results show that the proposed linear prediction methods outperform the previous methods. In addition, a novel fast exact nearest-neighbor search method is developed. The search method is used to speed up the Linde-Buzo-Gray (LBG) clustering method.

Semantics-supported cooperative learning for enhanced awareness

Awareness is required for supporting all forms of cooperation. In Computer Supported Collaborative Learning (CSCL), awareness can be used for enhancing collaborative opportunities across physical distances and in computer-mediated environments. Shared Knowledge Awareness (SKA) intends to increase the perception about the shared knowledge, students have in a collaborative learning scenario and also concerns the understanding that this group has about it. However, it is very difficult to produce accurate awareness indicators based on informal message exchange among the participants. Therefore, we propose a semantic system for cooperation that makes use of formal methods for knowledge representation based on semantic web technologies. From these semantics-enhanced repository and messages, it could be easier to compute more accurate awareness.

Definition of linear color models in the RGB vector color space to detect red peaches in orchard images taken under natural illumination

This work proposes the detection of red peaches in orchard images based on the definition of different linear color models in the RGB vector color space. The classification and segmentation of the pixels of the image is then performed by comparing the color distance from each pixel to the different previously defined linear color models. The methodology proposed has been tested with images obtained in a real orchard under natural light. The peach variety in the orchard was the paraguayo (Prunus persica var. platycarpa) peach with red skin. The segmentation results showed that the area of the red peaches in the images was detected with an average error of 11.6%; 19.7% in the case of bright illumination; 8.2% in the case of low illumination; 8.6% for occlusion up to 33%; 12.2% in the case of occlusion between 34 and 66%; and 23% for occlusion above 66%. Finally, a methodology was proposed to estimate the diameter of the fruits based on an ellipsoidal fitting. A first diameter was obtained by using all the contour pixels and a second diameter was obtained by rejecting some pixels of the contour. This approach enables a rough estimate of the fruit occlusion percentage range by comparing the two diameter estimates.

Randomized clinical trial on epidural versus patient-controlled analgesia for laparoscopic colorectal surgery within an enhanced recovery pathway.

OBJECTIVE: To compare epidural analgesia (EDA) to patient-controlled opioid-based analgesia (PCA) in patients undergoing laparoscopic colorectal surgery. BACKGROUND: EDA is mainstay of multimodal pain management within enhanced recovery pathways [enhanced recovery after surgery (ERAS)]. For laparoscopic colorectal resections, the benefit of epidurals remains debated. Some consider EDA as useful, whereas others perceive epidurals as unnecessary or even deleterious. METHODS: A total of 128 patients undergoing elective laparoscopic colorectal resections were enrolled in a randomized clinical trial comparing EDA versus PCA. Primary end point was medical recovery. Overall complications, hospital stay, perioperative vasopressor requirements, and postoperative pain scores were secondary outcome measures. Analysis was performed according to the intention-to-treat principle. RESULTS: Final analysis included 65 EDA patients and 57 PCA patients. Both groups were similar regarding baseline characteristics. Medical recovery required a median of 5 days (interquartile range [IQR], 3-7.5 days) in EDA patients and 4 days (IQR, 3-6 days) in the PCA group (P = 0.082). PCA patients had significantly less overall complications [19 (33%) vs 35 (54%); P = 0.029] but a similar hospital stay [5 days (IQR, 4-8 days) vs 7 days (IQR, 4.5-12 days); P = 0.434]. Significantly more EDA patients needed vasopressor treatment perioperatively (90% vs 74%, P = 0.018), the day of surgery (27% vs 4%, P < 0.001), and on postoperative day 1 (29% vs 4%, P < 0.001), whereas no difference in postoperative pain scores was noted. CONCLUSIONS: Epidurals seem to slow down recovery after laparoscopic colorectal resections without adding obvious benefits. EDA can therefore not be recommended as part of ERAS pathways in laparoscopic colorectal surgery.