Welfare of calves - 2. Increase in vertebral artery blood flow following exsanguination by neck sticking and evaluation of chest sticking as an alternative slaughter method

The role of the vertebral arteries in delaying loss of sensibility following neck sticking in slaughter calves was investigated. Vertebral artery blood flow was measured using probes before, during and after electrical stunning and slaughter. Systemic blood pressure, electrocorticogram, visually evoked responses and the occurrence of carotid occlusions were also recorded. When carotid occlusion occurred, the time to onset of brain failure was delayed based on the development of an isoelectric state. In addition, when carotid occlusion occurred the mean arterial blood pressure was sustained for longer following slaughter, and concurrently vertebral artery blood flow could be maintained at about 30% of its initial level for up to 3 min. In some animals vertebral artery flow increased substantially following sticking. When chest sticking was used no occlusion of vessels occurred, mean arterial blood pressure fell promptly (within 8 s) and the onset of an isoelectric state did not extend beyond one minute. In addition, visually evoked responses were not present after 5 s following chest sticking.     

Classification Based on Hybridization of Parametric and Nonparametric Classifiers

Parametric methods of classification assume specific parametric models for competing population densities (e.g., Gaussian population densities can lead to linear and quadratic discriminant analysis) and they work well when these model assumptions are valid. Violation in one or more of these parametric model assumptions often leads to a poor classifier. On the other hand, nonparametric classifiers (e.g., nearest-neighbor and kernel-based classifiers) are more flexible and free from parametric model assumptions. But, the statistical instability of these classifiers may lead to poor performance when we have small numbers of training sample observations. Nonparametric methods, however, do not use any parametric structure of population densities. Therefore, even when one has some additional information about population densities, that important information is not used to modify the nonparametric classification rule. This paper makes an attempt to overcome these limitations of parametric and nonparametric approaches and combines their strengths to develop some hybrid classification methods. We use some simulated examples and benchmark data sets to examine the performance of these hybrid discriminant analysis tools. Asymptotic results on their misclassification rates have been derived under appropriate regularity conditions.     

From template to image: reconstructing fingerprints from minutiae points

Most fingerprint-based biometric systems store the minutiae template of a user in the database. It has been traditionally assumed that the minutiae template of a user does not reveal any information about the original fingerprint. In this paper, we challenge this notion and show that three levels of information about the parent fingerprint can be elicited from the minutiae template alone, viz., 1) the orientation field information, 2) the class or type information, and 3) the friction ridge structure. The orientation estimation algorithm determines the direction of local ridges using the evidence of minutiae triplets. The estimated orientation field, along with the given minutiae distribution, is then used to predict the class of the fingerprint. Finally, the ridge structure of the parent fingerprint is generated using streamlines that are based on the estimated orientation field. Line integral convolution is used to impart texture to the ensuing ridges, resulting in a ridge map resembling the parent fingerprint. The salient feature of this noniterative method to generate ridges is its ability to preserve the minutiae at specified locations in the reconstructed ridge map. Experiments using a commercial fingerprint matcher suggest that the reconstructed ridge structure bears close resemblance to the parent fingerprint     

Detection of repolarization alternans with an implantable cardioverter defibrillator lead in a porcine model

Mechanistic links have been suggested between repolarization alternans (RPA) and the onset of ventricular tachycardia (VT) and/or fibrillation. Endocardial detection of RPA may, therefore, be an important step in future device-based treatments of arrhythmias. Here, we investigate if RPA could be detected during acute ischemia using an implantable cardioverter defibrillator (ICD) lead (tip to distal coil) located in the right ventricular apex. In 18 pigs, the right coronary (n = 10) or left anterior descending coronary (n = 8) artery was occluded for 10 min using a balloon catheter, followed by reperfusion for 30 min, and re-occlusion for 30 min. RPA magnitude, computed using the modified moving average (MMA) method, showed a sharp increase in all 18 animals, from a mean baseline level of 1.9 +/- 1.3 mV to 3.0 +/- 1.3 mV during first occlusion (p < 0.001). RPA magnitude showed a prominent increase in 10 animals during re-occlusion, from a mean baseline level of 1.7 +/- 1.0 mV to 3.3 +/- 1.5 mV (p < 0.001). The protocol was terminated during the first two stages of occlusion and reperfusion for the remaining 8 animals due to the occurrence of ventricular fibrillation (VF). These results confirm that RPA increases under ischemic conditions and that it is possible to detect and track RPA dynamics with an ICD lead that is positioned in a clinically realistic location. Such an approach may be useful in formulating improved arrhythmia detection and control algorithms.     

Significance of image representation for face verification

In this paper we discuss the significance of representation of images for face verification. We consider three different representations, namely, edge gradient, edge orientation and potential field derived from the edge gradient. These representations are examined in the context of face verification using a specific type of correlation filter, called the minimum average correlation energy (MACE) filter. The different representations are derived using one-dimensional (1-D) processing of image. The 1-D processing provides multiple partial evidences for a given face image, one evidence for each direction of the 1-D processing. Separate MACE filters are used for deriving each partial evidence. We propose a method to combine the partial evidences obtained for each representation using an auto-associative neural network (AANN) model, to arrive at a decision for face verification. Results show that the performance of the system using potential field representation is better than that using the edge gradient representation or the edge orientation representation. Also, the potential field representation derived from the edge gradient is observed to be less sensitive to variation in illumination compared to the gray level representation of images.     

Multichannel ECG data compression based on multiscale principal component analysis

In this paper, multiscale principal component analysis (MSPCA) is proposed for multichannel electrocardiogram (MECG) data compression. In wavelet domain, principal components analysis (PCA) of multiscale multivariate matrices of multichannel signals helps reduce dimension and remove redundant information present in signals. The selection of principal components (PCs) is based on average fractional energy contribution of eigenvalue in a data matrix. Multichannel compression is implemented using uniform quantizer and entropy coding of PCA coefficients. The compressed signal quality is evaluated quantitatively using percentage root mean square difference (PRD), and wavelet energy-based diagnostic distortion (WEDD) measures. Using dataset from CSE multilead measurement library, multichannel compression ratio of 5.98:1 is found with PRD value 2.09% and the lowest WEDD value of 4.19%. Based on, gold standard subjective quality measure, the lowest mean opinion score error value of 5.56% is found.     

Targeting Efficiency and Biodistribution of Zoledronate Conjugated Docetaxel Loaded Pegylated PBCA Nanoparticles for Bone Metastasis

In an attempt to improve tumor localization of docetaxel (DTX)‐loaded nanoparticles (NPs), zoledronic acid (ZOL) is used as a ligand to target bone metastasis. DTX‐loaded ZOL‐conjugated polyethylene glycol (PEG)ylated polybutyl cyanoacrylate (PBCA) NPs are prepared using an anionic polymerization technique. PBCA‐PEG‐ZOL NPs are subjected to cytotoxic assay in both BO2 and MCF‐7 cell lines. Cell cycle arrest and apoptosis induced by the PBCA‐PEG‐ZOL NPs are studied. Quantitative cellular uptake, NP uptake route characterization, confocal microscopy and IPP/ApppI levels are performed. PBCA‐PEG‐ZOL NPs show an enhanced cytotoxic effect in both BO2 as well as MCF‐7 cell lines due to higher uptake following ZOL‐mediated endocytosis. The molecular basis of apoptosis reveals the involvement of a cytoplasmic protein in activating the programmed cell death pathway. Route characterization studies reveal that PBCA‐PEG‐ZOL NPs uptake is not completely blocked even by using both inhibitors (genistein and phenyl arsinoxide) simultaneously, conferring that uptake is not entirely based upon clathrin or caveolae. PBCA‐PEG‐ZOL NPs showed 7 and 5.3 times increase in IPP and ApppI production, in comparison to ZOL treatment, and 138 times higher than the control group in MCF‐7 cell line. In BO2 cell line, after treatment with NPs, IPP was 5.35 times higher than ZOL solution. No ApppI in BO2 cell line after treatment with NPs and ZOL solution was found. NP distribution in tumor infected bone is also significantly high in comparison to the normal bone at any time point. It is concluded that ZOL‐conjugated NPs provide an efficient and targeted delivery of DTX, with synergistic effects. Thus, these NPs present a promising treatment in the near future, by actively targeting metastatic tumor.     

QoS aware and fault tolerant handovers in software defined LTE networks

Wireless Networks - In order to handle a huge number of mobile users connections and their requirements like higher throughput, lower delay and seamless mobility, telecom operators have started...     

A Hybrid Clustering Approach Based Q-Leach in TDMA to Optimize QOS-Parameters

An Orthogonal frequency part multiplexing suffers from a considerable challenge due to a high peak to average power ratio (PAPR). Hence, an effective method such as partial transmits sequence (PTS) can avert this defiance by limit the design of PAPR. Therefore, an improving PAPR reduction performance via a novel approach is proposed by detaching each subblock into two parts furthermore exchanges the first sample with the final selection in each portion of the subblock to generate a new partitioning scheme. Several typical traditional segmentation schemes are used to analyze and apply the presented algorithm, such as adjacent, interleaving, and pseudo-random schemes. Besides, two scenarios are adopted based on simulation software in which the number of subcarriers is set to 128 and 256. Based on the results, a superior PAPR reduction performance is achieved based on the improved segmentation schemes regarding traditional strategies in both systems. Moreover, the enhanced adjusted PTS scheme poses a low computational complexity compared with that of the conventional schemes.