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Background/Introduction Nocturnal blood pressure (BP) dipping status, defined by the night-to-day BP ratio, has been correlated with the cardiovascular (CV) risk in patients with arterial hypertension. The risk is higher in those with less than normal or no drop in nocturnal BP while data in extreme dippers are inconsistent. On the other hand, white-coat hypertension (WCHT), defined as an elevated office BP despite a normal out-of-office BP, is characterized by a lower CV risk than that of sustained hypertension and rather comparable with that of true normotension. Purpose The present study assessed the possible relation between the nocturnal BP dipping status and the underlying CV risk in WCHT individuals. Methods Among all individuals examined in our outpatient anti-hypertensive units over the past 15 years, 2310 (42% men, 52.2±13.1 years of age) were diagnosed with WCHT (increased office BP: 156.4±10.0/99.6±6.2 mmHg and normal 24-hour ambulatory BP: 122.4±6.3/75.3±5.4 mmHg) and were enrolled in the study. A night-to-day BP ratio (from the 24-hour ambulatory BP monitoring) 0.8–0.9 defined Normal nocturnal BP Dipping, 1 Absence of Dipping. The underlying 10-year CV risk of death in the studied population was calculated with the Hellenic version of the HeartScore (Hellenic Score), as proposed by the current 2018 European Society of Hypertension guidelines, based on age, gender, smoking status, systolic BP and total cholesterol levels. Results From 2310 individuals studied, 1208 (52.3%) were found with Normal Dipping, 386 (16.7%) with Extreme Dipping, 622 (26.9%) with Mild Dipping and 94 (4.1%) with Absence of Dipping. Hellenic Score was 3.21±4.67% in subjects with Normal Dipping, 3.49±4.97% in those with Extreme Dipping, 3.66±5.04% in those with Mild Dipping, 6.21±7.29%, in those with Absence of Dipping (p for trend Conclusions Nocturnal BP dipping status is closely associated with the underlying CV risk of WCHT individuals. Extreme Dipping, Mild Dipping and especially Absence of Dipping increase CV risk thus necessitating closer follow-up of these individuals and possibly faster initiation of BP-lowering drug treatment.

Objective:Nocturnal blood pressure (BP) dipping status, defined by the night-to-day BP ratio, has been correlated with the cardiovascular (CV) risk in patients with arterial hypertension. The risk is higher in those with less than normal or no drop in nocturnal BP while data in extreme dippers are i

Globodera pallida and G. rostochiensis are two cyst-forming nematodes known to infest potato crops, causing severe economic losses worldwide. In this study, a real-time TaqMan PCR assay was developed and optimized for the simultaneous detection of G. pallida and G. rostochiensis. The assay's analytical and diagnostic sensitivity and specificity were evaluated using reference isolates. Four different DNA extraction methods and one rapid crude template-preparation procedure were compared in terms of extraction purity, efficiency for PCR applications, utility and cost. Extraction methods A and B included two commercially available kits that utilize silica columns and magnetic beads, respectively. Method C was based on DNA isolation using Chelex resin, and method D was a standard chemistry in-house protocol. Procedure E included the direct use of crude mixture composed of disrupted cysts in Tris–EDTA buffer. The multiplex TaqMan PCR assay successfully discriminated the two nematode species from all reference cyst samples and its recorded diagnostic sensitivity (Dse) and specificity (Dsp) was 100%. On the contrary, in conventional (Co) PCR tests, the overall Dsp and Dse were lower and estimated at 94 and 87% for G. pallida, and 97 and 88% for G. rostochiensis, respectively. Spectrophotometric results showed that DNA extraction methods A, B and C yielded the purest DNA and gave the lowest mean Ct values as well as the most consistent results in Co PCR. Alternative crude preparation method E resulted in statistically similar and Ct values consistent with those obtained with methods A to C when tested by TaqMan PCR. The developed assay, using crude template-preparation E, allows the simple, accurate and cost-effective testing of a large number of cyst samples and can be applied in surveys and certification schemes.

The relevance of blood-based assays to monitor minimal residual disease (MRD) in non-metastatic prostate cancer (PCa) remains unclear. Proving that clinically relevant circulating tumor cells (CTCs) can be detected with available technologies could address this. This study aimed to improve CTC detection in non-metastatic PCa patients by combining three independent CTC assays: the CellSearch system, an in vivo CellCollector and the EPISPOT. Peripheral blood samples from high-risk PCa patients were screened for CTCs before and three months after radical prostatectomy (RP). Combining the results of both time points, CTCs were detected in 37%, 54.9% and 58.7% of patients using CellSearch, CellCollector and EPISPOT, respectively. The cumulative positivity rate of the three CTC assays was 81.3% (87/107) with 21.5% (23/107) of patients harboring ≥5 CTCs/7.5 ml blood. Matched pair analysis of 30 blood samples taken before and after surgery indicated a significant decrease in CTCs captured by the CellCollector from 66% before RP to 34% after therapy (p = 0.031). CTC detection by EPISPOT before RP significantly correlated with PSA serum values (p < 0.0001) and clinical tumor stage (p = 0.04), while the other assays showed no significant correlations. In conclusion, CTC-based liquid biopsies have the potential to monitor MRD in patients with non-metastatic prostate cancer. © The Author(s) 2016.

Background: The aim of this case report is to present our experience with two very rare cases of thoracic muscle tuberculosis. Muscle tuberculosis, as a primary disease, can only be detected in cases in which mycobacteria have been transplanted to a muscle through an infected needle. Case presentations: Case 1 is a 38-year-old immigrant man and Case 2 is a 24-year-old immigrant man, both originating from Sub-Saharan African Countries; they presented in the past two years to our hospital with swellings at the base of the hemithorax and were diagnosed as having muscle tuberculosis. Administration of anti-tuberculosis chemotherapy caused: (a) diminution of inflammation, (b) diminution of the size of local fusiform injury, and (c) clinical improvement. Conclusions: Thoracic muscle tuberculosis should be considered to be one of the etiologies of muscular disease in European countries with a high incidence of immigrants originating from endemic geographical areas. © 2016 The Author(s).

Museums are changing fast, yet they still need to respond to the challenges posed by a society that changes at an even faster pace. Human mobility, multi-culturalism and increasing economic assymetries create an environment, in which the role of museums as public spaces emerges as particularly complex. In this paper, we discuss issues of social inclusion in heritage museums from a conceptual point of view. In particular, we examine the conceptual barriers posed to accessibility and participation by current spatial, communicative, social and sensorial approaches in museum practice and suggest possible ways to shift such obstacles. Some of these ways may necessitate paradigmatic changes in museum policies. The paper draws on various aspects of social and communication theory. © ICOM 2017

Tomato chlorosis virus (ToCV) and Tomato infectious chlorosis virus (TICV) (genus: Crinivirus, family: Closteroviridae) are two emergent whitefly-transmitted viruses that have been associated with yellowing symptoms of tomato crops during the last two decades. A real-time, one-step reverse transcription (RT) TaqMan(®) polymerase chain reaction (PCR) assay was developed and optimized for the multiplex detection of TICV, ToCV and an internal control of mitochondrion cytochrome oxidase subunit I (mtCOXI) gene from plants. The plant mtCOXI assay can be used as an internal control in at least 77 plant species from 28 different families. The one-step RT TaqMan PCR assay successfully detected and discriminated the two virus species in infected tomato plants, other host plants and their whitefly vectors. In direct comparison, the assay was approximately 10,000-fold and 100-fold more sensitive than conventional one-step RT-PCR and two-step nested RT-PCR, respectively. The increased sensitivity allowed the use of alternative template preparation methods that do not require RNA purification. The assay can be performed either by the direct addition of crude plant extract into the real-time reaction mixture or alternatively, the sap extract can be blotted on a positively charged nylon membrane, eluted and added in the reaction mixture. The developed assay allows the simple, fast and cost-effective testing of a large number of samples and can be easily applied in surveys and certification schemes.

Scene analysis is an important area of research with the aim of identifying objects and their relationships in natural scenes. MINERVA benchmark has been recently introduced in this area for testing different image processing and classification schemes. In this paper we present results on the classification of eight natural objects in the complete set of 448 natural images using neural networks. An exhaustive set of experiments with this benchmark has been conducted using four different segmentation methods and five texture-based feature extraction methods. The results in this paper show the performance of a neural network classifier on a tenfold cross-validation task. On the basis of the results produced, we are able to rank how well different image segmentation algorithms are suited to the task of region of interest identification in these images, and we also see how well texture extraction algorithms rank on the basis of classification results.

We present a new framework for novelty detection. The framework evaluates neural networks as adaptive classifiers that are capable of novelty detection and retraining on the basis of newly discovered information. We apply our newly developed model to the application area of object recognition in video. We detail the tools and methods needed for novelty detection such that data from unknown classes can be reliably rejected without any a priori knowledge of its characteristics. The rejected data is postprocessed to determine which samples can be manually labeled of a new type and used for retraining. We compare the proposed framework with other novelty detection methods and discuss the results of adaptive retraining of neural network to recognize further unseen data containing the newly added objects.

Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains information about objects that were not known at the time of training the model. In this paper we provide state-of-the-art review in the area of novelty detection based on statistical approaches. The second part paper details novelty detection using neural networks. As discussed, there are a multitude of applications where novelty detection is extremely important including signal processing, computer vision, pattern recognition, data mining, and robotics.

Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. In this paper we focus on neural network-based approaches for novelty detection. Statistical approaches are covered in Part 1 paper.

Selenium (Se) is a key component of iodinases; higher Se levels are associated with lower titers of antithyroid peroxidase antibodies (anti-TPO). Pregnancy exerts profound effects on thyroid function and autoimmunity. To assess the relationship of urine Se levels with thyroid function and autoimmunity in pregnant women residing in Athens, Greece, we studied prospectively 47 euthyroid women in uncomplicated singleton pregnancies (mean age + SD: 30 + 5 years) in each trimester, measuring urine Se levels, urine iodine, plasma thyrotropin (TSH), free thyroxine and triiodothyronine (FT4 and FT3), as well as levels of anti-TPO antibodies. Changes of the measured parameters were assessed over each trimester; thyroid parameters were assessed with relation to Se levels. Urine Se dropped by the third trimester, whereas urine iodine did not change appreciably during pregnancy. TSH and anti-TPO did not show appreciable changes; FT4 and FT3 gradually decreased as the pregnancy advanced. No relationship between urine Se levels and anti-TPO was found. During pregnancy, changes in urine Se levels accompany mild changes in thyroid function. However, we did not find some association between these changes and thyroid autoimmune activity over this period, probably because the effect of Se on thyroid autoimmunity may only become apparent in case of excess Se fortification. © 2013 Springer Science+Business Media New York.

It is now well-established that k nearest-neighbour classi"ers o!er a quick and reliable method of data classi"cation. In this paper we extend the basic de"nition of the standard k nearest-neighbour algorithm to include the ability to resolve con#icts when the highest number of nearest neighbours are found for more than one training class (model-1). We also propose model-2 of nearest-neighbour algorithm that is based on "nding the nearest average distance rather than nearest maximum number of neighbours. These new models are explored using image understanding data. The models are evaluated on pattern recognition accuracy for correctly recognising image texture data of "ve natural classes: grass, trees, sky, river re#ecting sky and river re#ecting trees. On noise contaminated test data, the new nearest neighbour models show very promising results for further studies. We evaluate their performance with increasing values of neighbours (k) and discuss their future in scene analysis research. Crown Copyright 2001 Published by Elsevier Science Ltd. on behalf of Pattern Recognition Society. All rights reserved.

A small number of kidney transplant recipients abruptly lose function secondary to acute renal artery or vein thrombosis or more rarely a form of necrotizing vasculitis. We report a group of four kidney transplant recipients who lost renal function and share the following features: (1) diabetes (type I, insulin-dependent diabetes mellitus, type II or steroid-induced); (2) abrupt change/loss of renal function; (3) a concomitant clinical event (fever, viral symptoms, menometrorrhagia, viremia, bacteremia); (4) severe necrotizing vasculitis with hemorrhagic necrosis on histopathology; (5) patent renal artery and vein at time of transplant nephrectomy (i.e., no vascular thrombosis); and (6) high levels of peripheral serum gamma-IFN 1-5 days before transplant nephrectomy (467 +/- 175 pg/ml) compared with that of patients experiencing severe rejection (8.4 +/- 3.7 pg/ml) (P < 0.002). These data support the concept of a cytokine (IFN-gamma)-mediated accelerated inflammatory response resulting in graft loss from necrotizing vasculitis--the clinical equivalent of an organ-specific Shwartzman reaction.

AIM: To investigate the cellular and humoral immunity status of gliomas, and their association with the WHO grading system. MATERIAL AND METHODS: We have conducted a case-control study of 49 patients with gliomas and 30 healthy controls. We used ELISA assays, radial immunodiffusion, indirect immunofluorescence, latex test and flow cytometry assays to estimate preoperative in serum the immunological profile. RESULTS: Patients with glioma had significantly reduced amounts of IL2 (p=0.000), TNF-a (p=0.033), IgG (p=0.011), IgA (p=0.027),C4 (p=0.026) ,CD3+ (p=0.001), CD4+ (p=0.000), CD8+ (p=0.002), ratio CD4/CD8 (p=0.000), CD19+ (p=0.04) and elevated IL10 (p=0.05) compared with healthy controls. No statistically significant differences were observed concerning viral agents, total NK cells, IgM, IgE, IL16, granzyme-b, RF, ANA, ENA, anti-dsDNA and anti-cardiolipin antibodies. A higher WHO grade, after controlling for age and gender, was associated with decreased number of CD3+ (p=0.011), CD4+ (p=0.015), CD8+ (p=0.048) and ratio CD4/CD8 (p=0.027), as well as with decreased IL2 (p=0.018), C4 (p=0.02), and IgG (p=0.05). IL2 and CD4+ counts were significant predictors of grade. CONCLUSIONS: A shift from Th1 to Th2, a CD3+ and CD19+ lymphocytopenia, a diminished fraction CD4/CD8 and a reduced amount of immunoglobulins and complement were observed in the patients with gliomas. A higher WHO grade of the tumor was associated with greater impairments of immunity. Since defects of both humoral and cellular immunity were equally observed and significant predictors of grade were assessed, a preoperative evaluation of the immune system of patients with gliomas is being proposed. J Neuroimmunol

This chapter introduces the network buffer control techniques as a mean to provide QoS. This problem has been extensively studied in the context of wirelined networks; however, the proliferation of wireless networks and the introduction of multimedia applications has significantly changed the characteristics of the traffic mix that flows on the network. The objective of this chapter is to create a new methodology for automatically adapting the various buffer thresholds such that the network exhibits optimal or near optimal performance even as network conditions change. The behavior of the network (generally a discrete event system—DES) is approximated by that of a stochastic fluid model (SFM); then using infinitesimal perturbation analysis (IPA) we obtain sensitivity estimators of the performance measure(s) of interest with respect to the control parameter. These estimators are easy to compute using data observed from the DES’s sample path. Finally, the computed estimators are used in stochastic approximation algorithms to adjust the thresholds.

Background: To investigate the epidemiologic and clinical characteristics (age, sex, tumor location, socioeconomic status) and potential predisposing factors (alcohol, tobacco, mobile phone use, severe head trauma) of cerebral gliomas in a defined area of Northwest Greece. Methods: The prospective study was conducted in patients with gliomas referred to all 7 hospitals of a study area with a population of 488,435 inhabitants, from June 1, 2005, to May 31, 2007. Incidence rates (IR) were calculated as new cases diagnosed among residents of the study area during the study period per 100,000 inhabitants. A case-control study was carried out in order to study the possible association of the risk of glioma with smoking, alcohol, use of mobile phone, and severe cranial trauma. Results: A total of 56 glioma incident cases were identified with IRs of glioma and glioblastoma (GBM) at 5.73/105/year and 3.69/105/year, respectively. A male to female ratio of 1.25 was obtained in the GBM group. IRs of glioma and GBM for both males and females were higher in the age group 60–79. The most frequent anatomic location was the frontal lobe. 46.5% of the patients originated from the low, 25% from the middle and 28.5% from the high socioeconomic class. There was no significant association between glioma and alcohol consumption, smoking and mobile phone use. A trend for a positive association between the risk of glioma and a history of severe cranial trauma was observed, but this association was not statistically significant. Conclusion: The estimated IR of glioma and GBM in this study was higher compared with data from other studies carried out on European, Asian and US populations. Further studies may be needed to assess the possible association of genetic, environmental and lifestyle factors with the high occurrence of gliomas observed in this study.

The deployment of network applications (e.g., multimedia and other real time applications) has put extra pressure on network scarce resources (bandwidth and buffers) thus generating a need for effective resource allocation and management. In this paper we adopt a Stochastic Fluid Model (SFM) framework and derive sensitivity estimators for three performance measures of interest (workload, throughput and loss volume) with respect to the fluid inflow process parameters. The motivation is to use the sensitivity estimators for dynamic control and optimization of the systems performance. Subsequently these estimators are evaluated based on data observed from a single sample path of the discrete-event system and can be used to dynamically control the input process to the system allowing the network to work continuously at an optimal or near optimal point. Furthermore, the proposed analysis naturally leads to a distributed algorithm for evaluating the propagation of perturbations in a network (e.g., due to changes in the buffer size of upstream nodes).

This paper proposes a new model of "novelty detection" for image sequence analysis using neural networks. This model uses the concept of artificially generated negative data to form closed decision boundaries using a multilayer perceptron. The neural network output is novelty filtered by thresholding the output of multiple networks (one per known class) to which the sample is input and clustered for determining which clusters represent novel classes. After labeling these novel clusters, new networks are trained on this data. We perform experiments with video-based image sequence data containing a number of novel classes. The performance of the novelty filter is evaluated using two performance metrics and we compare our proposed model on the basis of these with five baseline novelty detectors. We also discuss the results of retraining each model after novelty detection. On the basis of Chi-square performance metric, we prove at 5 percent significance level that our optimized novelty detector performs at the same level as an ideal novelty detector that does not make any mistakes.

The value, in current prices, of livestock production in Cyprus exceeds the amount of CY£ 160 million and accounts for about 42% of the value of total agricultural production. The country is self-sufficient in milk and milk products, eggs, pig and poultry meat, while production covers the demand for beef by 70–75% and for sheep and goat meat by 90%. Over the last 40 years, the production of meat increased more than 10-fold, of milk 6-fold and of eggs doubled. These achievements were the result of the gradual transformation from low to high input production systems in an effort to improve productivity to satisfy the increasing demand, to reduce production risks associated with frequent droughts, to decrease pressure on the environment from overgrazing, and to lower production costs. Today, in dairy cattle, pigs and poultry, the production is based on a small number of high input and medium to large size commercial farms using employed labour force, while in sheep and goats, farms are smaller and rely on family labour. In cattle and sheep, one predominant breed in each species is utilized for production, while in goats, two breeds and their crosses are used. Pig and poultry farms rely on imported breeds and hybrids. The per capita consumption of livestock products is among the highest in Europe leaving little room for further increases. The present trends relate to quality aspects of livestock products, introduction of new technology, improved production management, reduced costs and production methods friendly to the environment.

One of the major issues in novelty detection for video and image analysis application is how to recognize objects that are not in full view. As the camera pans, tilts and zooms, image objects within a scene are never in full view and enter and exit image frames with time. These objects with partial view do not contain enough number of pixels that can generate robust color, texture and statistical features, and therefore result in classification samples that are often not representative of that class, e.g. outliers. From a data analysis point of view, it is impossible to known which samples are truly outliers because of partial view as opposed to outliers because of mistakes in object labeling. This can cause serious problems in novelty detection tasks using neural networks. In this paper we propose a novel methodology for automatically detecting image objects with partial view and discuss how to use this knowledge to improve novelty detection results.

Background Recent studies have shown that patients with single vessel coronary artery disease (CAD) suffering from acute coronary syndromes (ACS) have increased coronary sinus (CS) blood temperature compared with the right atrium (RA). The aim of this study was to investigate whether there is a correlation between systemic inflammatory indexes and CS temperature and whether there is a difference in CS temperature between patients with single vs. multivessel disease. Materials and methods We included consecutive patients scheduled for coronary angiography for recent-onset chest pain evaluation. We measured C-reactive protein (CRP) levels in the study population. Coronary sinus and RA blood temperature measurements were performed by a 7F thermography catheter. Delta T was calculated by subtracting the RA from the CS blood temperature. Results The study population comprised 53 patients with ACS, 25 patients with stable angina (SA) and 22 subjects without CAD (control group). Delta T was greater in patients with ACS and with SA compared with the control group (0.22 +/- 0.10 degrees C, 0.18 +/- 0.04 degrees C vs. 0.14 +/- 0.07 degrees C, P < 0.01 for both comparisons). The ACS group had greater Delta T compared with the SA group, although the difference did not reach statistical significance (P = 0.09). Eighteen (39.1%) out of 46 patients with multivessel disease had three-vessel disease and 28 (60.8%) had two-vessel disease. Delta T between patients with multivessel and single vessel disease was similar (0.22 +/- 0.01 degrees C, 0.19 +/- 0.01 degrees C, P = 0.17). The levels of CRP were well correlated with Delta T (R = 0.35b, P < 0.01). Conclusion Systemic inflammation is well correlated with CS temperature; thus, an inflammatory process could be the underlying mechanism for increased heat production from the myocardium.

OBJECTIVES We investigated whether there is increased temperature in non-culprit lesions, and the correlation of clinical syndrome with heat production of non-culprit lesions. BACKGROUND There is a controversy regarding whether there is widespread inflammation Involving non-culprit lesions, or whether inflammatory involvement is limited to the culprit lesion. Coronary thermography assesses the local inflammatory involvement in atherosclerotic lesions. METHODS We included patients suffering from stable angina (SA) or acute coronary syndrome (ACS). All patients had two or more angiographically detectable lesions at different arteries. Culprit lesions should be identified in all patients. Patients with chronic total occlusions and multiple significant lesions at the culprit vessel were excluded. We measured at each non-culprit lesion the temperature difference (Delta T) between the atherosclerotic plaque and the proximal vessel wall temperature. RESULTS The study population included 42 patients: 23 with SA, 19 with ACS. The Delta T in lion-culprit lesions was 0.08 +/- 0.07 degrees C. Patients with ACS had a higher temperature difference in non-culprit lesions compared with patients with SA (ACS 0.11 +/- 0.08 degrees C vs. SA 0.05 +/- 0.06 degrees C; p < 0.01). The mean value of Delta T in non-culprit lesions was higher in the untreated group compared with the treated group with statins (0.11 +/- 0.10 degrees C vs. 0.06 +/- 0.05 degrees C; p = 0.05). CONCLUSIONS The results of this study show that heat is generated in non-culprit lesions. Moreover, in patients with ACS, temperature difference is increased compared with patients with stable angina.

In this paper we investigate the role of colour spaces on texture analysis. We extract a range of correlogram and colour moment features for the VisTex colour texture benchmark in different colour spaces and find the average probabilistic distance of separation across different objects for different features and suggest the colour spaces that are best suited for the classification process. We also show the results of k-nearest neighbour classification for different features and their combined set.

The recognition and analysis of natural objects is an important area of research. In this paper, we demonstrate the results of natural object recognition using colour texture features. The results on colour features are generated using a large scene analysis benchmark and neural network classifier. These results are compared to the use of grey-scale features on the same data and found to be much superior.

Feature selection is an important consideration in several applications where one needs to choose a smaller subset of features from a complete set of raw measurements such that the improved subset generates as good or better classification performance compared to original data. In this paper, we describe a novel feature selection approach that is based on the estimation of classification complexity through data partitioning. This approach allows us to select the N best features from a given set in an order of their ability to separate data from different classes. In this paper, we perform our experiments on the ORL face database that consists of 400 images. The results show that the proposed approach outperforms the probability distance approach and is a viable method for implementing more advanced search methods of feature selection.

Scene analysis is an important area of research with the aim of identifying objects and their relationships in natural scenes. The MINERVA benchmark has recently been introduced in this area for testing different image processing and classification schemes. We present results on the classification of eight natural objects in the complete set of 448 natural images using neural networks. An exhaustive set of experiments with this benchmark has been conducted using four different segmentation methods and five texture-based feature extraction methods. The results in this paper show the performance of a neural network classifier on a ten fold cross-validation task. On the basis of the results produced, we are able to rank how well different image segmentation algorithms are suited to the task of region of interest identification in these images, and we also see how well texture extraction algorithms rank on the basis of classification results.