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WOS: 000468732700018, PubMed ID: 31054483, Background and aims: One of the objectives of the ESC-EORP EUROASPIRE V survey is to determine how well European guidelines on the management of dyslipidaemias are implemented in coronary patients. Methods: Standardized methods were used by trained technicians to collect information on 7824 patients from 130 centers in 27 countries, from the medical records and at a visit at least 6 months after hospitalization for a coronary event. All lipid measurements were performed in one central laboratory. Patients were divided into three groups: on high-intensity LDL-C-lowering-drug therapy (LLT), on low or moderate-intensity LLT and on no LLT. Results: At the time of the visit, almost half of the patients were on a high-intensity LLT. Between hospital discharge and the visit, LLT had been reduced in intensity or interrupted in 20.8% of the patients and had been started or increased in intensity in 11.7%. In those who had interrupted LLT or had reduced the intensity, intolerance to LLT and the advice of their physician were reported as the reason why in 15.8 and 36.8% of the cases, respectively. LDL-C control was better in those on a high-intensity LLT compared to those on low or moderate intensity LLT. LDL-C control was better in men than women and in patients with self-reported diabetes. Conclusions: The results of the EUROASPIRE V survey show that most coronary patients have a less than optimal management of LDL-C. More professional strategies are needed, aiming at lifestyle changes and LLT adapted to the need of the individual patient., ESC - EORP; AmgenAmgen; Eli LillyEli Lilly; PfizerPfizer; SanofiSanofi-Aventis; Ferrer; Novo NordiskNovo Nordisk, The EUROASPIRE V survey was carried out under the auspices of the ESC - EORP. Since the start of EORP, the following companies have supported the programme: Amgen, Eli Lilly, Pfizer, Sanofi, Ferrer and Novo Nordisk. The sponsors of the EUROASPIRE surveys had no role in the design, data collection, data analysis, data interpretation, decision to publish, or writing the manuscript.

UV−visible diffuse reflectance spectroscopy (UV−vis DRS) and the ethanol oxidation probe reaction were used to investigate the structure and function of binary transition metal oxide catalysts containing dispersed WOx and MoOx domains supported together on alumina. The efficacy of UV−vis DRS as a tool to identify segregated MoOx and WOx surface domains along with their growth into larger and interacting binary oxides is demonstrated for the first time. UV−vis absorption edge analysis of physical mixtures of single oxide catalysts indicates that spatially segregated domains of different composition result in multiple edges in the UV−vis absorption spectra. Binary oxide catalysts containing 0.5 Mo atoms/nm2 and 0.5 W atoms/nm2 show two distinct absorption edges at 3.60 and 4.13 eV, corresponding to spatially and compositionally segregated MoOx and WOx domains. At higher surface densities (2−8 total metal atoms/nm2) only one edge is observed, suggesting that MoOx and WOx are molecularly mixing and forming a ...

In spite of significant advancements in automatic mesh generation during the past decade, the construction of quality finite element discretizations on complex three-dimensional domains is still a difficult and time demanding task. In this paper, the partition of unity framework used in the generalized finite element method (GFEM) is exploited to create a very robust and flexible method capable of using meshes that are unacceptable for the finite element method, while retaining its accuracy and computational efficiency. This is accomplished not by changing the mesh but instead by clustering groups of nodes and elements. The clusters define a modified finite element partition of unity that is constant over part of the clusters. This so-called clustered partition of unity is then enriched to the desired order using the framework of the GFEM. The proposed generalized finite element method can correctly and efficiently deal with: (i) elements with negative Jacobian; (ii) excessively fine meshes created by automatic mesh generators; (iii) meshes consisting of several sub-domains with non-matching interfaces. Under such relaxed requirements for an acceptable mesh, and for correctly defined geometries, today's automated tetrahedral mesh generators can practically guarantee successful volume meshing that can be entirely hidden from the user. A detailed technical discussion of the proposed generalized finite element method with clustering along with numerical experiments and some implementation details are presented. Copyright © 2006 John Wiley & Sons, Ltd.

With the growing popularity in using social media to collect data, there is an increasing need to discover ways in which to productively use this data. Our objective is to form an interest profile from tweets and use this to recommend loosely related Reddit threads which the reader is most likely to be interested in. The problem is approached as a genre classification problem. Given a tweet, we want to deduce what genre(s) it might fall under if those words in the tweet were used in official texts. From there, we keep track of how many tweets fall under which genre, and generate a list of Reddit threads which similarly fall under those genre and are proportional to the interests of the user. Due to the complexity of genre classification, we chose to use an ensemble approach for classification. We use three classifiers in our ensemble: 1) a classic Naive Bayesian classifier, 2) a Naive Bayesian classifier trained only on the parts-of-speech of sentences, and 3) a Naive Bayesian classifier which will only make a decision if the probability P(x) ≥ 0.9. We measured the success of our classifiers by comparing the accuracy, precision, and recall of each model. Classifiers 1 and 2 had high accuracy than classifier 3 but classifier 3 had a much higher precision and recall rate. After creating the classifier, we were then able to form an interest profile on well-known people, one who has a small number of tweets versus one with a much larger number, and compile a list of recommended articles. The genres tagged to each person seemed to match their public personas and most of the articles chosen fit these genres. Our results are a valuable beginning for what constitutes a much larger project.

There may be a relationship between sleep and pain in patients with chronic back pain. We collected day-time pain and nighttime activity data from 18 patients diagnosed with chronic back pain. The patients were followed for 6 days and 5 nights. Pain levels were collected every 90 min between 0800 hours and 2,200 hours using a computerized electronic diary. Activity levels were collected using a wrist accelerometer (Actiwatch AW-64). The Actiwatch sampled activity counts every 1 min. Patients were asked to wear the Actiwatch on their non-dominant arm. The pain level measurements were interpolated using cubic splines. A mean pain level was calculated for each period 0800 hours to 2,200 hours as well as for the 6-day period. The difference between the mean pain levels for the 6-day period and each 0800 hours to 2,200 hours period was calculated for each patient. Nighttime activity data were analyzed using the Actiwatch Sleep Analysis software. Correlations were calculated between the Actiwatch Sleep Analysis variables and the mean pain level differences for each patient and period. The correlation analysis was performed with SPSS 7.5. We were unable to show any significant relationships.A different approach to analyze the data was used. A Self-Organizing Map (SOM) Neural Network was trained using the original nighttime activity level time series from 10 randomly selected patients. Recall was then performed on all the activity level data. Correlations were calculated between the pain level variance for the 6-day period for each patient and the corresponding difference in the SOM output coordinates. The correlation was found to be r = 0.73, p0.01). We conclude that daytime pain levels are not directly correlated with sleep in the following night and that sleep is not directly correlated with daytime pain levels on the following day in this group of patients. There appears to be a correlation between the difference in nighttime activity levels and patterns and the daytime pain variance. Patients who experience large fluctuations in daytime pain levels also show a higher variability in their nighttime activity levels and patterns. Even though we were unable to show a direct relationship between daytime pain and sleep, it may be reasonable to assume that better pain control resulting in less daytime pain fluctuations can provide more stable nighttime activity levels and patterns in this limited group of patients. By using a neural network model, we were able to extract information from the nighttime activity levels even though a traditional statistical analysis was unsuccessful.

We have developed and benchmarked real-time collection methods that exploit digital, packet-switched telephony services available in metropolitan areas. The Arrhythmia monitoring system collects real-time electrocardiogram signals from a mobile or homebound patient, and combines them with GPS location data, and transmits this information to a remote station for display and monitoring.

Low back pain represents a significant medical problem, both in its prevalence and its cost to society. Most episodes of acute low back pain resolve without significant long-term functional impact. However, a minority of patients experience extended chronic pain and disability. In this paper, we have explored new techniques of patient assessment that may prospectively identify this minority of patients at risk of developing poor outcomes. We studied 15 patients with acute low back pain and 25 patients with chronic low back pain over 4 month's time. Patients monitored their pain and activity levels continuously over the first 3 weeks. Pain and functional status were assessed at baseline and at 3 weeks following enrollment. Follow-up assessment of functional status and progress were performed at 2 and 4 months. The pain and activity levels were categorized using a self-organizing-map neural network. A back-propagation neural network was trained with the categorization and outcome data. There was a good correlation between the true and predicted values for general health (r e 0.96, p < 0.01) and mental health (r e0.80, p < 0.01). No significant correlation was found if activity and pain data were not entered into the analysis. Our results show that neural network techniques can be applied effectively to categorizing patients with acute and chronic low back pain. It is our hope that future research will allow these categorizations to be tied to prognostic and therapeutic decisions in patients who present with episodes of back pain.

This chapter focuses on synthetic applications of enzymes in monophasic organic solvents and is intended to illustrate many types of transformations that can be catalyzed by enzymes in organic solvents. Nonaqueous enzyme systems can be divided into two classes: homogeneous systems in which enzymes are modified to be soluble, and heterogeneous systems in which the catalyst is in an insoluble form. Colyophilisates for use with enzymes in organic solvents can be categorized according to three primary acting mechanisms: activating salts, molecular imprinting agents/molds, and lyoprotectants. This chapter summarizes many applications of enzymes in organic solvents, with focus placed on three classes of enzymes that have found a wide measure of use in organic solvents: hydrolases (EC 3), lyases (EC 4), and oxidoreductases (EC 1). Hydrolase enzymes (EC 3) comprise the predominant bio-catalysts for transformations reported in organic solvents, and two groups, lipases and proteases, enjoy widespread use. Chiral cyanohydrins produced by hydroxynitrile lyases (HNLs) in organic solvents have been used as building blocks in the synthesis of various bioactive compounds, such as epinephrine derivatives. Enzymes are not only active in organic solvents, they often display high regio-, chemo-, and enantioselectivities, making them particularly suited for the selective modification of complex molecules. Novel enzyme preparation methods and system conditions, along with multienzyme processes and the ability to engineer enzymes with improved properties for synthesis, will lead to new synthetic routes and ensure an expanding role of organic-phase biocatalysis in the synthetic chemist’s repertoire.

This paper is aimed at presenting a partition of unity method for the simulation of three-dimensional dynamic crack propagation. The method is a variation of the partition of unity finite element method and hp-cloud method. In the context of crack simulation, this method allows for modeling of arbitrary dynamic crack propagation without any remeshing of the domain. In the proposed method, the approximation spaces are constructed using a partition of unity (PU) and local enrichment functions. The PU is provided by a combination of Shepard and finite element partitions of unity. This combination of PUs allows the inclusion of arbitrary crack geometry in a model without any modification of the initial discretization. It also avoids the problems associated with the integration of moving least squares or conventional Shepard partitions of unity used in several meshless methods. The local enrichment functions can be polynomials or customized functions. These functions can efficiently approximate the singular fields around crack fronts. The crack propagation is modeled by modifying the partition of unity along the crack surface and does not require continuous remeshings or mappings of solutions between consecutive meshes as the crack propagates. In contrast with the boundary element method, the proposed method can be applied to any class of problems solvable by the classical finite element method. In addition, the proposed method can be implemented into most finite element data bases. Several numerical examples demonstrating the main features and computational efficiency of the proposed method for dynamic crack propagation are presented.

Digitized data from CTG (cardiotocography) measurements (fetal heart rate and uterine contractions) have been used for categorization of typical heart rate patterns before and during delivery. Short time series of CTG data, about 7 min duration, have been used in the categorization process. In the first part of the study, selected CTG data corresponding to 10 typical cases were used for purely auto associative unsupervised training of a Self-Organizing Map Neural Network (SOM). The network may then be used for objective categorization of CTG patterns through the map coordinates produced by the network. The SOM coordinates were then compared. In the second part of the study, a hybrid neural network consisting of a SOM network and a Back-Propagation network (BP) was trained with data corresponding to a number of basic heart rate patterns as described by eight manually selected indices. Test data (different than the training data) were then used to check the performance of the network. The present study shows that the categorization process, in which neural networks were used, can be reliable and agree well with the manual categorization. Since the categorization by neural networks is very fast and does not involve human efforts, it may be useful in patient monitoring.

Malware detection is a major challenge in today’s software security profession. Works exist for malware detection based on static analysis such as function length frequency, printable string information, byte sequences, API calls, etc. Some works also applied dynamic analysis using features such as function call arguments, returned values, dynamic API call sequences, etc. In this work, we applied a reverse engineering process to extract static and behavioral features from malware based on an assumption that behavior of a malware can be revealed by executing it and observing its effects on the operating environment. We captured all the activities including registry activity, file system activity, network activity, API Calls made, and DLLs accessed for each executable by running them in an isolated environment. Using the extracted features from the reverse engineering process and static analysis features, we prepared two datasets and applied data mining algorithms to generate classification rules. Essential features are identified by applying Weka’s J48 decision tree classifier to 1103 software samples, 582 malware and 521 benign, collected from the Internet. The performance of all classifiers are evaluated by 5-fold cross validation with 80-20 splits of training sets. Experimental results show that Naive Bayes classifier has better performance on the smaller data set with 15 reversed features, while J48 has better performance on the data set created from the API Call data set with 141 features. In addition, we applied a rough set based tool BLEM2 to generate and evaluate the identification of reverse engineered features in contrast to decision trees. Preliminary results indicate that BLEM2 rules may provide interesting insights for essential feature identification.

A methodology to build discrete models of boundary-value problems (BVP) is presented. The method is applicable to arbitrary domains and employs only a scattered set of nodes to build approximate solutions to BVPs. A version of moving least-square interpolation and the collocation method are used to discretize BVP equations, which results in a truly meshless method (i.e. without a background mesh of integration points). h- and p-adaptive strategies are tested and very good convergence of the method was observed. The improvements in the methodology, in particular the introduction of spectral degrees of freedom, result in a fast and accurate method, significantly more efficient than the Finite Element Method or Element Free Galerkin Method. Several practical applications of the method to solve various engineering problems are presented.

Many industrial processes used to produce chemicals and pharmaceuticals would benefit from enzymes that function under extreme conditions. Enzymes from extremophilic microorganisms have evolved to function in a variety of extreme environments, and bioprospecting for these microorganisms has led to the discovery of new enzymes with high tolerance to nonnatural conditions. However, bioprospecting is inherently limited by the diversity of enzymes evolved by nature. Protein engineering has also been successful in generating extremophilic enzymes by both rational mutagenesis and directed evolution, but screening for activity under extreme conditions can be difficult. This review examines the emerging synergy between bioprospecting and protein engineering in developing extremophilic enzymes. Specific topics include unnatural industrial conditions relevant to biocatalysis, biophysical properties of extremophilic enzymes, and industrially relevant extremophilic enzymes found either in nature or through protein engineering.

Microblogs are one of a growing group of social network tools. Twitter is, at present, one of the most popular forums for microblogging in online social networks, and the fastest growing. Fifty million messages flow through servers, computers, and cell phones on a wide variety of topics exchanged daily. With this considerable volume, Twitter is a natural and obvious target for spreading spam via the messages, called tweets. The challenge is how to determine if a tweet is a spam or not, and more specifically a special category advertising pharmaceutical products. The authors look at the essential characteristics of spam tweets and what makes microblogging spam unique from email or other types of spam. They review methods and tools currently available to identify general spam tweets. Finally, this work introduces a new methodology of applying text mining and data mining techniques to generate classifiers that can be used for pharmaceutical spam detection in the context of microblogging.

This paper presents a method of applying text mining techniques and data mining tools for pharmaceutical spam detection from Twitter data. A simple method based on a manually selected list of 65 pharmaceutical discriminating words is used for labeling spam training tweets. Preliminary experimental results show that J48 decision tree classifier has better performance over Naive Bayesian algorithm.

One challenge in malware analysis involves collecting useful data without risking experimenters' machines or systems. Static analysis of malware codebases is valuable in providing insights on malware development mechanisms, however, it cannot provide understanding in dynamic profiling of executable codes. In this paper, we present a case study of the well-known Nugache worm using existing reverse engineering tools to collect data from malwares running in a closed-lab environment. Useful dynamic patterns of malwares are generated by using a rough set based machine learning tool. The proposed approach can be used for the study of malware behaviors in a safe and pedagogical environment. The dynamic patterns generated by data mining tools may provide insights for specifying similarity measures used by network level Intrusion Detection Systems.

We studied the temporal relationship between pain and activity in patients with acute or chronic low back pain. We studied 15 patients with acute low back pain and 15 patients with chronic low back pain over 3 wk. The activity levels were collected automatically using a wrist accelerometer and were sampled every minute. The pain levels were recorded at least every 90 min using a pocket-sized electronic diary. The time series from each patient were then analyzed using the cross-correlation function at various time offsets. We found that during the first 7 days of acute low back pain, there was a significant (P < 0.01) degree of cross-correlation between activity and pain. On average, pain followed activity by approximately 30 min. As these patients improved and reported less pain, the relationship between activity and pain disappeared. There was no such relationship at any point among the patients with chronic low back pain.

The odd-even merge is a widely used and generally accepted merging network that uses O(N log/sup 2/N) comparators with O(log/sup 2/N) delay. A novel merging network is presented that generalizes the technique used in the odd-even merge. It is based on the division of the input keys by a specified modulus, not limited to 2. A special comparator is used in the final merge step that accepts m input lines and produces m sorted items, where m is the modulus selected for the merge. Alternatives are discussed that apply to the bitonic merging network. >

Digitized data from CTG (cardio-tocography) measurements (fetal heart rate and uterine contractions) have been used for categorization of typical heart rate patterns before and during delivery. Short time series of CTG data, about 7 minutes duration, have been used in the categorization process. In the first part of the study selected CTG data corresponding to 10 typical cases were used for purely autoassociative unsupervised training of a self-organizing map neural network (SOM). The network may then be used for objective categorization of CTG patterns through the map coordinates produced by the network. The SOM coordinates have then been compared In the second part of the study, a hybrid neural network consisting of a SOM network and a backpropagation network was trained with data corresponding to a number of basic heart rate pattern as described by 8 manually scaled indices. Test data (different than the training data) were then used to check the performance of the network. The present study shows that the categorization process, in which neural networks were used, can be reliable and agree well with the manual categorization. Since the categorization by neural networks is very fast and does not involve human efforts, it may be useful in patient monitoring. >

Low back pain represents a significant medical problem, both in its prevalence and its cost to society. Most episodes of acute low back pain resolve without significant long-term functional impact. However, a minority of patients experience extended chronic pain and disability. In this paper, we have explored new techniques of patient assessment that may prospectively identify this minority ofpatients at risk of developing poor outcomes. We studied 15 patients with acute low back pain and 25 patients with chronic low back pain over 4 month's time. Patients monitored their pain and activity levels continuously over the first 3 weeks. Pain and functional status were assessed at baseline and at 3 weeks following enrollment. Follow-up assessment of functional status and progress were performed at 2 and 4 months. The pain and activity levels were categorized using a self-organizing-map neural network. A back-propagation neural network was trained with the categorization and outcome data. There was a good correlation between the true and predicted values for general health (r = 0.96, p0.01) and mental health (r = 0.80, p0.01). No significant correlation was found if activity and pain data were not entered into the analysis. Our results show that neural network techniques can be applied effectively to categorizing patients with acute and chronic low back pain. It is our hope that future research will allow these categorizations to be tied to prognostic and therapeutic decisions in patients who present with episodes of back pain.

Digitized data from CTG (cardiotocography) measurements (fetal heart rate and uterine contractions) have been used for categorization of typical heart rate patterns before and during delivery. Short time series of CTG data, about 7 min duration, have been used in the categorization process. In the first part of the study, selected CTG data corresponding to 10 typical cases were used for purely auto associative unsupervised training of a Self-Organizing Map Neural Network (SOM). The network may then be used for objective categorization of CTG patterns through the map coordinates produced by the network. The SOM coordinates were then compared. In the second part of the study, a hybrid neural network consisting of a SOM network and a Back-Propagation network (BP) was trained with data corresponding to a number of basic heart rate patterns as described by eight manually selected indices. Test data (different than the training data) were then used to check the performance of the network. The present study shows that the categorization process, in which neural networks were used, can be reliable and agree well with the manual categorization. Since the categorization by neural networks is very fast and does not involve human efforts, it may be useful in patient monitoring.

The project aimed at the development of a new meshless technology, and computer code based on, for the analysis of crack propagation in dynamics fluid structure interaction. In the meshless formulation crack propagation is not restricted by the existence of domain discretisation (meshless method) and suitable error estimators combined with adaptively assure highly accurate solution. A general purpose, three dimensional meshless analysis code, called PHLEXcrack has been developed, tested and delivered to Navy. At present the code is capable of analysis of static and dynamic structure responses including the underwater shock analysis (provided via interface to "U.S.A." code) with dynamic crack propagation through the selected part of the domain. Crack propagation criteria for brittle materials is provided based on least square fit method and energy release rate data. Users can specify own routines for alternate fracture criteria and crack propagation prediction. Full suite of classical finite elements (comparable to basic Nastran capabilities), together with hp-adaptive volume finite elements, and 3 dimensional meshless formulation are implemented and can be freely mixed to obtain efficiently the solution of displacements, stresses and fracture measures in the structure. Nonlinear capabilities of the code consist of large deformations, nonlinear materials. (incl. plasticity) and contact with rigid surfaces.

Zalozenia i cel pracy Celem badania bylo wykazanie przydatności aspiracyjnej biopsji cienkoiglowej, jako źrodla materialu do analizy ploidii DNA komorek guzow nowotworowych oraz oceny stanu receptorow estrogenowych, progesteronowych i Ki-67 w pierwotnych rakach piersi. Material i metodyka Material do analizy ploidii DNA w cytometrze przeplywowym stanowila zawiesina komorek guzow piersi pobierana przez lekarzy patomorfologow metodą BAC. Badania immunocytochemiczne i preparaty cytologiczne przygotowywano w sposob typowy. Wyniki W latach (1999–2002 r.) wykonano badania u 275 kobiet w wieku od 24 do 85 lat. W tej grupie znalazlo sie 195 przypadkow guzow o typie aneuploidalnym cyklu komorkowego, gdzie wartośc CV mieścila sie w granicach od 1.4 – 9.0 (śr. 4,76), a liczba komorek bedących w fazie S cyklu aneuploidalnego wynosila od 0.4% – 87.4%. W 80 przypadkach guzow piersi o wylącznie diploidalnym typie cyklu komorkowego, wartośc CV mieścila sie w granicach od 2.1 – 7.8 (śr. 4.7), a liczba komorek bedących w fazie S cyklu diploidalnego wynosila od 0.1% – 28.5%. Ploidie DNA porownywano ze stanem receptorowym badanych komorek. Aneuploidia DNA korelowala ze slabą reakcją (27 przypadkow) lub brakiem reakcji (81 przypadkow) na obecnośc receptora estrogenowego oraz progesteronowego (odpowiednio 26 i 96 przypadkow), natomiast towarzyszyla silnej reakcji na obecnośc Ki-67 (15 – 60%). Wnioski Niskie wartości CV w parametrach zarowno guzow aneuploidalnych jak i diploidalnych wskazują na przydatnośc metody oceny ploidii DNA oraz fazy S cyklu aneuploidalnego w materiale cytologicznym uzyskanym na drodze BAC. Badania wskazują, ze cytometryczna analiza zawartości komorkowego DNA oraz fazy S cyklu aneuploidalnego, korelują z wynikami immunocytochemicznymi (PgR, ER, Ki-67). Metody te mogą byc szczegolnie uzyteczne klinicznie przy prognozowaniu i przewidywaniu odpowiedzi na indywidualne leczenie pacjentek, kwalifikujących sie do wcześniejszej chemioterapii lub/i hormonoterapii. Oznaczanie wymienionych markerow przy uzyciu BAC jest bardzo przydatne dla leczenia pacjentow (gdy nie dysponujemy materialem tkankowym a tylko cytologicznym) z np. nieoperacyjnym rakiem piersi lub w przypadku malych zmian pierwotnych.