Age- and activity-related differences in the abundance of Myosin essential and regulatory light chains in human muscle

Traditional methods for phenotyping skeletal muscle (e.g., immunohistochemistry) are labor-intensive and ill-suited to multixplex analysis, i.e., assays must be performed in a series. Addressing these concerns represents a largely unmet research need but more comprehensive parallel analysis of myofibrillar proteins could advance knowledge regarding age- and activity-dependent changes in human muscle. We report a label-free, semi-automated and time efficient LC-MS proteomic workflow for phenotyping the myofibrillar proteome. Application of this workflow in old and young as well as trained and untrained human skeletal muscle yielded several novel observations that were subsequently verified by multiple reaction monitoring (MRM).We report novel data demonstrating that human ageing is associated with lesser myosin light chain 1 content and greater myosin light chain 3 content, consistent with an age-related reduction in type II muscle fibers. We also disambiguate conflicting data regarding myosin regulatory light chain, revealing that age-related changes in this protein more closely reflect physical activity status than ageing per se. This finding reinforces the need to control for physical activity levels when investigating the natural process of ageing. Taken together, our data confirm and extend knowledge regarding age- and activity-related phenotypes. In addition, the MRM transitions described here provide a methodological platform that can be fine-tuned to suite multiple research needs and thus advance myofibrillar phenotyping.

A survey of fingerprint classification Part I: taxonomies on feature extraction methods and learning models

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.

Análise das atitudes e comportamentos dos jovens adultos face aos iogurtes

Dissertação de Mestrado apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Comunicação, especialização em Marketing e Publicidade.

A atitude dos consumidores face aos produtos alimentares de marca do distribuidor

Dissertação de Mestrado apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências da Comunicação, especialização em Marketing e Publicidade.

Um estudo sobre a personalidade da marca do distribuidor, no contexto da comunicação de produtos alimentares

Dissertação de Mestrado apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Relações Públicas.

Gestão do cooperativismo médico no Brasil

Tese apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Doutor em Ciências Empresariais, especialidade em Gestão

Automatic Detection of Relevant Head Gestures in American Sign Language Communication

An automated system for detection of head movements is described. The goal is to label relevant head gestures in video of American Sign Language (ASL) communication. In the system, a 3D head tracker recovers head rotation and translation parameters from monocular video. Relevant head gestures are then detected by analyzing the length and frequency of the motion signal's peaks and valleys. Each parameter is analyzed independently, due to the fact that a number of relevant head movements in ASL are associated with major changes around one rotational axis. No explicit training of the system is necessary. Currently, the system can detect "head shakes." In experimental evaluation, classification performance is compared against ground-truth labels obtained from ASL linguists. Initial results are promising, as the system matches the linguists' labels in a significant number of cases.

On the Scalability-Performance Tradeoffs in MPLS and IP Routing

MPLS (Multi-Protocol Label Switching) has recently emerged to facilitate the engineering of network traffic. This can be achieved by directing packet flows over paths that satisfy multiple requirements. MPLS has been regarded as an enhancement to traditional IP routing, which has the following problems: (1) all packets with the same IP destination address have to follow the same path through the network; and (2) paths have often been computed based on static and single link metrics. These problems may cause traffic concentration, and thus degradation in quality of service. In this paper, we investigate by simulations a range of routing solutions and examine the tradeoff between scalability and performance. At one extreme, IP packet routing using dynamic link metrics provides a stateless solution but may lead to routing oscillations. At the other extreme, we consider a recently proposed Profile-based Routing (PBR), which uses knowledge of potential ingress-egress pairs as well as the traffic profile among them. Minimum Interference Routing (MIRA) is another recently proposed MPLS-based scheme, which only exploits knowledge of potential ingress-egress pairs but not their traffic profile. MIRA and the more conventional widest-shortest path (WSP) routing represent alternative MPLS-based approaches on the spectrum of routing solutions. We compare these solutions in terms of utility, bandwidth acceptance ratio as well as their scalability (routing state and computational overhead) and load balancing capability. While the simplest of the per-flow algorithms we consider, the performance of WSP is close to dynamic per-packet routing, without the potential instabilities of dynamic routing.

Large-Scale Neural Systems for Vision and Cognition

— Consideration of how people respond to the question What is this? has suggested new problem frontiers for pattern recognition and information fusion, as well as neural systems that embody the cognitive transformation of declarative information into relational knowledge. In contrast to traditional classification methods, which aim to find the single correct label for each exemplar (This is a car), the new approach discovers rules that embody coherent relationships among labels which would otherwise appear contradictory to a learning system (This is a car, that is a vehicle, over there is a sedan). This talk will describe how an individual who experiences exemplars in real time, with each exemplar trained on at most one category label, can autonomously discover a hierarchy of cognitive rules, thereby converting local information into global knowledge. Computational examples are based on the observation that sensors working at different times, locations, and spatial scales, and experts with different goals, languages, and situations, may produce apparently inconsistent image labels, which are reconciled by implicit underlying relationships that the network’s learning process discovers. The ARTMAP information fusion system can, moreover, integrate multiple separate knowledge hierarchies, by fusing independent domains into a unified structure. In the process, the system discovers cross-domain rules, inferring multilevel relationships among groups of output classes, without any supervised labeling of these relationships. In order to self-organize its expert system, the ARTMAP information fusion network features distributed code representations which exploit the model’s intrinsic capacity for one-to-many learning (This is a car and a vehicle and a sedan) as well as many-to-one learning (Each of those vehicles is a car). Fusion system software, testbed datasets, and articles are available from http://cns.bu.edu/techlab.

An examination of the construction of meanings for obesity in Ireland

Obesity has been defined as a consequence of energy imbalance, where energy intake exceeds energy expenditure and results in a build-up of adipose tissue. However, this scientific definition masks the complicated social meanings associated with the condition. This research investigated the construction of meaning around obesity at various levels of inquiry to inform how obesity is portrayed and understood in Ireland. A multi-paradigmatic approach was adopted, drawing on theory and methods from psychology and sociology and an analytical framework combining the Common Sense Model and framing theory was employed. In order to examine the exo-level meanings of obesity, content analysis was performed on two media data sets (n=479, n=346) and a thematic analysis was also performed on the multiple newspaper sample (n=346). At the micro-level, obesity discourses were investigated via the thematic analysis of comments sampled from an online message board. Finally, an online survey assessed individual-level beliefs and understandings of obesity. The media analysis revealed that individual blame for obesity was pervasive and the behavioural frame was dominant. A significant increase in attention to obesity over time was observed, manifestations of weight stigma were common, and there was an emotive discourse of blame directed towards the parents of obese children. The micro-level analysis provided insight into the weight-based stigma in society and a clear set of negative ‘default’ judgements accompanied the obese label. The survey analysis confirmed that the behavioural frame was the dominant means of understanding obesity. One of the strengths of this thesis is the link created between framing and the Common Sense Model in the development of an analytical framework for application in the examination of health/illness representations. This approach helped to ascertain the extent of the pervasive biomedical and individual blame discourse on obesity, which establishes the basis for the stigmatisation of obese persons.

Mature results of a randomized phase II multicenter study of exemestane versus tamoxifen as first-line hormone therapy for postmenopausal women with metastatic breast cancer.

BACKGROUND: Women with hormone-responsive metastatic breast cancer (MBC) may respond to or have stable disease with a number of hormone therapies. We explored the efficacy and safety of the steroidal aromatase inactivator exemestane as first-line hormonal therapy in MBC in postmenopausal women. PATIENTS AND METHODS: Patients with measurable disease were eligible if they had received no prior hormone therapy for metastatic disease and had hormone receptor positive disease or hormone receptor unknown disease with a long disease-free interval from adjuvant therapy. They were randomized to tamoxifen 20 mg/day or exemestane 25 mg/day in this open-label study. RESULTS: Blinded independently reviewed response rates for exemestane and tamoxifen were 41% and 17%, respectively. Fifty-seven per cent of exemestane- and 42% of tamoxifen-treated patients experienced clinical benefit, defined as complete or partial response, or disease stabilization lasting at least 6 months. There was a low incidence of severe flushing, sweating, nausea and edema in women who received exemestane. One exemestane-treated patient had a pulmonary embolism with grade 4 dyspnea. CONCLUSIONS: Exemestane is well tolerated and active in the first-line treatment of hormone-responsive MBC. An ongoing EORTC phase III trial is comparing the efficacy, measuring time-to-disease progression, of exemestane and tamoxifen.

Promising results with exemestane in the first-line treatment of metastatic breast cancer: a randomized phase II EORTC trial with a tamoxifen control.

Because tamoxifen (TAM), a nonsteroidal antiestrogen, is routinely used in the adjuvant setting, other hormone therapies are needed as alternatives for first-line treatment of metastatic breast cancer (MBC). Currently, exemestane (EXE) and other antiaromatase agents are indicated for use in patients who experience failure of TAM. In this multicenter, randomized, open-label, TAM-controlled (20 mg/day), phase II trial, we examined the activity and tolerability of EXE 25 mg/day for the first-line treatment of MBC in postmenopausal women. Exemestane was well tolerated and demonstrated substantial first-line antitumor activity based on intent-to-treat analysis of peer-reviewed responses. In the EXE arm, values for complete, partial, and objective response, clinical benefit, and time to tumor progression (TTP) exceeded those reported for TAM although no statistical comparison was made. Based on these encouraging results, a phase III trial will compare EXE and TAM.

Safety and efficacy of rivastigmine in adolescents with Down syndrome: long-term follow-up.

Following the completion of a 20-week, open-label study of the safety and efficacy of liquid rivastigmine for adolescents with Down syndrome, 5 of the 10 adolescents in the clinical trial continued long-term rivastigmine therapy and 5 did not. After an average period of 38 months, all 10 subjects returned for a follow-up assessment to determine the safety and efficacy of long-term rivastigmine use. Rivastigmine was well tolerated and overall health appeared to be unaffected by long-term rivastigmine use. Performance change on cognitive and language measures administered at the termination of the open-label clinical trial was compared between the two groups. No between-group difference in median performance change across the long-term period was found, suggesting that the long-term use of rivastigmine does not improve cognitive and language performance. However, two subjects demonstrated remarkable improvement in adaptive function over the long-term period. Both subjects had received long-term rivastigmine therapy. The discussion addresses the challenge of assessing cognitive change in clinical trials using adolescents with Down syndrome as subjects and the use of group versus individual data to evaluate the relevance of medication effects.

Identification of beta-adrenergic receptors in human lymphocytes by (-) (3H) alprenolol binding.

Human lymphocytes are known to posessess a catecholamine-responsive adenylate cyclase which has typical beta-adrenergic specificity. To identify directly and to quantitate these beta-adenergic receptors in human lymphocytes, (-) [3H] alprenolol, a potent beta-adrenergic antagonist, was used to label binding sites in homogenates of human mononuclear leukocytes. Binding of (-) [3H] alprenolol to these sites demonstrated the kinetics, affinity, and stereospecificity expected of binding to adenylate cyclase-coupled beta-adrenergic receptors. Binding was rapid (t1/2 less than 30 s) and rapidly reversible (t1/2 less than 3 min) at 37 degrees C. Binding was a saturable process with 75 +/- 12 fmol (-) [3H] alprenolol bound/mg protein (mean +/- SEM) at saturation, corresponding to about 2,000 sites/cell. Half-maximal saturation occurred at 10 nM (-) [3H] alprenolol, which provides an estimate of the dissociation constant of (-) [3H] alprenolol for the beta-adrenergic receptor. The beta-adrenergic antagonist, (-) propranolol, potently competed for the binding sites, causing half-maximal inhibition of binding at 9 nM. beta-Adrenergic agonists also competed for the binding sites. The order of potency was (-) isoproterenol greater than (-) epinephrine greater than (-)-norepinephrine which agreed with the order of potency of these agents in stimulating leukocyte adenylate cyclase. Dissociation constants computed from binding experiments were virtually identical to those obtained from adenylate cyclase activation studies. Marked stereospecificity was observed for both binding and activation of adenylate cyclase. (-)Stereoisomers of beta-adrenergic agonists and antagonists were 9- to 300-fold more potent than their corresponding (+) stereoisomers. Structurally related compounds devoid of beta-adrenergic activity such as dopamine, dihydroxymandelic acid, normetanephrine, pyrocatechol, and phentolamine did not effectively compete for the binding sites. (-) [3H] alprenolol binding to human mononuclear leukocyte preparations was almost entirely accounted for by binding to small lymphocytes, the predominant cell type in the preparations. No binding was detectable to human erythrocytes. These results demonstrate the feasibility of using direct binding methods to study beta-adrenergic receptors in a human tissue. They also provide an experimental approach to the study of states of altered sensitivity to catecholamines at the receptor level in man.

An active learning approach for rapid characterization of endothelial cells in human tumors.

Currently, no available pathological or molecular measures of tumor angiogenesis predict response to antiangiogenic therapies used in clinical practice. Recognizing that tumor endothelial cells (EC) and EC activation and survival signaling are the direct targets of these therapies, we sought to develop an automated platform for quantifying activity of critical signaling pathways and other biological events in EC of patient tumors by histopathology. Computer image analysis of EC in highly heterogeneous human tumors by a statistical classifier trained using examples selected by human experts performed poorly due to subjectivity and selection bias. We hypothesized that the analysis can be optimized by a more active process to aid experts in identifying informative training examples. To test this hypothesis, we incorporated a novel active learning (AL) algorithm into FARSIGHT image analysis software that aids the expert by seeking out informative examples for the operator to label. The resulting FARSIGHT-AL system identified EC with specificity and sensitivity consistently greater than 0.9 and outperformed traditional supervised classification algorithms. The system modeled individual operator preferences and generated reproducible results. Using the results of EC classification, we also quantified proliferation (Ki67) and activity in important signal transduction pathways (MAP kinase, STAT3) in immunostained human clear cell renal cell carcinoma and other tumors. FARSIGHT-AL enables characterization of EC in conventionally preserved human tumors in a more automated process suitable for testing and validating in clinical trials. The results of our study support a unique opportunity for quantifying angiogenesis in a manner that can now be tested for its ability to identify novel predictive and response biomarkers.