Definitions and outcome measures for mucous membrane pemphigoid: recommendations of an international panel of experts

Mucous membrane pemphigoid encompasses a group of autoimmune bullous diseases with a similar phenotype characterized by subepithelial blisters, erosions, and scarring of mucous membranes, skin, or both. Although knowledge about autoimmune bullous disease is increasing, there is often a lack of clear definitions of disease, outcome measures, and therapeutic end points. With clearer definitions and outcome measures, it is possible to directly compare the results and data from various studies using meta-analyses. This consensus statement provides accurate and reproducible definitions for disease extent, activity, outcome measures, end points, and therapeutic response for mucous membrane pemphigoid and proposes a disease extent score, the Mucous Membrane Pemphigoid Disease Area Index.

Temporal variations of dyspnea, fatigue, and lung function in chronic lung disease

Background. Research investigating symptom management in patients with chronic obstructive pulmonary disease (COPD) largely has been undertaken assuming the homeostatic construct, without regard to potential roles of circadian rhythms. Temporal relations among dyspnea, fatigue, peak expiratory flow rate (PEFR) and objective measures of activity/rest have not been reported in COPD. ^ Objectives. The specific aims of this study were to (1) explore the 24-hour patterns of dyspnea, fatigue, and PEFR in subjects with COPD; (2) examine the relations among dyspnea, fatigue, and PEFR in COPD; and (3) examine the relations among objective measures of activity/rest and dyspnea, fatigue, and PEFR in COPD. ^ Methods. The repeated-measures design involved 10 subjects with COPD who self-assessed dyspnea and fatigue by 100 mm visual analog scales, and PEFR by peak flow meter in their home 5 times a day for 8 days. Activity/rest was measured by wrist actigraphy. Single and population mean cosinor analyses and correlations were computed for dyspnea, fatigue, and PEFR; correlations were done among these variables and activity/rest. ^ Results. Circadian rhythms were documented by single cosinor analysis in 40% of the subjects for dyspnea, 60% for fatigue, and 60% for PEFR. The population cosinor analysis of PEFR yielded a significant rhythm (p < .05). The 8-day 24-hour means of dyspnea and fatigue was moderately correlated (r = .48, p < .01). Dyspnea and PEFR, and fatigue and PEFR, were weakly correlated in a negative way (r = −.11, p < .05 and r = −.15, p < .01 respectively). Weak to moderate correlations (r = .12–.34, p < .05) were demonstrated between PEFR and mean activity level measured up to 4 hours before PEFR measurement. ^ Conclusions. The findings suggest that (1) the dyspnea and fatigue experienced by COPD patients are moderately related, (2) there is a weak to modest positive relation between PEFR and activity levels, and (3) temporal variation in lung function may not affect the dyspnea and fatigue experienced by patients with COPD. Further research, examining the relations among dyspnea, fatigue, PEFR, and activity/rest is needed. Replication of this study is suggested with a larger sample size. ^

Direct and Indirect Measures of Capacity Utilization: A Nonparametric Analysis of U.S. Manufacturing

We measure the capacity output of a firm as the maximum amount producible by a firm given a specific quantity of the quasi-fixed input and an overall expenditure constraint for its choice of variable inputs. We compute this indirect capacity utilization measure for the total manufacturing sector in the US as well as for a number of disaggregated industries, for the period 1970-2001. We find considerable variation in capacity utilization rates both across industries and over years within industries. Our results suggest that the expenditure constraint was binding, especially in periods of high interest rates.

AN INVESTIGATION OF THE POSSIBLE RELATIONSHIP BETWEEN CERTAIN PHYSIOLOGICAL AND PSYCHOLOGICAL MEASURES AND SMOKING CESSATION IN A SELF - SELECTED POPULATION

This study focused on the possible relationship between certain physiological and psychological variables and the cessation of smoking. The population studied was employees enrolled in a multimodality smoking cessation program at the local offices of a major American corporation. In order to be eligible to participate, each individual must have become a non-smoker by the end of the smoking cessation program.^ Three physiological measures were taken on each individual while performing a relaxation exercise; (1) Electromyogram (EMG), (2) Galvanic Skin Response (GSR), and (3) Skin Temperature. The psychological measure consisted of the variable "anxiety" in the Cattell 16-PF personality inventory. Individual's self report of their smoking status was verified through a test for expired carbon monoxide levels.^ For the total population (N-31) no significant relationships were found between the physiological and psychological variable measured and cessation; however, with the removal of two cases discovered during the post-test interview to be influenced by external factors of high caffeine level and a severe family crisis, the measure of EMG, attained significance in discriminating between the successful and unsuccessful in Smoking Cessation. ^

The Central Bank and bank credits in the Philippines : a survey on effectiveness of monetary policy and its measures

In the post-Asian crisis period, bank loans to the manufacturing sector have shown a slow recovery in the affected countries, unexceptionally in the Philippines. This paper provides a literacy survey on the effectiveness of the Central Bank’s monetary policy and the responsiveness of the financial market, and discusses on the future works necessary to better understand the monetary policy effectiveness in the Philippines. As the survey shows, most previous works focus on the correlation between the short-term policy rates and during the period of monetary tightening and relatively less interest in quantitative effectiveness. Future tasks would shed lights on (1) the asset side – other than loan outstanding – of banks to analyze their behavior/preference in structuring portfolios, and (2) the quantitative impacts during the monetary easing period.

European legislation and implementation measures in the management of construction and demolition waste.

The intense activity in the construction sector during the last decade has generated huge volumes of construction and demolition (C&D) waste. In average, Europe has generated around 890 million tonnes of construction and demolition waste per year. Although now the activity has entered in a phase of decline, due to the change of the economic cycle, we don’t have to forget all the problems caused by such waste, or rather, by their management which is still far from achieving the overall target of 70% for C&D waste --excludes soil and stones not containing dangerous substances-- should be recycled in the EU Countries by 2020 (Waste Framework Directive). But in fact, the reality is that only 50% of the C&D waste generated in EU is recycled and 40% of it corresponds to the recycling of soil and stones not containing dangerous substances. Aware of this situation, the European Countries are implementing national policies as well as different measures to prevent the waste that can be avoidable and to promote measures to increase recycling and recovering. In this aspect, this article gives an overview of the amount of C&D waste generated in European countries, as well as the amount of this waste that is being recycled and the different measures that European countries have applied to solve this situation.

Characterization of vineyard's canopy through fuzzy clustering and svm over color images

In this work we propose an image acquisition and processing methodology (framework) developed for performance in-field grapes and leaves detection and quantification, based on a six step methodology: 1) image segmentation through Fuzzy C-Means with Gustafson Kessel (FCM-GK) clustering; 2) obtaining of FCM-GK outputs (centroids) for acting as seeding for K-Means clustering; 3) Identification of the clusters generated by K-Means using a Support Vector Machine (SVM) classifier. 4) Performance of morphological operations over the grapes and leaves clusters in order to fill holes and to eliminate small pixels clusters; 5)Creation of a mosaic image by Scale-Invariant Feature Transform (SIFT) in order to avoid overlapping between images; 6) Calculation of the areas of leaves and grapes and finding of the centroids in the grape bunches. Image data are collected using a colour camera fixed to a mobile platform. This platform was developed to give a stabilized surface to guarantee that the images were acquired parallel to de vineyard rows. In this way, the platform avoids the distortion of the images that lead to poor estimation of the areas. Our preliminary results are promissory, although they still have shown that it is necessary to implement a camera stabilization system to avoid undesired camera movements, and also a parallel processing procedure in order to speed up the mosaicking process.

Semi-supervised subspace clustering and applications to neuroscience

Machine learning techniques are used for extracting valuable knowledge from data. Nowa¬days, these techniques are becoming even more important due to the evolution in data ac¬quisition and storage, which is leading to data with different characteristics that must be exploited. Therefore, advances in data collection must be accompanied with advances in machine learning techniques to solve new challenges that might arise, on both academic and real applications. There are several machine learning techniques depending on both data characteristics and purpose. Unsupervised classification or clustering is one of the most known techniques when data lack of supervision (unlabeled data) and the aim is to discover data groups (clusters) according to their similarity. On the other hand, supervised classification needs data with supervision (labeled data) and its aim is to make predictions about labels of new data. The presence of data labels is a very important characteristic that guides not only the learning task but also other related tasks such as validation. When only some of the available data are labeled whereas the others remain unlabeled (partially labeled data), neither clustering nor supervised classification can be used. This scenario, which is becoming common nowadays because of labeling process ignorance or cost, is tackled with semi-supervised learning techniques. This thesis focuses on the branch of semi-supervised learning closest to clustering, i.e., to discover clusters using available labels as support to guide and improve the clustering process. Another important data characteristic, different from the presence of data labels, is the relevance or not of data features. Data are characterized by features, but it is possible that not all of them are relevant, or equally relevant, for the learning process. A recent clustering tendency, related to data relevance and called subspace clustering, claims that different clusters might be described by different feature subsets. This differs from traditional solutions to data relevance problem, where a single feature subset (usually the complete set of original features) is found and used to perform the clustering process. The proximity of this work to clustering leads to the first goal of this thesis. As commented above, clustering validation is a difficult task due to the absence of data labels. Although there are many indices that can be used to assess the quality of clustering solutions, these validations depend on clustering algorithms and data characteristics. Hence, in the first goal three known clustering algorithms are used to cluster data with outliers and noise, to critically study how some of the most known validation indices behave. The main goal of this work is however to combine semi-supervised clustering with subspace clustering to obtain clustering solutions that can be correctly validated by using either known indices or expert opinions. Two different algorithms are proposed from different points of view to discover clusters characterized by different subspaces. For the first algorithm, available data labels are used for searching for subspaces firstly, before searching for clusters. This algorithm assigns each instance to only one cluster (hard clustering) and is based on mapping known labels to subspaces using supervised classification techniques. Subspaces are then used to find clusters using traditional clustering techniques. The second algorithm uses available data labels to search for subspaces and clusters at the same time in an iterative process. This algorithm assigns each instance to each cluster based on a membership probability (soft clustering) and is based on integrating known labels and the search for subspaces into a model-based clustering approach. The different proposals are tested using different real and synthetic databases, and comparisons to other methods are also included when appropriate. Finally, as an example of real and current application, different machine learning tech¬niques, including one of the proposals of this work (the most sophisticated one) are applied to a task of one of the most challenging biological problems nowadays, the human brain model¬ing. Specifically, expert neuroscientists do not agree with a neuron classification for the brain cortex, which makes impossible not only any modeling attempt but also the day-to-day work without a common way to name neurons. Therefore, machine learning techniques may help to get an accepted solution to this problem, which can be an important milestone for future research in neuroscience. Resumen Las técnicas de aprendizaje automático se usan para extraer información valiosa de datos. Hoy en día, la importancia de estas técnicas está siendo incluso mayor, debido a que la evolución en la adquisición y almacenamiento de datos está llevando a datos con diferentes características que deben ser explotadas. Por lo tanto, los avances en la recolección de datos deben ir ligados a avances en las técnicas de aprendizaje automático para resolver nuevos retos que pueden aparecer, tanto en aplicaciones académicas como reales. Existen varias técnicas de aprendizaje automático dependiendo de las características de los datos y del propósito. La clasificación no supervisada o clustering es una de las técnicas más conocidas cuando los datos carecen de supervisión (datos sin etiqueta), siendo el objetivo descubrir nuevos grupos (agrupaciones) dependiendo de la similitud de los datos. Por otra parte, la clasificación supervisada necesita datos con supervisión (datos etiquetados) y su objetivo es realizar predicciones sobre las etiquetas de nuevos datos. La presencia de las etiquetas es una característica muy importante que guía no solo el aprendizaje sino también otras tareas relacionadas como la validación. Cuando solo algunos de los datos disponibles están etiquetados, mientras que el resto permanece sin etiqueta (datos parcialmente etiquetados), ni el clustering ni la clasificación supervisada se pueden utilizar. Este escenario, que está llegando a ser común hoy en día debido a la ignorancia o el coste del proceso de etiquetado, es abordado utilizando técnicas de aprendizaje semi-supervisadas. Esta tesis trata la rama del aprendizaje semi-supervisado más cercana al clustering, es decir, descubrir agrupaciones utilizando las etiquetas disponibles como apoyo para guiar y mejorar el proceso de clustering. Otra característica importante de los datos, distinta de la presencia de etiquetas, es la relevancia o no de los atributos de los datos. Los datos se caracterizan por atributos, pero es posible que no todos ellos sean relevantes, o igualmente relevantes, para el proceso de aprendizaje. Una tendencia reciente en clustering, relacionada con la relevancia de los datos y llamada clustering en subespacios, afirma que agrupaciones diferentes pueden estar descritas por subconjuntos de atributos diferentes. Esto difiere de las soluciones tradicionales para el problema de la relevancia de los datos, en las que se busca un único subconjunto de atributos (normalmente el conjunto original de atributos) y se utiliza para realizar el proceso de clustering. La cercanía de este trabajo con el clustering lleva al primer objetivo de la tesis. Como se ha comentado previamente, la validación en clustering es una tarea difícil debido a la ausencia de etiquetas. Aunque existen muchos índices que pueden usarse para evaluar la calidad de las soluciones de clustering, estas validaciones dependen de los algoritmos de clustering utilizados y de las características de los datos. Por lo tanto, en el primer objetivo tres conocidos algoritmos se usan para agrupar datos con valores atípicos y ruido para estudiar de forma crítica cómo se comportan algunos de los índices de validación más conocidos. El objetivo principal de este trabajo sin embargo es combinar clustering semi-supervisado con clustering en subespacios para obtener soluciones de clustering que puedan ser validadas de forma correcta utilizando índices conocidos u opiniones expertas. Se proponen dos algoritmos desde dos puntos de vista diferentes para descubrir agrupaciones caracterizadas por diferentes subespacios. Para el primer algoritmo, las etiquetas disponibles se usan para bus¬car en primer lugar los subespacios antes de buscar las agrupaciones. Este algoritmo asigna cada instancia a un único cluster (hard clustering) y se basa en mapear las etiquetas cono-cidas a subespacios utilizando técnicas de clasificación supervisada. El segundo algoritmo utiliza las etiquetas disponibles para buscar de forma simultánea los subespacios y las agru¬paciones en un proceso iterativo. Este algoritmo asigna cada instancia a cada cluster con una probabilidad de pertenencia (soft clustering) y se basa en integrar las etiquetas conocidas y la búsqueda en subespacios dentro de clustering basado en modelos. Las propuestas son probadas utilizando diferentes bases de datos reales y sintéticas, incluyendo comparaciones con otros métodos cuando resulten apropiadas. Finalmente, a modo de ejemplo de una aplicación real y actual, se aplican diferentes técnicas de aprendizaje automático, incluyendo una de las propuestas de este trabajo (la más sofisticada) a una tarea de uno de los problemas biológicos más desafiantes hoy en día, el modelado del cerebro humano. Específicamente, expertos neurocientíficos no se ponen de acuerdo en una clasificación de neuronas para la corteza cerebral, lo que imposibilita no sólo cualquier intento de modelado sino también el trabajo del día a día al no tener una forma estándar de llamar a las neuronas. Por lo tanto, las técnicas de aprendizaje automático pueden ayudar a conseguir una solución aceptada para este problema, lo cual puede ser un importante hito para investigaciones futuras en neurociencia.

A data-globe and immersive virtual reality environment for upper limb rehabilitation after spinal cord injury

While a number of virtual data-gloves have been used in stroke, there is little evidence about their use in spinal cord injury (SCI). A pilot clinical experience with nine SCI subjects was performed comparing two groups: one carried out a virtual rehabilitation training based on the use of a data glove, CyberTouch combined with traditional rehabilitation, during 30 minutes a day twice a week along two weeks; while the other made only conventional rehabilitation. Furthermore, two functional indexes were developed in order to assess the patient’s performance of the sessions: normalized trajectory lengths and repeatability. While differences between groups were not statistically significant, the data-glove group seemed to obtain better results in the muscle balance and functional parameters, and in the dexterity, coordination and fine grip tests. Related to the indexes that we implemented, normalized trajectory lengths and repeatability, every patient showed an improvement in at least one of the indexes, either along Y-axis trajectory or Z-axis trajectory. This study might be a step in investigating new ways of treatments and objective measures in order to obtain more accurate data about the patient’s evolution, allowing the clinicians to develop rehabilitation treatments, adapted to the abilities and needs of the patients.

Impairment of synaptic vesicle clustering and of synaptic transmission, and increased seizure propensity, in synapsin I-deficient mice.

Synapsin I has been proposed to be involved in the modulation of neurotransmitter release by controlling the availability of synaptic vesicles for exocytosis. To further understand the role of synapsin I in the function of adult nerve terminals, we studied synapsin I-deficient mice generated by homologous recombination. The organization of synaptic vesicles at presynaptic terminals of synapsin I-deficient mice was markedly altered: densely packed vesicles were only present in a narrow rim at active zones, whereas the majority of vesicles were dispersed throughout the terminal area. This was in contrast to the organized vesicle clusters present in terminals of wild-type animals. Release of glutamate from nerve endings, induced by K+,4-aminopyridine, or a Ca2+ ionophore, was markedly decreased in synapsin I mutant mice. The recovery of synaptic transmission after depletion of neurotransmitter by high-frequency stimulation was greatly delayed. Finally, synapsin I-deficient mice exhibited a strikingly increased response to electrical stimulation, as measured by electrographic and behavioral seizures. These results provide strong support for the hypothesis that synapsin I plays a key role in the regulation of nerve terminal function in mature synapses.

Competing Lipid-Protein and Protein-Protein Interactions Determine Clustering and Gating Patterns in the Potassium Channel from Streptomyces lividans (KcsA)

There is increasing evidence to support the notion that membrane proteins, instead of being isolated components floating in a fluid lipid environment, can be assembled into supramolecular complexes that take part in a variety of cooperative cellular functions. The interplay between lipid-protein and protein-protein interactions is expected to be a determinant factor in the assembly and dynamics of such membrane complexes. Here we report on a role of anionic phospholipids in determining the extent of clustering of KcsA, a model potassium channel. Assembly/disassembly of channel clusters occurs, at least partly, as a consequence of competing lipid-protein and protein-protein interactions at nonannular lipid binding sites on the channel surface and brings about profound changes in the gating properties of the channel. Our results suggest that these latter effects of anionic lipids are mediated via the Trp67–Glu71–Asp80 inactivation triad within the channel structure and its bearing on the selectivity filter.

Impact of lung function testing on hemodynamic values : concordance between non-invasive and invasive measures

Trabalho Final do Curso de Mestrado Integrado em Medicina, Faculdade de Medicina, Universidade de Lisboa, 2014