994 resultados para vector processing
Resumo:
We present a novel filtering method for multispectral satellite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments carried out on multiclass one-against-all classification and target detection show the capabilities of the learned spatial filters.
Resumo:
The joint angles of multi-segment foot models have been primarily described using two mathematical methods: the joint coordinate system and the attitude vector. This study aimed to determine whether the angles obtained through these two descriptors are comparable, and whether these descriptors have similar sensitivity to experimental errors. Six subjects walked eight times on an instrumented walkway while the joint angles among shank, hindfoot, medial forefoot, and lateral forefoot were measured. The angles obtained using both descriptors and their sensitivity to experimental errors were compared. There was no overall significant difference between the ranges of motion obtained using both descriptors. However, median differences of more than 6° were noticed for the medial-lateral forefoot joint. For all joints and rotation planes, both descriptors provided highly similar angle patterns (median correlation coefficient: R>0.90), except for the medial-lateral forefoot angle in the transverse plane (median R=0.77). The joint coordinate system was significantly more sensitive to anatomical landmarks misplacement errors. However, the absolute differences of sensitivity were small relative to the joints ranges of motion. In conclusion, the angles obtained using these two descriptors were not identical, but were similar for at least the shank-hindfoot and hindfoot-medial forefoot joints. Therefore, the angle comparison across descriptors is possible for these two joints. Comparison should be done more carefully for the medial-lateral forefoot joint. Moreover, despite different sensitivities to experimental errors, the effects of the experimental errors on the angles were small for both descriptors suggesting that both descriptors can be considered for multi-segment foot models.
Resumo:
Standard practice in Bayesian VARs is to formulate priors on the autoregressive parameters, but economists and policy makers actually have priors about the behavior of observable variables. We show how this kind of prior can be used in a VAR under strict probability theory principles. We state the inverse problem to be solved and we propose a numerical algorithm that works well in practical situations with a very large number of parameters. We prove various convergence theorems for the algorithm. As an application, we first show that the results in Christiano et al. (1999) are very sensitive to the introduction of various priors that are widely used. These priors turn out to be associated with undesirable priors on observables. But an empirical prior on observables helps clarify the relevance of these estimates: we find much higher persistence of output responses to monetary policy shocks than the one reported in Christiano et al. (1999) and a significantly larger total effect.
Resumo:
Telomerase is a ribonucleoprotein complex responsible for the maintenance of the length of the telomeres during cell division, which is active in germ-line cells as well as in the vast majority of tumors but not in most normal tissues. The wide expression of the human telomerase catalytic subunit (hTERT) in tumors makes it an interesting candidate vaccine for cancer. hTERT-derived peptide 540-548 (hTERT(540)) has been recently shown to be recognized in an HLA-A*0201-restricted fashion by T cell lines derived from peptide-stimulated peripheral blood mononuclear cells (PBMC) from healthy donors. As a first step to the inclusion of this peptide in immunotherapy clinical trials, it is crucial to assess hTERT(540)-specific T cell reactivity in cancer patients as well as the ability of hTERT-specific CD8(+) T lymphocytes to recognize and lyse hTERT-expressing target cells. Here, we have analyzed the CD8(+) T cell response to peptide hTERT(540) in HLA-A*0201 melanoma patients by using fluorescent HLA-A*0201/hTERT(540) peptide tetramers. HLA-A*0201/hTERT(540) tetramer(+) CD8(+) T cells were readily detected in peptide-stimulated PBMC from a significant proportion of patients and could be isolated by tetramer-guided cell sorting. hTERT(540)-specific CD8(+) T cells were able to specifically recognize HLA-A*0201 cells either pulsed with peptide or transiently transfected with a minigene encoding the minimal epitope. In contrast, they failed to recognize hTERT-expressing HLA-A*0201(+) target cells. Furthermore, in vitro proteasome digestion studies revealed inadequate hTERT processing. Altogether, these results raise questions on the use of hTERT(540) peptide for cancer immunotherapy.
Resumo:
Olfactory processes were reported to be lateralized. The purpose of this study was to further explore this phenomenon and investigate the effect of the hemispheric localization of epileptogenic foci on olfactory deficits in patients with temporal lobe epilepsy (TLE). Olfactory functioning was assessed in 61 patients and 60 healthy control (HC) subjects. The patients and HC subjects were asked to rate the intensity, pleasantness, familiarity, and edibility of 12 common odorants and then identify them. Stimulations were delivered monorhinally in the nostril ipsilateral to the epileptogenic focus in TLE and arbitrarily in either the left or the right nostril in the HC subjects. The results demonstrated that regardless of the side of stimulation, patients with TLE had reduced performance in all olfactory tasks compared with the HC subjects. With regard to the side of the epileptogenic focus, patients with left TLE judged odors as less pleasant and had more difficulty with identification than patients with right TLE, underlining a privileged role of the left hemisphere in the emotional and semantic processing of odors. Finally, irrespective of group, a tendency towards a right-nostril advantage for judging odor familiarity was found in agreement with a prominent role of the right hemisphere in odor memory processing.
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Estudi realitzat a partir d’una estada al Institut de Génétique Moléculaire de Montpellier, França, entre 2010 i 2012. En aquest projecte s’ha avaluat les avantatges dels vectors adenovirals canins tipus 2 (CAV2) com a vectors de transferència gènica al sistema nerviós central (SNC) en un model primat no-humà i en un model caní del síndrome de Sly (mucopolisacaridosis tipus 7, MPS VII), malaltia monogènica que cursa amb neurodegeneració. En una primera part del projecte s’ha avaluat la biodistribució, l’eficàcia i la durada de l’expressió del transgen en un model primat no humà, (Microcebus murinus). Com ha vector s’ha utilitzat un CAV2 de primera generació que expressa la proteïna verda fluorescent (CAVGFP). Els resultats aportats en aquesta memòria demostren que en primats no humans, com en d’altres espècies testades anteriorment per l’equip de l’EJ Kremer, la injecció intracerebral de CAV2 resulta en una extensa transducció del SNC, siguent les neurones i els precursors neuronals les cèl•lules preferencialment transduïdes. Els vectors canins, servint-se de vesícules intracel•lulars són transportats, majoritàriament, des de les sinapsis cap al soma neuronal, aquest transport intracel•lular permet una extensa transducció del SNC a partir d’una única injecció intracerebral dels vectors virals. En una segona part d’aquest projecte s’ha avaluat l’ús terapèutic dels CAV2. S’ha injectat un vector helper-dependent que expressa el gen la b-glucuronidasa i el gen de la proteïna verda fluorescent (HD-RIGIE), en el SNC del model caní del síndrome de Sly (MPS VII). La biodistribució i la eficàcia terapèutica han estat avaluades. Els nivells d’activitat enzimàtica en animals malalts injectats amb el vector terapèutic va arribar a valors similars als dels animals no afectes. A més a més s’ha observat una reducció en la quantitat dels GAGs acumulats en les cèl•lules dels animals malalts tractats amb el vector terapèutic, demostrant la potencialitat terapèutica dels CAV2 per a malalties que afecten al SNC. Els resultats aportats en aquest treball ens permeten dir que els CAV2 són unes bones eines terapèutiques per al tractament de malalties que afecten al SNC.
Resumo:
Induced pluripotent stem (iPS) cells have generated keen interestdue to their potential use in regenerative medicine. They havebeen obtained from various cell types of both mice and humans byexogenous delivery of different combinations of Oct4, Sox2, Klf4,c-Myc, Nanog, and Lin28. The delivery of these transcription factorshas mostly entailed the use of integrating viral vectors (retrovirusesor lentiviruses), carrying the risk of both insertional mutagenesisand oncogenesis due to misexpression of these exogenousfactors. Therefore, obtaining iPS cells that do not carry integratedtransgene sequences is an important prerequisite for their eventualtherapeutic use. Here we report the generation of iPS cell linesfrom mouse embryonic fibroblasts with no evidence of integrationof the reprogramming vector in their genome, achieved by nucleofectionof a polycistronic construct coexpressing Oct4, Sox2, Klf4,and c-Myc
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Stylized facts regarding the industrial process include emphases on obtaining information about and control over the quality of raw materials. We provide a model that establishes conditions under which informed control involves ensuring uniformity in inputs and increased uniformity encourages more extensive processing. We show when the Boltzmann-Shannon entropy statistic is an appropriate measure of uniformity.
Resumo:
The increasing volume of data describing humandisease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the@neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system’s architecture is generic enough that it could be adapted to the treatment of other diseases.Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers cliniciansthe tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medicalresearchers gain access to a critical mass of aneurysm related data due to the system’s ability to federate distributed informationsources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access andwork on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand forperforming computationally intensive simulations for treatment planning and research.
Resumo:
The spectral efficiency achievable with joint processing of pilot and data symbol observations is compared with that achievable through the conventional (separate) approach of first estimating the channel on the basis of the pilot symbols alone, and subsequently detecting the datasymbols. Studied on the basis of a mutual information lower bound, joint processing is found to provide a non-negligible advantage relative to separate processing, particularly for fast fading. It is shown that, regardless of the fading rate, only a very small number of pilot symbols (at most one per transmit antenna and per channel coherence interval) shouldbe transmitted if joint processing is allowed.
Resumo:
Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.
Resumo:
In this article we present a hybrid approach for automatic summarization of Spanish medical texts. There are a lot of systems for automatic summarization using statistics or linguistics, but only a few of them combining both techniques. Our idea is that to reach a good summary we need to use linguistic aspects of texts, but as well we should benefit of the advantages of statistical techniques. We have integrated the Cortex (Vector Space Model) and Enertex (statistical physics) systems coupled with the Yate term extractor, and the Disicosum system (linguistics). We have compared these systems and afterwards we have integrated them in a hybrid approach. Finally, we have applied this hybrid system over a corpora of medical articles and we have evaluated their performances obtaining good results.