“Gender, Race and Interculturalism in TNT-El Vacie’s Romani Fuenteovejuna.”

This paper addresses the performance of Lope de Vega’s Fuenteovejuna undertaken by TNT-El Vacie (Territorio de Nuevos Tiempos), a theater company from Seville, Spain. I Consider how Antonio Álamo’s adaptation, Pepa Gamboa’s direction, and the staging of El Vacie, a company comprised exclusively of non-professional Roma women, constitutes a site of resistance as well as a performance of gender, race, and place. From this vantage point, I wrestle with issues of agency, dialogism and intercultural communication in order to make sense of ontological as well as hermeneutic aspects of the performance text: deletions and additions; orality and authority; acting and embodying. Furthermore, the fact that El Vacie is also the name of the impoverished settlement where the women reside, within a mere 200 yards of the theatre where the play is performed, enriches the text and further complicates their reading of Fuenteovejuna by adding levels of referentiality and indexicality that redefine boundaries, and processes of exclusion and inclusion. While this is not the first collaboration between TNT and El Vacie—it was preceded by the successful and awarded 2009 staging of La Casa de Bernarda Alba —I consider this performance a particularly important contribution to the construction of a historicized cultural politics of identity that makes the Romani community visible by positioning the women of El Vacie center stage as participants, creators, and interpreters of the national cultural patrimony. Through this cultural intervention, certain spaces and markers of the Roma community acquire visibility as they get inserted into a national historical discourse from which they previously had been historically excluded

Assembly, super-assembly and impared assembly of the mitochondrial electron trasport chain: in situ validation of the plasticity model

The oxidative phosphorylation system (OXPHOS) comprises three fundamental processes: electron transport, proton pumping and ATP synthesis. The OXPHOS system is organized as a branched chain of multi-protein complexes that can be assembled into supra-molecular structures (supercomplexes) to optimize the utilization of the different sources of electrons. We have proposed the plasticity model as a dynamic model where free and super-assembled RCs may coexist and be functional. This model is supported by the disruption of the mitochondrial membranes with mild detergents and visualization of supercomplexes (SCs) by blue native electrophoresis (BNGE) extracted from cell lines or tissues. In this thesis, we have developed an innovative and robust approach to visualize and quantitatively estimate the proximity of the mitochondrial complexes and SCs (I/III, III/IV, I/IV and I/III/IV) in intact cells, without the use of detergents. For that purpose, we have analyzed different combinations of mitochondrial endogenous subunits by Stimulated Emission Depletion super resolution microscopy (STED) using a variety of cellular tools: mtDNA depleted cells (º), complex III (CYTbM) and complex IV (Cox10KO) depleted cell lines, and their respective isogenic controls. Moreover, we have used different immunolabelling combinations to tag RCs and SCs (CI/CIII, CI/CIV, CIII/CIV and CI/CIII/CIV). Thus, STED imaging reveals the co-existence of free and superassembled complexes in intact cells demonstrating in situ that the cellular organization of the mitochondrial respiratory chain are correctly represented by the plasticity model. On the other hand, It is known that mutations in genes encoding subunits of the mitochondrial complexes may affect the stability of other complexes. Therefore, as a second main aim, we investigate the molecular mechanism that allows CIII mutants, to suppress the effect of mutations, which impede the assembly of respiratory complexes in normal circumstances

The macular retinal ganglion cell layer as a biomarker for diagnosis and prognosis in multiple sclerosis: A deep learning approach

AbstractPurposeThe macular ganglion cell layer (mGCL) is a strong potential biomarker of axonal degeneration in multiple sclerosis (MS). For this reason, this study aims to develop a computer‐aided method to facilitate diagnosis and prognosis in MS.MethodsThis paper combines a cross‐sectional study of 72 MS patients and 30 healthy control subjects for diagnosis and a 10‐year longitudinal study of the same MS patients for the prediction of disability progression, during which the mGCL was measured using optical coherence tomography (OCT). Deep neural networks were used as an automatic classifier.ResultsFor MS diagnosis, greatest accuracy (90.3%) was achieved using 17 features as inputs. The neural network architecture comprised the input layer, two hidden layers and the output layer with softmax activation. For the prediction of disability progression 8 years later, accuracy of 81.9% was achieved with a neural network comprising two hidden layers and 400 epochs.ConclusionWe present evidence that by applying deep learning techniques to clinical and mGCL thickness data it is possible to identify MS and predict the course of the disease. This approach potentially constitutes a non‐invasive, low‐cost, easy‐to‐implement and effective method

Comparison of Retinal Nerve Fiber Layer Thickness Measurements in Healthy Subjects Using Fourier and Time Domain Optical Coherence Tomography

Purpose. To compare the retinal nerve fiber layer (RNFL) measurements using two different ocular coherence tomography (OCT) devices: Cirrus Fourier domain OCT and Stratus time domain OCT. To analyze reproducibility of Fourier domain measurements in healthy subjects. Methods. One hundred and thirty-two eyes of 132 healthy subjects were scaned on the same day with both instruments, separated by 10 minutes from each other. Thickness of quadrant, average and the 12 different areas around the optic nerve were compared between Cirrus and Stratus. Repeatability, intraclass correlation coefficients (ICCs), and coefficients of variation (COVs) were calculated in RNFL measurements provided by Fourier domain device. Results. The average thickness in the optic cube was 95.50 μm using Cirrus and 97.85 μm using Stratus. Average thickness and temporal quadrant showed significant differences using Cirrus and Stratus methods. Reproducibility was better with Fourier domain OCT (mean COV of 4.54%) than with Stratus time-domain OCT (mean COV of 5.57%). Conclusions. Both scan options give reproducible RNFL thickness measurement, but there are differences between them. Measurements obtained using Fourier domain device show better reproducibility

Explainable artificial intelligence toward usable and trustworthy computer-aided early diagnosis of multiple sclerosis from Optical Coherence Tomography

Background: Several studies indicate that the anterior visual pathway provides information about the dynamics of axonal degeneration in Multiple Sclerosis (MS). Current research in the field is focused on the quest for the most discriminative features among patients and controls and the development of machine learning models that yield computer-aided solutions widely usable in clinical practice. However, most studies are conducted with small samples and the models are used as black boxes. Clinicians should not trust machine learning decisions unless they come with comprehensive and easily understandable explanations. Materials and methods: A total of 216 eyes from 111 healthy controls and 100 eyes from 59 patients with relapsing-remitting MS were enrolled. The feature set was obtained from the thickness of the ganglion cell layer (GCL) and the retinal nerve fiber layer (RNFL). Measurements were acquired by the novel Posterior Pole protocol from Spectralis Optical Coherence Tomography (OCT) device. We compared two black-box methods (gradient boosting and random forests) with a glass-box method (explainable boosting machine). Explainability was studied using SHAP for the black-box methods and the scores of the glass-box method. Results: The best-performing models were obtained for the GCL layer. Explainability pointed out to the temporal location of the GCL layer that is usually broken or thinning in MS and the relationship between low thickness values and high probability of MS, which is coherent with clinical knowledge. Conclusions: The insights on how to use explainability shown in this work represent a first important step toward a trustworthy computer-aided solution for the diagnosis of MS with OCT

Explainable artificial intelligence toward usable and trustworthy computer-aided diagnosis of multiple sclerosis from Optical Coherence Tomography

Background: Several studies indicate that the anterior visual pathway provides information about the dynamics of axonal degeneration in Multiple Sclerosis (MS). Current research in the field is focused on the quest for the most discriminative features among patients and controls and the development of machine learning models that yield computer-aided solutions widely usable in clinical practice. However, most studies are conducted with small samples and the models are used as black boxes. Clinicians should not trust machine learning decisions unless they come with comprehensive and easily understandable explanations. Materials and methods: A total of 216 eyes from 111 healthy controls and 100 eyes from 59 patients with relapsing-remitting MS were enrolled. The feature set was obtained from the thickness of the ganglion cell layer (GCL) and the retinal nerve fiber layer (RNFL). Measurements were acquired by the novel Posterior Pole protocol from Spectralis Optical Coherence Tomography (OCT) device. We compared two black-box methods (gradient boosting and random forests) with a glass-box method (explainable boosting machine). Explainability was studied using SHAP for the black-box methods and the scores of the glass-box method. Results: The best-performing models were obtained for the GCL layer. Explainability pointed out to the temporal location of the GCL layer that is usually broken or thinning in MS and the relationship between low thickness values and high probability of MS, which is coherent with clinical knowledge.Conclusions: The insights on how to use explainability shown in this work represent a first important step toward a trustworthy computer-aided solution for the diagnosis of MS with OCT

Neuro-Ophthalmologic Evaluation as a Biomarker for Diagnosis and Progression in Parkinson Disease

Objectives: The purpose of current neuro-ophthalmologic research is to evaluate visual dysfunction and its correlation with structural changes in the retina of patients with Parkinson’s disease and to examine whether there is an association between retinal thinning and disease progression

N+3 Small Commercial Efficient and Quiet Transportation for Year 2030-2035

This study develops a future scenario that enables convenient point-to-point commercial air travel via a large network of community airports and a new class of small airliners. A network demand and capacity study identifies current and future air travel demands and the capacity of this new network to satisfy these demands. A current technology small commercial airliner is defined to meet the needs of the new network, as a baseline for evaluating the improvement brought about by advanced technologies. Impact of this new mode of travel on the infrastructure and surrounding communities of the small airports in this new N+3 network are also evaluated. Year 2030-2035 small commercial airliner technologies are identified and a trade study conducted to evaluate and select those with the greatest potential for enhancing future air travel and the study metrics. The selected advanced air vehicle concept is assessed against the baseline aircraft, and an advanced, but conventional aircraft, and the study metrics. The key technologies of the selected advanced air vehicle are identified, their impact quantified, and risk assessments and roadmaps defined

Polychronous Interpretation of Synoptic, a Domain Specific Modeling Language for Embedded Flight-Software

The SPaCIFY project, which aims at bringing advances in MDE to the satellite flight software industry, advocates a top-down approach built on a domain-specific modeling language named Synoptic. In line with previous approaches to real-time modeling such as Statecharts and Simulink, Synoptic features hierarchical decomposition of application and control modules in synchronous block diagrams and state machines. Its semantics is described in the polychronous model of computation, which is that of the synchronous language Signal.Comment: Workshop on Formal Methods for Aerospace (FMA 2009