Transcription Factor Activity Inference in Systemic Lupus Erythematosus

Background: Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease with diverse clinical manifestations. Although most of the SLE-associated loci are located in regulatory regions, there is a lack of global information about transcription factor (TFs) activities, the mode of regulation of the TFs, or the cell or sample-specific regulatory circuits. The aim of this work is to decipher TFs implicated in SLE. Methods: In order to decipher regulatory mechanisms in SLE, we have inferred TF activities from transcriptomic data for almost all human TFs, defined clusters of SLE patients based on the estimated TF activities and analyzed the differential activity patterns among SLE and healthy samples in two different cohorts. The Transcription Factor activity matrix was used to stratify SLE patients and define sets of TFs with statistically significant differential activity among the disease and control samples. Results: TF activities were able to identify two main subgroups of patients characterized by distinct neutrophil-to-lymphocyte ratio (NLR), with consistent patterns in two independent datasets—one from pediatric patients and other from adults. Furthermore, after contrasting all subgroups of patients and controls, we obtained a significant and robust list of 14 TFs implicated in the dysregulation of SLE by different mechanisms and pathways. Among them, well-known regulators of SLE, such as STAT or IRF, were found, but others suggest new pathways that might have important roles in SLE. Conclusions: These results provide a foundation to comprehend the regulatory mechanism underlying SLE and the established regulatory factors behind SLE heterogeneity that could be potential therapeutic targets.Innovative Medicines Initiative 2 Joint Undertaking (JU) - 831434 (3TR)European Union’s Horizon 2020 research and innovation program and EFPIANIH AR69572 and NIH RO-1 grant AR06957

Síntesis sostenible de Metal-Organic Frameworks (MOF)

Treballs Finals de Grau de Química, Facultat de Química, Universitat de Barcelona, Any: 2016, Tutors: Montserrat Corbella Cordomí, Gerard Tobias RossellThe research presented in this work investigates the use of green solvents in the synthesisof a new-class of hybrid porous materials, named metal-organic frameworks (MOFs), which are formed by the combination of metallic centres and organic linkers. The fluid technology based on supercritical carbon dioxide (scCO2) has been studied as a promising alternative for safer and cleaner synthesis over the conventional solvothermal conditions. In particular, the specific goal stablished for this research is to successfully synthesize two widely-known compounds with MOF structures denominated HKUST-1 and ZIF-8 that will demonstrate the applicability of the method. The reactions were carried out in a reactor vessel at the conditions of 200 bar, 70 ºC for 20 hours in a medium of scCO2 with selected additives. The reactant quantities were used in stoichiometric ratios according to the formula unit of each MOF. The physicochemical and textural properties of the prepared samples were measured and compared to those obtained from conventional methods. The two products have been successfully synthesized with a high-degree of purity as demonstrated by the elemental analysis. The structure of HKUST-1 corresponds to a face-centred cubic lattice with an observed surface area of 1290 m2·g-1, in accordance with previous reported data. ZIF-8 was also obtained with the same structure than the one described in the literature, but with outstanding values of surface area (1730 m2·g-1), which overpass most of the values reported with other synthetic approaches. In the last part of this report, an explanation based on the ultraviolet-visible (UV-Vis) spectra and the Crystal Field Theory is provided as a tool to understand the observed colour change in HKUST-

Odontoameloblastoma: descripción de un caso y revisión de la literatura

El odontoameloblastoma (OA) es un tumor odontogénico mixto extremadamente raro que aparece en los huesos maxilares y que presenta tanto componentes epiteliales como mesenquimales. El término odontoameloblastoma fue incluido en la clasificación de 1971 de la OMS. Tan solo 23 casos bien documentados han sido publicados. Debido a su rareza, existe controversia en cuanto al tratamiento de este tumor. Presentamos un nuevo caso de OA que afecta a la mandíbula y simula un odontoma compuesto, así como una breve revisión de la literatura.Odontoameloblastoma (OA) is an extremely rare mixed odontogenic tumor appearing within the maxillary bone, with both epithelial and mesenchymal components. The term odontoameloblastoma (OA) was included in the 1971's WHO classification. Only 23 well-documented cases have been reported in the medical literature. Because of their rarity, controversy exists in the treatment of this tumor. We present a new case of OA involving the mandible mimicking a compound odontoma and a brief review of the related literature

Frame-by-frame language identification in short utterances using deep neural networks

This is the author’s version of a work that was accepted for publication in Neural Networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neural Networks, VOL 64, (2015) DOI 10.1016/j.neunet.2014.08.006This work addresses the use of deep neural networks (DNNs) in automatic language identification (LID) focused on short test utterances. Motivated by their recent success in acoustic modelling for speech recognition, we adapt DNNs to the problem of identifying the language in a given utterance from the short-term acoustic features. We show how DNNs are particularly suitable to perform LID in real-time applications, due to their capacity to emit a language identification posterior at each new frame of the test utterance. We then analyse different aspects of the system, such as the amount of required training data, the number of hidden layers, the relevance of contextual information and the effect of the test utterance duration. Finally, we propose several methods to combine frame-by-frame posteriors. Experiments are conducted on two different datasets: the public NIST Language Recognition Evaluation 2009 (3 s task) and a much larger corpus (of 5 million utterances) known as Google 5M LID, obtained from different Google Services. Reported results show relative improvements of DNNs versus the i-vector system of 40% in LRE09 3 second task and 76% in Google 5M LID

An Automated Fall Detection System Using Recurrent Neural Networks

Falls are the most common cause of fatal injuries in elderly people, causing even death if there is no immediate assistance. Fall detection systems can be used to alert and request help when this type of accident happens. Certain types of these systems include wearable devices that analyze bio-medical signals from the person carrying it in real time. In this way, Deep Learning algorithms could automate and improve the detection of unintentional falls by analyzing these signals. These algorithms have proven to achieve high effectiveness with competitive performances in many classification problems. This work aims to study 16 Recurrent Neural Networks architectures (using Long Short-Term Memory and Gated Recurrent Units) for falls detection based on accelerometer data, reducing computational requirements of previous research. The architectures have been tested on a labeled version of the publicly available SisFall dataset, achieving a mean F1-score above 0.73 and improving state-of-the-art solutions in terms of network complexity.Ministerio de Economía y Competitivida TEC2016-77785-

Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks

Breast cancer is one of the most frequent causes of mortality in women. For the early detection of breast cancer, the mammography is used as the most efficient technique to identify abnormalities such as tumors. Automatic detection of tumors in mammograms has become a big challenge and can play a crucial role to assist doctors in order to achieve an accurate diagnosis. State-of-the-art Deep Learning algorithms such as Faster Regional Convolutional Neural Networks are able to determine the presence of an object and also its position inside the image in a reduced computation time. In this work, we evaluate these algorithms to detect tumors in mammogram images and propose a detection system that contains: (1) a preprocessing step performed on mammograms taken from the Digital Database for Screening Mammography (DDSM) and (2) the Neural Network model, which performs feature extraction over the mammograms in order to locate tumors within each image and classify them as malignant or benign. The results obtained show that the proposed algorithm has an accuracy of 97.375%. These results show that the system could be very useful for aiding physicians when detecting tumors from mammogram images.Ministerio de Economía y Competitividad TEC2016-77785-

Application of catalytic wet peroxide oxidation for sunscreen agents breakdown

Sunscreen agents are chemical compounds widely used nowadays for skin protection from UV sunlight. Recently, their ubiquitous occurrence in aquatic systems has been evidenced, which poses a high risk for the environment and human health as they are associated with endocrine disrupting activity, reproductive toxicity, and genotoxicity. In this work, the feasibility of an economically and environmentally friendly catalytic system based on the thermally modified natural magnetite and hydrogen peroxide (Fe3O4-R400/H2O2), has been evaluated for the degradation of two representative sunscreen agents: benzophenone-3 (BP-3) and 4-aminobenzoic acid (PABA) in wastewater. The experiments were conducted under circumneutral pH (pH0 = 5), with temperature control (25 ◦C). Both compounds (500 μg L− 1 ) were successfully removed from water by using a relatively low catalyst concentration (0.5 g L− 1 ) and the theoretical stoichiometric H2O2 dose for their complete oxidation (~2.3 mg L− 1 ). Afterwards, a complete operating condition study was performed with BP-3, given its predominant occurrence in fresh waters, analysing the influence of H2O2 dose (1.2–4.6 mg L− 1 ), catalyst concentration (0.1–0.5 g L− 1 ), and temperature (25–45 ◦C). From the evolution of the identified by-products, a reaction pathway was proposed according to which oxidation of BP-3 gives rise to several aromatic intermediates, which finally evolve to short-chain organic acids. The generation of such aromatic by-products led to a considerably ecotoxicity increase in the initial stages of the reaction, but non-toxic effluents were ultimately achieved. Notably, the mineralization yield reached was above 60%. As a proof of concept, the feasibility of the system was finally demonstrated in real water matrices (WWTP effluent and surface water)PID2019-105079RB-I00, P2018/EMT-434

Multi-dataset Training for Medical Image Segmentation as a Service

Deep Learning tools are widely used for medical image segmentation. The results produced by these techniques depend to a great extent on the data sets used to train the used network. Nowadays many cloud service providers offer the required resources to train networks and deploy deep learning networks. This makes the idea of segmentation as a cloud-based service attractive. In this paper we study the possibility of training, a generalized configurable, Keras U-Net to test the feasibility of training with images acquired, with specific instruments, to perform predictions on data from other instruments. We use, as our application example, the segmentation of Optic Disc and Cup which can be applied to glaucoma detection. We use two publicly available data sets (RIM-One V3 and DRISHTI) to train either independently or combining their data.Ministerio de Economía y Competitividad TEC2016-77785-

Simulation, construction and characterization of a piezoelectric transducer using rexolite as acoustic matching

This article describes the simulation and characterization of an ultrasonic transducer using a new material called Rexolite to be used as a matching element. This transducer was simulated using a commercial piezoelectric ceramic PIC255 at 8 MHz. Rexolite, the new material, presents an excellent acoustic matching, specially in terms of the acoustic impedance of water. Finite elements simulations were used in this work. Rexolite was considered as a suitable material in the construction of the transducer due to its malleability and acoustic properties, to validate the simulations a prototype transducer was constructed. Experimental measurements were used to determine the resonance frequency of the prototype transducer. Simulated and experimental results were very similar showing that Rexolite may be an excellent matching, particularly for medical applications