¿Es Global o Local la Investigación? La Proliferación Situada de Polímeros, Transgénicos y Colectivos

El presente artículo presenta la construcción sociotécnica de dos experiencias de investigación: polímeros cerámicas y papas transgénicas. Ambas tienen por objeto constituirse en una alternativa de análisis a los estudios que resaltan el papel fundamental de la tecnología en la explicación de la globalización. El trabajo muestra dos etnografías de laboratorio. En el caso de los polímeros se muestra cómo la construcción de un saber lo cal se expande internacionalmente; en el segundo, las papas transgénicas, siendo un saber que ha comenzado a ser conocido mundialmente, es necesario situarlo localmente en su proceso de investigación.El presente artículo presenta la construcción sociotécnica de dos experiencias de investigación: polímeros cerámicas y papas transgénicas. Ambas tienen por objeto constituirse en una alternativa de análisis a los estudios que resaltan el papel fundamental de la tecnología en la explicación de la globalización. El trabajo muestra dos etnografías de laboratorio. En el caso de los polímeros se muestra cómo la construcción de un saber lo cal se expande internacionalmente; en el segundo, las papas transgénicas, siendo un saber que ha comenzado a ser conocido mundialmente, es necesario situarlo localmente en su proceso de investigación

Contribution of speckle noise in near-infrared spectroscopy measurements

Near-infrared spectroscopy (NIRS) is widely used in biomedical optics with applications ranging from basic science, such as in functional neuroimaging, to clinical, as in pulse oximetry. Despite the relatively low absorption of tissue in the near-infrared, there is still a significant amount of optical attenuation produced by the highly scattering nature of tissue. Because of this, designers of NIRS systems have to balance source optical power and source–detector separation to maximize the signal-to-noise ratio (SNR). However, theoretical estimations of SNR neglect the effects of speckle. Speckle manifests as fluctuations of the optical power received at the detector. These fluctuations are caused by interference of the multiple random paths taken by photons in tissue. We present a model for the NIRS SNR that includes the effects of speckle. We performed experimental validations with a NIRS system to show that it agrees with our model. Additionally, we performed computer simulations based on the model to estimate the contribution of speckle noise for different collection areas and source–detector separations. We show that at short source–detector separation, speckle contributes most of the noise when using long coherence length sources. Considering this additional noise is especially important for hybrid applications that use NIRS and speckle contrast simultaneously, such as in diffuse correlation spectroscopy.R01 EB025145 - NIBIB NIH HHS; R24 NS104096 - NINDS NIH HHSPublished versio

Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective

Within a decade, single trial analysis of functional Near Infrared Spectroscopy (fNIRS) signals has gained significant momentum, and fNIRS joined the set of modalities frequently used for active and passive Brain Computer Interfaces (BCI). A great variety of methods for feature extraction and classification have been explored using state-of-the-art Machine Learning methods. In contrast, signal preprocessing and cleaning pipelines for fNIRS often follow simple recipes and so far rarely incorporate the available state-of-the-art in adjacent fields. In neuroscience, where fMRI and fNIRS are established neuroimaging tools, evoked hemodynamic brain activity is typically estimated across multiple trials using a General Linear Model (GLM). With the help of the GLM, subject, channel, and task specific evoked hemodynamic responses are estimated, and the evoked brain activity is more robustly separated from systemic physiological interference using independent measures of nuisance regressors, such as short-separation fNIRS measurements. When correctly applied in single trial analysis, e.g., in BCI, this approach can significantly enhance contrast to noise ratio of the brain signal, improve feature separability and ultimately lead to better classification accuracy. In this manuscript, we provide a brief introduction into the GLM and show how to incorporate it into a typical BCI preprocessing pipeline and cross-validation. Using a resting state fNIRS data set augmented with synthetic hemodynamic responses that provide ground truth brain activity, we compare the quality of commonly used fNIRS features for BCI that are extracted from (1) conventionally preprocessed signals, and (2) signals preprocessed with the GLM and physiological nuisance regressors. We show that the GLM-based approach can provide better single trial estimates of brain activity as well as a new feature type, i.e., the weight of the individual and channel-specific hemodynamic response function (HRF) regressor. The improved estimates yield features with higher separability, that significantly enhance accuracy in a binary classification task when compared to conventional preprocessing—on average +7.4% across subjects and feature types. We propose to adapt this well-established approach from neuroscience to the domain of single-trial analysis and preprocessing wherever the classification of evoked brain activity is of concern, for instance in BCI

Automatic voice disorder detection using self-supervised representations

Many speech features and models, including Deep Neural Networks (DNN), are used for classification tasks between healthy and pathological speech with the Saarbruecken Voice Database (SVD). However, accuracy values of 80.71% for phrases or 82.8% for vowels /aiu/ are the highest reported for audio samples in SVD when the evaluation includes the wide amount of pathologies in the database, instead of a selection of some pathologies. This paper targets this top performance in the state-of-the-art Automatic Voice Disorder Detection (AVDD) systems. In the framework of a DNN-based AVDD system we study the capability of Self-Supervised (SS) representation learning for describing discriminative cues between healthy and pathological speech. The system processes the SS temporal sequence of features with a single feed-forward layer and Class-Token (CT) Transformer for obtaining the classification between healthy and pathological speech. Furthermore, there is evaluated a suitable data extension of the training set with out-of-domain data is also evaluated to deal with the low availability of data for using DNN-based models in voice pathology detection. Experimental results using audio samples corresponding to phrases in the SVD dataset, including all pathologies available, show classification accuracy values until 93.36%. This means that the proposed AVDD system achieved accuracy improvements of 4.1% without the training data extension, and 15.62% after the training data extension compared to the baseline system. Beyond the novelty of using SS representations for AVDD, the fact of obtaining accuracies over 90% in these conditions and using the whole set of pathologies in the SVD is a milestone for voice disorder-related research. Furthermore, the study on the amount of in-domain data in the training set related to the system performance show guidance for the data preparation stage. Lessons learned in this work suggest guidelines for taking advantage of DNN, to boost the performance in developing automatic systems for diagnosis, treatment, and monitoring of voice pathologies

A Novel, Quick, and Reliable Smartphone-Based Method for Serum PSA Quantification: Original Design of a Portable Microfluidic Immunosensor-Based System

We describe a versatile, portable, and simple platform that includes a microfluidic electrochemical immunosensor for prostate-specific antigen (PSA) detection. It is based on the covalent immobilization of the anti-PSA monoclonal antibody on magnetic microbeads retained in the central channel of a microfluidic device. Image flow cytometry and scanning electron microscopy were used to characterize the magnetic microbeads. A direct sandwich immunoassay (with horseradish peroxidase-conjugated PSA antibody) served to quantify the cancer biomarker in serum samples. The enzymatic product was detected at -100 mV by amperometry on sputtered thin-film electrodes. Electrochemical reaction produced a current proportional to the PSA level, with a linear range from 10 pg mL(-1) to 1500 pg mL(-1). The sensitivity was demonstrated by a detection limit of 2 pg mL(-1) and the reproducibility by a coefficient of variation of 6.16%. The clinical performance of this platform was tested in serum samples from patients with prostate cancer (PCa), observing high specificity and full correlation with gold standard determinations. In conclusion, this analytical platform is a promising tool for measuring PSA levels in patients with PCa, offering a high sensitivity and reduced variability. The small platform size and low cost of this quantitative methodology support its suitability for the fast and sensitive analysis of PSA and other circulating biomarkers in patients. Further research is warranted to verify these findings and explore its potential application at all healthcare levels.Universidad Nacional de San Luis PROICO 22/Q241ANPCyT PICT 2018-04443 PICT-2015-2246 PICT-2015-1575 PICT-2014-1184 PICT-2014-0375 PICT-2018-04443Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET) PIP 11220150100004COGENYOCentre for Genomics and Oncological Research: Pfizer-University of GranadaAndalusian Regional Government (Granada, Spain)ISCIII Health Research Institute P17/00989La Caixa FoundationHealth and Family Secretariat of the Andalusian Regional GovernmentSpanish GovernmentH2020-MSCA-IF-2019-89566

Autophagy-inducing peptides from mammalian VSV and fish VHSV rhabdoviral G glycoproteins (G) as models for the development of new therapeutic molecules

It has not been elucidated whether or not autophagy is induced by rhabdoviral G glycoproteins (G) in vertebrate organisms for which rhabdovirus infection is lethal. Our work provides the first evidence that both mammalian (vesicular stomatitis virus, VSV) and fish (viral hemorrhagic septicemia virus, VHSV, and spring viremia carp virus, SVCV) rhabdoviral Gs induce an autophagic antiviral program in vertebrate cell lines. The transcriptomic profiles obtained from zebrafish genetically immunized with either Gsvcv or Gvhsv suggest that autophagy is induced shortly after immunization and therefore, it may be an important component of the strong antiviral immune responses elicited by these viral proteins. Pepscan mapping of autophagy-inducing linear determinants of Gvhsv and Gvsv showed that peptides located in their fusion domains induce autophagy. Altogether these results suggest that strategies aimed at modulating autophagy could be used for the prevention and treatment of rhabdoviral infections such as rabies, which causes thousands of human deaths every year

Cervical Fluids Are a Source of Protein Biomarkers for Early, Non-Invasive Endometrial Cancer Diagnosis

Cervical sample; Endometrial cancer; ProteinMostra cervical; Càncer d'endometri; ProteïnaMuestra cervical; Cáncer de endometrio; ProteínaBackground: Abnormal uterine bleeding is the main symptom of endometrial cancer (EC), but it is highly nonspecific. This represents a huge burden for women’s health since all women presenting with bleeding will undergo sequential invasive tests, which are avoidable for 90–95% of those women who do not have EC. Methods: This study aimed to evaluate the potential of cervical samples collected with five different devices as a source of protein biomarkers to diagnose EC. We evaluated the protein quantity and the proteome composition of five cervical sampling methods. Results: Samples collected with a Rovers Cervex Brush® and the HC2 DNA collection device, Digene, were the most suitable samples for EC proteomic studies. Most proteins found in uterine fluids were also detected in both cervical samples. We then conducted a clinical retrospective study to assess the expression of 52 EC-related proteins in 41 patients (22 EC; 19 non-EC), using targeted proteomics. We identified SERPINH1, VIM, TAGLN, PPIA, CSE1L, and CTNNB1 as potential protein biomarkers to discriminate between EC and symptomatic non-EC women with abnormal uterine bleeding in cervical fluids (AUC > 0.8). Conclusions: This study opens an avenue for developing non-invasive protein-based EC diagnostic tests, which will improve the standard of care for gynecological patients.This research was funded by grants from the Instituto de Salud Carlos III (ISCIII) grant number PI17/02155, PI20/00644 to E.C. and S.C., and the IFI19/00029 to E.C.-d.l.-R.; from Fundación Científica Asociación Española Contra el Cáncer (AECC) grant number GCTRA1804MATI; and the INVES20051COLA to E.C.; the CIBERONC network grant number CB16/12/00328; and the ERA PerMed ERA-NET grant (PERME212443COLA funded by AECC and AEC21_2/00030 funded by ISCIII); and 2021 SGR 1157 by AGAUR. The present work has been also supported by a Televie grant 5/20/5—TLV/DD to G.D

In silico Approach for Validating and Unveiling New Applications for Prognostic Biomarkers of Endometrial Cancer

Bioinformática; Cáncer de endometrio; Biomarcador pronósticoBioinformàtica; Càncer d'endometri; Biomarcador pronòsticBioinformatics; Endometrial cancer; Prognostic biomarkerEndometrial cancer (EC) mortality is directly associated with the presence of prognostic factors. Current stratification systems are not accurate enough to predict the outcome of patients. Therefore, identifying more accurate prognostic EC biomarkers is crucial. We aimed to validate 255 prognostic biomarkers identified in multiple studies and explore their prognostic application by analyzing them in TCGA and CPTAC datasets. We analyzed the mRNA and proteomic expression data to assess the statistical prognostic performance of the 255 proteins. Significant biomarkers related to overall survival (OS) and recurrence-free survival (RFS) were combined and signatures generated. A total of 30 biomarkers were associated either to one or more of the following prognostic factors: histological type (n = 15), histological grade (n = 6), FIGO stage (n = 1), molecular classification (n = 16), or they were associated to OS (n = 11), and RFS (n = 5). A prognostic signature composed of 11 proteins increased the accuracy to predict OS (AUC = 0.827). The study validates and identifies new potential applications of 30 proteins as prognostic biomarkers and suggests to further study under-studied biomarkers such as TPX2, and confirms already used biomarkers such as MSH6, MSH2, or L1CAM. These results are expected to advance the quest for biomarkers to accurately assess the risk of EC patients.This research was funded by grants from the Instituto de Salud Carlos III (ISCIII) grant number PI17/02155, PI20/00644, and the IFI19/00029 to E.C.-d.l.R., the Ministerio de ciencia, Innovación y Universidades through a RETOS Colaboración (RTC-2017-6261-1), both co-financed by the European Regional Development Fund (FEDER); from Fundación Científica Asociación Española Contra el Cáncer (AECC) grant number GCTRA1804MATI and CIBERONC network grant number CB16/12/00328; and Grups Consolidats de la Generalitat de Catalunya (2017SGR1661). E.C. is supported by an Investigator Grant from AECC (INVES20051COLA). E.M.-G. was supported by Televie grant F5/20/5-TLV/DD

Campaña Publicitaria y Estrategia para Laive: Caso Muuushake

El siguiente trabajo de investigación tiene como finalidad conocer y empatizar con el público objetivo del producto Muuushake de la empresa Laive con la misión de generar una campaña relevante, memorable y con alto nivel de identificación. Para ello, nos servimos de herramientas de investigación como focus groups, entrevistas, social listening y encuestas que nos permitieron profundizar en las motivaciones e insights del consumidor para de esta manera poder crear una campaña con contenido realmente relevante para ellos. El reto era poder conectar con ellos en un contexto atípico donde la categoría de consumo on the go se había visto fuertemente afectada por la cuarentena. En ese sentido, era la oportunidad para trasladar este consumo al hogar aprovechando los nuevos momentos de consumo creados a partir de los nuevos estilos de vida del público objetivo. Encontramos que el trabajo y el estudio son la combinación perfecta para detonar el estrés en el consumidor, quien aplaca estos sentimientos con pequeños momentos de auto indulgencia. Sin embargo, descubrimos que muchas veces ellos se sienten culpables por darse esos descansos necesarios. En base a estas dos ideas, nació la campaña 2021 para Laive Muuushake que desarrollamos en las siguientes páginas.The purpose of this research is to study and emphatize Laive Muuushake’s consumers. Our mission was to create a meaningful and easy-to-remember campaign for the consumers of the product. In order to do that, we used focus group strategies, research through data, social listening and tools that allow us to go further on their daily motivations and struggles. With the insights we found, we were able to create a message that spoke directly to them. The challenge was to bring value to a very difficult category these days: on the go products. Due to the pandemic crisis, this category has been seriously affected. The opportunity was to move the consumption to the consumer's house, where new occasions of consumption appear. We found that during pandemic, the stress levels on young people increased due to home-office work and online classes. Thus, young people usually reward themselves with little moments of rest, food treats and others. Nonetheless, we discover that these breaks or moments of relaxation create a guilty feeling in them even if they really need it or deserve it. On the base of this two insights our new campaign was born