Investigation by laser doppler velocimetry of the effects of liquid flow rates and feed positions on the flow patterns induced in a stirred tank by an axial-flow impeller

The (ow patterns established in a continuously-fed stirred tank, equipped with a Mixel TT axial-(ow impeller, have been investigated bylaser Doppler velocimetry, for a high and a low value of mean residence time—mixing time ratio. The pseudo-two-dimensional axial– radial-velocityvector plots, as well as the spatial distributions of the tangential velocitycomponent and the velocitypro;les around the impeller, show that the interaction between the incoming liquid and the liquid entrained bythe agitator rotation cause the (ow pattern in the vessel to become stronglythree-dimensional, especiallyin the region between the plane, where the feeding tube lies, and the 180◦-downstream plane. The increase in the liquid (ow rate and the location of the feed entryboth a$ect the (ow pattern, with the latter having a more pronounced e$ect. The overall process, in this mode of operation, depends upon the appropriate con;guration and choice of parameters: for conditions corresponding to high liquid (ow rates, the (ow patterns indicate the possibilityof short-circuiting, when the liquid is fed into the stream being drawn bythe agitator and when the outlet is located at the bottom of the vessel

Literary review of algorithms for segmentation and classification of Artificial Intelligence pathologies applied to breast cancer

According to data, breast cancer is a significant health issue and has a considerable economic impact. This clearly justifies the need for breast cancer screening. However, the current diagnostic process used in clinical settings is prone to errors. Consequently, there is a requirement for a tool that can help doctors categorize mammograms into the four BI-RADS categories. This study presents an approach that uses deep learning. It examines the challenges and difficulties encountered and evaluates and compares its effectiveness. One dataset of mammograms was used, with experts having already classified the radiological images using the BI-RADS guidelines. The images in these datasets belong to categories 1 to 4. The deep learning approach employed in this study is based on a Convolutional Neural Network (CNN), namely a ResNet22. The propose is to use two inputs, one for the Cranio-Caudal (CC) view and another for the Medio-Lateral Oblique (MLO) view. Each input comprises a mammogram image and two heatmaps. Consequently, we have named the architecture MammoHeatNet (MHN). The algorithm initially processes the mammogram image by cropping it, extracting optimal centers, and obtaining the heatmaps. Once the pre-processing is complete, the inputs are fed into the model, which then classifies them into four BI-RADS categories. To obtain the best model, various parameter configurations have been tested. The ultimate model attained a maximum accuracy of 74.19%. The process of training and testing the model was time-intensive, requiring 150 hours to obtain the best possible model. In conclusion, the deep learning model used in this study achieve good performance. However, with the incorporation of a larger dataset for train it and various modifications to the model, even better results could be achieved. The main contribution of this work is the implementation of a deep neuronal network that process the images like a human specialist would do it, using to views of the same mammogram.Segons les dades, el càncer de mama és un important problema de salut i té un considerable impacte econòmic. Això justifica clarament la necessitat de realitzar revisions de càncer de mama. No obstant, el procés diagnòstic actual utilitzat en entorns clínics té tendència a cometre errors. En conseqüència, és necessari disposar d'una eina que pugui ajudar els metges a classificar les mamografies en les quatre categories BI-RADS. Aquest estudi presenta una enfocament que utilitza el "deep learning". S'examinen els desafiaments i dificultats trobades, i s'avalua i compara la seva eficàcia. S'utilitza un conjunt de dades de mamografies, amb experts que ja han classificat les imatges radiològiques utilitzant les directrius BI-RADS. Les imatges d'aquests conjunts de dades pertanyen a les categories 1 a 4. L’algoritme de "deep learning" utilitzat en aquest estudi es basa en una Xarxa Neuronal Convolucional (CNN), concretament un ResNet22. La proposta és utilitzar dues entrades, una per a la vista Cranio-Caudal (CC) i una altra per a la vista Medio-Lateral Oblique (MLO). Cada entrada comprèn una imatge de mamografia i dues "heatmaps". Per tant, s'ha nomenat a l'arquitectura MammoHeatNet (MHN). L'algoritme processa inicialment la imatge de mamografia, retallant-la, extraient centres òptims i obtenint les "heatmaps". Una vegada que el pre-processament està complet, les entrades es duen al model, que les classifica en les quatre categories BI-RADS. Per obtenir el millor model, s'han provat diverses configuracions de paràmetres. El model final assolit va obtenir una precisió màxima del 74.19%. El procés d'entrenament i prova del model va requerir molt de temps, amb un total de 150 hores per obtenir el millor model possible. En conclusió, el model de "deep learning" utilitzat en aquest estudi aconsegueix un bon rendiment. No obstant, amb la incorporació d'un conjunt de dades més gran per a l'entrenament i diverses modificacions al model, es podrien obtenir resultats encara millors. La principal contribució d'aquest treball és la implementació d'una xarxa neuronal profunda que processa les imatges com ho faria un especialista humà, utilitzant dues vistes de la mateixa mamografia.Según los datos, el cáncer de mama es un problema de salud significativo y tiene un impacto económico considerable. Esto justifica claramente la necesidad de realizar revisiones de cáncer de mama. Sin embargo, el proceso diagnóstico actual utilizado en entornos clínicos tiende a cometer errores. En consecuencia, es necesario disponer de una herramienta que pueda ayudar a los médicos a clasificar las mamografías en las cuatro categorías BI-RADS. Este estudio presenta un enfoque que utiliza el "deep learning". Se examinan los desafíos y dificultades encontradas, y se evalúa y compara su eficacia. Se utiliza un conjunto de datos de mamografías, con expertos que ya han clasificado las imágenes radiológicas utilizando las directrices BI-RADS. Las imágenes de estos conjuntos de datos pertenecen a las categorías 1 a 4. El algoritmo de "deep learning" empleado en este estudio se basa en una Red Neuronal Convolucional (CNN), concretamente un ResNet22. La propuesta es utilizar dos entradas, una para la vista Cranio-Caudal (CC) y otra para la vista Medio-Lateral Oblicua (MLO). Cada entrada comprende una imagen de mamografía y dos "heatmaps". Por tanto, se ha nombrado a la arquitectura MammoHeatNet (MHN). El algoritmo procesa inicialmente la imagen de mamografía, recortándola, extrayendo centros óptimos y obteniendo las "heatmaps". Una vez que el preprocesamiento está completo, las entradas se entran al modelo, que las clasifica en las cuatro categorías BI-RADS. Para obtener el mejor modelo, se han probado varias configuraciones de parámetros. El modelo final alcanzó una precisión máxima del 74,19%. El proceso de entrenamiento y prueba del modelo requirió mucho tiempo, con un total de 150 horas para obtener el mejor modelo posible. En conclusión, el modelo de "deep learning" utilizado en este estudio logra un buen rendimiento. Sin embargo, con la incorporación de un conjunto de datos más grande para el entrenamiento y diversas modificaciones al modelo, se podrían obtener resultados aún mejores. La principal contribución de este trabajo es la implementación de una red neuronal profunda que procesa las imágenes como lo haría un especialista humano, utilizando dos vistas de la misma mamografía

Granuloma encapsulation is a key factor for containing tuberculosis infection in minipigs

Altres ajuts: MISA/FIS/PI080785Altres ajuts: I+D+I/FIS/CM06/00123A transthoracic infection involving a low dose of Mycobacterium tuberculosis has been used to establish a new model of infection in minipigs. The 20-week monitoring period showed a marked Th1 response and poor humoral response for the whole infection. A detailed histopathological analysis was performed after slicing the formalin-fixed whole lungs of each animal. All lesions were recorded and classified according to their microscopic aspect, their relationship with the intralobular connective network and their degree of maturity in order to obtain a dissemination ratio (DR) between recent and old lesions. CFU counts and evolution of the DR with time showed that the proposed model correlated with a contained infection, decreasing from week 9 onwards. These findings suggest that the infection induces an initial Th1 response, which is followed by local fibrosis and encapsulation of the granulomas, thereby decreasing the onset of new lesions. Two therapeutic strategies were applied in order to understand how they could influence the model. Thus, chemotherapy with isoniazid alone helped to decrease the total number of lesions, despite the increase in DR after week 9, with similar kinetics to those of the control group, whereas addition of a therapeutic M. tuberculosis fragment-based vaccine after chemotherapy increased the Th1 and humoral responses, as well as the number of lesions, but decreased the DR. By providing a local pulmonary structure similar to that in humans, the mini-pig model highlights new aspects that could be key to a better understanding tuberculosis infection control in humans

Immune response development after vaccination of 1-day-old naïve pigs with a Porcine Reproductive and Respiratory Syndrome 1-based modified live virus vaccine

Background The development of the innate and adaptive immune responses to Porcine reproductive and respiratory syndrome virus (PRRSV) after vaccination of 1 day-old pigs with a PRRSV-1 based modified live virus (MLV) vaccine by intramuscular (IM) and intranasal (IN) routes was characterised, before and after challenge with a heterologous PRRSV-1 isolate at 18 weeks post-vaccination. Twenty-five PRRSV-seronegative piglets were used. At 1 day of age, pigs were administered with a single dose of vaccine via the IM (n = 10) or the IN route (n = 10). Control group (n = 5) received saline solution. After vaccination, pigs were bled at days 3, 7, 28, 56, 83, 113 and 125. Levels of cytokines IL-10, IL-8, IFN-α (measured by ELISA tests of serum), TNF-α and IFN-γ (measured by ELISA and ELISPOT, respectively, from stimulated peripheral blood mononuclear cells), and serum neutralising antibodies (NA) to the vaccine strain, were measured. Results The induction of IL-10 was rare, indicating that IL-10 mediated immunomodulation/immune dysfunction was not a feature of this vaccine or of the challenge virus. IL-8 was detected in only two pigs following vaccination, but in the majority of pigs after challenge, indicating that their ability to produce an innate immune response was not impaired. TNF-α was not detected in any vaccinated pigs until day 83. After challenge, only a minority of pigs produced TNF-α. IFN-α was detected in all vaccinated pigs following vaccination, indicating the potential for development of an effective Th1 adaptive immune response. IFN-γ-secreting cells were detected in all vaccinated pigs after vaccination. NA to the vaccine strain were first detected at day 56 in pigs vaccinated by both routes, and remained at similar levels until challenge. After challenge, a boost in NA was observed. The efficacy of the vaccine was demonstrated by reduction of viraemia and nasal shedding after challenge. Conclusions The administration of a PRRSV-1 based MLV vaccine to 1 day-old piglets was able to induce an immune response characterised by: (1) undetectable or low levels of IL-10, IL-8 and TNF-α, (2) an increase in IFN-α expression within the first seven days, (3) a gradual increase in the number of antigen-specific IFN-γ-secreting cells, and (4) induction of detectable NA. After challenge with a heterologous strain, there was a rapid boost in NA titres, indicating a priming effect of the vaccine.info:eu-repo/semantics/publishedVersio

HyperProbe consortium: innovate tumour neurosurgery with innovative photonic solutions

Recent advancements in imaging technologies (MRI, PET, CT, among others) have significantly improved clinical localisation of lesions of the central nervous system (CNS) before surgery, making possible for neurosurgeons to plan and navigate away from functional brain locations when removing tumours, such as gliomas. However, neuronavigation in the surgical management of brain tumours remains a significant challenge, due to the inability to maintain accurate spatial information of pathological and healthy locations intraoperatively. To answer this challenge, the HyperProbe consortium have been put together, consisting of a team of engineers, physicists, data scientists and neurosurgeons, to develop an innovative, all-optical, intraoperative imaging system based on (i) hyperspectral imaging (HSI) for rapid, multiwavelength spectral acquisition, and (ii) artificial intelligence (AI) for image reconstruction, morpho-chemical characterisation and molecular fingerprint recognition. Our HyperProbe system will (1) map, monitor and quantify biomolecules of interest in cerebral physiology; (2) be handheld, cost-effective and user-friendly; (3) apply AI-based methods for the reconstruction of the hyperspectral images, the analysis of the spatio-spectral data and the development and quantification of novel biomarkers for identification of glioma and differentiation from functional brain tissue. HyperProbe will be validated and optimised with studies in optical phantoms, in vivo against gold standard modalities in neuronavigational imaging, and finally we will provide proof of principle of its performances during routine brain tumour surgery on patients. HyperProbe aims at providing functional and structural information on biomarkers of interest that is currently missing during neuro-oncological interventions

Effect of resource spatial correlation and Hunter-Fisher-Gatherer mobility on social cooperation in Tierra del Fuego

This article presents an agent-based model designed to explore the development of cooperation in hunter-fisher-gatherer societies that face a dilemma of sharing an unpredictable resource that is randomly distributed in space. The model is a stylised abstraction of the Yamana society, which inhabited the channels and islands of the southernmost part of Tierra del Fuego (Argentina-Chile). According to ethnographic sources, the Yamana developed cooperative behaviour supported by an indirect reciprocity mechanism: whenever someone found an extraordinary confluence of resources, such as a beached whale, they would use smoke signals to announce their find, bringing people together to share food and exchange different types of social capital. The model provides insight on how the spatial concentration of beachings and agents’ movements in the space can influence cooperation. We conclude that the emergence of informal and dynamic communities that operate as a vigilance network preserves cooperation and makes defection very costly.MICINN http://www.idi.mineco.gob.es/ CSD2010-00034 (SimulPast CONSOLIDER-INGENIO 2010) and HAR2009-06996; the government of Castilla y Leónhttp://www.jcyl.es/ GREX251-2009; the Argentine CONICET http://www.conicet.gov.ar/PIP-0706; and the Wenner-Gren Foundation for Anthropological Researchhttp://www.wennergren.org/ "Social Aggregation: A Yamana Society's Short Term Episode to Analyse Social Interaction, Tierra del Fuego, Argentina". The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip

Functional annotation of human long noncoding RNAs via molecular phenotyping

Long noncoding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes, and yet, their functions remain largely unknown. As part of the FANTOM6 project, we systematically knocked down the expression of 285 lncRNAs in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNAs exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest-todate lncRNA knockdown data set with molecular phenotyping (over 1000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.Peer reviewe