47,395 research outputs found

    Revisão taxonómica do género Calendula L. (Asteraceae - Calenduleae) na Península Ibérica e Marrocos

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    The genus Calendula L. (Asteraceae - Calenduleae) includes, depending on the author, 10 to 25 species, distributed mainly in the Mediterranean basin. The taxonomy of this genus is considered to be extremely difficult, due to a great morphological variability, doubtfull relevance of some of the characters used to distinguish its species (e.g. the life form: annual or perennial; the habit: erect or diffuse, shape of the leaves, indumentum, relative size of the capitula and colour of disc or ray florets, achene morphology), but also due to the hybridization and polyploidization. Despite the numerous studies that have been published, no agreement on the classification and characters used to discriminate between taxa has been reached. A taxonomic study of the genus Calendula was conducted for the Iberian Peninsula and Morocco, aiming at (1) access the morphological variability between and within taxa, (2) confirm the chromosome numbers, (3) increase the nuclear DNA content estimations, (4) re-evaluate taxa delimitations and circumscription, and (5) reassess, and redefine, the descriptions and characters useful to distinguish taxa. In order to achieve a satisfying taxonomic core, extensive fieldwork, detailed morphometric analysis, chorological, karyological and genome size studies were conducted. For the Iberian Peninsula, four species were recognized, including nine subspecies (between these two new subspecies were described). For Morocco, including some taxa from Algeria and Tunisia 13 species were recognized (two new species and a nomenclatural change), including 15 subspecies (among these eight new subspecies were described). To corroborate the results obtained and to evaluate the evolutionary relationships among taxa, phylogenetic studies using molecular methods, such as ITS, microsatellites or other molecular markers, should be used.O g√©nero Calendula L. (Asteraceae - Calenduleae) inclui, dependendo do autor, 10 a 25 esp√©cies, distribu√≠das essencialmente na bacia do Mediterr√Ęneo. A taxonomia deste g√©nero √© considerada extremamente dif√≠cil, devido √† grande variabilidade morfol√≥gica, discutivel relev√Ęncia de alguns dos caracteres utilizados para distinguir suas esp√©cies (por exemplo, a forma de vida: anual ou perene, o h√°bito: erecto ou difuso, a forma das folhas, o indumento, o tamanho e a cor dos cap√≠tulos e a morfologia dos aqu√©nios), mas tamb√©m devido √† hibridiza√ß√£o e poliploidiza√ß√£o. Apesar dos in√ļmeros estudos que foram publicados, n√£o foi alcan√ßado um acordo sobre a classifica√ß√£o e os caracteres utilizados para discriminar as suas esp√©cies. Um estudo taxon√≥mico do g√©nero Calendula foi realizado para a Pen√≠nsula Ib√©rica e Marrocos, com o objectivo de (1) verificar a variabilidade morfol√≥gica, (2) confirmar o n√ļmero de cromossomas, (3) aumentar as estimativas de conte√ļdo em ADN, (4) reavaliar a delimita√ß√£o e a circunscri√ß√£o dos taxa, e (5) reavaliar e redefinir as descri√ß√Ķes e caracteres √ļteis para os distinguir. Para alcan√ßar uma robust√™s taxon√≥mica satisfat√≥ria, foram realizados extensos trabalhos de campo, an√°lise morfom√©trica detalhada, abordagens corol√≥gicas, cariol√≥gicas e quanto ao conte√ļdo em ADN. Para a Pen√≠nsula Ib√©rica, quatro esp√©cies foram reconhecidas, incluindo nove subesp√©cies (entre essas duas novas subesp√©cies foram descritas). Para Marrocos, incluindo alguns taxa da Argelia e Tunisia, foram reconhecidas 13 esp√©cies (duas novas e uma mudan√ßa nomenclatural), incluindo 15 subesp√©cies (entre essas oito novas subesp√©cies foram descritas). Para corroborar os resultados obtidos e avaliar as rela√ß√Ķes evolutivas e filogen√©ticas entre os taxa, estudos que utilizem diferentes m√©todos moleculares, tais como ITS, microsat√©lites ou outros marcadores moleculares, devem ser utilizados.Apoio financeiro do Laborat√≥rio Associado CESAM - Centro de Estudos do Ambiente e do Mar (AMB/50017) financiado por fundos nacionais atrav√©s da FCT/MCTES e cofinanciado pelo FEDER (POCI-01-0145-FEDER-007638), no √Ęmbito do Acordo de Parceria PT2020, e Compete 2020Programa Doutoral em Biologi

    Aprendizaje Automático No Supervisado para la Clasificación de Fuentes Astrofísicas de Rayos X

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    Contexto. El Chandra Source Catalog (CSC), que recoge las fuentes de rayos X detectadas por el Observatorio de Rayos X Chandra a lo largo de su historia, es un terreno f√©rtil para el descubrimiento, ya que muchas de las fuentes que contiene no han sido estudiadas en detalle. En el CSC podr√≠amos encontrar varios tipos de fuentes, desde objetos estelares j√≥venes (YSO) y sistemas binarios, hasta incluso cu√°sares muy lejanos (QSO) o galaxias activas con agujeros negros supermasivos en sus n√ļcleos. Entre las fuentes que podr√≠an cambiar el paradigma y que podr√≠amos buscar en los datos de Chandra est√°n las fusiones de objetos compactos, los tr√°nsitos de planetas extrasolares, los eventos de disrupci√≥n de mareas, etc. Sin embargo, s√≥lo se ha clasificado una peque√Īa fracci√≥n de las fuentes del CSC. Para llevar a cabo una investigaci√≥n exhaustiva de las fuentes del CSC, y estar preparados para los pr√≥ximos grandes estudios de rayos X, necesitamos clasificar tantas fuentes del cat√°logo como sea posible. Objetivos. Este trabajo propone un enfoque de aprendizaje no supervisado para clasificar el mayor n√ļmero posible de fuentes del Chandra Source Catalog, explorando primero las ventajas y los l√≠mites de utilizar s√≥lo los datos de rayos X disponibles. El aprendizaje no supervisado es especialmente adecuado dada la gran cantidad de detecciones que a√ļn no han sido clasificadas de forma independiente. Agrupando las observaciones de las fuentes por sus similitudes, y asociando despu√©s estos grupos con objetos previamente clasificados espectrosc√≥picamente, buscamos proponer una nueva metodolog√≠a que pueda proporcionarnos una clasificaci√≥n probabil√≠stica para una numerosa cantidad de fuentes. M√©todos. Empleamos m√©todos de aprendizaje no supervisado, primero K-means, y luego Gaussian Mixtures, aplicados a una lista de propiedades de rayos X, para clasificar probabil√≠sticamente las fuentes de alta energ√≠a en el Chandra Source Catalog (CSC). Esto lo conseguimos asociando clusters espec√≠ficos con aquellos objetos del CSC que tienen una clasificaci√≥n en la base de datos SIMBAD, y luego asignando clases probabil√≠sticas por asociaci√≥n a los objetos no clasificados en cada cluster con un algoritmo basado en la distancia de Mahalanobis. Resultados. Somos capaces de identificar con √©xito clusters de objetos previamente identificados que probablemente pertenezcan a la misma clase, e incluso dentro de los grupos que fueron identificados teniendo predominantemente un tipo de fuente, como "galaxias", "QSO", "YSO", encontramos subclases relacionadas con su variabilidad y propiedades espectrales √ļnicas. El resultado de este ejercicio es una clasificaci√≥n probabil√≠stica robusta (es decir, una posterior sobre las clases) para 10090 de las fuentes del CSC. Las tablas correspondientes a cada cluster y el c√≥digo respectivo est√°n disponibles en https://github.com/BogoCoder/astrox. Conclusiones. Hemos desarrollado una metodolog√≠a para proporcionar una asignaci√≥n probabil√≠stica de clases a numerosas fuentes de rayos X del Chandra Source Catalog. A trav√©s de este proceso hemos visto que es posible construir un pipeline basado en aprendizaje autom√°tico no supervisado para esta tarea. Hemos visto que nuestro enfoque funciona bien para determinados tipos de fuentes generales, como un YSO, o fuentes extragal√°cticas. En otros casos, tenemos ambig√ľedad en el n√ļmero de clases presentes en un cluster particular, teniendo clases predominantes muy diferentes dentro de ellos. Esta ambig√ľedad podr√≠a resolverse a√Īadiendo datos de otro r√©gimen de longitudes de onda, como datos √≥pticos del SDSS (Sloan Digital Survey Summary). Este an√°lisis est√° previsto para un futuro trabajo. Esta tesis presenta una primera aproximaci√≥n al objetivo final de clasificar todas las posibles fuentes CSC que carecen de una clase.Context. The Chandra Source Catalog (CSC), which collects the X-ray sources detected by the Chandra X-ray Observatory through its history, is a fertile ground for discovery, because many of the sources it contains have not been studied in detail. In CSC we could find several types of sources, from young stellar objects (YSO) and binary systems, to even very far quasars (QSO) or active galaxies with supermassive black holes in their cores. Among the potentially paradigm changing sources that we could look for in Chandra data are compact object mergers, extrasolar planet transits, tidal disruption events, etc. However, only a small fraction of the CSC sources have been classified. In order to conduct a thorough investigation of the CSC sources, and to be prepared for the coming very large X-ray surveys, we need to classify as many catalog sources as possible. Aims. This work proposes an unsupervised learning approach to classify as many Chandra Source Catalog sources as possible, first exploring the advantages and limits of using only the X-ray data available. Unsupervised learning is particularly suitable given the vast amount of detections that have not been independently classified yet. Clustering the source observations by their similarities, and then associating these clusters with objects previously classified spectroscopically, we aim to propose a new methodology that could provide us with a probabilistic classification for a numerous amount of sources. Methods. We employ unsupervised learning methods, first K-means, then focusing on Gaussian Mixtures, applied to a list of X-ray properties, to probabilistically classify high energy sources in the Chandra Source Catalog (CSC). We achieve this by associating specific clusters with those CSC objects that have a classification in the SIMBAD database, and then assigning probabilistic classes by association to unclassified objects in each cluster with an algorithm based on the Mahalanobis distance. Results. We are able to successfully identify clusters of previously identified objects that likely belong to the same class, and even within groups that were identified as having predominantly a type of source, such as "galaxies", "QSO", "YSO", we find sub-classes related to their unique variability and spectral properties. The result of this exercise is a robust probabilistic classification (i.e. a posterior over classes) for 10090 of CSC sources. The tables for each cluster and respective code is available at https://github.com/BogoCoder/astrox. Conclusions. We developed a methodology to provide probabilistic class assignation to numerous X-ray sources of the Chandra Source Catalog. Through this process we have seen that it is possible to construct a pipeline based on unsupervised machine learning for this task. We have seen that our approach works well for particular general type of sources, such as a YSO, or extra-galactic sources. In other cases, we have ambiguity in the number of classes presented in a particular cluster, having very different predominant types within them. This ambiguity might be solved by an addition of other wavelength regime data, such as optical from SDSS (Sloan Digital Survey Summary). This analysis is planned for a future work. This thesis present an early approach for the final goal of classifying all possible CSC sources that lacks of a class

    Molecular mechanisms underlying the acquisition of cisplatin resistance in ovarian cancer: from stemness to lipid metabolism

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    Il carcinoma ovarico (OC) √® la neoplasia ginecologica con il maggiore tasso di mortalit√†, in parte dovuto all‚Äôacquisizione della resistenza al chemioterapico, che si manifesta nel 75% dei casi. In diversi tipi di tumori la sopravvivenza delle cellule tumorali trattate con chemioterapici √® stata alla riprogrammazione del metabolismo. Date queste premesse l‚Äôobiettivo di questo studio √® verificare se trattamenti a base di platino possano indurre un‚Äôalterazione del profilo metabolico e come questa riprogrammazione metabolica influisca sul fenotipo cellulare. L‚Äôanalisi del profilo OCR in due linee cellulari di OC (OV90 e OC314) resistenti al farmaco ha mostrato una riprogrammazione metabolica verso una riduzione del profilo OXPHOS e un aumento del metabolismo glicolitico rispetto alle cellule sensibili . Inoltre, √® stato verificato che l‚Äôacquisizione della resistenza al farmaco √® associata ad un‚Äôaumentata espressione dei trasportatori ABCC2 e ABCG2 e ad un rallentamento della proliferazione cellulare. La resistenza √®, inoltre, associata all‚Äô'acquisizione del fenotipo staminale. √ą stato dimostrato un arricchimento della popolazione CSC nelle linee resistenti rispetto alle loro controparti sensibili. Infatti, la sovraespressione dei fattori di Yamanaka e di ABCG2 supportano l'acquisizione di un fenotipo stem-like nelle linee resistenti. Pertanto il trattamento cronico con cisplatino ha favorito l‚Äôarricchimento delle CSC. Quindi, al fine di investigare il legame meccanicistico fra la riprogrammazione metabolica e l‚Äôacquisizione della chemioresistenza e di caratteristiche staminali, √® stata effettuata un'analisi bioinformatica: gli elevati livelli di espressione dei marcatori CSC correlano con la sovraespressione dei geni coinvolti nel mantenimento dell‚Äôomeostasi del metabolismo lipidico. Questa analisi ha mostrato il coinvolgimento della via PPAR, come via di regolazione, e la ő≤-ossidazione come via metabolica. Le cellule resistenti, con caratteristiche stem-like, costituiscono una sotto-popolazione con un profilo metabolico glicolitico e lipidico. Tali cellule presentano un vantaggio selettivo in un microambiente ricco di lipidi, quale l‚Äôomento, e potrebbero pertanto concorrere all‚Äôinsorgenza della recidiva.Ovarian cancer (OC) is the gynecological malignancy with the highest mortality rate, partly due to the acquisition of resistance to chemotherapy, which occurs in 75% of cases. In several types of tumors the survival of cancer cells treated with chemotherapy has been due to metabolic reprogramming. Given these premises, the objective of this study is to verify whether platinum-based treatments could induce an alteration of metabolic profile and how this metabolic reprogramming affects the cellular phenotype. Analysis of the OCR profile in two drug-resistant OC cell lines (OV90 and OC314) showed metabolic reprogramming towards a reduction in the OXPHOS profile and an increase in glycolytic metabolism compared to sensitive cells. Furthermore, it has been verified that the acquisition of drug resistance is associated with an increased expression of the ABCC2 and ABCG2 transporters and a slowdown in cell proliferation. Resistance is also associated with the acquisition of the stemness phenotype. An enrichment in CSC population in resistant cells was demonstrated compared to their sensitive counterparts. Indeed, the overexpression of Yamanaka factors and ABCG2 support the acquisition of a stem-like phenotype in resistant cell lines. Therefore, chronic treatment with cisplatin has favored the enrichment of CSCs. Then, in order to investigate the mechanistic link between metabolic reprogramming, the acquisition of chemoresistance and stemness characteristics, a bioinformatic analysis was performed: the high levels of expression of the CSC markers correlate with the overexpression of the genes involved in the maintenance of homeostasis of lipid metabolism. This analysis showed the involvement of the PPAR pathway, as a regulatory pathway, and ő≤-oxidation as a metabolic pathway. Resistant cells, with stem-like characteristics, constitute a sub-population with a glycolytic and lipid metabolic profile. These cells have a selective advantage in a lipid-rich microenvironment, such as the omentum, and could therefore contribute to the onset of relapse

    Differentially private partitioned variational inference

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    Learning a privacy-preserving model from sensitive data which are distributed across multiple devices is an increasingly important problem. The problem is often formulated in the federated learning context, with the aim of learning a single global model while keeping the data distributed. Moreover, Bayesian learning is a popular approach for modelling, since it naturally supports reliable uncertainty estimates. However, Bayesian learning is generally intractable even with centralised non-private data and so approximation techniques such as variational inference are a necessity. Variational inference has recently been extended to the non-private federated learning setting via the partitioned variational inference algorithm. For privacy protection, the current gold standard is called differential privacy. Differential privacy guarantees privacy in a strong, mathematically clearly defined sense. In this paper, we present differentially private partitioned variational inference, the first general framework for learning a variational approximation to a Bayesian posterior distribution in the federated learning setting while minimising the number of communication rounds and providing differential privacy guarantees for data subjects. We propose three alternative implementations in the general framework, one based on perturbing local optimisation runs done by individual parties, and two based on perturbing updates to the global model (one using a version of federated averaging, the second one adding virtual parties to the protocol), and compare their properties both theoretically and empirically.Comment: Published in TMLR 04/2023: https://openreview.net/forum?id=55Bcghgic

    On some conjectures of Z.-W. Sun involving harmonic numbers

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    Harmonic numbers are significant in various branches of number theory. With the help of the digamma function, we prove ten conjectural series of Z.-W. Sun involving harmonic numbers. Several ones of them are also series expansions of log‚Ā°2/ŌÄ2\log2/\pi^2

    Establishment of a 7-gene prognostic signature based on oxidative stress genes for predicting chemotherapy resistance in pancreatic cancer

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    Background: Oxidative stress is involved in regulating various biological processes in human cancers. However, the effect of oxidative stress on pancreatic adenocarcinoma (PAAD) remained unclear.Methods: Pancreatic cancer expression profiles from TCGA were downloaded. Consensus ClusterPlus helped classify molecular subtypes based on PAAD prognosis-associated oxidative stress genes. Limma package filtered differentially expressed genes (DEGs) between subtypes. A multi-gene risk model was developed using Lease absolute shrinkage and selection operator (Lasso)-Cox analysis. A nomogram was built based on risk score and distinct clinical features.Results: Consistent clustering identified 3 stable molecular subtypes (C1, C2, C3) based on oxidative stress-associated genes. Particularly, C3 had the optimal prognosis with the greatest mutation frequency, activate cell cycle pathway in an immunosuppressed status. Lasso and univariate cox regression analysis selected 7 oxidative stress phenotype-associated key genes, based on which we constructed a robust prognostic risk model independent of clinicopathological features with stable predictive performance in independent datasets. High-risk group was found to be more sensitive to small molecule chemotherapeutic drugs including Gemcitabine, Cisplatin, Erlotinib and Dasatinib. The 6 of 7 genes expressions were significantly associated with methylation. Survival prediction and prognostic model was further improved through a decision tree model by combining clinicopathological features with RiskScore.Conclusion: The risk model containing seven oxidative stress-related genes may have a greater potential to assist clinical treatment decision-making and prognosis determination

    Shellfish Stocks and Fisheries Review 2022: an assessment of selected stocks

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    This review presents information on the status of selected shellfish stocks in Ireland. In addition, data on the fleet and landings of shellfish species (excluding Nephrops and mussels) are presented. The intention of this annual review is to present stock assessment and management advice for shellfisheries that may be subject to new management proposals or where scientific advice is required in relation to assessing the environmental impact of shellfish fisheries especially in areas designated under European Directives. The review reflects the recent work of the Marine Institute (MI) in the biological assessment of shellfish fisheries and their interaction with the environment. The information and advice presented here for shellfish is complementary to that presented in the MI Stock Book on demersal and pelagic fisheries. Separate treatment of shellfish is warranted as their biology and distribution, the assessment methods that can be applied to them and the system under which they are managed, all differ substantially to demersal and pelagic stocks. Shellfish stocks are not generally assessed by The International Council for the Exploration of the Sea (ICES) and although they come under the competency of the Common Fisheries Policy they are generally not regulated by EU TAC and in the main, other than crab and scallop, are distributed inside the national 12 nm fisheries limit. Management of these fisheries is within the competency of the Department of Agriculture, Food and Marine (DAFM). A co-operative management framework introduced by the Governing Department and BIM in 2005 (Anon 2005), and under which a number of fishery management plans were developed, was, in 2014, replaced by the National and Regional Inshore Fisheries Forums (NIFF, RIFFs). These bodies are consultative forums, the members of which are representative of the inshore fisheries sector and other stakeholder groups. The National forum (NIFF) provides a structure with which each of the regional forums can interact with each other and with the Marine Agencies, DAFM and the Minister. Management of oyster fisheries is the responsibility of The Department of Environment, Climate and Communications, implemented through Inland Fisheries Ireland (IFI). In many cases, however, management responsibility for oysters is devolved through Fishery Orders or Aquaculture licences to local co-operatives. The main customers for this review are DAFM, RIFFs, NIFF and other Departments and Authorities listed above.EMFAF; Government of Irelan

    RanBP1: A Potential Therapeutic Target for Cancer Stem Cells in Lung Cancer and Glioma

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    Cancer stem cells (CSCs) are known to be one of the factors that make cancer treatment difficult. Many researchers are thus conducting research to efficiently destroy CSCs. Therefore, we sought to suggest a new target that can efficiently suppress CSCs. In this study, we observed a high expression of Ran-binding protein 1 (RanBP1) in lung cancer stem cells (LCSCs) and glioma stem cells (GSCs). Upregulated RanBP1 expression is strongly associated with the expression of CSC marker proteins and CSC regulators. In addition, an elevated RanBP1 expression is strongly associated with a poor patient prognosis. CSCs have the ability to resist radiation, and RanBP1 regulates this ability. RanBP1 also affects the metastasis-associated epithelial‚Äďmesenchymal transition (EMT) phenomenon. EMT marker proteins and regulatory proteins are affected by RanBP1 expression, and cell motility was regulated according to RanBP1 expression. The cancer microenvironment influences cancer growth, metastasis, and cancer treatment. RanBP1 can modulate the cancer microenvironment by regulating the cytokine IL-18. Secreted IL-18 acts on cancer cells and promotes cancer malignancy. Our results reveal, for the first time, that RanBP1 is an important regulator in LCSCs and GSCs, suggesting that it holds potential for use as a potential therapeutic target

    Open Set Classification of GAN-based Image Manipulations via a ViT-based Hybrid Architecture

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    Classification of AI-manipulated content is receiving great attention, for distinguishing different types of manipulations. Most of the methods developed so far fail in the open-set scenario, that is when the algorithm used for the manipulation is not represented by the training set. In this paper, we focus on the classification of synthetic face generation and manipulation in open-set scenarios, and propose a method for classification with a rejection option. The proposed method combines the use of Vision Transformers (ViT) with a hybrid approach for simultaneous classification and localization. Feature map correlation is exploited by the ViT module, while a localization branch is employed as an attention mechanism to force the model to learn per-class discriminative features associated with the forgery when the manipulation is performed locally in the image. Rejection is performed by considering several strategies and analyzing the model output layers. The effectiveness of the proposed method is assessed for the task of classification of facial attribute editing and GAN attribution
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