Heterologous expression of AtNPR1 gene in olive for increasing fungal tolerance

The NPR1 gene encodes a key component of SAR signaling mediated by salicylic acid (SA). After a pathogen infection, the accumulation of SA releases NPR1 monomers in the cytosol that are translocated to the nucleus, activating the expression of pathogenesis-related (PR) genes. Overexpression of NPR1 has conferred resistance to fungal, viral and bacterial pathogens in several plant species. The aim of this research was to generate transgenic olive plants expressing the gene AtNPR1 from Arabidopsis thaliana to obtain material resistant to fungal pathogens. Three transgenic lines expressing AtNPR1 gene under the control of the constitutive promoter CaMV35S were obtained following the protocol of Torreblanca et al. (2010), using an embryogenic line derived from a seed of cv. Picual. Level of AtNPR1 expression in transgenic calli varied greatly among the different lines, being higher in the line NPR1-780. The elicitation of embryogenic calli in liquid medium with AS did not increase endochitinase activity, a PR protein. However, jasmonic acid induced a transient increase in chitinase activity after 24 h of treatment in all the lines, being the increment higher in transgenic NPR1 than in control. After maturation and germination of transgenic somatic embryos, plants were micropropagated and acclimated to ex vitro conditions. The expression of AtNPR1 did not alter the growth of transgenic plants neither in vitro nor in the greenhouse. Experiments are in progress to determine the resistance of transgenic AtNPR1 plants to V. dalihae and R. necatrix.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Research projects: Plan Nacional AGL2014-52518-C2-1-R; AGL2017-83368-C2-1-R and Junta de Andalucía P11-AGR799

On the physics of transient ejection from bubble bursting

Using a dynamical scaling analysis of the flow variables and their evolution due to bubble bursting, here we predict the size and speed of ejected droplets for the whole range of experimental Ohnesorge and Bond numbers where ejection occurs. The transient ejection, which requires the backfire of a vortex ring inside the liquid to preserve physical symmetry, shows a delicate balance between inertia, surface tension and viscous forces around a critical Ohnesorge number, akin to an apparent singularity. Like in other natural phenomena, this balance makes the process extremely sensitive to initial conditions. Our model generalizes or displaces other recently proposed ones, impacting on, for instance, the statistical description of sea spray.Ministerio de Economía, Industria y Competitividad DPI2016-78887Ministerio de Economía, Industria y Competitividad PID2019-108278RBJunta de Andalucía P18-FR-362

Controle da podridão cinzenta da maçã por produtos naturais biologicamente ativos

Indexación: Web of Science; ScieloBiorend SC (chitosan), BC-1000 EC (grapefruit extract plus bioflavonoids) and ECO-100 SC (bioflavonoids plus organic acids, citric phytoalexins, fatty acids, glycerides and sugars), respectively, suppressed grey rot of apple caused by B. cinerea by 80.1%, 79.0% and 76.5% when used as post-harvest treatments under controlled conditions. When applied as combined pre- and post-harvest treatments Biorend SC inhibited fruit rot by 49.9 %, while BC-1000 EC and ECO-100 SC were ineffective. None of the products inhibited fruit rot when applied as pre-harvest treatments under controlled conditions or as post-harvest treatments under commercial conditions. The algal polysaccharide ulvan used in post-harvest treatments suppressed grey rot by 56.0% under controlled conditions, but had no inhibitory effect on combined pre- and post-harvest treatments. The inability of products to activate defense mechanisms (chitinase and peroxidase) of fruits was consistent with the unsuccessful control of rot by pre-harvest treatment. The results suggest that the natural products used have potential for use in integrated management of Botrytis rot when applied after harvest.Biorend SC (quitosana), BC-1000 EC (extrato de toranja mais flavonóides), e ECO-100 SC (bioflavonóides mais ácidos orgânicos, fitoalexinas cítricas, ácidos graxos glicerídeos e açúcares) inibiram em 80,1%, 79,0% e 76,5%, respectivamente, a podridão causada por Botrytis cinerea quando utilizados no tratamento pós-colheita de frutos de maçã sob condições controladas.Tratamento combinado de Biorend SC, com aplicação tanto em pré como no pós-colheita, proporcionou 49,9% de inibição da podridão, enquanto BC-1000 e ECO-100 EC não foram efetivos. Nenhum desses produtos inibiu a podridão cinzenta, quando utilizados em tratamento de pré-colheita em condições controladas ou em tratamento de pós-colheita em condições comerciais. O polissacarídeo algal ulvana, utilizado nos tratamentos de pós-colheita, reduziu em 56% a podridão cinzenta das maçãs em condições controladas, mas não teve efeito inibitório nos tratamentos combinados de pré e pós-colheita. A incapacidade dos produtos em ativar mecanismos de defesa (quitinases e peroxidases) nos frutos, após o tratamento em pré-colheita, foi consistente com a falta de controle da podridão nesse tipo de ensaio. Pelos resultados, sugere-se que os produtos naturais utilizados apresentam potencial para a utilização no manejo integrado da podridão de Botrytis quando aplicados em pós-colheita.http://ref.scielo.org/kcxb9

Metabolic and Stress Responses in Senegalese Soles (Solea senegalensis Kaup) Fed Tryptophan Supplements: E ects of Concentration and Feeding Period

The objective of this study was to assess the impact of di erent dietary Trp concentrations on the stress and metabolism response of juvenile Senegalese soles (Solea senegalensis). Fish (38.1 1.9 g) were fed di erent Trp-enriched feeds (0%, 1% and 2% Trp added) for two and eight days, and later exposed to air stress for three min. Samples were taken pre- and 1 h post-stress (condition). Plasma cortisol, lactate, glucose and proteins were significantly a ected by the sampling time, showing higher values at 1 h post-stress. Trp concentration in food also had significant e ects on lactate and glucose levels. However, the feeding period did not a ect these parameters. Post-stress values were higher than in the pre-stress condition for every plasma parameter, except for lactate in two days and 1% Trp treatment. Nevertheless, cortisol, glucose and lactate did not vary significantly between pre- and post-stress samplings in fish fed the 1% Trp-enriched diet for two days. The lack of variability in cortisol response was also due to the high pre-stress value, significantly superior to pre-stress control. The exposure time to Trp feeding did not significantly a ect any enzyme activity; however, Trp added and condition influenced protein-related enzyme activities. In spite of decreasing stress markers, Trp-enriched diets altered the protein metabolism

On the Logistical Difficulties and Findings of Jopara Sentiment Analysis

[Abstract] This paper addresses the problem of sentiment analysis for Jopara, a code-switching language between Guarani and Spanish. We first collect a corpus of Guarani-dominant tweets and discuss on the difficulties of finding quality data for even relatively easy-to-annotate tasks, such as sentiment analysis. Then, we train a set of neural models, including pre-trained language models, and explore whether they perform better than traditional machine learning ones in this low-resource setup. Transformer architectures obtain the best results, despite not considering Guarani during pre-training, but traditional machine learning models perform close due to the low-resource nature of the problem.DV is supported by a 2020 Leonardo Grant for Researchers and Cultural Creators from the FBBVA. 15 DV also receives funding from MINECO (ANSWER-ASAP, TIN2017-85160-C2-1-R), from Xunta de Galicia (ED431C 2020/11), from Centro de Investigación de Galicia ‘CITIC’, funded by Xunta de Galicia and the European Union (European Regional Development Fund- Galicia 2014-2020 Program) by grant ED431G 2019/01Xunta de Galicia; ED431C 2020/11Xunta de Galicia; ED431G 2019/01https://aclanthology.org/2021.calcs-

Descubriendo temas en Twitter sobre el brote del COVID-19 en España

[Resumen] En este trabajo, analizamos lo que los usuarios han estado discutiendo en Twitter durante el comienzo de la pandemia causada por el COVID-19. Concretamente, analizamos tres fases diferenciadas de la crisis del COVID-19 en España: el propio tiempo de pre-crisis, el estallido de la enfermedad y el confinamiento. Para llevar esto a cabo, primero recolectamos una gran cantidad de tuits que son preprocesados. A continuación, agrupamos los tuits en distintas temáticas usando un modelo de Latent Dirichlet Allocation, y definimos estrategias generativas y discriminativas para extraer las palabras clave y oraciones más representativas para cada tema. Finalmente, incluimos un exhaustivo análisis cualitativo sobre dichos temas, y cómo estos se corresponden con distintas problemáticas surgidas en España en distintos momentos de la crisis.[Abstract] In this work, we apply topic modeling to study what users have been discussing in Twitter during the beginning of the COVID-19 pandemic. More particularly, we explore the period of time that includes three differentiated phases of the COVID-19 crisis in Spain: the pre-crisis time, the outbreak, and the beginning of the lockdown. To do so, we first collect a large corpus of Spanish tweets and clean them. Then, we cluster the tweets into topics using a Latent Dirichlet Allocation model, and define generative and discriminative routes to later extract the most relevant keywords and sentences for each topic. Finally, we provide an exhaustive qualitative analysis about how such topics correspond to the situation in Spain at different stages of the crisis.MMAT has been partially funded by Barcelona Supercomputing Center (BSC) through the Spanish Plan for advancement of Language Technologies `Plan TL' and the Secretaría de Estado de Digitalización e Inteligencia Artificial (SEDIA). DV is supported by MINECO (TIN2017-85160-C2-1-R), by Xunta de Galicia (ED431C 2020/11), by Centro de Investigación de Galicia `CITIC' (European Regional Development Fund-Galicia 2014-2020 Program, ED431G 2019/01), and by a 2020 Leonardo Grant for Researchers and Cultural Creators from the BBVA FoundationXunta de Galicia; ED431C 2020/11Xunta de Galicia; ED431G 2019/0

Regular and complex singularities of the generalized thin film equation in two dimensions

A20We use a generalized version of the equation of motion for a thin film of liquid on a solid, horizontal substrate as a model system to study the formation of singularities in space dimensions greater than one. Varying both the exponent controlling long-ranged forces, as well as the exponent of the nonlinear mobility, we predict the structure of the singularity as the film thickness goes to zero. The spatial structure of rupture may be either ‘pointlike’ (approaching axisymmetry) or ‘quasi-one-dimensional’, in which case a one-dimensional singularity is unfolded into two or higher space dimensions. The scaling of the profile with time may be either strictly self-similar (the ‘regular’ case) or discretely self-similar and perhaps chaotic (the ‘irregular’ case). We calculate the phase boundaries between these regimes, and confirm our results by detailed comparisons with time-dependent simulations of the nonlinear thin film equation in two space dimensions.Junta de Andalucía P18-FR-3623Ministerio de Economía y Competitividad (MINECO). España 108278-RB-C3

Can we infer the presence of Differential Privacy in Deep Learning models' weights? Towards more secure Deep Learning

Differential Privacy (DP) is a key property to protect data and models from integrity attacks. In the Deep Learning (DL) field, it is commonly implemented through the Differentially Private Stochastic Gradient Descent (DP-SGD). However, when a model is shared or released, there is no way to check whether it is differentially private, that is, it required to trust the model provider. This situation poses a problem when data privacy is mandatory, specially with current data regulations, as the presence of DP can not be certificated consistently by any third party. Thus, we face the challenge of determining whether a DL model has been trained with DP, according to the title question: Can we infer the presence of Differential Privacy in Deep Learning models' weights? Since the DP-SGD significantly changes the training process of a DL model, we hypothesize that DP leaves an imprint in the weights of a DL model, which can be used to predict whether a model has been trained with DP regardless of its architecture and the training dataset. In this paper, we propose to employ the imprint in model weights of using DP to infer the presence of DP training in a DL model. To substantiate our hypothesis, we developed an experimental methodology based on two datasets of weights of DL models, each with models with and without DP training and a meta-classifier to infer whether DP was used in the training process of a DL model, by accessing its weights. We accomplish both, the removal of the requirement of a trusted model provider and a strong foundation for this interesting line of research. Thus, our contribution is an additional layer of security on top of the strict private requirements of DP training in DL models, towards to DL models