Rhabdocaulon strictus (Benth.) Epling (Lamiaceae) in wetlands of the Paraná River: a new record for the Santa Fe flora, Argentina

El presente trabajo tiene como objetivo comunicar una nueva cita para la flora de los humedales de la Provincia de Santa Fe: Rhabdocaulon strictus (Benth.) Epling. El trabajo incluye además, aportes inherentes a su distribución, hábitat, nomenclatura, etimología, usos, y estado de conservación.The aim of this paper is to communicate a new citation for the flora of the Santa Fe Province’s wetlands, Rhabdocaulon strictus (Benth.) Epling. This work also includes details on its geographic distribution, habitat, nomenclature, etymology, uses and conservation status.Fil: Brumnich, Federico. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; ArgentinaFil: Marchetti, Zuleica Yael. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentin

Optimization Algorithms for Computational Systems Biology

Computational systems biology aims at integrating biology and computational methods to gain a better understating of biological phenomena. It often requires the assistance of global optimization to adequately tune its tools. This review presents three powerful methodologies for global optimization that fit the requirements of most of the computational systems biology applications, such as model tuning and biomarker identification. We include the multi-start approach for least squares methods, mostly applied for fitting experimental data. We illustrate Markov Chain Monte Carlo methods, which are stochastic techniques here applied for fitting experimental data when a model involves stochastic equations or simulations. Finally, we present Genetic Algorithms, heuristic nature-inspired methods that are applied in a broad range of optimization applications, including the ones in systems biology

Identification of a novel motif in DNA ligases exemplified by DNA ligase IV

DNA ligase IV is an essential protein that functions in DNA non-homologous end-joining, the major mechanism that rejoins DNA double-strand breaks in mammalian cells. LIG4 syndrome represents a human disorder caused by mutations in DNA ligase IV that lead to impaired but not ablated activity. Thus far, five conserved motifs in DNA ligases have been identified. We previously reported G469E as a mutational change in a LIG4 syndrome patient. G469 does not lie in any of the previously reported motifs. A sequence comparison between DNA ligases led us to identify residues 468¿476 of DNA ligase IV as a further conserved motif, designated motif Va, present in eukaryotic DNA ligases. We carried out mutational analysis of residues within motif Va examining the impact on adenylation, double-stranded ligation, and DNA binding. We interpret our results using the DNA ligase I:DNA crystal structure. Substitution of the glycine at position 468 with an alanine or glutamic acid severely compromises protein activity and stability. Substitution of G469 with an alanine or glutamic acid is better tolerated but still impacts upon activity and protein stability. These finding suggest that G468 and G469 are important for protein stability and provide insight into the hypomorphic nature of the G469E mutation identified in a LIG4 syndrome patient. In contrast, residues 470, 473 and 476 within motif Va can be changed to alanine residues without any impact on DNA binding or adenylation activity. Importantly, however, such mutational changes do impact upon double-stranded ligation activity. Considered in light of the DNA ligase I:DNA crystal structure, our findings suggest that residues 470¿476 function as part of a molecular pincer that maintains the DNA in a conformation that is required for ligation

Insights and expectations for Tdap vaccination of pregnant women in Italy

Abstract Background: Pertussis is a widespread vaccine-preventable disease, associated with an increasing trend to hospitalization among newborns. Pertussis in newborns can be fatal, and the most e..

Rotavirus and the web: analysis of online conversations in Italy during 2020

Rotavirus is the most common cause of severe diarrhea among children worldwide. In 2017, Italy included rotavirus vaccination in its National Immunization Program. The use of social media monitoring, an efficient tool to understand vaccine hesitancy, has increased in recent years; however, only a few examples of such monitoring are available for Italy. Present study analyzed content on online sources, including social media, to identify factors contributing to Italian parents' decisions to vaccinate or not their children against rotavirus. Blogmeter Suite was used to search and analyze conversations related to rotavirus in Italian on online sources during 2020. These data were compared with data from 2019. There were 2250 mentions of "rotavirus" recorded; 1080 were related to the rotavirus vaccine. Terms and hashtags used were similar in both years. Facebook was the main source of influence, Instagram dominated the engagement (the sum of interactions related to a post), and Google Trends showed a 5-year upward trend in searches for rotavirus vaccine. Of 1270 sentiment opinions, 60.7% were negative. More parents were familiar with the disease and the vaccine in 2020 compared with 2019. Pediatricians were the most influential healthcare professionals (59.2% of mentions), followed by vaccination staff (33.4%). The most relevant factors for vaccine hesitancy were fear of adverse events, concerns about the vaccination schedule, and COVID-19. Present study represents the first web listening analysis of online discussions about rotavirus. The results can be used to inform targeted communication to counteract misinformation and raise awareness about rotavirus vaccination among parents

Changes in forest diversity over a chronosequence of fluvial islands

The high environmental heterogeneity of large fluvial systems is reflected by the co-existence of contrasting plant communities and landforms. The main objective of this study was to assess the forest diversity changes in islands of the Middle Paraná River (Argentina) in order to discuss an integrative question: how synchronized are the major changes in the features of islands and forests? Persistence age, elevation and flood regime of 11 main channel islands were determined. Variables related to the vascular plant community and the tree stand structure of forests were also measured in 400 m2 plots. Islands were classified as young or old (YIs or OIs), according to their persistence age, which ranged from two to 108 years. Both island classes differed in their elevation but not in the proportion of low water phase. Only three out of nine tree species were dominant: Tessaria integrifolia and Croton urucurana (restricted to YIs and OIs, respectively), and Salix humboldtiana (distributed in both island classes). Alpha diversity was positively correlated with the age of the YIs and reach the highest value in the oldest island forest. Beta diversity was mainly due to processes of species replacement which differentiate floodplain forests. Gamma diversity reached 101 species, being the perennial herbs aclear majority. The stand structure and the complete floristic composition were significantly different between YIs and OIs, with three and seven indicator species of each island class, respectively. Considering integrative models ofsuccession, our findings suggest that the biogeomorphic phase, recognised by the fluvial biogeomorphic model, prevailed in the whole range of island persistence ages. Therefore, it seems that the increase in forest diversity in a large river is restricted to spatial refugia defined by major hydrogeomorphic shifts.Fil: Brumnich, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral; ArgentinaFil: Marchetti, Zuleica Yael. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral; ArgentinaFil: Pereira, María Soledad. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

MANTRA: Memory Augmented Networks for Multiple Trajectory Prediction

Autonomous vehicles are expected to drive in complex scenarios with several independent non cooperating agents. Path planning for safely navigating in such environments can not just rely on perceiving present location and motion of other agents. It requires instead to predict such variables in a far enough future. In this paper we address the problem of multimodal trajectory prediction exploiting a Memory Augmented Neural Network. Our method learns past and future trajectory embeddings using recurrent neural networks and exploits an associative external memory to store and retrieve such embeddings. Trajectory prediction is then performed by decoding in-memory future encodings conditioned with the observed past. We incorporate scene knowledge in the decoding state by learning a CNN on top of semantic scene maps. Memory growth is limited by learning a writing controller based on the predictive capability of existing embeddings. We show that our method is able to natively perform multi-modal trajectory prediction obtaining state-of-the art results on three datasets. Moreover, thanks to the non-parametric nature of the memory module, we show how once trained our system can continuously improve by ingesting novel patterns.Comment: Accepted at CVPR2

Multiple Trajectory Prediction of Moving Agents with Memory Augmented Networks

Pedestrians and drivers are expected to safely navigate complex urban environments along with several non cooperating agents. Autonomous vehicles will soon replicate this capability. Each agent acquires a representation of the world from an egocentric perspective and must make decisions ensuring safety for itself and others. This requires to predict motion patterns of observed agents for a far enough future. In this paper we propose MANTRA, a model that exploits memory augmented networks to effectively predict multiple trajectories of other agents, observed from an egocentric perspective. Our model stores observations in memory and uses trained controllers to write meaningful pattern encodings and read trajectories that are most likely to occur in future. We show that our method is able to natively perform multi-modal trajectory prediction obtaining state-of-the art results on four datasets. Moreover, thanks to the non-parametric nature of the memory module, we show how once trained our system can continuously improve by ingesting novel patterns

A comprehensive theoretical framework for the optimization of neural networks classification performance with respect to weighted metrics

In many contexts, customized and weighted classification scores are designed in order to evaluate the goodness of the predictions carried out by neural networks. However, there exists a discrepancy between the maximization of such scores and the minimization of the loss function in the training phase. In this paper, we provide a complete theoretical setting that formalizes weighted classification metrics and then allows the construction of losses that drive the model to optimize these metrics of interest. After a detailed theoretical analysis, we show that our framework includes as particular instances well-established approaches such as classical cost-sensitive learning, weighted cross entropy loss functions and value-weighted skill scores