Environmental control of terpene emissions from Cistus monspeliensis L. in natural Mediterranean shrublands

The large amount of volatile organic compound (VOC) emitted by vegetation modifies air quality contributing to both tropospheric ozone and secondary organic aerosol production. A better understanding of the factors controlling VOC emissions by vegetation is mandatory in order to improve emission estimates derived from tropospheric chemistry models. Although the Mediterranean shrublands are particularly abundant and rich in emitting species, their emission potential is poorly known. Focusing on a VOC-emitting shrub species widespread in the Mediterranean area (Cistus monspeliensis L.), we measured and analysed its emissions of terpenes taking into account the age of individuals, the season of sampling and the soil type. Sampling was done under natural environmental conditions. Species of the genus Cistus are frequently reported to be storing species, although we found only one stored monoterpene and three sesquiterpenes in very low amount. Major emitted compounds were a-pinene and b-myrcene. Total terpene emissions were not influenced by plant age but emission of some individual terpenes was positively correlated with age. A strong seasonal effect was evidenced. A larger amount of terpenes was emitted during spring and summer than during fall and winter. Summer emission rates were nearly 70 times higher than winter emission rates. Total and individual terpene emissions were influenced by soil type; emissions on siliceous substrate were ca. seven times higher than those on calcareous substrate. In conclusion, it appears clearly that environmental factors such as soil nature and season should be taken into account in order to achieve improved modelling of terpene emissions by shrub species

Biomassa e estoques de nutrientes em vegetação de pousio sob diferentes manejos em sistema agroflorestal seqüencial de corte-e-trituração na Amazônia Oriental.

O manejo da vegetação de pousio é importante para manutenção da produtividade em sistemas agroflorestais seqüenciais. Durante o período de pousio, o sistema acumula nutrientes para as culturas agrícolas subseqüentes. A introdução de espécies leguminosas associadas à adubação fosfatada de baixa solubilidade pode promover o acúmulo de biomassa e os estoques de nutrientes influenciando positivamente na produtividade das culturas agrícolas. O estudo da biomassa e dos estoques de nutrientes nesses agroecossistemas pode fornecer subsídios para o seu manejo. Este artigo compara estimativas da biomassa e estoques de nutrientes de três vegetações de pousio submetidos a diferentes tratamentos: (1) pousio espontâneo; (2) pousio enriquecido com leguminosas arbóreas (Sclerolobium paniculatum Vogel e Inga edulis Mart.), e (3) pousio enriquecido com leguminosas arbóreas submetidas à adubação fosfatada de baixa solubilidade. O experimento foi conduzido por 23 meses, em um sistema agroflorestal seqüencial de corte-e-trituração no município de Marapanim, Amazônia Oriental. Os resultados mostraram que o sistema de pousio enriquecido com leguminosas arbóreas, submetidas ou não à adubação fosfatada de baixa solubilidade, acumula maiores massas secas e estoques de nutrientes que o sistema com pousio espontâneoEditores técnicos: Roberto Porro, Milton Kanashiro, Maria do Socorro Gonçalves Ferreira, Leila Sobral Sampaio e Gladys Ferreira de Sousa

MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response

Predicting clinical outcome is remarkably important but challenging. Research efforts have been paid on seeking significant biomarkers associated with the therapy response or/and patient survival. However, these biomarkers are generally costly and invasive, and possibly dissatifactory for novel therapy. On the other hand, multi-modal, heterogeneous, unaligned temporal data is continuously generated in clinical practice. This paper aims at a unified deep learning approach to predict patient prognosis and therapy response, with easily accessible data, e.g., radiographics, laboratory and clinical information. Prior arts focus on modeling single data modality, or ignore the temporal changes. Importantly, the clinical time series is asynchronous in practice, i.e., recorded with irregular intervals. In this study, we formalize the prognosis modeling as a multi-modal asynchronous time series classification task, and propose a MIA-Prognosis framework with Measurement, Intervention and Assessment (MIA) information to predict therapy response, where a Simple Temporal Attention (SimTA) module is developed to process the asynchronous time series. Experiments on synthetic dataset validate the superiory of SimTA over standard RNN-based approaches. Furthermore, we experiment the proposed method on an in-house, retrospective dataset of real-world non-small cell lung cancer patients under anti-PD-1 immunotherapy. The proposed method achieves promising performance on predicting the immunotherapy response. Notably, our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.Comment: MICCAI 2020 (Early Accepted; Student Travel Award

Genetic diversity, phylogenetic and phylogeographic analyses of Oncideres impluviata (Germar, 1823) (Coleoptera: Cerambycidae) in Rio Grande do Sul state, Brazil.

The Cerambycidae Oncideres impluviata (Germar, 1823) is an important insect pest for Acacia mearnsii De Wild in Southern Brazil. The damage caused by their girdling behavior reduces tree productivity, specially in the early years of plant establishment, when girdling is performed on the main trunk of trees. Here, we used a fragment of the mtDNA COI gene to analyze the genetic diversity, population structure and demography of O. impluviata in Southern Brazil, as well as to present the first hypothesis of phylogenetic relationships among species of the genus Oncideres. Our results identified five distinct haplotypes among the populations of O. impluviata, with the most common haplotype identified as O.imp_COI_01. The phylogenetic inferences corroborated the monophyly of O. impluviata with maximum statistical support. In addition, the phylogeny recovered three main population strains that are largely congruent with the haplotype network, which includes two lineages that are found in different edaphic regions of Rio Grande do Sul (Serra do Sudeste and Encosta Inferior do Nordeste). This is the first molecular phylogenetic assessment of O. impluviata. Our findings provide insights into the evolution of a significant species for the Brazilian forestry sector, as well as new resources for planning of pest management strategies

The interaction between the proliferating macroalga Asparagopsis taxiformis and the coral Astroides calycularis induces changes in microbiome and metabolomic fingerprints

Mediterranean Sea ecosystems are considered as hotspots of biological introductions, exposed to possible negative effects of non-indigenous species. In such temperate marine ecosystems, macroalgae may be dominant, with a great percentage of their diversity represented by introduced species. Their interaction with temperate indigenous benthic organisms have been poorly investigated. To provide new insights, we performed an experimental study on the interaction between the introduced proliferative red alga Asparagopsis taxiformis and the indigenous Mediterranean coral Astroides calycularis. The biological response measurements included meta-barcoding of the associated microbial communities and metabolomic fingerprinting of both species. Significant changes were detected among both associated microbial communities, the interspecific differences decreasing with stronger host interaction. No short term effects of the macroalga on the coral health, neither on its polyp activity or its metabolism, were detected. In contrast, the contact interaction with the coral induced a change in the macroalgal metabolomic fingerprint with a significant increase of its bioactivity against the marine bacteria Aliivibrio fischeri. This induction was related to the expression of bioactive metabolites located on the macroalgal surface, a phenomenon which might represent an immediate defensive response of the macroalga or an allelopathic offense against coral.ERA-NET Biome project "SEAPROLIF"; CNRS; Provence Alpes Cote d'Azur Region; TOTAL Fundation; Fundacao para a Ciencia e a Tecnologia (FCT) [Netbiome/0002/2011]; FCT fellowships [SFRH/BPD/63703/2009, SFRH/BPD/107878/2015]info:eu-repo/semantics/publishedVersio

On the relevance of preprocessing in predictive maintenance for dynamic systems

The complexity involved in the process of real-time data-driven monitoring dynamic systems for predicted maintenance is usually huge. With more or less in-depth any data-driven approach is sensitive to data preprocessing, understood as any data treatment prior to the application of the monitoring model, being sometimes crucial for the final development of the employed monitoring technique. The aim of this work is to quantify the sensitiveness of data-driven predictive maintenance models in dynamic systems in an exhaustive way. We consider a couple of predictive maintenance scenarios, each of them defined by some public available data. For each scenario, we consider its properties and apply several techniques for each of the successive preprocessing steps, e.g. data cleaning, missing values treatment, outlier detection, feature selection, or imbalance compensation. The pretreatment configurations, i.e. sequential combinations of techniques from different preprocessing steps, are considered together with different monitoring approaches, in order to determine the relevance of data preprocessing for predictive maintenance in dynamical systems

Detecting human Activities Based on a multimodal sensor data set using a bidirectional long short-term memory model: a case study

Human falls are one of the leading causes of fatal unintentional injuries worldwide. Falls result in a direct financial cost to health systems, and indirectly, to society’s productivity. Unsurprisingly, human fall detection and prevention is a major focus of health research. In this chapter, we present and evaluate several bidirectional long short-term memory (Bi-LSTM) models using a data set provided by the Challenge UP competition. The main goal of this study is to detect 12 human daily activities (six daily human activities, five falls, and one post-fall activity) derived from multi-modal data sources - wearable sensors, ambient sensors, and vision devices. Our proposed Bi-LSTM model leverages data from accelerometer and gyroscope sensors located at the ankle, right pocket, belt, and neck of the subject. We utilize a grid search technique to evaluate variations of the Bi-LSTM model and identify a configuration that presents the best results. The best Bi-LSTM model achieved good results for precision and f1-score, 43.30% and 38.50%, respectivel