Antibiosis in Ascia monuste orseis Godart (Lepidoptera: Pieridae) caused by kale genotypes

Ascia monuste orseis (Lepidoptera: Pieridae) is one of the main insect pests of kale. The study was done to  identify kale varieties resistant to A. monuste orseis by the antibiosis resistance mechanism. Kale genotypes (26) were evaluated in experiments performed at the Laboratory of Agricultural Entomology of Goiano Federal Institute - Campus Urutaí. A completely randomized experimental design with 50 replicates was used. The biological parameters evaluated were (a) larval stage: development time, instars, viability and larval weight 15 days after hatching; (b) pupal stage: development time, weight of 24-h-old pupae, viability; (c) larvae-adult stage: development time and viability. The genotypes Gigante I-915 and Pires 1 de Campinas have antibiosis resistance. Gigante I-915 caused high larval mortality and Pires 1 de Campinas resulted in low larval and pupal viability of A. monuste orseis.Key words: Brassica oleracea L. var. acephala, Brassicaceae, Great Southern White, host plant resistance, integrated pest management (IPM)

Charcoal chronology of the Amazon forest: A record of biodiversity preserved by ancient fires

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.he Amazon region holds a wide variety of ethnic groups and microclimates, enabling different interactions between humans and environment. To better understand the evolution of this region, ancient remains need to be analysed by all possible means. In this context, the study of natural and/or anthropogenic fires through the analysis of carbonized remains can give information on past climate, species diversity, and human intervention in forests and landscapes. In the present work, we undertook an anthracological analysis along with the 14 C dating of charcoal fragments using accelerator mass spectrometry (AMS). Charcoal samples from forest soils collected from seven different locations in the Amazon Basin were taxonomically classified and dated. Out of the 16 groups of charcoal fragments identified, five contained more than one taxonomic type, with the Fabaceae, Combretaceae and Sapotaceae families having the highest frequencies. 14 C charcoal dates span ∼6000 years (from 6876 to 365 yr BP) among different families, with the most significant variation observed for two fragments from the same sampling location (spanning 4000 14 C yr). Some sample sets resulted in up to five different families. These findings demonstrate the importance of the association between anthracological identification and radiocarbon dating in the reconstruction of paleo-forest composition and fire history.The authors thank the Brazilian agencies Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), and Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ). KDM thanks CNPq for fellowship 305079/2014–0. CL thanks FAPEAM/FAPESP (09/53369-6, led by Flávia Regina Capellotto Costa) for financial support and Thaise Emílio, José Luiz Purri da Veiga Pinto, Rosineide Machado and Francislaide da Silva Costa for help with charcoal collection. TRF, BSM, and BHM acknowledge financial support from NERC (NE/N011570/1), CAPES/CNPq Science without Borders (PVE 177/2012 and PVE 401279/2014-6), CNPq/PPBio (457602/2012-0), CNPq/PELD (403725/2012-7) and the University of Exeter - College of Life and Environmental Sciences

Gamification, citizen science, and civic technologies: In search of the common good

In this paper, we discuss the importance of gameplay as a valuable tool in citizen sensing initiatives aimed at enabling creative collaboration and civic engagement. We present a review of selected citizen science and civic technologies’ projects highlighting an emerging culture of massive collaborative initiatives that make use of crowdsourcing, enabling users to voluntarily contribute their time, effort and resources towards scientific research and civic issues. Moreover, we discuss how these initiatives could benefit from the inclusion of gameplay in their interaction processes. For that matter, we present a gamified citizen sensing project we are devising for users to enter and retrieve information on commercially available food products which contain ingredients associated with an increased risk of cancer and other diseases. Through gameplay, we expect to crowdsource an open database of potentially unhealthy food products, raising awareness among consumers about the risks of certain artificial additives. Finally, we argue that the use of gamification processes can engage voluntary participation in initiatives aimed at citizenship – including those which demand complex and repetitive tasks for the collection of data – and call for a more ethical, critical, and meaningful use of these new potential technologies, and for greater awareness of our new civic responsibilities

Towards Intelligent Crowd Behavior Understanding through the STFD Descriptor Exploration

Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of input video signals. This integrated solution defines an image descriptor (named spatio-temporal feature descriptor - STFD) that reflects the global motion information of crowds over time. A CNN has then been adopted to classify dominant or large-scale crowd abnormal behaviors. The work reported has focused on: 1) detecting moving objects in online (or near real-time) manner through spatio-temporal segmentations of crowds that is defined by the similarity of group trajectory structures in temporal space and the foreground blocks based on Gaussian Mixture Model (GMM) in spatial space; 2) dividing multiple clustered groups based on the spectral clustering method by considering image pixels from spatio-temporal segmentation regions as dynamic particles; 3) generating the STFD descriptor instances by calculating the attributes (i.e., collectiveness, stability, conflict and crowd density) of particles in the corresponding groups; 4) inputting generated STFD descriptor instances into the devised convolutional neural network (CNN) to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the PETS database as the primary experimental data sets. Results against benchmarking models and systems have shown promising advancements of this novel approach in terms of accuracy and efficiency for detecting crowd anomalies

Towards Intelligent Crowd Behavior Understanding through the STFD Descriptor Exploration

Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of input video signals. This integrated solution defines an image descriptor (named spatio-temporal feature descriptor - STFD) that reflects the global motion information of crowds over time. A CNN has then been adopted to classify dominant or large-scale crowd abnormal behaviors. The work reported has focused on: 1) detecting moving objects in online (or near real-time) manner through spatio-temporal segmentations of crowds that is defined by the similarity of group trajectory structures in temporal space and the foreground blocks based on Gaussian Mixture Model (GMM) in spatial space; 2) dividing multiple clustered groups based on the spectral clustering method by considering image pixels from spatio-temporal segmentation regions as dynamic particles; 3) generating the STFD descriptor instances by calculating the attributes (i.e., collectiveness, stability, conflict and crowd density) of particles in the corresponding groups; 4) inputting generated STFD descriptor instances into the devised convolutional neural network (CNN) to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the PETS database as the primary experimental data sets. Results against benchmarking models and systems have shown promising advancements of this novel approach in terms of accuracy and efficiency for detecting crowd anomalies

Genotoxicity biomonitoring of sewage in two municipal wastewater treatment plants using the Tradescantia pallida var. purpurea bioassay

The genotoxicity of untreated and treated sewage from two municipal wastewater treatment plants (WTP BN and WTP SJN) in the municipality of Porto Alegre, in the southern Brazilian state of Rio Grande do Sul, was evaluated over a one-year period using the Tradescantia pallida var. purpurea (Trad-MCN) bioassay. Inflorescences of T. pallida var. purpurea were exposed to sewage samples in February (summer), April (autumn), July (winter) and October (spring) 2009, and the micronuclei (MCN) frequencies were estimated in each period. The high genotoxicity of untreated sewage from WTP BN in February and April was not observed in treated sewage, indicating the efficiency of treatment at this WTP. However, untreated and treated sewage samples from WTP SJN had high MCN frequencies, except in October, when rainfall may have been responsible for reducing these frequencies at both WTPs. Physicochemical analyses of sewage from both WTPs indicated elevated concentrations of organic matter that were higher at WTP SJN than at WTP BN. Chromium was detected in untreated and treated sewage from WTP SJN, but not in treated sewage from WTP BN. Lead was found in all untreated sewage samples from WTP SJN, but only in the summer and autumn at WTP BN. These results indicate that the short-term Trad-MCN genotoxicity assay may be useful for regular monitoring of municipal WTPs