Origins of the Xylella fastidiosa Prophage-Like Regions and Their Impact in Genome Differentiation

Xylella fastidiosa is a Gram negative plant pathogen causing many economically important diseases, and analyses of completely sequenced X. fastidiosa genome strains allowed the identification of many prophage-like elements and possibly phage remnants, accounting for up to 15% of the genome composition. To better evaluate the recent evolution of the X. fastidiosa chromosome backbone among distinct pathovars, the number and location of prophage-like regions on two finished genomes (9a5c and Temecula1), and in two candidate molecules (Ann1 and Dixon) were assessed. Based on comparative best bidirectional hit analyses, the majority (51%) of the predicted genes in the X. fastidiosa prophage-like regions are related to structural phage genes belonging to the Siphoviridae family. Electron micrograph reveals the existence of putative viral particles with similar morphology to lambda phages in the bacterial cell in planta. Moreover, analysis of microarray data indicates that 9a5c strain cultivated under stress conditions presents enhanced expression of phage anti-repressor genes, suggesting switches from lysogenic to lytic cycle of phages under stress-induced situations. Furthermore, virulence-associated proteins and toxins are found within these prophage-like elements, thus suggesting an important role in host adaptation. Finally, clustering analyses of phage integrase genes based on multiple alignment patterns reveal they group in five lineages, all possessing a tyrosine recombinase catalytic domain, and phylogenetically close to other integrases found in phages that are genetic mosaics and able to perform generalized and specialized transduction. Integration sites and tRNA association is also evidenced. In summary, we present comparative and experimental evidence supporting the association and contribution of phage activity on the differentiation of Xylella genomes

Occurrence of eggs and oocysts of gastrointestinal parasites in passerine birds kept in captivity in Para State, Brazil Ocorrência de ovos e oocistos de parasitos gastrointestinais em aves Passeriformes mantidas em cativeiro no estado do Pará, Brasil

ABSTRACT The objective of this study was to detect helminth eggs and protozoan oocysts in samples of feces from birds of the orde

Neurostimulation Combined With Cognitive Intervention in Alzheimer’s Disease (NeuroAD): Study Protocol of Double-Blind, Randomized, Factorial Clinical Trial

Despite advances in the treatment of Alzheimer’s disease (AD), there is currently no prospect of a cure, and evidence shows that multifactorial interventions can benefit patients. A promising therapeutic alternative is the use of transcranial direct current stimulation (tDCS) simultaneously with cognitive intervention. The combination of these non-pharmacological techniques is apparently a safe and accessible approach. This study protocol aims to compare the efficacy of tDCS and cognitive intervention in a double-blind, randomized and factorial clinical trial. One hundred participants diagnosed with mild-stage AD will be randomized to receive both tDCS and cognitive intervention, tDCS, cognitive intervention, or placebo. The treatment will last 8 weeks, with a 12-month follow-up. The primary outcome will be the improvement of global cognitive functions, evaluated by the AD Assessment Scale, cognitive subscale (ADAS-Cog). The secondary outcomes will include measures of functional, affective, and behavioral components, as well as a neurophysiological marker (Brain-derived neurotrophic factor, BDNF). This study will enable us to assess, both in the short and long term, whether tDCS is more effective than the placebo and to examine the effects of combined therapy (tDCS and cognitive intervention) and isolated treatments (tDCS vs. cognitive intervention) on patients with AD.Clinical Trial Registration: www.ClinicalTrials.gov, identifier NCT02772185—May 5, 2016

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

The AJ156 antibody recognizes the Dictyostelium Talin A protein by immunofluorescence

The AJ156 antibody, derived from the 169-477-5 hybridoma, detects by immunofluorescence the full-length Talin A protein from Dictyostelium discoideum

The AJ156 antibody recognizes the Dictyostelium Talin A protein by Western blot

The AJ156 antibody, derived from the 169-477-5 hybridoma, detects by Western blot the full-length Talin A protein from Dictyostelium discoideum

The AK423 antibody recognizes Dictyostelium actin network by immunofluorescence

The AK423 antibody, derived from the 224-236-1 hybridoma, detects by immunofluorescence the actin network of Dictyostelium discoideum