Processo de antagonismo de Dicyma pulvinata contra Fusicladium macrosporum em folhas de seringueira

The interaction between Dicyma pulvinata and Fusicladium macrosporum was studied by scanning electron microscopy. Spores of D. pulvinata germinated on the surface of F. macrosporum lesions induced on artificially infected rubber plants were fixed 8 h after inoculation. D. pulvinata germ tubes seemed to elongate toward F. macrosporum. Close contact between the antagonistic fungus and F. macrosporum spores was verified 24 h after application of D. pulvinata. At the end of the process, spores of F. macrosporum seemed to have disintegrated and to be devoid of content. The hyperparasite grew completely over the pathogen. Six to seven days after application of the antagonistic fungus, D. pulvinata conidiophores were observed emerging from F. macrosporum structures with profuse sporulation. Studies have also shown the possibility of D. pulvinata producing hydrolytic enzymes, which could be associated with the control of plant pathogens. This information may help to elucidate some of the modes of action of D. pulvinata, a potential biological control agent for South American leaf blight of Hevea rubber plant.Estudou-se a interação entre Dicyma pulvinata e F. macrosporum ao microscópio eletrônico de varredura. Esporos de D. pulvinata germinaram na superfície das lesões induzidas por F. macrosporum em plantas de seringueira (Hevea brasiliensis), infectadas artificialmente, fixadas 8 h após a inoculação do antagonista. Aparentemente, os tubos germinativos se alongaram em direção ao patógeno. O contato íntimo entre o hiperparasita e o patógeno foi verificado em amostras fixadas 24 h após a aplicação de D. pulvinata. Ao término do processo, os esporos de F. macrosporum aparentemente invadidos pelo antagonista mostraram-se desintegrados e esvaziados de seu conteúdo. D. pulvinata cresceu sobre as lesões, sobrepondo totalmente o patógeno. Seis dias após a aplicação, conidióforos do fungo antagonista foram observados emergindo das estruturas do patógeno, produzindo esporos em grande quantidade. Verificou-se, também, um possível envolvimento de enzimas hidrolíticas na associação antagonística entre D. pulvinata e o patógeno. Estas informações podem contribuir para elucidar o modo de ação de D. pulvinata, um potencial agente de controle biológico para o mal das folhas da seringueira

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

Initial findings of striatum tripartite model in OCD brain samples based on transcriptome analysis.

Obsessive-compulsive disorder (OCD) is a psychiatric disorder characterized by obsessions and/or compulsions. Different striatal subregions belonging to the cortico-striato-thalamic circuitry (CSTC) play an important role in the pathophysiology of OCD. The transcriptomes of 3 separate striatal areas (putamen (PT), caudate nucleus (CN) and accumbens nucleus (NAC)) from postmortem brain tissue were compared between 6 OCD and 8 control cases. In addition to network connectivity deregulation, different biological processes are specific to each striatum region according to the tripartite model of the striatum and contribute in various ways to OCD pathophysiology. Specifically, regulation of neurotransmitter levels and presynaptic processes involved in chemical synaptic transmission were shared between NAC and PT. The Gene Ontology terms cellular response to chemical stimulus, response to external stimulus, response to organic substance, regulation of synaptic plasticity, and modulation of synaptic transmission were shared between CN and PT. Most genes harboring common and/or rare variants previously associated with OCD that were differentially expressed or part of a least preserved coexpression module in our study also suggest striatum subregion specificity. At the transcriptional level, our study supports differences in the 3 circuit CSTC model associated with OCD

Initial findings of striatum tripartite model in OCD brain samples based on transcriptome analysis.

Obsessive-compulsive disorder (OCD) is a psychiatric disorder characterized by obsessions and/or compulsions. Different striatal subregions belonging to the cortico-striato-thalamic circuitry (CSTC) play an important role in the pathophysiology of OCD. The transcriptomes of 3 separate striatal areas (putamen (PT), caudate nucleus (CN) and accumbens nucleus (NAC)) from postmortem brain tissue were compared between 6 OCD and 8 control cases. In addition to network connectivity deregulation, different biological processes are specific to each striatum region according to the tripartite model of the striatum and contribute in various ways to OCD pathophysiology. Specifically, regulation of neurotransmitter levels and presynaptic processes involved in chemical synaptic transmission were shared between NAC and PT. The Gene Ontology terms cellular response to chemical stimulus, response to external stimulus, response to organic substance, regulation of synaptic plasticity, and modulation of synaptic transmission were shared between CN and PT. Most genes harboring common and/or rare variants previously associated with OCD that were differentially expressed or part of a least preserved coexpression module in our study also suggest striatum subregion specificity. At the transcriptional level, our study supports differences in the 3 circuit CSTC model associated with OCD