Genetic characterization of cassava (Manihot esculenta) landraces in Brazil assessed with simple sequence repeats

Based on nine microsatellite loci, the aim of this study was to appraise the genetic diversity of 42 cassava (Manihot esculenta) landraces from selected regions in Brazil, and examine how this variety is distributed according to origin in several municipalities in the states of Minas Gerais, São Paulo, Mato Grosso do Sul, Amazonas and Mato Grosso. High diversity values were found among the five above-mentioned regions, with 3.3 alleles per locus on an average, a high percentage of polymorphic loci varying from 88.8% to 100%, an average of 0.265 for observed heterozygosity and 0.570 for gene diversity. Most genetic diversity was concentrated within the regions themselves (HS = 0.52). Cluster analysis and principal component based scatter plotting showed greater similarity among landraces from São Paulo, Mato Grosso do Sul and Amazonas, whereas those from Minas Gerais were clustered into a sub-group within this group. The plants from Mato Grosso, mostly collected in the municipality of General Carneiro, provided the highest differentiation. The migration of human populations is one among the possible reasons for this closer resemblance or greater disparity among plants from the various regions

Convolutional Neural Networks to Estimate Dry Matter Yield in a Guineagrass Breeding Program Using UAV Remote Sensing.

Forage dry matter is the main source of nutrients in the diet of ruminant animals. Thus, this trait is evaluated in most forage breeding programs with the objective of increasing the yield. Novel solutions combining unmanned aerial vehicles (UAVs) and computer vision are crucial to increase the efficiency of forage breeding programs, to support high-throughput phenotyping (HTP), aiming to estimate parameters correlated to important traits. The main goal of this study was to propose a convolutional neural network (CNN) approach using UAV-RGB imagery to estimate dry matter yield traits in a guineagrass breeding program. For this, an experiment composed of 330 plots of full-sib families and checks conducted at Embrapa Beef Cattle, Brazil, was used. The image dataset was composed of images obtained with an RGB sensor embedded in a Phantom 4 PRO. The traits leaf dry matter yield (LDMY) and total dry matter yield (TDMY) were obtained by conventional agronomic methodology and considered as the ground-truth data. Different CNN architectures were analyzed, such as AlexNet, ResNeXt50, DarkNet53, and two networks proposed recently for related tasks named MaCNN and LF-CNN. Pretrained AlexNet and ResNeXt50 architectures were also studied. Ten-fold cross-validation was used for training and testing the model. Estimates of DMY traits by each CNN architecture were considered as new HTP traits to compare with real traits. Pearson correlation coefficient r between real and HTP traits ranged from 0.62 to 0.79 for LDMY and from 0.60 to 0.76 for TDMY; root square mean error (RSME) ranged from 286.24 to 366.93 kg·ha−1 for LDMY and from 413.07 to 506.56 kg·ha−1 for TDMY. All the CNNs generated heritable HTP traits, except LF-CNN for LDMY and AlexNet for TDMY. Genetic correlations between real and HTP traits were high but varied according to the CNN architecture. HTP trait from ResNeXt50 pretrained achieved the best results for indirect selection regardless of the dry matter trait. This demonstrates that CNNs with remote sensing data are highly promising for HTP for dry matter yield traits in forage breeding programs

In Vitro and In Vivo Activity of a Palladacycle Complex on Leishmania (Leishmania) amazonensis

Leishmaniasis is an important public health problem with an estimated annual incidence of 1.5 million of new human cases of cutaneous leishmaniasis and 500,000 of visceral leishmaniasis. Treatment of the diseases is limited by toxicity and parasite resistance to the drugs currently in use, validating the need to develop new leishmanicidal compounds. We evaluated the killing by the palladacycle complex DPPE 1.2 of Leishmania (Leishmania) amazonensis, an agent of human cutaneous leishmaniasis in the Amazon region, Brazil. DPPE 1.2 destroyed promastigotes of L. (L.) amazonensis in vitro at nanomolar concentrations, whereas intracellular amastigotes were killed at drug concentrations 10-fold less toxic than those displayed to macrophages. L. (L.) amazonensis-infected BALB/c mice treated by intralesional injection of DPPE 1.2 exhibited a significant decrease of foot lesion sizes and a 97% reduction of parasite burdens when compared to untreated controls. Additional experiments indicated the inhibition of the cathepsin B activity of L. (L.) amazonensis amastigotes by DPPE 1.2. Further studies are needed to explore the potential of DPPE 1.2 as an additional option for the chemotherapy of leishmaniasis

Imidacloprid-Induced Impairment of Mushroom Bodies and Behavior of the Native Stingless Bee Melipona quadrifasciata anthidioides

Declines in pollinator colonies represent a worldwide concern. The widespread use of agricultural pesticides is recognized as a potential cause of these declines. Previous studies have examined the effects of neonicotinoid insecticides such as imidacloprid on pollinator colonies, but these investigations have mainly focused on adult honey bees. Native stingless bees (Hymenoptera: Apidae: Meliponinae) are key pollinators in neotropical areas and are threatened with extinction due to deforestation and pesticide use. Few studies have directly investigated the effects of pesticides on these pollinators. Furthermore, the existing impact studies did not address the issue of larval ingestion of contaminated pollen and nectar, which could potentially have dire consequences for the colony. Here, we assessed the effects of imidacloprid ingestion by stingless bee larvae on their survival, development, neuromorphology and adult walking behavior. Increasing doses of imidacloprid were added to the diet provided to individual worker larvae of the stingless bee Melipona quadrifasciata anthidioides throughout their development. Survival rates above 50% were only observed at insecticide doses lower than 0.0056 µg active ingredient (a.i.)/bee. No sublethal effect on body mass or developmental time was observed in the surviving insects, but the pesticide treatment negatively affected the development of mushroom bodies in the brain and impaired the walking behavior of newly emerged adult workers. Therefore, stingless bee larvae are particularly susceptible to imidacloprid, as it caused both high mortality and sublethal effects that impaired brain development and compromised mobility at the young adult stage. These findings demonstrate the lethal effects of imidacloprid on native stingless bees and provide evidence of novel serious sublethal effects that may compromise colony survival. The ecological and economic importance of neotropical stingless bees as pollinators, their susceptibility to insecticides and the vulnerability of their larvae to insecticide exposure emphasize the importance of studying these species

Sensitivity of the Cherenkov Telescope Array to TeV photon emission from the Large Magellanic Cloud

A deep survey of the Large Magellanic Cloud at ∼0.1-100 TeV photon energies with the Cherenkov Telescope Array is planned. We assess the detection prospects based on a model for the emission of the galaxy, comprising the four known TeV emitters, mock populations of sources, and interstellar emission on galactic scales. We also assess the detectability of 30 Doradus and SN 1987A, and the constraints that can be derived on the nature of dark matter. The survey will allow for fine spectral studies of N 157B, N 132D, LMC P3, and 30 Doradus C, and half a dozen other sources should be revealed, mainly pulsar-powered objects. The remnant from SN 1987A could be detected if it produces cosmic-ray nuclei with a flat power-law spectrum at high energies, or with a steeper index 2.3-2.4 pending a flux increase by a factor of >3-4 over ∼2015-2035. Large-scale interstellar emission remains mostly out of reach of the survey if its >10 GeV spectrum has a soft photon index ∼2.7, but degree-scale 0.1-10 TeV pion-decay emission could be detected if the cosmic-ray spectrum hardens above >100 GeV. The 30 Doradus star-forming region is detectable if acceleration efficiency is on the order of 1−10 per cent of the mechanical luminosity and diffusion is suppressed by two orders of magnitude within <100 pc. Finally, the survey could probe the canonical velocity-averaged cross-section for self-annihilation of weakly interacting massive particles for cuspy Navarro-Frenk-White profiles

Major prospects for exploring canine vector borne diseases and novel intervention methods using 'omic technologies

Canine vector-borne diseases (CVBDs) are of major socioeconomic importance worldwide. Although many studies have provided insights into CVBDs, there has been limited exploration of fundamental molecular aspects of most pathogens, their vectors, pathogen-host relationships and disease and drug resistance using advanced, 'omic technologies. The aim of the present article is to take a prospective view of the impact that next-generation, 'omics technologies could have, with an emphasis on describing the principles of transcriptomic/genomic sequencing as well as bioinformatic technologies and their implications in both fundamental and applied areas of CVBD research. Tackling key biological questions employing these technologies will provide a 'systems biology' context and could lead to radically new intervention and management strategies against CVBDs

NEOTROPICAL XENARTHRANS: a data set of occurrence of xenarthran species in the Neotropics

Xenarthrans – anteaters, sloths, and armadillos – have essential functions for ecosystem maintenance, such as insect control and nutrient cycling, playing key roles as ecosystem engineers. Because of habitat loss and fragmentation, hunting pressure, and conflicts with 24 domestic dogs, these species have been threatened locally, regionally, or even across their full distribution ranges. The Neotropics harbor 21 species of armadillos, ten anteaters, and six sloths. Our dataset includes the families Chlamyphoridae (13), Dasypodidae (7), Myrmecophagidae (3), Bradypodidae (4), and Megalonychidae (2). We have no occurrence data on Dasypus pilosus (Dasypodidae). Regarding Cyclopedidae, until recently, only one species was recognized, but new genetic studies have revealed that the group is represented by seven species. In this data-paper, we compiled a total of 42,528 records of 31 species, represented by occurrence and quantitative data, totaling 24,847 unique georeferenced records. The geographic range is from the south of the USA, Mexico, and Caribbean countries at the northern portion of the Neotropics, to its austral distribution in Argentina, Paraguay, Chile, and Uruguay. Regarding anteaters, Myrmecophaga tridactyla has the most records (n=5,941), and Cyclopes sp. has the fewest (n=240). The armadillo species with the most data is Dasypus novemcinctus (n=11,588), and the least recorded for Calyptophractus retusus (n=33). With regards to sloth species, Bradypus variegatus has the most records (n=962), and Bradypus pygmaeus has the fewest (n=12). Our main objective with Neotropical Xenarthrans is to make occurrence and quantitative data available to facilitate more ecological research, particularly if we integrate the xenarthran data with other datasets of Neotropical Series which will become available very soon (i.e. Neotropical Carnivores, Neotropical Invasive Mammals, and Neotropical Hunters and Dogs). Therefore, studies on trophic cascades, hunting pressure, habitat loss, fragmentation effects, species invasion, and climate change effects will be possible with the Neotropical Xenarthrans dataset

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