N-acyl-homoserine lactone produced by Rahnella inusitata isolated from the gut of Galleria mellonella influences Salmonella phenotypes.

The most studied mechanism of quorum sensing in Gram-negative bacteria is mediated by autoinducer 1 (AI-1), namely, acyl-homoserine lactone (AHL). This system allows communication among different bacterial species and regulates the expression of virulence genes in many pathogens. Although AHL-producing bacteria have been detected in the intestines of humans and other animals, no report was found about AHL-producing bacteria in the insect gut and the possible effects of these autoinducers on enteropathogenic bacteria. Therefore, this study aimed to identify AHL-producing bacteria in the gut of larvae of Galleria mellonella and to evaluate the influence of this quorum sensing signal on the regulation of adhesion and motility phenotypes in the intestinal pathogen Salmonella. Sequencing of the 16S rRNA gene, 16S rRNA gene-based phylogenetic analyses, and phenotypic characterization of gut isolates was performed. The profile of AHLs produced by the isolates was determined using thin-layer chromatography (TLC) and revealed with the biosensor strain Chromobacterium violaceum CV026. Sequencing, phylogenetic analyses and phenotypic characterization of gut isolates showed that the three AHL-producing strains belong to the species Rahnella inusitata, named GM34, GM56, and GM60. The TLC showed that R. inusitata produces a six-carbon AHL. In the presence of cell-free extract of R. inusitata containing AHL and under anaerobic conditions, Salmonella enterica increased the adhesion to stainless steel coupons and presented swarming motility. Extracts from the culture medium of R. inusitata isolates containing AHL increased the adhesion on stainless steel coupons and swarming motility of Salmonella enterica serovar Enteritidis PT4 under anaerobic conditions. The results suggest the possibility of communication between members of the G. mellonella intestinal microbiota with pathogens such as Salmonella.

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.

Repercussões do tratamento quimioterápico sobre a função ovariana / Repercussions of chemotherapy on ovarian function

Os tratamentos quimioterápicos atuais têm aumentado bastante a sobrevida de pacientes acometidas por diversos tipos de câncer; porém, podem causar falência ovariana e infertilidade. É importante o médico estar atento aos riscos de ocorrência dessas sequelas para tentar preservar a qualidade de vida das pacientes. Este artigo apresenta uma revisão acerca de recursos propedêuticos que podem ser utilizados na avaliação da função ovariana e de dados sobre a possível disfunção ovariana pós-quimioterapia. Normalmente essa disfunção não ocorre imediatamente após o tratamento, manifestando-se mais tarde como menopausa precoce. Procedimentos podem ser realizados com o intuito de se preservar a fertilidade e/ou a função ovariana.

New chemotherapic treatment have been raising the lifetime of the patients with cancer. However, important sequelas of these treatments, as ovary failure and infertility, may occur. It is important to the doctor to be aware of the risks of these side effects occurrence in order to maintain the quality of life for those patients. This article presents a review about the propedeutic resources that can be used to analyze the ovarian function and important data about the possible ovary dysfunction after chemotherapy. In most patients the ovary dysfunction will not occur immediately after the treatment expressing later as an early menopause scene. Some procedures can be performed in order to try to preserve the fertility and the ovary function.