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The model selection stage has become a central theme in applying the additive main effects and multiplicative interaction (AMMI) model to determine the optimal number of bilinear components to be retained to describe the genotype-by-environment interaction (GEI). In the Bayesian context, this problem has been addressed by using information criteria and the Bayes factor. However, these procedures are computationally intensive, making their application unfeasible when the model’s parametric space is large. A Bayesian analysis of the AMMI model was conducted using the Reversible Jump algorithm (RJMCMC) to determine the number of multiplicative terms needed to explain the GEI pattern. Three a priori distributions were assigned for the singular value scale parameter under different justifications, namely: i) the insufficient reason principle (uniform); ii) the invariance principle (Jeffreys’ prior) and iii) the maximum entropy principle. Simulated and real data were used to exemplify the method. An evaluation of the predictive ability of models for simulated data was conducted and indicated that the AMMI analysis, in general, was robust, and models adjusted by the Reversible Jump method were superior to those in which sampling was performed only by the Gibbs sampler. In addition, the RJMCMC showed greater feasibility since the selection and estimation of parameters are carried out concurrently in the same sampling algorithm, being more attractive in terms of computational time. The use of the maximum entropy principle makes the analysis more flexible, avoiding the use of procedures for correcting prior degrees of freedom and obtaining improper posterior marginal distributions.

Composting is a promising source of new organisms and thermostable enzymes that may be helpful in environmental management and industrial processes. Here we present results of metagenomic- and metatranscriptomic-based analyses of a large composting operation in the São Paulo Zoo Park. This composting exhibits a sustained thermophilic profile (50 °C to 75 °C), which seems to preclude fungal activity. The main novelty of our study is the combination of time-series sampling with shotgun DNA, 16S rRNA gene amplicon, and metatranscriptome high-throughput sequencing, enabling an unprecedented detailed view of microbial community structure, dynamics, and function in this ecosystem. The time-series data showed that the turning procedure has a strong impact on the compost microbiota, restoring to a certain extent the population profile seen at the beginning of the process; and that lignocellulosic biomass deconstruction occurs synergistically and sequentially, with hemicellulose being degraded preferentially to cellulose and lignin. Moreover, our sequencing data allowed near-complete genome reconstruction of five bacterial species previously found in biomass-degrading environments and of a novel biodegrading bacterial species, likely a new genus in the order Bacillales. The data and analyses provided are a rich source for additional investigations of thermophilic composting microbiology.

Background Inefficient T-cell access to the tumor microenvironment (TME) is among the causes of tumor immune-resistance. Previous evidence demonstrated that targeting CXCR4 improves anti-PD-1/PD-L1 efficacy reshaping TME. To evaluate the role of newly developed CXCR4 antagonists (PCT/IB2011/000120/ EP2528936B1/US2013/0079292A1) in potentiating anti-PD-1 efficacy two syngeneic murine models, the MC38 colon cancer and the B16 melanoma-human CXCR4-transduced, were employed. Methods Mice were subcutaneously injected with MC38 (1 × 10 6 ) or B16-hCXCR4 (5 × 10 5 ). After two weeks, tumors bearing mice were intraperitoneally (ip) treated with murine anti-PD-1 [RMP1–14] (5 mg/kg, twice week for 2 weeks), Pep R (2 mg/kg, 5 days per week for 2 weeks), or both agents. The TME was evaluated through immunohistochemistry and flow-cytometry. In addition, the effects of the human-anti-PD-1 nivolumab and/or Peptide-R54 (Pep R54), were evaluated on human melanoma PES43 cells and xenografts treated. Results The combined treatment, Pep R plus anti-PD-1, reduced the MC38 Relative Tumor Volume (RTV) by 2.67 fold ( p  = 0.038) while nor anti-PD-1, neither Pep R significantly impacted on tumor growth. Significant higher number of Granzyme B (GZMB) positive cells was detected in MC38 tumors from mice treated with the combined treatment ( p  = 0.016) while anti-PD-1 determined a modest but significant increase of tumor-infiltrating GZMB positive cells ( p  = 0.035). Also, a lower number of FoxP3 positive cells was detected ( p  = 0.022). In the B16-hCXCR4 tumors, two weeks of combined treatment reduced tumor volume by 2.27 fold while nor anti-PD-1 neither Pep R significantly impacted on tumor growth. A significant higher number of GRZB positive cells was observed in B16-hCXCR4 tumors treated with combined treatment (p = 0,0015) as compared to anti-PD-1 ( p  = 0.028). The combined treatment reduced CXCR4, CXCL12 and PD-L1 expression in MC38 tumors. In addition, flow cytometry on fresh B16-hCXCR4 tumors showed significantly higher Tregs number following anti-PD-1 partially reversed by the combined treatment Pep R and anti-PD-1. Combined treatment determined an increase of CD8/Tregs and CD8/MDSC ratio. To dissect the effect of anti-PD-1 and CXCR4 targeting on PD-1 expressed by human cancer cells, PES43 human melanoma xenograft model was employed. In vitro human anti-PD-1 nivolumab or pembrolizumab (10 μM) reduced PES43 cells growth while nivolumab (10 μM) inhibited pERK1/2, P38 MAPK, pAKT and p4EBP. PES43 xenograft mice were treated with Pep R54, a newly developed Pep R derivative (AcHN-Arg-Ala-[DCys-Arg- Nal(2′)-His-Pen]- COOH), plus nivolumab. After 3 weeks of combined treatment a significant reduction in tumor growth was shown ( p  = 0.038). PES43 lung disseminated tumor cells (DTC) were detected in fresh lung tissues as melanoma positive MCSP-APC + cells. Although not statistically significant, DTC-PES43 cells were reduced in mice lungs treated with combined treatment while nivolumab or Pep R54 did not affect DTC number. Conclusion Combined treatment with the new developed CXCR4 antagonist, Pep R, plus anti-PD-1, reduced tumor-growth in two syngeneic murine models, anti-PD-1 sensitive and resistant, potentiating Granzyme and reducing Foxp3 cells infiltration. In addition, the human specific CXCR4 antagonist, Pep R54, cooperated with nivolumab in inhibiting the growth of the PD-1 expressing human PES43 melanoma xenograft. This evidence sheds light on PD-1 targeting mechanisms and paves the way for CXCR4/PD-1 targeting combination therapy.

Graphene nanoribbons (GNRs) are two-dimensional structures with a rich variety of electronic properties that derive from their semiconducting band gaps. In these materials, charge transport can occur via a hopping process mediated by carriers formed by self-interacting states between the excess charge and local lattice deformations. Here, we use a two-dimensional tight-binding approach to reveal the formation of bipolarons in GNRs. Our results show that the formed bipolarons are dynamically stable even for high electric field strengths when it comes to GNRs. Remarkably, the bipolaron dynamics can occur in acoustic and optical regimes concerning its saturation velocity. The phase transition between these two regimes takes place for a critical field strength in which the bipolaron moves roughly with the speed of sound in the material.

The genotype main effects plus the genotype × environment interaction effects model has been widely used to analyze multi-environmental trials data, especially using a graphical biplot considering the first two principal components of the singular value decomposition of the interaction matrix. Many authors have noted the advantages of applying Bayesian inference in these classes of models to replace the frequentist approach. This results in parsimonious models, and eliminates parameters that would be present in a traditional analysis of bilinear components (frequentist form). This work aims to extend shrinkage methods to estimators of those parameters that composes the multiplicative part of the model, using the maximum entropy principle for prior justification. A Bayesian version (non-shrinkage prior, using conjugacy and large variance) was also used for comparison. The simulated data set had 20 genotypes evaluated across seven environments, in a complete randomized block design with three replications. Cross-validation procedures were conducted to assess the predictive ability of the model and information criteria were used for model selection. A better predictive capacity was found for the model with a shrinkage effect, especially for unorthogonal scenarios in which more genotypes were removed at random. In these cases, however, the best fitted models, as measured by information criteria, were the conjugate flat prior. In addition, the flexibility of the Bayesian method was found, in general, to attribute inference to the parameters of the models which related to the biplot representation. Maximum entropy prior was the more parsimonious, and estimates singular values with a greater contribution to the sum of squares of the genotype + genotype × environmental interaction. Hence, this method enabled the best discrimination of parameters responsible for the existing patterns and the best discarding of the noise than the model assuming non-informative priors for multiplicative parameters.

Morphine is considered a highly potent analgesic agent used to relieve suffering of patients with cancer. Several in vitro and in vivo studies showed that morphine also modulates angiogenesis and regulates tumour cell growth. Unfortunately, the results obtained by these studies are still contradictory. In order to better dissect the role of morphine in cancer cell growth and angiogenesis we performed in vitro studies on ER-negative human breast carcinoma cells, MDA.MB231 and in vivo studies on heterotopic mouse model of human triple negative breast cancer, TNBC. We demonstrated that morphine in vitro enhanced the proliferation and inhibited the apoptosis of MDA.MB231 cells. In vivo studies performed on xenograft mouse model of TNBC revealed that tumours of mice treated with morphine were larger than those observed in other groups. Moreover, morphine was able to enhance the neoangiogenesis. Our data showed that morphine at clinical relevant doses promotes angiogenesis and increases breast cancer progression.

The metastatic process is the most serious cause of cancer death. Norepinephrine, secreted in chronic stress conditions, stimulates the motility of breast and colon cells through β-adrenergic receptor. On these bases, we examined its possible role in metastasis formation and development in vitro and in vivo. Treatments with norepinephrine (β2-adrenoreceptor agonist) in mice xenografted with human DU145 prostate cancer cells increased the metastatic potential of these cells. Specifically, we showed that treatment of mice with norepinephrine induced a significant increase of the migratory activity of cancer cells in a concentration-dependent manner and that this process was blocked by propanolol (β-adrenergic antagonist). Mice treated with norepinephrine, displayed an increased number of metastatic foci of DU145 cells in inguinal lymph nodes and also showed an increased expression of MMP2 and MMP9 in tumor samples compared to controls. Moreover, we demonstrated that propanolol induced in norepinephrine treated DU145 cells a E-cadherin finger-like membrane protrusions driven by vimentin remodeling. Altogether these data suggest that β2-AR plays an important role in prostate cancer metastasis formation and that the treatment with antagonist propanolol, could represents an interesting tool to control this process in cells overexpressing β2AR.

BackgroundColorectal cancer (CRC) is one of the most prevalent and deadly tumors worldwide. The majority of CRC is resistant to anti-programmed cell death-1 (PD-1)-based cancer immunotherapy, with approximately 15% with high-microsatellite instability, high tumor mutation burden, and intratumoral lymphocytic infiltration. Programmed death-ligand 1 (PD-L1)/PD-1 signaling was described in solid tumor cells. In melanoma, liver, and thyroid cancer cells, intrinsic PD-1 signaling activates oncogenic functions, while in lung cancer cells, it has a tumor suppressor effect. Our work aimed to evaluate the effects of the anti-PD-1 nivolumab (NIVO) on CRC cells.MethodsIn vitro NIVO-treated human colon cancer cells (HT29, HCT116, and LoVo) were evaluated for cell growth, chemo/radiotherapeutic sensitivity, apoptosis, and spheroid growth. Total RNA-seq was assessed in 6–24 hours NIVO-treated human colon cancer cells HT29 and HCT116 as compared with NIVO-treated PES43 human melanoma cells. In vivo mice carrying HT29 xenograft were intraperitoneally treated with NIVO, OXA (oxaliplatin), and NIVO+OXA, and the tumors were characterized for growth, apoptosis, and pERK1/2/pP38. Forty-eight human primary colon cancers were evaluated for PD-1 expression through immunohistochemistry.ResultsIn PD-1+ human colon cancer cells, intrinsic PD-1 signaling significantly decreased proliferation and promoted apoptosis. On the contrary, NIVO promoted proliferation, reduced apoptosis, and protected PD-1+ cells from chemo/radiotherapy. Transcriptional profile of NIVO-treated HT29 and HCT116 human colon cancer cells revealed downregulation of BATF2, DRAM1, FXYD3, IFIT3, MT-TN, and TNFRSF11A, and upregulation of CLK1, DCAF13, DNAJC2, MTHFD1L, PRPF3, PSMD7, and SCFD1; the opposite regulation was described in NIVO-treated human melanoma PES43 cells. Differentially expressed genes (DEGs) were significantly enriched for interferon pathway, innate immune, cytokine-mediated signaling pathways. In vivo, NIVO promoted HT29 tumor growth, thus reducing OXA efficacy as revealed through significant Ki-67 increase, pERK1/2 and pP38 increase, and apoptotic cell reduction. Eleven out of 48 primary human colon cancer biopsies expressed PD-1 (22.9%). PD-1 expression is significantly associated with lower pT stage.ConclusionsIn PD-1+ human colon cancer cells, NIVO activates tumor survival pathways and could protect tumor cells from conventional therapies.

One of the main challenges in plant breeding programs is the efficient quantification of the genotype-by-environment interaction (GEI). The presence of significant GEI may create difficulties for breeders in the selection and recommendation of superior genotypes for a wide environmental network. Among the diverse statistical procedures developed for this purpose, we highlight those based on mixed models and factor analysis that are called factor analytic (FA) models. However, some inferential issues are related to the factor analytic model, such as Heywood cases that make the model non-identifiable. Moreover, the representation of the loads and factors in the conventional biplot does not involve any measurement of uncertainty. In this work, we propose dealing with the FA model using the Bayesian framework with direct sampling of factor loadings via spectral decomposition; this guarantees identifiability in the estimation process and eliminates the need for the rotationality of factor loadings or imposition of any ad hoc constraints. We used simulated and real data to illustrate the method's application in multi-environment trials (MET) and to compare it with traditional FA mixed models on controlled unbalancing. In general, the Bayesian FA model was robust under different simulated unbalanced levels, presenting the superior predictive ability of missing data when compared to competing models, such as those based on FA mixed models. In addition, for some scenarios, the classical FA mixed model failed in estimating the full FA model, illustrating the parametric problems of convergence in these models. Our results suggest that Bayesian factorial models might be successfully used in plant breeding for MET analysis.