Morphology, Mineralogy, and Chemistry of Atmospheric Aerosols Nearby an Active Mining Area: Aljustrel Mine (SW Portugal)

Mining activities increase contaminant levels in the environment, so it is crucial to study the particulate matter in these areas to understand the impacts on nearby urban areas and populations. This work was conducted close to the active mine of Aljustrel (Portugal), where black dust deposition is evident. PM10 samples were collected in two periods: 10–17 July and 1–10 November of 2018. Two different techniques were used: SEM-EDX for the individual characterization of the aerosols and ICP-MS to quantify the elemental concentration of 11 elements (Ca, Na, Fe, Mn, As, Cd, Cu, Sb, Pb, and Zn). In this region, the observed PM10 mass concentration was 20 to 47 g m 3 (July) and 4 to 23 g m3 (November), which is lower than the limit of 50 g m3 established in the European Directive. The individual characterization of 2006 particles by SEM-EDX shows oxides (17%) and sulfides (10%), while Na, Si, Fe, S, Al, and Cu are the elements with the most representativeness in all the analyzed particles. The ICP-MS results indicate that the daily elemental concentration in the samples collected in July is higher than November, and only As exceeds the limit established for European legislation.info:eu-repo/semantics/publishedVersio

Global Disruption of α2A Adrenoceptor Barely Affects Bone Tissue but Minimizes the Detrimental Effects of Thyrotoxicosis on Cortical Bone

Evidence shows that sympathetic nervous system (SNS) activation inhibits bone formation and activates bone resorption leading to bone loss. Because thyroid hormone (TH) interacts with the SNS to control several physiological processes, we raised the hypothesis that this interaction also controls bone remodeling. We have previously shown that mice with double-gene inactivation of α2A- and -adrenoceptors (α2A/2C-AR−/−) present high bone mass (HBM) phenotype and resistance to thyrotoxicosis-induced osteopenia, which supports a TH-SNS interaction to control bone mass and suggests that it involves α2-AR signaling. Accordingly, we detected expression of α2A-AR, α2B-AR and α2C-AR in the skeleton, and that triiodothyronine (T3) modulates α2C-AR mRNA expression in the bone. Later, we found that mice with single-gene inactivation of α2C-AR (α2C-AR−/−) present low bone mass in the femur and HBM in the vertebra, but that both skeletal sites are resistant to TH-induce osteopenia, showing that the SNS actions occur in a skeletal site-dependent manner, and that thyrotoxicosis depends on α2C-AR signaling to promote bone loss. To further dissect the specific roles of α2-AR subtypes, in this study, we evaluated the skeletal phenotype of mice with single-gene inactivation of α2A-AR (α2A-AR−/−), and the effect of daily treatment with a supraphysiological dose of T3, for 4 or 12 weeks, on bone microarchitecture and bone resistance to fracture. Micro-computed tomographic (μCT) analysis revealed normal trabecular and cortical bone structure in the femur and vertebra of euthyroid α2A-AR−/− mice. Thyrotoxicosis was more detrimental to femoral trabecular bone in α2A-AR−/− than in WT mice, whereas this bone compartment had been previously shown to present resistance to thyrotoxicosis in α2C-AR−/− mice. Altogether these findings reveal that TH excess depends on α2C-AR signaling to negatively affect femoral trabecular bone. In contrast, thyrotoxicosis was more deleterious to femoral and vertebral cortical bone in WT than in α2A-AR−/− mice, suggesting that α2A-AR signaling contributes to TH actions on cortical bone. These findings further support a TH-SNS interaction to control bone physiology, and suggest that α2A-AR and α2C-AR signaling pathways have key roles in the mechanisms through which thyrotoxicosis promotes its detrimental effects on bone remodeling, structure and resistance to fracture

45S rDNA external transcribed spacer organization reveals new phylogenetic relationships in Avena genus

Research ArticleThe genus Avena comprises four distinct genomes organized in diploid (AA or CC), tetraploid (AABB or AACC) and hexaploid species (AACCDD), constituting an interesting model for phylogenetic analysis. The aim of this work was to characterize 45S rDNA intergenic spacer (IGS) variability in distinct species representative of Avena genome diversity±A. strigosa (AA), A. ventricosa (CvCv), A. eriantha (CpCp), A. barbata (AABB), A. murphyi (AACC), A. sativa (AACCDD) and A. sterilis (AACCDD) through the assessment of the 5' external transcribed spacer (5'-ETS), a promising IGS region for phylogenetic studies poorly studied in Avena genus. In this work, IGS length polymorphisms were detected mainly due to distinct 5'-ETS sequence types resulting from major differences in the number and organization of repeated motifs. Although species with A genome revealed a 5'-ETS organization (A-organization) similar to the one previously described in A. sativa, a distinct organization was unraveled in C genome diploid species (C-organization). Interestingly, such new organization presents a higher similarity with other Poaceae species than A-genome sequences, supporting the hypothesis of C-genome being the ancestral Avena genome. Additionally, polyploid species with both genomes mainly retain the A-genome 5'-ETS organization, confirming the preferential elimination of C-genome sequences in Avena polyploid species. Moreover, 5'-ETS sequences phylogenetic analysis consistently clustered the species studied according to ploidy and genomic constitution supporting the use of ribosomal genes to highlight Avena species evolutive pathways.info:eu-repo/semantics/publishedVersio

Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability

Background Predicting progression from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) is an utmost open issue in AD-related research. Neuropsychological assessment has proven to be useful in identifying MCI patients who are likely to convert to dementia. However, the large battery of neuropsychological tests (NPTs) performed in clinical practice and the limited number of training examples are challenge to machine learning when learning prognostic models. In this context, it is paramount to pursue approaches that effectively seek for reduced sets of relevant features. Subsets of NPTs from which prognostic models can be learnt should not only be good predictors, but also stable, promoting generalizable and explainable models. Methods We propose a feature selection (FS) ensemble combining stability and predictability to choose the most relevant NPTs for prognostic prediction in AD. First, we combine the outcome of multiple (filter and embedded) FS methods. Then, we use a wrapper-based approach optimizing both stability and predictability to compute the number of selected features. We use two large prospective studies (ADNI and the Portuguese Cognitive Complaints Cohort, CCC) to evaluate the approach and assess the predictive value of a large number of NPTs. Results The best subsets of features include approximately 30 and 20 (from the original 79 and 40) features, for ADNI and CCC data, respectively, yielding stability above 0.89 and 0.95, and AUC above 0.87 and 0.82. Most NPTs learnt using the proposed feature selection ensemble have been identified in the literature as strong predictors of conversion from MCI to AD. Conclusions The FS ensemble approach was able to 1) identify subsets of stable and relevant predictors from a consensus of multiple FS methods using baseline NPTs and 2) learn reliable prognostic models of conversion from MCI to AD using these subsets of features. The machine learning models learnt from these features outperformed the models trained without FS and achieved competitive results when compared to commonly used FS algorithms. Furthermore, the selected features are derived from a consensus of methods thus being more robust, while releasing users from choosing the most appropriate FS method to be used in their classification task.PTDC/EEI-SII/1937/2014; SFRH/BD/95846/2013; SFRH/BD/118872/2016info:eu-repo/semantics/publishedVersio

The FANCM:p.Arg658* truncating variant is associated with risk of triple-negative breast cancer

Breast cancer is a common disease partially caused by genetic risk factors. Germline pathogenic variants in DNA repair genes BRCA1, BRCA2, PALB2, ATM, and CHEK2 are associated with breast cancer risk. FANCM, which encodes for a DNA translocase, has been proposed as a breast cancer predisposition gene, with greater effects for the ER-negative and triple-negative breast cancer (TNBC) subtypes. We tested the three recurrent protein-truncating variants FANCM:p.Arg658*, p.Gln1701*, and p.Arg1931* for association with breast cancer risk in 67,112 cases, 53,766 controls, and 26,662 carriers of pathogenic variants of BRCA1 or BRCA2. These three variants were also studied functionally by measuring survival and chromosome fragility in FANCM (-/-) patient-derived immortalized fibroblasts treated with diepoxybutane or olaparib. We observed that FANCM:p.Arg658* was associated with increased risk of ER-negative disease and TNBC (OR = 2.44, P = 0.034 and OR = 3.79; P = 0.009, respectively). In a country-restricted analysis, we confirmed the associations detected for FANCM:p.Arg658* and found that also FANCM:p.Arg1931* was associated with ER-negative breast cancer risk (OR = 1.96; P = 0.006). The functional results indicated that all three variants were deleterious affecting cell survival and chromosome stability with FANCM:p.Arg658* causing more severe phenotypes. In conclusion, we confirmed that the two rare FANCM deleterious variants p.Arg658* and p.Arg1931* are risk factors for ER-negative and TNBC subtypes. Overall our data suggest that the effect of truncating variants on breast cancer risk may depend on their position in the gene. Cell sensitivity to olaparib exposure, identifies a possible therapeutic option to treat FANCM-associated tumors

Semen molecular and cellular features: these parameters can reliably predict subsequent ART outcome in a goat model

Currently, the assessment of sperm function in a raw or processed semen sample is not able to reliably predict sperm ability to withstand freezing and thawing procedures and in vivo fertility and/or assisted reproductive biotechnologies (ART) outcome. The aim of the present study was to investigate which parameters among a battery of analyses could predict subsequent spermatozoa in vitro fertilization ability and hence blastocyst output in a goat model. Ejaculates were obtained by artificial vagina from 3 adult goats (Capra hircus) aged 2 years (A, B and C). In order to assess the predictive value of viability, computer assisted sperm analyzer (CASA) motility parameters and ATP intracellular concentration before and after thawing and of DNA integrity after thawing on subsequent embryo output after an in vitro fertility test, a logistic regression analysis was used. Individual differences in semen parameters were evident for semen viability after thawing and DNA integrity. Results of IVF test showed that spermatozoa collected from A and B lead to higher cleavage rates (0 < 0.01) and blastocysts output (p < 0.05) compared with C. Logistic regression analysis model explained a deviance of 72% (p < 0.0001), directly related with the mean percentage of rapid spermatozoa in fresh semen (p < 0.01), semen viability after thawing (p < 0.01), and with two of the three comet parameters considered, i.e tail DNA percentage and comet length (p < 0.0001). DNA integrity alone had a high predictive value on IVF outcome with frozen/thawed semen (deviance explained: 57%). The model proposed here represents one of the many possible ways to explain differences found in embryo output following IVF with different semen donors and may represent a useful tool to select the most suitable donors for semen cryopreservation

Global chemical effects of the microbiome include new bile-acid conjugations

A mosaic of cross-phylum chemical interactions occurs between all metazoans and their microbiomes. A number of molecular families that are known to be produced by the microbiome have a marked effect on the balance between health and disease. Considering the diversity of the human microbiome (which numbers over 40,000 operational taxonomic units), the effect of the microbiome on the chemistry of an entire animal remains underexplored. Here we use mass spectrometry informatics and data visualization approaches to provide an assessment of the effects of the microbiome on the chemistry of an entire mammal by comparing metabolomics data from germ-free and specific-pathogen-free mice. We found that the microbiota affects the chemistry of all organs. This included the amino acid conjugations of host bile acids that were used to produce phenylalanocholic acid, tyrosocholic acid and leucocholic acid, which have not previously been characterized despite extensive research on bile-acid chemistry. These bile-acid conjugates were also found in humans, and were enriched in patients with inflammatory bowel disease or cystic fibrosis. These compounds agonized the farnesoid X receptor in vitro, and mice gavaged with the compounds showed reduced expression of bile-acid synthesis genes in vivo. Further studies are required to confirm whether these compounds have a physiological role in the host, and whether they contribute to gut diseases that are associated with microbiome dysbiosis