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We generalize the U(1) gauge transformations of electrodynamics by means of an analytical extension of their parameter space and observe that this leads naturally to two gauge potentials, one electric, one magnetic, which permit the writing of local Lagrangians describing elementary particles with electric and magnetic charges. Gauge invariance requires a conformal transformation of the metric tensor. We apply this approach, which borrows from Utiyama’s methodology, to a model with a massless scalar field and a model with a massless spinor field. We observed that for spinor models non-symmetrized Lagrangians can enable the existence of magnetic monopoles, but this is not possible with symmetrized Lagrangian. Such restrictions do not occur for spinless fields, but the model does not allow spin-one fields interacting with monopoles.

Marinas are semi-enclosed environments characterised by reduced hydrodynamic energy, high sedimentation rates, and reduced oxygen levels. The ongoing development of activities and infrastructure construction are leading to deterioration in the quality of coastal aquatic environments, creating environmental risks. Trace metal elements (TMEs) and organotins are significant contaminants, prompting this study to evaluate the added value of spatiotemporal monitoring compared to one-time sediment assessments. Two Mediterranean harbours, Port Camargue (PC) and Port Carnon (C), differing in morphology and size, were monitored for a year with regular water sampling, focusing on TMEs and organotins. Sediment contamination, notably in the technical zone, revealed concentrations of Cu (309 and 1210 mg kg−1 for C and PC, respectively), Zn (242 and 425 mg kg−1 for C and PC, respectively), and tributyltin (TBT) (198 and 4678 µg (Sn) kg−1 for C and PC, respectively) surpassing the effect range medium (ERM), while other marina stations generally stayed below this threshold. Spatiotemporal water monitoring highlighted concentrations above environmental quality standards (EQS) at all stations of the larger marina. This exceedance was systematic for Cu and Zn in all samples, ranging respectively between 2.54 and 37.56 µg (Sn) L−1 and 0.63 and 33.48 µg (Sn) L−1. A notable temporal dynamic for TBT and Cu was also observed. Conversely, the open marina, connected to the open sea, rarely exhibited concentrations above EQS in water, despite sediment concentrations occasionally exceeding ERM values. This underscores that risk assessment in these ecosystems cannot rely solely on sediment characterisation.

Objectives: Ventilator-associated pneumonia (VAP) is a severe care-related disease. The Centers for Disease Control defined the diagnosis criteria; however, the pediatric criteria are mainly subjective and retrospective. Clinical decision support systems have recently been developed in healthcare to help the physician to be more accurate for the early detection of severe pathology. We aimed at developing a predictive model to provide early diagnosis of VAP at the bedside in a pediatric intensive care unit (PICU). Methods: We performed a retrospective single-center study at a tertiary-care pediatric teaching hospital. All patients treated by invasive mechanical ventilation between September 2013 and October 2019 were included. Data were collected in the PICU electronic medical record and high-resolution research database. Development of the clinical decision support was then performed using open-access R software (Version 3.6.1®). Measurements and main results: In total, 2077 children were mechanically ventilated. We identified 827 episodes with almost 48 h of mechanical invasive ventilation and 77 patients who suffered from at least one VAP event. We split our database at the patient level in a training set of 461 patients free of VAP and 45 patients with VAP and in a testing set of 199 patients free of VAP and 20 patients with VAP. The Imbalanced Random Forest model was considered as the best fit with an area under the ROC curve from fitting the Imbalanced Random Forest model on the testing set being 0.82 (95% CI: (0.71, 0.93)). An optimal threshold of 0.41 gave a sensitivity of 79.7% and a specificity of 72.7%, with a positive predictive value (PPV) of 9% and a negative predictive value of 99%, and with an accuracy of 79.5% (95% CI: (0.77, 0.82)). Conclusions: Using machine learning, we developed a clinical predictive algorithm based on clinical data stored prospectively in a database. The next step will be to implement the algorithm in PICUs to provide early, automatic detection of ventilator-associated pneumonia.

The growing demand for molecules of interest from microalgal biomass, such as phycobiliproteins, has led to an accumulation of unused by-products. For example, phycocyanin, obtained by the extraction of Spirulina, generated cakes rich in non-polar molecules of interest, such as free fatty acids (FFAs). These FFAs were generally considered as markers of lipidome degradation, but represented a relevant alternative to topical antibiotics, based on a biomimetic approach. In order to develop a sustainable Spirulina biorefinery scheme, different pretreatments and alternative solvents were screened to identify the best combination for the valorization of FFAs. Thus, five pre-treatments were studied including a phycocyanin extraction by-product. The following three biobased solvents were selected: ethyl acetate (EtOAc), dimethyl carbonate (DMC) and a fatty acid-based natural deep eutectic solvent (NaDES). The pigment and fatty acid profiles were established by spectroscopic and chromatographic approaches. NaDES demonstrated superior extraction capacity and selectivity compared to other biobased solvents, regardless of pretreatment. In contrast, EtOAc and DMC showed a greater diversity of FFAs, with a predominance of polyunsaturated fatty acids (PUFAs). The by-product has also been highlighted as a relevant raw material facilitating the recovery of FFAs. These results pave the way for a green biorefinery of the lipid fraction and phycobiliproteins of microalgae.

We have previously reported that the female genital tract (FGT) of Beninese HIV highly-exposed seronegative (HESN) commercial sex workers (CSWs), presented elevated frequencies of a myeloid HLA-DR+CD14+CD11c+ population presenting “tolerogenic” monocyte derived dendritic cells (MoDC) features. In order to assess whether a differential profile of monocytes may be involved in the generation of these genital MoDCs, we have herein characterized the blood monocyte compartment of Beninese HESNs (HIV-uninfected ≥ 10 years CSWs) and relevant controls (HIV-uninfected 2.5–5 years CSWs herein termed “early HESNs”), HIV-infected CSWs, and low-risk HIV-uninfected women from the general population. Transcriptomic analyses by RNA-Seq of total sorted blood monocytes demonstrate that in comparison to the control groups, HESNs present increased expression levels of FCGR2C, FCAR, ITGAX, ITGAM, CR2, CD68, and CD163 genes, associated with effector functions. Moreover, we found increased expression levels of genes associated with protection/control against SHIV/HIV such as CCL3, CCL4, CCL5, BHLHE40, and TNFSF13, as well as with immune regulation such as IL-10, Ahr, CD83, and the orphan nuclear receptor (NR)4A1, NR4A2, and NR4A3. Through multicolor flow cytometry analyses, we noticed that the frequencies of intermediate and non-classical monocyte populations tended to be elevated in the blood of HESNs, and exhibited increased expression levels of effector CD16, CD11c, CD11b, as well as regulatory HLA-G, IL-10, and IFN-α markers when compared to HIV-uninfected women and/or HIV-infected CSWs. This profile is compatible with that previously reported in the FGT of HESNs, and likely confers an enormous advantage in their resistance to HIV infection.

Metabolomics has been increasingly used in animal and food sciences. Animal health is one of the most important factor that can also alter animal integrity and welfare. Some studies have already investigated the link between health and metabolic profile of dairy animals. These studies in metabolomics often consider a single type of sample using a single analytical platform (nuclear magnetic resonance or mass spectrometry). Only few studies with multi-platform approaches are also used with a single or a multi type of sample, but they mainly consider dairy cows’ metabolome although dairy goats present similar diseases, that it could be interesting to detect early to preserve animal health and milk production. This study aims to create a metabolic atlas of goat plasma, milk and feces, based on healthy animals. Our study describes a standard operating procedure for three goat matrices: blood plasma, milk, and feces using multiple platforms (NMR (1H), UHPLC (RP)-MS and UHPLC (HILIC)-MS) that follows a unique sample preparation procedure for each sample type to be analyzed on multi-platforms basis. Our method was evaluated for its robustness and allowed a better characterization of goat metabolic profile in healthy conditions.

Currently, most clinical studies in metabolomics only consider a single type of sample such as urine, plasma, or feces and use a single analytical platform, either NMR or MS. Although some studies have already investigated metabolomics data from multiple fluids, the information is limited to a unique analytical platform. On the other hand, clinical studies investigating the human metabolome that combine multi-analytical platforms have focused on a single biofluid. Combining data from multiple sample types for one patient using a multimodal analytical approach (NMR and MS) should extend the metabolome coverage. Pre-analytical and analytical phases are time consuming. These steps need to be improved in order to move into clinical studies that deal with a large number of patient samples. Our study describes a standard operating procedure for biological specimens (urine, blood, saliva, and feces) using multiple platforms (1H-NMR, RP-UHPLC-MS, and HILIC-UHPLC-MS). Each sample type follows a unique sample preparation procedure for analysis on a multi-platform basis. Our method was evaluated for its robustness and was able to generate a representative metabolic map.

Armillaria sinapina, a fungal pathogen of primary timber species of North American forests, causes white root rot disease that ultimately kills the trees. A more detailed understanding of the molecular mechanisms underlying this illness will support future developments on disease resistance and management, as well as in the decomposition of cellulosic material for further use. In this study, RNA-Seq technology was used to compare the transcriptome profiles of A. sinapina fungal culture grown in yeast malt broth medium supplemented or not with betulin, a natural compound of the terpenoid group found in abundance in white birch bark. This was done to identify enzyme transcripts involved in the metabolism (redox reaction) of betulin into betulinic acid, a potent anticancer drug. De novo assembly and characterization of A. sinapina transcriptome was performed using Illumina technology. A total of 170,592,464 reads were generated, then 273,561 transcripts were characterized. Approximately, 53% of transcripts could be identified using public databases with several metabolic pathways represented. A total of 11 transcripts involved in terpenoid biosynthesis were identified. In addition, 25 gene transcripts that could play a significant role in lignin degradation were uncovered, as well as several redox enzymes of the cytochromes P450 family. To our knowledge, this research is the first transcriptomic study carried out on A. sinapina.