Search Results (261)

Metabolomics, the comprehensive analysis of low-molecular-weight metabolites (typically below 1500 DA) in biological systems, relies heavily on mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Each technique has inherent strengths and weaknesses. MS offers high sensitivity and is commonly coupled with chromatography to analyze complex matrices, yet it is destructive, has limited reproducibility, and provides limited structural information. NMR, while less sensitive, is non-destructive and enables structural elucidation and precise quantification. Recent studies increasingly employ data fusion (DF) strategies to combine the complementary information from NMR and MS, aiming to enhance metabolomic analyses. This review summarizes DF methodologies using NMR and MS data in metabolomics studies over the past decade. A comprehensive search of SciFinder, Scopus, and Clarivate Web of Science databases was conducted to analyze fusion techniques, methods, and statistical models. The review emphasizes the growing importance of DF in metabolomics, showing its capacity to provide a more comprehensive view of biochemical processes across diverse biological systems, including clinical, plant, and food matrices. Full article

Background/Objectives: Severe mental illnesses such as schizophrenia, major depressive disorder, and bipolar disorder are often accompanied by metabolic comorbidities. While the role of vitamins in physical health is well-established, their involvement in psychiatric disorders has garnered increasing attention in recent years. Methods: We conducted a cross-sectional analysis of 1003 patients diagnosed with severe mental illnesses. Vitamin D, B9, and B12 serum levels were measured, and deficiencies were defined using established clinical cutoffs. Multivariate regression analyses were performed to identify associations between vitamin deficiencies and clinical outcomes. Results: Our findings revealed that vitamin deficiencies were prevalent across all diagnostic groups, with particularly high rates in patients with schizophrenia and major depressive disorder. Vitamin D deficiency was significantly associated with worse psychiatric outcomes, including increased depressive symptoms (adjusted OR = 1.89, p = 0.018), lower Global Assessment of Functioning scores (adjusted OR = −0.18, p < 0.001), and higher rates of metabolic syndrome (adjusted OR = 1.97, p = 0.007). Folate and B12 deficiencies were also linked to greater psychiatric symptom severity and metabolic disturbances, including increased risks of obesity and dyslipidemia. Conclusions: Our study highlights the critical role of vitamins deficiencies in both psychiatric and metabolic health of patients with severe mental illnesses. These findings underscore the importance of routine screening and correction of these deficiencies as part of comprehensive care in psychiatric populations. The integration of nutritional interventions may offer a novel and holistic approach to improving both mental and physical health outcomes. Full article

Semantic segmentation in deep learning is a crucial area of research within computer vision, aimed at assigning specific labels to each pixel in an image. The segmentation of crops, plants, and weeds has significantly advanced the application of deep learning in precision agriculture, leading to the development of sophisticated architectures based on convolutional neural networks (CNNs). This study proposes a segmentation algorithm for identifying plants and weeds using broadband multispectral images. In the first part of this algorithm, we utilize the PIF-Net model for feature extraction and fusion. The resulting feature map is then employed to enhance an optimized U-Net model for semantic segmentation within a broadband system. Our investigation focuses specifically on scenes from the CAVIAR dataset of multispectral images. The proposed algorithm has enabled us to effectively capture complex details while regulating the learning process, achieving an impressive overall accuracy of 98.2%. The results demonstrate that our approach to semantic segmentation and the differentiation between plants and weeds yields accurate and compelling outcomes. Full article

Background/Objectives: Circulating tumor DNA (ctDNA) analysis is a powerful tool for non-invasive monitoring of tumor burden and treatment response. Reliable quantification methods are critical for the effective use of ctDNA as a tumor biomarker. Digital PCR (dPCR) offers high sensitivity and quantification, but requires the prior knowledge of tumor-specific genomic alterations. Next-generation sequencing (NGS) provides a more comprehensive approach but is semi-quantitative, relying on variant allelic fraction (VAF), which can be influenced by non-tumor cell-free DNA. Methods: We developed a novel quantitative NGS (qNGS) method for absolute quantification of nucleotide variants, utilizing unique molecular identifiers (UMIs) and of quantification standards (QSs), short synthetic DNA sequences modified to include characteristic mutations for unique identification in sequencing data. We evaluated the performance of this method using plasma samples spiked with mutated DNA and plasma pools from cancer patients. We further applied our technique to plasma samples from four non-small cell lung cancer (NSCLC) patients enrolled in the ELUCID trial. Results: Our qNGS approach demonstrated robust linearity and correlation with dPCR in both spiked and patient-derived plasma samples. Notably, the analysis of clinical samples from the ELUCID trial revealed the ability of our method to simultaneously quantify multiple variants in a single plasma sample. Significant differences in ctDNA levels were observed between baseline and post-treatment samples collected after three weeks of front-line therapy. Conclusions: We introduce a novel qNGS method that enables the absolute quantification of ctDNA, independent of non-tumor circulating DNA variations. This technique was applied for the first time to serial samples from NSCLC patients, demonstrating its ability to simultaneously monitor multiple variants, making it a robust and versatile tool for precision oncology. Full article

In the past decades, satellite hyperspectral remote sensing instruments have been providing key measurements for environmental monitoring, such as the analysis of water and air quality, soil usage, weather forecasting, or climate change. The success of this technology, however, relies on an accurate knowledge of the instrument’s spectral response functions (SRFs). Usually, the SRFs are assessed on-ground and then monitored on-flight using tedious analysis of the acquired radiances coupled with instrumental models; nonetheless, the complete retrieval of the SRFs is generally out of reach. In this context, EUMETSAT has developed a novel SRF retrieval methodology, with the intention of applying it routinely to the current Metop IASI instruments and soon to Metop-SG IASI-NG, and MTG-S IRS. By making use of spatiotemporal colocations of different detectors within a single instrument or between different platforms, relative SRFs may be retrieved on-flight without any a priori knowledge. The presented methodology is suited for instruments acquiring radiances with contiguous sampling over large spectral bands as the SRFs are retrieved by analyzing the neighboring channels’ correlations. This article focuses on Fourier transform spectrometers (FTS) in the far infrared as they possess these characteristics per design, but it is believed that the method could be extended to other technology and spectral bands. The SRFs are further processed to evaluate the relative self-apodization functions (SAFs), as they represent the discrepancies between the detectors at the interferograms level, the primary measurements of FTS. The following article presents both simulations and applications of the SRF retrieval for the three IASI instruments aboard the Metop platforms of the EPS program. We analyze both IASI sensors aboard Metop-B and C as well as the evolution of Metop-A IASI over 13 years of operation. Full article

Coastal areas support seagrass meadows, which offer crucial ecosystem services, including erosion control and carbon sequestration. However, these areas are increasingly impacted by human activities, leading to habitat fragmentation and seagrass decline. In situ surveys, traditionally performed to monitor these ecosystems, face limitations on temporal and spatial coverage, particularly in intertidal zones, prompting the addition of satellite data within monitoring programs. Yet, satellite remote sensing can be limited by too coarse spatial and/or spectral resolutions, making it difficult to discriminate seagrass from other macrophytes in highly heterogeneous meadows. Drone (unmanned aerial vehicle—UAV) images at a very high spatial resolution offer a promising solution to address challenges related to spatial heterogeneity and the intrapixel mixture. This study focuses on using drone acquisitions with a ten spectral band sensor similar to that onboard Sentinel-2 for mapping intertidal macrophytes at low tide (i.e., during a period of emersion) and effectively discriminating between seagrass and green macroalgae. Nine drone flights were conducted at two different altitudes (12 m and 120 m) across heterogeneous intertidal European habitats in France and Portugal, providing multispectral reflectance observation at very high spatial resolution (8 mm and 80 mm, respectively). Taking advantage of their extremely high spatial resolution, the low altitude flights were used to train a Neural Network classifier to discriminate five taxonomic classes of intertidal vegetation: Magnoliopsida (Seagrass), Chlorophyceae (Green macroalgae), Phaeophyceae (Brown algae), Rhodophyceae (Red macroalgae), and benthic Bacillariophyceae (Benthic diatoms), and validated using concomitant field measurements. Classification of drone imagery resulted in an overall accuracy of 94% across all sites and images, covering a total area of 467,000 m². The model exhibited an accuracy of 96.4% in identifying seagrass. In particular, seagrass and green algae can be discriminated. The very high spatial resolution of the drone data made it possible to assess the influence of spatial resolution on the classification outputs, showing a limited loss in seagrass detection up to about 10 m. Altogether, our findings suggest that the MultiSpectral Instrument (MSI) onboard Sentinel-2 offers a relevant trade-off between its spatial and spectral resolution, thus offering promising perspectives for satellite remote sensing of intertidal biodiversity over larger scales. Full article

Immobilized enzyme reactors (IMERs) are critical tools for developing novel oligosaccharides based on the enzymatic catalysis of polysaccharides. In this paper, a novel glucuronan lyase from Peteryoungia rosettiformans was produced, purified, and then immobilized on a CIM^® CDI disk for cleaving glucuronan. The results showed that around 63.6% of glycuronan lyases (800.9 μg) were immobilized on the disk. The V_max values of immobilized glucuronan lyases did not significantly change (56.9 ± 4.7 μM∙min⁻¹), while the K_m values (0.310 ± 0.075 g∙L⁻¹) increased by 2.5 times. It is worth noting that immobilized glucuronan lyases overcame the catalytic inhibition of free enzymes observed under high glucuronan concentrations (0.5–2 g∙L⁻¹). circumscribed central composite design (CCCD) and response surface methodology (RSM) showed that glucuronan concentration, flow rate, and reaction time significantly affected the yield of oligoglucuronans. The degree of polymerization (DP) of degraded glucuronan ranged from DP 2–8 according to the results obtained by high performance anion exchange chromatography coupled with a pulsed amperometric detector (HPAEC-PAD). The IMER retained 50.9% activity after running 2373 column volumes of glucuronan. Finally, this glucuronan lyase reactor was tentatively connected to an immobilized laccase reactor to depolymerize, and gallic acid (GA) was added to glucuronan. Approximately 8.5 mg of GA was added onto 1 g of initial glucuronan, and the GA–oligoglucuronan conjugates showed notable antioxidant activity. Full article

Background/objectives: Pseudomonas aeruginosa can cause community-acquired infections affecting various body sites. The present retrospective study investigated the genetic diversity of 173 isolates (166 clinical, 7 environmental) of P. aeruginosa collected from clinical pathology laboratories in Abidjan, Côte d’Ivoire (2001–2011). Methods: Multiple-Locus Variable Number of Tandem Repeats (VNTR) Analysis (MLVA) using 13 loci was applied to all isolates and compared to published MLVA data. The antibiotics status of the isolates was compiled when available and compared to published profiles. Results: Among 95 isolates analyzed for their antibiotics status, 14 displayed concerning resistance profiles: five multidrug-resistant (MDR) and nine extensively drug-resistant (XDR). MLVA typing revealed a high genetic diversity (>130 genotypes), with many genotypes represented by a single strain. Notably, thirteen clusters (≥4 related isolates) were observed. Some clusters displayed close genetic relatedness to isolates from France, Korea, and well-studied strains (ST560, LES and PA14). Comparative analysis suggested the presence of international high-risk MDR clones (CC233, CC111) in Côte d’Ivoire. Importantly, MLVA clustering revealed a close relationship of CC235-MDR strains with a locally identified cluster (group 9). Conclusions: These findings support MLVA as a reliable and cost-effective tool for low-resource settings, allowing the selection of relevant strains for future whole genome sequence analyses. This approach can improve outbreak investigations and public health interventions aimed at curbing MDR P. aeruginosa transmission within hospitals and at the national level. Full article

Background: Systematic revascularization of asymptomatic coronary artery stenosis before transcatheter aortic valve replacement (TAVR) is controversial. Purpose: The purpose of this study was to evaluate the feasibility and safety of functional evaluation of coronary artery disease (CAD) followed by selective ischemia-guided percutaneous coronary revascularization following TAVR. Methods: This prospective, bi-centric, single-arm, open-label trial included all patients with severe aortic stenosis (AS) eligible for TAVR and with significant CAD defined as ≥1 coronary stenosis ≥ 70%. Patients with left main stenosis ≥ 50%, proximal left anterior descending artery (LAD) stenosis ≥ 90% or > class 2 Canadian Classification Society (CCS) angina were excluded. Myocardial ischemia was evaluated by stress cardiac imaging one month after TAVR. The primary endpoint was a composite of all-cause death, stroke, major bleeding (Bleeding Academic Research Consotium ≥ 3), major vascular complication (Valve Academic Research Consortium 3 criteria), acute coronary syndrome (ACS) and hospitalization for cardiac causes within 6 months of receiving TAVR. Results: Between June 2020 and June 2022, 64 patients were included in this study. The mean age was 84 ± 5.2 years. CAD mostly involved LAD (n = 27, 42%) with frequent multivessel disease (n = 30, 47%) and calcified lesions (n = 39, 61%). Stress cardiac imaging could be achieved in 70% (n = 46) of the patients, while 30% (n = 18) did not attend the stress test. Significant myocardial ischemia was observed in only three patients (4.5%). At 6-month follow-up, fifteen patients (23%) reached the primary endpoint, including death in six patients (9%), stroke in three patients (5%) and major bleeding in three patients (5%). ACS was observed in only two patients (3%) but both had severe coronary stenosis (≥90%) and did not refer for stress imaging for personal reasons. Hospital readmission (n = 27, 41%) was mostly related to non-cardiac causes (n = 17, 27%). Conclusions: In patients with asymptomatic CAD scheduled to undergo TAVR, a selective ischemia-guided coronary revascularization after TAVR seems to be safe, with a very low rate of ACS and few cases of myocardial ischemia requiring revascularization, despite low adherence to medical follow-up in this elderly population. This strategy could be evaluated in a randomized study. Full article

Objectives: To assess the impact of an Artificial Intelligence (AI) limb bone fracture diagnosis software (AIS) on emergency department (ED) workflow and diagnostic accuracy. Materials and Methods: A retrospective study was conducted in two phases—without AIS (Period 1: 1 January 2020–30 June 2020) and with AIS (Period 2: 1 January 2021–30 June 2021). Results: Among 3720 patients (1780 in Period 1; 1940 in Period 2), the discrepancy rate decreased by 17% (p = 0.04) after AIS implementation. Clinically relevant discrepancies showed no significant change (−1.8%, p = 0.99). The mean length of stay in the ED was reduced by 9 min (p = 0.03), and expert consultation rates decreased by 1% (p = 0.38). Conclusions: AIS implementation reduced the overall discrepancy rate and slightly decreased ED length of stay, although its impact on clinically relevant discrepancies remains inconclusive. Key Point: After AI software deployment, the rate of radiographic discrepancies decreased by 17% (p = 0.04) but this was not clinically relevant (−2%, p = 0.99). Length of patient stay in the emergency department decreased by 5% with AI (p = 0.03). Bone fracture AI software is effective, but its effectiveness remains to be demonstrated. Full article

A range of remote sensing platforms provide high spatial and temporal resolution insights which are useful for monitoring vegetation growth. Very few studies have focused on fruit orchards, largely due to the inherent complexity of their structure. Fruit trees are mixed with inter-rows that can be grassed or non-grassed, and there are no standard protocols for ground measurements suitable for the range of crops. The assessment of biophysical variables (BVs) for fruit orchards from optical satellites remains a significant challenge. The objectives of this study are as follows: (1) to address the challenges of extracting and better interpreting biophysical variables from optical data by proposing new ground measurements protocols tailored to various orchards with differing inter-row management practices, (2) to quantify the impact of the inter-row at the Sentinel pixel scale, and (3) to evaluate the potential of Sentinel 2 data on BVs for orchard development monitoring and the detection of key phenological stages, such as the flowering and fruit set stages. Several orchards in two pedo-climatic zones in southeast France were monitored for three years: four apricot and nectarine orchards under different management systems and nine cherry orchards with differing tree densities and inter-row surfaces. We provide the first comparison of three established ground-based methods of assessing BVs in orchards: (1) hemispherical photographs, (2) a ceptometer, and (3) the Viticanopy smartphone app. The major phenological stages, from budburst to fruit growth, were also determined by in situ annotations on the same fields monitored using Viticanopy. In parallel, Sentinel 2 images from the two study sites were processed using a Biophysical Variable Neural Network (BVNET) model to extract the main BVs, including the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of green vegetation cover (FCOVER). The temporal dynamics of the normalised FAPAR were analysed, enabling the detection of the fruit set stage. A new aggregative model was applied to data from hemispherical photographs taken under trees and within inter-rows, enabling us to quantify the impact of the inter-row at the Sentinel 2 pixel scale. The resulting value compared to BVs computed from Sentinel 2 gave statistically significant correlations (0.57 for FCOVER and 0.45 for FAPAR, with respective RMSE values of 0.12 and 0.11). Viticanopy appears promising for assessing the PAI (plant area index) and FCOVER for orchards with grassed inter-rows, showing significant correlations with the Sentinel 2 LAI (R² of 0.72, RMSE 0.41) and FCOVER (R² 0.66 and RMSE 0.08). Overall, our results suggest that Sentinel 2 imagery can support orchard monitoring via indicators of development and inter-row management, offering data that are useful to quantify production and enhance resource management. Full article

Oil palm is presently the most important oil-producing crop worldwide in terms of oil production and consumption. However, oil palm cultivation faces important challenges such as adverse climatic conditions, expensive fertilization requirements, and fungal pathogens, including Ganoderma. Intense efforts in oil palm breeding are devoted to improving both oil production yield and resistance to environmental cues. Metabolomics can be of interest because it provides many quantitative traits and metabolic signatures that can be selected for to optimize oil palm performance. Here, we briefly review how metabolomics can help oil palm breeding, and to do so, we give examples of recent metabolomics analyses and provide a roadmap to use metabolomics-assisted breeding. Full article

Objective: The objective was to compare the microbiological characteristics and treatment of early and late surgical site infections (SSIs) in instrumented spinal surgery. Methods: Those patients admitted for SSIs in a single center between January 2010 and December 2022 were included. The subjects were divided into early (eSSIs) and late (lSSIs) SSIs, and demographic, microbiological, treatment, and follow-up data were collected. Results: Instrumented spinal surgery was performed in 2136 patients. Ninety-six cases of infections were identified (prevalence = 4.5%), with 47.9% eSSIs and 52.1% lSSIs. In 58.7% of the cases, the eSSIs were monomicrobial: Staphylococcus aureus (37%) and Enterobacterales (33.3%) were the main bacteria involved. In 66% of the cases, the lSSIs, were monomicrobial: Cutibacterium acnes (30.3%) and staphylococci were predominant. Enterobacterales were isolated in more than 70% of the polymicrobial samples in both the eSSIs and lSSIs. The treatment of the eSSIs mostly consisted of lavage-debridement surgery associated with antibiotic treatment, while the treatment of the lSSIs combined hardware removal or replacement and long-duration antibiotic treatment. A negative outcome was observed in 17.1% of the eSSIs and 5.7% of the lSSIs. Enterobacterales were associated with negative outcomes of eSSIs. Conclusions: Enterobacterales were found in most of the polymicrobial infections regardless of the time of infection onset. Further large studies should be conducted to precisely determine the management and prevention regarding the increasing Gram-negative bacteria SSIs. Full article

The vast array of omics data in microbiology presents significant opportunities for studying bacterial pathogenesis and creating computational tools for predicting pathogenic potential. However, the field lacks a comprehensive, curated resource that catalogs bacterial strains and their ability to cause human infections. Current methods for identifying pathogenicity determinants often introduce biases and miss critical aspects of bacterial pathogenesis. In response to this gap, we introduce BacSPaD (Bacterial Strains’ Pathogenicity Database), a thoroughly curated database focusing on pathogenicity annotations for a wide range of high-quality, complete bacterial genomes. Our rule-based annotation workflow combines metadata from trusted sources with automated keyword matching, extensive manual curation, and detailed literature review. Our analysis classified 5502 genomes as pathogenic to humans (HP) and 490 as non-pathogenic to humans (NHP), encompassing 532 species, 193 genera, and 96 families. Statistical analysis demonstrated a significant but moderate correlation between virulence factors and HP classification, highlighting the complexity of bacterial pathogenicity and the need for ongoing research. This resource is poised to enhance our understanding of bacterial pathogenicity mechanisms and aid in the development of predictive models. To improve accessibility and provide key visualization statistics, we developed a user-friendly web interface. Full article

Background: HIV drug resistance (HIV-DR) may jeopardize the benefit of antiretroviral therapy (ART) in treatment and prevention. This study utilized viral phylogenetics to resolve the influence of transmission networks on sustaining the spread of HIV-DR in Quebec spanning 2002 to 2022. Methods: Time trends in acquired (ADR) and transmitted drug resistance (TDR) were delineated in treatment-experienced (n = 3500) and ART-naïve persons (n = 6011) with subtype B infections. Similarly, non-B-subtype HIV-DR networks were assessed pre- (n = 1577) and post-ART experience (n = 488). Risks of acquisition of resistance-associated mutations (RAMs) were related to clustering using 1, 2–5, vs. 6+ members per cluster as categorical variables. Results: Despite steady declines in treatment failure and ADR since 2007, rates of TDR among newly infected, ART-naive persons remained at 14% spanning the 2007–2011, 2012–2016, and 2017–2022 periods. Notably, half of new infections among men having sex with men and heterosexual groups were linked in large, clustered networks having a median of 35 (14–73 IQR) and 16 (9–26 IQR) members per cluster, respectively. Cluster membership and size were implicated in forward transmission of non-nucleoside reverse transcriptase inhibitor NNRTI RAMs (9%) and thymidine analogue mutations (TAMs) (5%). In contrast, transmission of M184V, K65R, and integrase inhibitors (1–2%) remained rare. Levels of TDR reflected viral replicative fitness. The median baseline viremia in ART-naïve groups having no RAMs, NNRTI RAMs, TAMs, and M184VI were 46.088, 38,447, 20,330, and 6811 copies/mL, respectively (p < 0.0001). Conclusion: Phylogenetics emphasize the need to prioritize ART and pre-exposure prophylaxis strategies to avert the expansion of transmission cascades of HIV-DR. Full article