Irene García

Climate change is expected to increase both the frequency and intensity of marine heatwaves, prolonging periods of extreme sea surface temperatures. These events can disrupt stratification, reduce oxygen availability, and alter nutrient cycling, ultimately reshaping marine community structure and function. Impacts are likely to be particularly severe in coastal lagoons, which could compromise their biodiversity and ecosystem services. In this study, we simulated a marine heatwave under controlled mesocosm conditions to investigate its influence on microbial communities in sediments colonized by the native seagrass Cymodocea nodosa and the opportunistic seaweed Caulerpa prolifera in the eutrophicated Mar Menor coastal lagoon. Community shifts and ecologically relevant taxa were assessed by using 16S rRNA gene metabarcoding and compositional data analysis, using complementary statistical approaches combining log ratio analysis and the glmnet algorithm. Our results show that sediments colonized by different macrophyte species harbor distinct microbial assemblages with different functional capacities. While sediments associated with the invasive macroalga Caulerpa prolifera were enriched in sulfate-reducing bacteria (mostly Desulfobacterota and Desulfosarcinaceae ), Cymodocea nodosa colonized sediments showed higher abundances of sulfur-oxidizing taxa such as Thiotrichaceae . During marine heatwaves, Cymodocea nodosa sediments exhibited an increase in sulfate reducers coupled with a decline in sulfur oxidizers that favored sulfide accumulation. These findings suggest that marine heatwaves may alter sediment microbial sulfur cycling in vegetated coastal systems, with potential implications for ecosystem resilience. Our results also highlight the importance of considering microbial processes when evaluating the responses of coastal lagoons to increasingly frequent and intense marine heatwaves. IMPORTANCE: Coastal lagoons are among the ecosystems most vulnerable to thermal anomalies; however, the responses of sediment-associated microbial communities remain poorly understood. Here, we assess the impact of simulated marine heatwaves on sediment microbiota associated with the seagrass Cymodocea nodosa and the macroalga Caulerpa prolifera under controlled laboratory conditions. Heatwave exposure led to the accumulation of sulfur compounds and pronounced shifts in microbial community composition, indicating altered biogeochemical functioning. These microbial responses suggest that marine heatwaves could potentially contribute to conditions that have been associated with dystrophic events in eutrophicated lagoons. Our findings provide evidence that future marine heatwaves could alter the microbial composition of coastal lagoon sediments and highlight the urgent need to incorporate microbial processes into ecosystem monitoring and management frameworks.

Although the association between risk factors and non-surgical root canal treatment (NSRCT) failure has been extensively studied, methods to predict the outcomes of NSRCT are in an early stage, and dentists currently make the treatment prognosis based mainly on their clinical experience. Since this involves different sources of error, we investigated the use of machine learning (ML) models as a second opinion to support the clinical decision on whether to perform NSRCT. We undertook a retrospective study of 119 confirmed and not previously treated Apical Periodontitis cases that received the same treatment by the same specialist. For each patient, we recorded the variables from a newly proposed data collection template and defined a binary outcome: Success if the lesion clears and failure otherwise. We conducted tests for detecting the association between the variables and the outcome and selected a set of variables as the initial inputs into four ML algorithms: Logistic Regression (LR), Random Forest (RF), Naive-Bayes (NB), and K Nearest Neighbors (KNN). According to our results, RF and KNN significantly improve (p-values < 0.05) the sensitivity and accuracy of the dentist’s treatment prognosis. Taking our results as a proof of concept, we conclude that future randomized clinical trials are worth designing to test the clinical utility of ML models as a second opinion for NSRCT prognosis.

Metabolism is characterised by chemical reactions linked to each other, creating a complex network structure. The whole metabolic network is divided into pathways of chemical reactions, such that every pathway is a metabolic function. A simplified representation of metabolism, which we call an abstract metabolic network, is a graph in which metabolic pathways are nodes and there is an edge between two nodes if their corresponding pathways share one or more compounds. The abstract metabolic network of a given organism results in a small network that requires low computational power to be analysed and makes it a suitable model to perform a large-scale comparison of organisms’ metabolism. To explore the potentials and limits of such a basic representation, we considered a comprehensive set of KEGG organisms, represented through their abstract metabolic network. We performed pairwise comparisons using graph kernel methods and analyse the results through exploratory data analysis and machine learning techniques. The results show that abstract metabolic networks discriminate macro evolutionary events, indicating that they are expressive enough to capture key steps in metabolism evolution.

Motivation Several protein-protein interaction networks (PPIN) aligners have been developed during the last 15 years. One of their goals is to help the functional annotation of proteins and the prediction of protein-protein interactions. A correct aligner must preserve the network’s topology as well as the biological coherence. However, this is a trade-off that is hard to achieve. In addition, most aligners require a considerable effort to use in practice and many researchers must choose an aligner without the opportunity to previously compare the performance of different aligners. Results We developed PINAWeb, a user-friendly web-based tool to obtain and compare the results produced by the aligners: AligNet, HubAlign, L-GRAAL, PINALOG and SPINAL. PPINs can be uploaded either from the STRING database or from a user database. The source code of PINAWeb is freely available on GitHub to enable researchers to add other aligners, network databases or alignment score metrics. In addition, PINAWeb provides a report with the analysis for every alignment in terms of topological and functional information scores, as well as the visualization of the alignments’ comparison (agreement/differences) when more than one aligner are considered. Availability https://bioinfo.uib.es/~recerca/PINAWeb

Differential diagnosis between bacterial and viral meningitis is crucial. In our study, to differentiate bacterial vs. viral meningitis, three machine learning (ML) algorithms (multiple logistic regression (MLR), random forest (RF), and naïve-Bayes (NB)) were applied for the two age groups (0–14 and >14 years) of patients with meningitis by both conventional (culture) and molecular (PCR) methods. Cerebrospinal fluid (CSF) neutrophils, CSF lymphocytes, neutrophil-to-lymphocyte ratio (NLR), blood albumin, blood C-reactive protein (CRP), glucose, blood soluble urokinase-type plasminogen activator receptor (suPAR), and CSF lymphocytes-to-blood CRP ratio (LCR) were used as predictors for the ML algorithms. The performance of the ML algorithms was evaluated through a cross-validation procedure, and optimal predictions of the type of meningitis were above 95% for viral and 78% for bacterial meningitis. Overall, MLR and RF yielded the best performance when using CSF neutrophils, CSF lymphocytes, NLR, albumin, glucose, gender, and CRP. Also, our results reconfirm the high diagnostic accuracy of NLR in the differential diagnosis between bacterial and viral meningitis.