Zooming in the plastisphere: the ecological interface for phytoplankton-plastic interactions in aquatic ecosystems.

Phytoplankton is an essential resource in aquatic ecosystems, situated at the base of aquatic food webs. Plastic pollution can impact these organisms, potentially affecting the functioning of aquatic ecosystems. The interaction between plastics and phytoplankton is multifaceted: while microplastics can exert toxic effects on phytoplankton, plastics can also act as a substrate for colonisation.

How does increasing temperature affect the toxicity of bisphenol A on Cryptomonas ovata and its consumer Daphnia magna?

The global rise in plastic production has led to significant plastic deposition in aquatic ecosystems, releasing chemical compounds as plastics degrade. Among these, bisphenol A (BPA) is a major global concern due to its endocrine-disrupting effects and widespread presence in aquatic environments. Furthermore, the toxicity of BPA on aquatic organisms can be modulated by global change stressors such as temperature, which plays an essential role in the metabolism of organisms, including the degradation and accumulation of toxic compounds.

Warming increases the compositional and functional variability of a temperate protist community.

Phototrophic protists are a fundamental component of the world's oceans by serving as the primary source of energy, oxygen, and organic nutrients for the entire ecosystem. Due to the high thermal seasonality of their habitat, temperate protists could harbour many well-adapted species that tolerate ocean warming. However, these species may not sustain ecosystem functions equally well.

Hybrid capture shotgun sequencing detected unexpected viruses in the cerebrospinal fluid of children with acute meningitis and encephalitis.

Purpose: Investigation of undiagnosed cases of infectious neurological diseases, especially in the paediatric population, remains a challenge. This study aimed to enhance understanding of viruses in CSF from children with clinically diagnosed meningitis and/or encephalitis (M/ME) of unknown aetiology using shotgun sequencing enhanced by hybrid capture (HCSS).

Methods: A single-centre prospective study was conducted at Sant Joan de Déu University Hospital, Barcelona, involving 40 M/ME episodes of unknown aetiology, recruited from May 2021 to July 2022.

The impact of extreme weather events exceeds those due to global-change drivers on coastal phytoplankton assemblages.

Extreme wind and rainfall events have become more frequent phenomena, impacting coastal ecosystems by inducing increased mixing regimes in the upper mixed layers (UML) and reduced transparency (i.e. browning), hence affecting phytoplankton photosynthesis.

Graph neural networks in EEG spike detection.

Objective: This study develops new machine learning architectures that are more adept at detecting interictal epileptiform discharges (IEDs) in scalp EEG. A comparison of results using the average precision (AP) metric is made with the proposed models on two datasets obtained from Baptist Hospital of Miami and Temple University Hospital.

Methods: Applying graph neural networks (GNNs) on functional connectivity (FC) maps of different frequency sub-bands to yield a novel architecture we call FC-GNN.

Metagenomic sequencing, molecular characterization, and Bayesian phylogenetics of imported type 2 vaccine-derived poliovirus, Spain, 2021.

Introduction: In 2021, a type 2 vaccine-derived poliovirus (VDPV2) was isolated from the stool of a patient with acute flaccid paralysis (AFP) admitted to Spain from Senegal. A virological investigation was conducted to characterize and trace the origin of VDPV2.

Methods: We used an unbiased metagenomic approach for the whole-genome sequencing of VDPV2 from the stool (pre-treated with chloroform) and from the poliovirus-positive supernatant.

Invasive bacterial infection in children with fever and petechial rash in the emergency department: a national prospective observational study.

Objective: To determine the incidence and clinical predictors of invasive bacterial infection (IBI) in well-appearing children who present to the emergency department (ED) with fever and petechiae.

Design: A prospective, observational, multicentre study was conducted in 18 hospitals between November 2017 and October 2019.

Patients: A total of 688 patients were recruited.

Impact of the bacterial nasopharyngeal microbiota on the severity of genus enterovirus lower respiratory tract infection in children: A case-control study.

Introduction: Rhinoviruses (RV) and enteroviruses (EV) are among the main causative etiologies of lower respiratory tract infection (LRTI) in children. The clinical spectrum of RV/EV infection is wide, which could be explained by diverse environmental, pathogen-, and host-related factors. Little is known about the nasopharyngeal microbiota as a risk factor or disease modifier for RV/EV infection in pediatric patients.

Browning, nutrient inputs, and fast vertical mixing from simulated extreme rainfall and wind stress alter estuarine phytoplankton productivity.

Browning and nutrient inputs from extreme rainfall, together with increased vertical mixing due to strong winds, are more frequent in coastal ecosystems; however, their interactive effects on phytoplankton are poorly understood. We conducted experiments to quantify how browning, together with different mixing speeds (fluctuating radiation), and a nutrient pulse alter primary productivity and photosynthetic efficiency in estuarine phytoplankton communities. Phytoplankton communities (grazers excluded) were exposed simultaneously to these drivers, and key photosynthetic targets were quantified: oxygen production, electron transport rates (ETRs), and carbon fixation immediately following collection and after a 2-d acclimation/adaptation period.

Luminescent Carbon Nanodots Doped with Gadolinium (III): Purification Criteria, Chemical and Biological Characterization of a New Dual Fluorescence/MR Imaging Agent.

Carbon Dots (CDs) are luminescent quasi-spherical nanoparticles, possessing water solubility, high biocompatibility, and tunable chemical and physical properties for a wide range of applications, including nanomedicine and theranostics. The evaluation of new purification criteria, useful to achieve more reliable CDs, free from the interference of artifacts, is currently an object of debate in the field. Here, new CDs doped with gadolinium (Gd (III)), named Gd@CNDs, are presented as multifunctional probes for Magnetic Resonance Imaging (MRI).

A transfer learning approach based on gradient boosting machine for diagnosis of Alzheimer's disease.

Early detection of Alzheimer's disease (AD) during the Mild Cognitive Impairment (MCI) stage could enable effective intervention to slow down disease progression. Computer-aided diagnosis of AD relies on a sufficient amount of biomarker data. When this requirement is not fulfilled, transfer learning can be used to transfer knowledge from a source domain with more amount of labeled data than available in the desired target domain.

Net effect of environmental fluctuations in multiple global-change drivers across the tree of life.

Jensen's inequality predicts that the response of any given system to average constant conditions is different from its average response to varying ones. Environmental fluctuations in abiotic conditions are pervasive on Earth; yet until recently, most ecological research has addressed the effects of multiple environmental drivers by assuming constant conditions. One could thus expect to find significant deviations in the magnitude of their effects on ecosystems when environmental fluctuations are considered.

Norovirus GII.3[P12] Outbreak Associated with the Drinking Water Supply in a Rural Area in Galicia, Spain, 2021.

On 30 September 2021, the city council of Muxia, Spain (population of 4,564 inhabitants), reported an unusual increase of patients with acute gastroenteritis (AGE). Because geographically widespread villages belonging to the same water supply were affected, a waterborne outbreak was suspected. Overall, 115 probable cases were ascertained during epidemiological investigations carried out by the local health authority (attack rate, 5.

A Tensorized Multitask Deep Learning Network for Progression Prediction of Alzheimer's Disease.

With the advances in machine learning for the diagnosis of Alzheimer's disease (AD), most studies have focused on either identifying the subject's status through classification algorithms or on predicting their cognitive scores through regression methods, neglecting the potential association between these two tasks. Motivated by the need to enhance the prospects for early diagnosis along with the ability to predict future disease states, this study proposes a deep neural network based on modality fusion, kernelization, and tensorization that perform multiclass classification and longitudinal regression simultaneously within a unified multitask framework. This relationship between multiclass classification and longitudinal regression is found to boost the efficacy of the final model in dealing with both tasks.

Molecular characterization of enterovirus detected in cerebrospinal fluid and wastewater samples in Monastir, Tunisia, 2014-2017.

Background: Enteroviruses (EVs) are considered the main causative agents responsible for aseptic meningitis worldwide. This study was conducted in the Monastir region of Tunisia in order to know the prevalence of EV infections in children with meningitis symptoms. Detected EV types were compared to those identified in wastewater samples.

A study of the longitudinal changes in multiple cerebrospinal fluid and volumetric magnetic resonance imaging biomarkers on converter and non-converter Alzheimer's disease subjects with consideration for their amyloid beta status.

Introduction: This study aims to determine whether newly introduced biomarkers Visinin-like protein-1 (VILIP-1), chitinase-3-like protein 1 (YKL-40), synaptosomal-associated protein 25 (SNAP-25), and neurogranin (NG) in cerebrospinal fluid are useful in evaluating the asymptomatic and early symptomatic stages of Alzheimer's disease (AD). It further aims to shed new insight into the differences between stable subjects and those who progress to AD by associating cerebrospinal fluid (CSF) biomarkers and specific magnetic resonance imaging (MRI) regions with disease progression, more deeply exploring how such biomarkers relate to AD pathology.

Methods: We examined baseline and longitudinal changes over a 7-year span and the longitudinal interactions between CSF and MRI biomarkers for subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI).