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​​​​​​​Vertically resolved information on aerosol particles represents a key aspect in many atmospheric studies, including aerosol–climate interactions and aerosol impacts on air quality and human health. This information is primarily derived by lidar active remote sensing, in particular with extensive networks currently in operation worldwide. In Italy, the Institute of Atmospheric Sciences and Climate (ISAC) of the National Research Council (CNR) established the ALICENET network of automated lidar ceilometers (ALCs) in 2015. Since then, ALICENET has grown as a cooperative effort of Italian institutions dealing with atmospheric science and monitoring, and it currently includes instruments run by regional environmental protection agencies, universities, research centres, and private companies. In the current configuration, the network makes use of both single-channel ALCs and dual-channel, polarisation-sensitive-system ALCs (referred to as PLCs). The systems operate in very different environments (urban, coastal, mountainous, and volcanic areas) from northern to southern Italy, thus allowing the continuous monitoring of the aerosol vertical distribution across the country. ALICENET also contributes to the EUMETNET programme E-PROFILE, filling an Italian observational gap compared to other EU member states, which generally run extended ALC networks through national meteorological services. In this work, we present the ALICENET infrastructure and the specifically developed data processing centralised at CNR-ISAC, converting raw instrumental data into quantitative, quality-controlled information on aerosol properties ranging from attenuated backscatter to aerosol mass and vertical stratifications. This setup allows us to get insights into the 4D aerosol field over Italy with applications from near-real-time monitoring to long-term analyses, examples of which are reported in this work. Specific comparisons of the ALICENET products to independent measurements obtained with different techniques, such as particulate matter (PM) concentrations from in situ samplers and aerosol optical depth (AOD) from sun photometers, are also included here, revealing the good performances of the ALICENET algorithms. Overall, ALICENET represents a valuable resource to extend the current aerosol observational capabilities in Italy and in the Mediterranean area, and it contributes to bridging the gap between atmospheric science and its application to specific sectors, among which are air quality, solar energy, and aviation safety.

Purpose: Developmental language disorder (DLD) is a multifaceted disorder. Recently, interest has grown in prosodic aspects of DLD, but most investigations of possible prosodic causes focus on speech perception tasks. Here, we focus on speech production from a speech amplitude envelope (AE) perspective. Perceptual studies have indicated a role for difficulties in AE processing in DLD related to sensory/neural processing of prosody. We explore possible matching AE difficulties in production. Method: Fifty-seven children with and without DLD completed a computerized imitation task, copying aloud 30 familiar targets such as "alligator." Children with DLD (n = 20) were compared with typically developing children (age-matched controls [AMC], n = 21) and younger language controls (YLC, n = 16). Similarity of the child's productions to the target in terms of the continuous AE and pitch contour was computed using two similarity metrics, correlation, and mutual information. Both the speech AE and the pitch contour contain important information about stress patterning and intonational information over time. Results: Children with DLD showed significantly reduced imitation for both the AE and pitch contour metrics compared to AMC children. The opportunity to repeat the targets had no impact on performance for any group. Word length effects were similar across groups. Conclusions: The spoken production of multisyllabic words by children with DLD is atypical regarding both the AE and the pitch contour. This is consistent with a theoretical explanation of DLD based on impaired sensory/neural processing of low-frequency (slow) amplitude and frequency modulations, as predicted by the temporal sampling theory.

The prevalent "core phonological deficit" model of dyslexia proposes that the reading and spelling difficulties characterizing affected children stem from prior developmental difficulties in processing speech sound structure, for example, perceiving and identifying syllable stress patterns, syllables, rhymes and phonemes. Yet spoken word production appears normal. This suggests an unexpected disconnect between speech input and speech output processes. Here we investigated the output side of this disconnect from a speech rhythm perspective by measuring the speech amplitude envelope (AE) of multisyllabic spoken phrases. The speech AE contains crucial information regarding stress patterns, speech rate, tonal contrasts and intonational information. We created a novel computerized speech copying task in which participants copied aloud familiar spoken targets like "Aladdin." Seventy-five children with and without dyslexia were tested, some of whom were also receiving an oral intervention designed to enhance multi-syllabic processing. Similarity of the child's productions to the target AE was computed using correlation and mutual information metrics. Similarity of pitch contour, another acoustic cue to speech rhythm, was used for control analyses. Children with dyslexia were significantly worse at producing the multi-syllabic targets as indexed by both similarity metrics for computing the AE. However, children with dyslexia were not different from control children in producing pitch contours. Accordingly, the spoken production of multisyllabic phrases by children with dyslexia is atypical regarding the AE. Children with dyslexia may not appear to listeners to exhibit speech production difficulties because their pitch contours are intact.

Abstract Single‐atom catalysis is gaining interest also because of its potential applications in a broad spectrum of electrochemical reactions. The reactivity of single‐atom catalysts (SACs) is typically modeled with first principles approaches taking insight from heterogenous catalysis. An increasing number of studies show that the chemistry of SACs is more complex than often assumed, and shares many aspects in common with coordination chemistry. This evidence raises challenges for computational electrocatalysis of SACs. In this perspective we highlight a few fundamental ingredients that one need to consider to provide reliable predictions on the reactivity of SACs for electrochemical applications. We discuss the role of the local coordination of the metal active phase, the need to use self‐interaction corrected functionals, in particular when systems have magnetic ground states. We highlight the formation of unconventional intermediates with respect to classical metal electrodes, the need to include the stability of SACs in electrochemical conditions and the role of solvation in the analysis of new potential catalytic systems. This brief account can be considered as a tutorial underlining the importance of treating the reactivity of SACs. In fact, neglecting some of these aspects could lead to unreliable predictions failing in the design of new electrocatalysts.

Abstract Background Lingual abscess corresponds to an infection that manifests itself as a painful swelling in the tongue with purulent material inside. This is a rare entity and the information available in the literature is limited and based on clinical case reports. Therefore, the diagnosis, etiology, clinical manifestations, and treatment are not fully defined. Case presentation The aim of this work was to describe the imaging of a case of lingual abscess, focusing on imaging evaluation using ultrasound and computed tomography (CT), and comparing the diagnostic utility of both modalities in smaller lingual abscesses. This case was treated with antibiotics and adequate drainage was performed, with good evolution. Conclusions Tongue abscess is a rare pathology due to the various mechanisms that the tongue has to avoid infections; however, it is a diagnosis that we must consider. CT may be sufficient to diagnose it on imaging, but in smaller lesions, ultrasound with a high-frequency transducer provides more information.

A disturbed balance between excitation and inhibition (E/I balance) is increasingly recognized as a key driver of neurodegeneration in multiple sclerosis (MS), a chronic inflammatory disease of the central nervous system. To understand how chronic hyperexcitability contributes to neuronal loss in MS, we transcriptionally profiled neurons from mice lacking inhibitory metabotropic glutamate signaling with shifted E/I balance and increased vulnerability to inflammation- induced neurodegeneration. This revealed a prominent induction of the nuclear receptor NR4A2 in neurons. Mechanistically, NR4A2 increased susceptibility to excitotoxicity by stimulating continuous VGF secretion leading to glycolysis-dependent neuronal cell death. Extending these findings to people with MS (pwMS), we observed increased VGF levels in serum and brain biopsies. Notably, neuron-specific deletion of Vgf in a mouse model of MS ameliorated neurodegeneration. These findings underscore the detrimental effect of a persistent metabolic shift driven by excitatory activity as a fundamental mechanism in inflammation-induced neurodegeneration., Introduction Inflammation-induced neurodegeneration is the principal driver of disease progression and the accumulation of neurological disabilities in people with multiple sclerosis (pwMS) (1, 2), the most prevalent inflammatory disease of [...]

Canopy Homolog 2 (CNPY2) is an endoplasmic reticulum (ER) localized protein belonging to the CNPY gene family. We show here that CNPY2 is protective against ER stress induced by tunicamycin in neuronal cells. Overexpression of CNPY2 enhanced, while downregulation of CNPY2 using shRNA expression, reduced the viability of neuroblastoma cells after tunicamycin. Likewise, recombinant CNPY2 increased survival of cortical neurons in culture after ER stress. CNPY2 reduced the activating transcription factor 6 (ATF6) branch of ER stress and decreased the expression of CCAT/Enhancer-Binding Protein Homologous Protein (CHOP) involved in cell death. Immunostaining using mouse brain sections revealed that CNPY2 is expressed by cortical and striatal neurons and is co-expressed with the transcription factor, COUPTF-interacting protein 2 (CTIP2). In transgenic N171-82Q mice, as a model for Huntington’s disease (HD), the number of CNPY2-immunopositive neurons was increased in the cortex together with CTIP2. In the striatum, however, the number of CNPY2 decreased at 19 weeks of age, representing a late-stage of pathology. Striatal cells in culture were shown to be more susceptible to ER stress after downregulation of CNPY2. These results demonstrate that CNPY2 is expressed by corticostriatal neurons involved in the regulation of movement. CNPY2 enhances neuronal survival by reducing ER stress and is a promising factor to consider in HD and possibly in other brain diseases.

la presente investigación tiene como finalidad describir cómo la ciudadanía ha contribuido a la elaboración de un Diagnóstico Territorial de Empleo (DET) promovido por la Generalitat Valenciana, previo al diseño de futuras políticas públicas para el fomento del empleo, en tres municipios alicantinos. Se consiguió a través de una Investigación-Acción-Participación (IAP). se realizaron talleres participativos y grupos focales que sirvieron para que la ciudadanía conociese de la existencia del proyecto y sus fines, además de aportar su experiencia, opinión y plantear posibles soluciones a la problemática del empleo en estos municipios. Se puede concluir que la ciudadanía está capacitada para aportar su perspectiva sobre una problemática que le concierne y cómo puede contribuir al diseño de políticas orientadas al fomento de la empleabilidad.

Background Coproduction is defined as patients and clinicians collaborating equally and reciprocally in healthcare and is a crucial concept for quality improvement (QI) of health services. Learning Health Networks (LHNs) provide insights to integrate coproduction with QI efforts from programmes from various health systems.Objective We describe interventions to develop and maintain patient and family partner (PFP) coproduction, measured by PFP-reported and programme-reported scales. We aim to increase percentage of programmes with PFPs reporting active QI work within their programme, while maintaining satisfaction in PFP-clinician relationships.Methods Conducted in the Cystic Fibrosis Learning Network (CFLN), an LHN comprising over 30 cystic fibrosis (CF) programmes, people with CF, caregivers and clinicians cocreated interventions in readiness awareness, inclusive PFP recruitment, onboarding process, partnership development and leadership opportunities. Interventions were adapted by CFLN programmes and summarised in a change package for existing programmes and the orientation of new ones. We collected monthly assessments for PFP and programme perceptions of coproduction and PFP self-rated competency of QI skills and satisfaction with programme QI efforts. We used control charts to analyse coproduction scales and run charts for PFP self-ratings.Results Between 2018 and 2022, the CFLN expanded to 34 programmes with 52% having ≥1 PFP reporting active QI participation. Clinicians from 76% of programmes reported PFPs were actively participating or leading QI efforts. PFPs reported increased QI skills competency (17%–32%) and consistently high satisfaction and feeling valued in their work.Conclusions Implementing system-level programmatic strategies to engage and sustain partnerships between clinicians and patients and families with CF improved perceptions of coproduction to conduct QI work. Key adaptable strategies for programmes included onboarding and QI training, supporting multiple PFPs simultaneously and developing financial recognition processes. Interventions may be applicable in other health conditions beyond CF seeking to foster the practice of coproduction.

Golden tomato (GT), harvested at the veraison stage, has gained attention due to its rich content of bioactive compounds and potential health benefits. Previous studies have highlighted GT’s antioxidant properties and its positive effects on metabolic syndrome (MetS), a condition characterized by obesity, dyslipidemia, and oxidative stress. This study investigates for the first time a derivative from GT, i.e., the juice (GTJ), which could be a potential candidate for development as a functional food. We first characterized GT juice, identifying 9-oxo-10(E),12(E)-octadecadienoic (9-oxo-10(E),12(E)-ODA) fatty acid, a known peroxisome proliferator-activated receptor alpha (PPAR-α) agonist, using High-Performance Liquid Chromatography (HPLC)–mass spectrometry. Then, using a high-fat-diet (HFD) rat model, we assessed the impact of daily GT juice supplementation in addressing MetS. We outlined that GTJ improved body weight and leptin-mediated food intake. Moreover, it ameliorated glucose tolerance, lipid profile, systemic redox homeostasis, hepatic oxidative stress, and steatosis in HFD rats. Furthermore, GT juice enhances the hepatic transcription of PPAR-α, thus putatively promoting fatty acid oxidation and lipid metabolism. These findings suggest that GT juice mitigates lipidic accumulation and putatively halters oxidative species at the hepatic level through PPAR-α activation. Our study underscores the protective effects of GT juice against MetS, highlighting its future potential as a nutraceutical for improving dysmetabolism and associated alterations.

Slow cortical oscillations play a crucial role in processing the speech amplitude envelope, which is perceived atypically by children with developmental dyslexia. Here we use electroencephalography (EEG) recorded during natural speech listening to identify neural processing patterns involving slow oscillations that may characterize children with dyslexia. In a story listening paradigm, we find that atypical power dynamics and phase-amplitude coupling between delta and theta oscillations characterize dyslexic versus other child control groups (typically-developing controls, other language disorder controls). We further isolate EEG common spatial patterns (CSP) during speech listening across delta and theta oscillations that identify dyslexic children. A linear classifier using four delta-band CSP variables predicted dyslexia status (0.77 AUC). Crucially, these spatial patterns also identified children with dyslexia when applied to EEG measured during a rhythmic syllable processing task. This transfer effect (i.e., the ability to use neural features derived from a story listening task as input features to a classifier based on a rhythmic syllable task) is consistent with a core developmental deficit in neural processing of speech rhythm. The findings are suggestive of distinct atypical neurocognitive speech encoding mechanisms underlying dyslexia, which could be targeted by novel interventions.

Abstract Even prior to producing their first words, infants are developing a sophisticated speech processing system, with robust word recognition present by 4–6 months of age. These emergent linguistic skills, observed with behavioural investigations, are likely to rely on increasingly sophisticated neural underpinnings. The infant brain is known to robustly track the speech envelope, however previous cortical tracking studies were unable to demonstrate the presence of phonetic feature encoding. Here we utilise temporal response functions computed from electrophysiological responses to nursery rhymes to investigate the cortical encoding of phonetic features in a longitudinal cohort of infants when aged 4, 7 and 11 months, as well as adults. The analyses reveal an increasingly detailed and acoustically invariant phonetic encoding emerging over the first year of life, providing neurophysiological evidence that the pre-verbal human cortex learns phonetic categories. By contrast, we found no credible evidence for age-related increases in cortical tracking of the acoustic spectrogram.

Abstract Realizing the full potential of quantum technologies requires precise real-time control on time scales much shorter than the coherence time. Model-free reinforcement learning promises to discover efficient feedback strategies from scratch without relying on a description of the quantum system. However, developing and training a reinforcement learning agent able to operate in real-time using feedback has been an open challenge. Here, we have implemented such an agent for a single qubit as a sub-microsecond-latency neural network on a field-programmable gate array (FPGA). We demonstrate its use to efficiently initialize a superconducting qubit and train the agent based solely on measurements. Our work is a first step towards adoption of reinforcement learning for the control of quantum devices and more generally any physical device requiring low-latency feedback.