Household energy demand data are essential for designing load-shifting strategies, storage solutions and demand response programs. This is the first publicly available dataset that integrates multiple household-level energy and mobility metrics. These include grid imports and exports (30-minute intervals), rooftop photovoltaic (PV) production (30-minute intervals), EV charging sessions, and detailed journey logs (start time, end time, distance, and duration) from the same households of a pilot energy community located on the Dingle Peninsula, Ireland. The dataset includes four volunteer households, each equipped with a 2.1 kWp rooftop PV system, 5 kWh Sonnen battery, a Hyundai Kona Electric (64 kWh), a Pulsar Plus EV charger (7.4 kW AC), and a Mitsubishi Electric Ecodan air-source heat pump.While grid and PV data span three years, EV mobility logs cover February 2021 to January 2022. Battery telemetry and heat pump demand data are available for the final six to eight months of the observation period (mid-2021 to early 2022), enabling detailed whole-home analysis for that specific window.
At the present time, the increasing use of lithium-ion batteries in electric vehicles has created unprecedented pressure for end-of-life management and resource recovery. This article reports on a direct recycling approach to regenerate spent cathode active materials, in particular Ni-rich NMC622, via a hydrothermal re-lithiation strategy and thermal annealing. An initial screening process was established to separate high purity spent cathode active materials from disassembled LG Chem pouch cells from Hyundai KONA battery packs. A full factorial design was applied to provide a meaningful statistical analysis of the influence of hydrothermal variables - LiOH concentration, temperature and reaction time. The results indicate that lithium concentration and temperature have a strong main effect on regeneration efficiency, while interaction effects with time are more influential for lithium incorporation. The regenerated cathode active materials exhibited structural, morphological and electrochemical performance comparable to commercial NMC622, especially for samples treated at 160 °C, 4 M LiOH and 1 h reaction time. This process demonstrates the feasibility of regenerating degraded cathode active materials for reuse in new batteries, contributing to circular economy strategies and critical raw material independence in Europe. On the other hand, detailed material characterization validated the recovery of layered crystalline structure and localized cation mixing, conditions required for best battery performance. Regenerated electrodes retained high specific capacity during electrochemical testing and displayed good stability over 50 cycles under the conditions tested. Interactions were quantitatively significant and through the statistical analysis approach, optimal synthesis conditions were directed based on interaction limits. Against this background, the proposed method circumvents the high energy consumption and material losses of the pyrometallurgical route and the secondary pollution and reagents needed in the hydrometallurgical leaching process. In summary, direct recycling appears to be a more resource-efficient and sustainable route for the recovery of cathode materials in future battery supply chains.
Scanning electrochemical microscopy (SECM) is an efficient operando technique that offers deep insight into the electrode-electrolyte interfacial process occurring during electrochemical reactions. This approach helps to identify the complexities of the reactions associated with the electrochemical steps and intermediates in various reaction conditions. This article focuses on employing SECM as a tool to study anodic oxygen evolution reaction (OER) in water splitting. Using this technique, the qualitative and quantitative evaluation of catalytic activity can be mapped on catalyst surface. This work critically investigates SECM's strengths in probing key catalytic parameters involved in OER, such as charge transfer kinetics, oxygen flux evaluation, and catalyst-substrate interactions. In addition to that, SECM's ability to detect reactive oxygen species, active site analysis, and determination of catalyst oxidation state are also emphasized, using various operational modes. Finally, the challenges of SECM are highlighted elaborately, which can pave the path for further innovation in related fields.
The association of long COVID with health-related quality-of-life (HrQOL) has not been well-characterized. Participants who received blinded placebo in the ACTIV-2/A5401 outpatient COVID-19 treatment trial were included in an analysis of the association of long COVID with HrQOL (both pre-specified exploratory trial endpoints) 9 months after acute COVID-19. Long COVID was defined as presence of self-assessed COVID-19 symptoms and HrQOL was assessed with EQ-5D-5L and SF-36v2 questionnaires. Associations were evaluated by Fisher's exact tests and Wilcoxon rank-sum tests. Of 546 participants, 13% had long COVID. Long COVID was associated with greater risk of reported problems in the EQ-5D-5L dimensions of mobility, usual activities, pain/discomfort, and anxiety/depression (risk ratios 3.45-6.00, all p < 0.001) and worse self-reported health scores (median 80 vs. 95, p < 0.001). Participants with long COVID also had worse SF-36v2 composite physical and mental component scores (both p < 0.001) and individual SF-36 domain scores (physical functioning, physical role, bodily pain, general health, vitality, social functioning, emotional role, and mental health; all p < 0.001). Associations were similar regardless of baseline (pre-COVID-19) medical history. Long COVID is associated with impaired HrQOL across multiple domains, highlighting the need to develop preventative and therapeutic interventions for this protean condition.
How anti-SARS-CoV-2 monoclonal antibodies (mAbs) change subsequent vaccine responses remains uncertain. We conducted a prospective, phase IV, open-label study of adults who received mRNA-1273 or BNT162b2. Cohort 1 included outpatients with acute COVID-19 previously randomized to mAbs (tixagevimab/cilgavimab or amubarvimab/romlusevimab), camostat, or placebo in ACTIV-2/A5401. Cohort 2 included unvaccinated adults without reported prior COVID-19 and was analyzed as naïve or non-naïve by baseline neutralizing antibodies (nAbs). We measured binding IgG, nAbs, spike-specific memory B cells, and CD4+/CD8+ T cells at baseline and days 28, 56, and 140. Forty-three participants were analyzed. At day 140, nAb titers were lower among prior mAb recipients and COVID-19-naïve participants than among placebo/camostat recipients and those with evidence of prior infection (overall p=0.018). RBD-specific, but not spike-specific, memory B cells were reduced after prior mAb therapy at days 56 and 140. Frequency of spike-specific CD4+ and CD8+ T-cell responses did not differ by prior mAb exposure. Adverse events were mostly grade 1-2 and consistent with vaccine trials. Prior anti-SARS-CoV-2 mAb treatment limits endogenous RBD-focused B-cell responses to later mRNA vaccination without measurably affecting T-cell immunity. Timing of vaccination after mAb therapy may matter and warrants study. NCT04952402.
Aqueous manganese batteries (AMBs) are promising alternatives to lithium-ion systems due to their safety and cost-effectiveness. However, organic cathodes often face challenges regarding material dissolution and slow ion kinetics. In this work, we introduce indanthrone (IDT) as a strategic organic cathode for high-performance Mn2+/H+ batteries. IDT features a massive eight-ring π-conjugated framework that enables extensive electronic delocalization. Unlike systems with excessively concentrated active groups, the IDT framework provides a more dispersed distribution of redox-active sites. This arch\itecture promotes smooth ion transport through enhanced charge delocalization across the extended aromatic system. The IDT electrode delivers a high specific capa\city of 194.8 mAh/g at 0.1 A/g, while the Mn-IDT full-cell exhibits 85.5% capacity retention after 3000 cycles at 0.3 A/g. Spectroscopic and crystallographic analyses confirm a low-strain pathway, where b-axis expansion (2.09%) is effectively compensated by minor contractions in the a and c-axes. This unique anisotropic response leads to minimal overall volume fluctuation, ensuring structural integrity during prolonged operation. This work provides a molecular-level design principle for constructing low-strain organic cathodes for high-voltage aqueous multivalent batteries.
Sound absorption in porous materials is fundamentally governed by their microstructural morphology yet establishing a quantitative and design-oriented relationship between microstructure and acoustic behavior remains challenging. To address this challenge, a deep learning-based acoustic modeling framework is proposed for analyzing the microstructure of flexible polyurethane (PU) foam and predicting its acoustic performance. A microscopic analysis model is developed to semantically segment SEM images using a U-Net model and quantitatively extract the distribution parameters of microstructural properties, including cell size, pore size, pore shape factor, and strut thickness. An artificial neural network model is developed to model the relationship between these microstructural parameters and acoustic performance measured using an impedance tube, based on 210 flexible PU foam samples including thermally aged and non-aged materials from multiple manufacturers. Finally, the proposed approach is validated through comprehensive performance evaluation, comparison with experiments and alternative methods, and analysis of microstructural parameter contributions. The validated framework establishes a quantitative link between microscale morphology and acoustic performance, providing data-driven acoustic insights and practical guidance for acoustic material design, including feature selection, optimization of acoustic performance, and fabrication of sound-absorbing materials for applications such as automotive and construction.
The COVID-19 pandemic has underscored the urgent need for broad-spectrum antivirals (BSAs) capable of countering diverse and rapidly emerging viral threats. Unlike virus-specific drugs, BSAs offer cross-family protection and can serve as adaptable therapeutic platforms for pandemic preparedness. Advances in nanotechnology have further strengthened this approach by improving the solubility, stability, and targeted delivery of antiviral agents. Several repurposed drugs, such as niclosamide, favipiravir, remdesivir, nitazoxanide, and zinc-ionophores, have demonstrated potential broad-spectrum activity when formulated at the nanoscale. These nanoengineered platforms enhance pharmacokinetic performance, tissue penetration, and bioavailability, thereby enabling lower effective doses and reduced systemic toxicity. Such nanotechnological strategies not only improve antiviral efficacy across multiple viral families, including Coronaviridae, Flaviviridae, Orthomyxoviridae, and Poxviridae, but also support scalable, cost-effective production suitable for global deployment. By integrating drug repurposing with nanoengineering, BSAs can form the cornerstone of future pandemic preparedness, bridging the gap between laboratory innovation and rapid clinical response to emerging infectious diseases.
Organic sludge (OS) contains high concentration of calcium, which can inhibit anaerobic digestion (AD) by limiting mass transfer between microbes and organics. In this study, we investigated the effects of carbonate-forming additives injection on methane production in AD of OS by mitigating calcium inhibition and clarified the underlying mechanisms. CO2, urea, and sodium bicarbonate were evaluated with focus on floc structure of OS and calcium dynamic, and energy analysis considered biogas-derived CO2 recycling. CO2 injection achieved highest methane production (113.9 mL-CH4/g-VS) and net energy gain (269 MJ/tonne-OS), representing 29 vol% and 9% increase, respectively, compared to control, while urea and sodium bicarbonate showed negligible effects. These improvements with CO2 injection resulted from dissolution of internal calcium decreasing particle size by 54% and increasing specific surface area by 8%. This study demonstrates the energetic feasibility of recycling biogas-derived CO2 to enhance AD of calcium-rich sludge, supporting the sustainability of waste-to-energy systems.
Medulloblastoma, the most common malignant brain tumor in children, shows a pronounced tendency to spread to the leptomeninges. Leptomeningeal metastasis accounts for nearly all medulloblastoma-related deaths, yet the cellular and molecular mechanisms driving this process remain poorly understood. Progress has been hindered by limited access to patient samples, the fragile anatomy of the leptomeninges, and the lack of robust preclinical models. Here, we developed an in vitro model that captures key features of the early leptomeningeal niche. We demonstrate that human meningeal cells promote medulloblastoma survival and proliferation under nutrient-deprived conditions both in vitro and in vivo. Using this system, we uncovered mechanisms that govern distinct patterns of leptomeningeal colonization in vivo. This physiologically informed and experimentally validated model offers a tractable platform for elucidating the molecular basis of leptomeningeal colonization and for identifying therapeutic vulnerabilities that can prevent medulloblastoma dissemination.
The rapid digitalization of modern energy systems—including smart grids, advanced metering infrastructures (AMI), and supervisory control and data acquisition (SCADA) networks—has increased their vulnerability to cyberattacks. Although encryption secures energy data, it also conceals malicious traffic, complicating intrusion detection. This vulnerability exposes critical systems to threats, such as false data injection, command tampering, and advanced persistent attacks. Consequently, distinguishing benign from malicious activity becomes increasingly challenging, especially when attackers exfiltrate sensitive data through encrypted channels. This study proposes an adaptive feature selection (AFS) method to enhance cybersecurity in energy systems. In contrast to conventional models that focus solely on statistical relevance, AFS incorporates gradient-based relevance to capture context-sensitive traffic patterns, thereby revealing malicious activities within encrypted, noisy environments. Experimental results, conducted on the CIRA-CIC-DoHBrw-2020 dataset, show that AFS improves detection accuracy by 24.74% and reduces training time by 35% compared to conventional PCA-based methods. This approach strengthens cybersecurity in energy systems by improving the detection performance of intrusion detection frameworks, thereby enhancing operational reliability, data integrity, and overall network security.
Schizophrenia (SCZ) and alcohol use disorder (AUD) are associated with physical decline and motor dysfunction, but objective wearable-based motor assessments remain underutilized in psychiatric research. This study compared handgrip strength (HGS) and gait features between healthy controls (HCs) and individuals with SCZ or AUD using wearable sensors. A total of 434 participants (HCs: n = 210; AUD: n = 80; SCZ: n = 144) completed instrumented Timed Up and Go, walking, and HGS tests. Fifteen motor features were extracted and analyzed using multivariable linear regression adjusted for age, sex, and BMI. Five features-HGS, relative HGS (rHGS), walk quality index, symmetry index, and mid-turning phase duration-significantly differentiated one or both diagnostic groups from HCs. In AUD, rHGS showed moderate associations with multiple gait parameters, consistent with more widespread motor dysfunction. In SCZ, these associations were weaker, suggesting reduced coupling between upper- and lower-limb motor function. Both groups showed reduced HGS and gait alterations, but with distinct coordination patterns. These findings support wearable-based grip and gait metrics as scalable and objective motor functional markers in SCZ and AUD.
Vision-language models trained using self-supervised learning are crucial to reduce the dependency on large volumes of labeled data. However, conventional self-supervised approaches that rely on precise image-text pairing are not always feasible for cardiovascular magnetic resonance imaging (CMR) given its ability to visualize cardiac anatomy, physiology, and microstructure in a single exam. We present CMR-contrastive language image pretraining (CMR-CLIP), a vision language model which treats CMR images as videos to jointly learn embeddings between the images in the study and associated reports. The model is trained on a large dataset consisting of 11,028 studies performed at a single healthcare institution and evaluated on an internal test (N = 2,758) and external dataset (N = 428). CMR-CLIP achieves remarkable performance in real-world clinical tasks, achieving accuracies of 88.5% for non-ischemic cardiomyopathy, 88.0% for ischemic cardiomyopathy, 96.2% for cardiac amyloidosis, and 98.6% for hypertrophic cardiomyopathy, potentially leading to more consistent diagnosis of cardiovascular disease.
The role of germline predisposition in pediatric cancer is increasingly recognized. However, the optimal approach to identifying cancer predisposing germline pathogenic variants (GPV) in children, and even the prevalence of GPVs among children with cancer, remain unclear. We examined GPV prevalence and diagnostic yield of different test approaches within a national pediatric precision oncology program. We performed prospective rapid-turnaround whole-genome and -transcriptome profiling of 496 consecutively recruited children with poor-prognosis cancer to identify genetic variants linked to cancer risk. Integration of tumor and germline molecular profiling identified GPVs in 15.5% of patients, an incremental GPV yield of 7.9% above that detected by standard care. Although the cancer type was outside the recognized phenotypic spectrum for 43.7% of reported GPVs, 63.2% of these were clinically actionable. Integrated germline-tumor analysis increased the GPV detection rate by 8.5%, informed germline interpretation in 14.3% of patients with GPVs, and provided biological insight into tumor etiology, together highlighting the value of integrated analyses. Cascade testing in first-degree relatives confirmed that the GPV was de novo in 21% of tested families. Within inherited GPVs (73.9%), 47.8% had direct implications for risk management recommendations in the relevant parent. Our findings establish the clinical benefit and feasibility of integrated tumor-germline whole-genome screening to detect GPVs in children with poor-prognosis cancers and their first-degree relatives.
Non-canonical (i.e. unannotated) open reading frames (ncORFs) have until recently been omitted from reference genome annotations, despite evidence of their translation, limiting their incorporation into biomedical research. To address this, in 2022, we initiated the TransCODE consortium and built the first community-driven consensus catalog of human ncORFs, which was openly distributed to the research community via Ensembl-GENCODE. While this catalog represented a starting point for reference ncORF annotation, major technical and scientific issues remained. In particular, this initial catalog had no standardized framework to judge the evidence of translation for individual ncORFs. Here, we present an expanded and refined catalog of the human reference annotation of ncORFs. By incorporating more datasets and by lifting constraints on ORF length and start codon, we define a comprehensive set of 28 359 ncORFs that is nearly four times the size of the previous catalog. Furthermore, to aid users who wish to work with ncORFs with the strongest and most reproducible signals of translation, we utilized a data-driven framework (i.e. translation signature scores) to assess the accumulated evidence for any individual ncORF. Using this approach, we derive a subset of 10 127 ncORFs with translation evidence on par with canonical protein-coding genes, which we refer to as the primary set. This set can serve as a reliable reference for downstream analyses and validation, with a particular emphasis on high quality. Overall, this update reflects continuous community-driven efforts to make ncORFs accessible and actionable to the broader research public, and further iterations of the catalog will continue to expand and refine this resource.
Ewing Sarcoma (ES) is a rare but aggressive malignancy of bone tissue in adolescents and young adults, where early detection of progression and real-time treatment monitoring remain unmet clinical needs. Tumor extracellular vesicles (EVs) carry surface markers and nucleic acid cargo that can serve as minimally invasive biomarkers, but single-marker EV assays often lack specificity, and colocalized-marker approaches may suffer from low sensitivity. Here, we report the ES EV Capture-Release-Capture (CaReCa) assay, a two-step enrichment strategy that combines desthiobiotin (DTB)-mediated capture/release of CD99+ EVs with click chemistry-mediated recapture of CD99+/B7-H3+ EVs, introducing molecular specificity to suppress background signals. To overcome limited yield from EVs with colocalized markers, we incorporated RT-digital PCR quantification of encapsulated ACTB mRNA, a stable housekeeping transcript, as a sensitive proxy for EV abundance. Using only 100 µL of plasma, the ES EV CaReCa assay distinguished ES patients (n = 20) from healthy donors (n = 20) with an AUROC of 0.98. Longitudinal analysis further demonstrated that dynamic changes in the assay readouts paralleled disease progression and treatment response, consistent with PET/CT findings. Together, these results establish CaReCa as a sensitive, specific, and scalable liquid biopsy platform with translational potential for noninvasive monitoring of ES patients.
Extracellular matrix (ECM) remodeling is increasingly recognized as a central determinant of inflammation, fibrosis, and therapeutic response across chronic diseases. This Perspective examines how post-COVID-19 pulmonary fibrosis and stromal-driven resistance in solid tumors converge on a shared phenomenon, ECM-driven pseudo-resistance, an extrinsic and reversible microenvironmental state in which pathological matrix architecture, fibro-inflammatory signaling, and immune exclusion transiently limit therapeutic efficacy without conferring stable, mutation-driven cellular resistance. We highlight how nanochemical strategies, including ECM-penetrating and ECM-modulating nanohybrids, can dismantle these physical and signaling barriers by reprogramming matrix stiffness, mechanotransduction, and immune accessibility. By integrating evidence from virology, oncology, and materials science, this review proposes that targeting conserved ECM pathways through advanced nanochemistry offers a cross-disease therapeutic paradigm for overcoming pseudo-resistance in fibrotic and malignant pathologies.
The Centers for Disease Control and Prevention has recommended contact precautions for healthcare personnel caring for COVID-19 patients since the beginning of the pandemic. However, current scientific evidence points to transmission through small respiratory droplets or aerosols and not contaminated fomites as the dominant routes of transmission of SARS-CoV-2. We believe science shows there is no benefit and thus only negative consequences to patients, the environment, and the U.S healthcare system associated with ongoing contact precautions for patients with SARS-CoV-2 infection, and we advocate for updated guidelines reflecting current science.
Restoring long-distance spinal cord connectivity remains a major challenge in regenerative medicine. Despite advances in stem cell therapy, biomaterial scaffolds, and neuromodulation, recovery after spinal cord injury (SCI) is limited by a hostile post-injury microenvironment marked by chronic inflammation, glial scarring, and extracellular matrix (ECM) stiffening. This Perspective proposes nanoengineered niclosamide, a repurposable multi-pathway modulator, as a strategy to reprogram this niche. By attenuating NF-κB/STAT3-driven inflammation, suppressing fibrotic signaling, and reducing ECM rigidity, nanoengineered niclosamide may synergize with scaffold- and stimulation-based approaches, highlighting microenvironmental modulation as a realistic path forward for SCI repair.