Individual Events Research Articles

Attribution of historical extreme heat events in different climate zones of China

Abstract China has a vast territory with diverse climates, including the arid Northwest China (NWC), semi-arid and semi-humid mixed North China (NC), humid Yangtze River Valley (YRV) and South China (SC). Previous studies on extreme heat event attribution mainly focus on individual events in specific regions, with less attention paid to comparisons of different historical events. Here, we use three indicators with daily maximum temperature exceeding 35°C, to investigate human influence on the strongest extreme heat events in early and recent periods. Based on the newest observations, we find clear increase of all the heat indicators across China, with a rapid rise in recent decade and the hottest event occurred in 2022. The Coupled Model Intercomparison Project Phase 6 (CMIP6) models generally capture the evolution of these indicators, but some biases exist. We utilize an annual cycle-based method to correct the model biases in climate state and then use the adjusted model results to conduct the event attribution in different historical periods. We find that human influence has greatly increased the probability of recent events across all regions, while having no impact on the early historical events. For the hottest 2022 event, the risk ratios for duration of extreme heat in a few regions could not be estimated due to zero probability in the natural world, indicating that such events could not happen without human influence. In different climate zones, the risk ratios for all indicators in NWC exceed those in other regions when using consistent observational thresholds, indicating a greater response of extreme heat to anthropogenic forcing in this arid region. For the same event, attribution results of different indicators yield varying risk ratios, highlighting the importance of considering multiple indicators in event attribution. Additionally, model performance notably affects attribution results; without bias correction, human influence may be incorrectly estimated.

Panopticon: A novel deep learning model to detect single transit events with no prior data filtering in PLATO light curves

To prepare for the analyses of the future PLATO light curves, we develop a deep learning model to detect transits in high precision photometric light curves. Since PLATO's main objective is the detection of temperate Earth-sized planets around solar-type stars, the code is designed to detect individual transit events. The filtering step, required by conventional detection methods, can affect the transit, which could be an issue for long and shallow transits. To protect the transit shape and depth, the code is also designed to work on unfiltered light curves. The model is based on the Unet family architecture, but it is able to more efficiently extract and combine features of various length scale, leading to a more robust detection scheme. We trained the model on a set of simulated PLATO light curves in which we injected, at the pixel level, planetary, eclipsing binary, or background eclipsing binary signals. We also included a variety of noises in our data, such as granulation, stellar spots, and cosmic rays. We then assessed the capacity of to detect transits in a separate dataset. The approach is able to recover 90% of our test population, including more than 25% in the Earth-analog regime, directly in unfiltered light curves. We report that the model also recovers transits irrespective of the orbital period, and it is therefore able to reliably retrieve transits on a single event basis. These figures were obtained when accepting a false alarm rate of 1%. When keeping the false alarm rate low ($<0.01$%) is still able to recover more than 85% of the transit signals. Any transit deeper than ∼180ppm is essentially guaranteed to be recovered. This method is able to recover transits on a single event basis, and it does so with a low false alarm rate. Due to the nature of machine learning, the inference time is minimal, around 0.2,s per light curve of 126,720 points. Thanks to light curves being one dimensional, the model training is also fast, on the order of a few hours per model. This speed in training and inference, coupled with the recovery effectiveness and precision of the model, make this approach an ideal tool to complement or be used ahead of classical approaches.

A cohort study reveals shared and distinct serum metabolic biomarkers for major adverse cardiovascular events in middle-aged and older adults.

We assessed the association of serum metabolites with the occurrence of major adverse cardiovascular events (MACE) in middle-aged and elderly individuals, explored the value of metabolomics in predicting MACE, and compared the distinctions in MACE risk-related metabolic biomarkers between middle-aged and elderly groups. Among the participants of the UK Biobank who underwent baseline assessment through nuclear magnetic resonance (NMR)-based metabolomic profiling of 168 serum metabolites and had complete covariates and clinical lipid parameters, we included those without a previous diagnosis of ischemic heart disease, cerebrovascular disease, heart failure, or cardiac arrest and not on lipid-lowering medications. Relevant covariates included sociodemographic characteristics, lifestyle factors, clinical information, and fasting time. Cox regression gave adjusted hazard ratios for metabolites, including the concentrations of various lipoprotein particles, compositional profiles of different lipoproteins, ketone bodies, amino acids, fatty acids, and additional low-molecular-weight metabolic biomarkers. The least absolute shrinkage and selection operator (LASSO) regression was applied to these metabolites to screen characteristic metabolic variables. Selected feature metabolic biomarkers were added to the established model for predicting MACE risk; risk differentiation (C-statistic) and reclassification (continuous net reclassification improvement [NRI], integrated differentiation index [IDI]) were evaluated. This study included 54,561 UK Biobank participants (34,797 middle-aged adults and 19,764 elderly adults) and was followed for a median of more than 12 years. Of these, there are 1799 middle-aged individuals and 2527 elderly individuals incident of MACE (ischemic heart disease, stroke, and cardiovascular deaths). After adjusting for relevant covariates, Cox regression yielded metabolic biomarkers associated with the occurrence of MACE in the population (false discovery rate controlled P < 0.05). In the elderly, the metabolites associated with increased MACE risk were notably diminished compared to the middle-aged; and the elderly group underscored the protective function of medium and small HDL and their constituents, docosahexaenoic acid, and glycine. The more comprehensive model, which additionally includes the feature metabolic biomarkers, demonstrated enhanced discriminatory power and predictive accuracy for MACE occurrence among middle-aged individuals, evidenced by improved C-statistics (from 0.711 [95% CI 0.699-0.722] to 0.723 [0.711-0.734]), a continuous NRI of 0.247 [0.207-0.315], and an absolute IDI of 0.005 [0.004-0.008]. Its evaluation value is superior to that in the elderly. Our study explored the association of circulating metabolites with MACE risk in middle-aged and elderly adults and made comparisons. Metabolomic insights have revealed biomarkers associated with new-onset MACE in different age populations, highlighting the value of protective metabolites in the elderly. This provides instrumental information to possibly implement precision medicine for preventing MACE.

Potential Biases in Mixed-Effects Ground-Motion Models and Variance Components Due to Uncertainty in Random Effects

ABSTRACT Empirical ground-motion models are typically estimated via mixed-effects regression, to account for correlation between records from the same event or from the same site or station. Estimated values of the random effects are often used in further analyses, for example, to develop additional submodels or to investigate physical characteristics of individual events or sites. Such analyses often do not account for uncertainty in the random effects. Using simulations, we show that neglecting these uncertainties can lead to a variety of biases, such as underestimation of variances or biased scaling with predictor variables. We demonstrate that these biases can often be overcome by estimating more complex (e.g., Bayesian) models on the total residuals of a mixed-effects regression.

Association Between Frailty and Mortality, Falls and Hospitalisation Among Patients Undergoing Dialysis: A Systematic Review and Meta-Analysis.

Frailty is a risk factor for adverse events in older individuals; however, it has not been fully verified in patients undergoing dialysis. Our aim was to verify the association between frailty and adverse outcomes consisting of mortality, falls and hospitalisation among patients undergoing dialysis by a systematic review and meta-analysis. Systematic review and meta-analysis. Multiple internet databases, were searched from the establishment of each database to April 2022, including the PubMed, EMbase, Cochrane, CNKI, WanFang and China Science and Technology Journal (VIP) databases. Cohort studies exploring the association between frailty and adverse outcomes among patients undergoing dialysis were analysed. The Newcastle Ottawa Scale (NOS) was used to assess the risk of bias in the included studies. A random effects model was used to pool the effect size, and comprehensive analyses consisting of subgroup analysis, sensitivity analysis and publication bias were assessed. The search initially identified 2744 studies from six databases. After the screening, 26 studies including 14,089 patients with dialysis aged 44.95-78.10 years were included in the final analysis, all of which were observational cohort studies. The pooled results showed that frailty was a powerful predictor of adverse outcomes (mortality, falls and hospitalisation) among the patients. Therefore, dialysis patients should be screened for early frailty and appropriate interventions should be implemented to improve adverse outcomes.

Predictive Value of Left Atrial Strain for Thrombotic Events in Hypertrophic Cardiomyopathy without Atrial Fibrillation.

Purpose To assess the predictive value of left atrial (LA) fast long-axis strain derived from cardiac MRI for thrombotic events (TEs) in individuals with hypertrophic cardiomyopathy (HCM). Materials and Methods This secondary analysis of an ongoing prospective trial (Chinese Clinical Trial Registry: ChiCTR1900024094) included consecutive participants with HCM without atrial fibrillation (AF) who underwent cardiac MRI from January 2012 to December 2020. The LA fast long-axis strain was obtained by semiautomatically tracking the distance between the atrioventricular junction and the midposterior LA wall. The primary end point was the occurrence of TEs, including ischemic stroke, transient ischemic attack, and systemic thromboembolism. The predictive value of LA strain was determined with Cox analysis. Results Overall, 714 participants with HCM (mean age ± SD, 50.1 years ± 14.3; 441 men, 273 women; obstructive HCM, n = 289; apical HCM, n = 144) were included (median follow-up: 51 months). Twenty-eight (3.9%) participants with HCM experienced TEs, 60% (17 of 28) of whom had no new-onset AF. Those who experienced TEs had lower LA reservoir and conduit strains (16.2% ± 7.3 vs 21.8% ± 8.3, P = .001; 5.9% ± 3.5 vs 9.7% ± 5.5, P = .01, respectively), with no evidence of a difference in LA booster strain between groups. LA reservoir and conduit strain were independent predictors of TEs in different multivariable models, even after adjusting for age, diabetes, and left ventricular ejection fraction (adjusted hazard ratios: reservoir strain [per 5% decrease], 1.29-1.34 [95% CI: 1.05, 1.50]; conduit strain [per 5% decrease], 1.42-1.47 [95% CI: 1.04, 1.67]). Conclusion Cardiac MRI-derived LA reservoir and conduit strain were independent predictors for the occurrence of TEs in individuals with HCM without AF. Keywords: MR-Imaging, Left Atrium, Cardiomyopathies, Hypertrophic Cardiomyopathy, Thromboembolism, Cardiac Magnetic Resonance Chinese Clinical Trial Registry no. ChiCTR1900024094 Supplemental material is available for this article. © RSNA, 2025.

Response of an obligate CAM plant to competition and increased watering intervals.

Climate change has exacerbated precipitation variability, profoundly impacting vegetation dynamics and community structures in arid ecosystems. There remains a notable knowledge gap regarding the ecological effects of altered precipitation on crassulacean acid metabolism (CAM) plants and their interactions with other photosynthetic types. This study investigated the response of the typical obligate CAM plant Orostachys fimbriata to extended watering intervals (WI4-WI8) and various competitive patterns (M1-M4) with the C3 grass Melilotus officinalis and the C4 grass Setaria viridis through greenhouse experiments. The results showed that: (1) In species mixtures, CAM plants had slightly reduced the total biomass (TB) compared to monocultures, yet maintained competitiveness by increasing the root-to-shoot biomass (R:S) ratio, stabilizing plant height, and sustaining their photosynthetic rates. (2) As watering intervals increased, CAM plants adapted by further elevating the R:S ratio, reducing height, and decreasing aboveground biomass. However, their height, CO2 assimilation rate, and above- and below-ground biomass were significantly suppressed, particularly when coexisting with C4 plants. More extreme watering regime caused a 47.6% decrease in TB of CAM plants in M4, while C3 and C4 grasses declined by 53.2% and 37.8%, respectively. (3) Given the predicted extension of drought intervals and the intensification of individual rainfall events under future climate conditions, the competitive pressure from C4 plants with high drought tolerance and resource acquisition advantages may limit the expansion potential of CAM plants in drylands. This study enhances the understanding of adaptive mechanisms of CAM plants competing and coexisting with grasses under variable environments, providing scientific bases for predicting arid ecosystem dynamics.

Efficacy of Army Combat Fitness Test 12-Week Exercise Program Among Females.

The dearth of research investigating the efficacy of virtual Army Combat Fitness Test (ACFT) training programs among female populations necessitates further exploration. This study aims to evaluate the effectiveness of a 12-week ACFT virtual exercise program among female participants. Nine female participants (age = 19.7 ± 1.3 years, height = 165.9 ± 4.9 cm, and body mass = 62.7 ± 5.9 kg) volunteered to complete 2 assessment sessions and the 12-week ACFT virtual exercise training program. In session 1, baseline height, body mass, body composition, and ACFT scores were collected. Upon completion of session 1, under supervisory guidance, all participants were scheduled to complete the 12-week exercise training program in-person. The ACFT exercise training program required participants to complete a warmup/cool down and exercise with suspension training straps 4 days per week, 60 minutes for 12 weeks. In session 2, post-assessments of body mass, body composition, and ACFT scores were collected. Statistical analyses were conducted utilizing paired sample t-tests to assess differences between pre- and post-intervention ACFT scores with P ≤ .05. Cohen's d was calculated to quantify the magnitude of the observed effect. Overall mean ACFT pre-intervention scores (487.2 ± 66.5) increased significantly when compared to mean ACFT post-intervention scores (521.6 ± 44.0) (Cohen's d = 0.86, P = .03). No statistical differences were displayed among the individual mean ACFT pre- versus post-intervention scores. A consistent decline in adherence of 3.9% per week ensued from week 1 through week 12. Following the intervention, female participants experienced a significant increase of approximately 7% in their overall ACFT scores, with upward trends observed across all 6 individual ACFT events. These findings suggest that 12-week virtual suspension training can be a valuable tool for enhancing physical fitness and performance among female participants, particularly within the context of military readiness.

Improving Earthquake Location Resolution through Dynamic Time Warping-Based Cross-correlation

Cross-correlation methods for seismic event location utilize the differential travel time of the signals presented in the cross-correlation records for positioning, eliminating the necessity for scanning the earthquake occurrence time, thereby ensuring efficient calculation of event locations. The finite-frequency characteristics of the signal can cause a decrease in the time resolution of the correlation recordings, resulting in a decrease in the spatial resolution of the location. Exploration of various combinations of station pairs, often referred to as imaging conditions, have been commonly employed to enhance the positioning resolution. Instead of modifying the imaging conditions, we developed a dynamic time warping-based cross-correlation method aimed at enhancing the time resolution of the cross-correlation records. This innovative approach utilizes the Pearson correlation coefficient as a distance metric to conduct a global search to obtain time shifts for different seismic signals, providing an optimal correlation coefficient. The proposed workflow enables the transformation of the differential time and the Pearson correlation coefficients into high-resolution cross-correlation records. It not only enhances the time resolution of individual seismic event cross-correlation records but also captures the time differences of distinct signals across multiple events. Simulations show that the proposed method markedly improves both the time resolution of cross-correlation records and the spatial resolution of locations, effectively handling multievent locations with varying magnitudes. A case study reveals that the new method is effective in handling complex waveforms from regional seismic networks and serves as a significant improvement in the precision of seismic event location.

Multi-spacecraft observations of the decay phase of solar energetic particle events

The parameters of solar energetic particle (SEP) event profiles such as the onset time and peak time have been researched extensively to obtain information on the acceleration and transport of SEPs. The corotation of particle-filled magnetic flux tubes with the Sun is generally thought to play a minor role in determining intensity profiles. However recent simulations have suggested that corotation affects the SEP decay phases and depends on the location of the observer with respect to the active region associated with the event. We aim to determine whether signatures of corotation are present in observations of the decay phases of SEP events, and we study the dependence of the parameters of the decay phase on the properties of the flares and coronal mass ejections associated with the events. We analysed multi-spacecraft observations of SEP intensity profiles from 11 events between 2020 and 2022 using data from Solar Orbiter, PSP, STEREO-A, and SOHO. We determined the decay-time constant, τ, in three energy channels; electrons ∼1 MeV, protons ∼25 MeV, and protons ∼60 MeV. We studied the dependence of τ on the longitudinal separation, Δ ϕ, between the source of the active region and the spacecraft magnetic footpoint on the Sun. Individual events show a tendency for the decay-time constant to decrease with increasing Δ ϕ. This agrees with test particle simulations. The magnitude of the event as measured through the intensity of the associated flare and SEP peak flux affects the measured τ values and likely is the cause of the observed large inter-event variability together with the varying solar wind and the conditions in the interplanetary magnetic field. We conclude that corotation affects decay phase of an SEP event and should be included in future simulations and interpretations of these events.

Opportunistic measurement of sagittal abdominal diameter with bone densitometry predicts death and cardiovascular events

Abstract Supine sagittal abdominal diameter (SAD), also known as abdominal height, has been proposed as a simple measure for assessing abdominal adiposity. We aimed to determine whether SAD from dual-energy X-ray absorptiometry (DXA) performed for osteoporosis assessment predicts major adverse cardiovascular events (MACE) using the population-based DXA registry for the Province of Manitoba, Canada. The study population comprised 72,974 individuals aged 40 years and older with baseline DXA assessment between February 1999 and March 2018. Incident MACE (composite of all-cause mortality, acute myocardial infarction [MI], non-hemorrhagic stroke) was ascertained from linked healthcare databases. During mean 8.4 years follow-up (611,862 person-years), 14,457 (18.8%) individuals experienced incident MACE. Risk stratification was greatest with SAD/weight ratio, with area under the curve (AUC) for MACE and its components ranging from 0.582 for acute MI to 0.620 for death (all p&amp;lt;0.001), all significantly better than with body mass index (BMI) (p&amp;lt;0.001). In multivariable-adjusted models, each SD increase in SAD/weight was associated with increased risk for MACE (hazards ratio [HR] 1.20, 95% CI 1.18-1.22), death (HR 1.22, 95% CI 1.20-1.25), acute MI (HR 1.19, 95% CI 1.14- 1.24), and stroke (HR 1.17, 95% CI 1.12-1.22). A linear gradient was seen across SAD/weight quintiles (all p-trend&amp;lt;0.001), with adjusted HR for MACE 1.61 (95% CI 1.50-1.72) for highest versus lowest quintile. Results were similar when further adjusted for BMI, in non-obese and obese individuals (p-interaction for obesity = 0.141), and in both women and men (p-interaction for sex = 0.471). In conclusion, SAD measured opportunistically at the time of DXA testing is predictive of death and major cardiovascular events in individuals undergoing osteoporosis assessment.

Mapping cell-cell fusion at single-cell resolution.

Cell-cell fusion is a tightly controlled process in the human body known to be involved in fertilization, placental development, muscle growth, bone remodeling, and viral response. Fusion between cancer cells results first in a whole-genome doubled state, which may be followed by the generation of aneuploidies; these genomic alterations are known drivers of tumor evolution. The role of cell-cell fusion in cancer progression and treatment response has been understudied due to limited experimental systems for tracking and analyzing individual fusion events. To meet this need, we developed a molecular toolkit to map the origins and outcomes of individual cell fusion events within a tumor cell population. This platform, ClonMapper Duo ('CMDuo'), identifies cells that have undergone cell-cell fusion through a combination of reporter expression and engineered fluorescence-associated index sequences paired to randomly generated nucleotide barcodes. scRNA-seq of the indexed barcodes enables the mapping of each set of parental cells and fusion progeny throughout the cell population. In triple-negative breast cancer cells CMDuo uncovered subclonal transcriptomic hybridization and unveiled distinct cell-states which arise in direct consequence of homotypic cell-cell fusion. CMDuo is a platform that enables mapping of cell-cell fusion events in high-throughput single cell data and enables the study of cell fusion in disease progression and therapeutic response.

Unveiling the functional connectivity of astrocytic networks with AstroNet, a graph reconstruction algorithm coupled to image processing

Astrocytes form extensive networks with diverse calcium activity, yet the organization and connectivity of these networks across brain regions remain largely unknown. To address this, we developed AstroNet, a data-driven algorithm that uses two-photon calcium imaging to map temporal correlations in astrocyte activation. By organizing individual astrocyte activation events chronologically, our method reconstructs functional networks and extracts local astrocyte correlations. We create a graph of the astrocyte network by tallying direct co-activations between pairs of cells along these activation pathways. Applied to the CA1 hippocampus and motor cortex, AstroNet reveals notable differences: astrocytes in the hippocampus display stronger connectivity, while cortical astrocytes form sparser networks. In both regions, smaller, tightly connected sub-networks are embedded within a larger, loosely connected structure. This method not only identifies astrocyte activation paths and connectivity but also reveals distinct, region-specific network patterns, providing new insights into the functional organization of astrocytic networks in the brain.

Differential analysis of alternative splicing events in gene regions using residual neural networks

Abstract Several computational methods for the differential analysis of alternative splicing (AS) events among RNA-Seq samples typically rely on estimating isoform-level gene expression. However, these approaches are often error-prone due to the interplay of individual AS events, which results in different isoforms with locally similar sequences. Moreover, methods based on isoform-level quantification usually need annotated transcripts. In this work, we leverage the ability of deep learning networks to learn features from images and propose , a novel method for event-based AS differential analysis between two RNA-Seq samples. Our method does not rely on isoform abundance estimation, neither on a specific annotation. employs an image embedding scheme to represent the alignments of the two samples on the same region and utilizes a residual neural network to predict the AS events possibly expressed within that region. To our knowledge, is the first deep learning approach for performing an event-based AS analysis of RNA-Seq samples. To validate , we also address the lack of high quality AS benchmark datasets. For this purpose, we manually curated a set of regions exhibiting AS events. These regions were used for training our model and for assessing the predictions of our method. Our results highlight that achieves higher precision at the expense of a small reduction in sensitivity. The tool and the manually curated regions are available at https://github.com/sciccolella/deepSpecas.

Complete 3D photoelectron momentum vector reconstruction from time-position charged particle imaging

Abstract Many charged particle imaging techniques exist which directly measure, at a detector, the transverse position (x, y) and time-of-flight (t) of individual events in order to obtain a full set of 3D coordinates. Where curved velocity-mapping electric field lines are implemented, as in the case of 3D Velocity Map Imaging (3D VMI) and certain COLTRIMS (Cold Target Recoil Ion Momentum Spectroscopy) instruments, the general transformation of (x, y, t)-data into initial 3D recoil momentum vectors (px , py , pz ) is challenging and has not yet been fully addressed. Here we present a detailed and general method for this transformation, illustrated using our 3D VMI spectrometer and the well-known narrow-band photoionization of nitric oxide, for which we demonstrate quantitative agreement with reported values. We additionally show how to measure and correct (i) small errors in the laser polarization axis alignment at the interaction region of a 3D charged particle imaging spectrometer, and (ii) the spatial variation of gain on a microchannel plate (MCP) detector. Improvements to and characterization of our 3D VMI spectrometer yield an electron time-of-flight resolution of 72 ps across the full 40 mm MCP, in combination with pixel-level spatial resolution.

Spatiotemporal patterns of individual and multiple simultaneous severe weather events co-occurring with power outages in the United States, 2018–2020

In the United States, severe weather-driven power outages are increasing in frequency and duration, likely with health consequences. Previous studies examined individual severe weather events (e.g., heatwaves) and focused on large outages in metropolitan areas. Here, we described nationwide spatiotemporal patterns of individual (e.g., tropical cyclone alone) and simultaneous (e.g., tropical cyclone + anomalous heat) severe weather events co-occurring with 8+ hour outages. We used hourly county-level PowerOutage.us data from 2018–2020 to define 8+ hour outages as whenever the daily proportion of customers without power was ≥0.1% for ≥8 continuous hours. We conducted analyses at the daily and county (county-day) level and identified county-days with severe weather events, including anomalous cold, anomalous heat, anomalous precipitation, snowfall, tropical cyclones, and wildfire. Of 1,657 counties with reliable outage data, 1,205 (72.7%) experienced an 8+ hour outage co-occurring with an individual severe weather event, and 904 (54.6%) with multiple simultaneous severe weather events. Anomalous precipitation events co-occurring with outages were the most common, affecting 1,170 (70.6%) counties. These outages concentrated along the Gulf Coast, the Northeast, Michigan, and Southern California. Co-occurrence with anomalous heat happened the second most frequently, affecting 839 (50.6%) counties, mostly in Southeastern states. Among all county-days with a severe weather event, tropical cyclones–though rarer and primarily affecting the Eastern Seaboard–co-occurred with an 8+ hour outage 24% of the time. On the West Coast, wildfires were increasingly likely to co-occur with weather-related outages from 2018–2020. Among multiple simultaneous weather events, 8+ hour outages co-occurred with anomalous precipitation-anomalous heat on 1,155 county-days in 40 states, anomalous precipitation-tropical cyclone on 705 county-days in 24 states, and anomalous cold-snowfall on 259 county-days in 27 states. Our results can help guide efforts to strengthen the electricity grid, prepare communities for multi-hazard events, and allocate resources for adaptation and recovery.

Tracking Cholesterol Flip-Flop in Mammalian Plasma Membrane through Coarse-Grained Molecular Dynamics Simulations.

Plasma membrane (PM) simulations at longer length and time scales at nearly atomistic resolution can provide invaluable insights into cell signaling, apoptosis, lipid trafficking, and lipid raft formation. We propose a coarse-grained (CG) model of a mammalian PM considering major lipid head groups distributed asymmetrically across the membrane bilayer and validate the model against bilayer structural properties from atomistic simulation. Using the proposed CG model, we identify a recurring pattern in the passive collective cholesterol transbilayer motion and study the individual cholesterol flip-flop events and associated pathways along with lateral ordering in the bilayer during a flip-flop event. We identify two discrete cholesterol flip-flop pathways: (i) a systematic rototranslational pathway and (ii) intraleaflet inversion followed by interleaflet translation (or reverse). We observe a periodic cholesterol enrichment in the exoplasmic leaflet of the PM bilayer and examine the underlying cholesterol-lipid affinities. We observe closer association between cholesterol and palmitoylsphingomyelin (PSM) lipid, relative to other lipids, and conclude that the cholesterol enrichment in the exoplasmic leaflet can be attributed to higher PSM content in that leaflet, together leading to formation of short-lived PSM-cholesterol-rich domains.

Nonlinear Rayleigh–Taylor instability in an accelerated soap film

Rupture and fragmentation of thin liquid films are of great importance in manufacturing, agriculture, disease transmission, and other fields. An individual rupture event typically results from the nonlinear growth of small perturbations excited by the classical Rayleigh–Taylor mechanism and opposed by surface tension on the film interfaces. We study an initially uniform liquid film accelerated in the direction perpendicular to its interfaces, constructing a new reduced description of the flow in the limit of large surface tension consisting of three coupled (nonlinear) partial differential equations. This system is linearly unstable to disturbances of small wavenumber; we derive a simplified asymptotic description to gain analytical understanding of the nonlinear development until rupture. An initial phase of exponential film thinning leads to the accumulation of liquid in a central bulge. At a well-defined time, a localised pinch-off mechanism, driven by a mismatch in the interfacial curvature at the edge of the bulge, results in a self-similar thinning that breaks the film in finite time. Across a wide parameter range, the resultant droplet formed from the central bulge contains a significant fraction of the initial film volume.

Influence of radiotherapy on cardiac implantable devices and leads-asingle-institution analysis and critical evaluation of current guidelines.

Increasing life expectancy and advances in cancer treatment will lead to more patients needing both radiation therapy (RT) and cardiac implantable electronic devices (CIEDs). CIEDs, including pacemakers and defibrillators, are essential for managing cardiac arrhythmias and heart failure. Telemetric monitoring of CIEDs checks battery status, lead function, settings, and diagnostic data, thereby identifying software deviations or damage. This study evaluates the German Society for Radiation Oncology (DEGRO)/German Society for Cardiology (DGK) guideline, assessing real-world complications and risk factors and analyzing pacemaker and implantable cardioverter-defibrillator (ICD) lead function for their predictive value concerning device malfunction. Atotal of 54patients with pacemakers or ICDs who underwent radiation therapy were identified. Demographics, treatment courses, and device information from physical and digital records were extracted. DEGRO/DGK risk groups and pacemaker dependency at the start of RT were assessed. Delineation of the devices and lead insertion sites was performed in the treatment planning system. Dosimetric information from the treatment plans was then correlated with reports of standardized device checks. Over 80% of patients were treated with dual-chamber pacemakers or cardiac resynchronization therapy (CRT), and 16.7% had ICDs. One third of patients were pacemaker dependent. 59.3% of patients were in the low-risk category, 29.3% in the medium-risk, and 11.1% in the high-risk category. Thoracic irradiation resulted in the highest median dose to devices. Lead parameter deviations exceeding thresholds were found in 14.8% for the stimulation threshold and 13.5% for sensing. Device malfunctions occurred in 3.7% of cases, both involving electrical resets and neutron-producing radiation (beam energy 10megaelectron volt (MV) or higher). Collecting lead parameters in addition to secure malfunctions like electrical restarts and memory erasure revealed asignificant proportion of treatment courses with temporary changes, though no correlation with individual treatment plans or adverse events was found. The focus on reducing neutron-producing radiation could be further supported.

Differentiation of Related Events in Hippocampus Supports Memory Reinstatement in Development.

Adults are capable of either differentiating or integrating similar events in memory based on which representations are optimal for a given situation. Yet how children represent related memories remains unknown. Here, children (7-10 years old) and adults formed memories for separate yet overlapping events. We then measured how successfully remembered events were represented and reinstated using fMRI. We found that children formed differentiated representations in the hippocampus-such that related events were stored as less similar to one another compared with unrelated events. Conversely, adults formed integrated representations, wherein related events were stored as more similar, including in medial prefrontal cortex. Furthermore, hippocampal differentiation among children and medial prefrontal cortex integration among adults tracked neocortical reinstatement of the specific features associated with the individual events. Together, these findings reveal that the same memory behaviors are supported by different underlying representations across development. Specifically, whereas differentiation underlies memory organization and retrieval in childhood, integration exhibits a protracted developmental trajectory.