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Neutron stars provide a unique opportunity to study strongly interacting matter under extreme density conditions. The intricacies of matter inside neutron stars and their equation of state are not directly visible, but determine bulk properties, such as mass and radius, which affect the star's thermal X-ray emissions. However, the telescope spectra of these emissions are also affected by the stellar distance, hydrogen column, and effective surface temperature, which are not always well-constrained. Uncertainties on these nuisance parameters must be accounted for when making a robust estimation of the equation of state. In this study, we develop a novel methodology that, for the first time, can infer the full posterior distribution of both the equation of state and nuisance parameters directly from telescope observations. This method relies on the use of neural likelihood estimation, in which normalizing flows use samples of simulated telescope data to learn the likelihood of the neutron star spectra as a function of these parameters, coupled with Hamiltonian Monte Carlo methods to efficiently sample from the corresponding posterior distribution. Our approach surpasses the accuracy of previous methods, improves the interpretability of the results by providing access to the full posterior distribution, and naturally scales to a growing number of neutron star observations expected in the coming years.

Knowledge distillation is a form of model compression that allows artificial neural networks of different sizes to learn from one another. Its main application is the compactification of large deep neural networks to free up computational resources, in particular on edge devices. In this article, we consider proton-proton collisions at the High-Luminosity Large Hadron Collider (HL-LHC) and demonstrate a successful knowledge transfer from an event-level graph neural network (GNN) to a particle-level small deep neural network (DNN). Our algorithm, DistillNet, is a DNN that is trained to learn about the provenance of particles, as provided by the soft labels that are the GNN outputs, to predict whether or not a particle originates from the primary interaction vertex. The results indicate that for this problem, which is one of the main challenges at the HL-LHC, there is minimal loss during the transfer of knowledge to the small student network, while improving significantly the computational resource needs compared to the teacher. This is demonstrated for the distilled student network on a CPU, as well as for a quantized and pruned student network deployed on an field programmable gate array. Our study proves that knowledge transfer between networks of different complexity can be used for fast artificial intelligence (AI) in high-energy physics that improves the expressiveness of observables over non-AI-based reconstruction algorithms. Such an approach can become essential at the HL-LHC experiments, e.g. to comply with the resource budget of their trigger stages.

BACKGROUND: Sensory processing dysfunction (SPD) is linked to altered white matter (WM) microstructure in school-age children. Sensory over-responsivity (SOR), a form of SPD, affects at least 2.5% of all children and has substantial deleterious impact on learning and mental health. However, SOR has not been well studied using microstructural imaging such as diffusion MRI (dMRI). Since SOR involves hypersensitivity to external stimuli, we test the hypothesis that children with SOR require compensatory neuroplasticity in the form of superior WM microstructural integrity to protect against internalizing behavior, leaving those with impaired WM microstructure vulnerable to somatization and depression. METHODS: Children ages 8-12 years old with neurodevelopmental concerns were assessed for SOR using a comprehensive structured clinical evaluation, the Sensory Processing 3 Dimensions Assessment, and underwent 3 Tesla MRI with multishell multiband dMRI. Tract-based spatial statistics was used to measure diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) metrics from global WM and nineteen selected WM tracts. Correlations of DTI and NODDI measures with measures of somatization and emotional disturbance from the Behavioral Assessment System for Children, 3rd edition (BASC-3), were computed in the SOR group and in matched children with neurodevelopmental concerns but not SOR. RESULTS: Global WM fractional anisotropy (FA) is negatively correlated with somatization and with emotional disturbance in the SOR group but not the non-SOR group. Also observed in children with SOR are positive correlations of radial diffusivity (RD) and free water fraction (FISO) with somatization and, in most cases, emotional disturbance. These effects are significant in boys with SOR, whereas the study is underpowered for girls. The most affected white matter are medial lemniscus and internal capsule sensory tracts, although effects of SOR are observed in many cerebral, cerebellar, and brainstem tracts. CONCLUSION: White matter microstructure is related to affective behavior in children with SOR.

Abstract Children with autism and other neurodevelopmental concerns (NDC) frequently exhibit an array of sensory processing dysfunction phenotypes, posing a significant challenge their adaptive development. Additionally, these children often encounter difficulties with self-regulation, including emotion dysregulation, anxiety, and symptoms associated with attention and hyperactivity. However, further research is required to comprehend how patterns of sensory processing differences across neurodevelopmental conditions may contribute to regulatory control problems. Adopting a transdiagnostic perspective within the Research Domain Criteria (RDoC) framework, this study examined the relationship between clusters of sensory processing phenotypes and differential patterns of self-regulation behaviors. We recruited a sample of 117 participants (8–12 years) with a diverse range of neurodevelopmental concerns including autism, ADHD, anxiety, and sensory processing differences. This study aimed to (1) establish the prevalence of self-regulation problems in a community-recruited cohort of children with diverse NDCs; (2) construct data-driven sensory processing latent subtypes; (3) investigate group differences in emotion dysregulation, anxiety, and ADHD symptoms. Results showed that 39% of NDC children met clinically concerning thresholds for emotion dysregulation, 19% for anxiety, and 62% for ADHD. Second, latent profile analysis identified five sensory processing subtypes categorized by modality: Typical Processing, Intermediate/Mixed, Sensory Over-Responsive, Sensory Seeking, and Sensory Under-Responsive. Notably, the Sensory Over-Responsive group exhibited distinctively elevated anxiety scores, while the Sensory Seeking and Sensory Under-Responsive groups showed heightened ADHD scores. Intriguingly, the Sensory Over-Responsive, Sensory Under-Responsive, and Sensory Seeking subgroups all demonstrated elevated emotion dysregulation scores, suggesting a potential shared mechanism of emotion dysregulation that might elucidate the connection between sensory processing differences and increased anxiety and ADHD behaviors in children with autism and other NDCs.

Background: Clinically important perioperative atrial fibrillation (POAF) is a common cardiac complication after noncardiac surgery. Little is known about how patients with POAF are managed acutely and whether practices have changed over time. Methods: We conducted an observational substudy of patients who had POAF, were at elevated cardiovascular risk, and were enrolled in the PeriOperative Ischemic Evaluation (POISE)-1, 2 and 3 trials between 2002 and 2021. POAF was defined as new, clinically important atrial fibrillation occurring within 30 days after surgery. We assessed the use of rhythm-control and anticoagulation treatment in response to POAF, at hospital discharge and at 30 days after surgery. We assessed for temporal trends using multivariable logistic regression. Results: Of the 27,896 patients included, 545 (1.9%) developed clinically important POAF. Patients received rhythm-control treatment in 48.6% of cases. The level of use of rhythm-control treatment increased over the course of the trials (POISE-1 vs POISE-2 vs POISE-3; 40.9% vs 49.5% vs 59.1%). A later randomization date was associated independently with use of rhythm-control treatment (odds ratio, 1.05 per year; 95% confidence interval, 1.01-1.09). Anticoagulation treatment was prescribed in 21% of POAF cases. The level of anticoagulation treatement use was higher in POISE-3, compared to that in the 2 previous trials (POISE-1 vs POISE-2 vs POISE-3—16.4% vs 16.5% vs 33.6%). A later randomization date was associated independently with use of anticoagulation treatment (odds ratio, 1.06 per year; 95% confidence interval, 1.02-1.11). Conclusions: Despite the absence of randomized controlled trials, the level of use of rhythm-control and anticoagulation treatment for POAF is rising. High-quality trials are needed urgently to determine whether these interventions are safe and effective in this population. Résumé: Contexte: En chirurgie non cardiaque, la fibrillation auriculaire postopératoire (FAPO) d’importance clinique est une complication cardiaque fréquente. La prise en charge ponctuelle des patients présentant une FAPO est mal connue et on ne sait pas précisément si les pratiques ont évolué au fil du temps. Méthodologie: Nous avons mené une sous-étude d’observation auprès de patients ayant présenté une FAPO et dont le risque cardiovasculaire était élevé, et qui avaient participé aux études 1, 2 ou 3 du programme d’essais cliniques POISE (PeriOperative Ischemic Evaluation) entre 2002 et 2021. La FAPO était définie comme un nouvel épisode de fibrillation auriculaire (FA) d’importance clinique au cours des 30 jours suivant une intervention chirurgicale. Nous avons évalué l’utilisation d’un traitement visant à régulariser le rythme cardiaque et de l’anticoagulothérapie dans la prise en charge d’une FAPO, au moment du congé de l’hôpital, puis 30 jours après l’intervention chirurgicale. Nous avons procédé à l’évaluation des tendances temporelles par régression logistique multivariée. Résultats: Sur les 27 896 patients inclus dans l’analyse, 545 (1,9 %) ont présenté une FAPO d’importance clinique. Dans 48,6 % des cas, les patients ont reçu un traitement visant à régulariser le rythme cardiaque. Le taux d’utilisation d’un traitement visant à régulariser le rythme cardiaque a augmenté d’une étude à l’autre (étude POISE-1 vs étude POISE-2 vs étude POISE-3; 40,9 % vs 49,5 % vs 59,1 %, respectivement). Une date plus tardive de la répartition aléatoire était un facteur indépendant de l’utilisation d’un traitement visant à régulariser le rythme cardiaque (rapport de cotes = 1,05 par an; intervalle de confiance [IC] à 95 % : 1,01-1,09). Une anticoagulothérapie a été prescrite à 21 % des patients ayant présenté une FAPO. Le taux d’utilisation d’une anticoagulothérapie était plus élevé dans l’étude POISE-3, comparativement aux deux essais précédents (étude POISE-1 vs étude POISE-2 vs étude POISE-3; – 16,4 % vs 16,5 % vs 33,6 %, respectivement). Une date plus tardive de la répartition aléatoire était un facteur indépendant d’utilisation d’une anticoagulothérapie (rapport de cotes = 1,06 par an; IC à 95 % : 1,02-1,11). Conclusions: Malgré l’absence d’essais comparatifs avec répartition aléatoire, le taux d’utilisation d’un traitement visant à régulariser le rythme cardiaque ou d’une anticoagulothérapie chez les patients présentant une FAPO est en augmentation. Il existe un besoin urgent de réaliser des études cliniques de grande qualité afin de déterminer si ces interventions sont sûres et efficaces dans cette population de patients.

CRISPR-Cas9 genome editing has enabled advanced T cell therapies, but occasional loss of the targeted chromosome remains a safety concern. To investigate whether Cas9-induced chromosome loss is a universal phenomenon and evaluate its clinical significance, we conducted a systematic analysis in primary human T cells. Arrayed and pooled CRISPR screens revealed that chromosome loss was generalizable across the genome and resulted in partial and entire loss of the targeted chromosome, including in preclinical chimeric antigen receptor T cells. T cells with chromosome loss persisted for weeks in culture, implying the potential to interfere with clinical use. A modified cell manufacturing process, employed in our first-in-human clinical trial of Cas9-engineered T cells (NCT03399448), reduced chromosome loss while largely preserving genome editing efficacy. Expression of p53 correlated with protection from chromosome loss observed in this protocol, suggesting both a mechanism and strategy for T cell engineering that mitigates this genotoxicity in the clinic.

Abstract Monocytes, the circulating macrophage precursors, contribute to diseases like atherosclerosis and asthma. Long non-coding RNAs (lncRNAs) have been shown to modulate the phenotype and inflammatory capacity of monocytes. We previously discovered the lncRNA SMANTIS, which contributes to cellular phenotype expression by controlling BRG1 in mesenchymal cells. Here, we report that SMANTIS is particularly highly expressed in monocytes and lost during differentiation into macrophages. Moreover, different types of myeloid leukemia presented specific SMANTIS expression patterns. Interaction studies revealed that SMANTIS binds RUNX1, a transcription factor frequently mutated in AML, primarily through its Alu-element on the RUNT domain. RNA-seq after CRISPR/Cas9-mediated deletion of SMANTIS or RUNX1 revealed an association with cell adhesion and both limited the monocyte adhesion to endothelial cells. Mechanistically, SMANTIS KO reduced RUNX1 genomic binding and altered the interaction of RUNX1 with EP300 and CBFB. Collectively, SMANTIS interacts with RUNX1 and attenuates monocyte adhesion, which might limit monocyte vascular egress.

Abstract Common half-cell measurements deliver oxygen reduction activities linked to the chosen scan rate, limiting their value for fundamental and comparative studies on platinum. Here we show a deconvolution of the intrinsic kinetics from the effect of surface oxygen on platinum. We find an electronic effect of the surface oxygen, substantiate a Tafel slope of ~120 mV/decade, obtain an exchange current density of 13 ± 4 µA/cm2, and an activity of 7 mA/cm2 at 900 mV. Eventually, we broaden the scope of this analysis to the effects of surface rearrangement, alloying, and supported Pt nanoparticles, the latter providing insight into discrepancies between half-cell and fuel cell measurements. We find through computational methods that binding energies of intermediates would be weakened by the presence of highly coordinated oxygen atoms. Finally, we obtain a phenomenological rate equation for the oxygen reduction and evolution reaction, suggesting that both reactions follow a shared mechanism.

Abstract Previous research has demonstrated a critical link between maternal mental health and infant development. However, there is limited understanding of the role of autonomic regulation in postpartum maternal mental health and infant outcomes. In the current study, we tested 76 mother-infant dyads from diverse socioeconomic backgrounds when infants were 3-months of age. We recorded simultaneous ECG from dyads while baseline EEG was collected from the infant; ECG heart rate variability (HRV) and EEG theta-beta ratio and alpha asymmetry were calculated. Dyadic physiological synchrony was also analyzed to better understand the role of autonomic co-regulation. Results demonstrated that lower maternal HRV was associated with higher self-reported maternal depression and anxiety. Additionally, mothers with lower HRV had infants with lower HRV. Maternal HRV was also associated with higher infant theta-beta ratios, but not alpha asymmetry. Exploratory analyses suggested that for mother-infant dyads with greater physiological synchrony, higher maternal HRV predicted increased infant theta-beta ratio via infant HRV. These findings support a model in which maternal mental health may influence infant neurophysiology via alterations in autonomic stress regulation and dyadic physiological co-regulation.

Abstract Large population-based cohort studies utilizing device-based measures of physical activity are crucial to close important research gaps regarding the potential protective effects of physical activity on chronic diseases. The present study details the quality control processes and the derivation of physical activity metrics from 100 Hz accelerometer data collected in the German National Cohort (NAKO). During the 2014 to 2019 baseline assessment, a subsample of NAKO participants wore a triaxial ActiGraph accelerometer on their right hip for seven consecutive days. Auto-calibration, signal feature calculations including Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD), identification of non-wear time, and imputation, were conducted using the R package GGIR version 2.10-3. A total of 73,334 participants contributed data for accelerometry analysis, of whom 63,236 provided valid data. The average ENMO was 11.7 ± 3.7 mg (milli gravitational acceleration) and the average MAD was 19.9 ± 6.1 mg. Notably, acceleration summary metrics were higher in men than women and diminished with increasing age. Work generated in the present study will facilitate harmonized analysis, reproducibility, and utilization of NAKO accelerometry data. The NAKO accelerometry dataset represents a valuable asset for physical activity research and will be accessible through a specified application process.

Studies have shown that dialogic instruction can promote reading comprehension, but its contribution to lower-level skills like reading fluency is not as well understood. The paper reports on a dialogically oriented small group intervention for struggling second-grade Hebrew readers, targeting both comprehension and fluency. Rather than top-down instruction, the program focused on providing ample opportunities for students to engage with literacy in enriching and meaningful ways. Nine schools from the same Israeli city were randomly assigned to the intervention or business-as-usual control conditions. Sixty students from the five intervention schools were selected as participants based on RTI Tier 2 criteria. The control group comprised 39 students from the remaining four schools. The groups were matched on measures of reading and reading comprehension. The intervention was administered by participants' teachers, each working with five children twice weekly for a total of 18-20 sessions. Teachers followed specially designed lesson plans while receiving guidance from the research team. Post-intervention assessments showed that the groups did not differ in reading comprehension, but the intervention group had a significantly higher average reading rate coupled with lower accuracy. Considering the well-known phenomenon in Hebrew reading development, where transitioning from piecemeal decoding to higher-order strategies results in a phase of faster but less accurate reading, these results point to an improvement in participants' reading fluency. The intervention's effect on reading fluency and lack of effect on reading comprehension are discussed, as well as the implications of dialogic instruction for broader aspects of literacy and student well-being.

Predicting the effects of coding variants is a major challenge. While recent deep-learning models have improved variant effect prediction accuracy, they cannot analyze all coding variants due to dependency on close homologs or software limitations. Here we developed a workflow using ESM1b, a 650-million-parameter protein language model, to predict all ~450 million possible missense variant effects in the human genome, and made all predictions available on a web portal. ESM1b outperformed existing methods in classifying ~150,000 ClinVar/HGMD missense variants as pathogenic or benign and predicting measurements across 28 deep mutational scan datasets. We further annotated ~2 million variants as damaging only in specific protein isoforms, demonstrating the importance of considering all isoforms when predicting variant effects. Our approach also generalizes to more complex coding variants such as in-frame indels and stop-gains. Together, these results establish protein language models as an effective, accurate and general approach to predicting variant effects.

Therapeutic alliance (TA) is a well-established predictor of clinical outcomes in traditional psychotherapy. However, its association with outcomes in eHealth interventions has been inconsistent, which might be due to the absence of measurements specifically designed to capture TA in eHealth settings. The eHealth Therapeutic Alliance Inventory (ETAI) incorporates conventional as well as unique eHealth TA subscales, enabling to examine the contribution of new facets of TA beyond traditional concepts. This study investigates the predictive contribution of eHealth TA subscales compared to conventional TA subscales on clinical outcomes and evaluates the concurrent criterion validity of the ETAI. The study was conducted within the framework of a randomized controlled trial involving a 10-week digital parent training program aimed at addressing child disruptive behaviors. Parents were randomly assigned to either an enhanced-quality or a standard-quality program. Parents from 68 families completed the ETAI at five weeks' post-program initiation and at the post-intervention phase. The primary outcome was the improvement in child behavior, measured by the Eyberg Child Behavior Inventory. Positive Pearson correlations were found between all ETAI subscales covering unique eHealth TA aspects, measured at the 5-week time-point, and improvement in child behavior at post-intervention (rs ≥ 0.23, ps 0.48), reinforcing ETAI's criterion validity. The development of TA scales that incorporates unique eHealth TA subscales show initial promise in predicting outcomes. Further research is needed to better understand how different factors of eHealth TA relate to clinical outcomes across diverse clinical targets and programs.

Introduction There are no established methods to identify children with atypical diabetes for further study. We aimed to develop strategies to systematically ascertain cases of atypical pediatric diabetes using electronic medical records (EMR).Research design and methods We tested two strategies in a large pediatric hospital in the USA. Strategy 1: we designed a questionnaire to rule out typical diabetes and applied it to the EMR of 100 youth with diabetes. Strategy 2: we built three electronic queries to generate reports of three atypical pediatric diabetes phenotypes: unknown type, type 2 diabetes (T2D) diagnosed