Background: Glial fibrillary acidic protein (GFAP), a brain-specific biomarker, shows promise in differentiating intracerebral hemorrhage (ICH) from acute ischemic stroke (IS) and stroke mimics (SM). A novel point-of-care platform measures GFAP in minutes, yet requires centrifugation to obtain plasma. We aim to determine whether participants recruited in an ongoing prospective biomarker study (during working hours) differ from non-recruited patients. Methods: An exploratory analysis of undifferentiated stroke <24h from onset, where plasma GFAP levels (pg/ml) are measured (i-STAT Alinity) at hospital arrival. Clinical characteristics are compared among recruited and non-recruited patients. Results: Among the first 101 patients recruited, mean (±SD) age (70.8±14.5 years), % females (48%), and median (IQR) NIHSS (9(3-20) were similar to the 270 non-recruited patients (70.3±16.3 years, 51% females, NIHSS 7 (3-17), respectively) in the same time period. Median ASPECTS was slightly lower in recruited patients (10(9-10) vs (10(10-10)) (p=0.03). ICH and SM were more common among non-recruited (52% IS/13% ICH/32% SM) compared to recruited patients (67% IS/5% ICH/29% SM, p=0.002), while large-vessel occlusion was more common among those recruited (44% vs 19%, p=0.001). Conclusions: Clinical characteristics do not differ among recruited vs. non-recruited patients in an ongoing biomarker study, yet sampling bias exists regarding underlying stroke condition, with efforts to mitigate this going forward.

Background: Brain-specific glial fibrillary acidic protein (GFAP) can discriminate stroke type [ischemic stroke (AIS), intracerebral hemorrhage (ICH), stroke mimics (SM)]. Novel point-of-care technology (GFAP levels <15 minutes) is a promising diagnostic tool. We aim to evaluate the feasibility of rapid GFAP analysis in acute stroke. Methods: Exploratory analysis of an ongoing prospective study of suspected undifferentiated stroke <24h from onset. Rapid plasma GFAP levels (pg/mL) are measured at hospital arrival using the i-STAT Alinity® instrument and commercially-available cartridges. Study endpoints include quantitative GFAP levels according to final diagnosis and time from stroke onset. Results: Among 200 patients (mean(±SD) 70.7±15.5 years, 44.5% female, median (IQR) NIHSS 9(4-19), diagnosis was AIS (n=132 (59 large-vessel occlusion), ICH (n=17), and SM (n=51). Median time from hospital arrival to GFAP result was 56.0 (47.0-69.5) minutes. Median rapid GFAP levels were highest in ICH (878.0 (70.5-3,906.5) pg/mL) compared to AIS (49.5 (29.0-95)pg/mL) and SM (29(29-64)pg/mL), p=0.001. Median GFAP was higher in AIS-known onset >4.5h (n=9) (110.0 (44.0-216.0) pg/mL) compared to AIS<4.5h (40.5 (29.0-68.8) pg/mL) (n=72), (p=0.047), while AIS-unknown onset (n=51) (68.0 (29.0-108.5) pg/mL) fell between these two groups, likely reflecting the subgroup’s heterogeneity. Conclusions: Preliminary findings suggest that rapid GFAP analysis is feasible in acute stroke and may inform treatment decisions.

Actuator faults in unmanned aerial vehicles (UAVs) can have significant and potentially adverse effects on their safety and performance, highlighting the critical importance of fault diagnosis in UAV design. Ensuring the reliability of these systems in various applications often requires the use of advanced diagnostic algorithms. Artificial intelligence methods, such as deep learning and machine learning techniques, enable fault diagnosis through sample-based learning without the need for prior knowledge of fault mechanisms or physics-based models. However, UAV fault datasets are typically small due to stringent safety standards, which presents challenges for achieving high-performance fault diagnosis. To address this, deep reinforcement learning (DRL) algorithms offer a unique advantage by combining deep learning’s automatic feature extraction with reinforcement learning’s interactive learning approach, improving both learning capabilities and robustness. In this study, we propose and evaluate two DRL-based fault diagnosis models, which demonstrate remarkable accuracy in fault diagnosis, consistently exceeding $99{\rm{\% }}$. Notably, under small sample scenarios, the proposed models significantly outperform traditional classifiers such as decision trees, support vector machines, and multilayer perceptron neural networks. These findings suggest that the integration of DRL enhances fault diagnosis performance, particularly in data-limited environments.

Randomized response is an interview technique for sensitive questions designed to eliminate evasive response bias. Since this elimination is only partially successful, two models have been proposed for modeling evasive response bias: the cheater detection model for a design with two sub-samples with different randomization probabilities and the self-protective no sayers model for a design with multiple sensitive questions. This paper shows the correspondence between these models, and introduces models for the new, hybrid “ever/last year” design that account for self-protective no saying and cheating. The model for one set of ever/last year questions has a degree of freedom that can be used for the inclusion of a response bias parameter. Models with multiple degrees of freedom are introduced for extensions of the design with a third randomized response question and a second set of ever/last year questions. The models are illustrated with two surveys on doping use. We conclude with a discussion of the pros and cons of the ever/last year design and its potential for future research.

This study aimed to expand our understanding of myelin basic protein (MBP), a key component of central nervous system myelin, by developing a protocol to track and quantifying individual MBP particles during oligodendrocyte (OL) differentiation. MBP particle directionality, confinement, and diffusion were tracked by rapid TIRF and HILO imaging of Dendra2 tagged MBP in three stages of mouse oligodendroglia: OL precursors, early myelinating OLs, and mature myelinating OLs. The directionality and confinement of MBP particles increased at each stage consistent with progressive transport toward, and recruitment into, emerging myelin structures. Unexpectedly, diffusion data presented a more complex pattern with subpopulations of the most diffusive particles disappearing at the transition between the precursor and early myelinating stage, before reemerging in the membrane sheets of mature OLs. This diversity of particle behaviors, which would be undetectable by conventional ensemble-averaged methods, are consistent with a multifunctional view of MBP involving roles in myelin expansion and compaction.

Objectives: Over the past 2 years, many infection prevention and control (IPC) resources have been diverted to manage the COVID-19 pandemic. Its impact on the incidence of antimicrobial-resistant organisms has not been adequately studied. We investigated the impact of the pandemic on the incidence of carbapenem-resistant Enterobacterales (CRE) in Singapore. Methods: We extracted data on unique CRE isolates (clinical and/or surveillance cultures) and patient days for 6 public hospitals in Singapore from the carbapenemase-producing Enterobacteriaceae (CaPES) study group database, and we calculated the monthly incidence of CRE (per 10,000 patient days). Interrupted time-series (ITS) analysis was conducted with the pre–COVID-19 period defined as before February 2020, and the COVID-19 period defined as after February 2020. Statistical analyses were performed using Stata version 15 software. Results: From January 2017 to March 2021, 6,770 CRE isolates and 9,126,704 patient days were documented. The trend in CRE monthly incidence increased significantly during the pre–COVID-19 period (0.060; 95% CI, 0.033–0.094; P < .001) but decreased during the COVID-19 period (−0.183; 95% CI, −0.390 to 0.023; P = .080) without stepwise change in the incidence (−1.496; 95% CI, −3.477 to 0.485; P = .135). The trend in monthly incidence rate of CRE clinical cultures increased significantly during the pre–COVID-19 period (0.046; 95% CI, 0.028–0.064; P < .001) and decreased significantly during COVID-19 period (−0.148; 95% CI, −0.249 to −0.048; P = .048) with no stepwise change in the incidence (−0.063; 95% CI, −0.803 to 0.677; P = .864). The trend in monthly incidence rate of CRE surveillance cultures decreased during the pre–COVID-19 period (−0.020; 95% CI, −0.062 to 0.022; P = .341) and the COVID-19 period (−0.067; 95% CI, −0.291to 0.158; P = .552) without stepwise change in the incidence (−1.327; 95% CI, −3.535 to 0.881; P = .233). Conclusions: The rate of CRE in clinical cultures decreased during COVID-19 but not the rate in surveillance cultures. Further studies are warranted to study the impact of COVID-19 on CREs.

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We present the pulse arrival times and high-precision dispersion measure estimates for 14 millisecond pulsars observed simultaneously in the 300$-$500 MHz and 1260$-$1460 MHz frequency bands using the upgraded Giant Metrewave Radio Telescope. The data spans over a baseline of 3.5 years (2018-2021), and is the first official data release made available by the Indian Pulsar Timing Array collaboration. This data release presents a unique opportunity for investigating the interstellar medium effects at low radio frequencies and their impact on the timing precision of pulsar timing array experiments. In addition to the dispersion measure time series and pulse arrival times obtained using both narrowband and wideband timing techniques, we also present the dispersion measure structure function analysis for selected pulsars. Our ongoing investigations regarding the frequency dependence of dispersion measures have been discussed. Based on the preliminary analysis for five millisecond pulsars, we do not find any conclusive evidence of chromaticity in dispersion measures. Data from regular simultaneous two-frequency observations are presented for the first time in this work. This distinctive feature leads us to the highest precision dispersion measure estimates obtained so far for a subset of our sample. Simultaneous multi-band upgraded Giant Metrewave Radio Telescope observations in 300$-$500 MHz and 1260$-$1460 MHz are crucial for high-precision dispersion measure estimation and for the prospect of expanding the overall frequency coverage upon the combination of data from the various Pulsar Timing Array consortia in the near future. Parts of the data presented in this work are expected to be incorporated into the upcoming third data release of the International Pulsar Timing Array.

Emotion dysregulation is cross-diagnostic and impairing. Most research has focused on dysregulated expressions of negative affect, often measured as irritability, which is associated with multiple forms of psychopathology and predicts negative outcomes. However, the Research Domain Criteria (RDoC) include both negative and positive valence systems. Emerging evidence suggests that dysregulated expressions of positive affect, or excitability, in early childhood predict later psychopathology and impairment above and beyond irritability. Typically, irritability declines from early through middle childhood; however, the developmental trajectory of excitability is unknown. The impact of excitability across childhood on later emotion dysregulation is also yet unknown. In a well-characterized, longitudinal sample of 129 children studied from ages 3 to 5.11 years through 14 to 19 years, enriched for early depression and disruptive symptoms, we assessed the trajectory of irritability and excitability using multilevel modeling and how components of these trajectories impact later emotion dysregulation. While irritability declines across childhood, excitability remains remarkably stable both within and across the group. Overall levels of excitability (excitability intercept) predict later emotion dysregulation as measured by parent and self-report and predict decreased functional magnetic resonance imaging activity in cognitive emotion regulation regions during an emotion regulation task. Irritability was not related to any dysregulation outcome above and beyond excitability.

The Abu Kharif area in the Northern Eastern Desert consists of contrasting granitic magma suites: a Cryogenian granodiorite suite (850–635 Ma), an Ediacaran monzogranite suite (635–541 Ma) and a Cambrian alkali riebeckite granite suite (541–485 Ma). Tungsten mineralization occurs within W-bearing quartz veins and a disseminated type confined to the monzogranite.

Whole-rock geochemical data classify the granodiorite as a late-orogenic I-type with calc-alkaline affinity, while the monzogranite and alkali riebeckite granite represent respectively a post-orogenic highly fractionated I-type with calc-alkaline affinity and an anorogenic A1-subtype with alkaline affinity. Geochemical modelling indicates that the three intrusions represent separate magmatic pulses where the granodiorite was generated by ∼75 % batch partial melting of an amphibolitic source followed by fractional crystallization of hornblende, biotite, apatite and titanite. The monzogranite was formed by 62 % batch partial melting of the normal ‘non-metasomatized’ Pan-African crust of calc-alkaline granite composition followed by fractional crystallization of plagioclase, biotite, K-feldspar, magnetite, ilmenite, with minor apatite and titanite. The alkali riebeckite granite was generated by 65 % batch partial melting of metasomatized Pan-African granite source followed by fractional crystallization of plagioclase, K-feldspar, amphibole and biotite with minor magnetite, apatite and titanite. In general, the parent magmas of the three intrusions were originally enriched in W, but with different concentrations. This W-enrichment would be caused by magmatic-related hydrothermal volatile-rich fluids and concentrated within the monzogranite.

There is a paucity of evidence about the prevalence and risk factors for symptomatic infection among children. This study aimed to describe the prevalence of symptomatic coronavirus disease 2019 (COVID-19) and its risk factors in children and adolescents aged 0–18 years in Qatar. We conducted a cross-sectional study of all children aged 0–18 years diagnosed with COVID-19 using polymerase chain reaction in Qatar during the period 1st March to 31st July 2020. A generalised linear model with a binomial family and identity link was used to assess the association between selected factors and the prevalence of symptomatic infection. A total of 11 445 children with a median age of 8 years (interquartile range (IQR) 3–13 years) were included in this study. The prevalence of symptomatic COVID-19 was 36.6% (95% confidence interval (CI) 35.7–37.5), and it was similar between children aged <5 years (37.8%), 5–9 years (34.3%) and 10 + years (37.3%). The most frequently reported symptoms among the symptomatic group were fever (73.5%), cough (34.8%), headache (23.2%) and sore throat (23.2%). Fever (82.8%) was more common in symptomatic children aged <5 years, while cough (38.7%) was more prevalent in those aged 10 years or older, compared to other age groups. Variables associated with an increased risk of symptomatic infection were; contact with confirmed cases (RD 0.21; 95% CI 0.20–0.23; P = 0.001), having visited a health care facility (RD 0.54; 95% CI 0.45–0.62; P = 0.001), and children aged under 5 years (RD 0.05; 95% CI 0.02–0.07; P = 0.001) or aged 10 years or older (RD 0.04; 95% CI 0.02–0.06; P = 0.001). A third of the children with COVID-19 were symptomatic with a higher proportion of fever in very young children and a higher proportion of cough in those between 10 and 18 years of age.

We introduce pinta, a pipeline for reducing the upgraded Giant Metre-wave Radio Telescope (uGMRT) raw pulsar timing data, developed for the Indian Pulsar Timing Array experiment. We provide a detailed description of the workflow and usage of pinta, as well as its computational performance and RFI mitigation characteristics. We also discuss a novel and independent determination of the relative time offsets between the different back-end modes of uGMRT and the interpretation of the uGMRT observation frequency settings and their agreement with results obtained from engineering tests. Further, we demonstrate the capability of pinta to generate data products which can produce high-precision TOAs using PSR J1909$-$3744 as an example. These results are crucial for performing precision pulsar timing with the uGMRT.