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Learning loss pertains to the decline or regression in knowledge and abilities, as well as setbacks in academic advancement. This phenomenon typically arises from prolonged interruptions or gaps in the pupil's educational journey. Learning loss can be observed in diverse manifestations due to many factors. One example that can be illustrated is the disruption of formal education due to the COVID-19 pandemic that struck the world in 2019. As a result, this phenomenon has impacted students' academics, whether at the school level or higher education institutions, especially primary school students, thus causing learning poverty. This scoping review aims to identify the degree of reading literacy among primary school students and to investigate the factors that contribute to the learning losses of primary school students from the reading aspect. Four databases, including Scopus, Education Resources Information Center (ERIC), Bielefeld Academic Search Engine (BASE), and Directory of Open Access Journals (DOAJ), were used in this research, which found 40 articles for eligibility. Only 20 articles are eligible for analysis and reference after the exclusion and inclusion process for data collection. The findings show that learning losses have impacted the education sector, leading to poverty, especially among primary school children.

We applied reaction microscopy to elucidate fast non-adiabatic dissociation dynamics of deuterated water molecules after direct photo-double ionization at 61 eV with synchrotron radiation. For the very rare D+ + O+ + D breakup channel, the particle momenta, angular, and energy distributions of electrons and ions, measured in coincidence, reveal distinct electronic dication states and their dissociation pathways via spin-orbit coupling and charge transfer at crossings and seams on the potential energy surfaces. Notably, we could distinguish between direct and fast sequential dissociation scenarios. For the latter case, our measurements reveal the geometry and orientation of the deuterated water molecule with respect to the polarization vector that leads to this rare 3-body molecular breakup channel. Aided by multi-reference configuration-interaction calculations, the dissociation dynamics could be traced on the relevant potential energy surfaces and particularly their crossings and seams. This approach also unraveled the ultrafast time scales governing these processes.

Motor vehicles are among the major sources of pollutants and greenhouse gases in urban areas and a transition to "zero emission vehicles" is underway worldwide. However, emissions associated with brake and tire wear will remain. We show here that previously unrecognized volatile and semi-volatile organic compounds, which have a similarity to biomass burning emissions are emitted during braking. These include greenhouse gases or, these classified as Hazardous Air Pollutants, as well as nitrogen-containing organics, nitrogen oxides and ammonia. The distribution and reactivity of these gaseous emissions are such that they can react in air to form ozone and other secondary pollutants with adverse health and climate consequences. Some of the compounds may prove to be unique markers of brake emissions. At higher temperatures, nucleation and growth of nanoparticles is also observed. Regions with high traffic, which are often disadvantaged communities, as well as commuters can be impacted by these emissions even after combustion-powered vehicles are phased out.

This study aimed to characterize the role of sex and pubertal markers in reward motivation behavior and neural processing in early adolescence. We used baseline and two-year follow-up data from the Adolescent Brain and Cognitive DevelopmentSM study (15844 observations; 52% from boys; age 9-13). Pubertal development was measured with parent-reported Pubertal Development Scale, and DHEA, testosterone, and estradiol levels. Reward motivation behavior and neural processing at anticipation and feedback stages were assessed with the Monetary Incentive Delay task. Boys had higher reward motivation than girls, demonstrating greater accuracy difference between reward and neutral trials and higher task earnings. Girls had lower neural activation during reward feedback than boys in the nucleus accumbens, caudate, rostral anterior cingulate, medial orbitofrontal cortex, superior frontal gyrus and posterior cingulate. Pubertal stage and testosterone levels were positively associated with reward motivation behavior, although these associations changed when controlling for age. There were no significant associations between pubertal development and neural activation during reward anticipation and feedback. Sex differences in reward-related processing exist in early adolescence, signaling the need to understand their impact on typical and atypical functioning as it unfolds into adulthood.

Nontuberculous mycobacteria pulmonary disease (NTM-PD) is a chronic respiratory condition of growing concern. Its management is often complicated by multiple biological, clinical, healthcare-associated, and patient factors. Increased NTM infection can [...]

The various empirical models built for enrollment management, operations, and program evaluation purposes may have lost their predictive power as a result of the recent collective impact of COVID restrictions, widespread social upheaval, and the shift in educational preferences. This statistical artifact is known as model drifting, data-shift, covariate-shift. Succinctly, these events drove changes in the stationarity of the target variable and the predictors. The result is a student body with unknown performance qualities entirely distinct from previous cohorts. This study explains and illustrates: (1) how to test for academic model drift in academe; and (2) sets forth two methods used to repair vitiated student-body performance properties. Formally, it frames the data-drift outcome as a One Class problem which allows the deployment of two well-known One-Class algorithms: Support Vector Machines and Isolated Random Forests. The study shows their use in reconstructing a representative sample of the student-body

We present results from a 3.25 kg-day target exposure of two silicon charge-coupled devices (CCDs), each with 24 megapixels and skipper readout, deployed in the DAMIC setup at SNOLAB. With a reduction in pixel readout noise of a factor of 10 relative to the previous detector, we investigate the excess population of low-energy events in the CCD bulk previously observed above expected backgrounds. We address the dominant systematic uncertainty of the previous analysis through a depth fiducialization designed to reject surface backgrounds on the CCDs. The measured bulk ionization spectrum confirms the presence of an excess population of low-energy events in the CCD target with characteristic rate of ∼7 events per kg-day and electron-equivalent energies of ∼80 eV, whose origin remains unknown.

Quantitative PET attenuation correction (AC) for cardiac PET/CT and PET/MR is a challenging problem. We propose and evaluate an AC approach that uses coincidences from a relatively weak and physically fixed sparse external source, in combination with that from the patient, to reconstruct μ -maps based on physics principles alone. The low 30 cm3 volume of the source makes it easy to fill and place, and the method does not use prior image data or attenuation map assumptions. Our supplemental transmission aided maximum likelihood reconstruction of attenuation and activity (sTX-MLAA) algorithm contains an attenuation map update that maximizes the likelihood of terms representing coincidences originating from tracer in the patient and a weighted expression of counts segmented from the external source alone. Both external source and patient scatter and randoms are fully corrected. We evaluated performance of sTX-MLAA compared to reference standard CT-based AC with FDG PET/CT phantom studies; including modeling a patient with myocardial inflammation. Through an ROI analysis we measured ≤ 5 % bias in activity concentrations for PET images generated with sTX-MLAA and a TX source strength ≥ 12.7 MBq, relative to CT-AC. PET background variability (from noise and sparse sampling) was substantially reduced with sTX-MLAA compared to using counts segmented from the transmission source alone for AC. Results suggest that sTX-MLAA will enable quantitative PET during cardiac PET/CT and PET/MR of human patients.

Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is currently the leading cause of chronic liver disease worldwide. Metabolic Dysfunction-Associated Steatohepatitis (MASH), an advanced form of MASLD, can progress to liver fibrosis, cirrhosis, and hepatocellular carcinoma. Based on recent findings by our team that liver 5HT2A knockout male mice suppressed steatosis and reduced fibrosis-related gene expression, we developed a peripheral 5HT2A antagonist, compound 11c for MASH. It shows good in vitro activity, stability, and in vivo pharmacokinetics (PK) in rats and dogs. Compound 11c also shows good in vivo efficacy in a diet-induced obesity (DIO) male mice model and in a choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD) male mice model, effectively improving histologic features of MASH and fibrosis. According to the tissue distribution study using [14C]-labeled 11c, the compound was determined to be a peripheral 5HT2A antagonist. Collectively, first-in-class compound 11c shows promise as a therapeutic agent for the treatment of MASLD and MASH.