Mcnamarahwang8916

This study contributes to the field studies of learner control by linking learner control with the critical dimensions of AR-enhanced museum learning to provide more guidance in exhibit design. Based on the findings, practical suggestions on incorporating learner control in AR-based interactive exhibits are provided.Sit-to-stand transitions are an important part of activities of daily living and play a key role in functional mobility in humans. The sit-to-stand movement is often affected in older adults due to frailty and in patients with motor impairments such as Parkinson's disease leading to falls. Studying kinematics of sit-to-stand transitions can provide insight in assessment, monitoring and developing rehabilitation strategies for the affected populations. We propose a three-segment body model for estimating sit-to-stand kinematics using only two wearable inertial sensors, placed on the shank and back. Reducing the number of sensors to two instead of one per body segment facilitates monitoring and classifying movements over extended periods, making it more comfortable to wear while reducing the power requirements of sensors. We applied this model on 10 younger healthy adults (YH), 12 older healthy adults (OH) and 12 people with Parkinson's disease (PwP). We have achieved this by incorporating unique sit-to-stand classification technique using unsupervised learning in the model based reconstruction of angular kinematics using extended Kalman filter. Our proposed model showed that it was possible to successfully estimate thigh kinematics despite not measuring the thigh motion with inertial sensor. We classified sit-to-stand transitions, sitting and standing states with the accuracies of 98.67%, 94.20% and 91.41% for YH, OH and PwP respectively. We have proposed a novel integrated approach of modelling and classification for estimating the body kinematics during sit-to-stand motion and successfully applied it on YH, OH and PwP groups.Brightly fluorescent solid-state materials are highly desirable for bioimaging, optoelectronic applications, and energy harvesting. However, the close contact between π-systems most often leads to quenching. Recently, we developed small-molecule ionic isolation lattices (SMILES) that efficiently isolate fluorophores while ensuring very high densities of the dyes. Nevertheless, efficient Förster resonance energy transfer (FRET) energy migration in such dense systems is inevitable. While attractive for energy harvesting applications, FRET also significantly compromises quantum yields of fluorescent solids by funneling the excitation energy to dark trap states. Here, we investigate the underlying property of FRET and exploit it to our favor by intentionally introducing fluorescent dopants into SMILES materials, acting as FRET acceptors with favorable photophysical properties. This doping is shown to outcompete energy migration to dark trap states while also ruling out reabsorption effects in dense SMILES materials, resulting in universal fluorescent solid-state materials (thin films, powders, and crystals) with superior properties. These include emission quantum yields reaching as high as 50-65%, programmable fluorescence lifetimes with mono-exponential decay, and independent selection of absorption and emission maxima. The volume normalized brightness of these FRET-based SMILES now reach values up to 32,200 M-1 cm-1 nm-3 and can deliver freely tunable spectroscopic properties for the fabrication of super-bright advanced optical materials. It is found that SMILES prohibit PET quenching between donor and acceptor dyes that is observed for non-SMILES mixtures of the same dyes. This allows a very broad selection of donor and acceptor dyes for use in FRET SMILES.

Vaccination of masses against coronavirus disease 2019 (COVID-19) is critical to overcome the pandemic and restore normalcy. However, vaccine refusal and hesitancy prevail in many countries. COVID-19 has rapidly spread in Saudi Arabia since 2020. The acceptance rate of COVID-19 vaccines has been investigated in adults aged >18 years in Saudi Arabia. This study aimed to understand the acceptance and hesitancy of parents to vaccinate children aged <12 years against COVID-19 in Saudi Arabia and identify strategies that can encourage their engagement.

We used an online cross-sectional survey distributed to parents who lived in all regions of Saudi Arabia to investigate parents' views on the acceptability of a future COVID-19 vaccine for their children aged <12 years. Five hundred parents living in Saudi Arabia completed the survey.

The survey indicated that mothers were more enthusiastic about participating in the study than fathers. The participant aged 37.31 ± 8.52 years. A total of 38.6% of partlevel of health education and promotion are the most common factors in parents in Saudi Arabia. However, some participants agreed to receive vaccines only to protect their family members, and due to governmental rules and school mandates. Therefore, vaccine efficacy and safety in children must be clearly communicated to the public. This information would aid in reducing the hesitancy of parents to vaccinate their children against COVID-19.Duffy binding protein region II (DBPII) is considered a strong potential vaccine candidate of blood-stage P. vivax. However, the highly polymorphic nature of this protein often misdirects immune responses, leading them to be strain-specific. Details of cross-reactive humoral immunity to DBPII variants have therefore become an important focus for the development of broadly protective vaccines. Here, cross-reactive humoral immunity against a panel of Thai DBPII variants (DBL-THs) was demonstrated in immunized BALB/c mice and P. vivax patients, by in vitro erythrocyte-binding inhibition assay. Sera from immunized animals showed both strain-transcending (anti-DBL-TH2 and -TH4) and strain-specific (anti-DBL-TH5, -TH6 and -TH9) binding to DBL-TH variants. Using anti-DBL-TH sera at 50% inhibitory concentration (IC50) of the homologous strain, anti-DBL-TH2 sera showed cross inhibition to heterologous DBL-TH strains, whereas anti-DBL-TH5 sera exhibited only strain-specific inhibition. In P. vivax patients, 6 of 15 subjects produced and maintained cross-reactive anti-DBL-TH inhibitory antibodies through the 1-year post-infection timepoint. Cross-reactive memory B cell (MBC) responses to DBL-TH variants were analyzed in subjects recovered from P. vivax infection (RC). The plasma samples from 5 RC subjects showed broad inhibition. However, MBC-derived antibodies of these patients did not reveal cross-inhibition. Altogether, broadly anti-DBP variant inhibitory antibodies developed and persisted in P. vivax infections. However, the presence of cross-reactive anti-DBL-TH inhibitory function post-infection was not related with MBC responses to these variants. More detailed investigation of long-lasting, broadly protective antibodies to DBPII will guide the design of vivax malaria vaccines.Background The objective of this study was to develop and validate machine learning models for data entry error detection in a national out-of-hospital cardiac arrest (OHCA) prehospital patient care report database.Methods Adult OHCAs of presumed cardiac etiology were included. Data entry errors were defined as discrepancies between the coded data and the free-text note documenting the intervention or event; for example, information that was recorded as "absent" in the coded data but "present" in the free-text note. Machine learning models using the extreme gradient boosting, logistic regression, extreme gradient boosting outlier detection, and K-nearest neighbor outlier detection algorithms for error detection within nine core variables were developed and then validated for each variable.Results Among 12,100 OHCAs, the proportion of cases with at least one error type was 16.2%. The area under the receiver operating characteristic curve (AUC) of the best-performing model (model with the highest AUC for each outcome variable) was 0.71-0.95. Machine learning models detected errors most efficiently for outcome place and initial rhythm errors; 82.6% of place errors and 93.8% of initial rhythm errors could be detected while checking 11 and 35% of data, respectively, compared to the strategy of checking all data.Conclusion Machine learning models can detect data entry errors in care reports of emergency medical services (EMS) clinicians with acceptable performance and likely can improve the efficiency of the process of data quality control. EMS organizations that provide more prehospital interventions for OHCA patients could have higher error rates and may benefit from the adoption of error-detection models.Gallium-based liquid metals (LMs) combine metallic properties with the deformability of a liquid, which makes them promising candidates for a variety of applications. To broaden the range of physical and chemical properties, a variety of solid additives have been incorporated into the LMs in the literature. In contrast, only a handful of secondary fluids have been incorporated into LMs to create foams (gas-in-LM) or emulsions (liquid-in-LM). LM foams readily form through mixing of LM in air, facilitated by the formation of a native oxide on the LM. In contrast, LM breaks up into microdroplets when mixed with a secondary liquid such as silicone oil. Stable silicone oil-in-LM emulsions form only during mixing of the oil with LM foam. In this work, we investigate the fundamental mechanism underlying this process. We describe two possible microscale mechanisms for emulsion formation (1) oil replacing air in the foam or (2) oil creating additional features in the foam. The associated foam-to-emulsion density difference demonstrates that emulsions predominantly form through the addition of oxide-covered silicone oil capsules to the LM foam. We demonstrate this through density and surface wettability measurements and multiscale imaging of LM foam mixed with varied silicone oil contents in air or nitrogen environments. We also demonstrate the presence of a continuous silicone oil film on the emulsion surface and that this oil film prevents the embrittlement of contacting aluminum.Murine ulcerative dermatitis (UD) is a common, multifactorial skin disease of C57BL/6 and C57BL/6-background strains of mice. Many treatment options have been previously reported but have been variably successful and may interfere with specific research studies. Janus kinase (JAK) inhibitors, such as oclacitinib, have been used to treat allergic dermatitis in humans, dogs, and other species. Additionally, topical oclacitinib was shown to improve an induced model of dermatitis in mice. We hypothesized that topical application of oclacitinib in conjunction with hind limb nail trimming would improve UD lesion scores more than our institutional standard treatment regime using meloxicam, topical antibiotic ointment, and nail trimming or nail trimming alone. To test this, mice with naturally occurring UD were recruited to the study and assigned to one of three treatment groups (n = 14/group) nail trim only; nail trim plus meloxicam and topical triple antibiotic ointment; or nail trim plus topical oclacitinib. SKF96365 UD was assessed on days 1, 7, and 14 for all treatment groups, and scored based on a previously published scoring system that quantitatively scored UD lesions based on pruritus, character of the lesion, size of lesion, and location of lesion.