WISC-IV and IVA+Plus Pattern Analysis: Assessment of Attention-Deficit /Hyperactivity Disorder

Diagnosis of attention-deficit/hyperactivity disorder (ADHD) is a challenging practice with no definitive diagnostic test. This study sought to evaluate the use of a pattern of performance method of inference in ADHD assessment that examines an individual\u27s general intellectual ability in comparison to his or her own attention skills. WISC-IV scores, IVA+Plus quotients, and basic demographic information were collected from patient files of children previously evaluated by the Eastern Virginia Medical School Neuropsychology Clinic. Children with and without ADHD were compared by analyzing the following difference or delta score discrepancies: (a) WISC-IV General Ability Index (GAI) minus WISC-IV Working Memory Index, (b) WISC-IV GAI minus WISC-IV Processing Speed Index, (c) WISC-IV GAI minus IVA+plus Full Scale Response Control Quotient, and (d) WISC-IV GAI minus IVA+plus Full Scale Attention Quotient. Contrary to preliminary hypotheses, analyses demonstrated no significant utility for the use of these delta scores in distinguishing children referred for psychological testing with and without ADHD. Potential reasons for this lack of predicted discriminate ability are posited

Distractions in a disruption: The soothing effect of the heritage bus ride during London Tube strikes

This paper explores the cultural significance of replacement bus services during three London Tube strikes in 2018. Strikes cause delays to journeys, and are often anticipated, framed, and reported as nuisances. Empirically informed by participant observation, the paper discusses how social interaction among passengers, triggered by a heritage bus journey, could redefine a disrupted commuter trip as a collective heritage journey, via its unusual materialities and sensations. Passengers notice the different material configuration of heritage buses, leading to the creation of an affective atmosphere, which then spreads among passengers as if by affective contagion. The resulting initiation of a temporary guide–audience relationship in this unexpected space enabled different forms of intercultural dialogue and knowledge exchange, which transformed an ordinary everyday experience into something extraordinary, in which heightened awareness of the bus environment and an increase in social interaction somewhat resembled a guided tour of the city combined with commuter transport. While the economic injustices at the heart of Tube strikes should not be neglected, I propose that the use of heritage buses as replacement transport contributes to the formation of affective atmosphere via the increase in social interactions triggered by their material configuration, and consequently to the sharing of everyday history

Application of machine learning to support self-management of asthma with mHealth

While there have been several efforts to use mHealth technologies to support asthma management, none so far offer personalised algorithms that can provide real-time feedback and tailored advice to patients based on their monitoring. This work employed a publicly available mHealth dataset, the Asthma Mobile Health Study (AMHS), and applied machine learning techniques to develop early warning algorithms to enhance asthma self-management. The AMHS consisted of longitudinal data from 5,875 patients, including 13,614 weekly surveys and 75,795 daily surveys. We applied several well-known supervised learning algorithms (classification) to differentiate stable and unstable periods and found that both logistic regression and naïve Bayes-based classifiers provided high accuracy (AUC > 0.87). We found features related to the use of quick-relief puffs, night symptoms, frequency of data entry, and day symptoms (in descending order of importance) as the most useful features to detect early evidence of loss of control. We found no additional value of using peak flow readings to improve population level early warning algorithms

Machine Learning–Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review

Background: An early warning tool to predict attacks could enhance asthma management and reduce the likelihood of serious consequences. Electronic health records (EHRs) providing access to historical data about patients with asthma coupled with machine learning (ML) provide an opportunity to develop such a tool. Several studies have developed ML-based tools to predict asthma attacks.Objective: This study aims to critically evaluate ML-based models derived using EHRs for the prediction of asthma attacks.Methods: We systematically searched PubMed and Scopus (the search period was between January 1, 2012, and January 31, 2023) for papers meeting the following inclusion criteria: (1) used EHR data as the main data source, (2) used asthma attack as the outcome, and (3) compared ML-based prediction models’ performance. We excluded non-English papers and nonresearch papers, such as commentary and systematic review papers. In addition, we also excluded papers that did not provide any details about the respective ML approach and its result, including protocol papers. The selected studies were then summarized across multiple dimensions including data preprocessing methods, ML algorithms, model validation, model explainability, and model implementation.Results: Overall, 17 papers were included at the end of the selection process. There was considerable heterogeneity in how asthma attacks were defined. Of the 17 studies, 8 (47%) studies used routinely collected data both from primary care and secondary care practices together. Extreme imbalanced data was a notable issue in most studies (13/17, 76%), but only 38% (5/13) of them explicitly dealt with it in their data preprocessing pipeline. The gradient boosting–based method was the best ML method in 59% (10/17) of the studies. Of the 17 studies, 14 (82%) studies used a model explanation method to identify the most important predictors. None of the studies followed the standard reporting guidelines, and none were prospectively validated.Conclusions: Our review indicates that this research field is still underdeveloped, given the limited body of evidence, heterogeneity of methods, lack of external validation, and suboptimally reported models. We highlighted several technical challenges (class imbalance, external validation, model explanation, and adherence to reporting guidelines to aid reproducibility) that need to be addressed to make progress toward clinical adoption

The influence of vision on the perceptual compensation for reverberation in simulated environments

In typical listening environments, auditory signals arrive at the ear as a fusion of the direct energy from sound sources and the indirect reflections via reverberation. The listener thus cannot directly access the source and reverberation components individually, highlighting that the perceptual separation of these components can be subject to ambiguity. Accurate expectations of reverberation have been shown to reduce such ambiguity. The visible features of the physical environment (e.g., spatial and surface properties) can reveal aspects of reverberation that inform such expectations, suggesting an inferential role of vision in disambiguating the source and reverberation components. The aim of this thesis was to evaluate the degree to which visual information from simulated environments can affect the expectations of reverberation to consequently improve judgements of sound sources. To investigate this aim, we conducted three behavioural studies that assessed perception in audiovisual environments via online simulations created from a database of real-world locations. Chapter 3 assessed whether visual cues to the environment could inform of the reverberant properties of physical locations in an audiovisual congruence task. The results indicated a greater impression of congruence when reverberant cues were identical or similar to those represented by the depicted environment, demonstrating a capacity for vision to inform meaningful expectations of reverberation. Chapter 4 evaluated the degree to which vision contributed to the identification of speech sources within reverberation by prior exposure to visual environments. We found that exposure to the visual environment had hardly any effect on improving the identification of reverberant speech sources in this context. Chapter 5 investigated if a concurrent visual depiction of the environment would affect the tendency for estimates of sound source duration to be consistent despite varying reverberation. The results showed that source duration estimates were influenced by the degree of reverberation present, and were seemingly unaffected by any visual exposure. Taken together, the findings of this thesis suggest that scene understanding from vision contributes to the overall spatial understanding of environments and their reverberant properties, but appears to contribute little to enhancing the perceptual separation of source and reverberation components used to improve judgements of auditory sources

Evaluating energy savings retrofits for residential buildings in China

Building retrofit plays an important role in reducing energy consumption and carbon dioxide emissions whilst increasing occupant thermal comfort. This study used DesignBuilder to predict the energy saved by retrofitting a typical flat in Chongqing, a city in the hot summer, cold winter region of China. To increase the reliability of predictions, the model was verified by measured indoor air temperature for a one-week period in April. Five retrofit measures were evaluated, external wall insulation, new windows, increased air tightness, external shading, and higher efficiency of air conditioning. Three types of households with different AC operating schedule were assumed, high, medium and low. The variance in the model predictions due to the uncertainty in the model input parameters was calculated. The results showed that the energy saved depended on the use that was made of the AC system. For high energy users, 40 to 68% of the annual space-conditioning energy could be saved depending on the retrofit, whereas for low energy users the savings were 30 to 58%. Thermal comfort has improved in winter for low and medium energy users, but no improvement in summer

Strong cardiovascular prognostic implication of quantitative left atrial contractile function assessed by cardiac magnetic resonance imaging in patients with chronic hypertension

<p>Abstract</p> <p>Background</p> <p>Progressive left ventricular (LV) diastolic dysfunction due to hypertension (HTN) alters left atrial (LA) contractile function in a predictable manner. While increased LA size is a marker of LV diastolic dysfunction and has been shown to be predictive of adverse cardiovascular outcomes, the prognostic significance of altered LA contractile function is unknown.</p> <p>Methods</p> <p>A consecutive group of patients with chronic hypertension but without significant valvular disease or prior MI underwent clinically-indicated CMR for assessment of left ventricular (LV) function, myocardial ischemia, or viability. Calculation of LA volumes used in determining LA emptying functions was performed using the biplane area-length method.</p> <p>Results</p> <p>Two-hundred and ten patients were included in this study. During a median follow-up of 19 months, 48 patients experienced major adverse cardiac events (MACE), including 24 deaths. Decreased LA contractile function (LAEF_Contractile) demonstrated strong unadjusted associations with patient mortality, non-fatal events, and all MACE. For every 10% reduction of LAEF_Contractile, unadjusted hazards to MACE, all-cause mortality, and non-fatal events increased by 1.8, 1.5, and 1.4-folds, respectively. In addition, preservation of the proportional contribution from LA contraction to total diastolic filling (Contractile/Total ratio) was strongly associated with lower MACE and patient mortality. By multivariable analyses, LAEF_Contractilewas the strongest predictor in each of the best overall models of MACE, all-cause mortality, and non-fatal events. Even after adjustment for age, gender, left atrial volume, and LVEF, LAEF_Contractilemaintained strong independent associations with MACE (p < 0.0004), all-cause mortality (p < 0.0004), and non-fatal events (p < 0.0004).</p> <p>Conclusions</p> <p>In hypertensive patients at risk for left ventricular diastolic dysfunction, a decreased contribution of LA contractile function to ventricular filling during diastole is strongly predictive of adverse cardiac events and death.</p

Using VIRTIS on Venus Express to Constrain the Properties of the Giant Dark Cloud Observed in Images of Venus by IR2 on Akatsuki

A cloud opacity contrast feature that has been called a “long-lived sharp disruption” has been seen in the atmosphere of Venus in the near-infrared using Akatsuki’s IR2 camera, most clearly at equatorial latitudes. This feature was found to have a consistent planet-circling period of 4.9 days, and subsequent searches of past imagery revealed that it has probably existed for at least 30 years, the duration of near-infrared investigation of the deep atmosphere of Venus. Guided by the remarkably consistent morphological appearance of this feature, we have identified at least one previous instance of it in the Venus Express Visible and Infrared Thermal Imaging Spectrometer (VIRTIS) data. We take advantage of the spectroscopic capabilities of VIRTIS to retrieve atmospheric parameters in the vicinity of this feature that cannot be retrieved using the limited filter selection on board Akatsuki. We find that the changes in measurable quantities, such as cloud particle acid mass fraction, water vapor, carbon monoxide, cloud base altitude, and particle size, suggest that the changes that take place in the vicinity of this feature are restricted to the lower clouds of Venus (below 50 km). We hypothesize that further evolution of this feature (over timescales of days to weeks) results in measurable variations in these parameters at altitudes in the middle clouds of Venus (50–57 km), lending credence to its identification as a baroclinic trough or Kelvin front

Effective Dispersion of MgO Nanostructure on Biochar Support as a Basic Catalyst for Glucose Isomerization

Glucose isomerization to fructose is one of the most important reactions in the field of biomass valorization. We demonstrate wood waste valorization with MgCl2 salt to synthesize an environment-friendly catalyst (i.e., MgO-biochar), which exhibits effective glucose-to-fructose isomerization with over 30% fructose yield and 80% selectivity at only 100 °C for 30 min in water as a green medium. This study highlights that one-step synthesis can effectively disperse and tether MgO nanostructures to the biochar matrix, which displays a significant reduction of Mg leaching compared to MgO-biochars produced by two-step synthesis and pure MgO. The MgCl2 acts as a porogen that facilitates the formation of a porous biochar structure and dispersion of nanostructured MgO. We identify key parameters of impregnation media (ethylene glycol, ethanol, and water) and pyrolysis conditions (600/750 °C in N2/CO2 atmosphere) that are responsible for adjusting the reactivity and stability of MgO, which enable the design of more effective and recyclable biochar catalysts. Weak interactions between MgCl2 and biomass in the presence of aqueous miscible organic solvents as shape-directing agents are accountable for fast leaching of Mg from the MgO-biochar surface. The FTIR spectra confirm the existence of various coordinations on the hydroxylated surfaces of MgO-biochar surfaces. The mesoporous structures of the biochar support enhance the stability of MgO moieties as revealed by BET, XRD, and Raman analyses. Given the benefits of effective MgO dispersion on the biochar support, we can reduce the amount of MgO active species involved in each reaction run, which mitigates over-reaction compared to pure MgO catalysts and achieves high fructose yield and selectivity for three consecutive cycles