Validation Data Research Articles

Sleep, 24-Hour Activity Rhythms, and Subsequent Amyloid-β Pathology.

Sleep disturbances are common among older adults and have been associated with the development of Alzheimer disease (AD), such as amyloid-β (Aβ) pathology. For effective AD prevention, it is essential to pinpoint the specific disturbances in sleep and the underlying 24-hour activity rhythms that confer the highest risk of Aβ deposition. To determine the associations of 24-hour activity rhythms and sleep with Aβ deposition in adults without dementia, to evaluate whether disrupted 24-hour activity and sleep may precede Aβ deposition, and to assess the role of the apolipoprotein E ε4 (APOE4) genotype. This was an observational cohort study using data from the Rotterdam Study. Of 639 participants without dementia who underwent Aβ positron emission tomography (PET) from September 2018 to November 2021, 319 were included in the current study. Exclusion criteria were no APOE genotyping and no valid actigraphy data at the baseline visits from 2004 to 2006 or from 2012 to 2014. The mean (SD) follow-up was 7.8 (2.4) years. Data were analyzed from March 2023 to April 2024. Actigraphy (7 days and nights, objective sleep, and 24-hour activity rhythms), sleep diaries (self-reported sleep), Aβ42/40, phosphorylated tau (p-tau)181 and p-tau217 plasma assays, 18F-florbetaben PET (mean standard uptake value ratio [SUVR] in a large cortical region of interest), and APOE4 genotype. Association of objective and self-reported sleep and 24-hour activity rhythms at baseline with brain Aβ PET burden at follow-up. The mean (range) age in the study population was 61.5 (48-80) years at baseline and 69.2 (60-88) years at follow-up; 150 (47%) were women. Higher intradaily variability at baseline, an indicator of fragmented 24-hour activity rhythms, was associated with higher Aβ PET burden at follow-up (β, 0.15; bootstrapped 95% CI, 0.04 to 0.26; bootstrapped P = .02, false discovery rate [FDR] P = .048). APOE genotype modified this association, which was stronger in APOE4 carriers (β, 0.38; bootstrapped 95% CI, 0.05 to 0.64; bootstrapped P = .03) compared to noncarriers (β, 0.07; bootstrapped 95% CI, -0.04 to 0.18; bootstrapped P = .19). The findings remained largely similar after excluding participants with AD pathology at baseline, suggesting that a fragmented 24-hour activity rhythm may have preceded Aβ deposition. No other objective or self-reported measure of sleep was associated with Aβ. Among community-dwelling adults included in this study, higher fragmentation of the 24-hour activity rhythms was associated with greater subsequent Aβ burden, especially in APOE4 carriers. These results suggest that rest-activity fragmentation could represent a modifiable risk factor for AD.

All‐nonlinear static‐dynamic neural networks versus Bayesian machine learning for data‐driven modelling of chemical processes

AbstractIn recent decades, the utilization of machine learning (ML) and artificial intelligence (AI) approaches have been explored for process modelling applications. However, different types of ML models may have contrasting advantages and disadvantages, which become critical during the optimal selection of a specific data‐driven model for a particular application as well as estimation of parameters during model training. This paper compares and contrasts two different types of data‐driven modelling approaches, namely the series/parallel all‐nonlinear static‐dynamic neural network models and models from a Bayesian ML approach. Both types of AI modelling approaches considered in this work have shown to significantly outperform several state‐of‐the‐art steady‐state and dynamic data‐driven modelling techniques for various performance measures, specifically, model sparsity, predictive capabilities, and computational expense. The performances of the proposed model structures and algorithms have been evaluated for two nonlinear dynamic chemical engineering systems—a plug‐flow reactor for vapour phase cracking of acetone for production of acetic anhydride and a pilot‐plant for post‐combustion CO2 capture using monoethanolamine as the solvent. For the validation data from the CO2 capture pilot plant, root mean squared error (RMSE) for flue gas outlet temperature, flowrate and CO2 concentration is 0.05%, 1.07%, and 5.0%, respectively, for the all‐nonlinear static‐dynamic neural networks and 0.1%, 1.75%, and 14.14%, respectively, for the Bayesian ML models. For the plug flow reactor data, the Bayesian ML models yield superior RMSE compared to the all‐nonlinear static‐dynamic neural networks when the measurement data are corrupted with Gaussian, auto‐correlated, or cross‐correlated noise.

Calculation of Visual Background Values of Major Groundwater Components Taking Karamay City as an Example

Based on the groundwater chemistry data from Karamay City, Xinjiang Province, this study examines seven major components: K+, Ca2+, Na+, Mg2+, SO42−, Cl−, and HCO3−. The sampling was conducted during two periods: flood and dry periods. On the basis of analyzing the regional geologic background and hydrogeologic conditions, the study area was divided into calculation units and the test data validity was screened. Then, the outliers were eliminated by Grubbs’ method and Piper’s trilinear plot method, and the effectiveness of the elimination was evaluated by the box-and-line diagram. After that, the distribution types of the groundwater chemistry data in each calculation unit were judged to obtain the background values of the seven test indexes for the groundwater in different calculation units. The results show that Grubbs’ method and Piper’s trilinear plot method are effective in removing outliers. Secondly, the background values of Na+ and Cl− in the groundwater of Karamay City are mostly higher than those of other anions and cations during the two sampling periods. This phenomenon may be due to the concentration effect of evaporation. Additionally, the groundwater background values of Ca2+, Na+, and Cl− showed more significant differences between the two sampling periods compared to other ions.

Developing a Three-Dimensional Bubble Dynamics Model for Centrifugal Nuclear Thermal Propulsion

Nuclear thermal propulsion (NTP) is a highly efficient type of propulsion system that could yield a specific impulse up to 800 s. High-performance NTP allows for possibilities of various mission types from flyby to interplanetary flight, manned or unmanned. Centrifugal nuclear thermal propulsion (CNTP) is a type of NTP that uses liquid fissile material instead of a solid core in a standard reactor configuration. CNTP requires an interaction between liquid fissile fuel and gaseous propellant to perform heat transfer and generate thrust in a spinning enclosure. Because of the liquid state of the fissile fuel, it is essential to monitor bubble behavior and trajectory. For example, the bubble dwell time within the liquid and hydrogen flow rates can influence the liquid volume fraction and volume, which in turn can affect reactivity. A model is developed and presented that, as it matures, will predict the bubbles’ behavior based on the various types of liquid and gas and their corresponding properties as well as assist in cross-validation with the concurring experiment. The model primarily utilizes the smooth particle hydrodynamics (SPH) approach to simulate the liquid and gas interaction within the design space. The physics include buoyancy and drag on the bubbles. The pressure field within the liquid is modeled using hydrostatics, in which centrifugal motion introduces a radial gradient. Boundary conditions are devised to confine the liquid and gas within the enclosure. In this paper, we present the progress to date in two centrifugal fuel element–relevant subscale devices, the so-called “Antfarm” and “Blender II.” Antfarm is a static stage in a rectangular enclosure where buoyancy is purely gravity driven. Blender II refers to a rectangular enclosure that spins at up to 7000 rpm. Both Antfarm and Blender II are experiments that provide important data for validation against our SPH model. Two liquid and gas combinations are modeled in the present study using the Antfarm setup: water-air, and Galinstan-Nitrogen. The bubbles’ behavior was comparable to the experiment, and the velocity was at the same order of magnitude. The liquid simulation performed for the Blender II model shows that the pressure gradient within the liquid in the radial direction matches that predicted analytically from the centrifugal acceleration. The Blender II liquid model awaits experimental data for validation and verification.

Examining the Impact of Virtual Health Influencers on Young Adults’ Willingness to Engage in Liver Cancer Prevention: Insights from Parasocial Relationship Theory

The emergence of virtual influencers and AI doctors has significantly increased the attention of Chinese users, especially their health awareness and cancer health literacy. In our current study, guided by parasocial relationship theory, we examined the psychological antecedents that influence Chinese young adults’ willingness to engage in liver cancer prevention. Specifically, we aimed to examine the mediated mechanism of reduced unrealistic optimism within this relationship. A total of 252 respondents participated in this study, and the valid data were analyzed using hierarchical regression and mediation analysis to test our hypotheses. The results demonstrated three positive correlations between psychological factors (including perceived severity, parasocial relationship, and response efficacy) and Chinese young adults’ willingness to engage in liver cancer prevention. Furthermore, we found that reduced unrealistic optimism mediated these relationships. These findings provide valuable practical insights for Chinese health departments and experts to develop effective health campaign strategies that utilize multiple media platforms for optimal promotion.

Measurement and prediction of the detachment of Aspergillus niger spores in turbulent flows

Aspergillus niger (A. niger) spores can induce numerous health problems. Once the airflow-imposed drag force on an A. niger spore exceeds its binding force with the colony, the spore is detached. Turbulent flow may considerably increase the spore detachment. No method is currently available for prediction of the drag force on a spore and its detachment in turbulent flows. This investigation measured the turbulent velocities and detachment of A. niger colonies in a wind tunnel. Computational fluid dynamics (CFD) was employed to model an A. niger unit subjected to turbulent flow blowing. The top 1 % quantile instantaneous velocity of the turbulent flow was specified as the steady entry flow boundary condition for solving the peak velocity distribution and the peak drag forces onto spores. The predicted spore detachment ratios were compared with the measurement data for model validation. The results revealed that the spore detachment ratios with a turbulence intensity of 17 % to 20 % can be twice to triple the ratio with a turbulence intensity of approximately 1 %, when the average velocity for blowing remains the same. The proposed CFD model can accurately predict the detachment ratios of the A. niger spores. Environmental ImplicationSome people are sensitive to the Aspergillus niger (A. niger) spores, and excessive exposure can cause nasal congestion, skin tingling, coughing, and even asthma. Turbulent flow can considerably increase the spore detachment, due to the increased airflow-imposed drag force on the spores during turbulence. This investigation developed a numerical model to solve for the peak velocity distribution and the peak drag forces onto spores in turbulent flows to predict the spore detachment. With the numerical tool, the airborne fungal spore concentrations would be predictable, which paves a way for intelligent and precise control of fungal aerosol pollution.

Effects of omega-3 fatty acids on coronary revascularization and cardiovascular events: a meta-analysis.

Benefits of pharmacologic omega-3 fatty acid administration in cardiovascular prevention are controversial. Particularly, effects on coronary revascularization are unclear; also debated are specific benefits of eicosapentaenoic acid (EPA). We investigated incident coronary revascularizations, myocardial infarction (MI), stroke, heart failure (HF), unstable angina, and cardiovascular death, in subjects randomized to receive EPA or EPA + docosahexaenoic acid (EPA + DHA) vs. control. Meta-analysis of randomized controlled trials (RCTs) was conducted after MEDLINE, Embase, Scopus, Web of Science, and Cochrane Library search. Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines were followed for abstracting data and assessing data quality and validity. Data were pooled using a random effects model. Eighteen RCTs with 134 144 participants (primary and secondary cardiovascular prevention) receiving DHA + EPA (n = 52 498), EPA alone (n = 14 640), or control/placebo (n = 67 006) were included. Follow-up ranged from 4.5 months to 7.4 years. Overall, compared with controls, omega-3 supplementation reduced the risk of revascularization [0.90, 95% confidence interval (CI) 0.84-0.98; P = 0.001; P-heterogeneity = 0.0002; I2 = 68%], MI (0.89, 95% CI 0.81-0.98; P = 0.02; P-heterogeneity = 0.06; I2 = 41%), and cardiovascular death (0.92, 95% CI 0.85-0.99; P = 0.02; P-heterogeneity = 0.13; I2 = 33%). Lower risk was still observed in trials where most participants (≥60%) were on statin therapy. Compared with DHA + EPA, EPA alone showed a further significant risk reduction of revascularizations (0.76, 95% CI 0.65-0.88; P = 0.0002; P-interaction = 0.005) and all outcomes except HF. Omega-3 fatty acid supplementation reduced the risk of cardiovascular events and coronary revascularization, regardless of background statin use. Eicosapentaenoic acid alone produced greater benefits. The role of specific omega-3 molecules in primary vs. secondary prevention and the potential benefits of reduced revascularizations on overall health status and cost savings warrant further research.

Validation of Multi-State EHR-Based Network for Disease Surveillance (MENDS) Data and Implications for Improving Data Quality and Representativeness.

Surveillance modernization efforts emphasize the potential use of electronic health record (EHR) data to inform public health surveillance and prevention. However, EHR data streams vary widely in their completeness, accuracy, and representativeness. We developed a validation process for the Multi-State EHR-Based Network for Disease Surveillance (MENDS) pilot project to identify and resolve data quality issues that could affect chronic disease prevalence estimates. We examined MENDS validation processes from December 2020 through August 2023 across 5 data-contributing organizations and outlined steps to resolve data quality issues. We identified gaps in the EHR databases of data contributors and in the processes to extract, map, integrate, and analyze their EHR data. Examples of source-data problems included missing data on race and ethnicity and zip codes. Examples of data processing problems included duplicate or missing patient records, lower-than-expected volumes of data, use of multiple fields for a single data type, and implausible values. Validation protocols identified critical errors in both EHR source data and in the processes used to transform these data for analysis. Our experience highlights the value and importance of data validation to improve data quality and the accuracy of surveillance estimates that use EHR data. The validation process and lessons learned can be applied broadly to other EHR-based surveillance efforts.

Pariwisata, Masyarakat dan Kebudayaan: Studi Antropologi Pariwisata Pantai Marina di Pajukukang Bantaeng, Sulawesi Selatan

This article discusses the development of tourism with an anthropological approach to tourism in the Marina Pajujukang Bantaeng Beach, South Sulawesi. The results of this study indicate that: (1) Government efforts in developing marina beach tourism are trying to improve the human resources of the village of Baruga by conducting socialization and trying to complete facilities incomplete such as clean water filter (2) Community involvement in tourism development by supporting government programs for the advancement of marina beach tourism objects and participating directly in the development of Marina beach such as being a builder at the beginning of Marina beach construction until the Marina beach is like being security officer, janitor and waitress at a hotel in the Marina beach area. (3) The impacts felt by the community in developing the Marina beach are two, namely in the economic field for example increasing community income, opening new jobs and opening business opportunities for the Baruga village community while in the socio-cultural field there are two impacts namely positive and negative impacts. An example of the positive impact is the increasing public insight about tourist culture through the temporary negative impact that is starting to affect the local community on the culture of tourists who come to visit the Marina beach. This research is a qualitative descriptive study. Data collection techniques used were observation, interviews, and documentation. The data obtained is then processed using data analysis techniques such as data reduction, data presentation and conclusion making. And also the data validation using Member Check. From the results of research that has been found it seems that every development of tourism needs to be involved by all groups, especially the community, not just prioritizing investors. Because the essence of the success of the trip tour if the surrounding community enjoys economic results.

Privacy-aware quantum convolutional neural network for blockchain-based IoT health care data

Nowadays, the Internet of Things (IoT) is widely used in many applications, including healthcare to monitor the health condition of patients. However, it faces privacy and security issues due to the massive growth of hacking systems that provide illegal access to confidential health information. Blockchain (BC) is applied in IoT healthcare systems to manage healthcare data securely by using transparency features. In this research, a novel CAViaR Jellyfish Swarm Optimization enabled Quantum Convolutional Neural Network (CJSO-QCNN) is developed for the removal of noise in a privacy-aware BC-based IoT healthcare system. The CJSO is the combination of Conditional Autoregressive Value at Risk (CAViaR) and Jellyfish Search Optimizer (JSO). The data privacy is ensured according to the user preference by the service provider. The data is classified initially for identifying the sensitive data and is allowed for the treatment of noise, which is then stored in BC. Later, the user accesses the data by removing the noise using the CJSO-QCNN model. The valid data credentials are stored at the service provider according to user preferences. In addition, the superiority of the designed model is computed by comparing the performance with other prevailing approaches. The experimental results revealed that the CJSO-QCNN attained a maximum accuracy of 88.79%, a True Positive Rate (TPR) of 88.20%, and a True Negative Rate (TNR) of 88.61%.

On current limitations of online eye-tracking to study the visual processing of source code

Context:Eye-tracking is an increasingly popular instrument to study how programmers process and comprehend source code. While most studies are conducted in controlled environments with lab-grade hardware, it would be desirable to simplify and scale participation in experiments for users sitting remotely, leveraging home equipment. Objective:This study investigates the possibility of performing eye-tracking studies remotely using open-source algorithms and consumer-grade webcams. It establishes the technology’s current limitations and evaluates the quality of the data collected by it. We conclude by recommending ways forward to address the shortcomings and make remote code-reading studies in support of eye-tracking feasible in the future. Method:We gathered eye-gaze data remotely from 40 participants performing a code reading experiment on a purpose-built web application. The utilized eye-tracker worked client-side and used ridge regression to generate x- and y-coordinates in real-time predicting the participants’ on-screen gaze points without the need to collect and save video footage. We processed and analysed the collected data according to common practices for isolating eye-movement events and deriving metrics used in software engineering eye-tracking studies. In response to the lack of an algorithm explicitly developed for detecting oculomotor fixation events in low-frequency webcam data, we also introduced a dispersion threshold algorithm for that purpose. The quality of the collected data was subsequently assessed to determine the adequacy and validity of the methodology for eye-tracking. Results:The collected data was found to be of varying quality despite extensive calibration and graphical user guidance. We present our results highlighting both the negative and positive observations from which the community hopefully can learn. Both accuracy and precision were low and ultimately deemed insufficient for drawing valid conclusions in a high-precision empirical study. We nonetheless contribute to identifying critical limitations to be addressed in future research. Apart from the overall challenge of vastly diverse equipment, setup, and configuration, we found two main problems with the current webcam eye-tracking technology. The first was the absence of a validated algorithm to isolate fixations in low-frequency data, compromising the assurance of the accuracy of the data derived from it. The second problem was the lack of algorithmic support for head movements when predicting gaze location. Unsupervised participants do not always keep their heads still, even if instructed to do so. Consequently, we frequently observed spatial shifts that corrupted many collected datasets. Three encouraging observations resulted from the study. Even when shifted, gaze points were consistently dispersed in patterns resembling both the shape and size of the stimuli without extreme deviations. We could also distinguish recognizable reading patterns. Linearity was significantly different when participants were reading source code compared to natural text, and we could detect the expected left-to-right and top-to-bottom reading directions for participants reading natural text snippets. Conclusion:The accuracy and precision levels were not sufficient for a word-by-word analysis of code reading but could be adequate for a broader, coarse-grained precision study. Additionally we identified two main issues compromising the collected data validity and contributed a fixation detection algorithm to approach one of these issues. With suitable solutions to the identified issues, remote eye-tracking studies with webcams on code reading could eventually be feasible.

Evaluation of Reliability Between Race and Ethnicity Data Obtained from Self-report Versus Electronic Health Record.

Disparities based on perceived race and ethnicity exist in all fields of medicine. Accurate data collection is crucial to addressing these disparities, yet few studies have evaluated the validity of data gathered. This study compares self-reported race and ethnicity data, considered the gold standard, with data documented in the electronic health record (EHR), to assess the validity of that data. Data from self-reported questionnaires was collected from adolescents admitted to a psychiatric inpatient unit from February 2019 to July 2022. Demographic questionnaires were self-administered as part of a larger battery completed during the admission process. Data was compared to demographic information collected from the hospital's EHR for the same patients and time. In a sample of 1191 patients (ages 11-18, 61.9% female, 89% response rate), substantial agreement was observed for Hispanic ethnicity (κ = 0.64), while agreement for specific racial groups ranged from slight to substantial (κ = 0.10-0.63). In addition, it was noted that there was discrepancy between multiracial identification, with 17.1% of patients identifying as more than one race in self-reported data compared to 3.1% in EHR data. The findings from this data set highlight the need for caution when using EHR data to draw conclusions about health disparities. It also suggests that the method of data collection meaningfully influences the responses patients provide. Addressing these challenges is essential for advancing equitable healthcare and mitigating disparities among patients.

Sensing Volatile Pollutants with Spin-Coated Films Made of Pillar[5]arene Derivatives and Data Validation via Artificial Neural Networks.

Different types of solvents, aromatic and aliphatic, are used in many industrial sectors, and long-term exposure to these solvents can lead to many occupational diseases. Therefore, it is of great importance to detect volatile organic compounds (VOCs) using economic and ergonomic techniques. In this study, two macromolecules based on pillar[5]arene, named P[5]-1 and P[5]-2, were synthesized and applied to the detection of six different environmentally volatile pollutants in industry and laboratories. The thin films of the synthesized macrocycles were coated by using the spin coating technique on a suitable substrate under optimum conditions. All compounds and the prepared thin film surfaces were characterized by NMR, Fourier transform infrared (FT-IR), elemental analysis, atomic force microscopy (AFM), scanning electron microscopy (SEM), and contact angle measurements. All vapor sensing measurements were performed via the surface plasmon resonance (SPR) optical technique, and the responses of the P[5]-1 and P[5]-2 thin-film sensors were calculated with ΔI/Io × 100. The responses of the P[5]-1 and P[5]-2 thin-film sensors to dichloromethane vapor were determined to be 7.17 and 4.11, respectively, while the responses to chloroform vapor were calculated to be 5.24 and 2.8, respectively. As a result, these thin-film sensors showed a higher response to dichloromethane and chloroform vapors than to other harmful vapors. The SPR kinetic data for vapors validated that a nonlinear autoregressive neural network was performed with exogenous input for the best molecular modeling by using normalized reflected light intensity values. It can be clearly seen from the correlation coefficient values that the nonlinear autoregressive with exogenous input artificial neural network (NARX-ANN) model for dichloromethane converged more successfully to the experimental data compared to other gases. The correlation coefficient values of the dichloromethane modeling results were approximately 0.99 and 0.98 for P[5]-1 and P[5]-2 thin-film sensors, respectively.

Understanding the Social Dimensions and Context in HIV Research: Instrument Adaptation Is More Than Just Translation

Abstract There is a shortage of health-related questionnaires developed specifically for African countries' cultural contexts and local languages. Researchers have translated some tools to the target local languages. However, poor-quality or even high-quality translation can lead to an instrument that is not comparable with the original language if linguistic nuances, and cultural differences are not considered. This can affect the validity and reliability of the research data. The objective of this commentary was to highlight limitations of the commonly accepted forward–backward translation approach and the importance of more rigorous cultural and linguistic adaptation processes in social and behavioral research related to HIV.

Cerebrovascular Effects of Sildenafil in Small Vessel Disease: The OxHARP Trial.

Vascular cognitive impairment due to cerebral small vessel disease is associated with cerebral pulsatility, white matter hypoperfusion, and reduced cerebrovascular reactivity (CVR), and is potentially improved by endothelium-targeted drugs such as cilostazol. Whether sildenafil, a phosphodiesterase-5 inhibitor, improves cerebrovascular dysfunction is unknown. OxHARP trial (Oxford Haemodynamic Adaptation to Reduce Pulsatility) was a double-blind, randomized, placebo-controlled, 3-way crossover trial after nonembolic cerebrovascular events with mild-moderate white matter hyperintensities (WMH), the most prevalent manifestation of cerebral small vessel disease. The primary outcome assessed the superiority of 3 weeks of sildenafil 50 mg thrice daily versus placebo (mixed-effect linear models) on middle cerebral artery pulsatility, derived from peak systolic and end-diastolic velocities (transcranial ultrasound), with noninferiority to cilostazol 100 mg twice daily. Secondary end points included the following: cerebrovascular reactivity during inhalation of air, 4% and 6% CO2 on transcranial ultrasound (transcranial ultrasound-CVR); blood oxygen-level dependent-magnetic resonance imaging within WMH (CVR-WMH) and normal-appearing white matter (CVR-normal-appearing white matter); cerebral perfusion by arterial spin labeling (magnetic resonance imaging pseudocontinuous arterial spin labeling); and resistance by cerebrovascular conductance. Adverse effects were compared by Cochran Q. In 65/75 (87%) patients (median, 70 years;79% male) with valid primary outcome data, cerebral pulsatility was unchanged on sildenafil versus placebo (0.02, -0.01 to 0.05; P=0.18), or versus cilostazol (-0.01, -0.04 to 0.02; P=0.36), despite increased blood flow (∆ peak systolic velocity, 6.3 cm/s, 3.5-9.07; P<0.001; ∆ end-diastolic velocity, 1.98, 0.66-3.29; P=0.004). Secondary outcomes improved on sildenafil versus placebo for CVR-transcranial ultrasound (0.83 cm/s per mm Hg, 0.23-1.42; P=0.007), CVR-WMH (0.07, 0-0.14; P=0.043), CVR-normal-appearing white matter (0.06, 0.00-0.12; P=0.048), perfusion (WMH: 1.82 mL/100 g per minutes, 0.5-3.15; P=0.008; and normal-appearing white matter, 2.12, 0.66-3.6; P=0.006) and cerebrovascular resistance (sildenafil-placebo: 0.08, 0.05-0.10; P=4.9×10-8; cilostazol-placebo, 0.06, 0.03-0.09; P=5.1×10-5). Both drugs increased headaches (P=1.1×10-4), while cilostazol increased moderate-severe diarrhea (P=0.013). Sildenafil did not reduce pulsatility but increased cerebrovascular reactivity and perfusion. Sildenafil merits further study to determine whether it prevents the clinical sequelae of small vessel disease. URL: https://www.clinicaltrials.gov; Unique identifier: NCT03855332.

Point prevalence surveys of antibiotic prescribing in children at a tertiary hospital in a resource constraint, low-income sub-Saharan African country—the impact of an antimicrobial stewardship program

BackgroundResistance to multiple antibiotics by several pathogens has been widely described in children and has become a global health emergency. This is due to increased use by parents, caregivers, and healthcare providers. This study aims to describe the prevalence rates of antibiotic prescribing, ascertain the impact of antimicrobial stewardship programs, and target improving the quality of antibiotic prescribing in the paediatric population over time in a hospital.MethodA point prevalence survey of antibiotic use was performed yearly for 4 years to monitor trends in antibiotic prescribing. Data from all patients admitted before 8 a.m. on the day of the PPS were included. A web-based application designed by the University of Antwerp was used for data entry, validation, and analysis (http://www.global-pps.com).ResultsA total of 260 children, including 90 (34.6%) neonates and 170 (65.4%) older children, were admitted during the four surveys. Overall, 179 (68.8%) patients received at least one antibiotic. In neonates, the prevalence of antibiotic use increased from 78.9 to 89.5% but decreased from 100 to 58.8% in older children. There was a reduction in the use of antibiotics for prophylaxis from 45.7 to 24.6%. The most frequently prescribed antibiotic groups were third generation cephalosporins and aminoglycosides. The most common indications for antibiotic prescription were sepsis in neonates and central nervous system infection in older children. The documentation of reason in notes increased from 33 to 100%, while the stop-review date also increased from 19.4 to 70%.ConclusionThe indicators for appropriate antibiotic prescription improved over time with the introduction of antibiotic stewardship program in the department.

Neural Architecture Search Using Covariance Matrix Adaptation Evolution Strategy.

Evolution-based neural architecture search methods have shown promising results, but they require high computational resources because these methods involve training each candidate architecture from scratch and then evaluating its fitness, which results in long search time. Covariance Matrix Adaptation Evolution Strategy (CMA-ES) has shown promising results in tuning hyperparameters of neural networks but has not been used for neural architecture search. In this work, we propose a framework called CMANAS which applies the faster convergence property of CMA-ES to the deep neural architecture search problem. Instead of training each individual architecture seperately, we used the accuracy of a trained one shot model (OSM) on the validation data as a prediction of the fitness of the architecture, resulting in reduced search time. We also used an architecture-fitness table (AF table) for keeping a record of the already evaluated architecture, thus further reducing the search time. The architectures are modeled using a normal distribution, which is updated using CMA-ES based on the fitness of the sampled population. Experimentally, CMANAS achieves better results than previous evolution-based methods while reducing the search time significantly. The effectiveness of CMANAS is shown on two different search spaces using four datasets: CIFAR-10, CIFAR-100, ImageNet, and ImageNet16-120. All the results show that CMANAS is a viable alternative to previous evolution-based methods and extends the application of CMA-ES to the deep neural architecture search field.

Movement Quality Assessment of Army Reserve Officers' Training Corps Cadets: A Report of Validity and Normative Data.

Movement quality screening in early-career military populations, like Army Reserve Officers' Training Corps (AROTC) cadets, could decrease the negative impact of musculoskeletal injury observed within the military. Movement quality screening techniques should be valid before being pursued in the field. Normative data describing movement quality of AROTC cadets are also needed. Therefore, the aims of this study were to determine criterion validity of several movement quality assessments and report normative jump-landing kinematics of AROTC cadets. This cross-sectional research was approved by the Institutional Review Board. As part of a larger study, 20 AROTC cadets (21.3 ± 3.4 years; 1.7 ± 0.1 m; 73.8 ± 14.8 kg) had 3-dimensional (3D) and 2-dimensional (2D) kinematic data collected simultaneously while performing a jump-landing task. Variables of interest were 3D hip and knee sagittal, frontal, and transverse joint angles at maximum knee flexion. An experienced rater calculated sagittal and frontal 2D joint angles at maximum knee flexion. Averages of 2D and 3D angles were calculated to describe normative data and for further data analysis. Bivariate correlations between 3D and 2D variables were used to determine criterion validity. Moderate correlations were found between 2D and 3D hip frontal plane angles (P = .05, r =-0.33), 2D and 3D knee sagittal plane angles (P = .04, r = 0.35), and 2D and 3D knee frontal plane angles (P = .03, r = -0.36). Normative values of knee and hip kinematics demonstrated averages of 17.58° of knee adduction, 16.48° of knee external rotation, 11.57° of hip abduction, 10.76° of hip internal rotation, and 103.47° of knee flexion during landings. However, ranges demonstrated that landing patterns vary within AROTC cadets. The normative values of 3D jump-landing kinematic data indicate that movement quality varies greatly within AROTC cadets, and some cadets display potentially injurious movements. Therefore, screening movement quality could be beneficial to determine musculoskeletal injury risk in AROTC cadets. Based on the correlations discovered in this study, we recommend the 2D techniques used in this study be researched further as they may serve as alternatives to expensive, timely 3D techniques that could be better utilized in military environments.

Sociodemographic and Health Factors of the Alcohol Treatment-seeking Population in New South Wales, Australia.

Although factors associated with alcohol use have been researched at a population level, descriptions of the alcohol and other drug (AOD) treatment-seeking population in New South Wales (NSW), Australia, are limited. This study addresses this gap by analyzing sociodemographic and health characteristics in the NSW AOD treatment-seeking population. Self-reported Australian Treatment Outcomes Profile data on substance use, health ratings, and sociodemographic factors were acquired from public AOD services (offering services from counseling to ambulatory/inpatient withdrawal management) in 6 administrative health districts from 2016 to 2019 (n = 14,287). Gaussian and multiple logistic regressions were conducted to examine associations between these factors and alcohol consumption quantity. Data were analyzed for patients seeking treatment for alcohol consumption specifically (n = 5929; median age, 44 years; 65% male). Valid alcohol consumption data were available for 5460 patients, among whom the mean volume of alcohol consumed was 311 standard drinks (3110 grams of ethanol) over the past 28 days and 15 standard drinks (150 grams of ethanol) per occasion. Higher volumes were consumed by males and those with recent experiences of violence and/or injecting drug use. Caring for children younger than 5 years and having above-median health ratings were associated with lower alcohol consumption. This study contributes to the characterization of the NSW public AOD treatment population and identifies associations between alcohol consumption, sociodemographic factors, and health ratings among people seeking treatment for alcohol consumption. Findings point towards multilevel assessment and comprehensive interventions for people engaging in treatment for alcohol use. Future research should address barriers to treatment.

Risk calculators for the detection of prostate cancer: a systematic review.

Prostate cancer (PCa) (early) detection poses significant challenges, including unnecessary testing and the risk of potential overdiagnosis. The European Association of Urology therefore suggests an individual risk-adapted approach, incorporating risk calculators (RCs) into the PCa detection pathway. In the context of 'The PRostate Cancer Awareness and Initiative for Screening in the European Union' (PRAISE-U) project ( https://uroweb.org/praise-u ), we aim to provide an overview of the currently available clinical RCs applicable in an early PCa detection algorithm. We performed a systematic review to identify RCs predicting detection of clinically significant PCa at biopsy. A search was performed in the databases Medline ALL, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar for publications between January 2010 and July 2023. We retrieved relevant literature by using the terms "prostate cancer", "screening/diagnosis" and "predictive model". Inclusion criteria included systematic reviews, meta-analyses, and clinical trials. Exclusion criteria applied to studies involving pre-targeted high-risk populations, diagnosed PCa patients, or a sample sizes under 50 men. We identified 6474 articles, of which 140 were included after screening abstracts and full texts. In total, we identified 96 unique RCs. Among these, 45 underwent external validation, with 28 validated in multiple cohorts. Of the externally validated RCs, 17 are based on clinical factors, 19 incorporate clinical factors along with MRI details, 4 were based on blood biomarkers alone or in combination with clinical factors, and 5 included urinary biomarkers. The median AUC of externally validated RCs ranged from 0.63 to 0.93. This systematic review offers an extensive analysis of currently available RCs, their variable utilization, and performance within validation cohorts. RCs have consistently demonstrated their capacity to mitigate the limitations associated with early detection and have been integrated into modern practice and screening trials. Nevertheless, the lack of external validation data raises concerns about numerous RCs, and it is crucial to factor in this omission when evaluating whether a specific RC is applicable to one's target population.