Real World Research Articles

Learning real-world heterogeneous noise models with a benchmark dataset

Noise modeling is an important research field in computer vision; the design of an accurate model for imaging sensor noise depends on not only a comprehensive benchmark dataset of the real world, but also a precise design of the noise modeling algorithm. However, due to the inaccurate estimation method of noise-free images and limited shooting scenes, the current realistic datasets could not describe the diverse noise properties sufficiently. Moreover, popular parametric noise models are not sophisticated enough to characterize the real-world noise exactly. In this work, we first construct a more comprehensive dataset of the real world by capturing more indoor and outdoor scenes under different lighting conditions using diverse smartphones, then we propose a non-parametric noise estimation method capable of modeling the spatial heterogeneity of real-world noise patterns. Specifically, in order to characterize the spatial heterogeneity of real-world noise, we assume a non-i.i.d Gaussian distribution and propose a deep convolutional neural network (DCNN)-based approach for learning pixel-wise noise variance maps. To learn the pixel-wise variance map, we have constructed a variance estimation network mapping from the conditional signals (clean image, ISO, and camera model) to surrogate labels obtained from the nonlocal search of similar patches from the clean-noisy image pair. Finally, we conducted denoising and classification experiments using different kinds of simulated noisy images, compared to the Poisson-Gaussian and Noise Flow noise models, the proposed method achieves denoising performance improvements (PSNR) of 1.13 dB and 2.51 dB respectively on the proposed real-world test dataset, denoising and classification results on the real noisy data captured by mobile phones have verified that our approach is more accurate than current noise modeling methods.

Short-term high-speed rail passenger flow prediction by integrating ensemble empirical mode decomposition with multivariate grey support vector machine

Short-term prediction of high-speed rail (HSR) passenger flow provides a daily ridership estimation for the near future, which is critical to HSR planning and operational decision making. This paper proposes a new methodology that integrates ensemble empirical mode decomposition with multivariate support vector machines (EEMD-MSVM). There are four steps in this hybrid forecasting approach: (i) explore the correlation of multivariate HSR passenger flows at various stations based on archived data; (ii) decompose empirical modes of historical passenger flows for each HSR station, using EEMD to generate a number of intrinsic mode functions (IMFs) and a trend term; (iii) predict the IMF for each correlated station pair using MSVM; and (iv) reconstruct the refined IMF components to predict daily multivariate HSR passenger flows. The proposed EEMD-MSVM approach is demonstrated with multiple OD pairs along the Wuhan-Guangzhou HSR in China. Results from various origin-destination pairs, show that the EEMD-MSVM approach outperforms the existing ensemble empirical mode decomposition with grey support vector machine approach (EEMD-GSVM). With the multivariate approach, the mean absolute percentage error in demand prediction is reduced by 13.9%, 1.2%, 1.0%, 2.0%, and 2.7% and the mean absolute deviation is reduced by 78.8, 38.0, 4.4, 4.6, and 3.9, between these OD pairs respectively. Such increase in short-term demand prediction accuracy can significantly improve HSR service planning, operations, and revenue management in the real world.

Actualizing Radical Loving Leadership in Real World Health Care.

With Chapman's Radical Loving Care as a framework, this article shares practical wisdom and habits that occur daily in a busy community hospital to help all nurse leaders to develop a holistic view to shape their vision and embed radical loving care in their connections. The application of Chapman's concepts to the mentoring of others is viewed as a form of love and helps to strengthen not only the mentee, but the entire system of nurses caring for each other. Radical loving care is seen as a far reaching, progressive and humanistic strategy to show a deep, abiding kindness and caring to other human beings in the world of health care.

A multiclass logistic regression, Markov chain, and cellular automata integration for modelling urban land use change

Cellular Automata (CA) models employ simple transition rules and neighbourhood interactions to determine cells’ evolution. Integrating CA with other algorithms is often essential in order to obtain necessary statistical inferences. The integration of Binary Logistic Regression (BLR) and Markov chain (MC) with CA forms common CA integrations. For convenience, urban land use change is often modelled as a binary process (e,g. ‘undeveloped land’ and ‘developed land’), whereas in the real world urban land use change phenomenon could go beyond two processes (e. g. ‘undeveloped land’, ‘sparsely developed land’, ‘semi-highly developed land’, ‘highly developed land’, ‘‘very highly developed land’ etc.). The BLR-MC-CA hybrid model yields a binary outcome. In order to extend the BLR-MC-CA hybrid model to multivariate problems, we introduce a novel hybrid model, by simply replacing the BLR with the new Multiclass Logistic Regression (MLR) to obtain the new MLR-MC-CA hybrid model. Satellite remote sensing imageries acquired in 1984, 2000, and 2020 are used as input data. Four scenarios of urban land use change are modelled: ‘nonurban region’, ‘highest urban region’, ‘high urban region’, and ‘medium urban region’. These four scenarios are modelled with twelve urban land use drivers: distance to water body, distance to residential areas, distance to industrial and commercial areas, distance to major roads, distance to railway, distance to Lagos Island, distance to international seaport, distance to University of Lagos, income potential, population potential, distance to international airport, and distance to Lagos State University. Urban transitions 1984-2000 and 2000-2020 are used to simulate the urban states in 2000 and 2020 respectively; as well as to obtain the urban state in 2060. The validation of the experiment is largely based on McNemar’s test and Receiver Operating Characteristics (ROC) curves. The results of the experiment indicate significant fit between the reference and the predicted data.

A hybrid quantum computing pipeline for real world drug discovery

Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing a hybrid quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the application of quantum computation in drug discovery and propels it towards more scalable system. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows.

Experimental Assessment of a Vision-Based Obstacle Avoidance Strategy for Robot Manipulators: Off-line Trajectory Planning and On-line Motion Control

Human-Robot Interaction is an increasingly important topic in both research and industry fields. Since human safety must be always guaranteed and accidental contact with the operator avoided, it is necessary to investigate real-time obstacle avoidance strategies. The transfer from simulation environments, where algorithms are tested, to the real world is challenging from different points of view, e.g., the continuous tracking of the obstacle and the configuration of different manipulators. In this paper, the authors describe the implementation of a collision avoidance strategy based on the potential field method for off-line trajectory planning and on-line motion control, paired with the Motion Capture system Optitrack PrimeX 22 for obstacle tracking. Several experiments show the performance of the proposed strategy in the case of a fixed and dynamic obstacle, disturbing the robot’s trajectory from multiple directions. Two different avoidance modalities are adapted and tested for both standard and redundant robot manipulators. The results show the possibility of safely implementing the proposed avoidance strategy on real systems.

Dynamic Changes in Circulating Tumor Fraction as a Predictor of Real-World Clinical Outcomes in Solid Tumor Malignancy Patients Treated with Immunotherapy.

A dynamic molecular biomarker that can identify early efficacy of immune checkpoint inhibitor (ICI) therapy remains an unmet clinical need. Here we evaluate if a novel circulating tumor DNA (ctDNA) assay, xM, used for treatment response monitoring (TRM), that quantifies changes in ctDNA tumor fraction (TF), can predict outcome benefits in patients treated with ICI alone or in combination with chemotherapy in a real-world (RW) cohort. This retrospective study consisted of patients with advanced cancer from the Tempus de-identified clinical genomic database who received longitudinal liquid-based next-generation sequencing. Eligible patients had a blood sample ≤ 40days prior to the start of ICI initiation and an on-treatment blood sample 15-180days post ICI initiation. TF was calculated via an ensemble algorithm that utilizes TF estimates derived from variants and copy number information. Patients with molecular response (MR) were defined as patients with a ≥ 50% decrease in TF between tests. In the subset of patients with rw-imaging data between 2 and 18weeks of ICI initiation, the predictive value of MR in addition to rw-imaging was compared to a model of rw-imaging alone. The evaluable cohort (N = 86) was composed of 14 solid cancer types. Patients received either ICI monotherapy (38.4%, N = 33) or ICI in combination with chemotherapy (61.6%, N = 53). Patients with MR had significantly longer rw-overall survival (rwOS) (hazard ratio (HR)0.4, P = 0.004) and rw-progression free survival (rwPFS) (HR0.4, P = 0.005) than patients with molecular non-response (nMR). Similar results were seen in the ICI monotherapy subcohort; HR0.2, P = 0.02 for rwOS and HR0.2, P = 0.01 for rwPFS. In the subset of patients with matched rw-imaging data (N = 51), a model incorporating both MR and rw-imaging was superior in predicting rwOS than rw-imaging alone (P = 0.02). xM used for TRM is a novel serial quantitative TF algorithm that can be used clinically to evaluate ICI therapy efficacy.

Augmented reality for chemistry education to promote the use of chemical terminology in teacher training

Chemistry as a whole is divided into three levels. The macroscopic level describes real, observable phenomena of the material world. The submicroscopic level focuses on particles. The representative level includes pictorial and symbolic representations to visualize substance in its nature. Students often have problems separating these levels and conceptually transfer each of the three levels to the other. Therefore, teachers need to use chemical terminology correctly when teaching the substance-particle concept. Augmented Reality (AR) connects real and virtual world. The observer physically moves in a real environment that integrates virtual elements. The AR technology has great potential for learning in the subject chemistry, especially when it comes to making the “invisible” visible and illustrating scientific phenomena at particle level. The simultaneous presentation should avoid split-attention and offers new possibilities to interactively deal with (M)ER. The question arises whether AR has a positive effect on the use of technical language and the associated understanding of the concept of dealing with (M)ER at the substance and particle levels. With an AR app on the tablet and the AR glasses, the chemical processes of a real experiment are represented by AR visualizations. Therefore, the AR app was piloted. This study captured the chemistry handling with (M)ER of chemistry teachers (N = 30) using a pre-post survey. The participating preservice teachers are described below. Each test includes five tasks elaborated by thinking aloud. The thinking-aloud protocols to acquire the use of the chemical terminology are evaluated in MAXQDA.

Digital Hybrid Technology and Literary Fantasy of Today's Cinema: A Case Study of Jumanji

This study examines how state-of-the-art cinematic techniques, such as computer-generated imagery (CGI), visual effects, and the integration of digital characters with real-world environments, are used to achieve a seamless blend of fantasy and reality in the "Jumanji" film series (1995, 2005, 2017, 2019). The research investigates bringing the "Jumanji" game to life and how these technologies materialize the fantasy elements of the original game within the real world. It explores crafting a fantastical narrative, i.e., how they establish a captivating and immersive space adventure experience in "Zathura." This study suggests that contemporary fans of "Jumanji" have a distinct perception of the film because they enjoy the mix of immersive fantasy and a sense of real-world connection.

Custom Anchorless Object Detection Model for 3D Synthetic Traffic Sign Board Dataset with Depth Estimation and Text Character Extraction

This paper introduces an anchorless deep learning model designed for efficient analysis and processing of large-scale 3D synthetic traffic sign board datasets. With an ever-increasing emphasis on autonomous driving systems and their reliance on precise environmental perception, the ability to accurately interpret traffic sign information is crucial. Our model seamlessly integrates object detection, depth estimation, deformable parts, and text character extraction functionalities, facilitating a comprehensive understanding of road signs in simulated environments that mimic the real world. The dataset used has a large number of artificially generated traffic signs for 183 different classes. The signs include place names in Japanese and English, expressway names in Japanese and English, distances and motorway numbers, and direction arrow marks with different lighting, occlusion, viewing angles, camera distortion, day and night cycles, and bad weather like rain, snow, and fog. This was done so that the model could be tested thoroughly in a wide range of difficult conditions. We developed a convolutional neural network with a modified lightweight hourglass backbone using depthwise spatial and pointwise convolutions, along with spatial and channel attention modules that produce resilient feature maps. We conducted experiments to benchmark our model against the baseline model, showing improved accuracy and efficiency in both depth estimation and text extraction tasks, crucial for real-time applications in autonomous navigation systems. With its model efficiency and partwise decoded predictions, along with Optical Character Recognition (OCR), our approach suggests its potential as a valuable tool for developers of Advanced Driver-Assistance Systems (ADAS), Autonomous Vehicle (AV) technologies, and transportation safety applications, ensuring reliable navigation solutions.

Enhancing identification performance of cognitive impairment high-risk based on a semi-supervised learning method

BackgroundCognitive assessment plays a pivotal role in the early detection of cognitive impairment, particularly in the prevention and management of cognitive diseases such as Alzheimer’s and Lewy body dementia. Large-scale screening relies heavily on cognitive assessment scales as primary tools, with some low sensitivity and others expensive. Despite significant progress in machine learning for cognitive function assessment, its application in this particular screening domain remains underexplored, often requiring labor-intensive expert annotations. AimsThis paper introduces a semi-supervised learning algorithm based on pseudo-label with putback (SS-PP), aiming to enhance model efficiency in predicting the high risk of cognitive impairment (HR-CI) by utilizing the distribution of unlabeled samples. DataThe study involved 189 labeled samples and 215,078 unlabeled samples from real world. A semi-supervised classification algorithm was designed and evaluated by comparison with supervised methods composed by 14 traditional machine-learning methods and other advanced semi-supervised algorithms. ResultsThe optimal SS-PP model, based on GBDT, achieved an AUC of 0.947. Comparative analysis with supervised learning models and semi-supervised methods demonstrated an average AUC improvement of 8% and state-of-art performance, repectively. ConclusionThis study pioneers the exploration of utilizing limited labeled data for HR-CI predictions and evaluates the benefits of incorporating physical examination data, holding significant implications for the development of cost-effective strategies in relevant healthcare domains.

REMODELLING EDUCATION FOR STUDENTS IN NIGERIA BY PROVIDING RELEVANT SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATICS LEARNING (STEM).

Over the past thirty years, it is evident that there has been little change in the methodology of teaching Science, Technology, Engineering, And Mathematics (STEM) in Nigeria. Consequently, Nigerian students have lagged, encountering difficulties in science and math proficiency. Despite national reform initiatives aimed at enhancing the situation, the absence of uniformity and a unified effort has resulted in negligible alterations in instructional methods. This research examined data from student and teacher programs conducted by State Universal Basic Education Board (SUBEB). The focus was on two programs implemented over a two-year period: STEM Bootcamps for children and Teacher training initiatives. The results indicated that these programs successfully enhanced learning and forged a robust link to the real world. Even though it was their initial encounter with STEM pedagogy, both educators and students recognized its worth and found it advantageous in grasping the concepts addressed during the programs. These favorable outcomes sparked conversations about expanding the pilot programs and considering the ramifications for policy and future research.

Tweeting under uncertainty: The relationship between uncertain language and negative emotions in the wild.

Despite decades of research characterizing the relationship between uncertainty and emotion, little is known about how these constructs interact in the wild. Using naturalistic, large-scale language produced on Twitter, we ask whether increases in environmental uncertainty and associated aversive emotional reactions can be captured by the millions of digital traces of people sharing their thoughts online. Analyzing more than 20 million tweets from more than 7.5 million unique users, we find that uncertainty expressions peak when environmental uncertainty is high. This effect, however, is modulated by the type of trigger that increases uncertainty. Pandemics (COVID-19 in 2020) and national U.S. elections (2021) exhibit an increase in uncertainty language and negative sentiment in the real world, illustrating the well-documented relationship between uncertainty and aversive emotional reactions acting in lockstep. In contrast, when uncertain events involve a moral violation (i.e., the 2021 U.S. Capitol attack), specific negative emotions (i.e., anger, fear, and moral outrage) sharply increase, while uncertainty language abruptly decreases. This reveals that in the real world, uncertainty and emotion have a more complex relationship than originally assumed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Suboptimal monitoring and management in patients with unrecorded stage 3 chronic kidney disease in real-world settings: Insights from REVEAL-CKD.

Clinical practice guidelines for patients with chronic kidney disease (CKD) recommend regular monitoring and management of kidney function and CKD risk factors. However, the majority of patients with stage 3 CKD lack a diagnosis code, and data on the implementation of these recommendations in the real world are limited. To assess the implementation of guideline-directed monitoring and management practices in the real world in patients with stage 3 CKD without a recorded diagnosis code. REVEAL-CKD (NCT04847531) is a multinational, observational study of patients with stage 3 CKD. Eligible patients had ≥2 consecutive estimated glomerular filtration rate (eGFR) measurements indicative of stage 3 CKD recorded >90 and ≤730 days apart, lacked an International Classification of Diseases 9/10 diagnosis code corresponding to CKD any time before and up to 6 months after the second eGFR measurement. Testing of key measures of care quality were assessed. The study included 435,971 patients from 9 countries. In all countries, the prevalence of urinary albumin-creatinine ratio and albuminuria testing was low. Angiotensin-converting enzyme inhibitor, angiotensin receptor blocker and statin prescriptions were highly variable, and sodium-glucose cotransporter-2 inhibitor prescriptions remained below 21%. Blood pressure measurements were recorded in 20.2%-89.9% of patients. Overall, a large proportion of patients with evidence of stage 3 CKD did not receive recommended, guideline-directed monitoring and management. The variability in standard of care among countries demonstrates a clear opportunity to improve monitoring and management of these patients, most likely improving long-term outcomes.

Emotional contextual effects of face perception: a test of the affective realism hypothesis

Affective feelings naturally infuse individuals’ perceptions, serving as valid windows onto the real world. The affective realism hypothesis further explains how these feelings work: as properties of individuals’ perceptual experiences, these feelings influence perception. Notably, this hypothesis based on affective feelings with different valences has been substantiated, whereas the existing evidence is not compelling enough. Moreover, whether specific affective feelings can be experienced as properties of target perception remains unclear. Addressing these two issues deepens our understanding of the nature of emotional representation. Hence, we investigated the affective realism hypothesis based on affective feelings with different valences and specific emotions, comparing it with the affective misattribution hypothesis. In Experiment 1, we examined the effects of affective feelings with various valences on targets’ perception through the AM (1a) and CFS paradigms (1b). In Experiment 2, we investigated the effects of affective feelings with anger, sadness, and disgust using similar methods. Results from Experiments 1a and 1b consistently indicated significant differences in valence ratings of neutral faces under emotional contexts with varying valences. Experiment 2a revealed significant differences in specific emotion ratings of neutral faces under different specific emotional contexts in the AM paradigm, whereas such differences were not observed in the CFS paradigm in Experiment 2b. We concluded that affective feelings with different valences, rather than specific emotions, can be experienced as inherent properties of target perception, validating the affective realism hypothesis. These findings supported the view that the nature of emotional representation should be described as affective dimensions.

Analisis Wacana Kritis Norman Fairclough Pada Film Barbie 2023 Sebagai Representasi Budaya Patriarki

Film is part of audio-visual mass media communication and aims to convey social messages to the audience in accordance with the reality that is growing and developing in society. One of the films that has this purpose is the film Barbie Live Action 2023. The purpose of this research is to dissect the discourse in three dimensions, namely textual, discourse practice, and sociocultural practice. In this research, the type of research used is qualitative-descriptive research. The paradigm used in this research is the critical paradigm. The appropriate research method used for this research is Norman Fairclough's Critical Discourse Analysis method. In the textual dimension, the linguistic tools of Diction, Cohesion, Modality and figure of speech metaphor are obtained. In the discourse practice dimension, at the production level the Barbie film is produced with two backgrounds, namely Barbieland and the real world, namely the city of Los Angles, United States with English language dialogue. At a text consumption level, the film is the highest-grossing box office film of all time with earnings of $1.45 billion. Sociocultural practices have 3 levels, namely situational, institutional and social levels. Situational Barbie script was written during the pandemic. For example, the film Barbie was a box office success in almost the entire world and became the hope of the global community regarding the end of the pandemic. Furthermore, at the institutional level, which includes the media, is Greta Gerwig herself as a film filmmaker. Warner Bros. is the production house for the 2023 Barbie Live Action film. Then there is Mattel as the company behind the production of Barbie dolls. Finally, at the social level, this film shows how patriarchy persists in reality, the social and cultural system of American society still adheres to patriarchal culture and neglects women's rights and protection.

Development and Evaluation of a Retrieval-Augmented Large Language Model Framework for Ophthalmology

Although augmenting large language models (LLMs) with knowledge bases may improve medical domain-specific performance, practical methods are needed for local implementation of LLMs that address privacy concerns and enhance accessibility for health care professionals. To develop an accurate, cost-effective local implementation of an LLM to mitigate privacy concerns and support their practical deployment in health care settings. ChatZOC (Sun Yat-Sen University Zhongshan Ophthalmology Center), a retrieval-augmented LLM framework, was developed by enhancing a baseline LLM with a comprehensive ophthalmic dataset and evaluation framework (CODE), which includes over 30 000 pieces of ophthalmic knowledge. This LLM was benchmarked against 10 representative LLMs, including GPT-4 and GPT-3.5 Turbo (OpenAI), across 300 clinical questions in ophthalmology. The evaluation, involving a panel of medical experts and biomedical researchers, focused on accuracy, utility, and safety. A double-masked approach was used to try to minimize bias assessment across all models. The study used a comprehensive knowledge base derived from ophthalmic clinical practice, without directly involving clinical patients. LLM response to clinical questions. Accuracy, utility, and safety of LLMs in responding to clinical questions. The baseline model achieved a human ranking score of 0.48. The retrieval-augmented LLM had a score of 0.60, a difference of 0.12 (95% CI, 0.02-0.22; P = .02) from baseline and not different from GPT-4 with a score of 0.61 (difference = 0.01; 95% CI, -0.11 to 0.13; P = .89). For scientific consensus, the retrieval-augmented LLM was 84.0% compared with the baseline model of 46.5% (difference = 37.5%; 95% CI, 29.0%-46.0%; P < .001) and not different from GPT-4 with a value of 79.2% (difference = 4.8%; 95% CI, -0.3% to 10.0%; P = .06). Results of this quality improvement study suggest that the integration of high-quality knowledge bases improved the LLM's performance in medical domains. This study highlights the transformative potential of augmented LLMs in clinical practice by providing reliable, safe, and practical clinical information. Further research is needed to explore the broader application of such frameworks in the real world.

Leveraging AI and Machine Learning in Six-Sigma Documentation for Pharmaceutical Quality Assurance.

The pharmaceutical industry must maintain stringent quality assurance standards to ensure product safety and regulatory compliance. A key component of the well-known Six Sigma methodology for process improvement and quality control is precise and comprehensive documentation. However, there are a number of significant issues with traditional documentation procedures, including as slowness, human error, and difficulties with regulatory standards. This review research looks at innovative ways to employ machine learning (ML) and artificial intelligence (AI) to enhance Six Sigma documentation processes in the pharmaceutical sector. AI and ML provide cutting-edge technologies that have the potential to drastically alter documentation processes by automating data entry, collection, and analysis. Natural language processing (NLP) and computer vision technologies have the potential to significantly reduce human error rates and increase the efficacy of documentation processes. By applying machine learning algorithms to support real-time data analysis, predictive analytics, and proactive quality management, pharmaceutical organizations may be able to identify potential quality issues early on and take proactive efforts to address them. Combining AI and ML improves documentation accuracy and reliability while also strengthening compliance with stringent regulatory criteria. The primary barriers and limitations to the current state of Six Sigma documentation in the pharmaceutical industry are identified in this study. It examines the fundamentals of AI and ML with an emphasis on their specific applications in quality assurance and potential benefits for Six Sigma processes. The report includes extensive case studies that highlight notable developments and explain how AI/ML enhanced documentation is used in the real world.

MovePort: Multimodal Dataset of EMG, IMU, MoCap and Insole Pressure for Analyzing Abnormal Movements and Postures in Rehabilitation Training.

In most real world rehabilitation training, patients are trained to regain motion capabilities with the aid of functional/epidural electrical stimulation (FES/EES), under the support of gravity-assist systems to prevent falls. However, the lack of motion analysis dataset designed specifically for rehabilitation-related applications largely limits the conduct of pilot research. We provide an open access dataset, consisting of multimodal data collected via 16 electromyography (EMG) sensors, 6 inertial measurement unit (IMU) sensors, and 230 insole pressure sensors (IPS) per foot, together with a 26-sensor motion capture system, under different MOVEments and POstures for Rehabilitation Training (MovePort). Data were collected under diverse experimental paradigms. Twenty four participants first imitated multiple normal and abnormal body postures including (1) normal standing still, (2) leaning forward, (3) leaning back, and (4) half-squat, which in practical applications, can be detected as feedback to tune the parameters of FES/EES and gravity-assist systems to keep patients in a target body posture. Data under imitated abnormal gaits, e.g., (1) with legs raised higher under excessive electrical stimulation, and (2) with dragging legs under insufficient stimulation, were also collected. Data under normal gaits with low, medium and high speeds are also included. Pathological gait data from a subject with spastic paraplegia further increases the clinical value of our dataset. We also provide source codes to perform both intra- and inter-participant motion analyses of our dataset. We expect our dataset can provide a unique platform to promote collaboration among neurorehabilitation engineers.

Cost-effectiveness of COVID rapid diagnostic tests for patients with severe/critical illness in low- and middle-income countries: A modeling study.

Rapid diagnostic tests (RDTs) for coronavirus disease (COVID) are used in low- and middle-income countries (LMICs) to inform treatment decisions. However, to date, it is unclear when this use is cost-effective. Existing analyses are limited to a narrow set of countries and uses. The aim of this study is to assess the cost-effectiveness of COVID RDTs to inform the treatment of patients with severe illness in LMICs, considering real world practice. We assessed the cost-effectiveness of COVID testing across LMICs using a decision tree model, differentiating results by country income level, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) prevalence, and testing scenario (none, RDTs, polymerase chain reaction tests-PCRs and combinations). LMIC experts defined realistic care pathways and treatment options. Using a healthcare provider perspective and net monetary benefit approach, we assessed both intended (COVID symptom alleviation) and unintended (treatment side effects) health and economic impacts for each testing scenario. We included the side effects of corticosteroids, which are often the only available treatment for COVID. Because side effects depend both on the treatment and the patient's underlying illness (COVID or COVID-like illnesses, such as influenza), we considered the prevalence of COVID-like illnesses in our analyses. We found that SARS-CoV-2 testing of patients with severe COVID-like illness can be cost-effective in all LMICs, though only in some circumstances. High influenza prevalence among suspected COVID cases improves cost-effectiveness, since incorrectly provided corticosteroids may worsen influenza outcomes. In low- and some lower-middle-income countries, only patients with a high index of suspicion for COVID should be tested with RDTs, while other patients should be presumed to not have COVID. In some lower-middle-income and upper-middle-income countries, suspected severe COVID cases should almost always be tested. Further, in these settings, negative test results in patients with a high initial index of suspicion should be confirmed through PCR and, during influenza outbreaks, positive results in patients with a low initial index of suspicion should also be confirmed with a PCR. The use of interleukin-6 receptor blockers, when supported by testing, may also be cost-effective in higher-income LMICs. The cost at which they would be cost-effective in low-income countries ($162 to $406 per treatment course) is below current prices. The primary limitation of our analysis is substantial uncertainty around some of the parameters in our model due to limited data, most notably on current COVID mortality with standard of care, and insufficient evidence on the impact of corticosteroids on patients with severe influenza. COVID testing can be cost-effective to inform treatment of LMIC patients with severe COVID-like disease. The optimal algorithm is driven by country income level and health budgets, the level of suspicion that the patient may have COVID, and influenza prevalence. Further research to better characterize the unintended effects of corticosteroids, particularly on influenza cases, could improve decision making around the treatment of those with COVID-like symptoms in LMICs.