Miracle-dz/UniFAR-FTUs huggingface dataset:
The Normal Accommodative Convergence/Accommodation (AC/A) Ratio

ABSTRACT Background and Purpose: To measure the Accommodative Convergence (PD)/Accommodation ratio (D) (AC/A) in a cohort of visually normal participants using common clinical methods. Patients and Methods: AC/A ratios of 50 visually normal subjects were measured using the distance gradient (DG), near gradient (NG), gradient using synoptophore, (SG) and heterophoria (H) methods in line with current clinical practice. Results: Median AC/A ratios for NG, DG, SG, and H were 2.0 (IQR 2.0), 1.0 (IQR 0.6), 1.0 (IQR 0.6), and 5.0 (IQR 1.7), respectively. There was a statistically significant difference in ratios calculated between all methods in the same subjects (p < 0.05). There were differences in DG vs NG, DG vs H, SG vs H, and NG vs H (p < 0.05); only DG vs SG did not differ significantly (p > 0.05). Lens power toleration was found to affect AC/A ratio in DG (p < 0.05) and latent deviation was significantly associated with (p < 0.05) AC/A ratio in NG. Conclusion: Calculated AC/A ratios in this cohort were lower than historically cited normal (3–5:1) in all gradient methods. There were differences in AC/A values in the same subjects calculated with different gradient methods, indicating that these methods are not interchangeable and a universal normal range should not be applied for all methods.
A Virtual Reality based Approach for Researching Pedestrian to Vehicle Collisions

This paper presents a novel approach for the research on pedestrian to vehicle collisions in a realistic virtual environment. The reactions and crossing behavior of test subjects were examined using a pedestrian simulator, consisting of a full body wireless motion capture system and virtual reality glasses in which a virtual traffic environment is displayed. The crossing behavior of 23 participants was investigated in 9 different virtual traffic scenarios in which the velocities of the approaching vehicles, driving direction and traffic density is varied. In case of a virtual collision the body posture and impact position at the vehicle front was captured. Based on the variety of captured body postures at time of collision, typical pedestrian accident postures can be determined. The data was used as input to investigate the influence of the body posture on kinematics of the pedestrian during a car-pedestrian-impact using the Finite-Element Method (FEM). Therefore, the current standard SAE pedestrian test posture was compared to a non-standard posture which was recorded using the pedestrian simulator. In summary, the virtual reality based approach can be used for reproducible research on pedestrian to vehicle collisions, precollision pedestrian behavior and reactions and the optimization of pedestrian test models.
Development and evaluation of gelatin microspheres of tramadol hydrochloride

To develop formulations for effective management of chronic pain without side effects associated with NSAIDs and COX-2 antagonists, Tramadol hydrochloride, an opioid antagonist, was encapsulated within Gelatin microspheres for controlled delivery for longer periods. The microspheres were prepared using a single emulsion technique and were investigated. Tramadol hydrochloride could be encapsulated into Gelatin microspheres with an entrapment efÞ ciency of 97.2%. Spherical, transparent, and free-s owing microspheres were obtained. Scanning electron microscopy revealed the spherical structures under the magniÞ cation of 200 µm. The FTIR and DSC analysis indicated the stability and compatibility of the drug in gelatin microspheres. The microspheres were in the suitable particle size range of 20 to 160 µm. The drug was released continuously for a period of 12 hrs with a maximum release of 99.79%. Key words: Gelatin, microspheres, tramadol HCL, sesame oil.
Sample-Efficient Learning of Nonprehensile Manipulation Policies via Physics-Based Informed State Distributions

This paper proposes a sample-efficient yet simple approach to learning closed-loop policies for nonprehensile manipulation. Although reinforcement learning (RL) can learn closed-loop policies without requiring access to underlying physics models, it suffers from poor sample complexity on challenging tasks. To overcome this problem, we leverage rearrangement planning to provide an informative physics-based prior on the environment's optimal state-visitation distribution. Specifically, we present a new technique, Learning with Planned Episodic Resets (LeaPER), that resets the environment's state to one informed by the prior during the learning phase. We experimentally show that LeaPER significantly outperforms traditional RL approaches by a factor of up to 5X on simulated rearrangement. Further, we relax dynamics from quasi-static to welded contacts to illustrate that LeaPER is robust to the use of simpler physics models. Finally, LeaPER's closed-loop policies significantly improve task success rates relative to both open-loop controls with a planned path or simple feedback controllers that track open-loop trajectories. We demonstrate the performance and behavior of LeaPER on a physical 7-DOF manipulator in this https URL.
A Systematic Mapping of Software Engineering Approaches to Develop Big Data Systems

[Context] Data is being collected at an unprecedented scale. Data sets are becoming so large and complex that traditionally engineered systems may be inadequate to deal with them. While software engineering comprises a large set of approaches to support engineering robust software systems, there is no comprehensive overview of approaches that have been proposed and/or applied in the context of engineering big data systems. [Goal] This study aims at surveying existing research on big data software engineering to unveil and characterize the development approaches and major contributions. [Method] We conducted a systematic mapping study, identifying 52 related research papers, dated from 2011 to 2016. We classified and analyzed the identified approaches, their objectives, application domains, development lifecycle phase, and type of contribution. [Results] As a result, we outline the current state of the art and gaps on employing software engineering approaches to develop big data systems. For instance, we observed that the major challenges are in the area of software architecture and that more experimentation is needed to assess the classified approaches. [Conclusion] The results of this systematic mapping provide an overview on existing approaches to support building big data systems and helps to steer future research based on the identified gaps.
The Duke treadmill score with bicycle ergometer: Exercise capacity is the most important predictor of cardiovascular mortality

Background The Duke treadmill score, a widely used treadmill testing tool, is a weighted index combining exercise time or capacity, maximum ST-segment deviation and exercise-induced angina. No previous studies have investigated whether the Duke treadmill score and its individual components based on bicycle exercise testing predict cardiovascular death. Design Two populations with a standard bicycle testing were used: 3936 patients referred for exercise testing (2371 men, age 56 ± 13 years) from the Finnish Cardiovascular Study (FINCAVAS) and a population-based sample of 2683 men (age 53 ± 5.1 years) from the Kuopio Ischaemic Heart Disease study (KIHD). Methods Cox regression was applied for risk prediction with cardiovascular mortality as the primary endpoint. Results In FINCAVAS, during a median 6.3-year (interquartile range (IQR) 4.5–8.2) follow-up period, 180 patients (4.6%) experienced cardiovascular mortality. In KIHD, 562 patients (21.0%) died from cardiovascular causes during the median follow-up of 24.1 (IQR 18.0–26.2) years. The Duke treadmill score was associated with cardiovascular mortality in both populations (FINCAVAS, adjusted hazard ratio (HR) 3.15 for highest vs. lowest Duke treadmill score tertile, 95% confidence interval (CI) 1.83–5.42, P < 0.001; KIHD, adjusted HR 1.71, 95% CI 1.34–2.18, P < 0.001). However, after progressive adjustment for the Duke treadmill score components, the score was not associated with cardiovascular mortality in either study population, as exercise capacity in metabolic equivalents of task was the dominant harbinger of poor prognosis. Conclusions The Duke treadmill score is associated with cardiovascular mortality among patients who have undergone bicycle exercise testing, but metabolic equivalents of task, a component of the Duke treadmill score, proved to be a superior predictor.
Localization of Secretory Mucins MUC5AC and MUC5B in Normal/Healthy Human Airways

Rationale: MUC5AC and MUC5B are the predominant gel‐forming mucins in the mucus layer of human airways. Each mucin has distinct functions and site‐specific expression. However, the regional distribution of expression and cell types that secrete each mucin in normal/healthy human airways are not fully understood. Objectives: To characterize the regional distribution of MUC5B and MUC5AC in normal/healthy human airways and assess which cell types produce these mucins, referenced to the club cell secretory protein (CCSP). Methods: Multiple airway regions from 16 nonsmoker lungs without a history of lung disease were studied. MUC5AC, MUC5B, and CCSP expression/colocalization were assessed by RNA in situ hybridization and immunohistochemistry in five lungs with histologically healthy airways. Droplet digital PCR and cell cultures were performed for absolute quantification of MUC5AC/5B ratios and protein secretion, respectively. Measurements and Main Results: Submucosal glands expressed MUC5B, but not MUC5AC. However, MUC5B was also extensively expressed in superficial epithelia throughout the airways except for the terminal bronchioles. Morphometric calculations revealed that the distal airway superficial epithelium was the predominant site for MUC5B expression, whereas MUC5AC expression was concentrated in proximal, cartilaginous airways. RNA in situ hybridization revealed MUC5AC and MUC5B were colocalized with CCSP‐positive secretory cells in proximal superficial epithelia, whereas MUC5B and CCSP‐copositive cells dominated distal regions. Conclusions: In normal/healthy human airways, MUC5B is the dominant secretory mucin in the superficial epithelium and glands, with distal airways being a major site of expression. MUC5B and MUC5AC expression is a property of CCSP‐positive secretory cells in superficial airway epithelia.
Enhancing the representation of functional connectivity networks by fusing multi‐view information for autism spectrum disorder diagnosis

Functional connectivity network provides novel insights on how distributed brain regions are functionally integrated, and its deviations from healthy brain have recently been employed to identify biomarkers for neuropsychiatric disorders. However, most of brain network analysis methods utilized features extracted only from one functional connectivity network for brain disease detection and cannot provide a comprehensive representation on the subtle disruptions of brain functional organization induced by neuropsychiatric disorders. Inspired by the principles of multi‐view learning which utilizes information from multiple views to enhance object representation, we propose a novel multiple network based framework to enhance the representation of functional connectivity networks by fusing the common and complementary information conveyed in multiple networks. Specifically, four functional connectivity networks corresponding to the four adjacent values of regularization parameter are generated via a sparse regression model with group constraint ( l2,1‐norm), to enhance the common intrinsic topological structure and limit the error rate caused by different views. To obtain a set of more meaningful and discriminative features, we propose using a modified version of weighted clustering coefficients to quantify the subtle differences of each group‐sparse network at local level. We then linearly fuse the selected features from each individual network via a multi‐kernel support vector machine for autism spectrum disorder (ASD) diagnosis. The proposed framework achieves an accuracy of 79.35%, outperforming all the compared single network methods for at least 7% improvement. Moreover, compared with other multiple network methods, our method also achieves the best performance, that is, with at least 11% improvement in accuracy.
The Relationship Between Cognitive Impairment and Upper Extremity Function in Older Primary Care Patients

BACKGROUND Individuals with mild cognitive impairment (MCI), often a precursor to dementia, experience limitations in completing daily activities. These limitations are particularly important to understand, as they predict risk for dementia. Relations between functional changes and both cognitive decline and upper extremity motor impairments have been reported, but the contribution of motor function to relations between cognitive function and functional independence remains poorly understood. We examined the relationship between cognition and upper extremity activities, and whether this relation was mediated by motor function. METHODS A total of 430 community-dwelling primary care patients aged at least 65 years from the Boston Rehabilitative Impairment Study of the Elderly completed self-report measures of upper extremity function, tests of neuromuscular attributes to measure motor function (reaction time, pronosupination of the hands), and neuropsychological measures. Participants were classified based on cognitive performance into groups: MCI and without MCI, with MCI further classified by cognitive subtype. Regression and mediation analyses examined group differences and relations between cognitive function, upper extremity function, and neuromuscular attributes. RESULTS MCI participants demonstrated poorer neuromuscular attributes and self-reported upper extremity function, and neuromuscular attributes significantly mediated positive relations between cognitive status and self-reported upper extremity function. Poorer self-reported upper extremity function was most prominent for groups with executive dysfunction. CONCLUSIONS Together with previous research, results suggest that the relationship between cognitive function, motor function, and functional activities is not confined to mobility tasks but universally related to body systems and functional activities. These findings inform new approaches for dementia risk screening and rehabilitative care.
The Association between Red Blood Cell Distribution Width and Mortality in Critically Ill Patients with Acute Kidney Injury

Background Several investigators have sought risk factors for mortality in acute kidney injury (AKI). However, no epidemiological studies have investigated the impact of red blood cell distribution width (RDW) on prognosis for critically ill patients with AKI. The aim of this study was to investigate the association of RDW with mortality in these patients. Methods We analyzed data from the MIMIC-III. RDW was measured upon ICU admission. The association between RDW and mortality of AKI was determined using a multivariate logistic regression and was expressed as the adjusted odds ratio with associated 95% confidence interval (CI). We also conducted subgroup analyses to determine the consistency of this association. Results A total of 14,078 critically ill patients with AKI were eligible for this analysis. In multivariate analysis, adjusted for age and gender and compared with the reference group (RDW 11.1-13.4%) related to hospital mortality, the adjusted ORs (95% CIs) for RDW levels 13.5-14.3%, 14.4-15.6%, and 15.7-21.2% were 1.22 (1.05, 1.43), 1.56 (1.35, 1.81), and 2.66 (2.31, 3.06), respectively. After adjusting for confounding factors, with high RDW linked to an increase in mortality (RDW 15.7-21.2% versus 11.1-13.4%: OR, 1.57; 95% CI, 1.22 to 2.01; P trend <0.0001). A similar trend was observed for 30-day mortality. Conclusions RDW appeared to be an independent prognostic marker in critically ill patients with AKI and higher RDW was associated with increased risk of mortality in these patients.
A test of the epidemiological paradox in a context of forced migration: low birthweight among Syrian newborns in Lebanon.

BACKGROUND Studies on immigrants revealed an epidemiological paradox whereby low-socioeconomic status (SES) immigrant mothers exhibit favourable birth outcomes compared with native-born mothers. We tested the epidemiological paradox in a context of forced migration, comparing associations of low birthweight (LBW) and maternal SES between Syrian and Lebanese newborns in Lebanon. METHODS We used data from the National Collaborative Perinatal Neonatal Network (NCPNN) of 31 Lebanese hospitals, including 45 442 Lebanese and 4910 Syrian neonates born 2011-13. We assessed associations between LBW and maternal SES for both groups. Logistic regression models examined interactions between maternal origin and SES. RESULTS Syrian births increased exponentially between 2011 and 2013, along with the group's forced migration into Lebanon. Although Syrian mothers are more socioeconomically disadvantaged compared with Lebanese mothers, Syrian LBW (6.2%) was only marginally higher than Lebanese LBW (5.6%; P = 0.059). Only 20-24-years-old Syrian women [odds ratio (OR) = 2.02 (0.98-4.16)] and those with ≥ university education [OR = 1.70 (1.22-2.36)] exhibited higher odds of delivering an LBW baby compared with Lebanese women of the same age and education. CONCLUSIONS The findings do not provide strong evidence for the epidemiological paradox in a forced migration context. However, the relatively advantageous LBW profile among Syrian neonates, despite their mothers' low SES and exposure to acute and chronic psychological stress, points to protective mechanisms. One of these mechanisms may be a collective response by the displaced population to improve neonatal outcomes as a way of recovering from loss and death.
Terahertz Imaging of Thin Film Layers with Matched Field Processing

Terahertz (THz) time of flight (TOF) tomography systems offer a new measurement modality for non-destructive evaluation (NDE) of the subsurface layers of protective coatings and/or laminated composite materials for industrial, security and biomedical applications. However, for thin film samples, the time-of-flight within a layer is less than the duration of the THz pulse and consequently there is insufficient range resolution for NDE of the sample under test. In this paper, matched field processing (MFP) techniques are applied to thickness estimation in THz TOF tomography applications, and these methods are demonstrated by using measured THz spectra to estimate the the thicknesses of a thin air gap and its depth below the surface. MFP methods have been developed over several decades in the underwater acoustics community for model-based inversion of geo-acoustic parameters. It is expected that this research will provide an important link for THz researchers to access and apply the robust methods available in the MFP literature.
Object Modeling from 3D Point Cloud Data for Self-Driving Vehicles

For autonomous vehicles to be deployed and used practically, many problems are still needed to be solved. One of them we are interested in is to make use of a cheap LIDAR for robust object modelling with 3D point cloud data. Self-driving vehicles require accurate information about the surrounding environments to decide the next course of actions. 3D point cloud data obtained from LIDAR give more accurate distance than the counterpart stereo images. As LIDAR generates lowresolution data, the object detection and modeling is prone to produce errors. In this work, we propose the use of multiple frames of LIDAR data in an urban environment to construct a comprehensive model of the object. We assume the use of LIDAR on a moving platform and the results are almost equal to the 3D CAD model representation of the object.
AI to enhance interactive simulation-based training in resuscitation medicine

When patients become acutely unwell, the ability of frontline healthcare professionals to act quickly and effectively can mean the difference between life and death. High-fidelity simulation is the gold standard by which medics acquire and maintain key resuscitation skills, but the resource-intensive nature of current, face-to-face training limits access to training and allows “skills fade” to creep in. We propose that human computer interaction-based simulations augmented by artificial intelligence could provide a cost-effective alternative to traditional training and allow clinicians much greater access to training. This paper is mostly an in-depth discussion; however, we also present a 3D simulator for resuscitation skills training which we developed using the Unity games physics engine.
Spatio-Temporal Depth Interpolation (STDI)

In the area of autonomous driving, sensing the environment is most important for self-localization and egomotion estimation. Visual odometry/SLAM methods have proven capable to achieve good results, even in real-time applications by operating in a sparse mode. Running on a sequence, these methods need to continuously incorporate new features well distributed over the image. Therefore, the performance of these methods can be further improved, if they are supplied with coarse but dense initial depth information, that can be utilized at arbitrary sparse image positions. Previously triangulated depths and even high quality depth measurements of a LIDAR sensor are not suitable for this task, since they only provide a sparse depth map. To solve this issue, we introduce a novel interpolation method called Spatio-Temporal Depth Interpolation (STDI), which exploits spatial and temporal correlations of the data (e.g. sequences of sparse depth maps) to give a consistent dense output including associated uncertainties. STDI is a fused approach, which makes use of the most important components of a principal component analysis (PCA) (spatial information) and additionally is capable to re-use information of previously interpolated depth maps in a regression based approach (temporal information). We evaluate the quality of STDI on the KITTI visual odometry benchmark, where a sequence of extremely sparsely sampled depth maps $(\approx 40$ depth values) is densified and on the KITTI depth completion benchmark. The latter deals with the densification of sparse LIDAR input. Of course, our method is not limited to these applications and can be used for any densification of sparse sequential data which is expected to contain spatial and/or temporal correlations (e.g. initialization for dense optical flow methods based on a sparse measurement).