Miracle-dz/UniFAR-FTUs huggingface dataset:
Force Field Parametrization of Metal Ions from Statistical Learning Techniques

A novel statistical procedure has been developed to optimize the parameters of nonbonded force fields of metal ions in soft matter. The criterion for the optimization is the minimization of the deviations from ab initio forces and energies calculated for model systems. The method exploits the combination of the linear ridge regression and the cross-validation techniques with the differential evolution algorithm. Wide freedom in the choice of the functional form of the force fields is allowed since both linear and nonlinear parameters can be optimized. In order to maximize the information content of the data employed in the fitting procedure, the composition of the training set is entrusted to a combinatorial optimization algorithm which maximizes the dissimilarity of the included instances. The methodology has been validated using the force field parametrization of five metal ions (Zn2+, Ni2+, Mg2+, Ca2+, and Na+) in water as test cases.
“Drugs don’t have age limits”: The challenge of setting age restrictions for supervised injection facilities

Abstract Aims: People under age 18 who inject drugs represent a population at risk of health and social harms. Age restrictions at harm reduction programmes often formally exclude this population, but the reason behind such restrictions is lacking in the literature. To help fill this gap, we examine the perspectives of people who use drugs and various other stakeholders regarding whether supervised injection facilities (SIFs) should have age restrictions. Methods: Interviews and focus groups were conducted with a total of 95 people who use drugs and 141 other stakeholders (including police, fire and emergency services personnel, other city employees and officials, healthcare providers, residents and business representatives) in two Canadian cities without SIFs. Findings: We highlight the following thematic areas: mixed opinions regarding specific age restrictions; safety as a priority; different experiences and understandings of youth, agency and drug use; and ideas regarding maturity, “help” and other approaches. We note throughout that a familiar vulnerability–agency dichotomy often surfaced in the discussions. Conclusions: This paper contributes new empirical insights regarding youth access to SIFs. We offer considerations that may inform discussions occurring in other jurisdictions debating SIF implementation and may help remove or clarify age-related policies for harm reduction programmes.
Fault-tolerant tracking of multiple targets in collaborative Camera Networks

Emerging Camera Sensor Networks (CSN) leverage the collaboration, processing and communication capabilities of modern cameras to handle a wide variety of security and safety-critical tasks, including target tracking. However, the performance of CSN in terms of tracking accuracy can be severely degraded when faults occur. Faults may be caused by unpredictable software errors (e.g., in the image processing, feature extraction and data association modules), hardware malfunctions (e.g., in the camera mechanical parts or lens), or as the result of a malicious attack. We propose a decentralized CSN-based system for tracking multiple targets that mitigates the effect of faulty cameras. According to our findings, the proposed solution can well handle faulty camera observations and is able to reliably track a number of targets that may change over time.
On attitude observers and inertial navigation for reference system fault detection and isolation in dynamic positioning

Marine craft employing a dynamic positioning system rely on measurements from position reference systems and gyrocompasses, and faults in these systems and sensors pose a serious risk of vessel drive-offs, that may in turn have dire consequences. An inertial measurement unit may provide independent position and orientation information, provided that the attitude estimates of the observer in use are accurate enough. In this paper we compare three nonlinear strapdown inertial-measurement-unit-based attitude estimators aided by global navigation satellite systems, and put them to test in a simulation of two fault detection and isolation scenarios. The comparison finds that all attitude estimators under test have the potential for use in a fault detection and isolation scheme.
Novel Foot Progression Angle Algorithm Estimation via Foot-Worn, Magneto-Inertial Sensing

Objective: The foot progression angle (FPA) is an important clinical measurement but currently can only be computed while walking in a laboratory with a marker-based motion capture system. This paper proposes a novel FPA estimation algorithm based on a single integrated sensor unit, consisting of an accelerometer, gyroscope, and magnetometer, worn on the foot. Methods: The algorithm introduces a real-time heading vector with a complementary filter and utilizes a gradient descent method and zero-velocity update correction. Validation testing was performed by comparing FPA estimation from the wearable sensor with the standard FPAs computed from a marker-based motion capture system. Subjects performed nine walking trials of 2.5 min each on a treadmill. During each trial, subjects walked at one speed out of three options (1.0, 1.2, and 1.4 m/s) and walked with one gait pattern out of three options (normal, toe-in, and toe-out). Results: The algorithm estimated FPA to within 0.2° of error or less for each walking conditions. Conclusion: A novel FPA algorithm has been introduced and described based on a single foot-worn sensor unit, and validation testing showed that FPA estimation was accurate for different walking speeds and foot angles. Significance: This study enables future wearable systems gait research to assess or train walking patterns outside a laboratory setting in natural walking environments.
NATURAL DISASTERS. Mud tsunami wreaks ecological havoc in Brazil.

On 5 November, a dam holding approximately 50 million cubic meters of waste from iron mines burst withoutwarning near the city of Mariana in Minas Gerais state. The disaster spawned a mud tsunami that buried villages downstream, killing at least 13 people. Within a day, the wave reached the Doce River and traversed 600 kilometers—snuffing out aquatic life in its path—before spilling into the Atlantic Ocean on 21 November. A network of scientists, backed by crowdfunding, is analyzing river samples to assess the environmental impacts independently of the government and the mining companies that managed the dam. One controversy they hope to settle is whether the sludge was laced with two dozen heavy metals, including arsenic, copper, and mercury, and whether such toxic contamination will persist, delaying recovery.
Is the Sequence of SuperAlarm Triggers More Predictive Than Sequence of the Currently Utilized Patient Monitor Alarms?

<italic>Objective:</italic> Our previous studies have shown that “code blue” events can be predicted by SuperAlarm patterns that are multivariate combinations of monitor alarms and laboratory test results cooccurring frequently preceding the events but rarely among control patients. Deploying these patterns to the monitor data streams can generate SuperAlarm sequences. The objective of this study is to test the hypothesis that SuperAlarm sequences may contain more predictive sequential patterns than monitor alarms sequences. <italic>Methods:</italic> Monitor alarms and laboratory test results are extracted from a total of 254 adult coded and 2213 control patients. The training dataset is composed of subsequences that are sampled from complete sequences and then further represented as fixed-dimensional vectors by the term frequency inverse document frequency method. The information gain technique and weighted support vector machine are adopted to select the most relevant features and train a classifier to differentiate sequences between coded patients and control patients. Performances are assessed based on an independent dataset using three metrics: sensitivity of lead time (Sen<inline-formula><tex-math notation="LaTeX">$^L$</tex-math> </inline-formula>@T), alarm frequency reduction rate (AFRR), and work-up to detection ratio (WDR). <italic>Results: </italic> The performance of 12-h-long sequences of SuperAlarm can yield a Sen<inline-formula> <tex-math notation="LaTeX">$^L$</tex-math></inline-formula>@2 of 93.33%, an AFRR of 87.28%, and a WDR of 3.01. At an AFRR = 87.28%, Sen<inline-formula><tex-math notation="LaTeX">$^L$</tex-math></inline-formula>@2 for raw alarm sequences and discretized alarm sequences are 73.33% and 70.19%, respectively. At a WDR = 3.01, Sen<inline-formula><tex-math notation="LaTeX">$^L$</tex-math></inline-formula>@2 are 49.88% and 43.33%. <italic>Conclusion and Significance:</italic> The results demonstrate that SuperAlarm sequences indeed outperform monitor alarm sequences and suggest that one can focus on sequential patterns from SuperAlarm sequences to develop more precise patient monitoring solutions.
Association of Sociodemographic Factors With Depression in Women of Reproductive Age

Introduction. Women suffer disproportionately from depression, which has a high prevalence in developing countries. Sociodemographic factors are associated with depression, some of which may be modifiable and lead to better outcomes for depression among women. Objectives. To determine sociodemographic factors associated with depression among women of reproductive age in Karachi. Methods. A case control study was conducted on 376 women in 2 hospitals in Karachi. Patients were diagnosed by psychiatrists and family physicians by Diagnostic and Statistical Manual of Mental Disorders (4th ed.) criteria and SRQ20. Results. The odds for women with depression being married for >5 years, being abused by in-laws, spending ≤3 hours per day with their spouses, and being just satisfied or not satisfied with their married life was more than that for controls. Conclusion. The authors’ findings highlight the important modifiable risk factors for depression, which if addressed will help decrease the burden of depression among women.
Positron Emission Tomography Studies of Cerebral Blood Flow and Glucose Metabolism

Positron emission tomography (PET) may be used to map cerebral blood flow, using 150-labelled water, and regional cerebral glucose metabolism using I8Fdeoxyglucose ('*FDG). PET is costly and scarce, but produces quantitative data with superior spatial resolution to single photon emission computerized tomography (SPECT). The radiation exposure from an "FDG PET scan is approximately 5 mSv, which is similar to that received from a series of I5O-H,O scans to determine areas of brain involved in a cerebral task. These exposures limit the comparative studies that may be carried out on patients and normal volunteers. MRI has advanced considerably over the last decade; this has had two major consequences for the application of PET techniques to epilepsy. First, the role of PET in the clinical investigation of patients needs to be reevaluated, because contemporary MRI may result in PET data being superfluous. In the second case, PET data should always be interpreted in the light of high quality anatomical MRI, providing a structural-functional correlation; the latter has been aided by the development of computer programs to coregister MRI and PET datasets on a pixel-by-pixel basis (1).
Implementation of a Registry for Acute Coronary Syndrome in Resource-Limited Settings: Barriers and Opportunities

Cardiovascular disease (CVD) is the leading cause of death in Egypt and worldwide, placing great strain on the world’s health systems. High-quality treatment of CVD requires a valid, reliable measurement for ensuring evidence-based care. Clinical outcomes registries have been used to support quality improvement activities in some countries, but there are few examples of their implementation in resource-limited settings. A registry for acute coronary syndrome was piloted in 5 hospitals in Egypt, and observations regarding barriers and enabling factors related to implementation are summarized. Themes that emerged from daily observations include the importance of rapid cycles of change, the need to build a culture of applied research, the importance of modeling a blame-free culture, and key constraints encountered related to human resources and technical infrastructure. This pilot demonstrates that clinical registries may be a cost-effective investment in data infrastructure to support quality improvement in low- and middle-income countries.
Stigma and the Term Chronic Fatigue Syndrome

Considerable discussion has occurred concerning the diagnostic label chronic fatigue syndrome (CFS). Many patient groups argue that using this term trivializes the seriousness of the syndrome. A federal government organization, the Chronic Fatigue Syndrome Coordinating Committee, appointed a Name Change Workgroup to attempt to develop different terminology for this disorder. This group distributed a poll to patients and health-care providers to obtain information about its recommendation to use an umbrella term, chronic neuroendocrineimmune dysfunction syndrome, and subtypes under it. In addition, the CFIDS Association, a national self-help advocacy organization, distributed a questionnaire to a group of health-care providers concerning these recommendations. The findings of both polls are presented. Overall findings indicated that the majority of patients want the name changed and that the term chronic neuroendocrineimmune dysfunction syndrome was acceptable to the majority of this group.
Analyzing Seismocardiogram Cycles to Identify the Respiratory Phases

Goal: the objective of this study was to develop a method to identify respiratory phases (i.e., inhale or exhale) of seismocardiogram (SCG) cycles. An SCG signal is obtained by placing an accelerometer on the sternum to capture cardiac vibrations. Methods: SCGs from 19 healthy subjects were collected, preprocessed, segmented, and labeled. To extract the most important features, each SCG cycle was divided to equal-sized bins in time and frequency domains, and the average value of each bin was defined as a feature. Support vector machines was employed for feature selection and identification. The features were selected based on the total accuracy. The identification was performed in two scenarios: leave-one-subject-out (LOSO), and subject-specific (SS). Results: time-domain features resulted in better performance. The time-domain features that had higher accuracies included the characteristic points correlated with aortic-valve opening, aortic-valve closure, and the length of cardiac cycle. The average total identification accuracies were 88.1% and 95.4% for LOSO and SS scenarios, respectively. Conclusion: the proposed method was an efficient, reliable, and accurate approach to identify the respiratory phases of SCG cycles. Significance: The results obtained from this study can be employed to enhance the extraction of clinically valuable information such as systolic time intervals.
Automated Planning With Multivariate Shape Descriptors for Fibular Transfer in Mandibular Reconstruction

Objective: This paper introduces methods to automate preoperative planning of fibular segmentation and placement for mandibular reconstruction with fibular flaps. Methods: Preoperative virtual planning for this type of surgery has been performed by manual adjustment of many parameters, or based upon a single feature of the reconstruction. We propose a novel planning procedure formulated as a nonconvex minimization problem of an objective function using the multilateral shape descriptors. Results: A retrospective study was designed and 120 reconstruction plans were reproduced using computed tomography images with oral surgeons. The proposed automated planning model was quantitatively compared with both the existing model and the surgeons’ plans. Conclusion: The results show that the developed framework attains stable automated planning that agrees with the surgeons’ decisions. Significance: This method addresses tradeoff problems between symmetric reconstruction and restoration of the native contour of the mandible.
Strategies to Strengthen Public Health Inputs to Water Policy in Response to Climate Change: An Australian Perspective

Under current climate change projections, the capacity to provide safe drinking water to Australian communities will be challenged. Part of this challenge is the lack of an adaptive governance strategy that transcends jurisdictional boundaries to support integrated policy making, regulation, or infrastructural adaptation. Consequently, some water-related health hazards may not be adequately captured or forecast under existing water resource management policies to ensure safe water supplies. Given the high degree of spatial and temporal variability in climate conditions experienced by Australian communities, new strategies for national health planning and prioritization for safe water supplies are warranted. The challenges facing public health in Australia will be to develop flexible and robust governance strategies that strengthen public health input to existing water policy, regulation, and surveillance infrastructure through proactive risk planning, adopting new technologies, and intersectoral collaborations. The proposed approach could assist policy makers avert or minimize risk to communities arising from changes in climate and water provisions both in Australia and in the wider Asia Pacific region.
Forecasting Hepatocellular Carcinoma Mortality in Taiwan Using an Age-Period-Cohort Model

Hepatocellular carcinoma (HCC) is a common primary malignancy of the liver. Estimates of the future HCC burden are important for health planning. The authors applied an age-period-cohort (APC) model for a 15-year forecast of HCC mortality in Taiwan (based on mortality data between the years 1976 and 2005). It is found that for men, the mortality trend reverses itself in the period 2011-2015, from an increasing one to a slightly deceasing one. For women, the increasing trend reverses itself in the period 2001-2005. The age-adjusted HCC mortality for men and women in the most recent available years, 2006-2008, both fall in the range forecast by the APC model. Forecasting using the APC model can provide an advanced warning of trend reversals.
Modeling business processes as part of the BOOSTER approach to business object-oriented system development based on UML

In order to be able to use the standardized multipurpose modeling language UML (Unified Modeling Language) for the integrated development of business and software systems based on OMG's business object technology, a specialized process model is needed to ensure purposeful and effective development activities. BOOSTER*Process, which is part of a project called BOOSTER (Business Object-Oriented Software Technology for Enterprise Reengineering), describes a multi-level software development process model which meets these requirements and especially emphasizes reuse aspects resulting from the component character of business objects. This paper presents the basic architecture of BOOSTER*Process, consisting of four main engineering levels and, in addition, picks out the business process modeling activities at the business engineering level as an example. It is shown how UML activity diagrams can be applied in this context, replacing independent process modeling notations.
EEG-Based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention

Objective: To build group-level classification models capable of recognizing affective states and mental workload of individuals with autism spectrum disorder (ASD) during driving skill training. Methods: Twenty adolescents with ASD participated in a six-session virtual reality driving simulator-based experiment, during which their electroencephalogram (EEG) data were recorded alongside driving events and a therapist's rating of their affective states and mental workload. Five feature generation approaches including statistical features, fractal dimension features, higher order crossings (HOC)-based features, power features from frequency bands, and power features from bins ($\Delta \boldsymbol{f} = 2\;{\rm{Hz}}$ ) were applied to extract relevant features. Individual differences were removed with a two-step feature calibration method. Finally, binary classification results based on the k-nearest neighbors algorithm and univariate feature selection method were evaluated by leave-one-subject-out nested cross-validation to compare feature types and identify discriminative features. Results: The best classification results were achieved using power features from bins for engagement (0.95) and boredom (0.78), and HOC-based features for enjoyment (0.90), frustration (0.88), and workload (0.86). Conclusion: Offline EEG-based group-level classification models are feasible for recognizing binary low and high intensity of affect and workload of individuals with ASD in the context of driving. However, while promising the applicability of the models in an online adaptive driving task requires further development. Significance: The developed models provide a basis for an EEG-based passive brain computer interface system that has the potential to benefit individuals with ASD with an affect- and workload-based individualized driving skill training intervention.
Towards Improved Design and Evaluation of Epileptic Seizure Predictors

Objective: Key issues in the epilepsy seizure prediction research are (1) the reproducibility of results (2) the inability to compare multiple approaches directly. To overcome these problems, the seizure prediction challenge was organized on Kaggle.com. It aimed at establishing benchmarks on a dataset with predefined train, validation, and test sets. Our main objective is to analyze the competition format, and to propose improvements, which would facilitate a better comparison of algorithms. The second objective is to present a novel deep learning approach to seizure prediction and compare it to other commonly used methods using patient centered metrics. Methods: We used the competition's datasets to illustrate the effects of data contamination. Having better data partitions, we compared three types of models in terms of different objectives. Results: We found that correct selection of test samples is crucial when evaluating the performance of seizure forecasting models. Moreover, we showed that models, which achieve state-of-the-art performance with respect to commonly used AUC, sensitivity, and specificity metrics, may not yet be suitable for practical usage because of low precision scores. Conclusion: Correlation between validation and test datasets used in the competition limited its scientific value. Significance: Our findings provide guidelines which allow for a more objective evaluation of seizure prediction models.
Relação entre a carga viral de HPV oncogênico determinada pelo método de captura híbrida e o diagnóstico citológico de lesões de alto grau

INTRODUCTION: Infection with oncogenic human papilloma virus (HPV) has been established as the main etiologic agent for cervical cancer and of cervical intraepithelial neoplasia (CIN). OBJECTIVE: To determine the association between viral loads of the high risk HPV using the hybrid capture II (HC II) system and CIN lesion stage. MATERIAL AND METHOD: A total of 982 women with diagnosis of negative or of CIN I-III with Pap or liquid-based cytology (DNA-Citoliq-Digene) were included. HC II testing was used to detected HPV DNA. Viral load was measured expressed as relative lights unit (RLU) ratio. RLU ratios were categorized for analysis into three groups: negative ( 100 pg/ml). RESULTS: A total of 152 (72.4%) of 210 (21.4%) patients with CIN II, 53 (61.6%) of 86 (43.6%) with CIN II-III were positive for HPV DNA, the higher the viral load (> 100 pg/ml) were observed in 101 (66.4%) and 53 (61.6%) patients with CIN I or CIN II-III, respectively. DISCUSSION AND CONCLUSIONS: Our results suggest a clear association between viral load HPV DNA determined by HC II (version 1) assay and CIN stage. We concluded that the high viral load has a clinical importance for the management of patients with CIN.