bluuebunny/crossref_metadata_2025 huggingface dataset:
Anomaly Detection for Individual Sequences with Applications in Identifying Malicious Tools
Authors: Siboni Shachar , Cohen Asaf
Published: June 2020

Anomaly detection refers to the problem of identifying abnormal behaviour within a set of measurements. In many cases, one has some statistical model for normal data, and wishes to identify whether new data fit the model or not. However, in others, while there are normal data to learn from, there is no statistical model for this data, and there is no structured parameter set to estimate. Thus, one is forced to assume an individual sequences setup, where there is no given model or any guarantee that such a model exists. In this work, we propose a universal anomaly detection algorithm for one-dimensional time series that is able to learn the normal behaviour of systems and alert for abnormalities, without assuming anything on the normal data, or anything on the anomalies. The suggested method utilizes new information measures that were derived from the Lempel–Ziv (LZ) compression algorithm in order to optimally and efficiently learn the normal behaviour (during learning), and then estimate the likelihood of new data (during operation) and classify it accordingly. We apply the algorithm to key problems in computer security, as well as a benchmark anomaly detection data set, all using simple, single-feature time-indexed data. The first is detecting Botnets Command and Control (C&C) channels without deep inspection. We then apply it to the problems of malicious tools detection via system calls monitoring and data leakage identification.We conclude with the New York City (NYC) taxi data. Finally, while using information theoretic tools, we show that an attacker’s attempt to maliciously fool the detection system by trying to generate normal data is bound to fail, either due to a high probability of error or because of the need for huge amounts of resources.
On Leader-Following Consensus in Multi-Agent Systems with Discrete Updates at Random Times
Authors: Almeida Ricardo , Girejko Ewa , Hristova Snezhana , Malinowska Agnieszka
Published: June 2020

This paper studies the leader-following consensus problem in continuous-time multi-agent networks with communications/updates occurring only at random times. The time between two consecutive controller updates is exponentially distributed. Some sufficient conditions are derived to design the control law that ensures the leader-following consensus is asymptotically reached (in the sense of the expected value of a stochastic process). The numerical examples are worked out to demonstrate the effectiveness of our theoretical results.
CBPR Partnerships and Near-Roadway Pollution: A Promising Strategy to Influence the Translation of Research into Practice
Authors: Sprague Martinez Linda S. , Zamore Wig , Finley Alex , Reisner Ellin , Lowe Lydia , Brugge Doug
Published: June 2020

Community-based participatory research (CBPR) aims to engage those traditionally left out of the research process. Partnering with community stakeholders to design, plan, implement and disseminate research can facilitate translation into practice. Using qualitative research methods, we set out to explore the policy and practice implications of a CBPR partnership focused on reducing exposure to near-roadway pollution. Key Informant interviews (n = 13) were conducted with individuals from various entities (municipal, state and private) for whom partners to the Community Assessment of Freeway Exposure and Health (CAFEH) provided technical assistance between 2013 and 2017. The findings indicate community research partnerships may have the power to inform local planning efforts. Developers and planners who the partnership consulted indicated a greater awareness of the implications of near-roadway exposure. They also described making changes in their practice based on study findings. The CAFEH partnership has demonstrated active attention to translating knowledge can influence local planning and practice, albeit with some challenges.
Natural Disturbance Dynamics Analysis for Ecosystem-Based Management—FORDISMAN
Authors: Jõgiste Kalev , Frelich Lee E. , Vodde Floortje , Kangur Ahto , Metslaid Marek , Stanturf John A.
Published: June 2020

Forest ecosystems are shaped by disturbances and functional features of vegetation recovery after disturbances. There is considerable variation in basic disturbance characteristics, magnitude, severity, and intensity. Disturbance legacies provide possible explanations for ecosystem resilience. The impact (length and strength) of the pool of ecosystem legacies and how they vary at different spatial and temporal scales is a most promising line of further research. Analyses of successional trajectories, ecosystem memory, and novel ecosystems are required to improve modelling in support of forests. There is growing evidence that managing ecosystem legacies can act as a driver in adaptive management to achieve goals in forestry. Managers can adapt to climate change and new conditions through anticipatory or transformational strategies of ecosystem management. The papers presented in this Special Issue covers a wide range of topics, including the impact of herbivores, wind, and anthropogenic factors, on ecosystem resilience.
A Comparative Evaluation of Three Stem Profile Equations for Three Precious Tree Species in Southern China
Authors: Jiang Jun , Li Jie , Pang Lifeng , Ming Angang
Published: June 2020

Accurately describing the stem curve of precious tree species and estimating the quantity of various types of wood and their volume in the tropics can provide technical support for reasonable bucking. This study utilized Erythrophleum fordii, Castanopsis hystrix and Tectona grandis as study objects. Forty replicates of each species were used for a total of 120 individual trees. Their tape equations were constructed using simple tape equations, segmented taper equations and variable form taper equations. Statistical indicators were utilized to determine the best taper equation for the three types of precious tree species. A number of methods were compared and analyzed, including the index of correlation, the residual sum of squares, the mean prediction error, the variance of prediction errors and the root mean square error. Finally, a preliminary quantitative analysis was conducted to determine the trends of these three types of tree species. The result shows that the precision of the three predictions developed for each species is high, and, in particular, the segmented taper equations with optimized algorithms is the best. The tendency of the three species to vary was shown to be the highest for T. grandis in the range of 0.0 to 0.8 for its relative height, followed by E. fordii, while the variation of C. hystrix was the smallest. However, in the range of 0.8 to 1.0 relative height, the variation of Castanopsis hystrix was the largest, and the variation of both E. fordii and T. grandis were almost the same. Therefore, the segmented taper equations with optimization algorithms was recommended to fit the three types of tree species in the tropics. These types of equations can be used to estimate the stumpage and timber quantity and as a guide reasonable bucking for these three species.
The Effect of Eutypella parasitica on the Wood Decay of Three Maple Species
Authors: Brglez Ana , Piškur Barbara , Humar Miha , Gričar Jožica , Ogris Nikica
Published: June 2020

Eutypella parasitica R.W. Davidson & R.C. Lorenz is the causative agent of Eutypella canker of maple, a destructive disease of maples in Europe and North America. The fungus E. parasitica is known to cause wood stain and decay. However, it is not known how effectively it decomposes the wood of the most widespread maple species in Europe. Wood samples of Acer pseudoplatanus L., A. platanoides L., and A. campestre L. were exposed to four isolates of E. parasitica and nine other fungal species for comparison, according to the modified EN 113 standard. After 15 weeks of incubation, mass loss and microscopical analysis of samples showed evidence of colonization and different wood decay potentials among fungal species. A highly significant positive correlation was found between mass loss and moisture content for all fungal species. Similarly, the measured cell wall thickness correlated well with the calculated mass loss of the samples. On average, the fungal species caused the lowest mass loss in A. pseudoplatanus (10.0%) and the highest in A. campestre (12.6%) samples. Among the samples exposed to E. parasitica isolates, the highest mass loss was recorded in A. pseudoplatanus (6.6%). Statistical analysis showed significant differences in mass loss and moisture content between different E. parasitica isolates. Based on the results of staining, we discuss the type of decay caused by E. parasitica. Although E. parasitica isolates caused smaller mass loss of samples compared to other more effective decay species, we should not disregard its capability of degrading maple wood. Because E. parasitica usually infects the lower portion of the trunk, which is the largest and most valuable part of the tree, any damage can cause significant economic and resource loss.
Genetic Diversity of Paeonia rockii (Flare Tree Peony) Germplasm Accessions Revealed by Phenotypic Traits, EST-SSR Markers and Chloroplast DNA Sequences
Authors: Guo Xin , Cheng Fangyun , Zhong Yuan
Published: June 2020

Research Highlights: This study, based on the first collection of cultivated Paeonia rockii (flare tree peony, FTP) germplasm across the main distribution area by our breeding desires, comprehensively evaluates these accessions by using phenotypic traits, expressed sequence tag (EST)-simple sequence repeat (SSR) markers and chloroplast DNA sequences (cpDNA). The results show that these accessions collected selectively by us can represent the genetic background information of FTP as a germplasm of tree crops. Background and Objectives: FTP has high cultural, ornamental and medicinal value traditionally, as well as recently presenting a significance as an emerging edible oil with high α-linolenic acid contents in the seeds. The objectives of this study are to reveal the characteristics of the genetic diversity of FTP, as well as to provide scientific suggestions for the utilization of tree peony breeding and the conservation of germplasm resource. Materials and Methods: Based on the phenotypic traits, EST-SSR markers and chloroplast DNA sequence variation, we studied the diversity of a newly established population of 282 FTP accessions that were collected and propagated by ourselves in our breeding project in recent years. Results: (1) There was an abundant variation in phenotype of the accessions, and the phenotypic variation was evenly distributed within the population, without significant hierarchical structure, (2) the EST-SSR data showed that these 282 accessions had relatively high genetic diversity, in which a total of 185 alleles were detected in 34 pairs of primers. The 282 accessions were divided into three distinct groups, and (3) the chloroplast DNA sequences (cpDNA) data indicated that these accessions had a higher genetic diversity than the population level and a lower genetic diversity than the species level of wild P. rockii, and the existing spatial genetic structure of these accessions can be divided into two branches. Conclusions: From the results of the three analyses, we found that these accessions can fully reflect the genetic background information of FTP germplasm resources, so their protection and utilization will be of great significance for genetic improvement of woody peonies.
Rainfall-Induced Landslide Prediction Using Machine Learning Models: The Case of Ngororero District, Rwanda
Authors: Kuradusenge Martin , Kumaran Santhi , Zennaro Marco
Published: June 2020

Landslides fall under natural, unpredictable and most distractive disasters. Hence, early warning systems of such disasters can alert people and save lives. Some of the recent early warning models make use of Internet of Things to monitor the environmental parameters to predict the disasters. Some other models use machine learning techniques (MLT) to analyse rainfall data along with some internal parameters to predict these hazards. The prediction capability of the existing models and systems are limited in terms of their accuracy. In this research paper, two prediction modelling approaches, namely random forest (RF) and logistic regression (LR), are proposed. These approaches use rainfall datasets as well as various other internal and external parameters for landslide prediction and hence improve the accuracy. Moreover, the prediction performance of these approaches is further improved using antecedent cumulative rainfall data. These models are evaluated using the receiver operating characteristics, area under the curve (ROC-AUC) and false negative rate (FNR) to measure the landslide cases that were not reported. When antecedent rainfall data is included in the prediction, both models (RF and LR) performed better with an AUC of 0.995 and 0.997, respectively. The results proved that there is a good correlation between antecedent precipitation and landslide occurrence rather than between one-day rainfall and landslide occurrence. In terms of incorrect predictions, RF and LR improved FNR to 10.58% and 5.77% respectively. It is also noted that among the various internal factors used for prediction, slope angle has the highest impact than other factors. Comparing both the models, LR model’s performance is better in terms of FNR and it could be preferred for landslide prediction and early warning. LR model’s incorrect prediction rate FNR = 9.61% without including antecedent precipitation data and 3.84% including antecedent precipitation data.
Convolution- and Attention-Based Neural Network for Automated Sleep Stage Classification
Authors: Zhu Tianqi , Luo Wei , Yu Feng
Published: June 2020

Analyzing polysomnography (PSG) is an effective method for evaluating sleep health; however, the sleep stage scoring required for PSG analysis is a time-consuming effort for an experienced medical expert. When scoring sleep epochs, experts pay attention to find specific signal characteristics (e.g., K-complexes and spindles), and sometimes need to integrate information from preceding and subsequent epochs in order to make a decision. To imitate this process and to build a more interpretable deep learning model, we propose a neural network based on a convolutional network (CNN) and attention mechanism to perform automatic sleep staging. The CNN learns local signal characteristics, and the attention mechanism excels in learning inter- and intra-epoch features. In experiments on the public sleep-edf and sleep-edfx databases with different training and testing set partitioning methods, our model achieved overall accuracies of 93.7% and 82.8%, and macro-average F1-scores of 84.5 and 77.8, respectively, outperforming recently reported machine learning-based methods.
Inclusion of Health in Impact Assessment: A Review of Current Practice in Sub-Saharan Africa
Authors: Dietler Dominik , Lewinski Ruth , Azevedo Sophie , Engebretsen Rebecca , Brugger Fritz , Utzinger Jürg , Winkler Mirko S.
Published: June 2020

Natural resource extraction projects, including those in the mining sector, have various effects on human health and wellbeing, with communities in resource-rich areas in sub-Saharan Africa (SSA) being particularly vulnerable. While impact assessments (IA) can predict and mitigate negative effects, it is unclear whether and to what extent health aspects are included in current IA practice in SSA. For collecting IA reports, we contacted 569 mining projects and 35 ministries regulating the mining sector. The reports obtained were complemented by reports identified in prior research. The examination of the final sample of 44 IA reports revealed a heavy focus on environmental health determinants and included health outcomes were often limited to a few aspects, such as HIV, malaria and injuries. The miniscule yield of reports (1.6% of contacted projects) and the low response rate by the contacted mining companies (18%) might indicate a lack of transparency in the IA process of the mining sector in SSA. To address the shortcomings identified, policies regulating IA practice should strengthen the requirements for public disclosure of IA reports and promote a more comprehensive inclusion of health in IA, be it through stand-alone health impact assessment or more rigorous integration of health in other forms of IA.
A Questionnaire Survey of Management of Patients with Aneurysmal Subarachnoid Haemorrhage in Poland
Authors: Hofman Mariusz , Hajder Norbert , Duda Izabela , Krzych Łukasz J.
Published: June 2020

Background: Aneurysmal subarachnoid haemorrhage (aSAH) remains a potentially devastating threat to the brain with a serious impact on mortality and morbidity. We attempted to investigate correspondence between the current guidelines for aSAH management and real clinical practice in Poland. Methods: A web-based questionnaire was performed between 03.2019 and 06.2019. Centres performing neuro-interventional radiology procedures and neuro-critical care were included (n = 29). One response from each hospital was recorded. Results: In three (10.4%) centres, there was no clear protocol for an interventional treatment plan. Endovascular embolisation was predominantly used in 11 (37.9%) hospitals, and microsurgical clipping, in 10 (34.5%). A written protocol for standard anaesthetic management was established only in six (20.7%) centres for coiling and in five (17.2%) for microsurgical clipping. The diagnosis of cerebral vasospasm was based on transcranial Doppler as the first-choice method in seven (24.1%) units. “3-H therapy” was applied by 15 (51.8%) respondents, and “2-H therapy”, by four (13.8%) respondents. In only eight (27.6%) centres were all patients with aSAH being admitted to the ICU. Conclusion: Many discrepancies exist between the available guidelines and clinical practice in aSAH treatment in Poland. Peri-procedural management is poorly standardised. Means must be undertaken to improve patient-oriented treatment and care.
Fear of COVID-19 Scale—Associations of Its Scores with Health Literacy and Health-Related Behaviors among Medical Students
Authors: Nguyen Hiep T. , Do Binh N. , Pham Khue M. , Kim Giang B. , Dam Hoa T.B. , Nguyen Trung T. , Nguyen Thao T.P. , Nguyen Yen H. , Sørensen Kristine , Pleasant Andrew , Duong Tuyen Van
Published: June 2020

The coronavirus disease 2019 (COVID-19) pandemic causes fear, as its immediate consequences for the public have produced unprecedented challenges for the education and healthcare systems. We aimed to validate the fear of COVID-19 scale (FCoV-19S) and examine the association of its scores with health literacy and health-related behaviors among medical students. A cross-sectional study was conducted from 7 to 29 April 2020 on 5423 students at eight universities across Vietnam, including five universities in the North, one university in the Center, two universities in the South. An online survey questionnaire was used to collect data on participants’ characteristics, health literacy, fear of COVID-19 using the FCoV-19S, and health-related behaviors. The results showed that seven items of the FCoV-19S strongly loaded on one component, explained 62.15% of the variance, with good item–scale convergent validity and high internal consistency (Cronbach’s alpha = 0.90). Higher health literacy was associated with lower FCoV-19S scores (coefficient, B, −0.06; 95% confidence interval, 95%CI, −0.08, −0.04; p < 0.001). Older age or last academic years, being men, and being able to pay for medication were associated with lower FCoV-19S scores. Students with higher FCoV-19S scores more likely kept smoking (odds ratio, OR, 1.11; 95% CI, 1.08, 1.14; p < 0.001) or drinking alcohol (OR, 1.04; 95% CI, 1.02, 1.06; p < 0.001) at an unchanged or higher level during the pandemic, as compared to students with lower FCoV-19S scores. In conclusion, the FCoV-19S is valid and reliable in screening for fear of COVID-19. Health literacy was found to protect medical students from fear. Smoking and drinking appeared to have a negative impact on fear of COVID-19. Strategic public health approaches are required to reduce fear and promote healthy lifestyles during the pandemic.
Italian Consensus Statement on Patient Engagement in Chronic Care: Process and Outcomes
Authors: Graffigna Guendalina , Barello Serena , Riva Giuseppe , Corbo Massimo , Damiani Gianfranco , Iannone Primiano , Bosio Albino Claudio , Ricciardi Walter
Published: June 2020

Patient engagement has been recognized as a key priority in chronic care. However, scholars agree that guidelines are needed to ensure effective patient engagement strategies. To this end, a Consensus Conference process was promoted with the following methodological steps: (1) extensive literature review about patient engagement initiatives in chronic care; (2) a stakeholders survey to collect best practices and (3) workshops with experts. On the basis of the information collected, a consensus statement was drafted, revised, and finalized by a panel of select renowned experts. These experts agreed in defining engagement as an eco-systemic concept involving multiple actors all of which contribute to influence patients’ willingness and ability to engage in chronic care. Moreover, experts recommended, whenever possible, to adopt standardized instruments to assess engagement levels and related unmet needs. Then, experts strongly advised appropriate trainings for healthcare professionals about patient engagement strategies. Furthermore, the importance of promoting healthcare professionals’ wellbeing has been advocated. Family caregivers, as well as patients’ organizations - should be trained and engaged to increase the effectiveness of interventions dedicated to patients. Finally, experts agreed that digital technologies should be considered as a crucial enhancer for patient engagement in chronic care.
Does Emotion Regulation Predict Gains in Exercise-Induced Fitness? A Prospective Mixed-Effects Study with Elite Helicopter Pilots
Authors: Cárdenas David , Madinabeitia Iker , Alarcón Francisco , Perales José C.
Published: June 2020

Emotion regulation (ER) is a strong predictor of different aspects of mental health and wellbeing. However, only recently has ER been examined in relation to physical activity and its effects on fitness. In the present study, 26 elite helicopter pilots, serving in the Spanish Air Force, were physically trained for 6 months, and their level of fitness (maximum oxygen consumption and time to exhaustion in a treadmill-running test) was assessed before and after that period. Additionally, two indices of emotion regulation (general adaptiveness of ER strategies, as measured by the Emotion Regulation Questionnaire (ERQ), and negative urgency, as measured by the UPPS-P questionnaire) measured at baseline were used as prospective predictors of fitness improvement. After controlling for individual features, baseline fitness, and type of training, better emotion regulation strategies (more cognitive reappraisal plus less expressive suppression) predicted larger fitness gains (p = 0.028). Incidental emotion regulation, as measured by the negative urgency index, failed to predict pre–post-fitness changes (p = 0.734). These results suggest that fostering emotion regulation skills may improve the effectiveness of fitness training programs.
Longitudinal Study of Metabolic Biomarkers among Conventional and Organic Farmers in Thailand
Authors: Kongtip Pornpimol , Nankongnab Noppanun , Kallayanatham Nichcha , Pundee Ritthirong , Yimsabai Jutharak , Woskie Susan
Published: June 2020

The aim of this longitudinal study is to assess how pesticide use may impact metabolic biomarkers by collecting and comparing data from conventional (n = 13) and organic farmers (n = 225) every eight months for four rounds. Farmers were interviewed about family health history, food consumption behaviors, self-reported health problems, agricultural activities, and history of pesticide use. Systolic and diastolic blood pressure and body mass index (BMI) were measured. Blood samples were collected for total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), blood glucose, and triglycerides. A linear mixed model with random intercepts for subjects was used to compare the metabolic biomarkers between conventional and organic farmers and to examine the impact of the number of pesticide spray days for all four rounds after controlling for covariates. The conventional farmers reported using insecticides, herbicides, and fungicides. The marginal means for chemical farmers were significantly higher than organic farmers for total cholesterol, LDL, HDL, glucose, systolic and diastolic blood pressure, BMI, and waist circumference. Increasing the number of days of spraying either insecticides or fungicides was associated with an increase in HDL, LDL, and cholesterol levels. Increasing the number of herbicide spray days was associated with an increase in systolic and diastolic blood pressure and a decrease in BMI. These findings suggest that pesticide-using conventional farmers may be at higher risk of metabolic disease in the future.
Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction
Authors: Lin Adrian Xi , Ho Andrew Fu Wah , Cheong Kang Hao , Li Zengxiang , Cai Wentong , Chee Marcel Lucas , Ng Yih Yng , Xiao Xiaokui , Ong Marcus Eng Hock
Published: June 2020

The accurate prediction of ambulance demand provides great value to emergency service providers and people living within a city. It supports the rational and dynamic allocation of ambulances and hospital staffing, and ensures patients have timely access to such resources. However, this task has been challenging due to complex multi-nature dependencies and nonlinear dynamics within ambulance demand, such as spatial characteristics involving the region of the city at which the demand is estimated, short and long-term historical demands, as well as the demographics of a region. Machine learning techniques are thus useful to quantify these characteristics of ambulance demand. However, there is generally a lack of studies that use machine learning tools for a comprehensive modeling of the important demand dependencies to predict ambulance demands. In this paper, an original and novel approach that leverages machine learning tools and extraction of features based on the multi-nature insights of ambulance demands is proposed. We experimentally evaluate the performance of next-day demand prediction across several state-of-the-art machine learning techniques and ambulance demand prediction methods, using real-world ambulatory and demographical datasets obtained from Singapore. We also provide an analysis of this ambulatory dataset and demonstrate the accuracy in modeling dependencies of different natures using various machine learning techniques.
Statistical and Network-Based Analysis of Italian COVID-19 Data: Communities Detection and Temporal Evolution
Authors: Milano Marianna , Cannataro Mario
Published: June 2020

The coronavirus disease (COVID-19) outbreak started in Wuhan, China, and it has rapidly spread across the world. Italy is one of the European countries most affected by COVID-19, and it has registered high COVID-19 death rates and the death toll. In this article, we analyzed different Italian COVID-19 data at the regional level for the period 24 February to 29 March 2020. The analysis pipeline includes the following steps. After individuating groups of similar or dissimilar regions with respect to the ten types of available COVID-19 data using statistical test, we built several similarity matrices. Then, we mapped those similarity matrices into networks where nodes represent Italian regions and edges represent similarity relationships (edge length is inversely proportional to similarity). Then, network-based analysis was performed mainly discovering communities of regions that show similar behavior. In particular, network-based analysis was performed by running several community detection algorithms on those networks and by underlying communities of regions that show similar behavior. The network-based analysis of Italian COVID-19 data is able to elegantly show how regions form communities, i.e., how they join and leave them, along time and how community consistency changes along time and with respect to the different available data.
Cochlear Implant Surgery: Endomeatal Approach versus Posterior Tympanotomy
Authors: Freni Francesco , Gazia Francesco , Slavutsky Victor , Perello Scherdel Enrique , Nicenboim Luis , Posada Rodrigo , Portelli Daniele , Galletti Bruno , Galletti Francesco
Published: June 2020

The aim of the present study was to compare the posterior tympanotomy (PT) technique to the endomeatal approach. The endomeatal approach (EMA) for Cochlear Implant (CI) surgery was performed on 98 patients with procident lateral sinus or a small mastoid cavity, on 103 ears (Group A). Conventional mastoidectomy and PT was performed on the other 104 patients, on 107 ears (Group B). Data on all patients were then collected for the following: intra- and post-operative complications, Tinnitus Handicap Inventory (THI), Vertigo Symptom Scale (VSS), duration of surgery, and postoperative discomfort. The difference in the total number of major and minor complications between the case group and the control group was not statistically significant. There was a statistically significant difference in discomfort between the two groups using the Visual Analogue Scale (VAS), both immediately postsurgery (p = 0.02) and after one month (p = 0.04). The mean duration of surgery was 102 ± 29 min for EMA and 118 ± 15 min for the PT technique (p = 0.008). EMA is a faster technique resulting in reduced postoperative patient discomfort in comparison to the PT method. The experience of the surgeon as well as the correct choice of surgical technique are fundamental to successful outcomes for cochlear implant surgery.
Air Pollution and Emergency Department Visits for Mental Disorders among Youth
Authors: Szyszkowicz Mieczysław , Zemek Roger , Colman Ian , Gardner William , Kousha Termeh , Smith-Doiron Marc
Published: June 2020

Although exposure to ambient air pollution has been linked to mental health problems, little is known about its potential effects on youth. This study investigates the association between short-term exposure to air pollutants and emergency department (ED) visits for mental health disorders. The National Ambulatory Care Reporting System database was used to retrieve ED visits for young individuals aged 8–24 years in Toronto, Canada. Daily average concentrations of nitrogen dioxide (NO2), fine particulate matter (PM2.5), and daily maximum 8 h ozone (O3) were calculated using measurement data from seven fixed stations. A case-crossover (CC) design was implemented to estimate the associations between ED visits and air pollution concentrations. Mental health ED visits were identified using International Classification of Diseases 10th Revision (ICD-10) codes, with seven categories considered. Models incorporating air pollutants and ambient temperature (with lags of 0–5 days) using a time-stratified CC technique were applied. Multivariable regression was performed by sex, three age groups, and seven types of mental health disorders to calculate relative risk (RR). The RRs were reported for one interquartile range (IQR) change in the air pollutant concentrations. Between April 2004 and December 2015 (4292 days), there were 83,985 ED visits for mental-health related problems in the target population. Several exposures to air pollutants were shown to have associations with ED visits for mental health including same day exposure to fine particulate matter (IQR = 6.03 μg/m3, RR = 1.01 (95% confidence interval: 1.00–1.02), RR = 1.02 (1.00–1.03)) for all and female-only patients, respectively. One-day lagged exposure was also associated with ED visits for PM2.5 (RR = 1.02 (1.01–1.03)), for nitrogen dioxide (IQR = 9.1 ppb, RR = 1.02 (1.00–1.04)), and ozone (IQR = 16.0 ppb, RR = 1.06 (1.01–1.10)) for males. In this study, urban air pollution concentration—mainly fine particulate matter and nitrogen dioxide—is associated with an increased risk for ED visits for adolescents and young adults with diagnosed mental health disorders.
Interdependent Healthcare Critical Infrastructure Analysis in a Spatiotemporal Environment: A Case Study
Authors: Nukavarapu Nivedita , Durbha Surya
Published: June 2020

During an urban flooding scenario, Healthcare Critical Infrastructure (HCI) represents a critical and essential resource. As the flood levels rise and the existing HCI facilities struggle to keep up with the pace, the under-preparedness of most urban cities to address this challenge becomes evident. Due to the disruptions in the interdependent Critical Infrastructures (CI) network (i.e., water supply, communications, electricity, transportation, etc.), during an urban flooding event, the operations at the healthcare CI facilities are inevitably affected. Hence, there is a need to identify cascading CI failure scenarios to visualize the propagation of failure of one CI facility to another CI, which can impact vast geographical areas. The goal of this work is to develop an interdependent HCI simulation model in a spatiotemporal environment to understand the dynamics in real-time and model the propagation of cascading CI failures in an interdependent HCI network. The model is developed based on a real-world cascading CI failure case study on an interdependent HCI network during the flood disaster event in December 2015 at Chennai, TamilNadu, India. The interdependencies between the CI networks are modeled by using the Stochastic Colored Petri Net (SCPN) based modeling approach. SCPN is used to model a real-word process that occurs in parallel or concurrently. Furthermore, a geographic information system-based interface is integrated with the simulation model, to visualize the dynamic behavior of the interdependent HCI SCPN simulation model in a spatiotemporal environment. Such a dynamic simulation model can assist the decision-makers and emergency responders to rapidly simulate ‘what if’ kind of scenarios and consequently respond rapidly.
Expression of MicroRNAs in Periodontal and Peri-Implant Diseases: A Systematic Review and Meta-Analysis
Authors: Asa’ad Farah , Garaicoa-Pazmiño Carlos , Dahlin Christer , Larsson Lena
Published: June 2020

Aim: The purpose of this review was to evaluate the expression patterns of miRNAs in periodontal and peri-implant diseases, while identifying potential miRNAs with the greatest diagnostic ability as an oral fluid biomarker. Materials and methods: Human and animal studies were included when evaluating expression of miRNAs between health and different forms/stages of diseases, in which microarray and/or real-time polymerase chain reaction (RT-PCR) was carried out to detect fold changes in gene expression. After full-text analysis, 43 articles were considered for a qualitative assessment, and 16 miRNAs were selected to perform meta-analysis. Results: Based on human studies, results showed an overall upregulation of most of the evaluated miRNAs in periodontitis, with miRNA-142-3p and miRNA-146a being the most conclusive on both microarray and RT-PCR values and potentially serving as diagnostic biomarkers for disease activity. Conversely, miR-155 was the only miRNA revealing a statistically significant difference (SSD) (p < 0.05*) in experimental periodontitis models from RT-PCR values. Scarce scientific evidence is available from peri-implant diseases, however, most explored miRNAs in peri-implantitis were downregulated except for miR-145. Conclusions: Although our results revealed that a distinct differential expression of specific miRNAs can be noted between the state of health and disease, future research remains necessary to explore the functional role of specific miRNAs and their potential as therapeutic targets in periodontal and peri-implant diseases. MeSH Terms: periodontitis, peri-implantitis, epigenomics, microarray analysis, real-time polymerase chain reaction, microRNAs. Clinical relevance: Scientific background: Although most research identified different expression levels of miRNAs in periodontal and peri-implant diseases compared to their counterparts, their actual role in the pathogenesis of these conditions remains unclear. Therefore, we aimed to present a systematic review and meta-analysis on the expression patterns of miRNAs in periodontitis and peri-implantitis, while identifying potential miRNAs with the greatest diagnostic ability as an oral fluid biomarker. Principal findings: In periodontitis-related studies, miRNA-142-3p and miRNA-146a were the most conclusive on both microarray and RT-PCR values. Scarce scientific evidence is available from peri-implant diseases. Practical implications: Both miRNA-142-3p and miRNA-146a might serve as future diagnostic biomarkers for disease activity in periodontitis. Yet, future research remains necessary to explore the functional role of specific miRNAs and their potential as therapeutic targets in periodontal and peri-implant diseases.
Anti-Inflammatory Potential of n-3 Polyunsaturated Fatty Acids Enriched Hen Eggs Consumption in Improving Microvascular Endothelial Function of Healthy Individuals—Clinical Trial
Authors: Stupin Ana , Mihalj Martina , Kolobarić Nikolina , Šušnjara Petar , Kolar Luka , Mihaljević Zrinka , Matić Anita , Stupin Marko , Jukić Ivana , Kralik Zlata , Grčević Manuela , Kralik Gordana , Šerić Vatroslav , Drenjančević Ines
Published: June 2020

The effects of consumption of n-3 polyunsaturated fatty acids (n-3 PUFAs) enriched hen eggs on endothelium-dependent and endothelium-independent vasodilation in microcirculation, and on endothelial activation and inflammation were determined in young healthy individuals. Control group (N = 21) ate three regular hen eggs/daily (249 mg n-3 PUFAs/day), and n-3 PUFAs group (N = 19) ate three n-3 PUFAs enriched hen eggs/daily (1053 g n-3 PUFAs/day) for 3 weeks. Skin microvascular blood flow in response to iontophoresis of acetylcholine (AChID; endothelium-dependent) and sodium nitroprusside (SNPID; endothelium-independent) was assessed by laser Doppler flowmetry. Blood pressure (BP), body composition, body fluid status, serum lipid and free fatty acids profile, and inflammatory and endothelial activation markers were measured before and after respective dietary protocol. Results: Serum n-3 PUFAs concentration significantly increased, AChID significantly improved, and SNPID remained unchanged in n-3 PUFAs group, while none was changed in Control group. Interferon-γ (pro-inflammatory) significantly decreased and interleukin-10 (anti-inflammatory) significantly increased in n-3 PUFAs. BP, fat free mass, and total body water significantly decreased, while fat mass, interleukin-17A (pro-inflammatory), interleukin-10 and vascular endothelial growth factor A significantly increased in the Control group. Other measured parameters remained unchanged in both groups. Favorable anti-inflammatory properties of n-3 PUFAs consumption potentially contribute to the improvement of microvascular endothelium-dependent vasodilation in healthy individuals.
Expanding Newborn Screening for Pompe Disease in the United States: The NewSTEPs New Disorders Implementation Project, a Resource for New Disorder Implementation
Authors: Hale Kshea , Kellar-Guenther Yvonne , McKasson Sarah , Singh Sikha , Ojodu Jelili
Published: June 2020

Public health programs in the United States screen more than four million babies each year for at least 30 genetic disorders. The Health and Human Services (HHS) Advisory Committee on Heritable Disorders in Newborns and Children (ACHDNC) recommends the disorders for state newborn screening (NBS) programs to screen. ACHDNC updated the Recommended Uniform Screening Panel (RUSP) to include Pompe disease in March 2015. To support the expansion of screening for Pompe disease, the Association of Public Health Laboratories (APHL) proposed the Newborn Screening Technical assistance and Evaluation Program (NewSTEPs) New Disorders Implementation Project, funded by the HHS’ Health Resources and Services Administration (HRSA) Maternal and Child Health Bureau (MCHB). Through this project, APHL provided financial support to 15 state NBS programs to enable full implementation of screening for Pompe disease. As of April 27, 2020, nine of the 15 programs had fully implemented Pompe disease newborn screening and six programs are currently pursuing implementation. This article will discuss how states advanced to statewide implementation of screening for Pompe disease, the challenges associated with implementing screening for this condition, the lessons learned during the project, and recommendations for implementing screening for Pompe disease.
Applications of Nonlinear Programming to the Optimization of Fractionated Protocols in Cancer Radiotherapy
Authors: Bertuzzi Alessandro , Conte Federica , Papa Federico , Sinisgalli Carmela
Published: June 2020

The present work of review collects and evidences the main results of our previous papers on the optimization of fractionated radiotherapy protocols. The problem under investigation is presented here in a unitary framework as a nonlinear programming application that aims to determine the optimal schemes of dose fractionation commonly used in external beam radiotherapy. The radiation responses of tumor and normal tissues are described by means of the linear quadratic model. We formulate a nonlinear, non-convex optimization problem including two quadratic constraints to limit the collateral normal tissue damages and linear box constraints on the fractional dose sizes. The general problem is decomposed into two subproblems: (1) analytical determination of the optimal fraction dose sizes as a function of the model parameters for arbitrarily fixed treatment lengths; and (2) numerical determination of the optimal fraction number, and of the optimal treatment time, in different parameter settings. After establishing the boundedness of the optimal number of fractions, we investigate by numerical simulation the optimal solution behavior for experimentally meaningful parameter ranges, recognizing the crucial role of some parameters, such as the radiosensitivity ratio, in determining the optimality of hypo- or equi-fractionated treatments. Our results agree with findings of the theoretical and clinical literature.
COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification
Authors: Samuel Jim , Ali G. G. Md. Nawaz , Rahman Md. Mokhlesur , Esawi Ek , Samuel Yana
Published: June 2020

Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naïve Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.
Gamified Evaluation in STEAM for Higher Education: A Case Study
Authors: Boytchev Pavel , Boytcheva Svetla
Published: June 2020

The process of converting non-game educational content and processes into game-like educational content and processes is called gamification. This article describes a gamified evaluation software for university students in Science, Technology, Engineering, the Arts and Mathematics (STEAM) courses, based on competence profiles of students and problems. The traditional learning management systems and learning tools cannot handle gamification to its full potential because of the unique requirements of gamified environments. We designed a novel gamification evaluation and assessment methodology implemented in a STEAM course through specially designed software. The results from end-user tests show a positive expectation of students’ performance and motivation. The preliminary results of over 100 students in the Fundamentals of Computer Graphics course are presented and the results of quantitative analysis are discussed. In addition we present an analysis of students’ surveys, where students expressed in free text form observations about the software.
Measuring the Inter and Intraspecific Sexual Shape Dimorphism and Body Shape Variation in Generalist Ground Beetles in Russia
Authors: Benítez Hugo A. , Sukhodolskaya Raisa A. , Órdenes-Clavería Rodrigo , Avtaeva Tamara A. , Kushalieva Shapaat A. , Saveliev Anatoly A.
Published: June 2020

Ground beetles in multiple species vary greatly in the expression of the shape on sexual traits, resulting in a sexual shape dimorphism as a consequence of sexual selection differences. The present research focuses on the study of inter and intrasexual sexual shape dimorphism of two generalist genera of ground beetles Pterostichus and Carabus. Geometric morphometric methods were applied to five generalist species of ground beetles Carabus exaratus, C. granulatus, Pterostichus melanarius, P. niger, and P. oblongopunctatus and several multivariate analyses were applied for two different traits, abdomen and elytra. Three of the five species analyzed showed high levels of sex-based shape dimorphism. However, the most generalist species, P. melanarius and P. oblongopunctatus, did not evidence shape-based sexual dimorphism differentiation in both of the analyzed traits, as statistically confirmed based on the permutation of pairwise comparison of the Mahalanobis distances of a sex–species classifier. It is generally known that environmental stress in natural populations can affect the fitness expression, principally related to sexual fecundity, being that this pattern is more evident in non-generalist species. In our results, the contrary pattern was found, with the absence of sexual shape dimorphism for two of the three generalist species analyzed. On the other hand, the interspecies shape variation was clearly identified using principal component analysis of both of the analyzed traits. Finally, this research is the first to analyze the relationship between sexual shape dimorphism in Russian ground beetles, evidencing the lack of understanding of the mechanism underlying the sexual dimorphism, especially in species living in extreme environments.