Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolutionary Epidemiology of Drug-Resistance in Space

How can we optimize the use of drugs against parasites to limit the evolution of drug resistance? This question has been addressed by many theoretical studies focusing either on the mixing of various treatments, or their temporal alternation. Here we consider a different treatment strategy where the use of the drug may vary in space to prevent the rise of drug-resistance. We analyze epidemiological models where drug-resistant and drug-sensitive parasites compete in a one-dimensional spatially heterogeneous environment. Two different parasite life-cycles are considered: (i) direct transmission between hosts, and (ii) vector-borne transmission. In both cases we find a critical size of the treated area, under which the drug-resistant strain cannot persist. This critical size depends on the basic reproductive ratios of each strain in each environment, on the ranges of dispersal, and on the duration of an infection with drug-resistant parasites. We discuss optimal treatment strategies that limit disease prevalence and the evolution of drug-resistance.
A Review of the Evidence on Strategies for Teaching Children Who Are DHH Grapheme–Phoneme Correspondence

Generally, learning to decode printed English has been a difficult skill to master for children who are deaf or hard of hearing (DHH). The purpose of this review was to examine the literature regarding strategies used to teach the grapheme–phoneme relationship to children who are DHH. The authors identified six strategies in the evidence base relative to decoding: cued speech, visual phonics, speechreading, fingerspelling, semantic and iconic representation, and morphographemics. Nine articles containing 11 studies met the standards of research rigor established by the authors. The specifics of these nine articles (e.g., participant age and degree of hearing loss, research design, results) are presented in a table format. In addition, an explanation of each of the six strategies, a summary of the articles found in their support, and implications for the field are included.
The Binary Tree Roll Operation: Definition, Explanation and Algorithm

The paper introduces an operation on a binary tree, called binary tree roll, or roll of a binary tree. Two versions of the binary tree roll, counterclockwise and clockwise, are presented. The operations are mathematically defined and graphically presented. It is explained how the binary tree roll actually coincides with the process of turning the entire tree 90 degrees counterclockwise or clockwise. To visually explain and perform the roll operation, the concepts of a wedge node, true ancestor, illusory ancestor, illusory root and illusory ancestral stem of nodes are introduced, as well as the visual operations of turning and downshift. Both roll operations are implemented using programming algorithms. The algorithms are explained, and all the situations that might be encountered during processing the roll operation are examined and resolved. Thus, the paper gives a mathematical introduction of both binary tree roll operations, gives their visual explanations and offers algorithms for their implementations using a computer. General Terms Algorithm, binary tree
Physical activity is negatively associated with the metabolic syndrome in the elderly.

BACKGROUND An inverse association between physical activity and metabolic syndrome has been reported in several cohorts, but very few specific studies are available in the elderly, in whom neurological and musculo-skeletal diseases are expected to lead to a remarkable age-related decline of physical activity. AIM AND DESIGN The relationships among physical activity, insulin resistance and metabolic syndrome were assessed in a cross-sectional study concerning 1144 subjects aged 65-91 years resident in Pianoro (northern Italy). Household and leisure-time activities were assessed by a self-administered questionnaire (Physical Activity Scale for Elderly--PASE). Routine clinical and biochemical data (including fasting insulin) were used to assess insulin resistance [Homeostasis Model Assessment (HOMA) method] and the prevalence of metabolic syndrome. RESULTS All PASE scores were inversely correlated with waist circumference, triglycerides and HOMA index, with highest significance for leisure-time activities (P < or = 0.005). The PASE score for household activities was also correlated inversely with blood glucose (P < 0.05), and directly with HDL cholesterol (P < 0.001). In logistic regression analysis, the metabolic syndrome was more prevalent among sedentary subjects (corresponding to the low tertile of leisure-time activities) than in the remaining more active population (odds ratio 1.51, 95% confidence interval 1.12-2.03, P = 0.007), independently of possible confounders. CONCLUSION Physical activity is inversely associated with insulin resistance and the metabolic syndrome even in the elderly. Community programs favoring physical activity are expected to significantly improve the health status in these subjects.
The influence of liposome-encapsulated prostaglandin E1 on hydrogen peroxide concentrations in the exhaled breath of patients with the acute respiratory distress syndrome.

UNLABELLED Hydrogen peroxide (H2O2) levels are increased in the exhaled breath of patients with the acute respiratory distress syndrome (ARDS). Because liposome-encapsulated prostaglandin E1 (PGE1) downregulates the CD11/CD18 receptor of the neutrophil, thereby limiting endothelial adhesion, the use of this drug should decrease the excretion of H2O2 in the expiratory condensate of patients with ARDS. Patients > 11 yr of age with ARDS (diffuse, patchy infiltrates by chest radiograph; Pao2/fraction of inspired oxygen [P/F] ratio < or = 200 mm Hg; pulmonary capillary wedge pressure < or = 18 mm Hg; and the requirement for mechanical ventilation) were randomized to receive placebo (n = 14) or escalating doses (0.15-3.6 micrograms/kg) of liposomal PGE1 (n = 14) every 6 h for up to 7 days. Condensate was collected every morning from the expiratory tubing that was submerged in an ice saltwater bath (-5 degrees C). H2O2 levels were measured by using a horseradish peroxidase assay. Other data collected included white blood cell count and P/F ratios. There was no significant difference in the concentration of H2O2 in the expiratory condensate between the liposomal PGE1 group and the control group either before (0.99 +/- 0.52 vs 0.93 +/- 0.48 mumol/L) or during treatment (1.04 +/- 0.45 vs 0.76 +/- 0.25 mumol/L). Liposomal PGE1 treatment improved the P/F ratio and decreased the white blood cell count over time. Despite its ability to downregulate the CD11/CD18 neutrophil receptor, liposomal PGE1 did not reduce exhaled H2O2 excretion. IMPLICATIONS White blood cells (WBC) are thought to be part of the cause of the acute respiratory distress syndrome, a lung disease. WBC in the lung produce hydrogen peroxide, which is exhaled. Liposomal PGE1 inhibits WBC function but was found to have no effect in decreasing exhaled hydrogen peroxide in patients with the acute respiratory distress syndrome.
Innate Immune Induction and Influenza Protection Elicited by a Response-Selective Agonist of Human C5a

The anaphylatoxin C5a is an especially potent mediator of both local and systemic inflammation. However, C5a also plays an essential role in mucosal host defense against bacterial, viral, and fungal infection. We have developed a response-selective agonist of human C5a, termed EP67, which retains the immunoenhancing activity of C5a at the expense of its inflammatory, anaphylagenic properties. EP67 insufflation results in the rapid induction of pulmonary cytokines and chemokines. This is followed by an influx of innate immune effector cells, including neutrophils, NK cells, and dendritic cells. EP67 exhibits both prophylactic and therapeutic protection when tested in a murine model of influenza A infection. Mice treated with EP67 within a twenty-four hour window of non-lethal infection were significantly protected from influenza-induced weight loss. Furthermore, EP67 delivered twenty-four hours after lethal infection completely blocked influenza-induced mortality (0% vs. 100% survival). Since protection based on innate immune induction is not restricted to any specific pathogen, EP67 may well prove equally efficacious against a wide variety of possible viral, bacterial, and fungal pathogens. Such a strategy could be used to stop the worldwide spread of emergent respiratory diseases, including but not limited to novel strains of influenza.
Human Gender Classification: A Review

Gender contains a wide range of information regarding to the characteristics difference between male and female. Successful gender recognition is essential and critical for many applications in the commercial domains such as applications of human-computer interaction and computer-aided physiological or psychological analysis. Some have proposed various approaches for automatic gender classification using the features derived from human bodies and/or behaviors. First, this paper introduces the challenge and application for gender classification research. Then, the development and framework of gender classification are described. Besides, we compare these state-of-the-art approaches, including vision-based methods, biological information-based method, and social network information-based method, to provide a comprehensive review in the area of gender classification. In mean time, we highlight the strength and discuss the limitation of each method. Finally, this review also discusses several promising applications for the future work.
Clinical Parameters vs Cytokine Profiles as Predictive Markers of IgE-Mediated Allergy in Young Children

Background Allergy afflicts one third of children, negatively impacting their quality of life and generating a significant socio-economic burden. To this day, this disorder remains difficult to diagnose early in young patients, with no predictive test available. Objective This study was designed to correlate cytokine profiles with clinical phenotypes of allergy development. Methods Three hundred patients were recruited and followed from birth to 18 months of age. They were given a clinical exam at birth and at 2, 6, 12, and 18 months of age, with skin prick tests at 6, and 18 months, in order to have a record of their medical history and determine their allergic status. In addition, mononuclear cells from 131 patients were isolated from cord blood and from peripheral blood samples at 2, 6 and 18 months of age, to analyse their cytokine and chemokine production. Results Cord blood mononuclear cells (CBMCs) from future Immunoglobulin (Ig) E-mediated allergic children produced significantly less Interleukin (IL)-12p70 and IL-15 than cells from the rest of the cohort. Multivariate analyses revealed that the best predictive model of allergy was built on cytokine data, whereas the best predictive model of IgE-mediated allergy was built on clinical parameters. Conclusions and clinical relevance Although univariate analyses can yield interesting information regarding the immune responses of allergic children, finding predictive markers of the disorder will likely rely on monitoring multiple parameters. Nonetheless these analyses suggest a potential key role for IL-15 in the development of atopic disease. In addition, the study highlights the importance of clinical parameters in predicting the development of IgE-mediated allergy.
Should Smartphone Users Mock Apps?

Smartphones represent the most serious threat to user privacy of any widely-deployed computing technology. Unfortunately, existing permission models provide smartphone users with limited protection, in part due to the difficulty users have distinguishing between legitimate and illegitimate use of their data. A mapping app may upload the same location information it uses to download maps (legitimate) to a marketing agency interested in delivering location-based ads (illegitimate). However, armed with the right technology users can turn apps' interest in personal data against them by intentionally manipulating the data that they expose. We refer to the intentional substitution of real data with artificial data intended to alter an apps perception of a user as mocking to differentiate this approach from other privacy-motivated techniques that focus on concealing data. In this paper, we explore the desirability and implications of this approach, present results from a survey suggesting that many users are interested in mocking apps, and discuss ethical and practical issues related to widespread app mocking.
Starting performance analysis for a single-phase capacitor-run permanent magnet motor with skewed rotor slots

This paper presents the novel starting performance analysis for a single-phase capacitor-run permanent magnet motor with skewed rotor slots. 2-D time-stepping finite element analysis has been used to successfully predict the starting performance of the prototype motor by using a multislice model. The agreement between computed and measured results of the prototype motor validates the proposed analysis. The run-up response corresponding to skew pitch has been given from the simulation results.
The challenge of integrating justice and care in neonatal nursing

The aim of this study was to explore neonatal nurses’and mothers of preterm infants’experiences of daily challenges. Interviews took place asking for good, bad and challenging experiences. Data were analysed using qualitative content analysis and findings were clustered in two categories: good and challenging experiences, each containing three themes. The good experiences were: managing with success as a nurse, small things matter for mothers, and a good day anyhow for mothers and nurses. The challenging experiences were: mothering in public, being pulled between responsibilities, and adverse things stick under the nurses’skin. The study shows that small daily clinical matters become big issues and could lead to moral distress, and that nurses integrate ethics of justice and ethics of care while mothers are concerned about health and well-being of their specific infant only. The challenge for nursing to integrate fairness and sensitive care in family-oriented neonatal care is discussed.
Hybrid Proximal-Point Methods for Systems of Generalized Equilibrium Problems and Maximal Monotone Operators in Banach Spaces

In this paper, by using Bregman’s technique, we introduce and study the hybrid proximal-point methods for finding a common element of the set of solutions to a system of generalized equilibrium Problems and zeros of a finite family of maximal monotone operators in reflexive Banach spaces. Strong convergence results of the proposed hybrid proximal-point algorithms are also established under some suitable conditions. As applications, the existence of solutions for a class of bilevel variational inequalities are established and some numerical examples are reported. Key–Words: Equilibrium problem, maximal monotone operator, bilevel variational inequalities, Bregman distance, Bregman projection, totally convex function, Legendre function.
On massive MIMO antenna topologies using total power in the azimuth and zenith domains

In this paper, we present an approximate expression for the spatial correlation of massive MIMO antenna topologies. The underlying concept of the proposed model is that the total power is relative to the true distribution of power in the azimuth and zenith domains. Simulation results using measured distribution of angles of departure (AOD) support the existing fact that massive MIMO convergence metrics do not demonstrate any sensitivity to antenna topology.
Expert system design for classification of brain waves and epileptic-seizure detection

Feature extraction and classification of electro-physiological signals is an important issue in development of disease diagnostic expert system (DDES). Classification of electroencephalogram (EEGs) signals (normal and abnormal) is still a challenge for engineers and scientists. Various signal processing techniques have already been proposed to solve this puzzle of classification of non linear signals like EEG. In this work, attempts have been taken to distinguish between normal, epileptic and non-epileptic EEG waves by use of Support Vector Machine (SVM). EEG signals from (healthy subject with eye open condition, healthy subject with eye close condition, signal from hippocampus region and signal from opposite to epileptogenic region and signal with seizure) were considered for the analysis. The signals were processed by using wavelet-chaos techniques. The nonlinear dynamics of the original EEGs are quantified in the form of the correlation dimension (CD, representing system complexity) and the largest Lyapunov exponent (LLE, representing system chaoticity), Capacitive Dimension (CAD) which show the randomness nature of the signal. SVM classifier applied on the extracted feature vectors for the classification purpose. From the results, it was clearly found that the classification accuracy was significantly higher i.e. more than ninety percentage. Hence the techniques can be implemented to design knowledge based expert disease diagnostic system.
Bayesian Networks for Knowledge-Based Authentication

Knowledge-based authentication (KBA) has gained prominence as a user authentication method for electronic transactions. This paper presents a Bayesian network model of KBA grounded in probabilistic reasoning and information theory. The probabilistic semantics of the model parameters naturally lead to the definitions of two key KBA metrics-guessability and memorability. The statistical modeling approach allows parameter estimation using methods such as the maximum likelihood estimator (MLE). The information-theoretic view helps to derive the closed-form solutions to estimating the guessability and guessing entropy metrics. The results related to KBA metrics and the models under different attacking strategies and factoid distributions are unified under a game-theoretic framework that yields lower and upper bounds of optimal guessability. The paper also proposes a methodology for implementing a Bayesian network-based KBA system. Further, an empirical evaluation of the relative merits of two Bayesian network structures for KBA, the Naive Bayes (NB) and the Tree Augmented Naive Bayes (TAN), confirms the hypothesis that the TAN structure is superior in terms of authentication accuracy and error rates. The results of the theoretical analysis and the empirical study provide insights into the KBA design problem and establish a foundation for future research in the KBA area.
Mining data records based on ontology evolution for deep web

Currently, the research for the extraction of information in deep web is pretty active. Although many researchers already adopted ontology in the data extraction, many problems still exist. This paper proposed an ontology evolution based method for mining in the data area. Not only will this method solve the problem when the website only consists of one record, but it also can identify he meaning of data that has no labels. With the evolution of ontology, the extraction of data records is being more accurate. Experiments indicate that this method could improve the accuracy and efficiency of data extraction
Social Network Analysis as a Toolkit for the Science of Social Work

The science of social work can be augmented by active engagement in social network analysis. We provide an overview of both egocentric analysis (also referred to as personal network analysis) and sociometric analysis (or whole network analysis), and discuss ways of incorporating these tools into social work research. Social network analysis is particularly useful in identifying mechanisms of social change; however, the science of social work is incomplete unless understanding is linked to the processes that promote positive change. We draw on 4 articles published in conjunction with this article to demonstrate how mechanisms for social change can be modeled, and to illustrate the ease with which implications for social work practice and directions for future intervention efforts emerge from such analyses.
Development of a screening tool to predict malnutrition among children under two years old in Zambia

ABSTRACT Background: Maternal and child undernutrition is an important issue, particularly in low- and middle-income countries. Children at high risk of malnutrition should be prioritized to receive necessary interventions to minimize such risk. Several risk factors have been proposed; however, until now, there has been no appropriate evaluation method to identify these children. In sub-Saharan Africa, children commonly receive regular check-ups from community health workers. A simple and easy nutrition assessment method is therefore needed for use by semi-professional health workers. Objectives: The aim of this study was to develop and test a practical screening tool for community use in predicting growth stunting in children under two years in rural Zambia. Methods: Field research was conducted from July to August 2014 in Southern Province, Zambia. Two hundred and sixty-four mother-child pairs participated in the study. Anthropometric measurements were performed on all children and mothers, and all mothers were interviewed. Risk factors for the screening test were estimated by using least absolute shrinkage and selection operator analysis. After re-evaluating all participants using the new screening tool, a receiver operating characteristic curve was drawn to set the cut-off value. Sensitivity and specificity were also calculated. Results: The screening tool included age, weight-for-age Z-score status, birth weight, feeding status, history of sibling death, multiple birth, and maternal education level. The total score ranged from 0 to 22, and the cut-off value was eight. Sensitivity and specificity were 0.963 and 0.697 respectively. Conclusions: A screening tool was developed to predict children at high risk of malnutrition living in Zambia. Further longitudinal studies are needed to confirm the test’s validity in detecting future stunting and to investigate the effectiveness of malnutrition treatment.
Neurotoxicity modeling for risk assessment.

The setting of acceptable exposure levels for neurotoxicants has followed the traditional approach of dividing experimental no-observed-adverse-effect-levels (NOAELs) by safety/uncertainty factors. NOAELs are believed by many toxicologists to represent levels having zero or negligible risk, while uncertainty factors are used to account for a number of sources of variation. Although the use of NOAELs in this manner has been criticized because of their imprecise quantitative definition, NOAELs for nonquantal neurotoxic effects have not been replaced by more precisely defined quantities (e.g., benchmark doses), partly due to the absence of a generally accepted methodology for attaching specific risk levels to low exposures. The present paper describes a quantitative approach to modeling nonquantal neurotoxic effects for risk assessment, which can be used to obtain results similar to the familiar results obtained in risk assessment for carcinogenicity and developmental toxicity. The steps involved in implementing the process are discussed, with particular attention being given to the critical step of defining an adverse neurologic effect. An experimental data set is used to illustrate the methodology.