Analyzing Particularities of Sensor Datasets for Supporting Data Understanding and Preparation.

Data scientists spend much time with data cleaning tasks, and this is especially important when dealing with data gathered from sensors, as finding failures is not unusual (there is an abundance of research on anomaly detection in sensor data). This work analyzes several aspects of the data generated by different sensor types to understand particularities in the data, linking them with existing data mining methodologies. Using data from different sources, this work analyzes how the type of sensor used and its measurement units have an important impact in basic statistics such as variance and mean, because of the statistical distributions of the datasets. The work also analyzes the behavior of outliers, how to detect them, and how they affect the equivalence of sensors, as equivalence is used in many solutions for identifying anomalies. Based on the previous results, the article presents guidance on how to deal with data coming from sensors, in order to understand the characteristics of sensor datasets, and proposes a parallelized implementation. Finally, the article shows that the proposed decision-making processes work well with a new type of sensor and that parallelizing with several cores enables calculations to be executed up to four times faster.

Safety and efficacy evaluation of the automatic stepwise recruitment maneuver in the neonatal population: An in vivo interventional study. Can anesthesiologists safely perform automatic lung recruitment maneuvers in neonates?

BACKGROUND: A new software has recently been incorporated in almost all new anesthesia machines to enable automatic lung recruitment maneuvers. To date, no studies have assessed the safety and efficacy of these automatic software programs in the neonatal population. AIMS: We aimed to evaluate the safety and efficacy of the lung recruitment maneuver performed using the automatic stepwise recruitment maneuver software of the FLOW-i 4.3 Anesthesia System® in a healthy and live neonatal model. METHODS: Eight male newborn piglets were included in the study. The lung recruitment maneuver was performed in pressure-controlled ventilation with a constant driving pressure (15 cmH2 O) in a stepwise increasing positive end-expiratory pressure (PEEP) model. The target peak inspiratory pressure (PIP) was 30 cmH2 O and PEEP was 15 cmH2 O. The maneuver lasted for 39 seconds. The hemodynamic variables were monitored using the PICCO® system. The following respiratory parameters were monitored: oxygen saturation, fraction of inspired oxygen, partial pressure of oxygen and carbon dioxide in the arterial blood, end-tidal carbon dioxide pressure, PIP, plateau pressure, PEEP, static compliance (Cstat ), and dynamic compliance (Cdyn ). Safety was evaluated by assessing the accuracy of the software, need for not interrupting the maneuver, hemodynamic stability, and absence of adverse respiratory events with the lung recruitment maneuver. Efficacy was evaluated by improvement in Cstat and Cdyn after performing the lung recruitment maneuver. RESULTS: All lung recruitment maneuvers were safely performed as scheduled without any interruptions. No pneumothorax or other side effects were observed. Hemodynamic stability was maintained during the lung recruitment maneuver. We observed an improvement of 33% in Cdyn and 24% in Cstat after the maneuver. CONCLUSIONS: The automatic stepwise recruitment maneuver software of the FLOW-i 4.3 Anesthesia System® is safe and efficacious in a healthy neonatal model. We did not observe any adverse respiratory or hemodynamic events during the implementation of the lung recruitment maneuver in the pressure-controlled ventilation mode using a stepwise increasing PEEP (30/15 cmH2 O) approach.

Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors.

This paper combines Bayesian networks (BN) and information theory to model the likelihood of severe loss of separation (LOS) near accidents, which are considered mid-air collision (MAC) precursors. BN is used to analyze LOS contributing factors and the multi-dependent relationship of causal factors, while Information Theory is used to identify the LOS precursors that provide the most information. The combination of the two techniques allows us to use data on LOS causes and precursors to define warning scenarios that could forecast a major LOS with severity A or a near accident, and consequently the likelihood of a MAC. The methodology is illustrated with a case study that encompasses the analysis of LOS that have taken place within the Spanish airspace during a period of four years.

Oral hygiene and cardiometabolic disease risk in the survey of the health of Wisconsin.

OBJECTIVES: Poor oral health is an increasingly recognized risk factor for cardiovascular disease (CVD) and type 2 diabetes (T2D), but little is known about the association between toothbrushing or flossing and cardiometabolic disease risk. The purpose of this study was to examine the degree to which an oral hygiene index was associated with CVD and T2D risk scores among disease-free adults in the Survey of the Health of Wisconsin. METHODS: All variables were measured in 2008-2010 in this cross-sectional design. Based on toothbrushing and flossing frequency, an oral hygiene index (poor, fair, good, excellent) was created as the primary predictor variable. The outcomes, CVD and T2D risk score, were based on previous estimates from large cohort studies. There were 712 and 296 individuals with complete data available for linear regression analyses in the CVD and T2D samples, respectively. RESULTS: After covariate adjustment, the final model indicated that participants in the excellent (ß ± SE = -0.019 ± 0.008, P = 0.020) oral hygiene category had a significantly lower CVD risk score as compared to participants in the poor oral hygiene category. Sensitivity analyses indicated that both toothbrushing and flossing were independently associated with CVD risk score, and various modifiable risk factors. Oral hygiene was not significantly associated with T2D risk score. CONCLUSIONS: Regular toothbrushing and flossing are associated with a more favorable CVD risk profile, but more experimental research is needed in this area to precisely determine the effects of various oral self-care maintenance behaviors on the control of individual cardiometabolic risk factors. These findings may inform future joint medical-dental initiatives designed to close gaps in the primary prevention of oral and systemic diseases.

Utilidad de las igg fraccionadas en el diagnóstico y seguimiento de la hidatidosis / Usefull of IgG subclasses in the diagnosis of human hydatid diasease and follow-up

Fundamento: Evaluar las IgG fraccionadas en el diagnóstico y seguimiento de la hidatidosis. Material y métodos: Se han estudiado 82 sueros de 50 enfermos diagnosticados previamente de hidatidosis. Estos pacientes fueron divididos en distintos grupos en función de su sintomatología. Se incluyeron 10 sueros de personas sanas como grupo control. Todos los sueros fueron previamente estudiados mediante una técnica de hemaglutinación indirecta. A todos los sueros positivos se les realizó un enzimoinmunoanálisis para determinar las IgG fraccionadas. Resultados: La IgG1 fue positiva en 81 de los 82 pacientes. La IgG2 y la IgG3 fueron positivas en 12 y en 2 pacientes, respectivamente. La IgG4 fue positiva en el 94,4 por ciento de los pacientes con sintomatología de hidatidosis, en el 100 por ciento de los enfermos intervenidos con anterioridad a los que no se practicó una cirugía radical, y en ningún caso en pacientes a los que se realizó cirugía radical o los quistes estaban calcificados. Conclusiones: Las IgG1 e IgG4 pueden ser utilizadas conjuntamente en el diagnóstico de hidatidosis, aportando gran especificidad y sensibilidad. La IgG4 se negativiza en poco tiempo si la evolución es favorable, se positiviza en pacientes que sufren recaídas y se mantiene positiva ante la presencia de un quiste residual. Todo esto hace de la IgG4 un buen marcador en el seguimiento de la hidatidosis (AU)