Two-stage quality monitoring of a laser welding process using machine learning – An approach for fast yet precise quality monitoring

In production, quality monitoring is essential to detect defective elements. State-of-the-art approaches are single-sensor systems (SSS) and multi-sensor systems (MSS). Yet, these approaches might not be suitable: Nowadays, one component may comprise several hundred meters of the weld seam, necessitating high-speed welding to produce enough components. To detect as many defects as possible in time, fast yet precise monitoring is required. However, information captured by SSS might not be sufficient and MSS suffer from long inference times. Therefore, we present a confidence-based cascaded system (CS). The key idea of the CS is that not all data are analyzed to obtain the quality weld, but only selected ones. As evidenced by our results, all CS outperform SSS in terms of accuracy and inference time. Further, compared to MSS, the CS has hardware advantages

Incorporación de la evaluación de tecnologías sanitarias en la toma de decisiones en el sistema de servicios de salud del seguro social del Perú: La experiencia del IETSI.

Public health systems have the great challenge of meeting the population's health needs with limited financial resources. Thus, to incorporate new health technologies (HT) in their coverage plans, they use tools that allow them to inform their decisions based on scientific evidence, such as health technology assessments (HTAs). These are developed in a multidisciplinary way, under an explicit methodology, which estimates the value of a technology. With this information, the decision-maker can support his decision, anticipate the impact of its implementation, plan actions, and set goals, all of which promote efficient use of resources, transparency of processes, and facilitate accountability. The Peruvian Social Security healthcare system (EsSalud) implemented, through the Instituto de Evaluación de Tecnologías en Salud e Investigación (IETSI), a decision-making process based on HTAs to decide on the coverage of new HT. From the creation of the IETSI in December 2014 to December 2021, 407 ETS have been carried out. Of these, 161 were approved, thus extending new treatments for clinical conditions. These incorporations have not translated into a sharp increase in the annual expending of medicines to put EsSalud's financial sustainability at risk, although it has increased patient access to innovative technologies. The average investment per patient treated with these technologies was reduced from S/ 133,270 in 2011 to S/ 47,779 in 2019.Los sistemas de salud públicos tienen el gran reto de atender las necesidades de salud de la población con recursos económicos limitados. Así, para incorporar nuevas tecnologías sanitarias (TS) en sus planes de cobertura usan herramientas que les permitan informar sus decisiones en evidencia científica, como las evaluaciones de tecnología sanitaria (ETS). Éstas son desarrolladas de forma multidisciplinaria, bajo una metodología explícita, lo que permite calcular el valor de una tecnología. Con esta información, el decisor puede sustentar su decisión, prever el impacto de su implementación, planificar acciones y establecer metas; todo lo cual potencia un uso eficiente de los recursos, transparencia de los procesos y facilita la rendición de cuentas. El Seguro Social de Salud del Perú (EsSalud) implementó, a través del Instituto de Evaluación de Tecnologías en Salud e Investigación (IETSI), un proceso de toma de decisiones basado en ETS para decidir sobre la cobertura de nuevas TS. Desde la creación del IETSI en diciembre 2014 a diciembre del año 2021 se han realizado 407 ETS. De éstas, 161 fueron aprobatorias, extendiéndose así nuevos tratamientos para condiciones clínicas. Estas incorporaciones no se han traducido en un incremento agudo en el gasto anual de medicamentos que ponga en riesgo la sostenibilidad financiera de EsSalud, aunque incrementó el acceso de pacientes a tecnologías innovadoras. La inversión promedio por paciente atendido con estas tecnologías se redujo después de la creación del IETSI de S/ 133,270.00 en el 2011 a S/ 47,779.00 en el 2019

Influenza Virus in Human Exhaled Breath: An Observational Study

Background: Recent studies suggest that humans exhale fine particles during tidal breathing but little is known of their composition, particularly during infection. Methodology/Principal Findings: We conducted a study of influenza infected patients to characterize influenza virus and particle concentrations in their exhaled breath. Patients presenting with influenza-like-illness, confirmed influenza A or B virus by rapid test, and onset within 3 days were recruited at three clinics in Hong Kong, China. We collected exhaled breath from each subject onto Teflon filters and measured exhaled particle concentrations using an optical particle counter. Filters were analyzed for influenza A and B viruses by quantitative polymerase chain reaction (qPCR). Twelve out of thirteen rapid test positive patients provided exhaled breath filter samples (7 subjects infected with influenza B virus and 5 subjects infected with influenza A virus). We detected influenza virus RNA in the exhaled breath of 4 (33%) subjects–three (60%) of the five patients infected with influenza A virus and one (14%) of the seven infected with influenza B virus. Exhaled influenza virus RNA generation rates ranged from <3.2 to 20 influenza virus RNA particles per minute. Over 87% of particles exhaled were under 1 µm in diameter. Conclusions: These findings regarding influenza virus RNA suggest that influenza virus may be contained in fine particles generated during tidal breathing, and add to the body of literature suggesting that fine particle aerosols may play a role in influenza transmission

The global network maternal newborn health registry: A multi-country, community-based registry of pregnancy outcomes

Background: The Global Network for Women\u27s and Children\u27s Health Research (Global Network) conducts clinical trials in resource-limited countries through partnerships among U.S. investigators, international investigators based in in low and middle-income countries (LMICs) and a central data coordinating center. The Global Network\u27s objectives include evaluating low-cost, sustainable interventions to improve women\u27s and children\u27s health in LMICs. Accurate reporting of births, stillbirths, neonatal deaths, maternal mortality, and measures of obstetric and neonatal care is critical to determine strategies for improving pregnancy outcomes. In response to this need, the Global Network developed the Maternal Newborn Health Registry (MNHR), a prospective, population-based registry of pregnant women, fetuses and neonates receiving care in defined catchment areas at the Global Network sites. This publication describes the MNHR, including participating sites, data management and quality and changes over time.Methods: Pregnant women who reside in or receive healthcare in select communities are enrolled in the MNHR of the Global Network. For each woman and her offspring, sociodemographic, health care, and the major outcomes through 42-days post-delivery are recorded. Study visits occur at enrollment during pregnancy, at delivery and at 42 days postpartum.Results: From 2010 through 2018, the Global Network MNHR sites were located in Guatemala, Belagavi and Nagpur, India, Pakistan, Democratic Republic of Congo, Kenya, and Zambia. During this period at these sites, 579,140 pregnant women were consented and enrolled in the MNHR, nearly 99% of all eligible women. Delivery data were collected for 99% of enrolled women and 42-day follow-up data for 99% of those delivered. In this supplement, the trends over time and assessment of differences across geographic regions are analyzed in a series of 18 manuscripts utilizing the MNHR data.Conclusions: Improving maternal, fetal and newborn health in countries with poor outcomes requires an understanding of the characteristics of the population, quality of health care and outcomes. Because the worst pregnancy outcomes typically occur in countries with limited health registration systems and vital records, alternative registration systems may prove to be highly valuable in providing data. The MNHR, an international, multicenter, population-based registry, assesses pregnancy outcomes over time in support of efforts to develop improved perinatal healthcare in resource-limited areas. Trial Registration The Maternal Newborn Health Registry is registered at Clinicaltrials.gov (ID# NCT01073475). Registered February 23, 2019. https://clinicaltrials.gov/ct2/show/NCT01073475

Ocean acidification with (de)eutrophication will alter future phytoplankton growth and succession

Human activity causes ocean acidification (OA) though the dissolution of anthropogenically generated CO2 into seawater, and eutrophication through the addition of inorganic nutrients. Eutrophication increases the phytoplankton biomass that can be supported during a bloom, and the resultant uptake of dissolved inorganic carbon during photosynthesis increases water-column pH (bloom-induced basification). This increased pH can adversely affect plankton growth. With OA, basification commences at a lower pH. Using experimental analyses of the growth of three contrasting phytoplankton under different pH scenarios, coupled with mathematical models describing growth and death as functions of pH and nutrient status, we show how different conditions of pH modify the scope for competitive interactions between phytoplankton species. We then use the models previously configured against experimental data to explore how the commencement of bloom-induced basification at lower pH with OA, and operating against a background of changing patterns in nutrient loads, may modify phytoplankton growth and competition. We conclude that OA and changed nutrient supply into shelf seas with eutrophication or de-eutrophication (the latter owing to pollution control) has clear scope to alter phytoplankton succession, thus affecting future trophic dynamics and impacting both biogeochemical cycling and fisheries

Trends and determinants of stillbirth in developing countries: results from the Global Network\u27s Population-Based Birth Registry.

BACKGROUND: Stillbirth rates remain high, especially in low and middle-income countries, where rates are 25 per 1000, ten-fold higher than in high-income countries. The United Nations\u27 Every Newborn Action Plan has set a goal of 12 stillbirths per 1000 births by 2030 for all countries. METHODS: From a population-based pregnancy outcome registry, including data from 2010 to 2016 from two sites each in Africa (Zambia and Kenya) and India (Nagpur and Belagavi), as well as sites in Pakistan and Guatemala, we evaluated the stillbirth rates and rates of annual decline as well as risk factors for 427,111 births of which 12,181 were stillbirths. RESULTS: The mean stillbirth rates for the sites were 21.3 per 1000 births for Africa, 25.3 per 1000 births for India, 56.9 per 1000 births for Pakistan and 19.9 per 1000 births for Guatemala. From 2010 to 2016, across all sites, the mean stillbirth rate declined from 31.7 per 1000 births to 26.4 per 1000 births for an average annual decline of 3.0%. Risk factors for stillbirth were similar across the sites and included maternal age \u3c 20 years and age \u3e 35 years. Compared to parity 1-2, zero parity and parity \u3e 3 were both associated with increased stillbirth risk and compared to women with any prenatal care, women with no prenatal care had significantly increased risk of stillbirth in all sites. CONCLUSIONS: At the current rates of decline, stillbirth rates in these sites will not reach the Every Newborn Action Plan goal of 12 per 1000 births by 2030. More attention to the risk factors and treating the causes of stillbirths will be required to reach the Every Newborn Action Plan goal of stillbirth reduction. TRIAL REGISTRATION: NCT01073475

Data quality monitoring and performance metrics of a prospective, population-based observational study of maternal and newborn health in low resource settings

BACKGROUND: To describe quantitative data quality monitoring and performance metrics adopted by the Global Network´s (GN) Maternal Newborn Health Registry (MNHR), a maternal and perinatal population-based registry (MPPBR) based in low and middle income countries (LMICs). METHODS: Ongoing prospective, population-based data on all pregnancy outcomes within defined geographical locations participating in the GN have been collected since 2008. Data quality metrics were defined and are implemented at the cluster, site and the central level to ensure data quality. Quantitative performance metrics are described for data collected between 2010 and 2013. RESULTS: Delivery outcome rates over 95% illustrate that all sites are successful in following patients from pregnancy through delivery. Examples of specific performance metric reports illustrate how both the metrics and reporting process are used to identify cluster-level and site-level quality issues and illustrate how those metrics track over time. Other summary reports (e.g. the increasing proportion of measured birth weight compared to estimated and missing birth weight) illustrate how a site has improved quality over time. CONCLUSION: High quality MPPBRs such as the MNHR provide key information on pregnancy outcomes to local and international health officials where civil registration systems are lacking. The MNHR has measures in place to monitor data collection procedures and improve the quality of data collected. Sites have increasingly achieved acceptable values of performance metrics over time, indicating improvements in data quality, but the quality control program must continue to evolve to optimize the use of the MNHR to assess the impact of community interventions in research protocols in pregnancy and perinatal health.Fil: Goudar, Shivaprasad S.. KLE University. Jawaharlal Nehru Medical College; IndiaFil: Stolka, Kristen B.. Research Triangle Institute International; Estados UnidosFil: Koso Thomas, Marion. Eunice Kennedy Shriver National Institute of Child Health and Human Development; Estados UnidosFil: Honnungar, Narayan V.. KLE University. Jawaharlal Nehru Medical College; IndiaFil: Mastiholi, Shivanand C.. KLE University. Jawaharlal Nehru Medical College; IndiaFil: Ramadurg, Umesh Y.. S. Nijalingappa Medical College; IndiaFil: Dhaded, Sangappa M.. KLE University. Jawaharlal Nehru Medical College; IndiaFil: Pasha, Omrana. Aga Khan University; PakistánFil: Patel, Archana. Indira Gandhi Government Medical College and Lata Medical Research Foundation; IndiaFil: Esamai, Fabian. University School of Medicine; KeniaFil: Chomba, Elwyn. University of Zambia; ZambiaFil: Garces, Ana. Universidad de San Carlos; GuatemalaFil: Althabe, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Efectividad Clínica y Sanitaria; ArgentinaFil: Carlo, Waldemar A.. University of Alabama at Birmingahm; Estados UnidosFil: Goldenberg, Robert L.. Columbia University; Estados UnidosFil: Hibberd, Patricia L.. Massachusetts General Hospital for Children; Estados UnidosFil: Liechty, Edward A.. Indiana University; Estados UnidosFil: Krebs, Nancy F.. University of Colorado School of Medicine; Estados UnidosFil: Hambidge, Michael K.. University of Colorado School of Medicine; Estados UnidosFil: Moore, Janet L.. Research Triangle Institute International; Estados UnidosFil: Wallace, Dennis D.. Research Triangle Institute International; Estados UnidosFil: Derman, Richard J. Christiana Care Health Services; Estados UnidosFil: Bhalachandra, Kodkany S.. KLE University. Jawaharlal Nehru Medical College; IndiaFil: Bose, Carl L.. University of North Carolina; Estados Unido

Collection of Aerosolized Human Cytokines Using Teflon® Filters

Background: Collection of exhaled breath samples for the analysis of inflammatory biomarkers is an important area of research aimed at improving our ability to diagnose, treat and understand the mechanisms of chronic pulmonary disease. Current collection methods based on condensation of water vapor from exhaled breath yield biomarker levels at or near the detection limits of immunoassays contributing to problems with reproducibility and validity of biomarker measurements. In this study, we compare the collection efficiency of two aerosol-to-liquid sampling devices to a filter-based collection method for recovery of dilute laboratory generated aerosols of human cytokines so as to identify potential alternatives to exhaled breath condensate collection. Methodology/Principal Findings: Two aerosol-to-liquid sampling devices, the SKC® Biosampler and Omni 3000™, as well as Teflon® filters were used to collect aerosols of human cytokines generated using a HEART nebulizer and single-pass aerosol chamber setup in order to compare the collection efficiencies of these sampling methods. Additionally, methods for the use of Teflon® filters to collect and measure cytokines recovered from aerosols were developed and evaluated through use of a high-sensitivity multiplex immunoassay. Our results show successful collection of cytokines from pg/m3 aerosol concentrations using Teflon® filters and measurement of cytokine levels in the sub-picogram/mL concentration range using a multiplex immunoassay with sampling times less than 30 minutes. Significant degradation of cytokines was observed due to storage of cytokines in concentrated filter extract solutions as compared to storage of dry filters. Conclusions: Use of filter collection methods resulted in significantly higher efficiency of collection than the two aerosol-to-liquid samplers evaluated in our study. The results of this study provide the foundation for a potential new technique to evaluate biomarkers of inflammation in exhaled breath samples