Evaluation of the performance of KOMPSAT-3 stereo images in terms of positioning and the generation of digital surface models

KOMPSAT-3 is an optical high-resolution earth observation satellite launched on 19 May 2012. It provides panchromatic optical images at a nominal ground sampling distance (GSD) of 70 cm. This letter provides geometric accuracy evaluation of KOMPSAT-3 stereo images, including position determination and automated digital surface model (DSM) generation. Bias-compensated KOMPSAT-3 stereo achieved positioning accuracy of 0.65 m and 0.70 m in horizontal and vertical directions, respectively. Bias-compensated KOMPSAT-3 images were utilized for automated DSM generation. A DSM showed height errors of about 3.8 m (RMS) when all grids were compared with reference LIDAR data. Geometric performance of KOMPSAT-3 images was also compared with that of WorldView-1 images whose nominal GSD is 46 cm, over the same test area. The positioning accuracy and the accuracy of DSM generation from KOMPSAT-3 were slightly inferior to those of WorldView-1. However, overall performance of the two satellite images was comparable, when the difference of GSD is taken into account.     

Intra-articular Chlamydial Antigen and Inflammatory Arthritis

Joint material from 133 patients with well-characterized inflammatoryarthritis, including individuals likely to have suffered reactivearthiritis, was studied. The majority of patients were alsoexamined for the presence of genital tract infection with Chlamydiatrachomatis. Fluorescein-conjugated monoclonal antibodies demonstratedthe presence of C. trachomatis antigen in synovial fluid celldeposits or synovial sections from inflamed knee joints of sevenpatients with reactive arthritis. The significance of thesefindings is discussed, as is the low rate of detection of chlamydialantigen in either the genital tract or the joint from patientsin this study. We emphasize the need for further work aimedat identifying the relevant immunogenic chlamydial antigensresponsible for the initiation of reactive arthritis.     

Cytokine production by primary bone marrow megakaryocytes

Primary human bone marrow megakaryocytes were studied for their ability to express and release cytokines potentially relevant to their proliferation and/or differentiation. The purity of the bone marrow megakaryocytes was assessed by morphologic and immunocytochemical criteria. Unstimulated marrow megakaryocytes constitutively expressed genes for interleukin-1 beta (IL-1 beta), IL-6, granulocyte-macrophage colony-stimulating factor (GM-CSF), and tumor necrosis factor-alpha (TNF-alpha), by the polymerase chain reaction (PCR) and Northern blot analysis. At the protein level, megakaryocytes secreted significant amounts of IL-1 beta (53.6 +/- 3.6 pg/mL), IL-6 (57.6 +/- 15.6 pg/mL), and GM-CSF (24 +/- 4 pg/mL) but not TNF-alpha. Exposure of human marrow megakaryocytes to IL-1 beta increased the levels of IL-6 (87.3 +/- 2.3 pg/mL) detected in the culture supernatants. Transforming growth factor- beta was also able to stimulate IL-6, IL-1 beta, and GM-CSF secretion, but was less potent than stimulation with phorbol-12-myristate-13- acetate (PMA). The secreted cytokines acted additively to maintain and increase the number of colony-forming unit-megakaryocytes colonies (approximately 35%). These studies demonstrate the production of multiple cytokines by isolated human bone marrow megakaryocytes constitutively or stimulated in vitro. The capacity of human megakaryocytes to synthesize several cytokines known to modulate hematopoietic cells supports the concept that there may be an autocrine mechanism operative in the regulation of megakaryocytopoiesis.     

Effects of salbutamol on exhaled breath condensate biomarkers in acute lung injury: prospective analysis

Introduction  

The benefits of β-adrenergic stimulation have been described in acute lung injury (ALI), but there is still no evidence of its anti-inflammatory effect in these patients. Biomarkers in exhaled breath condensate (EBC) were used to study the effects of salbutamol on lung inflammation in mechanically ventilated patients with ALI.     

Health Care Equity in the Use of Advanced Analytics and Artificial Intelligence Technologies in Primary Care

The integration of advanced analytics and artificial intelligence (AI) technologies into the practice of medicine holds much promise. Yet, the opportunity to leverage these tools carries with it an equal responsibility to ensure that principles of equity are incorporated into their implementation and use. Without such efforts, tools will potentially reflect the myriad of ways in which data, algorithmic, and analytic biases can be produced, with the potential to widen inequities by race, ethnicity, gender, and other sociodemographic factors implicated in disparate health outcomes. We propose a set of strategic assertions to examine before, during, and after adoption of these technologies in order to facilitate healthcare equity across all patient population groups. The purpose is to enable generalists to promote engagement with technology companies and co-create, promote, or support innovation and insights that can potentially inform decision-making and health care equity.

Primary care has a critical role to play in ensuring that mission-driven values aimed at eliminating health care disparities are prioritized in the development, selection, clinical implementation, and use of advanced analytics and AI technologies. Because the application of these technologies in primary care is in its infancy, primary care professionals have a unique opportunity to guide the growth of fair, transparent, and ethical AI and analytics applications that embody health equity principles that meet the needs of diverse populations.Today, clinical decision-making in primary care is influenced by the ongoing integration of advanced analytics and AI technologies into the practice of medicine.¹ Examples include patient risk stratification, predictive modeling for disease progression,^2,3 decision-support applications,^4,5 and population health management tools for cancer screenings,^6,7 diabetes,^8,9 cardiovascular disease,^10–12 and other chronic disease conditions.¹³ These and other similar tools may or may not explicitly address the needs of diverse patient populations in primary care. Unless explicit strategies are used to promote equity, advanced analytics may inadvertently perpetuate inequities in primary care delivery, such as the use of algorithms that erroneously treat race categories as biological rather than social attributes in clinical decision making.¹⁴The importance of articulating equity as a specific goal for integrating AI into care is described in the 2019 National Academy of Medicine (NAM) report, Artificial Intelligence in Health Care: The Hope, The Hype, The Promise, The Peril. The report describes a quintuple aim to improve population health, reduce costs, improve the patient experience, promote care team well-being and achieve health care equity.¹⁵ Specifically, the report suggests that embracing health care equity would challenge a siloed approach to health care by addressing the diversity of patient needs using varied sources of data that include social determinants of health and psychosocial risk factors (Fig. ​(Fig.1).1). Equity, integral to the quintuple aim, would also require engaging diverse stakeholders to inform the design of AI applications and to monitor the impact of these technologies. The NAM report underscores the need for explicit strategies to actively embrace health care equity; without such strategies, AI applications are likely to reflect human biases in ways that will widen inequities by race/ethnicity, gender identity, sexual orientation, disability status, age, social class, geography, and other dimensions of social identity.^15,16 Open in a separate windowFigure 1Building on the quintuple aims of equity and inclusion in health and healthcare (National Academy of Medicine).¹⁴Indifference to technology and passive acceptance of biased tools pose risks to health care equity among diverse groups. To prevent this, we must be willing to articulate the priorities for successful AI and advanced analytics implementation and adopt strategies and processes that lead to equitable outcomes. To further these aims, we propose the following series of questions that should be considered before and during the adoption of an AI technology or advanced analytic strategy into practice. First, what needs of diverse patient populations can be better served by applying advanced analytics and AI technology? How can novel and diverse data sources be leveraged to enhance equity in AI implementations? How can patients and community members engage with stakeholders involved in shaping the use of AI in the delivery of health care? And finally, how are principles of diversity and inclusion reflected among those who are involved in the development, selection, and use of technology solutions to enable equitable health care?     

The Association Between Toddlerhood Self-Control and Later Externalizing Problems

Lower self-control is a significant correlate or predictor of a wide range of adult outcomes, and this association may be due to more general tendencies toward childhood externalizing problems. The present study examined the association between toddlerhood self-control expressed within a “don’t” compliance task (at 14–36 months) and later externalizing problems (parent-reported externalizing problems from age 4 to 12 years, teacher-reported externalizing problems from age 7 to 12 years, and self-reported conduct disorder symptoms at age 17 years) in a longitudinal, genetically informative study. The slope of self-control, but not its intercept, predicted later teacher-reported, but not parent- or self-reported, externalizing problems. That is, increase in self-control during toddlerhood was associated with lower levels of later teacher-reported externalizing problems. The slope of self-control was no longer a significant predictor of teacher-reported externalizing problems after controlling for observed disregard for others, a robust predictor of externalizing problems. Thus, the hypothesis that self-control is the primary predictor of externalizing problems was not supported. Results from genetic analyses suggested that the covariance between the slope of self-control and teacher-reported externalizing problems is due to both genetic and shared environmental influences.