With software systems becoming more and more complex and configurable, failures due to misconfigurations are becoming a prominent problem. In addition to the severe consequences such failures can cause, the diagnosis process can be difficult and costly. Besides, configurations also heavily affect the system performance. The selection of optimal configuration settings to achieve high performance is thus desired but usually takes a long time. This paper presents the efforts to help with the system correctness and performance problem by configuration analysis. To tackle the correctness problem and automatically detect software Misconfigurations, we take into account two important factors that are unexploited before: the interaction between the executing environment and the configuration file, and the rich correlations between configuration entries. We leverage the fact that with the emerging cloud virtual machines, more system data than just the configuration files are accessible. With the training data enriched with whole system information, our tool learns multiple aspects of the configuration files from the whole system stack, and thus is able to deal with a much broader range of configuration errors. At the same time, our tool provides a highly customizable interface that helps fully utilize users' domain knowledge, making the learning phase adaptive and effective. Results show that EnCore is effective in detecting both injected errors and known real world problems. In addition, it finds 37 new misconfigurations in 25 existing Amazon EC2 public images, as well as 24 new configuration problems in 22 images in a commercial private cloud. These previously undiscovered errors can cause problems in various aspects such as service unavailability and security issues. While correctness standard is usually consistent across different platforms and thus can be learnt with a large data set, different systems usually need different configuration settings to achieve high performance according to their specific characteristics. Therefore only learning the configuration values is not enough. Previous works try to automatically select the optimal configuration settings by trying out the whole space. However it usually takes a long time. We significantly reduce the computation time by analyzing the correlation and constraints of performance settings from source code

Introduction: The number and characteristics of pregnant patients presenting to the emergency department (ED) has not been well described. Our objective in this study was to determine the prevalence and characteristics of pregnant patients presenting to EDs in the US between 2010–2020.

 

Methods: We completed a retrospective, cross-sectional study of patient encounters at hospital-based EDs in the US from 2010–2020. Using the ED subsample of the National Hospital Ambulatory Medical Care Survey (NHAMCS) we identified ED visits for female patients aged 15–44 years. We defined a subsample of these as visits for pregnant patients using discharge diagnosis codes specificto pregnancy. We compared this population of pregnant patient visits to those for non-pregnant patients and computed point estimates for nationally weighted values. Multivariable linear regression was used to determine factors independently associated with pregnant patient visits.

 

Results: The 2010–2020 NHAMCS dataset included 255,963 ED visits. Of these visits 59,080 were for female patients 15–44 years old, and 6,068 of those visits were for pregnant patients. Pregnant patients accounted for 3% (95% confidence interval [CI] 2.7–3.2) of all ED visits and 8.6% (95% CI 8–9.3) of all visits among female patients 15–44 years. Weighting to a national sample, this equates to 2.77 million pregnant patients presenting for ED visits annually. Pregnant patients were more likely to be Black, Hispanic, or to use public insurance.

 

Conclusion: Pregnant patients make up a significant number of ED visits annually and are more likely to be people of color or publicly insured. Interventions to address the effects of changing abortion legislation on emergency medicine practice may benefit from consideration that certain populations of pregnant people are more likely to present to the ED for care.

Heightened secretion of protumorigenic effector proteins is a feature of malignant cells. Yet, the molecular underpinnings and therapeutic implications of this feature remain unclear. Here, we identify a chromosome 1q region that is frequently amplified in diverse cancer types and encodes multiple regulators of secretory vesicle biogenesis and trafficking, including the Golgi-dedicated enzyme phosphatidylinositol (PI)-4-kinase IIIβ (PI4KIIIβ). Molecular, biochemical, and cell biological studies show that PI4KIIIβ-derived PI-4-phosphate (PI4P) synthesis enhances secretion and accelerates lung adenocarcinoma progression by activating Golgi phosphoprotein 3 (GOLPH3)-dependent vesicular release from the Golgi. PI4KIIIβ-dependent secreted factors maintain 1q-amplified cancer cell survival and influence prometastatic processes in the tumor microenvironment. Disruption of this functional circuitry in 1q-amplified cancer cells with selective PI4KIIIβ antagonists induces apoptosis and suppresses tumor growth and metastasis. These results support a model in which chromosome 1q amplifications create a dependency on PI4KIIIβ-dependent secretion for cancer cell survival and tumor progression.