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AbstractObjectiveTo evaluate trends in organisms causing early-onset neonatal sepsis (EONS). Congruent with recent reports, we hypothesized there would be an increase in EONS caused by Escherichia coli.Study DesignNational data on infants admitted to neonatal intensive care units from 2009 to 2014 were compared to previously reported data from 2003 to 2008. We report 430 cases of EONS from 2009 to 2014. Bivariate analyses were used to analyze the distribution of causative organisms over time and differences by gestational age. Linear regression was used to estimate trends in causative organisms.ResultsSince 2003, there has been a trend of increasing numbers of cases caused by E coli (P<0.01). The predominant organism was E coli in preterm infants and Group B Streptococcus in term infants.ConclusionsWith the majority of EONS cases now caused by E coli, our findings emphasize the importance of continued surveillance of causative organism patterns and developing approaches to reduce cases caused by E coli.

This dataset is composed of CT pulmonary angiograms and annotations related to pulmonary embolism. It is available at https://www.rsna.org/education/ai-resources-and-training/ai-image-challenge/rsn...

Brain tumors are a hazardous type of tumor, and they build pressure inside the skull when they grow, which can potentially cause brain damage or even death. Attention mechanisms are widely adopted in state-of-the-art deep learning architectures for computer vision and neural translation tasks since they enhance networks' ability to capture spatial and channel-wise relationships. We offer an attention-based image segmentation model that outlines the brain tumors in Magnetic Resonance Imaging (MRI) scans if present. In the paper, we mainly focus on integrating Squeeze-and-Excitation Block and CBAM into the commonly used segmentation model, U-Net, to resolve the problem of concatenating unnecessary information into the decoder blocks and attempt to locate the tumor boundaries. Our research clearly shows the application of the attention mechanism in U-Net, incorporates the Squeeze-and-Excitation with CBAM, and improves the performance in the brain tumor segmentation task. The model is delivered on an app with additional text to speech and chatbot features provided.

Although finite Blaschke products are a remarkable and exclusive class of functions, they appear in many important approximation problems.

Abstract During the processing of water containing food materials, water transport can be driven by pressure from gas, capillarity, swelling, or gravity. To describe these various modes of pressure-driven transport using Darcy's law, permeability is a critical parameter for which few data are available, primarily as a result of measurement difficulties. Permeability in the flow of liquid water through potato tissue was estimated by approximating the plant cellular structure, measured directly using flow-through and indirectly using nuclear magnetic resonance. The data from the three approaches show the same order of magnitude 10−18 m2, and thus are in agreement with each other and results reported in the literature. The dynamics of porosity and permeability changes in fresh, water-saturated, and cooked samples were described using cellular structure changes as observed in microscopy images. Simple mechanistic equations for predicting permeability and porosity, which can include swelling and shrinkage, are proposed based on approximating the geometry of the cellular tissue. Data corroborated in three ways will provide permeability values with confidence, while the simple prediction equation will make a reasonable estimation of permeability possible for a large class of food products.

Advancements in the domain of artificial intelligence, safety management, and on-board fault tolerance have led to autonomous devices to be considered as a key element for future remote defence and peaceful missions. Drones-Also known as autonomous or unmanned vehicles-with different capabilities and features-can be organized in a fleet and the fleet can be organized in a way that will increase the survivability of the drones and improve mission success. This can be accomplished by balancing system effectiveness design parameters such as endurance, communications, sensor fusion, domain awareness, area coverage rates and human operator interaction against mission costs.

Sickle cell disease (SCD) results in chronic hemolytic anemia, recurrent vascular occlusion, insidious vital organ deterioration, early mortality, and diminished quality of life. Life-threatening acute physiologic crises may occur on a background of progressive diminishing vital organ function. Sickle hemoglobin polymerizes in the deoxygenated state, resulting in erythrocyte membrane deformation, vascular occlusion, and hemolysis. Vascular occlusion and increased blood viscosity results in functional asplenia and immune deficiency in early childhood, resulting in life-long increased susceptibility to serious bacterial infections. Infection remains a main cause of overall mortality in patients with SCD in low- and middle-income countries due to increased exposure to pathogens, increased co-morbidities such as malnutrition, lower vaccination rates, and diminished access to definitive care, including antibiotics and blood. Thus, the greatest gains in preventing infection-associated mortality can be achieved by addressing these factors for SCD patients in austere environments. In contrast, in high-income countries, perinatal diagnosis of SCD, antimicrobial prophylaxis, vaccination, aggressive use of antibiotics for febrile episodes, and the availability of contemporary critical care resources have resulted in a significant reduction in deaths from infection; however, chronic organ injury is problematic. All clinicians, regardless of their discipline, who assume the care of SCD patients must understand the importance of infectious disease as a contributor to death and disability. In this concise narrative review, we summarize the data that describes the importance of infectious diseases as a contributor to death and disability in SCD and discuss pathophysiology, prevalent organisms, prevention, management of acute episodes of critical illness, and ongoing care.

Abstract Many recent studies on soil and crop management practices have demonstrated their possibility in mitigating greenhouse gas emissions (GHG) from cropland. The response of GHG emissions to management practices can be quantitatively simulated using biophysics-based agricultural system models. However, the economic feasibilities of such management adoption are yet to be evaluated, especially when the producers have to adopt only one profitable management plan. This paper presents a field-scale economic analysis software that is capable of estimating the net benefits under various greenhouse gas mitigating management practices. The calculated net benefits are based on the benefit-cost analysis (BCA), where GHG emissions are converted to the CO2 equivalent and priced using the information from the current carbon trade market. GHG emissions and crop yield are simulated using the Root Zone Water Quality Model (RZWQM2), an agricultural system model coupled with the BCA software. A case study for a cornfield at the Saint Emmanuel site near Montreal, Canada, from 2012 to 2015 under two water table management practices, i.e., free-drainage (FD) and controlled drainage (CD), results showed that FD was more profitable than CD. Although fewer greenhouse gases were emitted under CD than under FD, the potential benefit under current carbon credit payment from GHG reduction under CD was far less than the additional cost from installing new instruments and excessive maintenance fees. This study suggests that the government subsidy is needed to provide further incentives for producers to adopt new greenhouse gas emission mitigating management practices.

BACKGROUND Current guidelines endorse shared decision making (SDM) for prostate-specific antigen (PSA) screening. The relationship between a patient's health literacy (HL) and SDM remains unclear. In the current study, the authors sought to identify the impact of HL on the rates of PSA screening and on the relationship between HL and SDM following the 2012 US Preventive Services Task Force recommendations against PSA screening. METHODS Using data from the 2016 Behavioral Risk Factor Surveillance System, the authors examined PSA screening in the 13 states that administered the optional "Health Literacy" module. Men aged ≥50 years were examined. Complex samples multivariable logistic regression models were computed to assess the odds of undergoing PSA screening. The interactions between HL and SDM were also examined. RESULTS A weighted sample of 12.249 million men with a rate of PSA screening of 33.4% were identified. Approximately one-third self-identified as having optimal HL. Rates of PSA screening were found to be highest amongst the highest HL group (42.2%). Being in this group was a significant predictor of undergoing PSA screening (odds ratio, 1.214; 95% confidence interval, 1.051-1.403). There was a significant interaction observed between HL and SDM (P for interaction, <.001) such that higher HL was associated with a lower likelihood of undergoing PSA screening when SDM was present. CONCLUSIONS In the uncertain environment of multiple contradictory screening guidelines, men who reported higher levels of HL were found to have higher levels of screening. The authors demonstrated that increased HL may reduce the screening-promoting effect of SDM. These findings highlight the dynamic interplay between HL and SDM that should inform the creation and promulgation of SDM guidelines, specifically when considering patients with low HL.

The COnstrain Dark Energy with X-ray clusters (CODEX) sample contains the largest flux limited sample of X-ray clusters at $0.35 < z < 0.65$. It was selected from ROSAT data in the 10,000 square degrees of overlap with BOSS, mapping a total number of 2770 high-z galaxy clusters. We present here the full results of the CFHT CODEX program on cluster mass measurement, including a reanalysis of CFHTLS Wide data, with 25 individual lensing-constrained cluster masses. We employ $lensfit$ shape measurement and perform a conservative colour-space selection and weighting of background galaxies. Using the combination of shape noise and an analytic covariance for intrinsic variations of cluster profiles at fixed mass due to large scale structure, miscentring, and variations in concentration and ellipticity, we determine the likelihood of the observed shear signal as a function of true mass for each cluster. We combine 25 individual cluster mass likelihoods in a Bayesian hierarchical scheme with the inclusion of optical and X-ray selection functions to derive constraints on the slope $��$, normalization $��$, and scatter $��_{\ln ��| ��}$ of our richness-mass scaling relation model in log-space: $\left = ����+ ��$, with $��= \ln (M_{200c}/M_{\mathrm{piv}})$, and $M_{\mathrm{piv}} = 10^{14.81} M_{\odot}$. We find a slope $��= 0.49^{+0.20}_{-0.15}$, normalization $ \exp(��) = 84.0^{+9.2}_{-14.8}$ and $��_{\ln ��| ��} = 0.17^{+0.13}_{-0.09}$ using CFHT richness estimates. In comparison to other weak lensing richness-mass relations, we find the normalization of the richness statistically agreeing with the normalization of other scaling relations from a broad redshift range ($0.0 37 pages, 12 figures