Disciple Making Starts at a Different Point Today

Disciple-making starts at a different place today than it did forty years ago. Knocking on a stranger’s door today is unlikely to be effective for people distant from the gospel as is characteristic of today\u27s generations. We must now view evangelism as a process and we must have a non-judgmental acceptance of all people. We must be willing to change to communicate the gospel in a way persuasive way

A Radio Pulsar/X-ray Binary Link

Radio pulsars with millisecond spin periods are thought to have been spun up by transfer of matter and angular momentum from a low-mass companion star during an X-ray-emitting phase. The spin periods of the neutron stars in several such low-mass X-ray binary (LMXB) systems have been shown to be in the millisecond regime, but no radio pulsations have been detected. Here we report on detection and follow-up observations of a nearby radio millisecond pulsar (MSP) in a circular binary orbit with an optically identified companion star. Optical observations indicate that an accretion disk was present in this system within the last decade. Our optical data show no evidence that one exists today, suggesting that the radio MSP has turned on after a recent LMXB phase.Comment: published in Scienc

Toward Objective Quantification of Perfusion-weighted Computed Tomography in Subarachnoid Hemorrhage: Quantification of Symmetry and Automated Delineation of Vascular Territories

Rationale and Objectives: Perfusion-weighted computed tomography (CTP) is a relatively recent innovation that estimates a value for cerebral blood flow (CBF) using a series of axial head CT images tracking the time course of a signal from an intravenous contrast bolus. Materials and Methods: CTP images were obtained using a standard imaging protocol and were analyzed using commercially available software. A novel computer-based method was used for objective quantification of side-to-side asymmetries of CBF values calculated from CTP images. Results: Our method corrects for the inherent variability of the CTP methodology seen in the subarachnoid hemorrhage (SAH) patient population to potentially aid in the diagnosis of cerebral vasospasm (CVS). This method analyzes and quantifies side-to-side asymmetry of CBF and presents relative differences in a construct termed a Relative Difference Map (RDM). To further automate this process, we have developed a unique methodology that enables a computer to delineate vascular territories within a brain image, regardless of the size and shape of the brain. Conclusions: While both the quantification of image symmetry using RDMs and the automated assignment of vascular territories were initially designed for the analysis of CTP images, it is likely that they will be useful in a variety of applications

Application of Machine Learning Methodologies for Predicting Corn Economic Optimal Nitrogen Rate

Determination of in-season N requirement for corn (Zea mays L.) is challenging due to interactions of genotype, environment, and management. Machine learning (ML), with its predictive power to tackle complex systems, may solve this barrier in the development of locally based N recommendations. The objective of this study was to explore application of ML methodologies to predict economic optimum nitrogen rate (EONR) for corn using data from 47 experiments across the US Corn Belt. Two features, a water table adjusted available water capacity (AWCwt) and a ratio of in-season rainfall to AWCwt (RAWCwt), were created to capture the impact of soil hydrology on N dynamics. Four ML models— linear regression (LR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO) regression, and gradient boost regression trees (GBRT)—were assessed and validated using “leave-one-location-out” (LOLO) and “leave-one-year-out” (LOYO) approaches. Generally, RR outperformed other models in predicting both at planting and split EONR times. Among the 47 tested sites, for 33 sites the predicted split EONR using RR fell within the 95% confidence interval, suggesting the chance of using the RR model to make an acceptable prediction of split EONR is ~70%. When RR was used to test split EONR prediction with input weather features surrogated with 10 yr of historical weather data, the model demonstrated robustness (MAE, 33.6 kg ha–1; R2 = 0.46). Incorporating mechanistically derived hydrological features significantly enhanced the ability of the ML procedures to model EONR. Improvement in estimating in-season soil hydrological status seems essential for success in modeling N demand