How Donald McGavran Has Impacted One Urban Church Plant and Indirectly Influenced Thousands of Other Churches: An Analysis of the Journey Church of the City

This essay offers five specific principles from Donald McGavran that have directly influenced The Journey Church in New York City, San Francisco, and Boca Raton, Florida, and indirectly thousands of other churches (through the writing and coaching ministry of Nelson Searcy with Church Leader Insights). McGavran’s principles of missionary eyes, goal setting, assimilation, homogeneity, and a Great Commission focus have proven invaluable in this church plant and offer a similar value to other churches seeking to make a difference in their communities

Bird song as a signal of aggressive intent

Abstract A central question in animal communication research concerns the reliability of animal signals. The question is particularly relevant to aggressive communication, where there often may be advantages to signaling an exaggerated likelihood of attack. We tested whether aggressive signals are indeed reliable signals of attack in song sparrows (Melospiza melodia). We elicited aggressive signaling using a 1-min playback on a male's territory, recorded the behavior of the male for 5 min, and then gave him the opportunity to attack a taxidermic mount of a song sparrow associated with further playback. Twenty subjects attacked the mount and 75 did not. Distance to the speaker was a significant predictor of attack for both the initial recording period and the 1 min before attack. For the initial recording period, none of the measures of singing behavior that we made was a significant predictor of attack, including song-type matching, type-switching frequency, and song rate. For the 1-min period immediately before attack, only the number of low amplitude "soft songs" was a significant predictor of attack. Although most aggressive signals contained little information on attack likelihood, as some models suggest should be the case, the unreliability of these signals was not caused by convergence of individuals on a single signaling strategy, as those models argue should occur

Competition-induced stress does not explain deceptive alarm calling in tufted capuchin monkeys

Tactical deception has long attracted interest because it is often assumed to entail complex cognitive mechanisms. However, systematic evidence of tactical deception is rare and no study has attempted to determine whether such behaviours may be underpinned by relatively simple mechanisms. This study examined whether deceptive alarm calling among wild tufted capuchin monkeys, Cebus apella nigritus, feeding on contestable food resources can be potentially explained by a physiological mechanism, namely increased activation in the adrenocortex and the resulting production of glucocorticoids (GCs; ‘stress hormones’). This was tested experimentally in Iguazu? National Park, Argentina, by manipulating the potential for contest competition over food and noninvasively monitoring GC production through analysis of faecal hormone metabolites. If deceptive false alarms are indeed associated with adreno- cortical activity, it was predicted that the patterns of production of these calls would match the patterns of GC output, generally being higher in callers than noncallers in cases in which food is most contestable, and specifically being higher in callers on those occasions when a deceptive false alarm was produced. This hypothesis was not supported, as (1) GC output was significantly lower in association with the experimental introduction of contestable resources than in natural contexts wherein the potential for contest is lower, (2) within experimental contexts, there was a nonsignificant tendency for noncallers to show higher GC output than callers when food was most contestable, and (3) individuals did not show higher GC levels in cases in which they produced deceptive alarms relative to cases in which they did not. A learned association between the production of alarms and increased access to food may be the most likely cognitive explanation for this case of tactical deception, although unexplored physiological mechanisms also remain possible

Detecting Volunteer Cotton Plants in a Corn Field with Deep Learning on UAV Remote-Sensing Imagery

The cotton boll weevil, Anthonomus grandis Boheman is a serious pest to the U.S. cotton industry that has cost more than 16 billion USD in damages since it entered the United States from Mexico in the late 1800s. This pest has been nearly eradicated; however, southern part of Texas still faces this issue and is always prone to the pest reinfestation each year due to its sub-tropical climate where cotton plants can grow year-round. Volunteer cotton (VC) plants growing in the fields of inter-seasonal crops, like corn, can serve as hosts to these pests once they reach pin-head square stage (5-6 leaf stage) and therefore need to be detected, located, and destroyed or sprayed . In this paper, we present a study to detect VC plants in a corn field using YOLOv3 on three band aerial images collected by unmanned aircraft system (UAS). The two-fold objectives of this paper were : (i) to determine whether YOLOv3 can be used for VC detection in a corn field using RGB (red, green, and blue) aerial images collected by UAS and (ii) to investigate the behavior of YOLOv3 on images at three different scales (320 x 320, S1; 416 x 416, S2; and 512 x 512, S3 pixels) based on average precision (AP), mean average precision (mAP) and F1-score at 95% confidence level. No significant differences existed for mAP among the three scales, while a significant difference was found for AP between S1 and S3 (p = 0.04) and S2 and S3 (p = 0.02). A significant difference was also found for F1-score between S2 and S3 (p = 0.02). The lack of significant differences of mAP at all the three scales indicated that the trained YOLOv3 model can be used on a computer vision-based remotely piloted aerial application system (RPAAS) for VC detection and spray application in near real-time.Comment: 38 Page

Reverberation Mapping of the Kepler-Field AGN KA1858+4850

KA1858+4850 is a narrow-line Seyfert 1 galaxy at redshift 0.078 and is among the brightest active galaxies monitored by the Kepler mission. We have carried out a reverberation mapping campaign designed to measure the broad-line region size and estimate the mass of the black hole in this galaxy. We obtained 74 epochs of spectroscopic data using the Kast Spectrograph at the Lick 3-m telescope from February to November of 2012, and obtained complementary V-band images from five other ground-based telescopes. We measured the H-beta light curve lag with respect to the V-band continuum light curve using both cross-correlation techniques (CCF) and continuum light curve variability modeling with the JAVELIN method, and found rest-frame lags of lag_CCF = 13.53 (+2.03, -2.32) days and lag_JAVELIN = 13.15 (+1.08, -1.00) days. The H-beta root-mean-square line profile has a width of sigma_line = 770 +/- 49 km/s. Combining these two results and assuming a virial scale factor of f = 5.13, we obtained a virial estimate of M_BH = 8.06 (+1.59, -1.72) x 10^6 M_sun for the mass of the central black hole and an Eddington ratio of L/L_Edd ~ 0.2. We also obtained consistent but slightly shorter emission-line lags with respect to the Kepler light curve. Thanks to the Kepler mission, the light curve of KA1858+4850 has among the highest cadences and signal-to-noise ratios ever measured for an active galactic nucleus; thus, our black hole mass measurement will serve as a reference point for relations between black hole mass and continuum variability characteristics in active galactic nuclei

Computer Vision for Volunteer Cotton Detection in a Corn Field with UAS Remote Sensing Imagery and Spot Spray Applications

To control boll weevil (Anthonomus grandis L.) pest re-infestation in cotton fields, the current practices of volunteer cotton (VC) (Gossypium hirsutum L.) plant detection in fields of rotation crops like corn (Zea mays L.) and sorghum (Sorghum bicolor L.) involve manual field scouting at the edges of fields. This leads to many VC plants growing in the middle of fields remain undetected that continue to grow side by side along with corn and sorghum. When they reach pinhead squaring stage (5-6 leaves), they can serve as hosts for the boll weevil pests. Therefore, it is required to detect, locate and then precisely spot-spray them with chemicals. In this paper, we present the application of YOLOv5m on radiometrically and gamma-corrected low resolution (1.2 Megapixel) multispectral imagery for detecting and locating VC plants growing in the middle of tasseling (VT) growth stage of cornfield. Our results show that VC plants can be detected with a mean average precision (mAP) of 79% and classification accuracy of 78% on images of size 1207 x 923 pixels at an average inference speed of nearly 47 frames per second (FPS) on NVIDIA Tesla P100 GPU-16GB and 0.4 FPS on NVIDIA Jetson TX2 GPU. We also demonstrate the application of a customized unmanned aircraft systems (UAS) for spot-spray applications based on the developed computer vision (CV) algorithm and how it can be used for near real-time detection and mitigation of VC plants growing in corn fields for efficient management of the boll weevil pests.Comment: 39 page

Effects of Long-Term Pioglitazone Treatment on Peripheral and Central Markers of Aging

BACKGROUND: Thiazolidinediones (TZDs) activate peroxisome proliferator-activated receptor gamma (PPARgamma) and are used clinically to help restore peripheral insulin sensitivity in Type 2 diabetes (T2DM). Interestingly, long-term treatment of mouse models of Alzheimer\u27s disease (AD) with TZDs also has been shown to reduce several well-established brain biomarkers of AD including inflammation, oxidative stress and Abeta accumulation. While TZD\u27s actions in AD models help to elucidate the mechanisms underlying their potentially beneficial effects in AD patients, little is known about the functional consequences of TZDs in animal models of normal aging. Because aging is a common risk factor for both AD and T2DM, we investigated whether the TZD, pioglitazone could alter brain aging under non-pathological conditions. METHODS AND FINDINGS: We used the F344 rat model of aging, and monitored behavioral, electrophysiological, and molecular variables to assess the effects of pioglitazone (PIO-Actos® a TZD) on several peripheral (blood and liver) and central (hippocampal) biomarkers of aging. Starting at 3 months or 17 months of age, male rats were treated for 4-5 months with either a control or a PIO-containing diet (final dose approximately 2.3 mg/kg body weight/day). A significant reduction in the Ca2+-dependent afterhyperpolarization was seen in the aged animals, with no significant change in long-term potentiation maintenance or learning and memory performance. Blood insulin levels were unchanged with age, but significantly reduced by PIO. Finally, a combination of microarray analyses on hippocampal tissue and serum-based multiplex cytokine assays revealed that age-dependent inflammatory increases were not reversed by PIO. CONCLUSIONS: While current research efforts continue to identify the underlying processes responsible for the progressive decline in cognitive function seen during normal aging, available medical treatments are still very limited. Because TZDs have been shown to have benefits in age-related conditions such as T2DM and AD, our study was aimed at elucidating PIO\u27s potentially beneficial actions in normal aging. Using a clinically-relevant dose and delivery method, long-term PIO treatment was able to blunt several indices of aging but apparently affected neither age-related cognitive decline nor peripheral/central age-related increases in inflammatory signaling

Integrated supply chain design for commodity chemicals production via woody biomass fast pyrolysis and upgrading

This study investigates the optimal supply chain design for commodity chemicals (BTX, etc.) production via woody biomass fast pyrolysis and hydroprocessing pathway. The locations and capacities of distributed preprocessing hubs and integrated biorefinery facilities are optimized with a mixed integer linear programming model. In this integrated supply chain system, decisions on the biomass chipping methods (roadside chipping vs. facility chipping) are also explored. The economic objective of the supply chain model is to maximize the profit for a 20-year chemicals production system. In addition to the economic objective, the model also incorporates an environmental objective of minimizing life cycle greenhouse gas emissions, analyzing the trade-off between the economic and environmental considerations. The capital cost, operating cost, and revenues for the biorefinery facilities are based on techno-economic analysis, and the proposed approach is illustrated through a case study of Minnesota, with Minneapolis-St. Paul serving as the chemicals distribution hub