One of the most iconic wild equids, the plains zebra occupies a broad region of sub-Saharan Africa and exhibits a wide range of phenotypic diversity in stripe patterns that have been used to classify multiple subspecies. After decades of relative stability, albeit with a loss of at least one recognized subspecies, the total population of plains zebras has undergone an approximate 25% decline since 2002. Individuals with abnormal stripe patterns have been recognized in recent years but the extent to which their appearance is related to demography and/or genetics is unclear. Investigating population genetic health and genetic structure are essential for developing effective strategies for plains zebra conservation. We collected DNA from 140 plains zebra, including seven with abnormal stripe patterns, from nine locations across the range of plains zebra, and analyzed data from restriction site-associated and whole genome sequencing (RAD-seq, WGS) libraries to better understand the relationships between population structure, genetic diversity, inbreeding, and abnormal phenotypes. We found that genetic structure did not coincide with described subspecific variation, but did distinguish geographic regions in which anthropogenic habitat fragmentation is associated with reduced gene flow and increased evidence of inbreeding, especially in certain parts of East Africa. Further, zebras with abnormal striping exhibited increased levels of inbreeding relative to normally striped individuals from the same populations. Our results point to a genetic cause of stripe pattern abnormalities, and dramatic evidence of the consequences of habitat fragmentation.Time-temperature indicators (TTIs) are cost-efficient tools that may be used to predict food quality. In this paper, a diffusion TTI was used to predict fruit quality during storage. Both the color changing characters of TTI and the quality parameters, including weight loss, soluble solids content, vitamin C content, titratable acidity, and antioxidant capacity of three kinds of fruits (kiwifruit, strawberry, and mango), were investigated for storage temperatures (5, 10, 15, and 20 °C). The relationships between the color changing properties and fruit quality parameters have been built based on the activation energy (Ea ). The results showed that the storage temperature and time had significant effects on the color changing of TTI and fruit quality. The RGB value of TTI decreased with time, and the higher the storage temperature, the faster the RGB value reduced. Also, the higher the storage temperature, the faster the fruit quality changed and the poorer they were. Furthermore, all of the differences of Ea bduring storage and distribution based on visualization technology that can simplify the methods of detecting fruit quality and achieve fast quality detection. It provides the possibility for low-cost quality monitoring and has more application potential in food quality predicting. Further studies on diffusion TTI are needed to develop its application in more field of food and make the diffusion TTI an intelligent mean for food quality monitoring and predicting.The correct identification of change-points during ongoing outbreak investigations of infectious diseases is a matter of paramount importance in epidemiology, with major implications for the management of health care resources, public health and, as the COVID-19 pandemic has shown, social live. Onsets, peaks, and inflexion points are some of them. An onset is the moment when the epidemic starts. A "peak" indicates a moment at which the incorporated values, both before and after, are lower a maximum. The inflexion points identify moments in which the rate of growth of the incorporation of new cases changes intensity. In this study, after interpreting the concept of elasticity of a random variable in an innovative way, we propose using it as a new simpler tool for anticipating epidemic remission change-points. In particular, we propose that the "remission point of change" will occur just at the instant when the speed in the accumulation of new cases is lower than the average speed of accumulation of cases up to that moment. This gives stability and robustness to the estimation in the event of possible remission variations. This descriptive measure, which is very easy to calculate and interpret, is revealed as informative and adequate, has the advantage of being distribution-free and can be estimated in real time, while the data is being collected. We use the 2014-2016 Western Africa Ebola virus epidemic to demonstrate this new approach. A couple of examples analyzing COVID-19 data are also included.Reliability of the air transportation system heavily depends on the performance of communication, navigation, and surveillance facilities in the National Airspace System (NAS). These facilities are prone to outages caused by convective weather, such as lightning. Current lightning safety standards and risk assessments focus solely on lightning occurrence and omit the effect of lightning intensity from hazard characterization. We propose methods that incorporate lightning intensity and occurrence parameters to better understand the impact of lightning strike on the NAS using the National Lightning Detection Network and Federal Aviation Administration NAS facilities and equipment outage databases. Spatial analysis and clustering reveal different exposure profiles for 436 U.S. airports. Kernel Density estimation and Hot Spot analysis show that regardless of lightning intensity, Southern state airports are the most exposed to lightning hazards.  https://www.selleckchem.com/products/Gefitinib.html  K-means clustering reveal five different lightning exposure profiles that mimic the spatial patterns produced by the Kernel Density estimation and Hot Spot analysis. A scoring system ranks all airports according to their exposure profile taking into consideration lightning occurrence and intensity. It is complemented with a rising trend exposure analysis, which identifies airports whose exposure could be underestimated under the current standards, identifying airports with fewer lightning occurrences but higher intensities. Finally, a comparison between the exposure patterns and lightning-induced outages provide insights into U.S. lightning impact patterns. Similar patterns between lightning exposure and outages indicate that the results of the proposed lightning hazard assessment provide useful information for prioritizing airport hardening investments at the national scale and reducing lightning risk.