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Abstract Background Since its first occurrence in the New York City area during 1999, West Nile virus (WNV) has spread rapidly across North America and has become a major public health concern in North America. By 2002, WNV was reported in 40 states and the District of Columbia with 4,156 human and 14,539 equine cases of infection. Mississippi had the highest human incidence rate of WNV during the 2002 epidemic in the United States. Epidemics of WNV can impose enormous impacts on local economies. Therefore, it is advantageous to predict human WNV risks for cost-effective controls of the disease and optimal allocations of limited resources. Understanding relationships between precipitation and WNV transmission is crucial for predicting the risk of the human WNV disease outbreaks under predicted global climate change scenarios. Methods We analyzed data on the human WNV incidences in the 82 counties of Mississippi in 2002, using standard morbidity ratio (SMR) and Bayesian hierarchical models, to determine relationships between precipitation and human WNV risks. We also entertained spatial autocorrelations of human WNV risks with conditional autocorrelative (CAR) models, implemented in WinBUGS 1.4.3. Results We observed an inverse relationship between county-level human WNV incidence risk and total annual rainfall during the previous year. Parameters representing spatial heterogeneity in the risk of human exposure to WNV improved model fit. Annual precipitation of the previous year was a predictor of spatial variation of WNV risk. Conclusions Our results have broad implications for risk assessment of WNV and forecasting WNV outbreaks. Assessing risk of vector-born infectious diseases will require understanding of complex ecological relationships. Based on the climatologically characteristic drought occurrence in the past and on climate model predictions for climate change and potentially greater drought occurrence in the future, we suggest that the frequency and relative risk of WNV outbreaks could increase.

Atherosclerosis (AS) is the main cause of morbidity and mortality in the world. Mitochondrial dysfunction is closely related to AS. At present, several signaling pathways related to mitochondrial dysfunction have been found, one of which is around PGC-1 α. PGC-1 α is a transcription activator, which is related to mitochondrial biogenesis and antioxidant defense. In this study, we explored the effect of miR-18a-5p/PGC-1 α signaling pathway on mitochondrial energy metabolism in HAECs with ox-LDL treatment. The results showed that the mitochondrial energy metabolism disorder in HAECs treated by ox-LDL was related to the downregulation of LncRNA FENDRR and PGC-1 α. FENDRR could reverse ox-LDL induced mitochondrial energy metabolism disorder and upregulate the PGC-1 α expression. FENDRR could be used as ceRNA to inhibit the miR-18a-5p expression and reduce the negative regulation of miR-18a-5p on PGC-1 α. Downregulation of miR-18a-5p expression or upregulation of PGC-1 α in ox-LDL treated HAECs could reverse mitochondrial energy metabolism disorder. In conclusion, these findings suggested that FENDRR/miR-18a-5p/PGC-1 α signaling pathway regulated mitochondrial energy metabolism in HAECs; ox-LDL downregulated the expression of PGC-1 α and cause mitochondrial energy metabolism disorder by inhibiting this signal pathway. [ABSTRACT FROM AUTHOR]

Weather has been recognized as a density independent factor influencing the abundance, distribution, and behavior of vertebrates. Male wild turkeys' (Meleagris gallopavo) breeding behavior includes vocalizations and courtship displays to attract females, the phenology of which can vary with latitude. State biologists design spring turkey-hunting season frameworks centered on annual vocalization patterns to maximize hunter engagement. The Mississippi Department of Wildlife, Fisheries, and Parks has traditionally instituted a statewide, 7-week, spring harvest season. However, hunters routinely argue that different peaks in gobbling activity across the state exist. The objective of this study was to determine whether differences in peak gobbling activity existed across a latitudinal gradient of Mississippi and assess the effect of weather on gobbling. During 2008 and 2009, we conducted a statewide gobbling survey. We used generalized additive mixed models to describe the probability and frequency of gobbling activity within northern and southern regions of the state. We also investigated the effect of daily weather conditions on gobbling activity. Our results revealed an approximate 10–14-day difference in peak gobbling activity between southern and northern Mississippi. The majority of all gobbling activity occurred within the current spring harvest framework. Perhaps more importantly, gobbling activity was more prevalent on days of regionally dry conditions (i.e., less humid) according to the Spatial Synoptic Classification. Our results provide information on gobbling activity phenology relative to hunting-season dates and weather-response information. Our approach may be particularly applicable in states with relatively shorter seasons or highly variable daily weather conditions that moderate gobbling frequency. [ABSTRACT FROM AUTHOR]

Abstract Movement ecology has rapidly advanced owing to recent developments of animal-attached devices and wide applications of sophisticated statistical and machine learning techniques in analysis of animal movement data. Global Positioning System (GPS) transmitters used for estimating animal locations and tri-axial accelerometers used for measuring the 3-dimensional accelerations of animal's motion aid researchers in collecting location and locomotion data at fine spatial and temporal scales. Machine learning and other advanced statistical methods bridge conceptual models to data, providing insights into ecological and physiological mechanisms underlying animal behavior and movements. This study reviews the general principles and applications of state space models, hidden Markov models, random forests, and support vector machines in the inference of animal behavior from movement data. Unsupervised learning algorithms, including Bayesian state space models implemented for robust correlated random walk models and hidden Markov models, help infer different behavioral modes using GPS location data. State space models can account for measurement error in GPS locations and estimate the true locations of animals; however, without including movement-state switching, state space models do not infer behavioral modes directly. On the contrary, hidden Markov models estimate the probabilities that animals switch between different behavioral modes. Nevertheless, hidden Markov models neither directly estimate animal locations nor account for measurement error explicitly. Supervised learning algorithms integrate data on locations and directional accelerations with synchronized behavioral observations (i.e., labels) to classify behaviors to pre-defined behavioral categories. Unlike unsupervised learning, supervised learning requires behavioral observations to label locations and accelerometer data to train the learning algorithms. However, behavioral observations synchronized with relocations and acceleration records are often missing or unattainable in many species, hindering the applications of supervised learning, making unsupervised learning a suitable tool for behavioral annotation of movement paths in secretive (cryptic) or less studied species. Environmental and behavioral annotations of animal movement paths by machine learning improve understanding the effects of environmental conditions on animal movements and behavioral decisions. Highlights • Machine learning has been widely applied to movement and location data. • Unsupervised learning helps study behaviors of less studied species. • Supervised learning classifies behaviors to pre-defined categories. • Fast, efficient learning algorithms are needed for big data on animal movements. [ABSTRACT FROM AUTHOR]