Insights and approaches using deep learning to classify wildlife.

The implementation of intelligent software to identify and classify objects and individuals in visual fields is a technology of growing importance to operatives in many fields, including wildlife conservation and management. To non-experts, the methods can be abstruse and the results mystifying. Here, in the context of applying cutting edge methods to classify wildlife species from camera-trap data, we shed light on the methods themselves and types of features these methods extract to make efficient identifications and reliable classifications. The current state of the art is to employ convolutional neural networks (CNN) encoded within deep-learning algorithms. We outline these methods and present results obtained in training a CNN to classify 20 African wildlife species with an overall accuracy of 87.5% from a dataset containing 111,467 images. We demonstrate the application of a gradient-weighted class-activation-mapping (Grad-CAM) procedure to extract the most salient pixels in the final convolution layer. We show that these pixels highlight features in particular images that in some cases are similar to those used to train humans to identify these species. Further, we used mutual information methods to identify the neurons in the final convolution layer that consistently respond most strongly across a set of images of one particular species. We then interpret the features in the image where the strongest responses occur, and present dataset biases that were revealed by these extracted features. We also used hierarchical clustering of feature vectors (i.e., the state of the final fully-connected layer in the CNN) associated with each image to produce a visual similarity dendrogram of identified species. Finally, we evaluated the relative unfamiliarity of images that were not part of the training set when these images were one of the 20 species "known" to our CNN in contrast to images of the species that were "unknown" to our CNN

Decreased Information Replacement of Working Memory After Sleep Deprivation: Evidence From an Event-Related Potential Study

Working memory (WM) components are altered after total sleep deprivation (TSD), both with respect to information replacement and result judgment. However, the electrophysiological mechanisms of WM alterations following sleep restriction remain largely unknown. To identify such mechanisms, event-related potentials were recorded during the n-back WM task, before and after 36 h sleep deprivation. Thirty-one young volunteers participated in this study and performed a two-back WM task with simultaneous electroencephalography (EEG) recording before and after TSD and after 8 h time in bed for recovery (TIBR). Repeated measures analysis of variance revealed that, compared to resting wakefulness, sleep deprivation induced a decrease in the P200 amplitude and induced longer reaction times. ERP-component scalp topographies results indicated that such decrease primarily occurred in the frontal cortex. The N200 and P300 amplitudes also decreased after TSD. Our results suggest that decreased information replacement of WM occurs after 36 h of TSD and that 8 h TIBR after a long period of TSD leads to partial restoration of WM functions. The present findings represent the EEG profile of WM during mental fatigue

Evaluation of microstructure variation of TC11 alloy after electroshocking treatment

Electro-shocking treatment (EST) has been investigated as a pathway to optimise the microstructure and mechanical properties of titanium alloys. The thermal conditions introduced by EST resulted in a phase transformation from α to β. The fraction of β phase decreased from 25.27% to 19.47% after EST for 0.02 s, which was possibly caused by the recrystallization of α phase. The application of EST for 0.04 s resulted in an increase in volume fraction of the β phase to 26.95%. The energy introduced by EST resulted in changes to the direction and intensity of texture within the microstructure with the texture intensity of the α phase increasing from 4.94 to 8.52, and that of β both increased from 3.35 to 9.88 after 0.04 s EST. © 2020 The Authors

Molecular Level Comparison of Water Extractives of Maple and Oak with Negative and Positive Ion ESI FT-ICR Mass Spectrometry

Soluble extractives in wood function to protect living trees from destructive agents and also contribute to wood color and fragrance. Some extractive components have biological activities with medical applications. They also play important roles in wood processing and related applications. To increase the knowledge of wood chemistry, maple and oak were extracted by water. Ultraviolet/visible (UV/vis) spectroscopy indicated the presence of a phenolic compound, resorcinol, in maple extractives having higher molecular mass and more aromatic components than oak extractives. Negative and positive electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI FT-ICR-MS) identified thousands of formulas in the two samples in the m/z range of 200 to 800. They mainly fall into the lignin-like, carbohydrate-like, and tannin-like compound categories. The top 25 peaks (ie, formulas) with the highest relative magnitude in negative ESI represented nearly 50% of the summed total spectral magnitude of all formulas assigned in the maple and oak extractives. Furthermore, the base peak (ie, most abundant peak) accounted for about 14% of the total abundance in each wood sample. Literature comparisons identified 17 of 20 formulas in the top five peaks of the four spectra as specific bioactive compounds in trees and other plants, implying the potential to explore utilization of maple and oak extractives for functional and medicinal applications. The various profiling of the top 25 peaks from the two samples also suggested the possible application of FT-ICR-MS for detecting chemical markers useful in profiling and identification of wood types and sources

Carbon footprint and driving forces of saline agriculture in coastally reclaimed areas of eastern China: a survey of four staple crops

Carbon emissions have always been a key issue in agricultural production. Because of the specific natural factors in the soil of saline agriculture, there are distinctive characteristics in saline agricultural production as compared with traditional agricultural zones. Here, we have adopted the theory of life cycle assessment, and employed the Intergovernmental Panel on Climate Change (IPCC) greenhouse gas (GHG) field calculation to estimate the GHG emissions, derived from the staple crop productions (i.e., barley, wheat, corn and rice). In addition, our study further analyzed the main driving forces of carbon emissions, and proposed some effective measures to reduce them. Our results have showed that: (1) Carbon footprint from the four crops in the study area varies from 0.63 to 0.77 kg CO2 eq•kg-1, which is higher than that from traditional agriculture; (2) GHG emissions from Fertilizer-Nitrogen (N) manufacture and inorganic N application have contributed to the greatest percentage of carbon footprint. Compared with traditional agricultural zones, fertilizer-N application and paddy irrigation involved with crop productions have overall greater contributions to carbon footprint; (3) Carbon emissions from saline agriculture can be reduced significantly by planting-breeding combination to reduce the amount of N fertilizer application, improving the traditional rotation system, and developing water-saving agriculture and ecological agriculture