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Dysfunctional autophagy is associated with various human diseases, e.g., cancer. The discovery of small molecules modulating autophagy with therapeutic potential could be significant. To this end, we screened the ability of a series of metabolites isolated from marine microorganisms to modulate autophagy. Anhydrodebromoaplysiatoxin (ADAT), a metabolite yielded by the marine red algae Gracilaria coronopifolia, inhibited autophagosome-lysosome fusion in mammalian cells, thereby inducing the accumulation of autophagosomes. Treatment of cells with ADAT alkalinized lysosomal pH. Interestingly, ADAT also activated the mTOR/p70S6K/FoxO3a signaling pathway, likely leading to the inhibition of autophagy induction. ADAT had little effect on apoptosis. Our results suggest that ADAT is a dichotomic autophagy inhibitor that inhibits both late-stage (autophagosome-lysosome fusion) and early-stage (autophagy induction) autophagy. [ABSTRACT FROM AUTHOR]

Simple Summary: Mesopredators (i.e., carnivores of small or intermediate body size) are key species for ecosystem stability, especially when larger carnivore populations have declined, as they have important ecosystem functions, such as population size control. They are essential for ecosystem energy circulation. Studies of predators' dietary habits and dietary differentiation between species provide critical information for identifying the structure and dynamics of trophic interactions in an ecosystem. Past research studies focused only on the diet of large carnivores while few detailed dietary datasets of mesopredators exist that present knowledge gaps regarding their coexistence. Using DNA metabarcoding, we studied the prey of three sympatric Mustelidae species in the Northeast Tiger and Leopard National Park. We find that prey preferences were largely partitioned by the three Mustelidae species and that they likely competed over certain dietary items (e.g., frogs and rodents). Our results provide an initial characterization of prey relationships between different Mustelidae species and may be used to inform management on dietary preferences of mesopredators that are affected by anthropogenic activities. Although human disturbance has declined with the creation of the national park, extra management for the protection of ecosystem functions and food webs is still needed. Diet analysis is essential to fully understand the biology of a species and its function within the ecosystem, as well as being key in identifying food web interactions and the population dynamics of predators and prey. The understanding of the diet of small to mid-sized carnivores remains generally lacking or uninformative due to the inability for taxonomic resolution based on morphology. The yellow-throated marten (Martes flavigula), Eurasian river otter (Lutra lutra), and Siberian weasel (Mustela sibirica) are three important Mustelidae species in ecosystems of northeast China. Based on fecal DNA and a next-generation sequencing (NGS) approach, we analyzed the vertebrate prey of these three sympatric Mustelidae. Prey included 7 mammalian taxa, 10 fishes, 2 birds, and 2 amphibians, with 85% of the taxa assigned to the species level. In total, twenty-one vertebrate prey taxa were identified from seven yellow-throated martens, eight Eurasian river otters, and two Siberian weasels. Concerning identified dietary species, 10 taxa were consumed by yellow-throated martens, 14 by Eurasian river otters, and 4 by Siberian weasels. Some prey species were identified in more than one species. Amphibians and fishes were the most dominant Eurasian river otter prey categories, whereas Eurasian badger (Meles leucurus), birds, and rodents were the main yellow-throated marten prey; amphibians and rodents were largely contained in Siberian weasel prey. Among prey items, Dybowski's frog (Rana dybowskii) and Korean field mouse (Apodemus peninsulae) were identified in all three Mustelidae species but our analyses suggest potential diet preferences among Mustelidae species. Future studies should focus on understanding the trophic relationships of these three Mustelidae species, providing valuable information for their conservation planning. [ABSTRACT FROM AUTHOR]

Exploring the processes of interspecific relationships is crucial to understanding the mechanisms of biodiversity maintenance. Visually detecting interspecies relationships of large mammals is limited by the reconstruction accuracy of the environmental structure and the timely detection of animal behavior. Hence, we used backpack laser scanning (BLS) to reconstruct the high-resolution three-dimensional environmental structure to simulate the process of a predator approaching its prey, indicating that predator tigers would reduce their visibility by changing their behavior. Wild boars will nibble off about 5m of branches around the nest in order to create better visibility around the nest, adopting an anti-predation strategy to detect possible predators in advance. Our study not only points out how predator–prey relationships are affected by visibility as the environment mediates it, but also provides an operable framework for exploring interspecific relationships from a more complex dimension. Finally, this study provides a new perspective for exploring the mechanisms of biodiversity maintenance. [ABSTRACT FROM AUTHOR]

Simple Summary: The imagery captured by cameras provides important information for wildlife research and conservation. Deep learning technology can assist ecologists in automatically identifying and processing imagery captured from camera traps, improving research capabilities and efficiency. Currently, many general deep learning architectures have been proposed but few have evaluated their applicability for use in real camera trap scenarios. Our study constructed the Northeast Tiger and Leopard National Park wildlife dataset (NTLNP dataset) for the first time and compared the real-world application performance of three currently mainstream object detection models. We hope this study provides a reference on the applicability of the AI technique in wild real-life scenarios and truly help ecologists to conduct wildlife conservation, management, and research more effectively. Camera traps are widely used in wildlife surveys and biodiversity monitoring. Depending on its triggering mechanism, a large number of images or videos are sometimes accumulated. Some literature has proposed the application of deep learning techniques to automatically identify wildlife in camera trap imagery, which can significantly reduce manual work and speed up analysis processes. However, there are few studies validating and comparing the applicability of different models for object detection in real field monitoring scenarios. In this study, we firstly constructed a wildlife image dataset of the Northeast Tiger and Leopard National Park (NTLNP dataset). Furthermore, we evaluated the recognition performance of three currently mainstream object detection architectures and compared the performance of training models on day and night data separately versus together. In this experiment, we selected YOLOv5 series models (anchor-based one-stage), Cascade R-CNN under feature extractor HRNet32 (anchor-based two-stage), and FCOS under feature extractors ResNet50 and ResNet101 (anchor-free one-stage). The experimental results showed that performance of the object detection models of the day-night joint training is satisfying. Specifically, the average result of our models was 0.98 mAP (mean average precision) in the animal image detection and 88% accuracy in the animal video classification. One-stage YOLOv5m achieved the best recognition accuracy. With the help of AI technology, ecologists can extract information from masses of imagery potentially quickly and efficiently, saving much time. [ABSTRACT FROM AUTHOR]

Simple Summary: A variety of new survey technologies are continuously being developed and used in wildlife monitoring. Rapidly advancing and widely used camera trap survey technology has helped to capture data and gain insights into many species. The Eastern Himalayas is a global biodiversity hotspot with exceptionally high species diversity. The Asian golden cat is widely distributed in the Yarlung Zangbo Grand Canyon National Nature Reserve. It inhabits seasonal rain forests from 100 m above sea level to the Rhododendron forest up to 3500 m above sea level. Coat pattern variation in the Asian golden cat is particularly prominent in this region. The common color type is the most widely distributed, followed by nine other types. We found 10 coat pattern variations and two coat patterns with a broken tail made up 0.32% of independent photos taken during a long-term nine-year monitoring program. The variation in coat patterns is indicative of the geography of the region. Environmental conditions regulate and activate the genetic diversity of Asian golden cat phenotypes. This study further strengthened the understanding of the basic knowledge of golden cat color types and lays the foundation for exploring the diversity of golden cat color types at the molecular level. The Asian golden cat (Catopuma temminckii) is the most varied wild cat species in terms of coat color. Understanding coat pattern variation will help to elucidate the mechanisms behind it as well as its relationship with the environment. We conducted long-term (2013–2021) monitoring of Asian golden cats in the Yarlung Zangbo Grand Canyon National Nature Reserve, Tibet, using camera traps at 283 points over 89,991 camera days. A total of 620 cat photos were recorded, including 344 (55.48%) with recognizable color patterns. Vector graphics of the coat patterns were extracted from the field image data, which revealed 10 color types in the ratio common: cinnamon: reddish-brown long hair: ocelot: blackening: melanistic: gray: brown: brown short hair: pure black = 123:76:57:35:22:8:7:7:5:4. The genes for coat pattern variation are widespread in the Asian golden cat population and are relatively stable. The increase in population size intraspecific competition has led to the tail break phenotype in individual cats. The gene encoding for tail breakage in Asian golden cats remains unknown. This study provides basic information for understanding faunal diversity in the Eastern Himalayan biodiversity hotspot and serves as a reference for studies on the formation mechanisms for feline color pattern diversity. [ABSTRACT FROM AUTHOR]

The ambient formation of secondary particulate matter (ambient FSPM) is commonly recognized as the major cause of severe PM2.5 air pollution in China. We present observational evidence showing that the ambient FSPM was too weak to yield a detectable contribution to extreme PM2.5 pollution events that swept northern China between 11 and 14 January 2019. Although the Community Multiscale Air Quality (CMAQ) model (v5.2) reasonably reproduced the observations in January 2019, it largely underestimated the concentrations of the PM2.5 during the episode. We propose a novel mechanism, called the "in-fresh-stack-plume non-precipitation-cloud processing of aerosols" followed by the evaporation of semi-volatile components from the aerosols, to generate PM2.5 at extremely high concentrations because of highly concentrated gaseous precursors and large amounts of water droplets in fresh cooling combustion plumes under poor dispersion conditions, low ambient temperature, and high relative humidity. The recorded non-precipitation-cloud processing of the aerosols in fresh stack combustion plumes normally lasts 20–30 s, but it prolongs as long as 2–5 min under cold, humid, and stagnant meteorological conditions and expectedly causes severe PM2.5 pollution events. Regardless of the presence of the natural cloud in the planetary boundary layer during the extreme events, the fast conversion of air pollutants in water droplets and the generation of the PM2.5 through the non-precipitation-cloud processing of aerosols always occur in fresh combustion plumes. The processing of aerosols is detectable using a nano-scan particle sizer assembled on an unmanned aerial vehicle to monitor the particle formation in stack plumes. In-fresh-stack-plume processed aerosols under varying meteorological conditions need to be studied urgently. [ABSTRACT FROM AUTHOR]

Photodegradation is a key process impacting the lifetime of oxalate in the atmosphere, but few studies investigated this process in the field due to the complex mixing and sources of oxalate. Oxalate-containing particles were measured via single-particle aerosol mass spectrometry at coastal and suburban sites in Qingdao, a coastal city in North China in the summer of 2016. The mixing state and influence of different ambient conditions on the source and photodegradation of oxalate were investigated. Generally, 6.3% and 12.3% of the total particles (by number) contained oxalate at coastal and suburban sites, respectively. Twelve major types of oxalate-containing particles were identified, and they were classified into three groups. Biomass burning (BB)-related oxalate–K and oxalate–carbonaceous particles were the dominant groups, respectively, accounting for 68.9% and 13.6% at the coastal site and 72.0% and 16.8% at the suburban site. Oxalate–Heavy metals (HM)-related particles represented 14.6% and 9.3% of the oxalate particles at coastal and suburban sites, respectively, which were mainly from industrial emissions (Cu-rich, Fe-rich, Pb-rich), BB (Zn-rich), and residual fuel oil combustion (V-rich). The peak area of oxalate at the coastal site decreased immediately after sunrise, while it increased during the daytime at the suburban site. However, the oxalate peak area of Fe-rich particles at both sites decreased after sunrise, indicating that iron plays an important role in oxalate degradation in both environments. The decay rates (k) of Fe-rich and BB-Fe particles at the coastal site (−0.978 and −0.859 h−1, respectively), were greater than those at the suburban site (−0.512 and −0.178 h−1, respectively), owing to the high-water content of particles and fewer oxalate precursors. The estimated k values of oxalate peak area for different ambient conditions were in the same order of magnitude, which can help establish or validate the future atmospheric models. [ABSTRACT FROM AUTHOR]

Simple Summary: Abnormal coloration is very rare in any given population of wildlife; however, our research identified a yellow-throated marten population with a high ratio of this phenomenon for the first time. Across the main distribution of the species with relevant observational data, we observed abnormally-colored martens in only Northeast Tiger and Leopard National Park. Abnormal coloration had a variety of forms and individuals with white paws that accounted for a larger proportion of the overall population than normal individuals. This shows heritable variation in the region, which is worthy of further research. Mammalian coat color is determined by heritable variations such as disease, nutrition, and hormone levels. Variation in animal coat color is also considered an environmental indicator and provides clues for the study of population genetics and biogeography. Records of abnormal coloration in the wild are rare, not only because it is often selected against, but also because of the difficulties in detection of the phenomenon. We used long-term camera-trapping data to first report abnormal coat coloration in yellow-throated marten (Martes flavigula) in China. Six types of abnormal coloration were found only in the Northeast Tiger and Leopard National Park, Northeast China, which were not reported in other regions in China. A total of 268 videos of Martes flavigula contained normal coloration, 455 videos of individuals of the species contained abnormal coloration, 437 contained the 'gloving' type (martens with de-pigmented front toes, paws or lower forelimbs), while the remaining other 18 videos contained five types (different degrees of white-spotting and dilution). The higher relative abundance index (0.428, 'gloving' to 0.329, normal) and wide distribution area of the 'gloving' type indicated that this abnormal coat coloration type is usual in Northeast China, which may reflect genetic variability in the local population. These records will contribute to further research on animal coat color and its corresponding adaptive strategy. [ABSTRACT FROM AUTHOR]