On Global Attractivity of a Class of Nonautonomous Difference Equations

We mainly investigate the global behavior to the family of higher-order nonautonomous recursive equations given by y n p ry n−s / q φ n y n−1 , y n−2 , . . . , y n−m y n−s , n ∈ N 0 , with p ≥ 0, r, q > 0, s, m ∈ N and positive initial values, and present some sufficient conditions for the parameters and maps φ n : R m → R , n ∈ N 0 , under which every positive solution to the equation converges to zero or a unique positive equilibrium. Our main result in the paper extends some related results from the work of Gibbons et al

The single-cell landscape of cystic echinococcosis in different stages provided insights into endothelial and immune cell heterogeneity

IntroductionHydatid cysts and angiogenesis are the key characteristics of cystic echinococcosis, with immune cells and endothelial cells mediating essential roles in disease progression. Recent single-cell analysis studies demonstrated immune cell infiltration after Echinococcus granulosus infection, highlighting the diagnostic and therapeutic potential of targeting certain cell types in the lesion microenvironment. However, more detailed immune mechanisms during different periods of E. granulosus infection were not elucidated.MethodsHerein, we characterized immune and endothelial cells from the liver samples of mice in different stages by single-cell RNA sequencing.ResultsWe profiled the transcriptomes of 45,199 cells from the liver samples of mice at 1, 3, and 6 months after infection (two replicates) and uninfected wild-type mice. The cells were categorized into 26 clusters with four distinct cell types: natural killer (NK)/T cells, B cells, myeloid cells, and endothelial cells. An SPP1+ macrophage subset with immunosuppressive and pro-angiogenic functions was identified in the late infection stage. Single-cell regulatory network inference and clustering (SCENIC) analysis suggested that Cebpe, Runx3, and Rora were the key regulators of the SPP1+ macrophages. Cell communication analysis revealed that the SPP1+ macrophages interacted with endothelial cells and had pro-angiogenic functions. There was an obvious communicative relationship between SPP1+ macrophages and endothelial cells via Vegfa–Vegfr1/Vegfr2, and SPP1+ macrophages interacted with other immune cells via specific ligand–receptor pairs, which might have contributed to their immunosuppressive function.DiscussionOur comprehensive exploration of the cystic echinococcosis ecosystem and the first discovery of SPP1+ macrophages with infection period specificity provide deeper insights into angiogenesis and the immune evasion mechanisms associated with later stages of infection

UADB: Unsupervised Anomaly Detection Booster

Unsupervised Anomaly Detection (UAD) is a key data mining problem owing to its wide real-world applications. Due to the complete absence of supervision signals, UAD methods rely on implicit assumptions about anomalous patterns (e.g., scattered/sparsely/densely clustered) to detect anomalies. However, real-world data are complex and vary significantly across different domains. No single assumption can describe such complexity and be valid in all scenarios. This is also confirmed by recent research that shows no UAD method is omnipotent. Based on above observations, instead of searching for a magic universal winner assumption, we seek to design a general UAD Booster (UADB) that empowers any UAD models with adaptability to different data. This is a challenging task given the heterogeneous model structures and assumptions adopted by existing UAD methods. To achieve this, we dive deep into the UAD problem and find that compared to normal data, anomalies (i) lack clear structure/pattern in feature space, thus (ii) harder to learn by model without a suitable assumption, and finally, leads to (iii) high variance between different learners. In light of these findings, we propose to (i) distill the knowledge of the source UAD model to an imitation learner (booster) that holds no data assumption, then (ii) exploit the variance between them to perform automatic correction, and thus (iii) improve the booster over the original UAD model. We use a neural network as the booster for its strong expressive power as a universal approximator and ability to perform flexible post-hoc tuning. Note that UADB is a model-agnostic framework that can enhance heterogeneous UAD models in a unified way. Extensive experiments on over 80 tabular datasets demonstrate the effectiveness of UADB

The distinct role of orbitofrontal and medial prefrontal cortex in encoding impulsive choices in an animal model of attention deficit hyperactivity disorder

Attention deficit hyperactivity disorder (ADHD) is a complex neurodevelopmental disorder affecting up to 5% of children worldwide. The lack of understanding of ADHD etiology prevented the development of effective treatment for the disease. Here, using in vivo electrophysiology recordings, we have recorded and analyzed the neuronal encoding of delay discounting behavior in prefrontal and orbitofrontal cortex of spontaneously hypertensive rat (SHR). We found that in the presence of rewards, neurons in the orbitofrontal cortex (OFC) were activated regardless to the value of the rewards and OFC neurons in SHR exhibited significantly higher rates of neuronal discharging towards the presence of rewards. While in the medial prefrontal cortex (mPFC), neurons of SHR responded similarly in the presence of large rewards compared with control rats whereas they displayed higher firing rates towards smaller rewards. In addition, the reward-predicting neurons in the OFC encodes for value of rewards in control animals and they were strongly activated upon receiving a small immediate reinforcer in the SHR whereas the reward-predicting neurons in the mPFC neurons generally did not respond to the value of the rewards. Our study characterized the neuronal discharging patterns of OFC and mPFC neurons in the SHR and the control animals and provided novel insights for further understanding the neuronal basis of ADHD pathology

Spatially explicit analysis identifies significant potential for bioenergy with carbon capture and storage in China

As China ramped-up coal power capacities rapidly while CO2 emissions need to decline, these capacities would turn into stranded assets. To deal with this risk, a promising option is to retrofit these capacities to co-fire with biomass and eventually upgrade to CCS operation (BECCS), but the feasibility is debated with respect to negative impacts on broader sustainability issues. Here we present a data-rich spatially explicit approach to estimate the marginal cost curve for decarbonizing the power sector in China with BECCS. We identify a potential of 222 GW of power capacities in 2836 counties generated by co-firing 0.9 Gt of biomass from the same county, with half being agricultural residues. Our spatially explicit method helps to reduce uncertainty in the economic costs and emissions of BECCS, identify the best opportunities for bioenergy and show the limitations by logistical challenges to achieve carbon neutrality in the power sector with large-scale BECCS in China

Potential impacts of pandemics on global warming, agricultural production, and biodiversity loss

The rising frequency of infectious diseases under climate change poses an emerging threat to environmental and agricultural sustainability by consuming large quantities of materials. The demand for crops to produce personal protective equipment (PPE) competes for land and fertilizers, leads to cropland expansion, and accelerates climate change, but the ecological impacts remain unclear. Here we explore the impacts of pandemics on global warming, agricultural production, and biodiversity loss in an Earth system model by developing relationships between consumption of PPE and the rate of infection during COVID-19. Meeting the demand for PPE would increase production of cotton lint, corn, and natural rubber, which accelerates global warming by 0.2°C with 1.8% additional species losses by 2100. Our results suggest that the risks of public health, food security, climate change, and ecological integrity have been connected to each other, which should be considered when predicting the impacts of future pandemics

Overview to the Hard X-ray Modulation Telescope (Insight-HXMT) Satellite

As China's first X-ray astronomical satellite, the Hard X-ray Modulation Telescope (HXMT), which was dubbed as Insight-HXMT after the launch on June 15, 2017, is a wide-band (1-250 keV) slat-collimator-based X-ray astronomy satellite with the capability of all-sky monitoring in 0.2-3 MeV. It was designed to perform pointing, scanning and gamma-ray burst (GRB) observations and, based on the Direct Demodulation Method (DDM), the image of the scanned sky region can be reconstructed. Here we give an overview of the mission and its progresses, including payload, core sciences, ground calibration/facility, ground segment, data archive, software, in-orbit performance, calibration, background model, observations and some preliminary results.Comment: 29 pages, 40 figures, 6 tables, to appear in Sci. China-Phys. Mech. Astron. arXiv admin note: text overlap with arXiv:1910.0443