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ABSTRACT Observing 3D magnetic fields, including orientation and strength, within the interstellar medium is vital but notoriously difficult. However, recent advances in our understanding of anisotropic magnetohydrodynamic (MHD) turbulence demonstrate that MHD turbulence and 3D magnetic fields leave their imprints on the intensity features of spectroscopic observations. Leveraging these theoretical frameworks, we propose a novel Convolutional Neural Network (CNN) model to extract this embedded information, enabling the probe of 3D magnetic fields. This model examines the plane-of-the-sky magnetic field orientation (ϕ), the magnetic field’s inclination angle (γ) relative to the line-of-sight, and the total magnetization level (M$$_{\rm A}^{-1}$$) of the cloud. We train the model using synthetic emission lines of 13CO (J  = 1–0) and C18O (J  = 1–0), generated from 3D MHD simulations that span conditions from sub-Alfvénic to super-Alfvénic molecular clouds. Our tests confirm that the CNN model effectively reconstructs the 3D magnetic field topology and magnetization. The median uncertainties are under 5° for both ϕ and γ, and less than 0.2 for MA in sub-Alfvénic conditions (MA ≈ 0.5). In super-Alfvénic scenarios (MA ≈ 2.0), they are under 15° for ϕ and γ, and 1.5 for MA. We applied this trained CNN model to the L1478 molecular cloud. Results show a strong agreement between the CNN-predicted magnetic field orientation and that derived from Planck 353 GHz polarization. The CNN approach enabled us to construct the 3D magnetic field map for L1478, revealing a global inclination angle of ≈76° and a global MA of ≈1.07. 

Abstract Powdery mildew fungi are obligate biotrophic pathogens that only invade plant epidermal cells. There are two epidermal surfaces in every plant leaf: the adaxial (upper) side and the abaxial (lower) side. While both leaf surfaces can be susceptible to adapted powdery mildew fungi in many plant species, there have been observations of leaf abaxial immunity in some plant species including Arabidopsis. The genetic basis of such leaf abaxial immunity remains unknown. In this study, we tested a series of Arabidopsis mutants defective in one or more known defense pathways with the adapted powdery mildew isolate Golovinomyces cichoracearum UCSC1. We found that leaf abaxial immunity was significantly compromised in mutants impaired for both the EDS1/PAD4- and PEN2/PEN3-dependent defenses. Consistently, expression of EDS1–yellow fluorescent protein and PEN2–green fluorescent protein fusions from their respective native promoters in the respective eds1-2 and pen2-1 mutant backgrounds was higher in the abaxial epidermal cells than in the adaxial epidermal cells. Altogether, our results indicate that leaf abaxial immunity against powdery mildew in Arabidopsis is at least partially due to enhanced EDS1/PAD4- and PEN2/PEN3-dependent defenses. Such transcriptionally pre-programmed defense mechanisms may underlie leaf abaxial immunity in other plant species such as hemp and may be exploited for engineering adaxial immunity against powdery mildew fungi in crop plants. 

With a growing body of literature on the topic of ecosystem service (ES), there is an urgent need to summarize ES research in the context of ecological restoration programs (ERPs) in China and identify knowledge gaps for future directions. We conducted a systematic literature review of articles to examine the use of ES approaches for ERP assessments. Our results showed that previous studies mainly focused on the Shaanxi Province, and more than half of the reviewed studies considered no more than three ES types simultaneously. All ES categories were not covered equally; most of the studies focused on provisioning and regulating services, while cultural services have received little attention. Although regional-scale and short-term assessments dominated the reviewed papers, we suggest that multiple temporal and spatial scales for ERP assessments should be given more attention in future work. Moreover, we highlight that an oversimplified land use/land cover (LULC) categorization scheme may potentially lead to inaccuracies and biases in ESs detection under restoration programs. Based on this review, our findings can guide future ERP assessments by using the ES approach. Meanwhile, given the global LULC change brought by the proliferation of plantations under ERPs, our results are also expected to provide a path forward to assess ESs associated with LULC change globally. 

This study aimed to evaluate the impact of Nystatin oral rinse on salivary and supragingival microbiota in adults with oral candidiasis and identify predictive factors related to individuals’ responses to Nystatin. The trial involved twenty participants who used 600,000 International Units/application of Nystatin oral rinse for seven days, four times a day, and were followed up at one week and three months after the rinse. The salivary and plaque microbiome of the participants were assessed via 16S rDNA amplicon sequencing. Overall, salivary and plaque microbiomes remained stable. However, among the participants (53 percent) who responded to Nystatin rinse (defined as free of oral Candida albicans post treatment), Veillonella emerged as a core genus alongside Streptococcus and Actinomyces in supragingival plaque at the 3-month follow-up. Furthermore, statistical models were fit to identify predictive factors of Nystatin rinse success (elimination of C. albicans) or failure (remaining C. albicans). The results revealed that an increased level of salivary Interferon (IFN)-γ-inducible protein (IP-10), also known as C-X-C motif chemokine ligand 10 (CXCL10), was an indicator of a failure of responding to Nystatin rinse. Future clinical trials are warranted to comprehensively assess the impact of antifungal treatment on the oral flora. 

Early childhood caries (ECC) is a disease that globally affects pre-school children. It is important to identify both protective and risk factors associated with this disease. This paper examined a set of saliva samples of Thai mother–child dyads and aimed to analyze how the maternal factors and oral microbiome of the dyads influence the development of ECC. However, heterogeneous latent subpopulations may exist that have different characteristics in terms of caries development. Therefore, we introduce a novel method to cluster the correlated outcomes of dependent observations while selecting influential independent variables to unearth latent groupings within this dataset and reveal their association in each group. This paper describes the discovery of three heterogeneous clusters in the dataset, each with its own unique mother–child outcome trend, as well as identifying several microbial factors that contribute to ECC. Significantly, the three identified clusters represent three typical clinical conditions in which mother–child dyads have typical (cluster 1), high–low (cluster 2), and low–high caries experiences (cluster 3) compared to the overall trend of mother–child caries status. Intriguingly, the variables identified as the driving attributes of each cluster, including specific taxa, have the potential to be used in the future as caries preventive measures. 

Perceiving and manipulating 3D articulated objects (e.g., cabinets, doors) in human environments is an important yet challenging task for future home-assistant robots. The space of 3D articulated objects is exceptionally rich in their myriad semantic categories, diverse shape geometry, and complicated part functionality. Previous works mostly abstract kinematic structure with estimated joint parameters and part poses as the visual representations for manipulating 3D articulated objects. In this paper, we propose object-centric actionable visual priors as a novel perception-interaction handshaking point that the perception system outputs more actionable guidance than kinematic structure estimation, by predicting dense geometry-aware, interaction-aware, and task-aware visual action affordance and trajectory proposals. We design an interaction-for-perception framework VAT-Mart to learn such actionable visual representations by simultaneously training a curiosity-driven reinforcement learning policy exploring diverse interaction trajectories and a perception module summarizing and generalizing the explored knowledge for pointwise predictions among diverse shapes. Experiments prove the effectiveness of the proposed approach using the large-scale PartNet-Mobility dataset in SAPIEN environment and show promising generalization capabilities to novel test shapes, unseen object categories, and real-world data. 

Abstract Spin-orbit coupling is an important ingredient to regulate the many-body physics, especially for many spin liquid candidate materials such as rare-earth magnets and Kitaev materials. The rare-earth chalcogenides Equation missing<#comment/>(Ch = O, S, Se) is a congenital frustrating system to exhibit the intrinsic landmark of spin liquid by eliminating both the site disorders between Equation missing<#comment/>and Equation missing<#comment/>ions with the big ionic size difference and the Dzyaloshinskii-Moriya interaction with the perfect triangular lattice of the Equation missing<#comment/>ions. The temperature versus magnetic-field phase diagram is established by the magnetization, specific heat, and neutron-scattering measurements. Notably, the neutron diffraction spectra and the magnetization curve might provide microscopic evidence for a series of spin configuration for in-plane fields, which include the disordered spin liquid state, 120° antiferromagnet, and one-half magnetization state. Furthermore, the ground state is suggested to be a gapless spin liquid from inelastic neutron scattering, and the magnetic field adjusts the spin orbit coupling. Therefore, the strong spin-orbit coupling in the frustrated quantum magnet substantially enriches low-energy spin physics. This rare-earth family could offer a good platform for exploring the quantum spin liquid ground state and quantum magnetic transitions. 

In this investigation, the improved electrochemical behavior in Si-doped Li-rich cathodes is studied with scanning transmission electron microscopy (STEM) and electron energy loss spectroscopy (EELS). Z-contrast images show a layered structure that develops a thin, spinel-like surface layer after the first charge cycle. Si-doping increases discharge capacity by ∼25% and appears to retard the surface phase transformation. Based on electron energy loss spectra, the surface layer in the doped material has an altered oxygen electronic environment, which supports the STEM findings. Furthermore, Si-doping changes the redox behavior during the activation cycle. Density functional theory calculations indicate that Si-doping can increase oxygen vacancy formation, and change the sequence of the redox couples by introducing more oxygen vacancies before or during the typical high voltage activation process. The results of this work indicate that the type of doping employed here is an effective strategy for controlling the complex charge compensation mechanisms in lithium-rich cathodes.