Quantification of absolute labeling efficiency at the single-protein level.

State-of-the-art super-resolution microscopy allows researchers to spatially resolve single proteins in dense clusters. However, accurate quantification of protein organization and stoichiometries requires a general method to evaluate absolute binder labeling efficiency, which is currently unavailable. Here we introduce a universally applicable approach that uses a reference tag fused to a target protein of interest. By attaching high-affinity binders, such as antibodies or nanobodies, to both the reference tag and the target protein, and then employing DNA-barcoded sequential super-resolution imaging, we can correlate the location of the reference tag with the target molecule binder. This approach facilitates the precise quantification of labeling efficiency at the single-protein level.

A hybrid spatiotemporal deep belief network and sparse representation-based framework reveals multilevel core functional components in decoding multitask fMRI signals.

Decoding human brain activity on various task-based functional brain imaging data is of great significance for uncovering the functioning mechanism of the human mind. Currently, most feature extraction model-based methods for brain state decoding are shallow machine learning models, which may struggle to capture complex and precise spatiotemporal patterns of brain activity from the highly noisy fMRI raw data. Moreover, although decoding models based on deep learning methods benefit from their multilayer structure that could extract spatiotemporal features at multiscale, the relatively large populations of fMRI datasets are indispensable, and the explainability of their results is elusive. To address the above problems, we proposed a computational framework based on hybrid spatiotemporal deep belief network and sparse representations to differentiate multitask fMRI (tfMRI) signals. Using a relatively small cohort of tfMRI data as a test bed, our framework can achieve an average classification accuracy of 97.86% and define the multilevel temporal and spatial patterns of multiple cognitive tasks. Intriguingly, our model can characterize the key components for differentiating the multitask fMRI signals. Overall, the proposed framework can identify the interpretable and discriminative fMRI composition patterns at multiple scales, offering an effective methodology for basic neuroscience and clinical research with relatively small cohorts.

Hydrothermal carbonization of biogas slurry and cattle manure into soil conditioner mitigates ammonia volatilization from paddy soil.

Hydrothermal carbonization of biogas slurry and animal manure into hydrochar could enhance waste recycling waste and minimize ammonia (NH3) volatilization from paddy fields. In this study, cattle manure-derived hydrochar prepared in the presence of Milli-Q water (CMWH) and biogas slurry (CMBSH), and biogas slurry-based hydrochar embedded with zeolite (ZHC) were applied to rice-paddy soil. The results demonstrated that CMBSH and ZHC treatments could significantly mitigate the cumulative NH3 volatilization and yield-scale NH3 volatilization by 27.9-45.2% and 28.5-45.4%, respectively, compared to the control group (without hydrochar addition), and significantly correlated with pH and ammonium-nitrogen (NH4+-N) concentration in floodwater. Nitrogen (N) loss via NH3 volatilization in the control group accounted for 24.9% of the applied N fertilizer, whereas CMBSH- and ZHC-amended treatments accounted for 13.6-17.9% of N in applied fertilizer. The reduced N loss improved soil N retention and availability for rice; consequently, grain N content significantly increased by 6.5-14.9% and N-use efficiency increased by 6.4-16.0% (P < 0.05), respectively. Based on linear fitting results, NH3 volatilization mitigation resulted from lower pH and NH4+-N concentration in floodwater that resulted from the acidic property and specific surface area of hydrochar treatments. Moreover, NH3-oxidizing archaea abundance in hydrochar-treated soil decreased by 40.9-46.9% in response to CMBSH and ZHC treatments, potentially suppressing NH4+-N transformation into nitrate and improving soil NH4+-N retention capacity. To date, this study applied biogas slurry-based hydrochar into paddy soil for the first time and demonstrated that ZHC significantly mitigated NH3 and increased N content. Overall, this study proposes an environmental-friendly strategy to recycle the wastes, biogas slurry, to the paddy fields to mitigate NH3 volatilization and increase grain yield of rice.

Hierarchical Individual Naturalistic Functional Brain Networks with Group Consistency uncovered by a Two-Stage NAS-Volumetric Sparse DBN Framework.

The functional magnetic resonance imaging under naturalistic paradigm (NfMRI) showed great advantages in identifying complex and interactive functional brain networks due to its dynamics and multimodal information. In recent years, various deep learning models, such as deep convolutional autoencoder (DCAE), deep belief network (DBN) and volumetric sparse deep belief network (vsDBN), can obtain hierarchical functional brain networks (FBN) and temporal features from fMRI data. Among them, the vsDBN model revealed a good capability in identifying hierarchical FBNs by modelling fMRI volume images. However, due to the high dimensionality of fMRI volumes and the diverse training parameters of deep learning methods, especially the network architecture that is the most critical parameter for uncovering the hierarchical organization of human brain function, researchers still face challenges in designing an appropriate deep learning framework with automatic network architecture optimization to model volumetric NfMRI. In addition, most of the existing deep learning models ignore the group-wise consistency and inter-subject variation properties embedded in NfMRI volumes. To solve these problems, we proposed a two-stage neural architecture search and vs DBN model (two-stage NAS-vsDBN model) to identify the hierarchical human brain spatio-temporal features possessing both group-consistency and individual-uniqueness under naturalistic condition. Moreover, our model defined reliable network structure for modelling volumetric NfMRI data via NAS framework, and the group-level and individual-level FBNs and associated temporal features exhibited great consistency. In general, our method well identified the hierarchical temporal and spatial features of the brain function and revealed the crucial properties of neural processes under natural viewing condition.Significance StatementIn this paper, we proposed and applied a novel analytical strategy - a two-stage NAS-vsDBN model to identify both group-level and individual-level spatio-temporal features at multi-scales from volumetric NfMRI data. The proposed PSO-based NAS framework can find optimal neural structure for both group-wise and individual-level vs-DBN models. Furthermore, with well-established correspondence between two stages of vsDBN models, our model can effectively detect group-level FBNs that reveal the consistency in neural processes across subjects and individual-level FBNs that maintain the subject specific variability, verifying the inherent property of brain function under naturalistic condition.

Resveratrol decreases local inflammatory markers and systemic endotoxin in patients with aggressive periodontitis.

BACKGROUND: Inflammation is hypothesized to contribute to the pathogenesis of periodontitis. Resveratrol (RV) is known for its anti-inflammatory properties. The purpose of this study was to investigate the inhibitory effect of RV on local inflammatory markers and systemic endotoxin in patients with periodontitis. METHODS: A total of 160 patients with periodontitis were enrolled in this study. The selected patients were randomly divided into four groups and received placebo, high-dose (500 mg/d) of RV (HRV, n = 40), middle-dose (250 mg/d) of RV (middle dose RV (MRV), n = 40) and low-dose (125 mg/d) of RV (low dose RV (LRV), n = 40) with orally administration. All patients received an 8-week treatment. The periodontal status of patients with periodontitis was recorded by using clinical attachment level (CAL), bleeding index (BI), oral hygiene index-simplified (OHI-S), and probing pocket depth (PPD). The levels of inflammatory markers in serum and gingival crevicular fluid (GCF), and systemic levels of endotoxin were evaluated using high sensitivity enzyme-linked immuno sorbent assay. RESULTS: Outcomes showed that symptoms of periodontitis determined by CAL, BI OHI-S and PPD were improved by RV compared to placebo. RV treatment decreased inflammatory markers in serum and GCF compared to placebo in patient with periodontitis. Systemic endotoxin declined more in the RV group than the placebo-treated group. CONCLUSION: In conclusion, data in the current study indicate that RV is an efficient drug for the treatment of patients with periodontitis. The findings of the present study find that RV inhibits systemic local inflammatory markers and systemic endotoxin and suggest that 500 mg/d RV is the ideal dose for patients with periodontitis.

A non-chemical weed control strategy, introducing duckweed into the paddy field.

BACKGROUND: Herbicide resistance in weeds and environmental pollution resulting from excessive application of chemical herbicides keeps increasing. Development of environment-friendly and effective weed management strategies are required for sustainable agricultural production. In this study we investigated the effects of duckweeds (Landoltia punctata (G. Meyer) Les & D. J. Crawford and Spirodela polyrhiza (Linnaeus) Schle iden) introduction on the weed community and rice growth in paddy fields. RESULTS: The study was conducted in the two rice-growing seasons (2018 and 2019) with three treatments: rice grown without duckweed introduction (CK), with L. punctata introduction (LP), and with S. polyrhiza introduction (SP). On average, LP and SP significantly reduced total weed density by more than 90% and 97%, respectively. Early in the rice-growing season, both duckweed species completely prevented weed growth. Further, both species significantly promoted rice plant growth in the advanced stages. SP significantly improved grain yield of rice by 23%. Light transmittance, temperature of the floodwater and soil, floodwater pH, and dissolved oxygen content significantly decreased following introduction of the duckweeds, indicating that the duckweeds introduction might inhibit weeds growth by altering environmental factors. CONCLUSION: This study provides a possible environment-friendly way to inhibit weed biomass in the paddy field by introducing duckweeds and interpreted the possible reasons of the impacts of duckweed on environmental variables. Weed control is beneficial for rice growth. Duckweed coverage might be limited in open fields and the associated practice requires additional investigation. © 2022 Society of Chemical Industry.

Hierarchical Spatio-Temporal Modeling of Naturalistic Functional Magnetic Resonance Imaging Signals via Two-Stage Deep Belief Network With Neural Architecture Search.

Naturalistic functional magnetic resonance imaging (NfMRI) has become an effective tool to study brain functional activities in real-life context, which reduces the anxiety or boredom due to difficult or repetitive tasks and avoids the problem of unreliable collection of brain activity caused by the subjects' microsleeps during resting state. Recent studies have made efforts on characterizing the brain's hierarchical organizations from fMRI data by various deep learning models. However, most of those models have ignored the properties of group-wise consistency and inter-subject difference in brain function under naturalistic paradigm. Another critical issue is how to determine the optimal neural architecture of deep learning models, as manual design of neural architecture is time-consuming and less reliable. To tackle these problems, we proposed a two-stage deep belief network (DBN) with neural architecture search (NAS) combined framework (two-stage NAS-DBN) to model both the group-consistent and individual-specific naturalistic functional brain networks (FBNs), which reflected the hierarchical organization of brain function and the nature of brain functional activities under naturalistic paradigm. Moreover, the test-retest reliability and spatial overlap rate of the FBNs identified by our model reveal better performance than that of widely used traditional methods. In general, our model provides a promising method for characterizing hierarchical spatiotemporal features under the natural paradigm.

Hollow hierarchical hydroxyapatite/Au/polyelectrolyte hybrid microparticles for multi-responsive drug delivery.

In this study, hollow hierarchical hydroxyapatite (HAP)/Au/polyelectrolyte hybrid microparticles with a hollow HAP core and polymer multilayer/Au nanoparticle (AuNPs) shell for multi-responsive drug delivery have been prepared via a layer-by-layer (LbL) technique. Thermal-/pH-dual responsive aliphatic poly(urethane-amine) (PUA) was employed as the smart component. The aggregated AuNPs inside hybrid microparticles could potentially obstruct the diffusion of doxorubicin hydrochloride (DOX) from the hollow microparticles and assuage the initial burst release of DOX. Upon irradiation with near-infrared (NIR) laser, AuNP aggregates can effectively convert light to heat and result in the rapid release of DOX due to the partial collapse of the polyelectrolyte multilayers (PUA/sodium poly(styrenesulfonate) (PSS)). In addition, due to the dissolution of HAP in the acidic media and the shrinkage of aliphatic PUA above its lower critical solution temperature (LCST), the drug release of hollow hybrid carriers exhibits distinguished pH- and thermal-dependent properties. The results indicate that the hollow HAP/Au/PUA/PSS hybrid microparticles show great potential as novel smart drug carriers for controllable drug delivery.