Morphology controlled synthesis of Fe3+-doped upconversion nanomaterials.

This work details the synthesis of paramagnetic upconversion nanoparticles doped with Fe3+ in various morphologies via the thermal decomposition method, followed by comprehensive characterization of their structures, optical properties and magnetism using diverse analytical techniques. Our findings demonstrate that by precisely modulating the ratio of oleic acid to octadecene in the solvent, one can successfully obtain hexagonal nanodiscs with a consistent and well-defined morphology. Further adjustments in the oleic acid to octadecene ratio, coupled with fine-tuning of the Na+/F- ratio, led to the production of small-sized nanorods with uniform morphology. Significantly, all Fe3+-doped nanoparticles displayed pronounced paramagnetism, with magnetic susceptibility measurements at 1 T and room temperature of 0.15 emu g-1 and 0.14 emu g-1 for the nanodiscs and nanorods, respectively. To further enhance their magnetic properties, we replaced the Y-matrix with a Gd-matrix, and by fine-tuning the oleic acid/octadecene and Na+/F- ratios, we achieved nanoparticles with uniform morphology. The magnetic susceptibility was 0.82 emu g-1 at 1 T and room temperature. Simultaneously, we could control the nanoparticle size by altering the synthesis temperature. These upconversion nanostructures, characterized by both paramagnetic properties and regular morphology, represent promising dual-mode nanoprobe candidates for optical biological imaging and magnetic resonance imaging.

Associations of arsenic exposure and arsenic metabolism with the risk of non-alcoholic fatty liver disease.

Growing evidences supported that arsenic exposure contributes to non-alcoholic fatty liver disease (NAFLD) risk, but findings were still inconsistent. Additionally, once absorbed, arsenic is methylated into monomethyl and dimethyl arsenicals. However, no studies investigated the association of arsenic metabolism with NAFLD. Our objectives were to evaluate the associations of arsenic exposure and arsenic metabolism with NAFLD prevalence. We conducted a case-control study with 1790 participants derived from Dongfeng-Tongji cohort and measured arsenic species (arsenite, arsenate, monomethylarsonate [MMA], dimethylarsinate [DMA], and arsenobetaine) in urine. Arsenic exposure (∑As) was defined as the sum of inorganic arsenic (iAs), MMA, and DMA. Arsenic metabolism was evaluated as the proportions of inorganic-related species (iAs%, MMA%, and DMA%) and methylation efficiency ratios (primary methylation index [PMI], secondary methylation index [SMI]). NAFLD was diagnosed by liver ultrasound. Logistic regression was used to evaluate the associations. The median of ∑As was 13.24 µg/g creatinine. The ∑As showed positive and nonlinear association with moderate/severe NAFLD (OR: per log-SD = 1.33, 95% CI: [1.03,1.71]; Pfor nonlinearity = 0.021). The iAs% (OR: per SD = 1.16, 95% CI: [1.03,1.30]) and SMI (OR: per log-SD = 1.16, 95% CI: [1.03,1.31]) showed positive while MMA% (OR: per SD = 0.80, 95% CI: [0.70,0.91]) and PMI (OR: per log-SD = 0.86, 95% CI: [0.77,0.96]) showed inverse associations with NAFLD. Moreover, the ORs (95% CI) of NAFLD for each 5% increase in iAs% was 1.36 (1.17,1.58) when MMA% decreased and 1.07 (1.01,1.13) when DMA% decreased; and for each 5% increase in MMA%, it was 0.74 (0.63,0.86) and 0.79 (0.69,0.91) when iAs% and DMA% decreased, respectively. The results suggest that inorganic arsenic exposure is positively associated with NAFLD risk and arsenic methylation efficiency plays a role in the NAFLD. The findings provide clues to explore potential interventions for the prevention of NAFLD. Prospective studies are needed to validate our findings.

Association of serum phthalates exposure with incident type 2 diabetes risk in Chinese population: A nested case-control study.

Prospective epidemiological evidence was lacking on the association of phthalates (PAEs) exposure with incident type 2 diabetes mellitus (T2DM) risk. In present nested case-control study, we identified 1006 T2DM cases and matched 1006 controls based on Dongfeng-Tongji cohort study, and 6 PAEs were detected in baseline serum. The conditional logistic regression model, Bayesian kernel machine regression (BKMR) model and Quantile-based g-computation were applied to evaluate the associations of determined PAEs, either as individuals or as a mixture, with incident T2DM risk. Subgroup analysis was conducted to identify the potential sensitive population of PAEs effects on T2DM. After multiple adjustment, no statistically significant association was observed between single or mixture of PAEs and incident T2DM risk in the whole population. However, serum levels of Di-n-butyl phthalate (DnBP) [OR= 2.06; 95% CI: (1.11-3.96)], Σdibutyl phthalate (ΣDBP) [OR= 1.96; 95% CI: (1.06-3.76)], and Σlow-molecular- weight phthalate (ΣLMW) [OR= 2.27; 95% CI: (1.17-4.57)] were significantly associated with T2DM in current drinker group. Moreover, significant potential interactions were observed among Di-iso-butyl phthalate (DiBP), DnBP, Butyl-benzyl phthalate (BBP), ΣDBP, and ΣLMW with drinking status on T2DM risk (P for interaction = 0.036, 0.005, 0.049. 0.010, and 0.005). We did not find significant associations between serum PAEs levels and T2DM in the whole population. However, current alcohol drinkers expose to higher levels of DnBP, ΣDBP, and ΣLMW had higher risk of T2DM.

A brain-inspired theory of mind spiking neural network improves multi-agent cooperation and competition.

During dynamic social interaction, inferring and predicting others' behaviors through theory of mind (ToM) is crucial for obtaining benefits in cooperative and competitive tasks. Current multi-agent reinforcement learning (MARL) methods primarily rely on agent observations to select behaviors, but they lack inspiration from ToM, which limits performance. In this article, we propose a multi-agent ToM decision-making (MAToM-DM) model, which consists of a MAToM spiking neural network (MAToM-SNN) module and a decision-making module. We design two brain-inspired ToM modules (Self-MAToM and Other-MAToM) to predict others' behaviors based on self-experience and observations of others, respectively. Each agent can adjust its behavior according to the predicted actions of others. The effectiveness of the proposed model has been demonstrated through experiments conducted in cooperative and competitive tasks. The results indicate that integrating the ToM mechanism can enhance cooperation and competition efficiency and lead to higher rewards compared with traditional MARL models.

BrainCog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired AI and brain simulation.

Spiking neural networks (SNNs) serve as a promising computational framework for integrating insights from the brain into artificial intelligence (AI). Existing software infrastructures based on SNNs exclusively support brain simulation or brain-inspired AI, but not both simultaneously. To decode the nature of biological intelligence and create AI, we present the brain-inspired cognitive intelligence engine (BrainCog). This SNN-based platform provides essential infrastructure support for developing brain-inspired AI and brain simulation. BrainCog integrates different biological neurons, encoding strategies, learning rules, brain areas, and hardware-software co-design as essential components. Leveraging these user-friendly components, BrainCog incorporates various cognitive functions, including perception and learning, decision-making, knowledge representation and reasoning, motor control, social cognition, and brain structure and function simulations across multiple scales. BORN is an AI engine developed by BrainCog, showcasing seamless integration of BrainCog's components and cognitive functions to build advanced AI models and applications.

Nature-inspired self-organizing collision avoidance for drone swarm based on reward-modulated spiking neural network.

Biological systems can exhibit intelligent swarm behavior through relatively independent individual, local interaction and decentralized decision-making. A major research challenge of self-organized swarm intelligence is the coupling influences between individual behaviors. Existing methods optimize the behavior of multiple individuals simultaneously from a global perspective. However, these methods lack in-depth inspiration from swarm behaviors in nature, so they are short of flexibly adapting to real multi-robot online decision-making tasks. To overcome such limits, this paper proposes a self-organized collision avoidance model for real drones incorporating a bio-inspired reward-modulated spiking neural network (RSNN). The local interaction and autonomous learning of a single individual leads to the emergence of swarm intelligence. We validated the proposed model on swarm collision avoidance tasks (a swarm of unmanned aerial vehicles without central control) in a bounded space, carrying out simulation and real-world experiments. Compared with artificial neural network-based online learning methods, our proposed method exhibits superior performance and better stability.

Corrigendum: A Brain-Inspired Theory of Mind Spiking Neural Network for Reducing Safety Risks of Other Agents.

[This corrects the article DOI: 10.3389/fnins.2022.753900.].

A Brain-Inspired Theory of Mind Spiking Neural Network for Reducing Safety Risks of Other Agents.

Artificial Intelligence (AI) systems are increasingly applied to complex tasks that involve interaction with multiple agents. Such interaction-based systems can lead to safety risks. Due to limited perception and prior knowledge, agents acting in the real world may unconsciously hold false beliefs and strategies about their environment, leading to safety risks in their future decisions. For humans, we can usually rely on the high-level theory of mind (ToM) capability to perceive the mental states of others, identify risk-inducing errors, and offer our timely help to keep others away from dangerous situations. Inspired by the biological information processing mechanism of ToM, we propose a brain-inspired theory of mind spiking neural network (ToM-SNN) model to enable agents to perceive such risk-inducing errors inside others' mental states and make decisions to help others when necessary. The ToM-SNN model incorporates the multiple brain areas coordination mechanisms and biologically realistic spiking neural networks (SNNs) trained with Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP). To verify the effectiveness of the ToM-SNN model, we conducted various experiments in the gridworld environments with random agents' starting positions and random blocking walls. Experimental results demonstrate that the agent with the ToM-SNN model selects rescue behavior to help others avoid safety risks based on self-experience and prior knowledge. To the best of our knowledge, this study provides a new perspective to explore how agents help others avoid potential risks based on bio-inspired ToM mechanisms and may contribute more inspiration toward better research on safety risks.

Microbial characteristics across different tongue coating types in a healthy population.

Background The physical appearance of tongue coatings is vital for traditional Chinese medicine (TCM) to diagnose health and disease status. The microbiota of different tongue coatings could also influence coating formation and be further associated with specific diseases. Previous studies have focused on bacteria from different tongue coatings in the context of specific diseases, but the normal variations in healthy individuals remain unknown.Aim: We examined the tongue microbiota by metagenomics in 94 healthy individuals classified into eight different tongue types.Results: The overall composition of the tongue coating microbiome is not drastically different among different coating types, similar to the findings of previous studies in healthy populations. Further analysis revealed microbiota characteristics of each coating type, and many of the key bacteria are reported to be implicated in diseases. Moreover, further inclusion of diabetic patients revealed disease-specific enrichment of Capnocytophaga, even though the same tongue coatings were studied.Conclusions: This work revealed the characteristic compositions of distinctive tongue coatings in a healthy population, which serves as a basis for understanding the tongue coating formation mechanism and provides a valuable reference to further investigate disease-specific tongue coating bacterial markers.

Distribution and Metabolism of Bt-Cry1Ac Toxin in Tissues and Organs of the Cotton Bollworm, Helicoverpa armigera.

Crystal (Cry) proteins derived from Bacillus thuringiensis (Bt) have been widely used in transgenic crops due to their toxicity against insect pests. However, the distribution and metabolism of these toxins in insect tissues and organs have remained obscure because the target insects do not ingest much toxin. In this study, several Cry1Ac-resistant strains of Helicoverpa armigera, fed artificial diets containing high doses of Cry1Ac toxin, were used to investigate the distribution and metabolism of Cry1Ac in their bodies. Cry1Ac was only detected in larvae, not in pupae or adults. Also, Cry1Ac passed through the midgut into other tissues, such as the hemolymph and fat body, but did not reach the larval integument. Metabolic tests revealed that Cry1Ac degraded most rapidly in the fat body, followed by the hemolymph, peritrophic membrane and its contents. The toxin was metabolized slowly in the midgut, but was degraded in all locations within 48 h. These findings will improve understanding of the functional mechanism of Bt toxins in target insects and the biotransfer and the bioaccumulation of Bt toxins in arthropod food webs in the Bt crop ecosystem.

Functional analysis of synthetic DELLA domain peptides and bioactive gibberellin assay using surface plasmon resonance technology.

DELLA proteins and phytohormone gibberellin act together to control convergence point of plant development. A gibberellin-bound nuclear receptor that interacts with the N-terminal domain of DELLA proteins is required for gibberellin induced degradation of DELLA proteins. N-terminal DELLA domain includes two conserved motifs: DELLA and VHYNP. However, their respective functions remain unclear. Meanwhile, the identification and detection of several bioactive gibberellins from the more than 100 gibberellin metabolites are overwhelmingly difficult for their similar structures. Using in vitro biochemical approach, our work demonstrates for the first time that the synthetic GAI N-terminal DELLA domain peptides have similar bioactive function as the expressed protein to interact with AtGID1a receptor. Furthermore, our results reveal that DELLA motif is vitally important region and DELLA segment is essentially required region to recognize AtGID1a receptor. Finally, based on bioactive GA-dependent of the interaction between AtGID1a and DELLA protein, we generated a new method that could identify and detect bioactive GAs accurately and rapidly with surface plasmon resonance assays.

Surface plasmon resonance detection of transgenic Cry1Ac cotton ( Gossypium spp.).

The detection and identification of genetically modified (GM) plants are challenging issues that have arisen from the potential negative impacts of extensive cultivation of transgenic plants. The screening process is a long-term focus and needs specific detection strategies. Surface plasmon resonance (SPR) has been used to detect a variety of biomolecules including proteins and nucleic acids due to its ability to monitor specific intermolecular interactions. In the present study, two high-throughput, label-free, and specific methods based on SPR technology were developed to detect transgenic Cry1Ac cotton ( Gossypium spp.) by separately targeting protein and DNA. In the protein-based detection system, monoclonal anti-Cry1Ac antibodies were immobilized on the surface of a CM5 sensor chip. Conventional cotton samples were used to define the detection threshold. Transgenic cotton was easily identified within 5 min per sample. For the DNA-based model, a 25-mer biotinylated oligonucleotide probe was immobilized on an SA sensor chip. PCR products of Cry1Ac (230 bp) were used to investigate the reaction conditions. The sensitivity of the constructed sensor chip was identified at concentrations as low as 0.1 nM based on its complementary base pairing.