Methyl mercury concentrations in seafood collected from Zhoushan Islands, Zhejiang, China, and their potential health risk for the fishing community

Seafood is an important exposure route for mercury, especially methyl mercury (MeHg). Therefore, we quantified MeHg concentrations in 69 species of seafood including fish, crustaceans and mollusks collected from Zhoushan Islands, China. MeHg concentrations ranged from 1. The daily dietary intake and hazard quotient for MeHg were calculated to estimate exposure and health risk through seafood consumption by local inhabitants. The calculated HQ was lower than 1, thus indicating that the exposure was below the risk threshold of related chronic diseases. However, higher MeHg concentrations in fish species such as Scoliodon sorrakowah and Auxis thazard are concerning and may pose health risk through continuous consumption by local inhabitants.China Spark Program (2015GA700094); Medical Health Science Foundation Program of the Health Department of Zhejiang Province (2020RC137); Science and technology Program of Zhoushan City (2017C32089); Medical Health Science Foundation Program of the Health Department of Zhoushan City (2018G02)) and the Chinese Academy of Sciences Fellowships under the Chinese Academy of Sciences President's International Fellowship for Visiting Scientists (2018VCC0002).info:eu-repo/semantics/publishedVersio

4-Nitro­phenyl α-l-rhamnopyran­oside hemihydrate1

The absolute configuration of the title compound, C12H15NO7·0.5H2O, was assigned from the synthesis. There are two rhamnoside mol­ecules and one water mol­ecule in the asymmetric unit, displaying O—H⋯O hydrogen bonding. One of the nitro groups does not conjugate efficiently with the benzene ring

Dynamic in Static:Hybrid Visual Correspondence for Self-Supervised Video Object Segmentation

Conventional video object segmentation (VOS) methods usually necessitate a substantial volume of pixel-level annotated video data for fully supervised learning. In this paper, we present HVC, a \textbf{h}ybrid static-dynamic \textbf{v}isual \textbf{c}orrespondence framework for self-supervised VOS. HVC extracts pseudo-dynamic signals from static images, enabling an efficient and scalable VOS model. Our approach utilizes a minimalist fully-convolutional architecture to capture static-dynamic visual correspondence in image-cropped views. To achieve this objective, we present a unified self-supervised approach to learn visual representations of static-dynamic feature similarity. Firstly, we establish static correspondence by utilizing a priori coordinate information between cropped views to guide the formation of consistent static feature representations. Subsequently, we devise a concise convolutional layer to capture the forward / backward pseudo-dynamic signals between two views, serving as cues for dynamic representations. Finally, we propose a hybrid visual correspondence loss to learn joint static and dynamic consistency representations. Our approach, without bells and whistles, necessitates only one training session using static image data, significantly reducing memory consumption ($\sim$16GB) and training time ($\sim$\textbf{2h}). Moreover, HVC achieves state-of-the-art performance in several self-supervised VOS benchmarks and additional video label propagation tasks

Brain-inspired Graph Spiking Neural Networks for Commonsense Knowledge Representation and Reasoning

How neural networks in the human brain represent commonsense knowledge, and complete related reasoning tasks is an important research topic in neuroscience, cognitive science, psychology, and artificial intelligence. Although the traditional artificial neural network using fixed-length vectors to represent symbols has gained good performance in some specific tasks, it is still a black box that lacks interpretability, far from how humans perceive the world. Inspired by the grandmother-cell hypothesis in neuroscience, this work investigates how population encoding and spiking timing-dependent plasticity (STDP) mechanisms can be integrated into the learning of spiking neural networks, and how a population of neurons can represent a symbol via guiding the completion of sequential firing between different neuron populations. The neuron populations of different communities together constitute the entire commonsense knowledge graph, forming a giant graph spiking neural network. Moreover, we introduced the Reward-modulated spiking timing-dependent plasticity (R-STDP) mechanism to simulate the biological reinforcement learning process and completed the related reasoning tasks accordingly, achieving comparable accuracy and faster convergence speed than the graph convolutional artificial neural networks. For the fields of neuroscience and cognitive science, the work in this paper provided the foundation of computational modeling for further exploration of the way the human brain represents commonsense knowledge. For the field of artificial intelligence, this paper indicated the exploration direction for realizing a more robust and interpretable neural network by constructing a commonsense knowledge representation and reasoning spiking neural networks with solid biological plausibility

Cross-Covariate Gait Recognition: A Benchmark

Gait datasets are essential for gait research. However, this paper observes that present benchmarks, whether conventional constrained or emerging real-world datasets, fall short regarding covariate diversity. To bridge this gap, we undertake an arduous 20-month effort to collect a cross-covariate gait recognition (CCGR) dataset. The CCGR dataset has 970 subjects and about 1.6 million sequences; almost every subject has 33 views and 53 different covariates. Compared to existing datasets, CCGR has both population and individual-level diversity. In addition, the views and covariates are well labeled, enabling the analysis of the effects of different factors. CCGR provides multiple types of gait data, including RGB, parsing, silhouette, and pose, offering researchers a comprehensive resource for exploration. In order to delve deeper into addressing cross-covariate gait recognition, we propose parsing-based gait recognition (ParsingGait) by utilizing the newly proposed parsing data. We have conducted extensive experiments. Our main results show: 1) Cross-covariate emerges as a pivotal challenge for practical applications of gait recognition. 2) ParsingGait demonstrates remarkable potential for further advancement. 3) Alarmingly, existing SOTA methods achieve less than 43% accuracy on the CCGR, highlighting the urgency of exploring cross-covariate gait recognition. Link: https://github.com/ShinanZou/CCGR.Comment: AAAI202

Awake chronic mouse model of targeted pial vessel occlusion via photothrombosis

Animal models of stroke are used extensively to study the mechanisms involved in the acute and chronic phases of recovery following stroke. A translatable animal model that closely mimics the mechanisms of a human stroke is essential in understanding recovery processes as well as developing therapies that improve functional outcomes. We describe a photothrombosis stroke model that is capable of targeting a single distal pial branch of the middle cerebral artery with minimal damage to the surrounding parenchyma in awake head-fixed mice. Mice are implanted with chronic cranial windows above one hemisphere of the brain that allow optical access to study recovery mechanisms for over a month following occlusion. Additionally, we study the effect of laser spot size used for occlusion and demonstrate that a spot size with small axial and lateral resolution has the advantage of minimizing unwanted photodamage while still monitoring macroscopic changes to cerebral blood flow during photothrombosis. We show that temporally guiding illumination using real-time feedback of blood flow dynamics also minimized unwanted photodamage to the vascular network. Finally, through quantifiable behavior deficits and chronic imaging we show that this model can be used to study recovery mechanisms or the effects of therapeutics longitudinally.R01 EB021018 - NIBIB NIH HHS; R01 MH111359 - NIMH NIH HHS; R01 NS108472 - NINDS NIH HHSPublished versio

Impact of Minor Alloy Components on the Electrocapillarity and Electrochemistry of Liquid Metal Fractals

Exploring and controlling surface tension‐driven phenomena in liquid metals may lead to unprecedented possibilities for next‐generation microfluidics, electronics, catalysis, and materials synthesis. In pursuit of these goals, the impact of minor constituents within liquid alloys is largely overlooked. Herein, it is showed that the presence of a fraction of solute metals such as tin, bismuth, and zinc in liquid gallium can significantly influence their electrocapillarity and electrochemistry. The instability‐driven fractal formation of liquid alloy droplets is investigated with different solutes and reveals the formation of distinctive non‐branched droplets, unstable fractals, and stable fractal modes under controlled voltage and alkaline solution conditions. In their individually unique fractal morphology diagrams, different liquid alloys demonstrate significantly shifted voltage thresholds in transition between the three fractal modes, depending on the choice of the solute metal. Surface tension measurements, cycle voltammetry and surface compositional characterizations provide strong evidence that the minor alloy components drastically alter the surface tension, surface electrochemical oxidation, and oxide dissolution processes that govern the droplet deformation and instability dynamics. The findings that minor components are able to regulate liquid alloys’ surface tensions, surface element distributions and electrochemical activities offer great promises for harnessing the tunability and functionality of liquid metals