Zhoushan Science and Technology Bureau

Nanoplastics are ubiquitous in aquatic environments, and elucidating their bioaccumulation behavior is essential for assessing toxicity and trophic transfer risks. While most studies focus on nanoplastics properties (e.g., type, size, surface charge), the influence of organismal growth stage remains unclear. Through bioaccumulation kinetic experiments with 10 mg/L polystyrene nanoplastics (PSNPs), this study found that Tetrahymena thermophila (T. thermophila) in the lag phase (cell density 4 × 104 cells/mL) exhibited the highest uptake rate of nanoplastics, 1.2-5.8 times that of the exponential phase (1-5 × 105 cells/mL) and 7.7 times that of the stationary phase (>5 × 105 cells/mL). Lag phase cells also had a larger specific surface area (0.319 vs. 0.271/0.269 μm-1), supporting their heightened uptake capacity. Under PSNP exposure, exponential and stationary phase cells showed significantly elevated reactive oxygen species (ROS) levels, accompanied by downregulated superoxide dismutase (SOD) and stable catalase (CAT) activity, indicating impaired antioxidant defense and potential redirection of energy toward stress mitigation. Consistent with efficient internalization, confocal imaging revealed clear PSNP colocalization within food vacuoles of lag period cells. Proteomic and transcriptomic analysis further confirmed the upregulation of carrier proteins, FAD/FMN oxidoreductases, and pathways associated with cellular components (membrane and organelle membrane) and molecular functions (transporter activity and transmembrane transporter activity) in lag-phase T. thermophila. Collectively, these findings provide a molecular-level understanding of the multi-phase-dependent bioaccumulation of PSNPs, offering critical insights for assessing the environmental risks of polystyrene nanoplastics in dynamic aquatic ecosystems.

河豚毒素是一种高毒性小分子神经毒素，可通过抑制神经细胞膜对Na+的通透性，影响神经信号传导，进而导致神经麻痹，严重者可因呼吸衰竭而死亡。我国部分沿海地区存在食用河豚鱼、织纹螺等海产品的习惯，因摄食含河豚毒素海产品导致中毒的事件时有发生。尽早识别毒素并进行对症解毒治疗可提高中毒患者的救治成功率，同时人体生物样本中毒素的浓度在一定程度上可以反映中毒程度和患者的预后状况。本研究建立了二维液相色谱-串联质谱法（2D-LC-MS/MS）定量检测突发中毒生物样本中河豚毒素的新方法。血浆或尿液样品经0.5%（v/v）乙酸乙腈溶液提取后高速离心，上清液先经第一维反相C18液相色谱柱粗分离，随后通过六通阀切换将目标物转移至亲水Amide液相色谱柱进行第二维色谱分离，电喷雾电离，选择反应监测（SRM）模式检测，基质匹配内标法定量。在0.2~40.0 μg/L（相当于生物样品中河豚毒素的含量为1.0~200.0 μg/L）范围内，河豚毒素呈现良好的线性关系，相关系数达到0.999 4以上；以3倍和10倍信噪比所对应的浓度为检出限和定量限时，血浆、尿液中河豚毒素的检出限均为0.3 μg/L，定量限均为1.0 μg/L；在2.0、10.0、50.0和200.0 μg/L的加标水平下，血浆和尿液中河豚毒素的日内回收率分别为84.4%~98.4%和84.4%~96.9%；日间回收率分别为87.7%~96.2%和84.8%~95.7%。检测方法日内、日间相对标准偏差均≤7.5%。该检测方法快速准确，无需复杂前处理，已成功应用于河豚毒素中毒生物样本的检测。

This brief presents a 3.4-GS/s 12-bit time-interleaved pipelined SAR ADC fabricated in 28-nm CMOS, incorporating a high-linearity track-and-hold amplifier (THA) based on the proposed non-Foster ring amplifier (NFRA). By leveraging interstage delay reduction and feedback factor compensation based on non-Foster capacitors (NFCs), the proposed NFRA enables fast and stable settling while preserving high linearity, effectively overcoming the stability limitations of conventional ring amplifiers (RingAmps). The prototype ADC achieves 57.3-dB SNDR and 77.6-dB SFDR at near-Nyquist input, while maintaining 53-dB SNDR and 73.2-dB SFDR up to 3.5 GHz with a total power consumption of 92 mW.

Abstract This study, for the first time, presented a novel Bayesian inference deep learning model based on a dilated convolutional ResNet framework, utilizing acoustic emission (AE) signals as inputs to quantify the uncertainty of the compression after impact (CAI) strength in composites. The output of the deep learning model included the mean CAI and the predictive aleatoric uncertainty. The epistemic uncertainty of the model was estimated by randomly sampling the input multiple times. AE signals were divided into a complete training dataset and a partially missing training dataset to investigate the impact of data insufficiency on the uncertainty of deep learning models. The results demonstrate that the deep learning model can provide highly accurate predictions of CAI performance. It is worth noting that on incomplete datasets, the epistemic uncertainty of the predictions increased significantly. To evaluate the effect of noise on the prediction of CAI strength, Gaussian noise with signal‐to‐noise ratios (SNRs) of 10, 5, and 2 dB was introduced during the prediction phase. The Bayesian posterior probability density function becomes lower and wider under the same impact energy as external noise increases. The model exhibited excellent regression performance under SNRs of 10 and 5 dB, validating the robustness of the proposed framework. As noise levels increased, the range of aleatoric uncertainty in the predictions expanded.Highlights A dilated convolution Bayesian deep learning framework for uncertainty quantification of compression after impact strength in laminated composites is proposed. The prediction and uncertainty quantification performance of the framework is evaluated. The effect of data missing and background noise on the uncertainty is investigated.

In this paper, an underwater wireless optical communication (UWOC) system that can simultaneously achieve beam steering and pulse amplitude modulation (PAM) enabled by the acousto-optic effect is proposed and experimentally demonstrated. An approach to manipulate the driving signal of an acousto-optic modulator (AOM) is utilized to simultaneously achieve precise laser emission angle control and signal modulation, which enables high-speed link-switching for multi-user access. The system is tested in a 7-m water tank with a water attenuation coefficient of 0.87 dB/m, and a bit error rate (BER) of 2.99 × 10-3 is realized at a maximum data rate of 1.6 Mbps. The theoretical deflection angle of a 450-nm laser is 4.6 mrad, while the actual steering range could reach up to 5.2 mrad with a horizontal displacement of 36.4 mm at the receiver plane under experimental conditions. We further measured the relationship between the diffraction efficiency of the 1st-order diffracted light and the amplitude as well as the central frequency of the driving signal, which is essential for the generation of the driving signal. The system provides an effective solution for link-switching in the Internet of Underwater Things (IoUT) and high-throughput underwater wireless optical networks.

With the rapid development of massive multiple-input multiple-output (mMIMO) techniques, the network capacity, number of served users and communication efficiency have been improved dramatically compared to that with limited number of antennas. These advantages are established based on accurate channel state information (CSI) at the base station (BS), which comes with a very high cost due to continuous CSI feedback from all the user equipments (UEs). In this letter, we propose a neural network-based CSI compression scheme with simple encoder-decoder framework for mMIMO systems. To achieve high accuracy, our proposed framework constructs an overall perceptual encoder-decoder structure with holistic cross-joint convolution (HCC) modules of different scales. In addition, a perceptual loss is introduced into the proposed design to further improve the accuracy in matrix recovery and limits the computational cost. Substantial experimental results demonstrate that the proposed HCC network (HCC-Net) is superior to several advanced algorithms in terms of estimation accuracy and computational complexity, such as the CSiNet+ and TransNet.

Wire-arc additive manufacturing (WAAM) process has advantages such as high deposition efficiency, large-scale parts, and low production costs. However, the TA15 titanium alloy fabricated by WAAM still exhibits issues including coarse grain, low strength and hardness, as well as poor corrosion resistance. This study introduces activating fluxes of CaF₂ into the WAAM process. The results show that CaF₂ additions refines the plate-like α phase and inhibits the transition of β to α phase. The improved microstructure increases the tensile strength by 8.36%, the hardness by 14.2%. Moreover, the corrosion potential and current increase (or decrease) from -0.395V, 6.384×10^-6A/cm³ to -0.294V, 2.499×10^-6 A/cm². Therefore, this study provides a potential approach for improving the microstructural properties of WAAM-prepared titanium alloy (TA15).