A label-free H1N1 influenza virus immunosensor based on an N-LIG/Au laser induced graphene microelectrode.

A label-free immunosensor based on an N-doped laser direct graphene (N-LIG)/Au electrode was proposed for H1N1 influenza virus detection. By utilizing the instantaneous high temperature of laser irradiation, N atoms are generated by the decomposition of melamine dripped onto the surface of an LIG electrode to obtain N-LIG with higher conductivity. The doping of N atoms provides a large number of active sites for LIG microelectrodes. Combined with the electrodeposition of Au NPs, and covalently crosslinking antibodies, a simple, highly sensitive and stable immunosensing interface is constructed. The proposed H1N1 influenza virus immunosensor has a detection range of 0.01 fg mL-1 to 10 ng mL-1 with a detection limit as low as 0.004 fg mL-1. The constructed sensor has ultra-high sensitivity and good selectivity and can be used for complex biological sample analysis, with potential application prospects in preventing the large-scale spread of influenza. Taking advantage of N-LIG electrode's properties will provide opportunities for developing portable electrochemical biosensors for health and environmental applications.

Co(III) complexes of the pentadentate NHC ligand PY4Im: carbene-induced trans influences and the non-disappearing 13C NMR peak.

Co(III) complexes of the N-heterocyclic carbene ligand PY4Im (PY4Im = (1,3-bis(bis(2-pyridyl)methyl)imidazol-2-ylidene)) having the general formula [(PY4Im)Co(X)](ClO4)n (X = NCMe; n = 3: OH-, N3-, NCS-, ONO-, F-; n = 2: O2CO2-, n = 1; (N3-)3, n = 0) were prepared and structurally characterised. X-ray structural data are consistent with the presence of a trans influence due to the coordinated carbene carbon, and this is also supported by computational results. 13C NMR spectra of the complexes did not display peaks corresponding to the carbene carbon, except in the case of the [(PY4Im)Co(O2CO)]+ cation, where a peak at δ = 170.21 ppm was observed. However, HMBC spectra allowed indirect determination of the chemical shifts of the carbene carbon in the remaining complexes, owing to the geometry of the PY4Im ligand. Calculated 13C chemical shifts for the complexes showed very good agreement with the experimental values for all but the carbene carbon atoms in all cases.

Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and prediction.

Although high-dimensional clinical data (HDCD) are increasingly available in biobank-scale datasets, their use for genetic discovery remains challenging. Here we introduce an unsupervised deep learning model, Representation Learning for Genetic Discovery on Low-Dimensional Embeddings (REGLE), for discovering associations between genetic variants and HDCD. REGLE leverages variational autoencoders to compute nonlinear disentangled embeddings of HDCD, which become the inputs to genome-wide association studies (GWAS). REGLE can uncover features not captured by existing expert-defined features and enables the creation of accurate disease-specific polygenic risk scores (PRSs) in datasets with very few labeled data. We apply REGLE to perform GWAS on respiratory and circulatory HDCD-spirograms measuring lung function and photoplethysmograms measuring blood volume changes. REGLE replicates known loci while identifying others not previously detected. REGLE are predictive of overall survival, and PRSs constructed from REGLE loci improve disease prediction across multiple biobanks. Overall, REGLE contain clinically relevant information beyond that captured by existing expert-defined features, leading to improved genetic discovery and disease prediction.

Digital economy and the synergistic governance of pollutants and carbon emissions: Facilitation or obstruction?

As an emerging force enabling high-quality economic development, digital economy (DE) still requires further investigation regarding its impact on synergistic governance of pollutants and carbon emissions (SGPCE). This study examines the impact of DE on SGPCE using two-way fixed effects model, intermediary effect model, and spatial Durbin model using provincial panel data from 2011 to 2020. The research reveals that: (1) DE has a significant promoting effect on SGPCE. (2) Enhancing the degree of green technology innovation is a crucial means of transmission for DE to propel SGPCE. (3) DE additionally exerts a constructive influence on SGPCE in adjacent regions, manifesting a spatial spillover effect. (4) Furthermore, DE demonstrates a notably heightened impact on SGPCE in the western region with respect to regional heterogeneity. Additionally, in the realm of dimension heterogeneity, the industrial digitization yields more favorable dividends for SGPCE compared to digital industrialization. The above conclusions provide novel insights and empirical evidence to validate the connection between DE and SGPCE. It also gives new policy recommendations for China to combat pollution prevention and climate warming under the wave of global digitization.

Time Division Colorful Multiplexing Based on Carbon Nanodots with Modifiable Colors and Lifetimes.

Optical multiplexing technology plays a crucial role in various fields such as data storage, anti-counterfeiting, and time-resolved biological imaging. Nevertheless, employing single-wavelength phosphorescence for multiplexing often results in spectral overlap among the emission peaks of various channels, which can precipitate crosstalk and misinterpretation in the information-decoding process, thereby compromising the integrity and precision of the encrypted data. This paper proposes a time-divided colorful multiplexing technology based on phosphorescent carbon nanodots with different colors and lifetimes. Using different luminescence colors to symbolize varying information levels helps achieve multitiered information encryption and storage. By modulation of the lifetime and the emission wavelength, intricate information can be encoded, thereby enhancing the intricacy and security of the encryption mechanism. By assigning different data bits to each color, more information can be encoded in the same physical space. This method enables higher-density information storage and fortifies encryption, ensuring the compactness and security of information.

A study on the spatial distribution characteristics and driving factors of traditional villages in the southeast coast of China: A case study of Puxian, Fujian.

Traditional villages are the common historical and cultural heritage of all mankind. With the intensification of urbanization, the continuation of traditional villages and the inheritance of historical and cultural heritage are facing risks. The research on the driving factors affecting the spatial distribution characteristics, heterogeneity and human land interaction of traditional villages provides a new idea for the protection of traditional villages. This study takes 137 traditional villages in Puxian area, a typical cultural area in the southeast coast, as the research object, analyzes the spatial distribution pattern of traditional villages by using spatial analysis method, and selects 13 factors to analyze the main driving forces and interaction mechanisms through geographical detectors. The results show that: (1) Puxian traditional villages are clustered and distributed, and the distribution among counties is uneven, mainly in the state of "one cluster and many scattered points" with more coastal areas and less mountainous areas. (2) Puxian traditional villages are mainly affected by many factors such as nature, space, society and culture. They are more densely distributed in areas with rich cultural heritage, fertile land, flat terrain, suitable climate, close to water systems, developed transportation, backward economy and dense population. (3) Cultural factors are the primary factors affecting the spatial distribution of traditional villages, the order of driving factors' explanatory power is: intangible cultural heritage (0.5160) > protected cultural relic units (0.3591) > distance from railway (0.3255) > night light remote sensing (0.3179) > elevation (0.3012) > population density (0.2671) > slope (0.2032) > soil type (0.1804) > precipitation (0.1750) > temperature (0.1744) > land use (0.1492) > distance from river (0.0691)>distance from highway (0.0530). The interaction of intangible cultural heritage, protected cultural relic units and distance from the railway is the dominant factor for the spatial differentiation of traditional villages. Among them, the interaction of intangible cultural heritage∩distance from the railway is the strongest, and the q-value is 0.79, which proves that the interpretation ability of the two factor model is much higher than that of the single factor model. The results of this study reflect that traditional villages and nature, space, society and culture are interdependent, so the protection of traditional villages should be adapted to local conditions.

Dearomative Intramolecular (4+3) Cycloadditions of Thiophenes.

Unexpectedly facile dearomative intramolecular (4+3) cycloadditions of thiophenes with epoxy enolsilanes, providing sulfur-bridged cycloadducts, are reported. A total of fifteen thiophene substrates have been found to undergo (4+3) cycloaddition smoothly to produce endo and exo (4+3) adducts in yields of up to 83 % with moderate to good diastereoselectivity. Complete conservation of enantiomeric purity was observed when the optically enriched epoxide was used. The desulfurizing transformations of the sulfur-bridged skeleton of the cycloadducts provide functionalized 6,7-fused bicyclic frameworks consisting of 1,3-cycloheptadiene subunits. Density functional theory calculations reveal the origins of the facile dearomatization of thiophenes in these (4+3) cycloadditions.

Fermented Gracilaria lemaneiformis polysaccharides alleviate food allergy by regulating Treg cells and gut microbiota.

Food allergy has a significant impact on the health and well-being of individuals, affecting both their physical and mental states. Research on natural bioactive compounds, such as polysaccharides extracted from seaweeds, holds great promise in the treatment of food allergies. In this study, fermented Gracilaria lemaneiformis polysaccharides (F-GLSP) were prepared using probiotic fermentation. Probiotic fermentation of Gracilaria lemaneiformis reduces the particle size of polysaccharides. To compare the anti-allergic activity of F-GLSP with unfermented Gracilaria lemaneiformis polysaccharides (UF-GLSP), an OVA-induced mouse food allergy model was established. F-GLSP exhibited a significant reduction in OVA-specific IgE and mMCP levels in allergic mice. Moreover, it significantly inhibited Th2 differentiation and IL-4 production and significantly promoted Treg differentiation and IL-10 production in allergic mice. In contrast, UF-GLSP only reduced OVA-specific IgE and mMCP in the serum of allergic mice. Furthermore, F-GLSP demonstrated a more pronounced regulation of intestinal flora abundance compared to UF-GLSP, significantly influencing the populations of Firmicutes, Bacteroidetes, Lactobacillus, and Clostridiales in the intestines of mice with food allergy. These findings suggest that F-GLSP may regulate food allergies in mice through multiple pathways. In summary, this study has promoted further development of functional foods with anti-allergic properties based on red algae polysaccharides.

Utilizing multimodal AI to improve genetic analyses of cardiovascular traits.

Electronic health records, biobanks, and wearable biosensors contain multiple high-dimensional clinical data (HDCD) modalities (e.g., ECG, Photoplethysmography (PPG), and MRI) for each individual. Access to multimodal HDCD provides a unique opportunity for genetic studies of complex traits because different modalities relevant to a single physiological system (e.g., circulatory system) encode complementary and overlapping information. We propose a novel multimodal deep learning method, M-REGLE, for discovering genetic associations from a joint representation of multiple complementary HDCD modalities. We showcase the effectiveness of this model by applying it to several cardiovascular modalities. M-REGLE jointly learns a lower representation (i.e., latent factors) of multimodal HDCD using a convolutional variational autoencoder, performs genome wide association studies (GWAS) on each latent factor, then combines the results to study the genetics of the underlying system. To validate the advantages of M-REGLE and multimodal learning, we apply it to common cardiovascular modalities (PPG and ECG), and compare its results to unimodal learning methods in which representations are learned from each data modality separately, but the downstream genetic analyses are performed on the combined unimodal representations. M-REGLE identifies 19.3% more loci on the 12-lead ECG dataset, 13.0% more loci on the ECG lead I + PPG dataset, and its genetic risk score significantly outperforms the unimodal risk score at predicting cardiac phenotypes, such as atrial fibrillation (Afib), in multiple biobanks.

DyCARS: A dynamic context-aware recommendation system.

Dynamic recommendation systems aim to achieve real-time updates and dynamic migration of user interests, primarily utilizing user-item interaction sequences with timestamps to capture the dynamic changes in user interests and item attributes. Recent research has mainly centered on two aspects. First, it involves modeling the dynamic interaction relationships between users and items using dynamic graphs. Second, it focuses on mining their long-term and short-term interaction patterns. This is achieved through the joint learning of static and dynamic embeddings for both users and items. Although most existing methods have achieved some success in modeling the historical interaction sequences between users and items, there is still room for improvement, particularly in terms of modeling the long-term dependency structures of dynamic interaction histories and extracting the most relevant delayed interaction patterns. To address this issue, we proposed a Dynamic Context-Aware Recommendation System for dynamic recommendation. Specifically, our model is built on a dynamic graph and utilizes the static embeddings of recent user-item interactions as dynamic context. Additionally, we constructed a Gated Multi-Layer Perceptron encoder to capture the long-term dependency structure in the dynamic interaction history and extract high-level features. Then, we introduced an Attention Pooling network to learn similarity scores between high-level features in the user-item dynamic interaction history. By calculating bidirectional attention weights, we extracted the most relevant delayed interaction patterns from the historical sequence to predict the dynamic embeddings of users and items. Additionally, we proposed a loss function called the Pairwise Cosine Similarity loss for dynamic recommendation to jointly optimize the static and dynamic embeddings of two types of nodes. Finally, extensive experiments on two real-world datasets, LastFM, and the Global Terrorism Database showed that our model achieves consistent improvements over state-of-the-art baselines.

Discovery and Development of Luvangetin from Zanthoxylum avicennae as a New Fungicide Candidate for Fusarium verticillioides.

Pathogenic fungi pose a significant threat to crop yields and human healthy, and the subsequent fungicide resistance has greatly aggravated these agricultural and medical challenges. Hence, the development of new fungicides with higher efficiency and greater environmental friendliness is urgently required. In this study, luvangetin, isolated and identified from the root of Zanthoxylum avicennae, exhibited wide-spectrum antifungal activity in vivo and in vitro. Integrated omics and in vitro and in vivo transcriptional analyses revealed that luvangetin inhibited GAL4-like Zn(II)2Cys6 transcriptional factor-mediated transcription, particularly the FvFUM21-mediated FUM cluster gene expression, and decreased the biosynthesis of fumonisins inFusarium verticillioides. Moreover, luvangetin binds to the double-stranded DNA helix in vitro in the groove mode. We isolated and identified luvangetin, a natural metabolite from a traditional Chinese edible medicinal plant and uncovered its multipathogen resistance mechanism. This study is the first to reveal the mechanism underlying the antifungal activity of luvangetin and provides a promising direction for the future use of plant-derived natural products to prevent and control plant and animal pathogenic fungi.

Binding of the plant-derived toxin simplexin to bovine protein kinase C: insights from molecular dynamics.

Pimelea poisoning of cattle is toxicologically linked to the activation of bovine protein kinase C (PKC) by the plant-derived toxin simplexin. To understand the affinity of PKC for simplexin, we performed molecular dynamics (MD) studies of simplexin, simplexin analogues, and several other activators of PKC. Binding enthalpy calculations indicated that simplexin had the strongest affinity for PKCα-C1B among the activators studied. Key to simplexin's affinity is its ability to form more hydrogen bonds to PKC, compared to the other activators. The C-3 carbonyl group and C-20 hydroxyl group of simplexin were identified as especially important for stabilizing the PKC binding interaction. The hydrophobic alkyl chain of simplexin induces deep membrane embedding of the PKC-simplexin complex, enhancing the protein-ligand hydrogen bonding. Our findings align with previous experiments on structure-activity relationships (SAR) for simplexin analogues, and provide insights that may guide the development of interventions or treatments for Pimelea poisoning.

Terpolymer Containing a meta-Octyloxy-phenyl-Modified Dithieno[3,2-f:2',3'-h]quinoxaline Unit Enabling Efficient Organic Solar Cells.

With the rapid development of small-molecule electron acceptors, polymer electron donors are becoming more important than ever in organic photovoltaics, and there is still room for the currently available high-performance polymer donors. To further develop polymer donors with finely tunable structures to achieve better photovoltaic performances, random ternary copolymerization is a useful technique. Herein, by incorporating a new electron-withdrawing segment 2,3-bis(3-octyloxyphenyl)dithieno[3,2-f:2',3'-h]quinoxaline derivative (C12T-TQ) to PM6, a series of terpolymers were synthesized. It is worth noting that the introduction of the C12T-TQ unit can deepen the highest occupied molecular orbital energy levels of the resultant polymers. In addition, the polymer Z6 with a 10% C12T-TQ ratio possesses the highest film absorption coefficient (9.86 × 104 cm-1) among the four polymers. When blended with Y6, it exhibited superior miscibility and mutual crystallinity enhancement between Z6 and Y6, suppressed recombination, better exciton separation and charge collection characteristics, and faster hole transfer in the D-A interface. Consequently, the device of Z6:Y6 successfully achieved enhanced photovoltaic parameters and yielded an efficiency of 17.01%, higher than the 16.18% of the PM6:Y6 device, demonstrating the effectiveness of the meta-octyloxy-phenyl-modified dithieno[3,2-f:2',3'-h]quinoxaline moiety to build promising terpolymer donors for high-performance organic solar cells.

Automatic origin prediction of liver metastases via hierarchical artificial-intelligence system trained on multiphasic CT data: a retrospective, multicentre study.

Background: Currently, the diagnostic testing for the primary origin of liver metastases (LMs) can be laborious, complicating clinical decision-making. Directly classifying the primary origin of LMs at computed tomography (CT) images has proven to be challenging, despite its potential to streamline the entire diagnostic workflow. Methods: We developed ALMSS, an artificial intelligence (AI)-based LMs screening system, to provide automated liver contrast-enhanced CT analysis for distinguishing LMs from hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), as well as subtyping primary origin of LMs as six organ systems. We processed a CECT dataset between January 1, 2013 and June 30, 2022 (n = 3105: 840 HCC, 354 ICC, and 1911 LMs) for training and internally testing ALMSS, and two additional cohorts (n = 622) for external validation of its diagnostic performance. The performance of radiologists with and without the assistance of ALMSS in diagnosing and subtyping LMs was assessed. Findings: ALMSS achieved average area under the curve (AUC) of 0.917 (95% confidence interval [CI]: 0.899-0.931) and 0.923 (95% [CI]: 0.905-0.937) for differentiating LMs, HCC and ICC on both the internal testing set and external testing set, outperformed that of two radiologists. Moreover, ALMSS yielded average AUC of 0.815 (95% [CI]: 0.794-0.836) and 0.818 (95% [CI]: 0.790-0.842) for predicting six primary origins on both two testing sets. Interestingly, ALMSS assigned origin diagnoses for LMs with pathological phenotypes beyond the training categories with average AUC of 0.761 (95% [CI]: 0.657-0.842), which verify the model's diagnostic expandability. Interpretation: Our study established an AI-based diagnostic system that effectively identifies and characterizes LMs directly from multiphasic CT images. Funding: National Natural Science Foundation of China, Guangdong Provincial Key Laboratory of Medical Image Processing.

A hybrid transistor with transcriptionally controlled computation and plasticity.

Organic electrochemical transistors (OECTs) are ideal devices for translating biological signals into electrical readouts and have applications in bioelectronics, biosensing, and neuromorphic computing. Despite their potential, developing programmable and modular methods for living systems to interface with OECTs has proven challenging. Here we describe hybrid OECTs containing the model electroactive bacterium Shewanella oneidensis that enable the transduction of biological computations to electrical responses. Specifically, we fabricated planar p-type OECTs and demonstrated that channel de-doping is driven by extracellular electron transfer (EET) from S. oneidensis. Leveraging this mechanistic understanding and our ability to control EET flux via transcriptional regulation, we used plasmid-based Boolean logic gates to translate biological computation into current changes within the OECT. Finally, we demonstrated EET-driven changes to OECT synaptic plasticity. This work enables fundamental EET studies and OECT-based biosensing and biocomputing systems with genetically controllable and modular design elements.

Visualization of Mesoscopic Conductivity Fluctuations in Amorphous Semiconductor Thin-Film Transistors.

Charge transport in amorphous semiconductors is considerably more complicated than the process in crystalline materials due to abundant localized states. In addition to device-scale characterization, spatially resolved measurements are important to unveiling electronic properties. Here, we report gigahertz conductivity mapping in amorphous indium gallium zinc oxide (a-IGZO) thin-film transistors by microwave impedance microscopy (MIM), which probes conductivity without Schottky barrier's influence. The difference between the dc and microwave conductivities reflects the efficacy of the injection barrier in an accumulation-mode transistor. The conductivity exhibits significant nanoscale inhomogeneity in the subthreshold regime, presumably due to trapping and release from localized states. The characteristic length scale of local fluctuations, as determined by the autocorrelation analysis, is about 200 nm. Using a random-barrier model, we can simulate the spatial variation of the potential landscape, which underlies the mesoscopic conductivity distribution. Our work provides an intuitive way to understand the charge transport mechanism in amorphous semiconductors at the microscopic level.

Characterizing Pairwise U-Turn Behavior in Fish: A Data-Driven Analysis.

We applied the time-series clustering method to analyze the trajectory data of rummy-nose tetra (Hemigrammus rhodostomus), with a particular focus on their spontaneous paired turning behavior. Firstly, an automated U-turn maneuver identification method was proposed to extract turning behaviors from the open trajectory data of two fish swimming in an annular tank. We revealed two distinct ways of pairwise U-turn swimming, named dominated turn and non-dominated turn. Upon comparison, the dominated turn is smoother and more efficient, with a fixed leader-follower relationship, i.e., the leader dominates the turning process. Because these two distinct ways corresponded to different patterns of turning feature parameters over time, we incorporated the Toeplitz inverse covariance-based clustering (TICC) method to gain deeper insights into this process. Pairwise turning behavior was decomposed into some elemental state compositions. Specifically, we found that the main influencing factor for a spontaneous U-turn is collision avoidance with the wall. In dominated turn, when inter-individual distances were appropriate, fish adjusted their positions and movement directions to achieve turning. Conversely, in closely spaced non-dominated turn, various factors such as changes in distance, velocity, and movement direction resulted in more complex behaviors. The purpose of our study is to integrate common location-based analysis methods with time-series clustering methods to analyze biological behavioral data. The study provides valuable insights into the U-turn behavior, motion characteristics, and decision factors of rummy-nose tetra during pairwise swimming. Additionally, the study extends the analysis of fish interaction features through the application of time-series clustering methods, offering a fresh perspective for the analysis of biological collective data.

Co-embedding of edges and nodes with deep graph convolutional neural networks.

Graph neural networks (GNNs) have significant advantages in dealing with non-Euclidean data and have been widely used in various fields. However, most of the existing GNN models face two main challenges: (1) Most GNN models built upon the message-passing framework exhibit a shallow structure, which hampers their ability to efficiently transmit information between distant nodes. To address this, we aim to propose a novel message-passing framework, enabling the construction of GNN models with deep architectures akin to convolutional neural networks (CNNs), potentially comprising dozens or even hundreds of layers. (2) Existing models often approach the learning of edge and node features as separate tasks. To overcome this limitation, we aspire to develop a deep graph convolutional neural network learning framework capable of simultaneously acquiring edge embeddings and node embeddings. By utilizing the learned multi-dimensional edge feature matrix, we construct multi-channel filters to more effectively capture accurate node features. To address these challenges, we propose the Co-embedding of Edges and Nodes with Deep Graph Convolutional Neural Networks (CEN-DGCNN). In our approach, we propose a novel message-passing framework that can fully integrate and utilize both node features and multi-dimensional edge features. Based on this framework, we develop a deep graph convolutional neural network model that prevents over-smoothing and obtains node non-local structural features and refined high-order node features by extracting long-distance dependencies between nodes and utilizing multi-dimensional edge features. Moreover, we propose a novel graph convolutional layer that can learn node embeddings and multi-dimensional edge embeddings simultaneously. The layer updates multi-dimensional edge embeddings across layers based on node features and an attention mechanism, which enables efficient utilization and fusion of both node and edge features. Additionally, we propose a multi-dimensional edge feature encoding method based on directed edges, and use the resulting multi-dimensional edge feature matrix to construct a multi-channel filter to filter the node information. Lastly, extensive experiments show that CEN-DGCNN outperforms a large number of graph neural network baseline methods, demonstrating the effectiveness of our proposed method.

A review of traditional Chinese medicine Curcumae Rhizoma for treatment of glioma.

Glioma is the most common primary central nervous tumor and its malignant and high recurrence rate are seriously threatening patient's life. The prognosis of glioma patients is still poor with a variety of modern treatments. Traditional Chinese medicine (TCM) is widely used in the adjuvant treatment or alternative medicine of glioma. Curcumae Rhizoma is one of the most commonly used in traditional Chinese medicine prescriptions for its anti-tumor characteristics. There are also many studies that reveals the anti-tumor effect of its active ingredients and some of which have been made into drugs and have been used in clinical practice. This review summarizes the new research progress on Curcumae Rhizoma for the treatment of glioma in recent years.

Tunable Electronic Transport of New-Type 2D Iodine Materials Affected by the Doping of Metal Elements.

2D iodine structures under high pressures are more attractive and valuable due to their special structures and excellent properties. Here, electronic transport properties of such 2D iodine structures are theoretically studied by considering the influence of the metal-element doping. In equilibrium, metal elements in Group 1 can enhance the conductance dramatically and show a better enhancement effect. Around the Fermi level, the transmission probability exceeds 1 and can be improved by the metal-element doping for all devices. In particular, the device density of states explains well the distinctions between transmission coefficients originating from different doping methods. Contrary to the "big" site doping, the "small" site doping changes transmission eigenstates greatly, with pronounced electronic states around doped atoms. In non-equilibrium, the conductance of all devices is almost weaker than the equilibrium conductance, decreasing at low voltages and fluctuating at high voltages with various amplitudes. Under biases, K-big doping shows the optimal enhancement effect, and Mg-small doping exhibits the most effective attenuation effect on conductance. Contrastingly, the currents of all devices increase with bias linearly. The metal-element doping can boost current at low biases and weaken current at high voltages. These findings contribute much to understanding the effects of defects on electronic properties and provide solid support for the application of new-type 2D iodine materials in controllable electronics and sensors.