Ti₃C₂ MXene-based Schottky Photocathode for Enhanced Photoelectrochemical Sensing

Nanomaterials are vital to the realization of photoelectrochemical (PEC) sensing platfrom that provides the sensitive detection and quantification of low-abundance biological samples. Here, this work reports a Schottky junction-based BiOI/Ti₃C₂ heterostructure, used as a photocathode for PEC bioanalysis. Specially, we realize in situ growth of flower-like BiOI on 2D intrinsically negatively charged Ti₃C₂ MXene nanosheet that endows BiOI/Ti₃C₂ heterostructure with admirably combined merits, noting in particular the generation of built-in electric field and the decrease of contact resistance between BiOI and Ti₃C₂. Under the visible light irradiation, the BiOI/Ti₃C₂ heterostructure-modified PEC platform displays superior cathodic photocurrent signal, while PEC response cuts down with the presence of L-Cysteine (L-Cys) as a representative analyte owing to the metal-S bond formation. The “signal-off” PEC sensing strategy shows good performance in terms of sensitivity, limit of detection (LOD, 0.005 nM) and stability. This research reveals the great potentials of MXene-based heterostructure in the application field of PEC sensor establishment

Automated Knowledge Extraction in the Field of Wheat Sharp Eyespot Control

Wheat sharp eyespot is a soil-borne fungal disease commonly found in wheat areas in China, which can occur throughout the entire reproductive period of wheat and has a great impact on the yield and quality of wheat in China. By constructing a domain ontology for wheat sharp eyespot control and modeling the domain knowledge, we aim to integrate and share the knowledge in the field of wheat sharp eyespot control, which can provide important support and guidance for agricultural decision-making and disease control. In this study, the literature in the field of wheat sharp eyespot control was used as a data source, the KeyBERT keyword extraction algorithm was used to mine the core concepts of the ontology, and the hierarchical relationships among the ontology concepts were extracted through clustering. Based on the constructed ontology of wheat sharp eyespot control, the schema of knowledge extraction was formed, and the knowledge extraction model was trained using the ERNIE 3.0 knowledge enhancement pretraining model. This study proposes a model and algorithm to realize knowledge extraction based on domain ontology, describes the construction method and process framework of wheat sharp eyespot control domain ontology, and details the training and reasoning effect of the knowledge extraction model. The knowledge extraction model constructed in this study for wheat sharp eyespot control contains a more complete conceptual system of wheat sharp eyespot. The F1 value of the model reaches 91.26%, which is a 17.86% improvement compared with the baseline model, and it can satisfy the knowledge extraction needs in the field of wheat sharp eyespot control. This study can provide a reference for domain knowledge extraction and provide strong support for knowledge discovery and downstream applications such as intelligent Q&A and intelligent recommendation in the field of wheat sharp eyespot control

Ti₃C₂ MXene-based Schottky Photocathode for Enhanced Photoelectrochemical Sensing

Nanomaterials are vital to the realization of photoelectrochemical (PEC) sensing platform that provides the sensitive detection and quantification of low-abundance biological samples. Here, this work reports a Schottky junction-based BiOI/Ti₃C₂ heterostructure, used as a photocathode for PEC bioanalysis. Specially, we realize in situ growth of flower-like BiOI on 2D intrinsically negatively charged Ti₃C₂ MXene nanosheet that endows BiOI/Ti₃C₂ heterostructure with admirably combined merits, noting in particular the generation of built-in electric field and the decrease of contact resistance between BiOI and Ti₃C₂. Under the visible light irradiation, the BiOI/Ti₃C₂ heterostructure-modified PEC platform displays superior cathodic photocurrent signal, while PEC response cuts down with the presence of L-Cysteine (L-Cys) as a representative analyte owing to the metal-S bond formation. The “signal-off” PEC sensing strategy shows good performance in terms of sensitivity, limit of detection (LOD, 0.005 nM) and stability. This research reveals the great potentials of MXene-based heterostructure in the application field of PEC sensor establishment

Exploring the Temporal Patterns of Dynamic Information Flow during Attention Network Test (ANT)

The attentional processes are conceptualized as a system of anatomical brain areas involving three specialized networks of alerting, orienting and executive control, each of which has been proven to have a relation with specified time-frequency oscillations through electrophysiological techniques. Nevertheless, at present, it is still unclear how the idea of these three independent attention networks is reflected in the specific short-time topology propagation of the brain, assembled with complexity and precision. In this study, we investigated the temporal patterns of dynamic information flow in each attention network via electroencephalograph (EEG)-based analysis. A modified version of the attention network test (ANT) with an EEG recording was adopted to probe the dynamic topology propagation in the three attention networks. First, the event-related potentials (ERP) analysis was used to extract sub-stage networks corresponding to the role of each attention network. Then, the dynamic network model of each attention network was constructed by post hoc test between conditions followed by the short-time-windows fitting model and brain network construction. We found that the alerting involved long-range interaction among the prefrontal cortex and posterior cortex of brain. The orienting elicited more sparse information flow after the target onset in the frequency band 1–30 Hz, and the executive control contained complex top-down control originating from the frontal cortex of the brain. Moreover, the switch of the activated regions in the associated time courses was elicited in attention networks contributing to diverse processing stages, which further extends our knowledge of the mechanism of attention networks

Electromagnetic radiation estimation at the ground plane near fifth‐generation base stations in China by using machine learning method

Abstract A novel method based on machine learning is proposed to estimate the electromagnetic radiation level at the ground plane near fifth‐generation (5G) base stations. The machine learning model was trained using data from various 5G base stations, enabling it to estimate the electric field intensity at any arbitrary radiation point when the base station provides service to different numbers of 5G terminals which are in different service modes. The inputs required for the model include the transmit power of the antenna, the antenna gain, the distance between the 5G base station and 5G terminals, terminal service modes, the number of 5G terminals and the environmental complexity around the 5G base station. Experimental results demonstrate the feasibility and effectiveness of the estimation method, with the mean absolute percentage error of the machine learning model being approximately 5.98%. This level of accuracy showcases the reliability of the approach. Moreover, the proposed method offers low costs when compared with on‐site measurements. The estimated results can be utilised to reduce test costs and provide valuable guidance for optimal site selection, thereby facilitating radio wave coverage or electromagnetic radiation regulation of 5G base stations

A high-affinity native human antibody neutralizes human cytomegalovirus infection of diverse cell types.

Human cytomegalovirus (HCMV) is the most common infection causing poor outcomes among transplant recipients. Maternal infection and transplacental transmission are major causes of permanent birth defects. Although no active vaccines to prevent HCMV infection have been approved, passive immunization with HCMV-specific immunoglobulin has shown promise in the treatment of both transplant and congenital indications. Antibodies targeting the viral glycoprotein B (gB) surface protein are known to neutralize HCMV infectivity, with high-affinity binding being a desirable trait, both to compete with low-affinity antibodies that promote the transmission of virus across the placenta and to displace nonneutralizing antibodies binding nearby epitopes. Using a miniaturized screening technology to characterize secreted IgG from single human B lymphocytes, 30 antibodies directed against gB were previously cloned. The most potent clone, TRL345, is described here. Its measured affinity was 1 pM for the highly conserved site I of the AD-2 epitope of gB. Strain-independent neutralization was confirmed for 15 primary HCMV clinical isolates. TRL345 prevented HCMV infection of placental fibroblasts, smooth muscle cells, endothelial cells, and epithelial cells, and it inhibited postinfection HCMV spread in epithelial cells. The potential utility for preventing congenital transmission is supported by the blockage of HCMV infection of placental cell types central to virus transmission to the fetus, including differentiating cytotrophoblasts, trophoblast progenitor cells, and placental fibroblasts. Further, TRL345 was effective at controlling an ex vivo infection of human placental anchoring villi. TRL345 has been utilized on a commercial scale and is a candidate for clinical evaluation