PbbHLH155 enhances iron deficiency tolerance in pear by directly activating PbFRO2 and PbbHLH38.

Iron (Fe) deficiency is a general stress for many horticulture crops, causing leaf chlorosis and stunted growth. The basic-helix-loop-helix (bHLH) transcription factor (TF) was reported to function in Fe absorption; however, the regulatory mechanism of bHLH genes on iron absorption remains largely unclear in pear. In this study, we found that PbbHLH155 was significantly induced by Fe deficiency. Overexpression of PbbHLH155 in Arabidopsis thaliana and pear calli significantly increases resistance to Fe deficiency. The PbbHLH155-overexpressed Arabidopsis lines exhibited greener leaf color, higher Fe content, stronger Fe chelate reductase (FCR) and root acidification activity. The PbbHLH155 knockout pear calli showed lower Fe content and weaker FCR activity. Interestingly, PbbHLH155 inhibited the expressions of PbFRO2 and PbbHLH38, which were positive regulators in Fe-deficiency responses (FDR). Furthermore, yeast one-hybrid (Y1H) and Dual-Luciferase Reporter (DLR) assays revealed that PbbHLH155 directly binds to the promoters of PbFRO2 and PbbHLH38, thus activating their expression. Overall, our results showed that PbbHLH155 directly promote the expression of PbFRO2 and PbbHLH38 to activate FCR activity for iron absorption. This study provided valuable information for pear breeding.

BTN3A3 inhibits clear cell renal cell carcinoma progression by regulating the ROS/MAPK pathway via interacting with RPS3A.

Butyrophilin subfamily 3 member A3 (BTN3A3) is a member of the immunoglobulin superfamily and functions as a tumor suppressor in multiple cancer types. Our study has revealed that in clear cell renal cell carcinoma (ccRCC), patients who express high levels of BTN3A3 experience longer survival times than those with lower expression. Further, we have observed that BTN3A3 inhibits the proliferation, migration, and invasion of ccRCC cells. Through the utilization of an immunoprecipitation assay followed by mass spectrometry, we have discovered that BTN3A3 binds directly to RPS3A. Knockdown of BTN3A3 led to increased cell proliferation, migration, and invasion. However, this effect was significantly reduced when RPS3A was simultaneously overexpressed. Previous reports have demonstrated that RPS3A positively regulates mitochondrial function and reactive oxygen species (ROS) levels. Our study has shown that overexpression of both BTN3A3 and RPS3A can increase cellular oxygen consumption rate (OCR) and ROS levels. Furthermore, we have observed that the addition of H2O2 can reverse the effects of BTN3A3 knockdown on cell proliferation and migration by increasing the cellular ROS level. ROS play a crucial role in regulating the MAPK pathway and tumor cell growth. To further explore this relationship, we examined RNA-Seq and immunoblotting data and found that BTN3A3 can negatively regulate the degree of activation of the MAPK signaling pathway. This finding suggests that the BTN3A3/RPS3A complex may regulate ccRCC progression by modulating MAPK pathways. Therefore, BTN3A3 could serve as both a prognostic marker and a potential therapeutic target for ccRCC patients.

Evidence That PbrSAUR72 Contributes to Iron Deficiency Tolerance in Pears by Facilitating Iron Absorption.

Iron is an essential trace element for plants; however, low bioactive Fe in soil continuously places plants in an Fe-deficient environment, triggering oxidative damage. To cope with this, plants make a series of alterations to increase Fe acquisition; however, this regulatory network needs further investigation. In this study, we found notably decreased indoleacetic acid (IAA) content in chlorotic pear (Pyrus bretschneideri Rehd.) leaves caused by Fe deficiency. Furthermore, IAA treatment slightly induced regreening by increasing chlorophyll synthesis and Fe2+ accumulation. At that point, we identified PbrSAUR72 as a key negative effector output of auxin signaling and established its close relationship to Fe deficiency. Furthermore, the transient PbrSAUR72 overexpression could form regreening spots with increased IAA and Fe2+ content in chlorotic pear leaves, whereas its transient silencing does the opposite in normal pear leaves. In addition, cytoplasm-localized PbrSAUR72 exhibits root expression preferences and displays high homology to AtSAUR40/72. This promotes salt tolerance in plants, indicating a putative role for PbrSAUR72 in abiotic stress responses. Indeed, transgenic plants of Solanum lycopersicum and Arabidopsis thaliana overexpressing PbrSAUR72 displayed less sensitivity to Fe deficiency, accompanied by substantially elevated expression of Fe-induced genes, such as FER/FIT, HA, and bHLH39/100. These result in higher ferric chelate reductase and root pH acidification activities, thereby hastening Fe absorption in transgenic plants under an Fe-deficient condition. Moreover, the ectopic overexpression of PbrSAUR72 inhibited reactive oxygen species production in response to Fe deficiency. These findings contribute to a new understanding of PbrSAURs and its involvement in Fe deficiency, providing new insights for the further study of the regulatory mechanisms underlying the Fe deficiency response.

WYSIWYG: IoT Device Identification Based on WebUI Login Pages.

With the improvement of intelligence and interconnection, Internet of Things (IoT) devices tend to become more vulnerable and exposed to many threats. Device identification is the foundation of many cybersecurity operations, such as asset management, vulnerability reaction, and situational awareness, which are important for enhancing the security of IoT devices. The more information sources and the more angles of view we have, the more precise identification results we obtain. This study proposes a novel and alternative method for IoT device identification, which introduces commonly available WebUI login pages with distinctive characteristics specific to vendors as the data source and uses an ensemble learning model based on a combination of Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) for device vendor identification and develops an Optical Character Recognition (OCR) based method for device type and model identification. The experimental results show that the ensemble learning model can achieve 99.1% accuracy and 99.5% F1-Score in the determination of whether a device is from a vendor that appeared in the training dataset, and if the answer is positive, 98% accuracy and 98.3% F1-Score in identifying which vendor it is from. The OCR-based method can identify fine-grained attributes of the device and achieve an accuracy of 99.46% in device model identification, which is higher than the results of the Shodan cyber search engine by a considerable margin of 11.39%.

Fine-grained identification of camera devices based on inherent features.

Camera devices are being deployed everywhere. Cities, enterprises, and more and more smart homes are using camera devices. Fine-grained identification of devices brings an in-depth understanding of the characteristics of these devices. Identifying the device type helps secure the device safe. But, existing device identification methods have difficulty in distinguishing fine-grained types of devices. To address this challenge, we propose a fine-grained identification method based on the camera deviceso inherent features. First, feature selection is based on the coverage and differences of the inherent features type. Second, the features are classified according to their representation. A design feature similarity calculation strategy (FSCS) for each type of feature is established. Then the feature weights are determined based on feature entropy. Finally, we present a device similarity model based on the FSCS and feature weights. And we use this model to identify the fine-grained type of a target device. We have evaluated our method on Dahua and Hikvision camera devices. The experimental results show that we can identify the deviceos fine-grained type when some inherent feature values are missing. Even when the inherent feature pmissing rateq is 50%, the average accuracy still exceeds 80%.