Portable Mass Spectrometry for On-site Detection of Hazardous Volatile Organic Compounds via Robotic Extractive Sampling.

Various hazardous volatile organic compounds (VOCs) are frequently released into environments during accidental events that cause many hazards to ecosystems and humans. Therefore, rapid, sensitive, and on-site detection of hazardous VOCs is crucial to understand their compositions, characteristics, and distributions in complex environments. However, manual handling of hazardous VOCs remains a challenging task, because of the inaccessible environments and health risk. In this work, we designed a quadruped robotic sampler to reach different complex environments for capturing trace hazardous VOCs using a needle trap device (NTD) by remote manipulation. The captured samples were rapidly identified by portable mass spectrometry (MS) within minutes. Rapid detection of various hazardous VOCs including toxicants, chemical warfare agents, and burning materials from different environments was successfully achieved using this robot-MS system. On-site detection of 83 typical hazardous VOCs was examined. Acceptable analytical performances including low detection limits (at subng/mL level), good reproducibility (relative standard deviation (RSD) < 20%, n = 6), excellent quantitative ability (R2 > 0.99), and detection speed (within minutes) were also obtained. Our results show that the robot-MS system has excellent performance including safety, controllability, applicability, and robustness under dangerous chemical conditions.

Traditional and non-traditional lipid parameters as risk factors for sudden sensorineural hearing loss.

OBJECTIVE: The purpose was to explore the effects of traditional and non-traditional lipid parameters on Sudden Sensorineural Hearing Loss (SSNHL). METHODS: The study included 452 patients diagnosed with SSNHL, among whom 206 patients had a level of hearing improvement ≥10 dB after one month of follow-up. A propensity score-matched (2:1) control group was used. Conditional and unconditional logistic regression were used to analyze the risk factors for SSNHL. RESULTS: Patients with SSNHL had a higher risk of concomitant hypertension and elevated atherosclerogenic lipid levels, with apolipoprotein B and apolipoprotein E identified as independent risk factors for the onset of SSNHL. Additionally, the Lipid Comprehensive Index (LCI) was an independent risk factor for the degree of hearing loss. A positive linear correlation was revealed between triglyceride, non-high-density lipoprotein cholesterol, atherogenic index, Castelli risk index, atherogenic index of plasma, LCI and hearing loss. However, no linear relationship was observed between hearing gain and any lipid parameters. When Total Cholesterol (TC) was in the range of borderline high, the treatment effect was the best. However, the statistical significance disappeared upon adjusting for confounding factors. CONCLUSION: Patients with SSNHL exhibited markedly dysregulated lipid metabolism. Elevated serum lipid levels may be a causative factor in auditory impairment and can influence the extent of hearing loss. Promptly improving cochlear microcirculation may benefit patients with borderline elevated TC.

Electrochemical Knocking-Down of Zn Metal Clusters into Single Atoms.

Single Atoms Catalysts (SACs) have emerged as a class of highly promising heterogeneous catalysts, where the traditional bottom-up synthesis approaches often encounter considerable challenges in relation to aggregation issues and poor stability. Consequently, achieving densely dispersed atomic species in a reliable and efficient manner remains a key focus in the field. Herein, we report a new facile electrochemical knock-down strategy for the formation of SACs, whereby the metal Zn clusters are transformed into single atoms. While a defect-rich substrate plays a pivotal role in capturing and stabilizing isolated Zn atoms, the feasibility of this novel strategy is demonstrated through a comprehensive investigation, combining experimental and theoretical studies. Furthermore, when studied in exploring for potential applications, the material prepared shows a remarkable improvement of 58.21% for the Li+ storage and delivers a capacity over 300 Wh kg-1 after 500 cycles upon the transformation of Zn clusters into single atoms.

Challenges and Opportunities in Preserving Key Structural Features of 3D-Printed Metal/Covalent Organic Framework.

Metal-organic framework (MOF) and covalent organic framework (COF) are a huge group of advanced porous materials exhibiting attractive and tunable microstructural features, such as large surface area, tunable pore size, and functional surfaces, which have significant values in various application areas. The emerging 3D printing technology further provides MOF and COFs (M/COFs) with higher designability of their macrostructure and demonstrates large achievements in their performance by shaping them into advanced 3D monoliths. However, the currently available 3D printing M/COFs strategy faces a major challenge of severe destruction of M/COFs' microstructural features, both during and after 3D printing. It is envisioned that preserving the microstructure of M/COFs in the 3D-printed monolith will bring a great improvement to the related applications. In this overview, the 3D-printed M/COFs are categorized into M/COF-mixed monoliths and M/COF-covered monoliths. Their differences in the properties, applications, and current research states are discussed. The up-to-date advancements in paste/scaffold composition and printing/covering methods to preserve the superior M/COF microstructure during 3D printing are further discussed for the two types of 3D-printed M/COF. Throughout the analysis of the current states of 3D-printed M/COFs, the expected future research direction to achieve a highly preserved microstructure in the 3D monolith is proposed.

Two-Phase Fermentation Systems for Microbial Production of Plant-Derived Terpenes.

Microbial cell factories, renowned for their economic and environmental benefits, have emerged as a key trend in academic and industrial areas, particularly in the fermentation of natural compounds. Among these, plant-derived terpenes stand out as a significant class of bioactive natural products. The large-scale production of such terpenes, exemplified by artemisinic acid-a crucial precursor to artemisinin-is now feasible through microbial cell factories. In the fermentation of terpenes, two-phase fermentation technology has been widely applied due to its unique advantages. It facilitates in situ product extraction or adsorption, effectively mitigating the detrimental impact of product accumulation on microbial cells, thereby significantly bolstering the efficiency of microbial production of plant-derived terpenes. This paper reviews the latest developments in two-phase fermentation system applications, focusing on microbial fermentation of plant-derived terpenes. It also discusses the mechanisms influencing microbial biosynthesis of terpenes. Moreover, we introduce some new two-phase fermentation techniques, currently unexplored in terpene fermentation, with the aim of providing more thoughts and explorations on the future applications of two-phase fermentation technology. Lastly, we discuss several challenges in the industrial application of two-phase fermentation systems, especially in downstream processing.

Switch Volmer-Heyrovsky to Volmer-Tafel Pathway for Efficient Acidic Electrocatalytic Hydrogen Evolution by Correlating Pt Single Atoms with Clusters.

As cost-effective catalysts, platinum (Pt) single-atom catalysts (SACs) have attracted substantial attention. However, most studies indicate that Pt SACs in acidic hydrogen evolution reaction (HER) follow the slow Volmer-Heyrovsky (VH) mechanism instead of the fast kinetic Volmer-Tafel (VT) pathway. Here, this work propose that the VH mechanism in Pt SACs can be switched to the faster VT pathway for efficient HER by correlating Pt single atoms (SAs) with Pt clusters (Cs). Our calculations reveal that the correlation between Pt SAs and Cs significantly impacts the electronic structure of exposed Pt atoms, lowering the adsorption barrier for atomic hydrogen and enabling a faster VT mechanism. To validate these findings, this work purposely synthesize three catalysts: l-Pt@MoS2 , m-Pt@MoS2 and h-Pt@MoS2 with low, moderate, and high Pt-loading, having different distributions of Pt SAs and Cs. The m-Pt@MoS2 catalyst with properly correlating Pt SAs and Cs exhibits outstanding performance with an overpotential of 47 mV and Tafel slope of 32 mV dec-1 . Further analysis of the Tafel values confirms that the m-Pt@MoS2 sample indeed follows the VT reaction mechanism, aligning with the theoretical findings. This study offers a deep understanding of the synergistic mechanism, paving a way for designing novel-advanced catalysts.

PEGA: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization.

EA, such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users lack the capability to implement evolutionary algorithms (EAs) for solving COPs. An intuitive and promising solution is to outsource evolutionary operations to a cloud server, however, it poses privacy concerns. To this end, this article proposes a novel computing paradigm called evolutionary computation as a service (ECaaS), where a cloud server renders evolutionary computation services for users while ensuring their privacy. Following the concept of ECaaS, this article presents privacy-preserving genetic algorithm (PEGA), a privacy-preserving GA designed specifically for COPs. PEGA enables users, regardless of their domain expertise or resource availability, to outsource COPs to the cloud server that holds a competitive GA and approximates the optimal solution while safeguarding privacy. Notably, PEGA features the following characteristics. First, PEGA empowers users without domain expertise or sufficient resources to solve COPs effectively. Second, PEGA protects the privacy of users by preventing the leakage of optimization problem details. Third, PEGA performs comparably to the conventional GA when approximating the optimal solution. To realize its functionality, we implement PEGA falling in a twin-server architecture and evaluate it on two widely known COPs: 1) the traveling Salesman problem (TSP) and 2) the 0/1 knapsack problem (KP). Particularly, we utilize encryption cryptography to protect users' privacy and carefully design a suite of secure computing protocols to support evolutionary operators of GA on encrypted chromosomes. Privacy analysis demonstrates that PEGA successfully preserves the confidentiality of COP contents. Experimental evaluation results on several TSP datasets and KP datasets reveal that PEGA performs equivalently to the conventional GA in approximating the optimal solution.

Interfacial Synergy in Mo2C/MoC Heterostructure Promoting Sequential Polysulfide Conversion in High-Performance Lithium-Sulfur Battery.

A rational design of sulfur host is the key to conquering the"polysulfide shuttle effects" by accelerating the polysulfide conversion. Since the process involves solid-liquid-solid multistep phase transitions, purposely-engineered heterostructure catalysts with various active regions for catalyzing conversion steps correspondingly are beneficial to promote the overall conversion process. However, the functionalities of the materials surface and interface in heterostructure catalysts remain unclear. In this work, an Mo2C/MoC catalyst with abundant Mo2C surface-interface-MoC surface tri-active-region is developed by in situ converting the MoZn-metal organic framework. The experimental and simulation studies demonstrate the interface can catch long-chain polysulfides and promote their conversion. Instead, the Mo2C and MoC tend to accommodate the short-chain polysulfide and accelerate their conversion and the Li2S dissociation. Benefitting from the high catalytic ability, the Li-S battery assembled with the Mo2C/MoC-S cathode shows more discrete redox reactions and delivers a high initial capacity of 1603.6 mAh g-1 at 1 C charging-discharging rate, which is over twofolds of the one assembled using individual hosts, and 80.4% capacity can be maintained after 1000 cycles at 3 C rate. This work has demonstrated a novel synergy between the interface and material surface, which will help the future design of high-performance Li-S batteries.

Laser-Ironing Induced Capping Layer on Co-ZIF-L Promoting In Situ Surface Modification to High-Spin Oxide-Carbon Hybrids on the "Real Catalyst" for High OER Activity and Stability.

Enhancing electrocatalytic performance through structural and compositional engineering attracts considerable attention. However, most materials only function as pre-catalysts and convert into "real catalysts" during electrochemical reactions. Such transition involves dramatic structural and compositional changes and disrupts their designed properties. Herein, for the first time, a laser-ironing (LI) approach capable of in-situ forming a laser-ironing capping layer (LICL) on the Co-ZIF-L flakes is developed. During the oxygen evolution reaction (OER) process, the LICL sustains the leaf-like morphology and promotes the formation of OER-active Co3 O4 nanoclusters with the highest activity and stability. In contrast, the pristine and conventional heat-treated Co-ZIF-Ls both collapse and transform to less active CoOOH. The density functional theory (DFT) calculations pinpoint the importance of the high spin (HS) states of Co ions and the narrowed band gap in Co3 O4 nanoclusters. They enhance the OER activity by promoting spin-selected electron transport, effectively lowering the energy barrier and realizing a spontaneous O2 -releasing step that is the potential determining step (pds) in CoOOH. This study presents an innovative approach for modulating both structural and compositional evolutions of electrocatalysts during the reaction, sustaining stability with high performance during dynamic electrochemical reactions, and providing new pathways for facile and high-precision surface microstructure control.

Accelerating Sulfur Redox Kinetics by Electronic Modulation and Drifting Effects of Pre-Lithiation Electrocatalysts.

Efficient catalyst design is crucial for addressing the sluggish multi-step sulfur redox reaction (SRR) in lithium-sulfur batteries (LiSBs), which are among the promising candidates for the next-generation high-energy-density storage systems. However, the limited understanding of the underlying catalytic kinetic mechanisms and the lack of precise control over catalyst structures pose challenges in designing highly efficient catalysts, which hinder the LiSBs' practical application. Here, drawing inspiration from the theoretical calculations, the concept of precisely controlled pre-lithiation SRR electrocatalysts is proposed. The dual roles of channel and surface lithium in pre-lithiated 1T'-MoS2 are revealed, referred to as the "electronic modulation effect" and "drifting effect", respectively, both of which contribute to accelerating the SRR kinetics. As a result, the thus-designed 1T'-Lix MoS2 /CS cathode obtained by epitaxial growth of pre-lithiated 1T'-MoS2 on cubic Co9 S8 exhibits impressive performance with a high initial specific capacity of 1049.8 mAh g-1 , excellent rate-capability, and remarkable long-term cycling stability with a decay rate of only 0.019% per cycle over 1000 cycles at 3 C. This work highlights the importance of precise control in pre-lithiation parameters and the synergistic effects of channel and surface lithium, providing new valuable insights into the design and optimization of SRR electrocatalysts for high-performance LiSBs.

The visual stimulation in disorders of consciousness.

Severe brain damage usually leads to disorders of consciousness (DOC), which include coma, unresponsive wakefulness syndrome (UWS) and a minimally conscious state (MCS). Visual stimulation is widely used, especially in the diagnosis and treatment and treatment of DOC. Researchers have indicated that tests based on visual stimulation including visual pursuit, when used in conjunction with the Coma Recovery Scale-Revised, are able to differentiate between UWS from an MCS. Recently, targeting patients' circadian rhythms has been proposed to be a possible treatment target for DOC. Indeed, light therapy has been applied in some other fields, including treating seasonal affective disorder, sleep problems, and Parkinson's disease. However, at present, although visual stimulation and light therapy are frequently used in DOC, there is still no international unified standard. Therefore, we recommend the development of an international consensus in regard to the definitions, operational criteria and assessment procedures of visual stimulation and light therapy. This review combines visual stimulation, circadian rhythm recovery, and light therapy in DOC patients and presents the mechanisms and current advances in applications related to light therapy and visual stimulation in an attempt to provide additional ideas for future research and treatment of DOC.

Quasi-Copper-Mers Enable High-Performance Catalysis for CO2 Reduction.

As the atmospheric carbon dioxide (CO2 ) level keeps hitting the new record, humanity is facing an ever-daunting challenge to efficiently mitigate CO2 from the atmosphere. Though electrochemical CO2 reduction presents a promising pathway to convert CO2 to valuable fuels and chemicals, the general lack of suitable electrocatalysts with high activity and selectivity severely constrains this approach. Herein, a novel class of electrocatalysts is investigated, the quasi-copper-mers, in which the CuN4 rather than Cu atom itself serve as the basic building block. The respective quasi-copper-monomers, -dimers, and -trimers hosted in a graphene-like substrate are first synthesized and then performed both experimental characterization and density functional theory (DFT) calculations to examine their atomic structures, evaluate their electrocatalytical performance and understand their underlying mechanisms. The experimental results show that the quasi-copper-trimers not only outperform the quasi-copper-dimer and quasi-copper-monomer when catalyzing CO2 to CO, it also shows a superior selectivity against the competing hydrogen evolution reaction (HER). The DFT calculations not only support the experimental observations, but also reveal the volcano curve and the physical origin for the qausi-copper-trimer superiority. The present work thus presents a new strategy in the design of high-performance electrocatalysts with high activity and selectivity.

A Cost-Driven Method for Deep-Learning-Based Hardware Trojan Detection.

The Cyber-Physical System and even the Metaverse will become the second space in which human beings live. While bringing convenience to human beings, it also brings many security threats. These threats may come from software or hardware. There has been a lot of research on managing malware, and there are many mature commercial products, such as antivirus software, firewalls, etc. In stark contrast, the research community on governing malicious hardware is still in its infancy. Chips are the core component of hardware, and hardware Trojans are the primary and complex security issue faced by chips. Detection of hardware Trojans is the first step for dealing with malicious circuits. Due to the limitation of the golden chip and the computational consumption, the existing traditional detection methods are not applicable to very large-scale integration. The performances of traditional machine-learning-based methods depend on the accuracy of the multi-feature representation, and most of the methods may lead to instability because of the difficulty of extracting features manually. In this paper, employing deep learning, a multiscale detection model for automatic feature extraction is proposed. The model is called MHTtext and provides two strategies to balance the accuracy and computational consumption. After selecting a strategy according to the actual situations and requirements, the MHTtext generates the corresponding path sentences from the netlist and employs TextCNN for identification. Further, it can also obtain non-repeated hardware Trojan component information to improve its stability performance. Moreover, a new evaluation metric is established to intuitively measure the model's effectiveness and balance: the stabilization efficiency index (SEI). In the experimental results for the benchmark netlists, the average accuracy (ACC) in the TextCNN of the global strategy is as high as 99.26%, and one of its stabilization efficiency index values ranks first with a score of 71.21 in all comparison classifiers. The local strategy also achieved an excellent effect, according to the SEI. The results show that the proposed MHTtext model has high stability, flexibility, and accuracy, in general.

Real-world treatment patterns in patients with nontuberculous mycobacterial lung disease in the Netherlands based on medication dispensing data.

PURPOSE: Real-world data on antibiotic management of nontuberculous mycobacterial lung disease (NTM-LD) is limited for many countries. This study aimed to evaluate real-world treatment practices of NTM-LD in the Netherlands using medication dispensing data. METHODS: A retrospective longitudinal real-world study was conducted using IQVIA's Dutch pharmaceutical dispensing database. The data are collected monthly and include approximately 70% of all outpatient prescriptions in the Netherlands. Patients initiated on specific NTM-LD treatment regimens between October 2015 and September 2020 were included. The main areas of investigation were initial treatment regimens, persistence on treatment, treatment switching, treatment compliance in terms of medication possession rate (MPR) and restarts of treatment. RESULTS: The database included 465 unique patients initiated on triple- or dual-drug regimens for the treatment of NTM-LD. Treatment switches were common and occurred approximately 1.6 per quarter throughout the treatment period. The average MPR of patients initiated on triple-drug therapy was 90%. The median time on therapy for these patients was 119 days; after six months and one year, 47% and 20% of the patients, respectively, were still on antibiotic therapy. Of 187 patients initiated on triple-drug therapy, 33 (18%) patients restarted antibiotic therapy after the initial treatment had been stopped. CONCLUSION: When on therapy, patients were compliant with the NTM-LD treatment; however, many patients stopped their therapy prematurely, treatment switches often occurred, and part of patients had to restart their therapy after a longer treatment gap. NTM-LD management should be improved through greater guideline adherence and appropriate involvement of expert centers.

Portable Mass Spectrometry Approach Combined with Machine Learning for Onsite Field Detection of Huanglongbing Disease.

Huanglongbing (HLB) is one of the most serious citrus diseases in the world. Rapid, onsite, and accurate field detection of HLB is a challenging task in analytical science for a long time. Herein, we have developed a novel HLB detection method that combines headspace solid phase microextraction with portable gas chromatography-mass spectrometry (PGC-MS) approach for onsite field detection of volatile metabolites of citrus leaves. Detectability and characteristics of HLB-affected metabolites from leaves were validated, and the important biomarkers were verified by authentic compounds. A machine learning approach based on random forest algorithm is established to model the volatile metabolites from healthy, symptomatic, and asymptomatic citrus leaves. In this work, a total of 147 citrus leaf samples were analyzed. Analytical performances of this newly developed method were investigated by in-field detection of various volatile metabolites. Results demonstrated limits of detection and quantification of 0.04-0.12 and 0.17-0.44 ng/mL for different metabolites, respectively. Linear calibration curves of various metabolites were established over a concentration dynamic range of at least three orders (R2 > 0.96). Good reproducibility was obtained for intraday (3.0-17.5%, n = 6) and interday precision (8.7-18.2%, n = 7). This new HLB field detection method provides a rapid detection with 6 min for each sample via a simple optimized procedure, including onsite sampling, PGC-MS analysis, and data process and provides a high accuracy (93.3%) for simultaneous identification of healthy, symptomatic, and asymptomatic trees. These data support the use of this new method for reliable field detection of HLB. Furthermore, metabolic pathways of HLB-affected metabolites were also proposed. Overall, our results not only provide a rapid and onsite field HLB detection method but also provide valuable information for understanding metabolic change of HLB infection.

Electrochromic-Induced Rechargeable Aqueous Batteries: An Integrated Multifunctional System for Cross-Domain Applications.

Multifunctional electrochromic-induced rechargeable aqueous batteries (MERABs) integrate electrochromism and aqueous ion batteries into one platform, which is able to deliver the conversion and storage of photo-thermal-electrochemical sources. Aqueous ion batteries compensate for the drawbacks of slow kinetic reactions and unsatisfied storage capacities of electrochromic devices. On the other hand, electrochromic technology can enable dynamically regulation of solar light and heat radiation. However, MERABs still face several technical issues, including a trade-off between electrochromic and electrochemical performance, low conversion efficiency and poor service life. In this connection, novel device configuration and electrode materials, and an optimized compatibility need to be considered for multidisciplinary applications. In this review, the unique advantages, key challenges and advanced applications are elucidated in a timely and comprehensive manner. Firstly, the prerequisites for effective integration of the working mechanism and device configuration, as well as the choice of electrode materials are examined. Secondly, the latest advances in the applications of MERABs are discussed, including wearable, self-powered, integrated systems and multisystem conversion. Finally, perspectives on the current challenges and future development are outlined, highlighting the giant leap required from laboratory prototypes to large-scale production and eventual commercialization.

Mask device as a new wearable sampler for breath analysis: what can we expect in the future?

Human exhaled breath is becoming an attractive clinical source as it is foreseen to enable noninvasive diagnosis of many diseases. Because mask devices can be used for efficiently filtering exhaled substances, mask-wearing has been required in the past few years in daily life since the unprecedented COVID-19 pandemic. In recent years, there is a new development of mask devices as new wearable breath samplers for collecting exhaled substances for disease diagnosis and biomarker discovery. This paper attempts to identify new trends in mask samplers for breath analysis. The couplings of mask samplers with different (bio)analytical approaches, including mass spectrometry (MS), polymerase chain reaction (PCR), sensor, and others for breath analysis, are summarized. The developments and applications of mask samplers in disease diagnosis and human health are reviewed. The limitations and future trends of mask samplers are also discussed.

PriMPSO: A Privacy-Preserving Multiagent Particle Swarm Optimization Algorithm.

Centralized particle swarm optimization (PSO) does not fully exploit the potential of distributed or parallel computing and suffers from single-point-of-failure. Particularly, each particle in PSO comprises a potential solution (e.g., traveling route and neural network model parameters) which is essentially viewed as private data. Unfortunately, previously neither centralized nor distributed PSO algorithms fail to protect privacy effectively. Inspired by secure multiparty computation and multiagent system, this article proposes a privacy-preserving multiagent PSO algorithm (called PriMPSO) to protect each particle's data and enable private data sharing in a privacy-preserving manner. The goal of PriMPSO is to protect each particle's data in a distributed computing paradigm via existing PSO algorithms with competitive performance. Specifically, each particle is executed by an independent agent with its own data, and all agents jointly perform global optimization without sacrificing any particle's data. Thorough investigations show that selecting an exemplar from all particles and updating particles through the exemplar are critical operations for PSO algorithms. To this end, this article designs a privacy-preserving exemplar selection algorithm and a privacy-preserving triple computation protocol to select exemplars and update particles, respectively. Strict privacy analyses and extensive experiments on a benchmark and a realistic task confirm that PriMPSO not only protects particles' privacy but also has uniform convergence performance with the existing PSO algorithm in approximating an optimal solution.

Genome-Wide Identification, Evolutionary and Functional Analyses of WRKY Family Members in Ginkgo biloba.

WRKY transcription factors (TFs) are one of the largest families in plants which play essential roles in plant growth and stress response. Ginkgo biloba is a living fossil that has remained essentially unchanged for more than 200 million years, and now has become widespread worldwide due to the medicinal active ingredients in its leaves. Here, 37 WRKY genes were identified, which were distributed randomly in nine chromosomes of G. biloba. Results of the phylogenetic analysis indicated that the GbWRKY could be divided into three groups. Furthermore, the expression patterns of GbWRKY genes were analyzed. Gene expression profiling and qRT-PCR revealed that different members of GbWRKY have different spatiotemporal expression patterns in different abiotic stresses. Most of the GbWRKY genes can respond to UV-B radiation, drought, high temperature and salt treatment. Meanwhile, all GbWRKY members performed phylogenetic tree analyses with the WRKY proteins of other species which were known to be associated with abiotic stress. The result suggested that GbWRKY may play a crucial role in regulating multiple stress tolerances. Additionally, GbWRKY13 and GbWRKY37 were all located in the nucleus, while GbWRKY15 was located in the nucleus and cytomembrane.

ATOH8 binds SMAD3 to induce cellular senescence and prevent Ras-driven malignant transformation.

The process of oncogene-induced senescence (OIS) and the conversion between OIS and malignant transformation during carcinogenesis is poorly understood. Here, we show that following overactivation of oncogene Ras in lung epithelial cells, high-level transforming growth factor ß1 (TGF-ß1)-activated SMAD3, but not SMAD2 or SMAD4, plays a determinant role in inducing cellular senescence independent of the p53/p16/p15 senescence pathways. Importantly, SMAD3 binds a potential tumor suppressor ATOH8 to form a transcriptional complex that directly represses a series of cell cycle-promoting genes and consequently causes senescence in lung epithelial cells. Interestingly, the prosenescent SMAD3 converts to being oncogenic and essentially facilitates oncogenic Ras-driven malignant transformation. Furthermore, depleting Atoh8 rapidly accelerates oncogenic Ras-driven lung tumorigenesis, and lung cancers driven by mutant Ras and Atoh8 loss, but not by mutant Ras only, are sensitive to treatment of a specific SMAD3 inhibitor. Moreover, hypermethylation of the ATOH8 gene can be found in approximately 12% of clinical lung cancer cases. Together, our findings demonstrate not only epithelial cellular senescence directed by a potential tumor suppressor-controlled transcriptional program but also an important interplay between the prosenescent and transforming effects of TGF-ß/SMAD3, potentially laying a foundation for developing early detection and anticancer strategies.