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Contamination of crop seeds and feed with Aspergillus flavusand its associated aflatoxins presents a significant threat to human and animal health due to their hepatotoxic and carcinogenic properties. To address this challenge, researchers have screened for potential biological control agents in peanut soil and pods. This study identified a promising candidate, a strain of the nonpigmented bacterium, Achromobacter xylosoxidansZJS2-1, isolated from the peanut rhizosphere in Zhejiang Province, China, exhibiting notable antifungal and antiaflatoxin activities. Further investigations demonstrated that ZJS2-1 active substances (ZAS) effectively inhibited growth at a MIC of 60 μL/mL and nearly suppressed AFB1 production by 99%. Metabolomic analysis revealed that ZAS significantly affected metabolites involved in cell wall and membrane biosynthesis, leading to compromised cellular integrity and induced apoptosis in A. flavusthrough the release of cytochrome c. Notably, ZAS targeted SrbA, a key transcription factor involved in ergosterol biosynthesis and cell membrane integrity, highlighting its crucial role in ZJS2-1’s biocontrol mechanism. Moreover, infection of crop seeds and plant wilt caused by A. flavuscan be efficiently alleviated by ZAS. Additionally, ZJS2-1 and ZAS demonstrated significant inhibitory effects on various Aspergillusspecies, with inhibition rates ranging from 80 to 99%. These findings highlight the potential of ZJS2-1 as a biocontrol agent against Aspergillusspecies, offering a promising solution to enhance food safety and protect human health.

To realize passive vibration monitoring of power transformers, this study proposes a self-powered method for transformer vibration monitoring sensors based on the Sm-doped-Pb(Mg1/3Nb2/3)O3-PbTiO3 (PMN-PT) piezoelectric bimorph array. This study conducts the theoretical analysis and field measurement of the transformer vibration signals. In addition, the preparation method of a high-performance Sm-doped-PMN-PT piezoelectric bimorph is investigated. The properties of the xSm-PMN-yPT ceramics are also analyzed. Further, the output voltage and power characteristics of the piezoelectric bimorph arrays with different combinations are compared. Finally, the validation and application of the prototype are presented. The experimental results show that the 2.5%Sm-PMN-32PT has a high piezoelectric coefficient (d33) of 1710 pC/N. The results also indicate that the piezoelectric self-powered device with a parallel structure consisting of nine piezoelectric bimorphs has a maximum power generation of 2.341 mW under the measured transformer vibration excitation. Moreover, it is shown that it takes 75 min to complete the charging of the 7.5 V-1-F faradic capacitor from 4.4 to 5 V to satisfy the condition for the required energy of 2.82 J for the vibration signal acquisition, processing, and transmission, realizing the intermittent passive vibration monitoring of the transformer for 16 times per day. Therefore, the proposed method has a promising engineering application prospect.

AbstractThis study investigates the effectiveness of virtual reality (VR) advertising in promoting charitable donations. The results indicated that, when compared with the traditional two-dimensional format, VR advertising can increase the number of people who decide to donate, as well as the amount donated. This favorable effect was achieved through a serial mediating effect of vicarious experience and existential guilt. Additionally, the findings also identified the need for stimulation as a boundary condition, suggesting that individuals with a low (versus high) need for stimulation were not influenced by the mediating process of VR prosocial advertising on charitable donations.

This study aimed to investigate effects of heat treatment temperatures (60℃, 70℃, 80℃, 90℃, 100℃) and times (10 min, 30 min, 60 min) on the aggregation behavior of Moringa Oleiferaseed salt-soluble protein (MOSP). With the increase of heating temperature and time, the MOSP solutions were changed from a more transparent state to a cloudy and opaque state, the absorbance value of the MOSP solution increased significantly, and the particle size gradually increased to a maximum of 1220.33 nm (100℃, 30 min). The polymer dispersion index (PDI) also increased and the maximum PDI of MOSP (100℃, 10 min) was 0.53, but the absolute value of the zeta-potential was gradually decreased. With the increase of heating temperature and heating time, the β-sheet content of MOSP was increased to 30.8%, while the α-helix content of MOSP decreased. There was no obvious change in the subunit distribution of the heat-induced MOSP. The spectrum analysis displayed that heat treatment led to the exposure of the hydrophobic residues in MOSP, but as the heating temperature was further increased to 90℃, re-embedding of the hydrophobic groups was observed due to the formation of aggregates. The total sulfhydryl group (-SHT) increased with the increase of temperature, while the free sulfhydryl group (-SHF)first decreased and then increased during short-term heating (10 and 30 min). The maximum content of -SHFreached 4.40% and the content of disulfide bonds was 11.97% (90℃, 30 min). These changes could have a significant impact on protein products and might spur innovation in new protein applications.

Class incremental learning from a pre-trained DNN model is gaining lots of popularity. Unfortunately, the pre-trained model also introduces a new attack vector, which enables an adversary to inject a backdoor into it and further compromise the downstream models learned from it. Prior works proposed backdoor attacks against the pre-trained models in the transfer learning scenario. However, they become less effective when the adversary does not have the knowledge of the downstream tasks or new data, which is more practical and considered in this paper. To this end, we design the first latent backdoor attacks against incremental learning. We propose two novel techniques, which can effectively and stealthily embed a backdoor into the pre-trained model. Such backdoor can only be activated when the pre-trained model is extended to a downstream model with incremental learning. It has a very high attack success rate, and is able to bypass existing backdoor detection approaches. Extensive experiments confirm the effectiveness of our attacks over different datasets and incremental learning methods, as well as strong robustness against state-of-the-art backdoor defense mechanisms including Neural Cleanse, Fine-Pruning and STRIP.

Deep Neural Networks are well-known to be vulnerable to Adversarial Examples. Recently, advanced gradient-based attacks were proposed (e.g., BPDA and EOT), which can significantly increase the difficulty and complexity of designing effective defenses. In this paper, we present a study towards the opportunity of mitigating those powerful attacks with only pre-processing operations. We make the following two contributions. First, we perform an in-depth analysis of those attacks and summarize three fundamental properties that a good defense solution should have. Second, we design a lightweight preprocessing function with these properties and the capability of preserving the model’s usability and robustness against these threats. Extensive evaluations indicate that our solutions can effectively mitigate all existing standard and advanced attack techniques, and beat 11 state-of-the-art defense solutions published in top-tier conferences over the past 2 years.

Collaborative learning has gained great popularity due to its benefit of data privacy protection: participants can jointly train a Deep Learning model without sharing their training sets. However, recent works discovered that an adversary can fully recover the sensitive training samples from the shared gradients. Such reconstruction attacks pose severe threats to collaborative learning. Hence, effective mitigation solutions are urgently desired. In this paper, we systematically analyze existing reconstruction attacks and propose to leverage data augmentation to defeat these attacks: by preprocessing sensitive images with carefully-selected transformation policies, it becomes infeasible for the adversary to extract training samples from the corresponding gradients. We first design two new metrics to quantify the impacts of transformations on data privacy and model usability. With the two metrics, we design a novel search method to automatically discover qualified policies from a given data augmentation library. Our defense method can be further combined with existing collaborative training systems without modifying the training protocols. We conduct comprehensive experiments on various system settings. Evaluation results demonstrate that the policies discovered by our method can defeat state-of-the-art reconstruction attacks in collaborative learning, with high efficiency and negligible impact on the model performance.

Modern information and communication systems have become increasingly challenging to manage. The ubiquitous system logs contain plentiful information and are thus widely exploited as an alternative source for system management. As log files usually encompass large amounts of raw data, manually analyzing them is laborious and error-prone. Consequently, many research endeavors have been devoted to automatic log analysis. However, these works typically expect structured input and struggle with the heterogeneous nature of raw system logs. Log parsing closes this gap by converting the unstructured system logs to structured records. Many parsers were proposed during the last decades to accommodate various log analysis applications. However, due to the ample solution space and lack of systematic evaluation, it is not easy for practitioners to find ready-made solutions that fit their needs. This paper aims to provide a comprehensive survey on log parsing. We begin with an exhaustive taxonomy of existing log parsers. Then we empirically analyze the critical performance and operational features for 17 open-source solutions both quantitatively and qualitatively, and whenever applicable discuss the merits of alternative approaches. We also elaborate on future challenges and discuss the relevant research directions. We envision this survey as a helpful resource for system administrators and domain experts to choose the most desirable open-source solution or implement new ones based on application-specific requirements.

Autonomous Vehicles (AVs) are closely connected in the Cooperative Intelligent Transportation System (C-ITS). They are equipped with various sensors and controlled by Autonomous Driving Systems (ADSs) to provide high-level autonomy. The vehicles exchange different types of real-time data with each other, which can help reduce traffic accidents and congestion, and improve the efficiency of transportation systems. However, when interacting with the environment, AVs suffer from a broad attack surface, and the sensory data are susceptible to anomalies caused by faults, sensor malfunctions, or attacks, which may jeopardize traffic safety and result in serious accidents. In this paper, we propose ADS-Lead, an efficient collaborative anomaly detection methodology to protect the lane-following mechanism of ADSs. ADS-Lead is equipped with a novel transformer-based one-class classification model to identify time series anomalies (GPS spoofing threat) and adversarial image examples (traffic sign and lane recognition attacks). Besides, AVs inside the C-ITS form a cognitive network, enabling us to apply the federated learning technology to our anomaly detection method, where the vehicles in the C-ITS jointly update the detection model with higher model generalization and data privacy. Experiments on Baidu Apollo and two public data sets (GTSRB and Tumsimple) indicate that our method can not only detect sensor anomalies effectively and efficiently but also outperform state-of-the-art anomaly detection methods.

ABSTRACTIntroductionIndoleamine 2,3-dioxygenase 1 (IDO1) is highly related to the immune evasion of a wide range of malignancies due to its role in the immune suppression caused by the depletion of tryptophan (Trp) and the accumulation of kynurenine (Kyn). The combination of IDO1 inhibitors with other treatments represents a promising strategy in immunotherapy, although considerable challenges lie ahead.Areas coveredThis review focuses on patent publications searched from Espacenet and Google Scholar, and related to IDO1 inhibitors with potential anti-cancer utilization during the period 2018–2022.Expert opinionDespite the clinical trial failure of the first-in-class IDO1 inhibitor epacadostat in combination with pembrolizumab, numerous studies have been carried on to pursue more efficient IDO1-based immune-modulating therapeutic solutions. A large number of IDO1 inhibitors with new structures and design concepts have been produced with the impetus of crystallographic studies, and have shown great research potential. The elaboration on the combination of IDO1 inhibitors with other targeting agents, the more precise selection of patients, the identification of more reliable biomarkers for evaluating the IDO1 treatment, and the investigation of possible toxicity, are critical factors to promote IDO1-based immunotherapies from bench to bedside.

Fungi play an important role in soil nitrous oxide (N2O) emission in many agricultural soil systems. However, the effect of fungi on N2O emission in Chinese milk vetch (CMV)-containing soils has not been examined sufficiently. This study investigated the contribution of bacteria and fungi to soil N2O emission in CMV-amended soils. We compared soils from an experimental field in the Fujian Academy of Agricultural Sciences that had been treated with 30 000 kg of CMV per 667 m2per year with one that was not treated with CMV. We incubated soil using cycloheximide and streptomycin to differentiate fungal and bacterial N2O emissions, respectively. Quantitative PCR (qPCR) was performed to investigate bacterial and fungal abundances in the two agricultural soil ecosystems. The contribution of fungi to soil N2O emission in CMV-amended soils was greater than that in non-CMV-amended paddy soils, with fungi accounting for more than 56% of the emissions in CMV-amended soils. Quantitative PCR showed that the ratio of the internal transcribed spacer to 16S rDNA was significantly higher in CMV-amended soils than in non-CMV-amended paddy soils. Furthermore, soil properties, such as pH (P<0.05) and NH4+concentration (P<0.05), significantly and negatively affected N2O emission by fungi in soil, whereas the total organic carbon (P<0.05) and NO3−concentration (P<0.05) showed significant positive effects. Fungi may be important contributors to N2O production in CMV-amended soils, which may create challenges for mitigating N2O production.

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ABSTRACTIntroductionThe mammalian target of rapamycin (mTOR) kinase is a central component in the PI3K/Akt/mTOR pathway and plays a crucial role in tumor biology, making it one appealing therapeutic target. In the past decade, the mTORi (mTOR inhibitor) development field has made great progress, with more agents entering key trials and the proposal of third-generation mTORi concept. Yet to achieve significant clinical success, combined efforts from multiple disciplines are ever needed.Areas coveredThis review focuses on the progress of mTORi development with anticancer potential from the perspective of the patent literature proposed between 2011 and 2020.Expert opinionThe highly complex regulatory mechanism network of mTOR proposes huge challenges to the development of clinically efficient mTORis. While in-depth biological research and fundamental medchemistry research are of importance to provide guidelines for improving mTORis, new technologies to pre-diagnose applicable populations is another key to provide precise personal cancer treatment. New mTOR agents are ever needed to tackle the common problems of side effects and drug resistance.

With the upsurge of "emotional storm" in the field of organizational behavior, the studies on individual emotions in organizational context are rising. Especially the relationship between emotions and knowledge innovation has attracted much attention by scholars. In particular, individual emotions may exert great effect on knowledge innovation whereas the mechanism is still unclear. Based on the emotional event theory, this paper constructs a model which explores the interaction of positive and negative emotions with individual knowledge innovation. Based on questionnaire data analysis, the results show that knowledge sharing partly mediate the relationship between positive emotion and knowledge innovation as well as the relationship between negative emotion and knowledge innovation; team trust accentuates the relationship between positive emotion and knowledge innovation as well as the relationship between negative emotion and knowledge innovation. The above findings are helpful to clarify the impact mechanism of emotions on knowledge innovation.

Background: Pulmonary surfactant dysfunction is an important pathological factor in acute respiratory distress syndrome (ARDS) and pulmonary fibrosis (PF). Objective: In this study, the characteristics of recombinant mature surfactant protein B (SP-B) and reteplase (rPA) fusion protein maintaining good pulmonary surface activity and rPA fibrinolytic activity in acute lung injury cell model were studied. Methods: We studied the characteristics of SP-B fusion expression, cloned rPA gene and N-terminal rPA/C-terminal SP-B co-expression gene, and constructed them into eukaryotic expression vector pEZ-M03 to obtain recombinant plasmids pEZ-rPA and pEZ-rPA/SP-B. The recombinant plasmids was transfected into Chinese hamster ovary (CHO) K1 cells and the expression products were analyzed by Western Blot. Lipopolysaccharide (LPS) was used to induce CCL149 (an alveolar epithelial cell line) cell injury model. Fluorescence staining of rPA and rPA/SP-B was carried out with the enhanced green fluorescent protein (eGFP) that comes with pEZ-M03; the cell Raman spectroscopy technique was used to analyze the interaction between rPA/SP-B fusion protein and the phospholipid structure of cell membrane in CCL149 cells. The enzyme activity of rPA in the fusion protein was determined by fibrin-agarose plate method. Results: The rPA/SP-B fusion protein was successfully expressed. In the CCL149 cell model of acute lung injury (ALI), the green fluorescence of rPA/SP-B is mainly distributed on the CCL149 cell membrane. The rPA/SP-B fusion protein can reduce the disorder of phospholipid molecules and reduce cell membrane damage. The enzyme activity of rPA/SP-B fusion protein was 3.42, and the fusion protein still had good enzyme activity. Conclusion: The recombinant eukaryotic plasmid pEZ-rPA/SP-B is constructed and can be expressed in the eukaryotic system. Studies have shown that rPA/SP-B fusion protein maintains good SP-B lung surface activity and rPA enzyme activity in acute lung injury cell model.

Soil erosion and sediment deposition are relevant to multiple important ecosystem services essential for natural and human systems. The present study aims to project future soil erosion and sediment deposition in the Upper Mississippi River Basin (UMRB) using climate projections by five Global Circulation Models (GCMs) under the Representative Concentrations Pathway (RCP) 8.5 scenario. To understand the importance of freeze–thaw cycles (FTCs) for soil erosion and sediment deposition estimation with climate change, this study compared two Soil and Water Assessment Tool (SWAT) models with different representations of the FTCs, with the standard SWAT using a simple regression method and SWAT-FT employing a physically based method. Modeling results show that future climate change can pronouncedly intensify soil erosion and increase sediment deposition, and the impacts are sensitive to how FTCs are represented in the model. The standard SWAT projected an increase in soil erosion by nearly 40% by the end of the 21st century, which is much lower than the projected over 65% increase in soil erosion by SWAT-FT. For sediment deposition, the projected percent changes by the standard SWAT and SWAT-FT also deviate from each other (i.e., about 70% by the standard SWAT vs about 120% by SWAT-FT). Overall, these results demonstrate the important roles of FTCs in projecting future soil erosion and sediment deposition and underline the need to consider the effects of conservation practices on FTCs to realistically assess the effectiveness of those measures.

In this study, the interactions between a Ga-based liquid metal, GaInSn, and several metal materials, including pure metals (Ni and Ti) and alloys (316H stainless steel (SS) and GH3535), at 650 °C were investigated. The aim was to evaluate the corrosion performance and select a suitable candidate material for use as a molten salt manometer diaphragm in thermal energy storage systems. The results indicated that the alloys (316H SS and GH3535) exhibited less corrosion than pure metals (Ni and Ti) in liquid GaInSn. Ga-rich binary intermetallic compounds were found to form on the surfaces of all the tested metal materials exposed to liquid GaInSn, as a result of the decomposition of liquid GaInSn and its reaction with the constituent elements of the metal materials. The corrosion mechanism for all the tested materials exposed to liquid GaInSn was also investigated and proposed, which may aid in selecting the optimal candidate material when liquid GaInSn is used as the pressure-sensing medium.

ABSTRACTIntroductionCyclin-dependent kinases 4 and 6 (CDK4/6) along with their upstream/downstream components are pivotal regulators for the cell cycle progression. The dysfunction of CDK4/6 is the common feature and promoting factor in various cancer types. In-depth research on CDK4/6 inhibitors has afforded therapeutic agents, while new challenges and ideas are emerging concomitantly.Areas coveredThis review focuses on patent publications related to CDK4/6 inhibitors which could be utilized for anti-cancer purposes during the period 2015–2019.Expert opinionThe increasingly comprehensive and thorough understanding of CDK4/6 inhibitors facilitates them to break through the current limitations. Hence the utilization of CDK4/6 inhibitors for cancer therapy in the near future is likely to be performed in diverse forms and for distinct purposes. Selectivity over kinases is still crucial to new agent development but shall be prudently dealt with. The gradually revealing of resistance and adverse events proposed another issue that calls for new tackling strategies.

Purpose: The impact of asymptomatic intracranial hemorrhage (aICH) on functional outcomes after endovascular thrombectomy (EVT) remains unclear, and tools for forecasting this complication are lacking. We aim to evaluate the clinical relevance of aICH and establish a prediction model.Methods: Data of patients who received EVT for acute anterior-circulation large vessel occlusion in 3 comprehensive hospitals were retrospectively analyzed. Asymptomatic intracranial hemorrhage was defined as any hemorrhage detected after EVT that did not fulfill the definition of symptomatic intracranial hemorrhage in the European Cooperative Acute Stroke Study. Logistic regression models were performed to assess the impact of aICH on 90-day functional outcomes and identify the predictors of aICH, which were then used to establish a prediction model. The discrimination, calibration, and clinical utility of the model were evaluated.Results: This study included 460 patients, among whom 152 (33.0%) developed aICH after EVT. Asymptomatic intracranial hemorrhage was negatively associated with 90-day excellent outcomes (adjusted odds ratio [OR]: 0.414, 95% confidence interval [CI]: 0.230–0.745, p=0.003) and good outcome (adjusted OR: 0.603, 95% CI: 0.374–0.971, p=0.037), but not with mortality (adjusted OR: 1.110, 95% CI: 0.611–2.017, p=0.732) after adjusted for other predictors of functional outcome. Pre-stroke anticoagulant therapy (OR: 2.233, 95% CI: 1.073–4.647, p=0.032), Alberta stroke program early CT score (OR: 0.842, 95% CI: 0.754–0.939, p=0.002), site of occlusion (internal carotid artery occlusion as the reference; M1 segment of middle cerebral artery occlusion, OR: 2.827, 95% CI: 1.409–5.674, p=0.003; tandem occlusion, OR: 3.928, 95% CI: 1.752–8.806, p=0.001), intravenous thrombolysis (OR: 2.091, 95% CI: 1.362–3.209, p=0.001), and successful recanalization (OR: 0.383, 95% CI: 0.213–0.689, p=0.001) were identified as the predictors of aICH, which were incorporated into a nomogram model. The area under the receiver operating characteristic curve of the model was 0.707 (95% CI: 0.657–0.757), and the calibration plot demonstrated good consistency between actual observed and predicted probability of aICH. Decision curve analysis showed that patients might benefit from the model.Conclusion: Asymptomatic intracranial hemorrhage was negatively associated with favorable functional outcome after EVT. We established a nomogram model for predicting aICH, which requires external clinical validation.Clinical Impact The impact of asymptomatic intracranial hemorrhage after endovascular thrombectomy on mid-term functional outcome has been controversial. We found that asymptomatic intracranial hemorrhage may also decreased the likelihood of 90-day favourable functional outcome after endovascular thrombectomy, supporting the notion that asymptomatic intracranial hemorrhage at the acute stage may not be benign. Moreover, we established a prediction model for this complication, which may improve clinical evaluation and management of patients who would receive endovascular thrombectomy for large vessel occlusion.

Nucleic acid detection plays a pivotal role in the accurate diagnosis of diseases. The CRISPR/Cas detection system, noted for its significant utility in a variety of applications, often necessitates enhanced sensitivity or specific signal amplification strategies, particularly for detecting low-abundance biomarkers. In this study, we present a quantum-dot-encoded beads (QDB)-energized CRISPR/Cas12-based lateral-flow assay (QDB-CRISPR-LFA). This method enables amplification-free, sensitive, and rapid detection (<40 min) of BRCA-1. We validated our method using contrived reference samples and nucleic acids extracted from tumor cells. The QDB-CRISPR-LFA provides a visual, more rapid alternative to the traditional BRCA-1 real-time RT-PCR assay. Significantly, through the integration of CRISPR’s specificity and the high signal output of QDB, the detection threshold for BRCA-1 has been reduced to the femtomolar level, representing an enhancement of 2–4 orders of magnitude over existing CRISPR/Cas detection methods. This advancement underscores the potential of our approach in advancing nucleic acid detection techniques, which is crucial for the early and precise diagnosis of diseases.

ABSTRACTIntroduction: As a key element in arguably the most important pathway MAPK signaling, the BRAF kinase gives rise to severe diseases including cancers when pathologically activated. Extensive research on BRAFi (BRAF inhibitor) has been carried out to profile the characters for optimized agents and to elaborate the therapeutic strategies for the related cancer treatment.Areas covered: This review gives an overview of recently approved BRAF agents on function mode, therapeutic efficacy, and deficiency, based on which current challenges and corresponding strategies were presented. New entities as BRAFi for medical purpose in patent literature during the period 2013–2018 were also briefly introduced.Expert opinion: With the disclosure of paradox-breaker BRAFi PLX7904 crystal in complex with BRAF, the rational design for next-generation BRAFi is becoming ever more feasible. Accompanying therapeutic strategies in BRAFi elaboration may also provide flexible choice in the future ‘personal medicine’. Further digging in the greatly enriched BRAFi pool will greatly benefit the drug design processes such as FBDD- and SBDD-driven development.Trial registration:ClinicalTrials.gov identifier: NCT02012231.Trial registration:ClinicalTrials.gov identifier: NCT02428712.Trial registration:ClinicalTrials.gov identifier: NCT02014116.

Stream temperature is a key variable that controls both physical and biogeochemical processes in aquatic ecosystems. Complex physical interactions between land surface and subsurface processes make accurate simulations of stream temperature dynamics at catchment scales a challenging task. In this study we propose an integrated, catchment‐scale framework to model stream, soil, streambed, and groundwater temperatures under the influence of hydrologic and vegetation dynamics in a mixed land use catchment in central England. The phenology and surface energy modules in the coupled model were used to quantify the impacts of vegetation processes on radiation fluxes (e.g., canopy shading and the effect of vegetation growth on optical parameters). The model enabled accurate simulations of the movement and partitioning of water and thermal fluxes in different hydrologic domains with R2values of observed and simulated temperatures in the range 0.60–0.87. Simulated groundwater heads and stream stages allowed the identification of gaining and losing portions of stream reaches and the estimation of Darcy fluxes. Simulation results show significantly dampened diel streambed temperature fluctuations below 0.3 m in gaining reaches, while in losing reaches the diel fluctuations showed relatively strong fluctuations below 0.3 m. The model enabled evaluation of the relative contributions of different processes to the stream thermal budget. Results indicate that net radiation was the dominant heat source, while latent heat flux was the primary heat sink. The model provides a useful tool to explicitly simulate water and heat fluxes as well as temperature‐dependent reaction rates in biogeochemical analyses. A catchment‐scale framework to model temperature dynamics is proposedDetailed model testing using observations of stream, streambed, soil, and groundwater temperaturesModel allowed identification of dominant controls on stream temperature and gaining and losing portions of stream reaches

Coordination to form polymer is emerging as a new technology for modifying or enhancing the properties of the existed energetic substances in energetic materials area. In this work, guanidine cation CN3H6+(Gu) and 3-amino-1,2,4- triazole C2H4N4(ATz) were crystallized into NaN5 and two novel energetic coordination polymers (CPs), (NaN5)5[(CH6- N3)N5](N5)3–(1) and (NaN5)2(C2H4N4) (2) were prepared respectively via a self-assembly process. The crystal structure reveals the co-existence of the chelating pentazole anion and organic component in the solid state. In polymer 1, Na+and N5–were coordinated to form a cage structure in which guanidine cation [C(NH2)3]+was trapped; for polymer 2, a mixedligand system was observed; N5–and ATz coordinate separately with Na+and form two independent but interweaved nets. In this way, coordination polymer has been successfully utilized to modify specific properties of energetic materials through crystallization. Benefiting from the coordination and weak interactions, the decomposition temperatures of both polymers increase from 111°C (1D structure [Na(H2O)(N5)] ∙2H2O) to 118.4 and 126.5°C respectively. Moreover, no crystallized H2O was generated in products to afford the anhydrous compounds of pentazole salts with high heats of formation (>800 kJ mol–1). Compared to traditional energetic materials, the advantage in heats of formation is still obvious for the cyclo-N5–based CPs, which highlights cyclo-N5–as a promising energetic precursor for high energy density materials (HEDMs). 在含能材料领域, 通过协同作用形成聚合物已成为改善或增强现有含能物质性能的一种新技术. 本文将胍阳离子CH3H6+(GU)和氨基-1,2,4-三唑C2H4N4(ATZ)与NaN5一起结晶, 通过自组装过程分别制备了两种新型含能配位聚合物(CPs), (NaN5)5[(CH3H6)N5](N5)3(1)和(NaN5)2(C2H4N4) (2). 晶体结构表明, 在固体状态下, 螯合的五唑阴离子实现了与其他有机成分共存. 聚合物1, Na+和N5−形成笼状, 并将胍阳离子[C(NH2)3]+围在里面; 而聚合物2是一个混合配体体系, N5−和ATZ与Na+分别形成两个独立但相互交织的网. 这些都说明了通过结晶形成配位聚合物, 来改变含能材料的特定性能是可行的. 受益于配位和弱相互作用, 两种聚合物的热分解温度分别从111°C (一维结构[Na(H2O)(N5)]·2H2O)提高到了118.4和126.5°C. 此外, 他们成功地除去了产物中的结晶水, 成为具有高生成热特点的五唑无水盐(> 800 kJ mol−1). 聚合物1和2比传统能量材料高得多的生成热, 表明N5−作为高能量密度材料(HEDMs)的前驱体, 具有很好的前景.

BackgroundAn in situ recanalization procedure of endovascular therapy (ET) or carotid endarterectomy (CEA) has been attempted in patients with symptomatic chronic internal carotid artery occlusions (ICAOs), though the recanalization rates of both are low.ObjectiveTo investigate the outcomes of Multimodality In situ Recanalization for ICAOs in a Hybrid Operating Room (MIRHOR) at the same session.MethodsSymptomatic chronic ICAOs were classified into type A or B (short occlusion with or without a tapered residual root [TRR]), and C or D (long occlusion with or without TRR), and managed in a hybrid operating room with ET, CEA, or both, as needed. Primary efficacy outcome was technical success of recanalization with Thrombolysis in Myocardial Infarction 3. Secondary efficacy outcome was any stroke or death within 30 days (primary safety outcome) plus an ipsilateral ischemic stroke after 30 days.ResultsTechnical success was finally achieved in 35 (83.3%) of 42 consecutively enrolled patients with ICAO, which was significantly higher than 35.7% (15/42, p<0.001) from the initial ET or CEA alone. Furthermore, the success rate was in descending order: 100% (18/18) for type A and B occlusions, 75% (6/8) for type C occlusions, and 69% (11/16) for type D occlusions (p=0.017). Two secondary efficacy outcome events (5.1%) without mortality, including one (2.4%) primary safety outcome, were observed during a mean follow-up of 10.5 months.ConclusionThe MIRHOR for symptomatic chronic ICAOs at the same session significantly improves technical success, with low periprocedural complications and favorable clinical outcomes. The ICAO classification appears valuable in predicting technical success.

The number of beach closings caused by bacterial contamination has continued to rise in recent years, putting beachgoers at risk of exposure to contaminated water. Current approaches predict levels of indicator bacteria using regression models containing a number of explanatory variables. Data-based modeling approaches can supplement routine monitoring data and provide highly accurate short-term forecasts of beach water quality. In this paper, we apply the nonlinear autoregressive network with exogenous inputs (NARX) method with explanatory variables to predict Escherichia coliconcentrations at four Lake Michigan beach sites. We also apply the nonlinear input–output network (NIO) and nonlinear autoregressive neural network (NAR) methods in addition to a hybrid wavelet-NAR (WA-NAR) model and demonstrate their application. All models were tested using 3 months of observed data. Results revealed that the NARX models provided the best performance and that the WA-NAR model, which requires no explanatory variables, outperformed the NIO and NAR models; therefore, the WA-NAR model is suitable for application to data scarce regions. The models proposed in this paper were evaluated using multiple performance metrics, including sensitivity and specificity measures, and produced results comparable or superior to those of previous mechanistic and statistical models developed for the same beach sites. The relatively high R2values between data and the NARX models (R2values of ∼0.8 for the beach sites and ∼0.9 for the river site) indicate that the new class of models shows promise for beach management.

Rotavirus enteritis (RVE) accounts for 37% of all death in children (<5 years) with diarrhea. Chinese herbal injections (CHIs) have drawn more attention from practitioners because of the valid effects for RVE. However, the most beneficial one has not yet been determined.

Duchenne muscular dystrophy (DMD) is a severe genetic disease caused by the loss of the dystrophin protein. Exon skipping is a promising strategy to treat DMD by restoring truncated dystrophin. Here, we demonstrate that base editors (e.g., targeted AID-mediated mutagenesis [TAM]) are able to efficiently induce exon skipping by disrupting functional redundant exonic splicing enhancers (ESEs). By developing an unbiased and high-throughput screening to interrogate exonic sequences, we successfully identify novel ESEs in DMDexons 51 and 53. TAM-CBE (cytidine base editor) induces near-complete skipping of the respective exons by targeting these ESEs in patients’ induced pluripotent stem cell (iPSC)-derived cardiomyocytes. Combined with strategies to disrupt splice sites, we identify suitable single guide RNAs (sgRNAs) with TAM-CBE to efficiently skip most DMDhotspot exons without substantial double-stranded breaks. Our study thus expands the repertoire of potential targets for CBE-mediated exon skipping in treating DMD and other RNA mis-splicing diseases.

Energy storage is an effective way to facilitate renewable energy (RE) development. Its technical performance and economic performance are key factors for large scale applications. As battery energy storage system (BESS) is one commercially-developed energy storage technology at present, BESS is utilized to connect to RE generation. BESS couple with RE can balance the generation and load, and provide auxiliary services. Thus, the technical and economic performance of this coupling system was investigated. The coupling system generates extra revenue compared to RE-only through arbitrage considering peak-valley electricity price and ancillary services. In order to maximize the net revenues of BESS, a multi-objective three-level model for the optimal configuration of BESS was developed. The outer layer was a model for the optimal configuration of BESS, the middle layer was a multi-objective optimal model for BESS to participate in electricity price arbitrage and reserve ancillary services, and the inner layer was an optimal scheduling model that coordinated wind power, photovoltaic (PV) power and BESS. The multi-objective genetic algorithm (GA) based on the roulette method was employed to solve the optimal model. A case study was conducted, and the annual net revenues of BESS under different BESS capacities were evaluated. When the annual net revenues of BESS reach the maximum, the optimal BESS capacity is obtained. Sensitive analysis was also conducted considering different price difference, environment conditions of irradiance, wind speed. The effective trend and optimization values were calculated. The study presented a solution including methodology and values for how to determine the installation of energy storage to RE.

Evaluating node importance in inter-domain routing networks to protect key nodes is crucial to the stability of the Internet. Existing evaluation methods struggle to balance accuracy and speed: centrality-based methods are quick but lack accuracy, while impact-based methods are precise but have high computational complexity. Approaches in the complex network field do not consider the business relationships in the inter-domain routing network, so their results may not match reality. This paper proposes a novel method for node importance evaluation in the inter-domain routing networks, called NIE-GAT. The key idea of NIE-GAT is to map a node's static features to its dynamic cascading failure impact by a deep learning model based on the graph attention network. Additionally, several node centrality features based on the business relationships are defined. A method for calculating node impact labels with the cascading failure model is also designed. The experimental results on real inter-domain routing networks of different scales show that the evaluation accuracy of NIE-GAT for the top 3% nodes reaches more than 93%, which is over 7% higher than the existing best method. For large-scale networks with more than ten thousand nodes, NIE-GAT can complete the evaluation within 2 min. • A novel method based on GAT is proposed to evaluate node importance in the inter-domain routing network. • NIE-GAT learns the mapping relationship between a node's static features and its dynamic cascading failure impacts. • Local and global node centrality features based on the business relationships are defined to improve evaluation accuracy. • NIE-GAT outperforms five classic and state-of-the-art methods in terms of accuracy. • NIE-GAT has low computational complexity and it can be applied to large-scale networks. [ABSTRACT FROM AUTHOR]

To study the microstructural evolution of pearlite steel subjected to pure rolling and rolling-sliding contact loading, a hypoeutectoid pearlite steel with composition and microstructure similar to BS11 was designed and twin-disc tests of this pearlite steel were performed to simulate the wheel/rail system. After a series of twin-disc tests, optical microscope (OM) observation, scanning electron microscope (SEM) observation, X-ray diffraction (XRD), and micro-hardness tests were conducted to characterize the microstructure. Under the pure rolling contact condition, a large amount of reticular cracks emerged within 60 μm below the contact surface of the samples after 120000 revolutions. The largest deformation was approximately 200 μm below the contact surface. Under the rolling-sliding contact condition, the nodularization of pearlite within 100 μm below the contact surface was obvious. The microstructure and stress-strain distribution of the area within 2 mm below the contact surface were investigated. The distribution of micro-hardness under the contact surface varied with contact conditions. Finite element method (FEM) was used to simulate the stress-strain distribution. The results of SEM, FEM, and micro-hardness tests indicated that under the pure rolling contact condition, the maximum plastic strain was approximately 200–400 μm below the contact surface. Conversely, under the rolling-sliding contact condition, the maximum plastic strain emerged on the contact surface. Under the pure rolling contact condition, the distribution of micro-hardness was almost identical to that of the equivalent plastic strain. Under the rolling-sliding contact condition, the distribution of micro-hardness was affected by the equivalent plastic strain and tangential stress.

To study the microstructural evolution of pearlite steel subjected to pure rolling and rolling-sliding contact loading, a hypoeutectoid pearlite steel with composition and microstructure similar to BS11 was designed and twindisc tests of this pearlite steel were performed to simulate the wheel/rail system. After a series of twin-disc tests, optical microscope (OM) observation, scanning electron microscope (SEM) observation, X-ray diffraction (XRD), and micro-hardness tests were conducted to characterize the microstructure. Under the pure rolling contact condition, a large amount of reticular cracks emerged within 60 µm below the contact surface of the samples after 120 000 revolutions. The largest deformation was approximately 200 µm below the contact surface. Under the rolling-sliding contact condition, the nodularization of pearlite within 100 µm below the contact surface was obvious. The microstructure and stress-strain distribution of the area within 2 mm below the contact surface were investigated. The distribution of micro-hardness under the contact surface varied with contact conditions. Finite element method (FEM) was used to simulate the stress-strain distribution. The results of SEM, FEM, and micro-hardness tests indicated that under the pure rolling contact condition, the maximum plastic strain was approximately 200 – 400 µm below the contact surface. Conversely, under the rolling-sliding contact condition, the maximum plastic strain emerged on the contact surface. Under the pure rolling contact condition, the distribution of micro-hardness was almost identical to that of the equivalent plastic strain. Under the rolling-sliding contact condition, the distribution of micro-hardness was affected by the equivalent plastic strain and tangential stress.

•Magnetic nanoparticles were synthesized and modified with hydrophilic external layer.•Modified magnetite nanoparticles were used to decorate A.succinogenescell wall.•Single cell-nanoparticle assembly was fabricated.•CO2was capture and in situbioconversion to CO2-derived succninate.

The authors are interested in image protection within resource environments offered by commodity computers such as desktops, laptops, tablets, or even smartphones. Additionally, the authors have in mind use cases where a large amount images are to be protected. Traditional encryption is not fast enough for such environments and such use cases. The authors derived a new solution by parallelizing selective encryption and using available GPU (Graphic Process Unit) acceleration. Progress obtained in terms of performance allows considering selective encryption as a general purpose solution for the use cases considered. After presenting related works, a ‘first level' of protection is described and a new ‘strong level' of protection method is introduced. Different architecture designs and implementation choices are extensively discussed, considering various criteria: performance indeed, but also image reconstruction quality and quality of data protection.

Ovarian aging is associated with depletion of the ovarian follicle reserve, which is the key determinant of fertility potential in females. In this study, we found that the small, secreted protein complement 1Q-like (C1QL1) is involved in the regulation of follicle depletion through intraovarian and endocrine control in a multidimensional collaborative manner. C1ql1was detected to be conserved in the ovary and showed high transcript levels during folliculogenesis. Blockade of C1QL1 by IP and ovarian intrabursal injection of C1QL1 antiserum into prepubertal mice impaired folliculogenesis accompanied by reductions in body weight, fat mass, and intraovarian lipid accumulation. An elevation of circulating estradiol levels, reduction of hypothalamic KISS1 and GnRH expression, and a decrease in serum FSH levels were found in C1QL1-deficient mice. In C1QL1-deficient ovaries, many primordial follicles were recruited and developed into medium follicles but underwent atresia at the large follicle stages, which resulted in depletion of follicle reserve. Depletion of C1QL1 alleviated the inhibitory effect of C1QL1 on granulosa cell apoptosis and the stimulatory effect of C1QL1 on granulosa cell autophagy, which resulted in accumulation in the preantral and early antral follicles and an increase in the atretic follicles. The abnormal profile of endocrine hormones accelerated the intraovarian effect of C1QL1 deficiency and further led to depletion of ovarian reserve. Altogether, this study revealed the expression patterns and the mechanism of action of C1QL1 during folliculogenesis and demonstrated that deficiency of C1QL1 caused ovarian follicular depletion.