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Phytopathogens represent an ongoing threat to crop production and a significant impediment to global food security. During the infection process, these pathogens spatiotemporally deploy a large array of effectors to sabotage host defense machinery and/or manipulate cellular pathways, thereby facilitating colonization and infection. However, besides their pivotal roles in pathogenesis, certain effectors, known as avirulence (AVR) effectors, can be directly or indirectly perceived by plant resistance (R) proteins, leading to race-specific resistance. An in-depth understanding of the intricate AVR-R interactions is instrumental for genetic improvement of crops and safeguarding them from diseases. Magnaporthe oryzae (M. oryzae), the causative agent of rice blast disease, is an exceptionally virulent and devastating fungal pathogen that induces blast disease on over 50 monocot plant species, including economically important crops. Rice-M. oryzae pathosystem serves as a prime model for functional dissection of AVR effectors and their interactions with R proteins and other target proteins in rice due to its scientific advantages and economic importance. Significant progress has been made in elucidating the potential roles of AVR effectors in the interaction between rice and M. oryzae over the past two decades. This review comprehensively discusses recent advancements in the field of M. oryzae AVR effectors, with a specific focus on their multifaceted roles through interactions with corresponding R/target proteins in rice during infection. Furthermore, we deliberated on the emerging strategies for engineering R proteins by leveraging the structural insights gained from M. oryzae AVR effectors.

The precise monitoring of sea ice parameters, including sea ice concentration and stage of development, is imperative for tactical navigation. Recent studies have showcased the enhanced mapping accuracy achieved by incorporating multi-source auxiliary data, such as passive microwave data, with Synthetic Aperture Radar (SAR) images. However, there remains a lack of research assessing the impact of individual features on model performance. This paper addresses this knowledge gap through ablation studies and alternate comparisons of data inputs. Building on the success in the AutoIce Challenge, we leverage the AI4Arctic Sea Ice Challenge Dataset to train multitask sea ice mapping models employing a U-Net architecture. Results from cross-validation and testing sets with all season data reveal the significant enhancement in estimation accuracy for all parameters when utilizing most of the AMSR2 channels. Additionally, the incorporation of time and location information as ancillary channels further amplifies the classification accuracy of all major ice types. Furthermore, among the various available ERA5 weather parameters, the inclusion of wind speed data proves effective in mitigating misclassifications in ice regions, particularly under melting scenarios. The paper culminates with a feature importance ranking table encompassing all available features, providing valuable guidance for the selection of pertinent data inputs. This comprehensive comparative study not only contributes to advancing sea ice mapping methodologies but also offers valuable insights into the nuanced impact of individual features on model performance.

Abstract Background Previous research has revealed that the 18 glycoside hydrolase gene family (GH18) member Chitinase 3-like 1 (Chi3l1) can regulate osteoclast differentiation and bone resorption. However, its downstream receptors and molecular mechanisms during osteoclastogenesis have yet to be elucidated. Methods Initially, we conducted a comprehensive investigation to evaluate the effects of recombinant Chi3l1 protein or Chi3l1 siRNA on osteoclast differentiation and the RANKL-induced MAPK/AKT signaling pathways. Moreover, we used immunofluorescence and immunoprecipitation assays to identify IL13Rα2 as the downstream receptor of Chi3l1. Subsequently, we investigated the impact of IL13Rα2 recombinant protein or IL13Rα2-siRNA on osteoclast differentiation and the associated signaling pathways. Finally, we performed in vivo experiments to examine the effect of recombinant IL13Rα2 protein in an LPS-induced mouse model of cranial osteolysis. Results Our findings highlight that the administration of recombinant Chi3l1 protein increased the formation of osteoclasts and bolstered the expression of several osteoclast-specific genes (TRAP, NFATC1, CTR, CTSK, V-ATPase d2, and Dc-STAMP). Additionally, Chi3l1 significantly promoted the RANKL-induced MAPK (ERK/P38/JNK) and AKT pathway activation, whereas Chi3l1 silencing inhibited this process. Next, using immunofluorescence and co-immunoprecipitation assays, we identified IL13Rα2 as the binding partner of Chi3l1 during osteoclastogenesis. IL13Rα2 recombinant protein or IL13Rα2-siRNA also inhibited osteoclast differentiation, and IL13Rα2-siRNA attenuated the RANKL-induced activation of the MAPK (ERK/P38/JNK) and AKT pathways, similar to the effects observed upon silencing of Chi3l1. Moreover, the promoting effect of recombinant Chi3l1 protein on osteoclastogenesis and the activation of the MAPK and AKT pathways was reversed by IL13Rα2 siRNA. Finally, recombinant LI13Rα2 protein significantly attenuated the LPS-induced cranial osteolysis and the number of osteoclasts in vivo. Conclusions Our findings suggested that IL13Rα2 served as a crucial receptor for Chi3l1, enhancing RANKL-induced MAPK and AKT activation to promote osteoclast differentiation. These findings provide valuable insights into the molecular mechanisms of Chi3l1 in osteoclastogenesis, with potential therapeutic implications for osteoclast-related diseases. Video Abstract

Abstract Age is closely related to human health and disease risks. However, chronologically defined age often disagrees with biological age, primarily due to genetic and environmental variables. Identifying effective indicators for biological age in clinical practice and self-monitoring is important but currently lacking. The human lens accumulates age-related changes that are amenable to rapid and objective assessment. Here, using lens photographs from 20 to 96-year-olds, we develop LensAge to reflect lens aging via deep learning. LensAge is closely correlated with chronological age of relatively healthy individuals (R2 > 0.80, mean absolute errors of 4.25 to 4.82 years). Among the general population, we calculate the LensAge index by contrasting LensAge and chronological age to reflect the aging rate relative to peers. The LensAge index effectively reveals the risks of age-related eye and systemic disease occurrence, as well as all-cause mortality. It outperforms chronological age in reflecting age-related disease risks (p

Abstract N4-acetylcytidine (ac4C) is a post-transcriptional RNA modification that regulates in various important biological processes. However, its role in human cancer, especially lymph node metastasis, remains largely unknown. Here, we demonstrated N-Acetyltransferase 10 (NAT10), as the only known “writer” of ac4C mRNA modification, was highly expressed in head and neck squamous cell carcinoma (HNSCC) patients with lymph node metastasis. High NAT10 levels in the lymph nodes of patients with HNSCC patients are a predictor of poor overall survival. Moreover, we found that high expression of NAT10 was positively upregulated by Nuclear Respiratory Factor 1 (NRF1) transcription factor. Gain- and loss-of-function experiments displayed that NAT10 promoted cell metastasis in mice. Mechanistically, NAT10 induced ac4C modification of Glycosylated Lysosomal Membrane Protein (GLMP) and stabilized its mRNA, which triggered the activation of the MAPK/ERK signaling pathway. Finally, the NAT10-specific inhibitor, remodelin, could inhibit HNSCC tumorigenesis in a 4-Nitroquinoline 1-oxide (4NQO)-induced murine tumor model and remodel the tumor microenvironment, including angiogenesis, CD8+ T cells and Treg recruitment. These results demonstrate that NAT10 promotes lymph node metastasis in HNSCC via ac4C-dependent stabilization of the GLMP transcript, providing a potential epitranscriptomic-targeted therapeutic strategy for HNSCC.

Abstract Excessive osteoclast formation and bone resorption are related to osteolytic diseases. Delta drosophila homolog-like 2 (Dlk2), a member of the epidermal growth factor (EGF)-like superfamily, reportedly regulates adipocyte differentiation, but its roles in bone homeostasis are unclear. In this study, we demonstrated that Dlk2 deletion in osteoclasts significantly inhibited osteoclast formation in vitro and contributed to a high-bone-mass phenotype in vivo. Importantly, Dlk2 was shown to interact with synapse-associated protein 1 (Syap1), which regulates Akt phosphorylation at Ser473. Dlk2 deletion inhibited Syap1-mediated activation of the AktSer473, ERK1/2 and p38 signaling cascades. Additionally, Dlk2 deficiency exhibits increased bone mass in ovariectomized mice. Our results reveal the important roles of the Dlk2-Syap1 signaling pathway in osteoclast differentiation and osteoclast-related bone disorders.

Indigoids, a class of bis-indoles, have long been applied in dyeing, food, and pharmaceutical industries. Recently, interest in these ‘old’ molecules has been renewed in the field of organic semiconductors as functional building blocks for organic electronics due to their excellent chemical and physical properties. However, these indigo derivatives are difficult to access through chemical synthesis. In this study, we engineer cytochrome P450 BM3 from an NADPH-dependent monooxygenase to peroxygenases through directed evolution. A select number of P450 BM3 variants are used for the selective oxidation of indole derivatives to form different indigoid pigments with a spectrum of colors. Among the prepared indigoid organic photocatalysts, a majority of indigoids demonstrate a reduced band gap than indigo due to the increased light capture and improved charge separation, making them promising candidates for the development of new organic electronic devices. Thus, we present a useful enzymatic approach with broad substrate scope and cost-effectiveness by using low-cost H2O2 as a cofactor for the preparation of diversified indigoids, offering versatility in designing and manufacturing new dyestuff and electronic/sensor components.

Monitoring sea ice in the Arctic region is crucial for polar maritime activities. The Canadian Ice Service (CIS) wants to augment its manual interpretation with machine learning-based approaches due to the increasing data volume received from newly launched synthetic aperture radar (SAR) satellites. However, fully supervised machine learning models require large training datasets, which are usually limited in the sea ice classification field. To address this issue, we propose a semi-supervised interactive system to classify sea ice in dual-pol RADARSAT-2 imagery using limited training samples. First, the SAR image is oversegmented into homogeneous regions. Then, a graph is constructed based on the segmentation results, and the feature set of each node is characterized by a convolutional neural network. Finally, a graph convolutional network (GCN) is employed to classify the whole graph using limited labeled nodes automatically. The proposed method is evaluated on a published dataset. Compared with referenced algorithms, this new method outperforms in both qualitative and quantitative aspects.

[This corrects the article DOI: 10.1371/journal.pone.0295565.].

BackgroundLockdowns in COVID-19 pandemic led to less physical activity and more intake of unhealthy food in children. The aim of this study was to investigate the negative impact of major lockdowns on the growth of children aged 3-6 years during COVID-19 pandemic period.MethodsPhysical examination results in 2019 to 2022 from 5834 eligible children (2972 males and 2862 females) from Southwestern China who were 3 years old in 2019 were retrospectively collected. Height and weight data points were extracted from the results, and percentiles of height (height%), weight (weight%), and BMI (BMI%), and rates of overweight and obesity were calculated and compared between different years during the pandemic.ResultsAfter analyzing the 15404 growth data points from 5834 children, a slowly increasing trend of height% from 2019 to 2022 was observed. Weight%, BMI%, overweight rate, obesity rate, and combined overweight and obesity rate had two peaks in 2020 and 2022 when major lockdowns were adopted and a drop in between (year 2021), except for obesity rate which did not drop in 2021. Similar results were shown after stratification by gender.ConclusionThe lockdowns in COVID-19 pandemic promoted obesity of kindergarten children, but did not show any negative impact on their height growth possibly due to over-nutrition of children during lockdowns. More efforts need to be made to limit the increase of obesity rate in kindergarten children during possible future lockdowns.

Inflammatory macrophages within the synovium play a pivotal role in the progression of arthritis inflammation. Effective drug therapy targeting inflammatory macrophages has long been a goal for clinicians and researchers. The standard approach for treating osteoarthritis (OA) involves systemic treatment and local injection. However, the high incidence of side effects associated with long-term drug administration increases the risk of complications in patients. Additionally, the rapid clearance of the joint cavity poses a biological barrier to the therapeutic effect. NADPH oxidase 4 (NOX4) is an enzyme protein regulating the cellular redox state by generating reactive oxygen species (ROS) within the cell. In this study, we designed and fabricated a hydrogel microsphere consisting of methyl methacrylate (MMA) and polyvinyl acetate (PVA) as the outer layer structure. We then loaded GLX351322 (GLX), a novel selective NOX4 inhibitor, into hydrogel microspheres through self-assembly with the compound polyethylene glycol ketone mercaptan (mPEG-TK) containing a disulfide bond, forming nanoparticles (mPEG-TK-GLX), thus creating a two-layer drug-loaded microspheres capsule with ROS-responsive and slow-releasing capabilities. Our results demonstrate that mPEG-TK-GLX@PVA-MMA effectively suppressed TBHP-induced inflammation, ROS production, and ferroptosis, indicating a promising curative strategy for OA and other inflammatory diseases in the future.

The rapid advancement of modern technology has significantly driven progress in various IT-related activities, resulting in a substantial increase in internet penetration rates, particularly among college students. The utilization of the internet has become one of the most essential tools in our modern society. However, internet addiction (IA) has emerged as a serious concern, particularly among college students, adversely affecting academic performance and having significant psychological and psychiatric implications. The aim of the current study was to determine the impact of physical exercise, gender and academic year on IA among college students. In the present study, we investigated internet usage, engagement in sports activities, and academic performance among college students from Western, Middle, and Eastern regions of Chinese universities. It’s noteworthy that most of the respondents were freshmen. Our findings indicate that freshmen students were more susceptible to experiencing IA. Approximately 75% of students engaged in leisure sports activities, revealing an inverse correlation between sports activity and IA. This correlation aligns with the level of sports involvement, emphasizing the potential benefits of physical activity in mitigating IA. However, our study did not uncover any correlation between geographic location and the occurrence of IA, nor did it find differences between medical and non-medical students. Furthermore, our study revealed no significant variations in IA among students from different ethnic backgrounds. The underlying mechanism of IA is being currently determined. Our data suggest that physical exercise, gender, and academic year have a significant impact on IA among college students.

Abstract To investigate the role of periosteum on the treatment of buccal dehiscence defects comparing with collagen membrane in canine model. Bilateral dehiscence-type defects at the buccal side on the distal root of the lower 3rd/4th premolars were created in six beagle dogs with a total of 24 defects and assigned into three groups: Group A: blood clot in an untreated defect; Group B: deproteinized bovine bone material (DBBM) covered with an absorbable membrane; Group C: DBBM covered with the periosteum. The structural parameters for trabecular architecture and vertical bone regeneration were evaluated. Histological and histomorphometric evaluation were carried out to observe new bone formation and mineralization in the graft site. Immunohistochemical analysis was performed to identify the expression of osteopontin (OPN) and osteocalcin (OCN) at postoperative 3 months. Group C achieved greater vertical alveolar bone gain than that of group A and group B. The periosteum-covered group showed significantly greater new bone formation and accelerated mineralization. The greater immunolabeling for OPN and OCN was observed in group C than in group A. Periosteal coverage has explicit advantages over collagen membranes for the quality and quantity of new bone regeneration in dehiscence defects repairing.

ObjectivePrevious studies have reported that white matter hyperintensities (WMHs) are associated with freezing of gait (FOG), but it is not clear whether their distribution areas have correlations with FOG in Parkinson’s disease (PD) and the potential influencing factors about WMHs.MethodsTwo hundred and forty-six patients with PD who underwent brain MRI were included. Participants were divided into PD with FOG (n = 111) and PD without FOG (n = 135) groups. Scheltens score was used to assess the WMHs burden in the areas of deep white matter hyperintensities (DWMHs), periventricular hyperintensities (PVHs), basal ganglia hyperintensities (BGHs), and infratentorial foci of hyperintensities (ITF). Whole brain WMHs volume was evaluated by automatic segmentation. Binary logistic regression was used to evaluate relationships between WMHs and FOG. The common cerebrovascular risk factors that may affect WMHs were evaluated by mediation analysis.ResultsThere were no statistical differences between PD with and without FOG groups in whole brain WMHs volume, total Scheltens score, BGHs, and ITF. Binary logistic regression showed that the total scores of DWMHs (OR = 1.094; 95% CI, 1.001, 1.195; p = 0.047), sum scores of PVHs and DWMHs (OR = 1.080; 95% CI, 1.003, 1.164; p = 0.042), especially the DWMHs in frontal (OR = 1.263; 95% CI, 1.060, 1.505 p = 0.009), and PVHs in frontal caps (OR = 2.699; 95% CI, 1.337, 5.450; p = 0.006) were associated with FOG. Age, hypertension, and serum alkaline phosphatase (ALP) are positively correlated with scores of DWMHs in frontal and PVHs in frontal caps.ConclusionThese results indicate that WMHs distribution areas especially in the frontal of DWMHs and PVHs play a role in PD patients with FOG.

Identification of sugarcane stem nodes is generally dependent on high-performance recognition equipment in sugarcane seed pre-cutting machines and inefficient. Accordingly, this study proposes a novel lightweight architecture for the detection of sugarcane stem nodes based on the YOLOv5 framework, named G-YOLOv5s-SS. Firstly, the study removes the CBS and C3 structures at the end of the backbone network to fully utilize shallow-level feature information. This enhances the detection performance of sugarcane stem nodes. Simultaneously, it eliminates the 32 times down-sampled branches in the neck structure and the 20x20 detection heads at the prediction end, reducing model complexity. Secondly, a Ghost lightweight module is introduced to replace the conventional convolution module in the BottleNeck structure, further reducing the model's complexity. Finally, the study incorporates the SimAM attention mechanism to enhance the extraction of sugarcane stem node features without introducing additional parameters. This improvement aims to enhance recognition accuracy, compensating for any loss in precision due to lightweight modifications. The experimental results showed that the average precision of the improved network for sugarcane stem node identification reached 97.6%, which was 0.6% higher than that of the YOLOv5 baseline network. Meanwhile, a model size of 2.6MB, 1,129,340 parameters, and 7.2G FLOPs, representing respective reductions of 82%, 84%, and 54.4%. Compared with mainstream one-stage target detection algorithms such as YOLOv4-tiny, YOLOv4, YOLOv5n, YOLOv6n, YOLOv6s, YOLOv7-tiny, and YOLOv7, G-YOLOv5s-SS achieved respective average precision improvements of 12.9%, 5.07%, 3.6%, 2.1%, 1.2%, 3%, and 0.4% in sugarcane stem nodes recognition. Meanwhile, the model size was compressed by 88.9%, 98.9%, 33.3%, 72%, 92.9%, 78.8% and 96.3%, respectively. Compared with similar studies, G-YOLOv5s-SS not only enhanced recognition accuracy but also considered model size, demonstrating an overall excellent performance that aligns with the requirements of sugarcane seed pre-cutting machines.

Abstract Real-time rapid detection of toxic gases at room temperature is particularly important for public health and environmental monitoring. Gas sensors based on conventional bulk materials often suffer from their poor surface-sensitive sites, leading to a very low gas adsorption ability. Moreover, the charge transportation efficiency is usually inhibited by the low defect density of surface-sensitive area than that in the interior. In this work, a gas sensing structure model based on CuS quantum dots/Bi2S3 nanosheets (CuS QDs/Bi2S3 NSs) inspired by artificial neuron network is constructed. Simulation analysis by density functional calculation revealed that CuS QDs and Bi2S3 NSs can be used as the main adsorption sites and charge transport pathways, respectively. Thus, the high-sensitivity sensing of NO2 can be realized by designing the artificial neuron-like sensor. The experimental results showed that the CuS QDs with a size of about 8 nm are highly adsorbable, which can enhance the NO2 sensitivity due to the rich sensitive sites and quantum size effect. The Bi2S3 NSs can be used as a charge transfer network channel to achieve efficient charge collection and transmission. The neuron-like sensor that simulates biological smell shows a significantly enhanced response value (3.4), excellent responsiveness (18 s) and recovery rate (338 s), low theoretical detection limit of 78 ppb, and excellent selectivity for NO2. Furthermore, the developed wearable device can also realize the visual detection of NO2 through real-time signal changes.

Abstract Connected and Automated Vehicle (CAV) is a transformative technology that has great potential to improve urban traffic and driving safety. Electric Vehicle (EV) is becoming the key subject of next-generation CAVs by virtue of its advantages in energy saving. Due to the limited endurance and computing capacity of EVs, it is challenging to meet the surging demand for computing-intensive and delay-sensitive in-vehicle intelligent applications. Therefore, computation offloading has been employed to extend a single vehicle’s computing capacity. Although various offloading strategies have been proposed to achieve good computing performace in the Vehicular Edge Computing (VEC) environment, it remains challenging to jointly optimize the offloading failure rate and the total energy consumption of the offloading process. To address this challenge, in this paper, we establish a computation offloading model based on Markov Decision Process (MDP), taking into consideration task dependencies, vehicle mobility, and different computing resources for task offloading. We then design a computation offloading strategy based on deep reinforcement learning, and leverage the Deep Q-Network based on Simulated Annealing (SA-DQN) algorithm to optimize the joint objectives. Experimental results show that the proposed strategy effectively reduces the offloading failure rate and the total energy consumption for application offloading.

The Permian Fengcheng Formation in the Mahu sag of the Junggar Basin is a crude oil reservoir and source rock. However, its stratigraphic characteristics, the boundary marks of the three members (lower, middle, and upper), and the sediment-filling processes are unclear. This study presents the sedimentary systems and sediment-filling processes in an intracontinental rift basin of this area using sedimentary-faces analysis, sequence stratigraphy, well logs, and two-dimensional seismic interpretations. The results show that the Fengcheng Formation consists of three third-order sequence stratigraphy (SQf1, SQf2, and SQf3). The lowest member of the Fengcheng Formation is composed of gray to dark gray thin middle layers of tuff, lacustrine dolomitic mudstone, and argillaceous dolomite near the depocenter. Tuff, siltstone, a small amount of fan–delta glutenite, volcanic breccia, and basalt are present near the fault zone. The logging curve is characterized by a high gamma-ray (GR) value, high amplitude, wavelength growth, and medium frequency. Near the depocenter, the middle member of the Fengcheng Formation comprises gray and gray–black thin layers of lacustrine muddy dolomite and dolomitic mudstone and thin sandstone and mudstone interbedded between tuff and gravel near the orogenic belt. The logging curve displays high-to-low GR values, high amplitude, short wavelength, and high frequency. The upper member of the Fengcheng Formation is not characterized by lesser tuffaceous and dolomitic components but by an increased fan–delta sandy content. Next, the bottom of the upper member is composed of lacustrine mudstone and siltstone interbedded between dolomitic mudstones; whereas, the top of the upper member is dominated by fan–delta coarse sandstone and sandy conglomerate. The logging curve shows low GR values, small amplitude, and continuous stability. The sediment-filling process of the Fengcheng Formation is controlled by fault activity. Overall, the lower and middle members of the Fengcheng Formation expand during the sediment-filling process of the lake basin of the Mahu sag; whereas, the upper member contracts. The strong uplift of the Horst near the Baiquan and Mahu orogenic belts led to an increase in provenance supply, resulting in a contraction of the southwestern margin of the lake basin during the sedimentation period from lower member to upper member of Fengcheng Formation.

The presence of rain may blur surface wave signatures and cause additional radar backscatter, which negatively affects the performance of ocean remote sensing applications (e.g., ocean surface wind and wave parameter measurement) using X-band marine radars. In this article, a novel end-to-end model is developed to detect and locate rain-contaminated pixels in X-band marine radar images based on a type of deep neural network called SegNet, which is able to segment rain-contaminated regions by classifying each pixel into three classes: rain-free, rain-contaminated, and wind-dominated rain cases. Shipborne marine radar images collected during a sea trial on the East Coast of Canada are first preprocessed and then utilized to train an ensemble of SegNet-based networks. The final classification result of each pixel will be the class chosen by most individual networks. Testing results using images obtained from both shipborne and shore-based marine radar systems manifest that the proposed model effectively segment between rain-free, rain-contaminated, and wind-dominated rain regions, with a pixel classification accuracy of 94.6% and 90.4% for Decca and Koden radar images, respectively.

The presence of rain degrades the performance of sea surface parameter estimation using X-band marine radar. In this article, a novel scheme is proposed to improve wind measurement accuracy from rain-contaminated X-band marine radar data. After extracting texture features from each image pixel, the rain-contaminated regions with blurry wave signatures are first identified using a self-organizing map (SOM)-based clustering model. Then, a convolutional neural network used for image haze removal, i.e., DehazeNet is introduced and incorporated into the proposed scheme for correcting the influence of rain on radar images. In order to obtain wind direction information, curve fitting is conducted on the average azimuthal intensities of rain-corrected radar images. On the other hand, wind speed is estimated from rain-corrected images by training a support vector regression-based model. Experiments conducted using datasets from both shipborne and onshore marine radar show that compared to results obtained from images without rain correction, the proposed method achieves relatively high estimation accuracy by reducing measurement errors significantly.

In this article, the accuracy of wave direction and period estimation from X-band marine radar images under different rain rates is analyzed, and a simple subimage selection scheme is proposed to mitigate the rain effect. First, each radar image is divided into multiple subimages, and the subimages with relatively clear wave signatures are identified based on the random-forest-based classification model. Then, wave direction is estimated by performing the Radon transform on each valid subimage. As for wave period estimation, a new method is proposed. Texture features are first extracted from each pixel of the selected subimage using the gray-level co-occurrence matrix and combined as a feature vector. Those feature vectors extracted from both rain-free and rain-contaminated training samples are then used to train a random-forest-based wave period regression model. The shore-based X-band marine radar images, simultaneous rain rate data, as well as buoy-measured wave data collected on the West Coast of the United States are used to analyze the rain effect on wave parameter estimation accuracy and validate the proposed method. Experimental results show that the proposed subimage selection scheme improves the estimation accuracy of both wave direction and wave period under different rain rates, with reductions of root-mean-square errors (RMSEs) by $\text{6.9}^\circ$, $\text{6.0}^\circ$, $\text{4.9}^\circ$, and $\text{1.0}^\circ$ for wave direction under rainless, light rain, moderate rain, and heavy rain conditions, respectively. As for wave period estimation, the RMSEs decrease by 0.13, 0.20, 0.30, and 0.20 s under those four rainfall intensity levels, respectively.

In this article, a temporal convolutional network (TCN)-based model is proposed to retrieve significant wave height ($H_s$) from X-band nautical radar images. Three types of features are first extracted from radar image sequences based on signal-to-noise ratio (SNR), ensemble empirical mode decomposition (EEMD), and gray level cooccurrence matrix methods, respectively. Then, feature vectors are input into the proposed TCN-based regression model to produce $H_s$ estimation. Radar data are collected from a moving vessel at the East Coast of Canada, as well as the simultaneous wave data measured by several wave buoys deployed nearby are used for model training and testing. Experimental results after averaging show that TCN-based model further improves the $H_s$ estimation accuracy, with reductions of root-mean-square errors by 0.33 and 0.10 m, respectively, compared to the SNR-based and the EEMD-based linear fitting methods. It has also been found that under the same feature extraction scheme, TCN outperforms other machine learning-based algorithms including support vector regression and the gated recurrent unit network.

This study aimed to compare the protective effects of different selenium supplements against heat stress and exercise fatigue-induced liver injury and to investigate the potential mechanisms of action. Selenium-enriched soybean protein (SePro), selenium-enriched soybean peptides (SePPs), and selenomethionine (SeMet) are organic selenium supplements in which selenium replaces the sulfur in their sulfur-containing amino acids. Common peptides (PPs) are obtained by enzymatic hydrolysis of soybean protein which was extracted from common soybean. The SePPs with higher hydrolysis degree and selenium retention were isolated via alkaline solubilization and acid precipitation and the enzymatic hydrolysis of alkaline protease, neutral protease, and papain. The results showed that SePPs could significantly increase the antioxidant levels in rats, inhibit lipid peroxidation, and reduce liver enzyme levels in rat serum, while the histological findings indicated that the inflammatory cell infiltration in the liver tissue was reduced, and new cells appeared after treatment with SePPs. Moreover, SePPs could increase glutathione (GSH) and GSH peroxidase (GSH-Px) in the liver, as well as protect the liver by regulating the NF-κB/IκB pathway, prevent interleukin 1β (IL-1β), interleukin 6 (IL-6), and tumor necrosis factor α (TNF-α) release in the liver. The SePPs displayed higher antioxidant and anti-inflammatory activity in vivo than SePro, SeMet, Sodium selenite (Na2SeO3), and PPs. Therefore, SePPs could be used as a priority selenium resource to develop heatstroke prevention products or nutritional supplements.

Background: Gestational diabetes mellitus (GDM) is associated with adverse maternal and neonatal outcomes, however the underlying mechanisms remain elusive. The aim of this study was to find efficient regulator of FGFs in response to the pathogenesis of GDM and explore the role of the FGFs in GDM.Methods: We performed a systematic screening of placental FGFs in GDM patients and further in two different GDM mouse models to investigate their expression changes. Significant changed FGF4 was selected, engineered, purified, and used to treat GDM mice in order to examine whether it can regulate the adverse metabolic phenotypes of the diabetic mice and protect their fetus.Results: We found FGF4 expression was elevated in GDM patients and its level was positively correlated to blood glucose, indicating a physiological relevance of FGF4 with respect to the development of GDM. Recombinant FGF4 (rFGF4) treatment could effectively normalize the adverse metabolic phenotypes in high fat diet induced GDM mice but not in STZ induced GDM mice. However, rFGF4 was highly effective in reduce of neural tube defects (NTDs) of embryos in both the two GDM models. Mechanistically, rFGF4 treatment inhibits pro-inflammatory signaling cascades and neuroepithelial cell apoptosis of both GDM models, which was independent of glucose regulation.Conclusions/interpretation: Our study provides novel insight into the important roles of placental FGF4 and suggests that it may serve as a promising diagnostic factor and therapeutic target for GDM.

Late blight, caused by Phytophthora infestans (P. infestans), is a devastating disease in potato worldwide. Our previous study revealed that the Solanum andigena genotype 03112-233 is resistant to P. infestans isolate 90128, but susceptible to the super race isolate, CN152. In this study, we confirmed by diagnostic resistance gene enrichment sequencing (dRenSeq) that the resistance of 03112-233 toward 90128 is most likely based on a distinct new R gene(s). To gain an insight into the mechanism that governs resistance or susceptibility in 03112-223, comparative transcriptomic profiling analysis based on RNAseq was initiated. Changes in transcription at two time points (24 h and 72 h) after inoculation with isolates 90128 or CN152 were analyzed. A total of 8,881 and 7,209 genes were differentially expressed in response to 90128 and CN152, respectively, and 1,083 differentially expressed genes (DEGs) were common to both time points and isolates. A substantial number of genes were differentially expressed in an isolate-specific manner with 3,837 genes showing induction or suppression following infection with 90128 and 2,165 genes induced or suppressed after colonization by CN152. Hierarchical clustering analysis suggested that isolates with different virulence profiles can induce different defense responses at different time points. Further analysis revealed that the compatible interaction caused higher induction of susceptibility genes such as SWEET compared with the incompatible interaction. The salicylic acid, jasmonic acid, and abscisic acid mediated signaling pathways were involved in the response against both isolates, while ethylene and brassinosteroids mediated defense pathways were suppressed. Our results provide a valuable resource for understanding the interactions between P. infestans and potato.

In this study, we built a single photon avalanche diode (SPAD) receiver based underwater wireless optical communication (UWOC) system. The bit error rate (BER) and signal-to-noise ratio (SNR) performance of UWOC with different distances and data transmission rates were obtained. Based on the water attenuation coefficient of 0.12 m-1, a series of neutral density (ND) filters were exploited to attenuate the light output power from the blue laser diode (LD) to simulate the long distance UWOC. The maximum estimated distances of 144 m and 117 m with corresponding BERs of 1.89 × 10-3 and 5.31 × 10-4 at data transmission rates of 500 bps and 2 Mbps were acquired in UWOC system using on-off keying (OOK) modulation scheme, respectively. Furthermore, we compared the differences between free-space and underwater channels, and a divergence angle of ~1.02 mrad was measured experimentally at a distance of 50 m in the free space. The long UWOC distances obtained in this study partly benefit from high sensitivity SPAD, the small laser divergence angle and low light attenuation. This study provides an approach to achieve long distance UWOC using SPAD.

In this paper, a multi-beam antenna based on metasurface is proposed. By employing the metasurface as radiating element, steering the beam in the elevation plane is realized. Also, the substrate placing above the metasurface helps to further improve the beam performance. The results indicate that the realized the antenna radiate four beam pointing at (θ,φ) = (32°, 0°), (32°, 90°), (32°, 180°), (32°, 270°). Based on this, by adjusting the feeding network, beam is steered in azimuth plane. When the length of the left patch decrease from 11.95 mm to 10.45 mm, the azimuth angle of four beams can be adjustable between 0° ~22°, 90°~112°, 180°~202°, 270° ~292°, respectively.

Background: Nasopharyngeal carcinoma (NPC) is a common tumor in China. Accurate stages of NPC are crucial for treatment. We therefore aim to develop radiomics models for discriminating early-stage (I–II) and advanced-stage (III–IVa) NPC based on MR images. Methods: 329 NPC patients were enrolled and randomly divided into a training cohort (n = 229) and a validation cohort (n = 100). Features were extracted based on axial contrast-enhanced T1-weighted images (CE-T1WI), T1WI, and T2-weighted images (T2WI). Least absolute shrinkage and selection operator (LASSO) was used to build radiomics signatures. Seven radiomics models were constructed with logistic regression. The AUC value was used to assess classification performance. The DeLong test was used to compare the AUCs of different radiomics models and visual assessment. Results: Models A, B, C, D, E, F, and G were constructed with 13, 9, 7, 9, 10, 7, and 6 features, respectively. All radiomics models showed better classification performance than that of visual assessment. Model A (CE-T1WI + T1WI + T2WI) showed the best classification performance (AUC: 0.847) in the training cohort. CE-T1WI showed the greatest significance for staging NPC. Conclusion: Radiomics models can effectively distinguish early-stage from advanced-stage NPC patients, and Model A (CE-T1WI + T1WI + T2WI) showed the best classification performance.

Applying a three-dimensional (3-D) reconstruction from mapping-oriented offline modeling to intelligent agent-oriented environment understanding and real-world environment construction oriented to agent autonomous behavior has important research and application value. Using a scanner to scan objects is a common way to obtain a 3-D model. However, the existing scanning methods rely heavily on manual work, fail to meet efficiency requirements, and are not sufficiently compatible with scanning objects of different sizes. In this article, we propose a creative visual coverage path planning approach for the robotic multi-model perception system (RMMP) in a 3-D environment under photogrammetric constraints. To realize the 3-D scanning of real scenes automatically, we designed a new robotic multi-model perception system. To reduce the influence of image distortion and resolution loss in 3-D reconstruction, we set scanner-to-scene projective geometric constraints. To optimize the scanning efficiency, we proposed a novel path planning method under photogrammetric and kinematics constraints. Under the RMMP system, a constraints-satisfied coverage path could be generated, and the 3-D reconstruction from the images collected along the way was carried out. In this way, the autonomous planning of the pose of the end scanner in scanning tasks was effectively solved. Experimental results show that the RMMP-based 3-D visual coverage method can improve the efficiency and quality in 3-D reconstruction.

The biological trait analysis (BTA) is regarded as a promising approach to unravel how ecosystem functions respond to human-induced disturbances. This study considered the four sampling locations associated with different human activities in Daya Bay, that is, the domestic and industrial sewage discharge area (SED), mariculture area (MRC), nuclear power plants thermal discharge area (NTD), and an area with relatively low human disturbance as a reference (REF). Thirty modalities of nine traits were selected in BTA. Our results showed a clear shift in the functional structure of macrobenthic communities between the sampling locations, except for the case between NTD and REF. The trait composition in the communities did not highlight any seasonal patterns. Bioturbation, longevity, tolerance, body size, feeding habit, and environmental position were the key traits to characterize the functional structure of macrobenthic communities and demonstrated predictable responses along the environmental gradients. Water depth, DO, Chl-a, NH4+, and petroleum contaminants in sediments were the main variables influencing the trait composition. In addition, the taxonomic index (H′) and functional diversity index (Rao’s Q) showed clear differences among the sampling locations. Although there were no significant differences between NTD and REF in terms of the trait composition and functional diversity, a potential function loss in NTD still can be detected through the integrated analysis with taxonomic diversity. We suggest that the traits (except for fragility, larval development, and living habits) selected and the diversity indices (H′ and Rao’s Q) could serve as promising indicators of ecological conditions in Daya Bay.

The Faraday probe and cylindrical Langmuir probe were used to characterize the downstream ion and electron spatial evolution of a 300 W class low-power Hall thruster. The time-averaged ion current density, electron energy probability function, plasma potential, electron temperature, and electron density measurements were conducted at discharge voltages of 200–400 V and anode mass flow rates of 0.65 and 0.95 mg s−1 in the range of 100–500 mm axially and −100 to 100 mm radially downstream of the thruster. The results show that the ion and electron flows exhibit a bipolar diffusion characteristic along the radial direction. Meanwhile, the radial diffusion rate of ions in the plume is greater than the axial diffusion rate. The plasma potential decreases from 18 V at 100 mm axially from the thruster exit to 4 V at 500 mm axially and 100 mm radially. Correspondingly, the electron temperature decreases from 4.2 to 1.0 eV. The electron number density decreases from 2.6 × 1016 to 4 × 1014 m−3. A variable exponential relationship between electron temperature and electron density was observed from the measurements of electron energy probability distribution functions, with an adiabatic factor γ ranging between 1.3 and 1.4 (below the adiabatic value of 5/3). The adiabatic factor γ is considered to correlate with the anode mass flux and the spatial location of plasma, which suggests a possible dependence on the collision rate. These data are of great importance for plume model validation, improvement, plume effect evaluation, and protection mechanisms.

Inflammation is considered to be a crucial factor in the development of chronic diseases, eight of which were listed among the top ten causes of death worldwide in the World Health Organization’s World Health Statistics 2019. Moreover, traditional drugs for inflammation are often linked to undesirable side effects. As gentler alternatives to traditional anti-inflammatory drugs, plant-derived bioactive peptides have been shown to be effective interventions against various chronic diseases, including Alzheimer’s disease, cardiovascular disease and cancer. However, an adequate and systematic review of the structures and anti-inflammatory activities of plant-derived bioactive peptides has been lacking. This paper reviews the latest research on plant-derived anti-inflammatory peptides (PAPs), mainly including the specific regulatory mechanisms of PAPs; the structure–activity relationships of PAPs; and their enzymatic processing based on the structure–activity relationships. Moreover, current research problems for PAPs are discussed, such as the shallow exploration of mechanisms, enzymatic solution determination difficulty, low yield and unknown in vivo absorption and metabolism and proposed future research directions. This work aims to provide a reference for functional activity research, nutritional food development and the clinical applications of PAPs.

Fillers play a critical role in the performance of mixed matrix membranes (MMMs). Microporous metal azolate frameworks (MAFs) are a subclass material of metal–organic frameworks (MOFs). Due to the uncoordinated nitrogen of the organic ligands, MAF-7 (SOD-[Zn(mtz)2], Hmtz = 3-methyl-1,2,4-triazole, window: d = 0.34 nm) shows excellent CO2 adsorption performance. In this work, Pebax 1657/MAF-7 MMMs were prepared by a sample solution casting method with MAF-7 particles as fillers for the first time. By means of X-ray diffraction (XRD), scanning electron microscope (SEM), infrared radiation (IR), and thermogravimetry (TG), the compositional and structural properties of the mixed matrix membrane with different filler content were analyzed. The results show that the compatibility of MAF-7 and Pebax is good with a filler content of 5 wt.%. The pure gas testing showed that mixed matrix membrane has a high ideal CO2/N2 selectivity of 124.84 together with a better CO2 permeability of 76.15 Barrer with the optimized filler content of 5 wt.%. The obtained membrane showed 323.04% enhancement in selectivity of CO2/N2 and 27.74% increase in the permeability of CO2 compared to the pristine membrane at 25 °C and 3 bar. The excellent separation performance may be due to the ligands that can afford a Lewis base active site for CO2 binding with the uniform dispersion of MAF-7 particles in Pebax and the favorable interface compatibility. The obtained membrane overcomes the Robeson’s upper bound in 2008 for CO2/N2 separation. This work provides a new strategy by utilizing MAFs as fillers with triazole ligand to enhance the gas separation performance of mixed matrix membranes.

Background: To assess the associations of platelet traits and obesity indices with aging biomarkers (telomere length (TL) and mitochondrial DNA copy number (mtDNA-CN)). Methods: A cross-sectional study was performed among 5091 rural Chinese adults. Obesity indices (waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR)) and platelet traits (plateletcrit (PCT), platelet large cell ratio (P-LCR), mean platelet volume (MPV) and platelet distribution width (PDW)) were collected by physical examination. The aging biomarkers were determined by quantitative real-time polymerase chain reaction. Generalized linear regression models and mediation analysis were applied to explore the relationships between platelet traits, obesity indices, and aging biomarkers. Results: The mean age of the participants was 56 years (range: 18–79). Each one-unit increment in WC, WHR and WHtR were related to a 0.316 (95% confidence interval (CI): −0.437, −0.196), 0.323 (95% CI: −0.513, −0.134) and 0.277 (95% CI: −0.400, −0.153) decrease in relative TL; or a 0.102 (95% CI: −0.197, −0.007), 0.109 (95% CI: −0.258, −0.041) and 0.101 (95% CI: −0.199, −0.004) decrease in relative mtDNA-CN. The proportions of obesity indices with aging biomarkers mediated by platelet indices ranged from 2.85% to 10.10%. Conclusions: Increased central obesity indices in relation to shortened relative TL or decreased mtDNA-CN were mediated by platelet traits, indicating that obesity in association with the accelerated aging process may be partially attributable to abnormal platelet activity.

This study aimed at discussing the laws of the design parameters of a radio frequency (RF) ion thruster that influence the thruster performance, guide the performance optimization-oriented design of the thruster, and realize the high-accuracy continuous adjustment of the thrust performance. The key influencing parameters of the thrust performance were analyzed by the numerical simulation method. The influencing laws of RF parameters on key plasma parameters, as well as the thermal characteristics of the thruster under the rated parameters, were explored. Moreover, a LRIT-30 RF ion thruster was developed and subjected to the performance adjustment test. The research results demonstrated that the simulation model can describe the plasma parameter distribution of the discharge chamber and the thermal distribution of key components. The 3 cm RF ion thruster performs well under 2 MHz operational frequency. The RF power and gas flow rate are the key influencing factors of beam extraction and are appropriate for the accurate adjustment of parameters. The wide-range adjustment of thrust (0.5–2.3 mN) and specific impulse (869–2564 s) can be realized when the screen-grid voltage, decelerating screen voltage, RF power, and gas flow rate are 1500 V, −200 V, 40–65 W, and 0.4–0.8 SCCM, respectively. The performance indices of the proposed RF ion thruster are close to the international advanced level.

Potato cyst nematodes (PCN) are economically important pests with a worldwide distribution in all temperate regions where potatoes are grown. Because above ground symptoms are non-specific, and detection of cysts in the soil is determined by the intensity of sampling, infestations are frequently spread before they are recognised. PCN cysts are resilient and persistent; their cargo of eggs can remain viable for over two decades, and thus once introduced PCN are very difficult to eradicate. Various control methods have been proposed, with resistant varieties being a key environmentally friendly and effective component of an integrated management programme. Wild and landrace relatives of cultivated potato have provided a source of PCN resistance genes that have been used in breeding programmes with varying levels of success. Producing a PCN resistant variety requires concerted effort over many years before it reaches what can be the biggest hurdle—commercial acceptance. Recent advances in potato genomics have provided tools to rapidly map resistance genes and to develop molecular markers to aid selection during breeding. This review will focus on the translation of these opportunities into durably PCN resistant varieties.

The capacitive voltage transformer (CVT) is a special measuring and protecting device, which is commonly applied in high-voltage power systems. Its measurement accuracy is affected seriously by the stray capacitances of the capacitance voltage divider (CVD) to ground and other charged parts. In this study, based on the boundary element method, a mathematical model was established firstly to calculate the stray capacitance. Then, the voltage distribution of the CVD was obtained by the CVD’s equivalent circuit model. Next, the effect of stray capacitance on the voltage distribution and the voltage difference ratio (VDR) of CVD was analysed in detail. We finally designed three types of shield and optimized their structure parameters to reduce VDR. The results indicated that the average deviation rate between calculated and experimental measured voltages is only 0.015%; that is to say, the method has high calculation precision. The stray capacitance of the CVD to ground is far larger than that of the CVD to the high-voltage terminal. It results in the inhomogeneous distribution of voltage and the increase of VDR. For the test CVT, its VDR exceeds the requirement of class 0.2. Among all of the three types of shield, the C type reduced the VDR of the test CVT the most. After optimizing the structure parameters of C-type shield, the VDR is further reduced to 0.08%. It is not only in accord with the requirement of class 0.2 but also has an adequate margin.

For the task of obstacle detection in a complex traffic environment, this paper proposes a road-free space extraction and obstacle detection method based on stereo vision. The proposed method combines the advantages of the V-disparity image and the Stixel method. Firstly, the depth information and the V-disparity image are calculated according to the disparity image. Then, the free space on the road surface is calculated through the RANSAC algorithm and dynamic programming (DP) algorithm. Furthermore, a new V-disparity image and a new U-disparity image are calculated by the disparity image after removing the road surface information. Finally, the height and width of the obstacles on the road are extracted from the new V-disparity image and U-disparity image, respectively. The detection of obstacles is realized by the height and width information of obstacles. In order to verify the method, we adopted the object detection benchmarks and road detection benchmarks of the KITTI dataset for verification. In terms of the accuracy performance indicators quality, detection rate, detection accuracy, and effectiveness, the method in this paper reaches 0.820, 0.863, 0.941, and 0.900, respectively, and the time consumption is only 5.145 milliseconds. Compared with other obstacle detection methods, the detection accuracy and real-time performance in this paper are better. The experimental results show that the method has good robustness and real-time performance for obstacle detection in a complex traffic environment.

Introduction Traumatic brain injury (TBI) is the leading cause of paediatric trauma death and disability worldwide. The ‘Guidelines for the Management of Severe Traumatic Brain Injury (Fourth Edition)’ recommend that nutritional goals should be achieved within 5–7 days of injury. Immune-enhancing nutrition or immunonutrition, referring to the addition of specialised nutrients, including glutamine, alanine, omega-3 fatty acids and nucleotides, to standard nutrition formulas, may improve surgical outcomes in the perioperative period. However, the role of immune-enhancing nutritional supplements for patients with paediatric TBI remains unclear. We will conduct a systematic review to determine the efficacy and safety of immunonutrition for patients with paediatric TBI and provide evidence for clinical decision-making.Methods and analysis Studies reporting immune-enhancing nutrition treatments for patients with paediatric TBI will be included. Outcomes of interest include the length of hospital stay, wound infections, all-cause mortality, non-wound infection, including pneumonia, urinary tract infection and bacteraemia, and the reports adverse events. Duration of follow-up has no restriction. Primary studies consisting of randomised controlled trials (RCTs) and non-RCTs will be eligible for this review, and only studies published in English will be included. We will search the Medline, Embase and Cochrane Library databases from their inception dates to January 2020. We will also search clinicaltrials.gov and the WHO International Clinical Trials Registry Platform for additional information. Two reviewers will independently select studies and extract data. Risk-of-bias will be assessed with tools based on the Cochrane risk-of-bias criteria and Newcastle-Ottawa Quality Assessment Scale. A meta-analysis will be used to pool data when there are sufficient studies with homogeneity. Heterogeneity of the estimates across studies will be assessed; if necessary, a subgroup analysis will be performed to explore the source of heterogeneity. The Grades of Recommendation, Assessment, Development and Evaluation method will be applied to assess the level of evidence obtained from this systematic review.Ethics and dissemination The proposed systematic review and meta-analysis will be based on published data, and thus ethical approval is not required. The results of this review will be published.PROSPERO registration number CRD42020154814.

At present, marine resources, especially the deep-sea resources, are becoming more and more important in resource exploitation globally, and hence, widely used deep-sea cranes are playing essential roles. For such systems, the bridge frames and trolleys are set up above the water, while payloads are transported under the water. In this underwater situation, there exist hydrodynamic forces such as complicated disturbances to the crane systems, making the payload vibration and rope flexibility more obvious. For the sake of improving working efficiency, considering the constraints of all the state variables, an anti-vibration trajectory is designed for the trolley motion, which can not only ensure trolley positioning but also suppress the flexible payload’s vibrations. Then, the state variables are constrained within preset safety ranges. Finally, numerical simulation results prove the satisfactory performance of the designed method.

Background: The novel coronavirus pneumonia COVID-19 caused by SARS-CoV-2 infection could lead to a serious of clinical symptoms and severe illness, including acute respiratory distress syndrome (ARDS) and fatal organ failure. We report the fundamental pathological investigation in the lungs and other organs of fatal cases for the mechanistic understanding of severe COVID-19 and the development of specific therapy in these cases. Methods: The autopsy and pathological investigations of specimens were performed on bodies of two deceased cases with COVID-19. Gross anatomy and histological investigation by Hematoxylin and eosin (HE) stained were reviewed on each patient. Alcian blue/periodic acid-Schiff (AB-PAS) staining and Masson staining were performed for the examinations of mucus, fibrin and collagen fiber in lung tissues. Immunohistochemical staining were performed on the slides of lung tissues from two patients. Real-time PCR was performed to detect the infection of SARS-CoV-2. Flow cytometry analyses were performed to detect the direct binding of S protein and the expression of ACE2 on the cell surface of macrophages. Findings: The main pathological features in lungs included extensive impairment of type I alveolar epithelial cells and atypical hyperplasia of type II alveolar cells, with formation of hyaline membrane, focal hemorrhage, exudation and pulmonary edema, and pulmonary consolidation. The mucous plug with fibrinous exudate in the alveoli and the dysfunction of alveolar macrophages were characteristic abnormalities. The type II alveolar epithelial cells and macrophages in alveoli and pulmonary hilum lymphoid tissue were infected by SARS-CoV-2. S protein of SARS-CoV-2 directly bound to the macrophage via the S-protein-ACE2 interaction. Interpretation: Infection of Alveolar macrophage by SARS-CoV-2 might be drivers of the “cytokine storm”, which might result in damages in pulmonary tissues, heart and lung, and leading to the failure of multiple organs . Funding: Shanghai Guangci Translational Medical Research Development Foundation, Shanghai, China

Water pollution by various toxic substances remains a serious environmental problem, especially the occurrence of organic micropollutants including endocrine disruptors, pharmaceutical pollutants and naphthol pollutants. Adsorption process has been an effective method for pollutant removal in wastewater treatment. However, the thermal regeneration process for the most widely used activated carbon is costly and energy-consuming. Therefore, there has been an increasing need to develop alternative low-cost and effective adsorption materials for pollutant removal. Herein, β-cyclodextrin (β-CD), a cheap and versatile material, was modified with methacrylate groups by reacting with methacryloyl chloride, giving an average degree of substitution of 3 per β-CD molecule. β-CD-methacrylate, which could function as a crosslinker, was then copolymerized with acrylamide monomer via free-radical copolymerization to form β-CD-polyacrylamide (β-CD-PAAm) hydrogel. Interestingly, in the structure of the β-CD-PAAm hydrogel, β-CD is not only a functional unit binding pollutant molecules through inclusion complexation, but also a structural unit crosslinking PAAm leading to the formation of the hydrogel 3D networks. Morphological studies showed that β-CD-PAAm gel had larger pore size than the control PAAm gel, which was synthesized using conventional crosslinker instead of β-CD-methacrylate. This was consistent with the higher swelling ratio of β-CD-PAAm gel than that of PAAm gel (29.4 vs. 12.7). In the kinetic adsorption studies, phenolphthalein, a model dye, and bisphenol A, propranolol hydrochloride, and 2-naphthol were used as model pollutants from different classes. The adsorption data for β-CD-PAAm gel fitted well into the pseudo-second-order model. In addition, the thermodynamic studies revealed that β-CD-PAAm gel was able to effectively adsorb the different dye and pollutants at various concentrations, while the control PAAm gel had very low adsorption, confirming that the pollutant removal was due to the inclusion complexation between β-CD units and pollutant molecules. The adsorption isotherms of the different dye and pollutants by the β-CD-PAAm gel fitted well into the Langmuir model. Furthermore, the β-CD-PAAm gel could be easily recycled by soaking in methanol and reused without compromising its performance for five consecutive adsorption/desorption cycles. Therefore, the β-CD-PAAm gel, which combines the advantage of an easy-to-handle hydrogel platform and the effectiveness of adsorption by β-CD units, could be a promising pollutant removal system for wastewater treatment applications.

Soybean dreg is a by-product of soybean products production, with a large consumption in China. Low utilization value leads to random discarding, which is one of the important sources of urban pollution. In this work, porous biochar was synthesized using a one-pot method and potassium bicarbonate (KHCO3) with low-cost soybean dreg (SD) powder as the carbon precursor to investigating the adsorption of methylene blue (MB). The prepared samples were characterized with scanning electron microscopy (SEM), transmission electron microscopy (TEM), elemental analyzer (EA), Brunauer-Emmett-Teller (BET), X-ray diffractometer (XRD), Raman spectroscopy (Raman), Fourier transform infrared spectrometer (FTIR), and X-ray photoelectron spectroscopy (XPS). The obtained SDB-K-3 showed a high specific surface area of 1620 m2 g−1, a large pore volume of 0.7509 cm3 g−1, and an average pore diameter of 1.859 nm. The results indicated that the maximum adsorption capacity of SDB-K-3 to MB could reach 1273.51 mg g−1 at 318 K. The kinetic data were most consistent with the pseudo-second-order model and the adsorption behavior was more suitable for the Langmuir isotherm equation. This study demonstrated that the porous biochar adsorbent can be prepared from soybean dreg by high value utilization, and it could hold significant potential for dye wastewater treatment in the future.

Engineering resistance genes to gain effector recognition is emerging as an important step in attaining broad, durable resistance. We engineered potato resistance gene R3a to gain recognition of the virulent AVR3aEM effector form of Phytophthora infestans. Random mutagenesis, gene shuffling and site-directed mutagenesis of R3a were conducted to produce R3a* variants with gain of recognition towards AVR3aEM. Programmed cell death following gain of recognition was enhanced in iterative rounds of artificial evolution and neared levels observed for recognition of AVR3aKI by R3a. We demonstrated that R3a*-mediated recognition responses, like for R3a, are dependent on SGT1 and HSP90. In addition, this gain of response is associated with re-localisation of R3a* variants from the cytoplasm to late endosomes when co-expressed with either AVR3aKI or AVR3aEM a mechanism that was previously only seen for R3a upon co-infiltration with AVR3aKI. Similarly, AVR3aEM specifically re-localised to the same vesicles upon recognition by R3a* variants, but not with R3a. R3a and R3a* provide resistance to P. infestans isolates expressing AVR3aKI but not those homozygous for AVR3aEM.

BackgroundGenetic resistance to barley leaf rust caused by Puccinia hordei involves both R genes and quantitative trait loci. The R genes provide higher but less durable resistance than the quantitative trait loci. Consequently, exploring quantitative or partial resistance has become a favorable alternative for controlling disease. Four quantitative trait loci for partial resistance to leaf rust have been identified in the doubled haploid Steptoe (St)/Morex (Mx) mapping population. Further investigations are required to study the molecular mechanisms underpinning partial resistance and ultimately identify the causal genes.Methodology/principal findingsWe explored partial resistance to barley leaf rust using a genetical genomics approach. We recorded RNA transcript abundance corresponding to each probe on a 15K Agilent custom barley microarray in seedlings from St and Mx and 144 doubled haploid lines of the St/Mx population. A total of 1154 and 1037 genes were, respectively, identified as being P. hordei-responsive among the St and Mx and differentially expressed between P. hordei-infected St and Mx. Normalized ratios from 72 distant-pair hybridisations were used to map the genetic determinants of variation in transcript abundance by expression quantitative trait locus (eQTL) mapping generating 15685 eQTL from 9557 genes. Correlation analysis identified 128 genes that were correlated with resistance, of which 89 had eQTL co-locating with the phenotypic quantitative trait loci (pQTL). Transcript abundance in the parents and conservation of synteny with rice allowed us to prioritise six genes as candidates for Rphq11, the pQTL of largest effect, and highlight one, a phospholipid hydroperoxide glutathione peroxidase (HvPHGPx) for detailed analysis.Conclusions/significanceThe eQTL approach yielded information that led to the identification of strong candidate genes underlying pQTL for resistance to leaf rust in barley and on the general pathogen response pathway. The dataset will facilitate a systems appraisal of this host-pathogen interaction and, potentially, for other traits measured in this population.