Search

Abstract The outbreaks of severe acute respiratory syndrome coronavirus (SARS-CoV-1), Middle East respiratory syndrome coronavirus (MERS-CoV), and SARS-CoV-2 highlight the need for countermeasures to prevent future coronavirus pandemics. Given the unpredictable nature of spillover events, preparing antibodies with broad coronavirus-neutralizing activity is an ideal proactive strategy. Here, we investigated whether SARS-CoV-2 infection and vaccination could provide cross-neutralizing antibodies (nAbs) against zoonotic sarbecoviruses. We evaluated the cross-neutralizing profiles of plasma and monoclonal antibodies constructed from B cells from coronavirus disease 2019 (COVID-19) convalescents and vaccine recipients; against sarbecoviruses originating from bats, civets, and pangolins; and against SARS-CoV-1 and SARS-CoV-2. We found that the majority of individuals with natural infection and vaccination elicited broad nAb responses to most tested sarbecoviruses, particularly to clade 1b viruses, but exhibited very low cross-neutralization to SARS-CoV-1 in both natural infection and vaccination, and vaccination boosters significantly augmented the magnitude and breadth of nAbs to sarbecoviruses. Of the nAbs, several exhibited neutralization activity against multiple sarbecoviruses by targeting the spike receptor-binding domain (RBD) and competing with angiotensin-converting enzyme 2 (ACE2) binding. SCM12-61 demonstrated exceptional potency, with half-maximal inhibitory concentration (IC50) values of 0.001–0.091 μg/mL against tested sarbecoviruses; while VSM9-12 exhibited remarkable cross-neutralizing breadth against sarbecoviruses and SARS-CoV-2 Omicron subvariants, highlighting the potential of these two nAbs in combating sarbecoviruses and SARS-CoV-2 Omicron subvariants. Collectively, our findings suggest that vaccination with an ancestral SARS-CoV-2 vaccine, in combination with broad nAbs against sarbecoviruses, may provide a countermeasure for preventing further sarbecovirus outbreaks in humans.

Abstract Mycobacterium tuberculosis (Mtb) employs multiple mechanisms, such as phagocytosis and autophagy, to evade innate immune clearance and establish infection. In the present study, we identified the ESX-1 secretion-associated protein EspL, which promotes Mtb survival by inhibiting phagosome maturation and autophagy initiation. EspL knockout decreased Mtb intracellular survival, while EspL overexpression increased bacterial survival by interfering with phagocytosis and autophagy. EspL interacts with ULK1 and promotes its phosphorylation at Ser757, leading to the inhibition of autophagy initiation. Additionally, overexpression of EspL reduced antigen presentation and T-cell responses both in vitro and in vivo. Our findings revealed that EspL interferes with autophagy and antigen presentation by suppressing ULK1 activation. These insights provide a novel understanding of Mtb pathogenicity.

In this paper, we revisit the classical energy transport equation for pure tidal flows in estuaries by including the effects of residual water level and freshwater discharge. Starting from the one-dimensional mass and momentum conservations, we derive analytical expressions for energy flux and its dissipation, which can be used to explore the interaction between tide and river from an energy perspective. It has been shown that the potential energy flux is dominant over the kinetic energy flux, and the energy contribution made by tide-river interaction is negligible compared with that due to tidal flow and freshwater discharge. A critical position corresponding with zero energy flux can be identified, reflecting a balance between along-channel tidal amplitude and residual water level. Assuming the variation of system energy tends to zero, we derive the long-term equilibrium bed profile with a convex-up shape, where we identify the location of a second estuarine turbidity maximum (ETM) at a position determined by the balance between river- and tide-induced energy flux. The dynamic response of tidal propagation extent and critical position with zero energy flux are shown to adjust to reflect seasonal changes towards the formation of a bed profile that is in dynamic equilibrium.Key policy insights A new analytical method is derived to calculate energy transport and energy dissipation in river-tide estuaries.Equilibrium bed profile determined by minimum entropy production has a convex-up shape where the minimum depth coincides with the location of zero along-channel energy flux.The location of a second estuarine turbidity maximum is consistent with the location where there is a balance between river- and tide-induced energy flux.

Detecting small and densely packed objects in images remains a significant challenge in computer vision. Existing object detection methods often exhibit low accuracy and frequently miss detection when identifying dense small objects and require larger model parameters. This study introduces a novel detection framework designed to address these limitations by integrating advanced feature fusion and optimization techniques. Our approach focuses on enhancing both detection accuracy and parameter efficiency. The approach was evaluated on the open-source VisDrone2019 data set and compared with mainstream algorithms. Experimental results demonstrate a 70.2% reduction in network parameters and a 6.3% improvement in mAP@0.5 over the original YOLOv7 algorithm. These results demonstrate that the enhanced model surpasses existing algorithms in detecting small objects.

Under-deposit corrosion is widely present in the pipelines of oil and gas production, causing significant corrosion damage. In this paper, a novel electrochemical cathodic-polarization method was carried out to accelerate the formation of CaCO3 scale on a X65 steel surface in a simulated solution containing scaling ions. Subsequently, pre-scaled X65 steel was placed in a high temperature and pressure autoclave to conduct corrosion weight-loss experiments and in situ electrochemical measurements. The study mainly compared the corrosion inhibition behavior of four quaternary ammonium salt corrosion inhibitors, pyridinium quaternary salt (BPC), quinolinium quaternary salt (BQC), 8-hydroxyquinolinium quaternary salt (BHQ) and pyridinium (1-chloromethyl naphthalene) quaternary salt (1-CPN), in a simulated oilfield scale under corrosive conditions. The results of the weight-loss experiments demonstrated that the inhibition efficiencies of the corrosion inhibitors from high to low were as follows: 1-CPN < BHQ < BQC < BPC. The in situ electrochemical measurements showed that the immersion time and type of corrosion inhibitor had a pronounced influence on the corrosion and corrosion inhibition behavior of X65 steel with CaCO3 coating. It was also proved using both EIS and PC that 1-CPN shows the best inhibition performance in all. Lastly, the inhibition mechanism of corrosion inhibitors at under-deposit conditions was analyzed via a surface morphology observation of SEM.

Abstract Background Precancerous lesions of gastric cancer (PLGC) refer to a kind of histopathological changes in the gastric mucosa that can progress to gastric cancer. Elian granules (ELG), a Chinese medicinal prescription, have achieved satisfactory results in the treatment of PLGC. However, the exact mechanism underlying the therapeutic effect of ELG remains unclear. Here, this study aims to explore the mechanism of ELG alleviating PLGC in rats. Methods The chemical ingredients of ELG were analyzed using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS). Specific Pathogen Free SD rats were randomly assigned to 3 groups: the control, model, and ELG groups. The 1-Methyl-3-nitro-1-nitrosoguanidine (MNNG) integrated modeling method was adopted to construct the PLGC rat model in groups except for the control group. Meanwhile, normal saline was used as an intervention for the control and model groups, and ELG aqueous solution for the ELG group, lasting 40 weeks. Subsequently, the stomach of rats was harvested for further analysis. Hematoxylin-eosin staining of the gastric tissue was conducted to assess the pathological changes. Immunofluorescence was carried out for the expression of CD68, and CD206 proteins. Real-time quantitative PCR combined with Western blot was conducted to analyze the expression of arginase-1(Arg-1), inducible nitric oxide synthase (iNOS), p65, p-p65, nuclear factor inhibitor protein-α (IκBα), and p-IκBα in gastric antrum tissue. Results Five chemical ingredients including Curcumol, Curzerenone, Berberine, Ferulic Acid, and 2-Hydroxy-3-Methylanthraquine were identified in ELG. The gastric mucosal glands of rats treated with ELG were orderly arranged, with no intestinal metaplasia and no dysplasia. Furthermore, ELG decreased the percentage of M2-type TAMs marked with CD68 and CD206 proteins, and the ratio of Arg-1 to iNOS in the gastric antrum tissue of rats with PLGC. In addition, ELG could also down-regulate the protein and mRNA expression of p-p65, p65, and p-IκBα, but up-regulate the expression of IκBα mRNA in rats with PLGC. Conclusions The results showed that ELG attenuates PLGC in rats by suppressing the M2-type polarization of tumor-associated macrophages (TAMs) through NF-κB signaling pathway.

IntroductionSince December 2019, the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) has presented considerable public health challenges. Multiple vaccines have been used to induce neutralizing antibodies (nAbs) and memory B-cell responses against the viral spike (S) glycoprotein, and many essential epitopes have been defined. Previous reports have identified severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike-reactive naïve B cells and preexisting memory B cells in unexposed individuals. However, the role of these spike-reactive B cells in vaccine-induced immunity remains unknown.MethodsTo elucidate the characteristics of preexisting SARS-CoV-2 S-reactive B cells as well as their maturation after antigen encounter, we assessed the relationship of spike-reactive B cells before and after vaccination in unexposed human individuals. We further characterized the sequence identity, targeting domain, broad-spectrum binding activity and neutralizing activity of these SARS-CoV-2 S-reactive B cells by isolating monoclonal antibodies (mAbs) from these B cells.ResultsThe frequencies of both spike-reactive naïve B cells and preexisting memory B cells before vaccination correlated with the frequencies of spike-reactive memory B cells after vaccination. Isolated mAbs from spike-reactive naïve B cells before vaccination had fewer somatic hypermutations (SHMs) than mAbs isolated from spike-reactive memory B cells before and after vaccination, but bound SARS-CoV-2 spike in vitro. Intriguingly, these germline-like mAbs possessed broad binding profiles for SARS-CoV-2 and its variants, although with low or no neutralizing capacity. According to tracking of the evolution of IGHV4-4/IGKV3-20 lineage antibodies from a single donor, the lineage underwent SHMs and developed increased binding activity after vaccination.DiscussionOur findings suggest that spike-reactive naïve B cells can be expanded and matured by vaccination and cocontribute to vaccine-elicited antibody responses with preexisting memory B cells. Selectively and precisely targeting spike-reactive B cells by rational antigen design may provide a novel strategy for next-generation SARS-CoV-2 vaccine development.

Summary: Blood flow is known to regulate cerebrovascular development through acting on vascular endothelial cells (ECs). As an indispensable component of the neurovascular unit, brain pericytes physically couple with ECs and play vital roles in blood-brain barrier integrity maintenance and neurovascular coupling. However, it remains unclear whether blood flow affects brain pericyte development. Using in vivo time-lapse imaging of larval zebrafish, we monitored the developmental dynamics of brain pericytes and found that they proliferate to expand their population and increase their coverage to brain vessels. In combination with pharmacological and genetic approaches, we demonstrated that blood flow enhances brain pericyte proliferation through Piezo1 expressed in ECs. Moreover, we identified that EC-intrinsic Notch signaling is downstream of Piezo1 to promote the activation of Notch signaling in pericytes. Thus, our findings reveal a role of blood flow in pericyte proliferation, extending the functional spectrum of hemodynamics on cerebrovascular development.

Understanding different aspects of public concerns and sentiments during large health emergencies, such as the COVID-19 pandemic, is essential for public health agencies to develop effective communication strategies, deliver up-to-date and accurate health information, and mitigate potential impacts of emerging misinformation. Current infoveillance systems generally focus on discussion intensity (i.e., number of relevant posts) as an approximation of public awareness, while largely ignoring the rich and diverse information in texts with granular information of varying public concerns and sentiments. In this study, we address this grand challenge by developing a novel natural language processing (NLP) infoveillance workflow based on bidirectional encoder representation from transformers (BERT). We first used a smaller COVID-19 tweet sample to develop a content classification and sentiment analysis model using COVID-Twitter-BERT. The classification accuracy was between 0.77 and 0.88 across the five identified topics. In the sentiment analysis with a three-class classification task (positive/negative/neutral), BERT achieved decent accuracy, 0.7. We then applied the content topic and sentiment classifiers to a much larger dataset with more than 4 million tweets in a 15-month period. We specifically analyzed non-pharmaceutical intervention (NPI) and social issue content topics. There were significant differences in terms of public awareness and sentiment towards the overall COVID-19, NPI, and social issue content topics across time and space. In addition, key events were also identified to associate with abrupt sentiment changes towards NPIs and social issues. This novel NLP-based AI workflow can be readily adopted for real-time granular content topic and sentiment infoveillance beyond the health context.

ObjectiveThe pathogenesis of peptic ulcer diseases (PUDs) involves multiple factors, and the contribution of gut microbiota to this process remains unclear. While previous studies have associated gut microbiota with peptic ulcers, the precise nature of the relationship, whether causal or influenced by biases, requires further elucidation.DesignThe largest meta-analysis of genome-wide association studies was conducted by the MiBioGen consortium, which provided the summary statistics of gut microbiota for implementation in the Mendelian randomization (MR) analysis. Summary statistics for five types of PUDs were compiled using the FinnGen Consortium R8 release data. Various statistical techniques, including inverse variance weighting (IVW), MR-Egger, weighted median (WM), weighted mode, and simple mode, were employed to assess the causal relationships between gut microbiota and these five PUDs.ResultIn the intestinal microbiome of 119 known genera, we found a total of 14 causal associations with various locations of PUDs and reported the potential pathogenic bacteria of Bilophila et al. Among them, four had causal relationships with esophageal ulcer, one with gastric ulcer, three with gastroduodenal ulcer, four with duodenal ulcer, and two with gastrojejunal ulcer.ConclusionIn this study, the pathogenic bacterial genera in the gut microbiota that promote the occurrence of PUDs were found to be causally related. There are multiple correlations between intestinal flora and PUDs, overlapping PUDs have overlapping associated genera. The variance in ulcer-related bacterial genera across different locations underscores the potential influence of anatomical locations and physiological functions.

In recent years, object detection based on deep learning has been widely applied and developed. When using object detection methods to process remote sensing images, the trade-off between the speed and accuracy of models is necessary, because remote sensing images pose additional difficulties such as complex backgrounds, small objects, and dense distribution to the detection task. This paper proposes YOLO-RS, an optimized object detection algorithm based on YOLOv4 to address the challenges. The Adaptively Spatial Feature Fusion (ASFF) structure is introduced after the feature enhancement network of YOLOv4. It assigns adaptive weight parameters to fuse multi-scale feature information, improving detection accuracy. Furthermore, optimizations are applied to the Spatial Pyramid Pooling (SPP) structure in YOLOv4. By incorporating residual connections and employing 1 × 1 convolutions after maximum pooling, both computation complexity and detection accuracy are improved. To enhance detection speed, Lightnet is introduced, inspired by Depthwise Separable Convolution for reducing model complexity. Additionally, the loss function in YOLOv4 is optimized by introducing the Intersection over Union loss function. This change replaces the aspect ratio loss term with the edge length loss, enhancing sensitivity to width and height, accelerating model convergence, and improving regression accuracy for detected frames. The mean Average Precision (mAP) values of the YOLO-RS model are 87.73% and 92.81% under the TGRS-HRRSD dataset and RSOD dataset, respectively, which are experimentally verified to be 2.15% and 1.66% higher compared to the original YOLOv4 algorithm. The detection speed reached 43.45 FPS and 43.68 FPS, respectively, with 5.29 Frames Per Second (FPS) and 5.30 FPS improvement.

Pine wilt disease (PWD), caused by pine wood nematode (PWN, Bursaphelenchus xylophilus), poses a serious threat to the coniferous forests in China. This study used unmanned aerial vehicle (UAV)-based hyperspectral imaging conducted at different altitudes to investigate the impact of spatio-temporal scales on PWD detection in an monoculture Masson pine plantation. The influence of spatio-temporal scales on hyperspectral responses of pine trees infected with PWD and detection accuracies were evaluated by Jeffries–Matusita (J-M) distances and the random forest (RF) algorithm. The optimal vegetation indices (VIs) and spatial resolutions were identified by comparing feature importance and model accuracy. The main results showed that the VIs and J-M distances were greatly affected by spatio-temporal scales. In the early, mid-, and late infection stages, the RF-based PWD detection model had accuracies ranging between 72.05 and 79.48%, 83.71 and 89.59%, and 96.81 and 99.28%, peaking at the 10 cm, 8 cm, and 4 cm spatial resolutions, respectively. The green normalized difference vegetation index (GNDVI) and red edge position (REP) were the optimal VIs in early and mid-infection stages, respectively. This study can be important to improve the efficiency of PWD detection and reducing the loss of forests resources.

Whole grain rice is recommended in a natural healthy diet because of its high nutritional and healthful benefits compared to polished or white rice. The whole grain contains the pericarp with many assorted colors (such as brown, red, and black) associated with taste and commercial quality. The color attributes of whole grain or brown rice are usually undesirable and need to be improved. To decipher the genetic basis of color variation in the whole grain rice, we conducted a genome-wide association analysis of three parameters of grain colors (brightness, redness, and yellowness) in a panel of 682 rice accessions. Twenty-six loci were identified for the color parameters, implying that grain color is under polygenic control. Among them, some major-effect loci were co-localized with the previously identified genes such as Rc and Rd. To eliminate the possible mask of Rc on other loci influencing grain color, we performed the association analysis in a subset of the panel that excluded the pigmented (red and black) rice. Eighteen loci or SNPs were detected to be associated with grain color in the subpopulation, many of which were not reported before. Two significant peak SNP regions on chromosomes 1 and 9 were validated using near-isogenic lines. Based on differential expression analysis of annotated genes within the SNP regions and metabolic analysis of pooled extreme samples, we found at least three annotated genes as potential candidates involved in the flavonoid metabolic pathway related to pericarp color. These results provide insights into the genetic basis of rice grain color and facilitate genomic breeding to improve appearance and commercial quality of whole grain rice.

Fraxinus mandshurica is one of the precious tree species in northeast China and has important economic and ecological value. Ascorbic acid (ASA) is a strong antioxidant that can significantly improve plant photosynthetic efficiency and stress resistance and participate widely in plant growth and development. In this study, we investigated the development process of mature zygotic embryos of F. mandshurica under different concentrations of ASA and found that 100 mg·L−1 exogenous ASA was the optimal concentration and that the induction rate of somatic embryos (SEs) was the highest at 72.89%, which was 7.13 times higher than that of the control group. The polyphenol content, peroxidase (POD) activity, nitric oxide (NO) content, nitrate reductase (NR) activity, total ascorbic acid (T-ASA) content, ASA content, ASA/Dehydroascorbic acid (DHA) ratio, GSH/GSSG ratio, and ascorbate peroxidase (APX) activity were significantly increased under the application of exogenous ASA in explants, whereas the polyphenol oxidase (PPO) activity, phenylalanine ammonia-lyase (PAL) activity, superoxide dismutase (SOD) activity, and catalase (CAT) activity, malondialdehyde (MDA) content and nitric oxide synthase (NOS) activity were decreased. At the same time, the content of T-ASA and ASA, T-GSH and GSSG, and PAL and SOD had the same change pattern in the control group and the treatment group. These results suggested that high or low concentrations of ASA could not promote the somatic embryogenesis of F. mandshurica and that exogenous ASA had significant effects on the physiology of F. mandshurica explants. ASA was also highly related to somatic embryogenesis and the explant browning of F. mandshurica. Our results could provide a reference for further study on the browning mechanism of F. mandshurica explants and lay the foundation for optimizing the condition of somatic embryogenesis in F. mandshurica.

Rice, as one of the main food crops, provides a vital source of dietary energy for over half the world’s population. The OsFAD3 gene encodes fatty acid desaturase, catalyzing the conversion of linoleic acid (LA) to alpha-linolenic acid (ALA) in rice. However, the genetic characterization of OsFAD3 and its role in the conversion of LA to ALA remains elusive. Here, we validated the effects of two homologous genes, OsFAD3-1 and OsFAD3-2, on the ALA and LA/ALA ratio in rice grains using near-isogenic lines. Two major haplotypes of OsFAD3-1 are identified with different effects on the ALA and LA/ALA ratio in rice germplasm. High expression of OsFAD3-1 is associated with high ALA accumulation and eating quality of rice grains. Overexpression of OsFAD3-1 driven by a seed-specific promoter increases the ALA content up to 16-fold in the endosperm. A diagnostic marker is designed based on an 8-bp insertion/deletion in the OsFAD3-1 promoter, which can recognize OsFAD3-1 alleles in rice. These results indicate that OsFAD3-1 is a useful target gene in marker-assisted breeding programs to improve varieties with high ALA and appropriate LA/ALA ratio in brown rice.

Knowledge-based planning (KBP) utilizes experienced planners’ knowledge embedded in prior plans to estimate optimal achievable dose volume histogram (DVH) of new cases. In the regression-based KBP framework, previously planned patients’ anatomical features and DVHs are extracted, and prior knowledge is summarized as the regression coefficients that transform features to organ-at-risk DVH predictions. In our study, we find that in different settings, different regression methods work better. To improve the robustness of KBP models, we propose an ensemble method that combines the strengths of various linear regression models, including stepwise, lasso, elastic net, and ridge regression. In the ensemble approach, we first obtain individual model prediction metadata using in-training-set leave-one-out cross validation. A constrained optimization is subsequently performed to decide individual model weights. The metadata is also used to filter out impactful training set outliers. We evaluate our method on a fresh set of retrospectively retrieved anonymized prostate intensity-modulated radiation therapy (IMRT) cases and head and neck IMRT cases. The proposed approach is more robust against small training set size, wrongly labeled cases, and dosimetric inferior plans, compared with other individual models. In summary, we believe the improved robustness makes the proposed method more suitable for clinical settings than individual models.

Functional verification has become one of the main bottlenecks in the cost-effective design of embedded systems, particularly for symmetric multiprocessors. It is estimated that verification in its entirety accounts for up to 60% of design resources, including duration, computer resources, and total personnel. Simulation-based verification is a long-standing approach used to locate design errors in the symmetric multiprocessor verification. The greatest challenge of simulation-based verification is the creation of the reference model of the symmetric multiprocessor. In this paper, we propose an efficient symmetric multiprocessor reference model, Hybrid Model, written with SystemC. SystemC can provide a high-level simulation environment and is faster than the traditional hardware description languages. Hybrid Model has been implemented in an efficient 32-bit symmetric multiprocessor verification. Experimental results show our proposed model is a fast, accurate, and efficient symmetric multiprocessor reference model and it is able to help designers to locate design errors easily and accurately.