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Abstract Background Inflammatory bowel disease (IBD) is a progressive and debilitating inflammatory disease of the gastrointestinal tract (GIT). Despite recent advances, precise treatment and noninvasive monitoring remain challenging. Methods Herein, we developed orally-administered, colitis-targeting and hyaluronic acid (HA)-modified, core-shell curcumin (Cur)- and cerium oxide (CeO2)-loaded nanoprobes (Cur@PC-HA/CeO2 NPs) for computed tomography (CT) imaging-guided treatment and monitoring of IBD in living mice. Results Following oral administration, high-molecular-weight HA maintains integrity with little absorption in the upper GIT, and then actively accumulates at local colitis sites owing to its colitis-targeting ability, leading to specific CT enhancement lasting for 24 h. The retained NPs are further degraded by hyaluronidase in the colon to release Cur and CeO2, thereby exerting anti-inflammatory and antioxidant effects. Combined with the ability of NPs to regulate intestinal flora, the oral NPs result in substantial relief in symptoms. Following multiple treatments, the gradually decreasing range of the colon with high CT attenuation correlates with the change in the clinical biomarkers, indicating the feasibility of treatment response and remission. Conclusion This study provides a proof-of-concept for the design of a novel theranostic integration strategy for concomitant IBD treatment and the real-time monitoring of treatment responses.

Abstract Almond hulls can be used as an inexpensive source of nutrients for producing fungi that can be used as nontraditional foods such as alternative protein, probiotics, and food ingredients. Nutrients were extracted from almond hulls using hot water and their utility in cultivating filamentous fungus Aspergillus awamori (A. awamori) in submerged cultivation was investigated using batch fermentation in flasks with passive aeration. The almond hulls extract supplied all the nutrients required for A. awamori growth. The uptake preference of various sugars was investigated and the growth kinetic parameters of A. awamori were determined. Utilization of sugars by A. awamori grown in almond hull extract proceeded sequentially with preference for glucose followed by sucrose, fructose, and then xylose. Compared to potato dextrose broth medium, the fungal biomass produced using almond hull extract as growth medium had higher protein (19.59%) and lower fat (1.50%) contents. The modified Gompertz model described the kinetics of A. awamori well with a lag phase of 0.18 days and a specific growth rate of 1.77 d−1.

Abstract Background Although abnormal accumulation of amyloid beta (Aβ) protein is thought to be the main cause of Alzheimer’s disease (AD), emerging evidence suggests a pivotal vascular contribution to AD. Aberrant amyloid β induces neurovascular dysfunction, leading to changes in the morphology and function of the microvasculature. However, little is known about the underlying mechanisms between Aβ deposition and vascular injuries. Recent studies have revealed that pericytes play a substantial role in the vasculopathy of AD. Additional research is imperative to attain a more comprehensive understanding. Methods Two-photon microscopy and laser speckle imaging were used to examine cerebrovascular dysfunction. Aβ oligomer stereotactic injection model was established to explain the relationship between Aβ and vasculopathy. Immunofluorescence staining, western blot, and real-time PCR were applied to detect the morphological and molecular alternations of pericytes. Primary cultured pericytes and bEnd.3 cells were employed to explore the underlying mechanisms. Results Vasculopathy including BBB damage, hypoperfusion, and low vessel density were found in the cortex of 8 to 10-month-old 5xFAD mice. A similar phenomenon accompanied by pericyte degeneration appeared in an Aβ-injected model, suggesting a direct relationship between Aβ and vascular dysfunction. Pericytes showed impaired features including low PDGFRβ expression and increased pro-inflammatory chemokines secretion under the administration of Aβ in vitro, of which supernatant cultured with bEND.3 cells led to significant endothelial dysfunction characterized by TJ protein deficiency. Conclusions Our results provide new insights into the pathogenic mechanism underlying Aβ-induced vasculopathy. Targeting pericyte therapies are promising to ameliorate vascular dysfunction in AD.

BackgroundThe relationship between sodium-glucose cotransporter 2 (SGLT2) inhibitors and prostate cancer is still unknown. Although these inhibitors can influence tumor glycolysis, the underlying mechanism requires further exploration.MethodsA two-sample two-step MR was used to determine 1) causal effects of SGLT2 inhibition on prostate cancer; 2) causal effects of 1,400 circulating metabolites or metabolite ratios on prostate cancer; and 3) mediation effects of these circulating metabolites. Genetic proxies for SGLT2 inhibition were identified as variants in the SLC5A2 gene and glycated hemoglobin level (HbA1c). Additionally, positive control analysis on type 2 diabetes mellitus (T2DM) was conducted to test the selection of genetic proxies. Phenome Wide Association Study (PheWAS) and MR-PheWAS analysis were used to explore potential treatable diseases and adverse outcomes of SGLT2 inhibitors.ResultsGenetically predicted SGLT2 inhibition (per 1 SD decrement in HbA1c) was associated with reduced risk of T2DM [odds ratio (OR) = 0.66 (95% CI 0.53, 0.82), P = 1.57 × 10−4]; prostate cancer [0.34 (0.23, 0.49), P = 2.21 × 10−8] and prostate-specific antigen [0.26 (0.08, 0.81), P = 2.07 × 10−2]. The effect of SGLT2 inhibition on prostate cancer was mediated by uridine level, with a mediated proportion of 9.34% of the total effect. In MR-PheWAS, 65 traits were found to be associated with SLGT2 inhibitors (P < 1.78 × 10−5), and among them, 13 were related to diabetes.ConclusionOur study suggested that SGLT2 inhibition could lower prostate cancer risk through uridine mediation. More mechanistic and clinical research is necessary to explore how uridine mediates the link between SGLT2 inhibition and prostate cancer.

Transforming optical facial images into sketches while preserving realism and facial features poses a significant challenge. The current methods that rely on paired training data are costly and resource-intensive. Furthermore, they often fail to capture the intricate features of faces, resulting in substandard sketch generation. To address these challenges, we propose the novel hierarchical contrast generative adversarial network (HCGAN). Firstly, HCGAN consists of a global sketch synthesis module that generates sketches with well-defined global features and a local sketch refinement module that enhances the ability to extract features in critical areas. Secondly, we introduce local refinement loss based on the local sketch refinement module, refining sketches at a granular level. Finally, we propose an association strategy called “warmup-epoch” and local consistency loss between the two modules to ensure HCGAN is effectively optimized. Evaluations of the CUFS and SKSF-A datasets demonstrate that our method produces high-quality sketches and outperforms existing state-of-the-art methods in terms of fidelity and realism. Compared to the current state-of-the-art methods, HCGAN reduces FID by 12.6941, 4.9124, and 9.0316 on three datasets of CUFS, respectively, and by 7.4679 on the SKSF-A dataset. Additionally, it obtained optimal scores for content fidelity (CF), global effects (GE), and local patterns (LP). The proposed HCGAN model provides a promising solution for realistic sketch synthesis under unpaired data training.

The phenotyping of plant roots is essential for improving plant productivity and adaptation. However, traditional techniques for assembling root phenotyping information are limited and often labor-intensive, especially for woody plants. In this study, an advanced approach called accurate and detailed quantitative structure model-based (AdQSM-based) root phenotypic measurement (ARPM) was developed to automatically extract phenotypes from Ginkgo tree root systems. The approach involves three-dimensional (3D) reconstruction of the point cloud obtained from terrestrial laser scanning (TLS) to extract key phenotypic parameters, including root diameter (RD), length, surface area, and volume. To evaluate the proposed method, two approaches [minimum spanning tree (MST)-based and triangulated irregular network (TIN)-based] were used to reconstruct the Ginkgo root systems from point clouds, and the number of lateral roots along with RD were extracted and compared with traditional methods. The results indicated that the RD extracted directly from point clouds [coefficient of determination (R2) = 0.99, root-mean-square error (RMSE) = 0.41 cm] outperformed the results of 3D models (MST-based: R2 = 0.71, RMSE = 2.20 cm; TIN-based: R2 = 0.54, RMSE = 2.80 cm). Additionally, the MST-based model (F1 = 0.81) outperformed the TIN-based model (F1 = 0.80) in detecting the number of first-order and second-order lateral roots. Each phenotyping trait fluctuated with a different cloud parameter (CP), and the CP value of 0.002 (r = 0.94, p < 0.01) was found to be advantageous for better extraction of structural phenotypes. This study has helped with the extraction and quantitative analysis of root phenotypes and enhanced our understanding of the relationship between architectural parameters and corresponding physiological functions of tree roots.

IntroductionAs an exceptional geographical entity, the vegetation of the Qinghai-Tibetan Plateau (QTP) exhibits high sensitivity to climate change. The Baima Snow Mountain National Nature Reserve (BNNR) is located in the south-eastern sector of the QTP, serving as a transition area from sub-tropical evergreen broadleaf forest to high-mountain vegetation. However, there has been limited exploration into predicting the temporal and spatial variability of vegetation cover using anti-interference methods to address outliers in long-term historical data. Additionally, the correlation between these variables and environmental factors in natural forests with complex terrain has rarely been analyzed.MethodsThis study has developed an advanced approach based on TS (Theil-Sen slope estimator) MK (Mann-Kendall test)-FVC (fractional vegetation cover) to accurately evaluate and predict the time and spatial shifts in FVC within the BNNR, utilizing the GEE (Google Earth Engine). The satellite data utilized in this paper consisted of Landsat images spanning from 1986 to2020. By integrating TS and MK methodologies to monitor and assess the FVC trend, the Hurst index was employed to forecast FVC. Furthermore, the association between FVC and topographic factors was evaluated, the partial correlation between FVC and climatic influences was analyzed at the pixel level (30×30m).Results and discussionHere are the results of this research: (1) Overall, the FVC of the BNNR exhibits a growth trend, with the mean FVC value increasing from 59.40% in 1986 to 68.67% in 2020. (2) The results based on the TS-MK algorithm showed that the percentage of the area of the study area with an increasing and decreasing trend was 59.03% (significant increase of 28.04%) and 22.13% (significant decrease of 6.42%), respectively. The coupling of the Hurst exponent with the Theil-Sen slope estimator suggests that the majority of regions within the BNNR are projected to sustain an upward trend in FVC in the future. (3) Overlaying the outcomes of TS-MK with the terrain factors revealed that the FVC changes were notably influenced by elevation. The partial correlation analysis between climate factors and vegetation changes indicated that temperature exerts a significant influence on vegetation cover, demonstrating a high spatial correlation.

Recent studies have demonstrated that postbiotics possess bioactivities comparable to those of probiotics. Therefore, our experiment aimed to evaluate the effects of postbiotics derived from Enterococcus faecium on the growth performance and intestinal health of growing male minks. A total of 120 growing male minks were randomly assigned to 4 groups, each with 15 replicates of 2 minks. The minks in the 4 groups were fed a basal diet supplemented with 0 (control), 0.05, 0.1, and 0.15% postbiotics derived from E. faecium (PEF), respectively. Compared to the control, PEF improved feed/gain (F/G) during the first 4 weeks and the entire 8 weeks of the study (p

Introduction: Contrary to the traditional recognition that CO2 has large injectivity (the ratio of CO2 injection volume to pressure drop) in low permeability oil reservoirs to keep the formation pressure at a high level, the CO2 injection rate usually cannot attain the set value. It is essential to study the factors that influence CO2 injectivity and propose the optimal strategies to improve the CO2 injectivity.Methods: Therefore, in this study, several core samples collected from low permeability oil reservoirs are used to experimentally investigate the influences of CO2 injection rate, formation permeability, pressure and water saturation on CO2 injectivity, and the corresponding pressure drop, oil and gas production are examined. To determine the primary factor that influences the CO2 injectivity, orthogonal experimental design (ODE) and numerical simulations are utilized. In addition, to improve CO2 injectivity, the techniques of mini-fracturing and radial perforation are presented, and the threshold values for these two parameters are determined.Result and discussion: The results demonstrate that according to the magnitude of the extent that influences CO2 injectivity, the rank for the above factors is CO2 injection rate, reservoir pressure, formation water saturation and permeability. The oil recovery is mainly influenced by CO2 injection rate and formation permeability, and the influences of reservoir pressure and water saturation on oil recovery are smaller. The threshold values for radial perforation and fracture half-length are 25 m and 50m, respectively, which can provide some guidance for the strategies that should be taken to improve the CO2 injectivity and recover more oil from low permeability oil reservoirs.

Abstract Background The surge in omicron variants has caused nationwide breakthrough infections in mainland China since the December 2022. In this study, we report the neutralization profiles of serum samples from the patients with breast cancer and the patients with liver cancer who had contracted subvariant breakthrough infections. Methods In this real‐world study, we enrolled 143 COVID‐19‐vaccinated (81 and 62 patients with breast and liver cancers) and 105 unvaccinated patients with cancer (58 and 47 patients with breast and liver cancers) after omicron infection. Anti‐spike receptor binding domain (RBD) IgGs and 50% pseudovirus neutralization titer (pVNT50) for the preceding (wild type), circulating omicron (BA.4‐BA.5, and BF.7), and new subvariants (XBB.1.5) were comprehensively analyzed. Results Patients with liver cancer receiving booster doses had higher levels of anti‐spike RBD IgG against circulating omicron (BA.4‐BA.5, and BF.7) and a novel subvariant (XBB.1.5) compared to patients with breast cancer after breakthrough infection. Additionally, all vaccinated patients produced higher neutralizing antibody titers against circulating omicron (BA.4‐BA.5, and BF.7) compared to unvaccinated patients. However, the unvaccinated patients produced higher neutralizing antibody against XBB.1.5 than vaccinated patients after Omicron infection, with this trend being more pronounced in breast cancer than in liver cancer patients. Moreover, we found that there was no correlation between anti‐spike RBD IgG against wildtype virus and the neutralizing antibody titer, but a positive correlation between anti‐spike RBD IgG and the neutralizing antibody against XBB.1.5 was found in unvaccinated patients. Conclusion Our study found that there may be differences in vaccine response and protective effect against COVID‐19 infection in patients with liver and breast cancer. Therefore, we recommend that COVID‐19 vaccine strategies should be optimized based on vaccine components and immunology profiles of different patients with cancer.

Abstract The consumption of fructose has increased dramaticly during the last few decades, inducing a great increase in the risk of intrahepatic lipid accumulation, hypertriglyceridemia, hyperuricemia and cancer. However, the underlying mechanism has not yet been fully elucidated. Amino acid metabolism may play an important role in the process of the diseases caused by fructose, but there is still a lack of corresponding evidence. In present study, we provide an evidence of how fructose affects amino acids metabolism in 1895 ordinary residents in Chinese community using UPLC-QqQMS based amino acid targeted metabolomics and the underlying mechanism of fructose exposure how interferes with amino acid metabolism related genes and acetylated modification of proteome in the liver of rats model. We found people with high fructose exposure had higher levels of Asa, EtN, Asp, and Glu, and lower levels of 1MHis, PEtN, Arg, Gln, GABA, Aad, Hyl and Cys. The further mechanism study displayed amino acid metabolic genes of Aspa , Cndp1 , Dbt , Dmgdh, and toxic metabolites such as N-acetylethanolamines accumulation, interference of urea cycle, as well as acetylated modification of key enzymes in glutamine metabolic network and glutamine derived NEAAs synthesis pathway in liver may play important roles in fructose caused reprogramming in amino acid metabolism. This research provides novel insights of the mechanism of amino acid metabolic disorder caused by fructose and supplies new targets for clinical therapy.

Abstract Sketch face recognition has a wide range of applications in criminal investigation, but it remains a challenging task due to the small‐scale sample and the semantic deficiencies caused by cross‐modality differences. The authors propose a light semantic Transformer network to extract and model the semantic information of cross‐modality images. First, the authors employ a meta‐learning training strategy to obtain task‐related training samples to solve the small sample problem. Then to solve the contradiction between the high complexity of the Transformer and the small sample problem of sketch face recognition, the authors build the light semantic transformer network by proposing a hierarchical group linear transformation and introducing parameter sharing, which can extract highly discriminative semantic features on small–scale datasets. Finally, the authors propose a domain‐adaptive focal loss to reduce the cross‐modality differences between sketches and photos and improve the training effect of the light semantic Transformer network. Extensive experiments have shown that the features extracted by the proposed method have significant discriminative effects. The authors’ method improves the recognition rate by 7.6% on the UoM‐SGFSv2 dataset, and the recognition rate reaches 92.59% on the CUFSF dataset.

There is no clear explanation for the extraordinary rebound in China’s population mortality over the past decade. This paper utilizes panel data from 31 Chinese provinces from 2010 to 2020 to determine the distinct impacts of public sports services (PSS), public health services (PMS), and their interaction on population mortality. Empirical results show that public sports services significantly reduce mortality. Every unit increase in public sports services reduces mortality by about 2.3%. It is characterized by delayed realization. Public health services were surprisingly associated with a rebound in mortality. Further studies found strong health effect from interaction of public sports and health services. The effect was significantly strengthened in areas with fewer extreme temperatures or developed economy. The findings have important policy implications for the high-quality development of public sports and health services. It also emphasizes integration of sports and medicine and mitigates health risks associated with extreme temperatures.

The caching behavior of rodents is widely considered to facilitate plant seed dispersal and seedling regeneration in forest ecosystems. Studies supporting this theory have been based mainly on experimental results from natural forests; however, whether scatter-hoarding rodents play an important role in seed dispersal and seedling regeneration in tree plantations remains unclear. In this study, we tracked 4000 seeds over 2 years in a Pinus armandii plantation in southwest China to investigate their fate from seed removal to survival after hoarding. We found that rodents removed more than 30.0% of the released seeds in both years, 9.9% to 29.1% of the seeds were scatter-hoarded under the leaf litter or on the soil surface, and a few seeds were removed two to three times. The dispersal distance of scatter-hoarded seeds ranged from 0.4 to 14.5 m with a mean of 3.55 m, and re-caching processes increased the dispersal distance of cached seeds. Additionally, 18.9% of the removed seeds survived in the cache until the end of the observation period. Our results suggested that scatter-hoarding rodents play an important role in seed dispersal in planted forests.

Postoperative delirium (POD) is a severe postoperative complication characterized by delirium-like symptoms. So far, no effective preventable strategy for POD prevention has been identified. Reports show that the consumption of green tea polyphenols (GTP) is associated with better cognitive function by modulating the composition of gut microbiota. Whether GTP also play a role in alleviating POD through gut microbiota is unknown. Herein, we studied the effect of prolonged (eight weeks) GTP intake on postoperative delirium in C57BL/6 mice with laparotomies under isoflurane anesthesia (anesthesia/surgery). We subsequently investigated anesthesia/surgery caused behavioral changes and increased the expression of malondialdehyde (MAD), an oxidative stress marker, and the activities of superoxide dismutase (SOD), an antioxidant marker, in the mice at 6 h after anesthesia/surgery. However, GTP administration reversed these changes and alleviated anesthesia/surgery-induced decrease in the abundance of gut bacterial genera, Roseburia. Further, fecal microbiota transplant demonstrated that compared with mice in the control group, treatment of C57BL/6 mice with feces from GTP-treated mice had a slight effect on the behavioral changes of mice. These data suggest that daily consumption of GTP could protect against anesthesia/surgery-induced behavioral changes, which is closely associated with gut microbiota modification by GTP.

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.

The purpose of this experiment was to explore the effects of dietary Enterococcus faecium (EF) on the growth performance, antioxidant capacity, immunity, and intestinal microbiota of growing male minks. A total of 60 male Regal White minks at 12 weeks of age were randomly assigned to two groups, each with 15 replicates of two minks per replicate. The minks in two groups were fed the basal diets and the basal diets with viable Enterococcus faecium (more than 107 cfu/kg of diet), respectively. Compared with the minks in control, Enterococcus faecium minks had heavier body weight (BW) at week 4 and week 8 of the study (p < 0.05), greater average daily gain (ADG), and a lower feed/gain ratio (F/G) of male minks during the initial 4 weeks and the entire 8-week study period (p < 0.05). Furthermore, Enterococcus faecium increased the apparent digestibility of crude protein (CP) and dry matter (DM) compared to the control (p < 0.05). Moreover, Enterococcus faecium enhanced the serum superoxide dismutase (SOD) activity and decreased the malondialdehyde (MDA) contents (p < 0.05). The results also confirmed that Enterococcus faecium increased the levels of serum immunoglobulin A (IgA), immunoglobulin G (IgG), and the concentrations of secretory immunoglobulin A (SIgA) in the jejunal mucosa while decreasing the interleukin-8 (IL-8) and interleukin-1β (IL-1β) levels in the jejunal mucosa (p < 0.05). Intestinal microbiota analysis revealed that Enterococcus faecium increased the species numbers at the OUT level. Compared with the control, Enterococcus faecium had significant effects on the relative abundance of Paraclostridium, Brevinema, and Comamonas (p < 0.05). The results showed that Enterococcus faecium could improve the growth performance, increase the antioxidant capacity, improve the immunity of growing male minks, and also modulate the gut microbiota.

Forest canopy height is a fundamental parameter of forest structure, and plays a pivotal role in understanding forest biomass allocation, carbon stock, forest productivity, and biodiversity. Spaceborne LiDAR (Light Detection and Ranging) systems, such as GEDI (Global Ecosystem Dynamics Investigation), provide large-scale estimation of ground elevation, canopy height, and other forest parameters. However, these measurements may have uncertainties influenced by topographic factors. This study focuses on the calibration of GEDI L2A and L1B data using an airborne LiDAR point cloud, and the combination of Sentinel-2 multispectral imagery, 1D convolutional neural network (CNN), artificial neural network (ANN), and random forest (RF) for upscaling estimated forest height in the Guangxi Gaofeng Forest Farm. First, various environmental (i.e., slope, solar elevation, etc.) and acquisition parameters (i.e., beam type, Solar elevation, etc.) were used to select and optimize the L2A footprint. Second, pseudo-waveforms were simulated from the airborne LiDAR point cloud and were combined with a 1D CNN model to calibrate the L1B waveform data. Third, the forest height extracted from the calibrated L1B waveforms and selected L2A footprints were compared and assessed, utilizing the CHM derived from the airborne LiDAR point cloud. Finally, the forest height data with higher accuracy were combined with Sentinel-2 multispectral imagery for an upscaling estimation of forest height. The results indicate that through optimization using environmental and acquisition parameters, the ground elevation and forest canopy height extracted from the L2A footprint are generally consistent with airborne LiDAR data (ground elevation: R2 = 0.99, RMSE = 4.99 m; canopy height: R2 = 0.42, RMSE = 5.16 m). Through optimizing, ground elevation extraction error was reduced by 45.5% (RMSE), and the canopy height extraction error was reduced by 30.3% (RMSE). After training a 1D CNN model to calibrate the forest height, the forest height information extracted using L1B has a high accuracy (R2 = 0.84, RMSE = 3.13 m). Compared to the optimized L2A data, the RMSE was reduced by 2.03 m. Combining the more accurate L1B forest height data with Sentinel-2 multispectral imagery and using RF and ANN for the upscaled estimation of the forest height, the RF model has the highest accuracy (R2 = 0.64, RMSE = 4.59 m). The results show that the extrapolation and inversion of GEDI, combined with multispectral remote sensing data, serve as effective tools for obtaining forest height distribution on a large scale.

Over the past 4 decades, Southwest China has the fast vegetation growth and aboveground biomass carbon (AGC) accumulation, largely attributed to the active implementation of ecological projects. However, Southwest China has been threatened by frequent extreme drought events recently, potentially countering the expected large AGC increase caused by the ecological projects. Here, we used the L-band vegetation optical depth to quantify the AGC dynamics over Southwest China during the period 2013-2021. Our results showed a net AGC sink of 0.064 [0.057, 0.077] Pg C year−1 (the range represents the maximum and minimum AGC values), suggesting that Southwest China acted as an AGC sink over the study period. Note that the AGC loss of 0.113 [0.101, 0.136] Pg C year−1 was found during 2013-2014, which could mainly be attributed to the negative influence of extreme droughts on AGC changes in Southwest China, particularly in the Yunnan province. For each land use type (i.e., dense forests, persistent forests, nonforests, afforestation, and forestry), the largest AGC stock increase of 0.032 [0.028, 0.036] Pg C year−1 was found in nonforests, owing to their widespread land cover rate over Southwest China. For AGC density (i.e., AGC per unit area), the afforestation areas showed the largest AGC density increase of 0.808 [0.724, 0.985] Mg C ha−1 year−1, reflecting the positive effect of afforestation on AGC increase. Moreover, the karst areas exhibited a higher increasing rate of AGC density than nonkarst areas, suggesting that the karst ecosystems have a high carbon sink capacity over Southwest China.

The Coronavirus Disease 2019 (COVID-19) pandemic has now persisted globally for four years, resulting in a staggering death toll of over 4 million individuals. The COVID-19 vaccine has emerged as a highly effective tool in controlling the spread of this virus. However, as the number of individuals receiving COVID-19. In this context, the investigation of adverse reactions related to COVID-19 vaccines holds paramount importance in relevant research. The purpose is to evaluate the current research status regarding adverse reactions associated with COVID-19 vaccines, offering insights for future research. A total of 3,746 articles were included in this analysis, and there has been a notable upward trajectory in the volume of published articles. The CiteSpace v6.1.R6, VOSviewer, SCImago Graphica, and Excel 2019 were employed to analyze and visualize the results. The institutions, countries, journals, authors, co-cited references, and keywords of these articles were analyzed. Furthermore, this study delves into the characteristics of articles on adverse reactions associated with COVID-19 vaccines. It was observed that the number of studies on COVID-19 vaccines has increased year by year since 2019 and witnessed a surge in output in 2021. The vast majority of studies have affirmed the overall safety of COVID-19 vaccines, with adverse reactions tending to be more concentrated in specific diseases. These findings provide valuable ideas for future research in this field and suggest the importance of strengthening international cooperation on adverse reactions to COVID-19 vaccines.

AbstractBackground & Objective The systemic immune-inflammation indicator (SII) has been extensively employed in various diseases for course change, treatment efficacy, or prediction, whereas whether it applies to iron overload or iron deficiency remains unclear. This study aimed at investigating the correlation between SII and serum ferritin in people aged over 20 in the US.Methods The measurements of the systemic immune-inflammation indicator (SII = platelet count × neutrophil-to-lymphocyte ratio) and serum ferritin of 5491 participants in the NHANES database served as the independent and dependent variables for the present cross-sectional study, respectively. Moreover, the correlation was investigated through and used multiple linear regression, smooth curve fitting, and threshold effect.Results After rigorous inclusion and exclusion of 19,225 participants, a grand total of 5,491 participants conforming to the requirements were covered for relevant analysis. SII showed a significant negative correlation with serum ferritin in unregulated ([β=-0.05,p

Abstract Recent years have witnessed significant progress in quantum communication and quantum internet with the emerging quantum photonic chips, whose characteristics of scalability, stability, and low cost, flourish and open up new possibilities in miniaturized footprints. Here, we provide an overview of the advances in quantum photonic chips for quantum communication, beginning with a summary of the prevalent photonic integrated fabrication platforms and key components for integrated quantum communication systems. We then discuss a range of quantum communication applications, such as quantum key distribution and quantum teleportation. Finally, the review culminates with a perspective on challenges towards high-performance chip-based quantum communication, as well as a glimpse into future opportunities for integrated quantum networks.

Accurate forest structural parameters (such as forest height and canopy cover) support forest carbon monitoring, sustainable forest management, and the implementation of silvicultural practices. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is a spaceborne Light Detection and Ranging (LiDAR) satellite, offers significant potential for acquiring precise and extensive information on forest structural parameters. However, the ICESat-2 ATL08 product is significantly influenced by the geographical environment and forest characteristics, maintaining considerable potential for enhancing the accuracy of forest height estimation. Meanwhile, it does not focus on providing canopy cover data. To acquire accurate forest structural parameters, the Terrain Signal Neural Network (TSNN) framework was proposed, integrating Computer Vision (CV), Ordering Points to Identify the Clustering Structure (OPTICS), and deep learning. It encompassed an advanced approach for detecting terrain vegetation signals and constructing deep learning models for estimating forest structural parameters using ICESat-2 ATL03 raw data. First, the ATL03 footprints were visualized as Profile Raster Images of Footprints (PRIF), implementing image binarization through adaptive thresholding and median filtering denoising to detect the terrain. Second, the rough denoising buffers were created based on the terrain, combining with the OPTICS clustering and Gaussian denoising algorithms to recognize the terrain vegetation signal footprints. Finally, deep learning models (convolutional neural network (CNN), ResNet50, and EfficientNetB3) were constructed, training standardized PRIF to estimate forest structural parameters (including forest height and canopy cover). The results indicated that the TSNN achieved high accuracy in terrain detection (coefficient of determination (R2) = 0.97) and terrain vegetation signal recognition (F-score = 0.72). The EfficientNetB3 model achieved the highest accuracy in forest height estimation (R2 = 0.88, relative Root Mean Squared Error (rRMSE) = 13.5%), while the CNN model achieved the highest accuracy in canopy cover estimation (R2 = 0.80, rRMSE = 18.5%). Our results have significantly enhanced the accuracy of acquiring ICESat-2 forest structural parameters, while also proposing an original approach combining CV and deep learning for utilizing spaceborne LiDAR data.

The living vegetation volume (LVV) can accurately describe the spatial structure of greening trees and quantitatively represent the relationship between this greening and its environment. Because of the mostly line shape distribution and the complex species of street trees, as well as interference from artificial objects, current LVV survey methods are normally limited in their efficiency and accuracy. In this study, we propose an improved methodology based on vehicle-mounted LiDAR data to estimate the LVV of urban street trees. First, a point-cloud-based CSP (comparative shortest-path) algorithm was used to segment the individual tree point clouds, and an artificial objects and low shrubs identification algorithm was developed to extract the street trees. Second, a DBSCAN (density-based spatial clustering of applications with noise) algorithm was utilized to remove the branch point clouds, and a bottom-up slicing method combined with the random sampling consistency iterative method algorithm (RANSAC) was employed to calculate the diameters of the tree trunks and obtain the canopy by comparing the variation in trunk diameters in the vertical direction. Finally, an envelope was fitted to the canopy point cloud using the adaptive AlphaShape algorithm to calculate the LVVs and their ecological benefits (e.g., O2 production and CO2 absorption). The results show that the CSP algorithm had a relatively high overall accuracy in segmenting individual trees (overall accuracy = 95.8%). The accuracies of the tree height and DBH extraction based on vehicle-mounted LiDAR point clouds were 1.66~3.92% (rRMSE) and 4.23~15.37% (rRMSE), respectively. For the plots on Zijin Mountain, the LVV contribution by the maple poplar was the highest (1049.667 m3), followed by the sycamore tree species (557.907 m3), and privet’s was the lowest (16.681 m3).

BCL-xL and BCL-2 are validated therapeutic targets in small-cell lung cancer (SCLC). Targeting these proteins with navitoclax (formerly ABT263, a dual BCL-xL/2 inhibitor) induces dose-limiting thrombocytopenia through on-target BCL-xL inhibition in platelets. Therefore, platelet toxicity poses a barrier in advancing the clinical translation of navitoclax. We have developed a strategy to selectively target BCL-xL in tumors, while sparing platelets, by utilizing proteolysis-targeting chimeras (PROTACs) that hijack the cellular ubiquitin proteasome system for target ubiquitination and subsequent degradation. In our previous study, the first-in-class BCL-xL PROTAC, called DT2216, was shown to have synergistic antitumor activities when combined with venetoclax (formerly ABT199, BCL-2-selective inhibitor) in a BCL-xL/2 co-dependent SCLC cell line, NCI-H146 (hereafter referred to as H146), in vitro and in a xenograft model. Guided by these findings, we evaluated our newly developed BCL-xL/2 dual degrader, called 753b, in three BCL-xL/2 co-dependent SCLC cell lines and the H146 xenograft models. 753b was found to degrade both BCL-xL and BCL-2 in these cell lines. Importantly, it was considerably more potent than DT2216, navitoclax, or DT2216 + venetoclax in reducing the viability of BCL-xL/2 co-dependent SCLC cell lines in cell culture. In vivo, 5 mg/kg weekly dosing of 753b was found to lead to significant tumor growth delay, similar to the DT2216 + venetoclax combination in H146 xenografts, by degrading both BCL-xL and BCL-2. Additionally, 753b administration at 5 mg/kg every four days induced tumor regressions. At this dosage, 753b was well tolerated in mice, without observable induction of severe thrombocytopenia as seen with navitoclax, and no evidence of significant changes in mouse body weights. These results suggest that the BCL-xL/2 dual degrader could be an effective and safe therapeutic for a subset of SCLC patients, warranting clinical trials in future.

Crowd movement analysis (CMA) is a key technology in the field of public safety. This technology provides reference for identifying potential hazards in public places by analyzing crowd aggregation and dispersion behavior. Traditional video processing techniques are susceptible to factors such as environmental lighting and depth of field when analyzing crowd movements, so cannot accurately locate the source of events. Radar, on the other hand, offers all-weather distance and angle measurements, effectively compensating for the shortcomings of video surveillance. This paper proposes a crowd motion analysis method based on radar particle flow (RPF). Firstly, radar particle flow is extracted from adjacent frames of millimeter-wave radar point sets by utilizing the optical flow method. Then, a new concept of micro-source is defined to describe whether any two RPF vectors originated from or reach the same location. Finally, in each local area, the internal micro-sources are counted to form a local diffusion potential, which characterizes the movement state of the crowd. The proposed algorithm is validated in real scenarios. By analyzing and processing radar data on aggregation, dispersion, and normal movements, the algorithm is able to effectively identify these movements with an accuracy rate of no less than 88%.

Mapping woody vegetation from aerial images is an important task bluein environment monitoring and management. A few studies have shown that semantic segmentation methods involving deep learning achieve significantly better performance in mapping than methods involving field-based measurement and handcrafted features. However, current deep networks used for mapping vegetation require labour-intensive pixel-level annotations. Thus, this paper proposes the use of image-level annotations and a weakly supervised semantic segmentation (WSSS) network for mapping woody vegetation based on Unmanned Aerial Vehicle (UAV) imagery. The network comprises a Localization Branch (LB) and an Attention Relocation Branch (ARB). The LB is trained in stage 1 of the mapping to identify regions with the most discriminative vegetation, while the ARB is introduced to better mine semantic information, which enhances the ability of the class activation maps (CAMs) to represent useful information. The ARB inherits the weights from the LB in stage 2 and uses a Multi-layer Attention Refocus Structure (MARS) into the network to expand the receptive field to enable the model to process global features. Thus, same-category regions that are located farther apart are better captured. Finally, the region focused by the dual branches are integrated to more accurately cover the areas to be segmented. Using UAV imagery datasets, namely UOPNOA and MiniFrance, along with quantitative metrics and qualitative results, the network demonstrates performance better than existing state-of-the-art related methods. The effectiveness and generalization of each module of the network are validated by ablation experiments. The code for implementing the network will be accessible on https://github.com/Mr-catc/DWSLNet.

CONTEXT: Closure rate of endovenous thermal ablation is limited by the trunk diameter of great saphenous vein. AIMS: This study aimed to investigate the safety and efficacy of transluminal injection of foam sclerotherapy (TLFS) after thermal ablation in improving the closure rate of primary varicose veins with large trunk. SUBJECTS AND METHODS: In this retrospective cohort study with 170 patients (189 legs), the outcomes of varicose veins patients treated with thermal ablation plus injection of sclerotherapy were compared with those treated with thermal ablation only. The primary outcome of this study was the rate of ablated segment closure after surgery and during patient follow-up. Propensity score matching model was used to eliminate the bias. RESULTS: After matching, 75 legs (65 patients) from the thermal ablation plus sclerotherapy group and 71 legs (66 patients) from the thermal ablation only group were further analyzed. The closure rate of the thermal ablation plus sclerotherapy group was 97.3%, which was significantly higher than that of the thermal ablation only group (87.3%, P = 0.048), and the closure rate of the thermal ablation plus sclerotherapy group remained higher during 1-year follow-up. No serious complications were observed in groups. CONCLUSIONS: TLFS can improve the efficacy of thermal ablation, increasing the closure rate of the ablation segment, and thus improving the symptoms.

The aquaculture of marine ranching is of great significance for scientific aquaculture and the practice of statistically grasping existing information on the types of living marine resources and their density. However, underwater environments are complex, and there are many small and overlapping targets for marine organisms, which seriously affects the performance of detectors. To overcome these issues, we attempted to improve the YOLOv8 detector. The InceptionNeXt block was used in the backbone to enhance the feature extraction capabilities of the network. Subsequently, a separate and enhanced attention module (SEAM) was added to the neck to enhance the detection of overlapping targets. Moreover, the normalized Wasserstein distance (NWD) loss was proportionally added to the original CIoU loss to improve the detection of small targets. Data augmentation methods were used to improve the dataset during training to enhance the robustness of the network. The experimental results showed that the improved YOLOv8 achieved the mAP of 84.5%, which was an improvement over the original YOLOv8 of approximately 6.2%. Meanwhile, there were no significant increases in the numbers of parameters and computations. This detector can be applied on platforms for seafloor observation experiments in the field of marine ranching to complete the task of real-time detection of marine organisms.

Although COVID-19 vaccines are an effective public health tool to combat the global pandemic, serious adverse events, such as hemophagocytic lymphohistiocytosis (HLH), caused by them are a concern. In this systematic review, cases of HLH reported after COVID-19 vaccination have been examined to understand the relationship between the two and propose effective therapeutic strategies. Furthermore, ruxolitinib’s potential as a cytokine inhibitor and its affinity for CD25 were initially assessed through molecular docking, aiming to aid targeted HLH therapy. PubMed and Web of Science databases were searched for published individual case reports on the occurrence of HLH after the administration of any COVID-19 vaccine. A total of 17 articles (25 patients) were included in this qualitative analysis. Furthermore, molecular docking was employed to investigate the therapeutic potential of ruxolitinib for HLH after COVID-19 vaccination. The mean age of patients who developed HLH after COVID-19 vaccination was 48.1 years. Most HLH episodes occurred after the BNT162b2 mRNA COVID-19 vaccination (14/25 cases) and to an extent after the ChAdOx1 nCov‐19 vaccination (5/25 cases). Almost all affected patients received steroid and antibiotic therapy. Three patients died despite treatment because of esophagus rupture, neutropenic fever, bacteroides bacteremia, refractory shock, and encephalopathy and shock. Visual docking results of IL-2 Rα and ruxolitinib using the Discovery Studio 2019 Client software yielded a model score of 119.879. The findings highlight the importance of considering and identifying the adverse effects of vaccination and the possibility of using ruxolitinib for treating HLH after COVID-19 vaccination.

Vogt-Koyanagi-Harada (VKH) disease is a rare and serious ocular adverse reaction following COVID-19 vaccination. This study aimed to evaluate the clinical features, diagnosis and management of COVID-19 vaccine-associated VKH disease. Case reports of VKH disease after COVID-19 vaccination were collected up to February 11, 2023 for retrospective analysis. Twenty-one patients (9 males and 12 females) were included, with a median age of 45 years (range 19–78), from three main regions, Asia (12/21), the Mediterranean region (4/21), and South America (5/21). Fourteen patients developed symptoms after the first dose of the vaccine, and 8 after the second dose. Vaccines included mRNA vaccine (10 cases), virus vector vaccine (6 cases), and inactivated vaccine (5 cases). The average time interval from vaccination to onset of symptoms was 7.5 days (range 12 hours to 4 weeks). All 21 patients experienced visual impairment after vaccination, with 20 cases involving both eyes. Sixteen patients showed symptoms of meningitis. Serous retinal detachment was observed in 16 patients, choroidal thickening was observed in 14, aqueous cell in 9, and subretinal fluid in 6. CSF pleocytosis was detected in 7 patients and skin symptoms were found in 3 patients. All patients received corticosteroid therapy, and 8 also received immunosuppressive agents. All patients recovered well, with a mean recovery time of 2 months. Early diagnosis and early treatment are crucial to the prognosis of patients with VKH after vaccination with COVID-19 vaccine. The risk of vaccination against COVID-19 in patients with a history of VKH disease should be evaluated clinically.

Abstract Salivary adenoid cystic carcinoma (SACC) with a unique MYB‐NFIB fusion has been considered an “immune‐cold” tumor, but the mechanisms behind this remain unclear. In this study, we analyzed the immune status of 29 SACC patients and found that most lung metastases exhibited an immunoinflammatory state, in contrast to the primary SACC tissues. Single‐cell sequencing data showed that anergic T‐cell types were low in primary inflammatory tissues, while inflammatory metastatic lung tissues had elevated levels of anergic CD8+ natural killer T (NKT)‐like cells and low levels of memory T cells. Primary exclusive tissues had high levels of myeloid‐derived suppressor cells (MDSCs) and low levels of activated CD8+ NKT‐like cells. These data support the fact that metastatic SACC cells might induce a stronger immune response in the lung. Additionally, an in vivo experiment showed that a minimally invasive SACC cell line with higher expression of human leukocyte antigens ‐B and ‐C induced NKT cell activation in mice and effectively attenuated the incidence of lung metastases caused by a highly invasive SACC cell line. This suggests that NKT therapy may be active in treating SACC lung metastasis. Conclusively, this study sheds light on the immune microenvironment of SACC and highlights the potential of NKT‐based therapy.

Abstract Chemoresistance in pancreatic cancer cells may be caused by the expansion of inherently resistant cancer cells or by the adaptive plasticity of initially sensitive cancer cells. We investigated how CD44 isoforms switching contributed to gemcitabine resistance. Treating CD44 null/low single-cell clones with increasing amounts of gemcitabine caused an increase in expression of CD44 and development of gemcitabine resistant (GR) cells. Drug sensitivity, invasiveness, and EMT process was evaluated by MTT, Matrigel invasion assays, and western blots. Genetic knockdown and pharmacological inhibitors were used to examine the roles of CD44 and IGF1R in mediating gemcitabine resistance. CD44 promoter activity and its interactive EMT-related transcription factors were evaluated by luciferase reporter assay and chromatin immunoprecipitation assay. Kaplan–Meier curve was created by log-rank test to reveal the clinical relevance of CD44 and IGF1R expression in patients. We found silence of CD44 in GR cells partially restored E-cadherin expression, reduced ZEB1 expression, and increased drug sensitivity. The gemcitabine-induced CD44 expressing and isoform switching were associated with an increase in nuclear accumulation of phosphor-cJun, Ets1, and Egr1 and binding of these transcription factors to the CD44 promoter. Gemcitabine treatment induced phosphorylation of IGF1R and increased the expression of phosphor-cJun, Ets1, and Egr1 within 72 h. Stimulation or suppression of IGF1R signaling or its downstream target promoted or blocked CD44 promoter activity. Clinically, patients whose tumors expressed high levels of CD44/IGF1R showed a poor prognosis. This study suggests that IGF1R-dependent CD44 isoform switching confers pancreatic cancer cells to undergo an adaptive change in response to gemcitabine and provides the basis for improved targeted therapy of pancreatic cancer.

Abstract Background Abnormal glycosylation in a variety of cancer types is involved in tumor progression and chemoresistance. Glycosyltransferase C1GALT1, the key enzyme in conversion of Tn antigen to T antigen, is involved in both physiological and pathological conditions. However, the mechanisms of C1GALT1 in enhancing oncogenic phenotypes and its regulatory effects via non-coding RNA are unclear. Methods Abnormal expression of C1GALT1 and its products T antigen in human bladder cancer (BLCA) were evaluated with BLCA tissue, plasma samples and cell lines. Effects of C1GALT1 on migratory ability and proliferation were assessed in YTS-1 cells by transwell, CCK8 and colony formation assay in vitro and by mouse subcutaneous xenograft and trans-splenic metastasis models in vivo. Dysregulated circular RNAs (circRNAs) and microRNAs (miRNAs) were profiled in 3 pairs of bladder cancer tissues by RNA-seq. Effects of miR-1-3p and cHP1BP3 (circRNA derived from HP1BP3) on modulating C1GALT1 expression were investigated by target prediction program, correlation analysis and luciferase reporter assay. Functional roles of miR-1-3p and cHP1BP3 on migratory ability and proliferation in BLCA were also investigated by in vitro and in vivo experiments. Additionally, glycoproteomic analysis was employed to identify the target glycoproteins of C1GALT1. Results In this study, we demonstrated upregulation of C1GALT1 and its product T antigen in BLCA. C1GALT1 silencing suppressed migratory ability and proliferation of BLCA YTS-1 cells in vitro and in vivo. Subsets of circRNAs and miRNAs were dysregulated in BLCA tissues. miR-1-3p, which is reduced in BLCA tissues, inhibited transcription of C1GALT1 by binding directly to its 3′-untranslated region (3′-UTR). miR-1-3p overexpression resulted in decreased migratory ability and proliferation of YTS-1 cells. cHP1BP3 was upregulated in BLCA tissues, and served as an miR-1-3p “sponge”. cHP1BP3 was shown to modulate migratory ability, proliferation, and colony formation of YTS-1 cells, and displayed tumor-suppressing activity in BLCA. Target glycoproteins of C1GALT1, including integrins and MUC16, were identified. Conclusions This study reveals the pro-metastatic and proliferative function of upregulated glycosyltransferase C1GLAT1, and provides preliminary data on mechanisms underlying dysregulation of C1GALT1 via miR-1-3p / cHP1BP3 axis in BLCA.

Abstract Background Exposure to sunlight may decrease the risk of developing Alzheimer’s disease (AD), and visible and near infrared light have been proposed as a possible therapeutic strategy for AD. Here, we investigated the effects of the visible, near infrared and far infrared (FIR) light on the cognitive ability of AD mice, and found that FIR light also showed potential in the improvement of cognitive dysfunction in AD. However, the related mechanism remains to be elucidated. Methods Morris water maze was used to evaluate the cognitive ability of APPswe/PSEN1dE9 double-transgenic AD mice after light treatment. Western blot was carried out to detect the expression of protein involved in synaptic function and amyloid-β (Aβ) production. The protein amount of interleukin (IL)-1β, IL-6, Aβ1-40 and Aβ1-42 were determined using enzyme-linked immunosorbent assay. The mRNA level of receptors was performed using real-time quantitative polymerase chain reaction. Immunostaining was performed to characterize the Aβ burden and microglial Aβ phagocytosis in the brain of AD mice. The Aβ phagocytosis of primary cultured microglia and BV2 were assessed by flow cytometry. The energy metabolism changes were evaluated using related assay kits, including adenosine triphosphate (ATP), lactate content, mitochondrial respiratory chain complex enzymatic activity and oxidized/reduced nicotinamide adenine dinucleotide assay kits. Results Our results showed that FIR light reduced Aβ burden, a hallmark of AD neuropathology, alleviated neuroinflammation, restored the expression of the presynaptic protein synaptophysin, and ameliorated learning and memory impairment in the AD mice. FIR light enhanced mitochondrial oxidative phosphorylation pathway to increase ATP production. This increased intracellular ATP promoted the extracellular ATP release from microglia stimulated by Aβ, leading to the enhanced Aβ phagocytosis through phosphoinositide 3-kinase/mammalian target of rapamycin pathways for Aβ clearance. Conclusions Our findings have uncovered a previously unappreciated function of FIR light in inducing microglial phagocytosis to clean Aβ, which may be the mechanisms for FIR light to improve cognitive dysfunction in AD mice. These results suggest that FIR light treatment is a potential therapeutic strategy for AD.

In recent years, sketch face recognition has a wide application in law enforcement agencies and criminals. Deep learning plays a crucial role in the recent developments of face recognition, however, it is challenging to employ deep learning methods for sketch face recognition due to insufficient face photo–sketch data. Moreover, compared to photos, sketches lack detailed texture, and there exists a domain gap between photos and sketches, hence, traditional homogeneous face recognition methods perform poorly in sketch face recognition. In this paper, a novel deep learning method termed Domain Adaptation Scaled Entropy Meta-Network (DASEMN) is proposed to tackle sketch face recognition tasks. Specifically, a meta-learning training strategy is designed to tackle the few-shot problem and improve the generalization ability of the network. Then, a generalized entropy loss termed scaled mean entropy loss is proposed to guide the network to extract discriminate features. Finally, a domain adaptation module is introduced in the training set to reduce the domain gap between the sketch domain and the photo domain. Experiments on UoM-SGFS and CUFSF sketch face databases show that the proposed method is superior to other sketch face recognition methods.

This paper proposes a fast direction of arrival (DOA) estimation method based on positive incremental modified Cholesky decomposition atomic norm minimization (PI-CANM) for augmented coprime array sensors. The approach incorporates coprime sampling on the augmented array to generate a non-uniform, discontinuous virtual array. It then utilizes interpolation to convert this into a uniform, continuous virtual array. Based on this, the problem of DOA estimation is equivalently formulated as a gridless optimization problem, which is solved via atomic norm minimization to reconstruct a Hermitian Toeplitz covariance matrix. Furthermore, by positive incremental modified Cholesky decomposition, the covariance matrix is transformed from positive semi-definite to positive definite, which simplifies the constraint of optimization problem and reduces the complexity of the solution. Finally, the Multiple Signal Classification method is utilized to carry out statistical signal processing on the reconstructed covariance matrix, yielding initial DOA angle estimates. Experimental outcomes highlight that the PI-CANM algorithm surpasses other algorithms in estimation accuracy, demonstrating stability in difficult circumstances such as low signal-to-noise ratios and limited snapshots. Additionally, it boasts an impressive computational speed. This method enhances both the accuracy and computational efficiency of DOA estimation, showing potential for broad applicability.

The present experiment was conducted to investigate the effects of Lactobacillus plantarum postbiotics on growth performance, immune status, and intestinal microflora of growing minks. A total of 80 minks (40 males and 40 females) were divided into four groups, each group contained 20 minks (10 males and 10 females). The minks in the four groups were fed a basal diet supplemented with 0, 0.15%, 0.3%, and 0.45% Lactobacillus plantarum postbiotics (PLP), respectively. After one week of adaptation, the experiment ran for eight weeks. The results showed that Lactobacillus plantarum postbiotics tended to have effects on average daily again (ADG) during the first 4 wk of the study (p < 0.1), and had effects on immune status (p < 0.05). Lactobacillus plantarum postbiotics also affected the abundance of intestinal bacteria at genus level (p < 0.05), but had no effects on α diversity of growing minks (p > 0.05). Compared to the minks in the control group, minks in 0.30% PLP group tended to have greater ADG, and IgA and IgM content in serum as well as SIgA content in jejunal mucosa (p < 0.05), and had less jejunal mucosal TNF-α and IL-8 levels, while minks in 0.45% PLP group had less IL-2 (p < 0.05). Compared to the control, Lactobacillus plantarum postbiotics decreased the relative abundances of Bacteroides_vulgatus and Luteimonas_sp. in male minks, and the relative abundances of Streptococcus_halotolerans in female minks (p < 0.05), respectively. Males grew faster and ate more associated with less F/G than females (p < 0.05). Males also had greater serum IgA and IgG content (p < 0.05), and males had less jejunal mucosal IL-1β, IL-8, IL-2, IL-6, IL-12, IL-10, TNF-α, and IFN-γ levels (p < 0.05). These results suggest that dietary supplementation of 0.3% postbiotics harvested from Lactobacillus plantarum could improve growth performance and immune status, and modulated the intestinal bacteria abundance of growing minks.

Abstract Cells cultivated in bioreactors offer many possibilities for the production of novel and nutritious food products. Scientific and technological advances in cellular agriculture and processing technologies have allowed for the development of new techniques to utilize in vitro animal cells, plant cells, and microorganisms to mimic the organoleptic and nutritional properties of traditional foods as well as to potentially develop entirely new product classes. This review compiles and discusses the state‐of‐the‐art cellular production and processing systems including 3D printing of customizable cell‐cultivated food products. In addition to the technological state‐of‐the art, this article reviews the nutritional characteristics of cell‐cultivated foods, introduces examples of new food products, and compiles economic characteristics and environmental impacts of each production system as assessed through technoeconomic analyses and lifecycle assessments. The factors influencing consumer acceptance of cell‐cultivated foods are articulated and the potential implications of these new technologies on traditional agricultural industries and food chains are discussed. Lastly, future research and development trajectories are introduced with suggestions for continued development.

Sulfolipid-1 (SL-1) is located in the Mycobacterium tuberculosis (M. tb) cell wall, and is essential for pathogen virulence and intracellular growth. Multiple proteins (e.g., Pks2, FadD23, PapA1, and MmpL8) in the SL-1 synthesis pathway can be treated as drug targets, but, to date, their structures have not been solved. The crystal structures of FadD23 bound to ATP or hexadecanoyl adenylate was determined in this study. We have also investigated long-chain saturated fatty acids as biological substrates of FadD23 through structural, biological, and chemical analyses. The mutation at the active site of FadD23 greatly influences enzymatic activity. Meanwhile, the FadD23 N-terminal domain alone cannot bind palmitic acid without C-terminal domain facilitation since it is almost inactive after removing the C-terminal domain. FadD23 is the first protein in the SL-1 synthesis pathway whose structure has been solved. These results reveal the importance of the C-terminal domain in the catalytic mechanism.

With the concept of sustainable management of plantations, individual trees with excellent characteristics in plantations have received attention from breeders. To improve and maintain long-term productivity, accurate and high-throughput access to phenotypic characteristics is essential when establishing breeding strategies. Meanwhile, genetic diversity is also an important issue that must be considered, especially for plantations without seed source information. This study was carried out in a ginkgo timber plantation. We used simple sequence repeat (SSR) markers for genetic background analysis and high-density terrestrial laser scanning for growth structural characteristic extraction, aiming to provide a possibility of applying remote sensing approaches for forest breeding. First, we analyzed the genetic diversity and population structure, and grouped individual trees according to the genetic distance. Then, the growth structural characteristics (height, diameter at breast height, crown width, crown area, crown volume, height to living crown, trunk volume, biomass of all components) were extracted. Finally, individual trees in each group were comprehensively evaluated and the best-performing ones were selected. Results illustrate that terrestrial laser scanning (TLS) point cloud data can provide nondestructive estimates of the growth structural characteristics at fine scale. From the ginkgo plantation containing high genetic diversity (average polymorphism information content index was 0.719) and high variation in growth structural characteristics (coefficient of variation ranged from 21.822% to 85.477%), 11 excellent individual trees with superior growth were determined. Our study guides the scientific management of plantations and also provides a potential for applying remote sensing technologies to accelerate forest breeding.

The steerable needle becomes appealing in the neurosurgery intervention procedure because of its flexibility to bypass critical regions inside the brain; with proper path planning, it can also minimize the potential damage by setting constraints and optimizing the insertion path. Recently, reinforcement learning (RL)-based path planning algorithm has shown promising results in neurosurgery, but because of the trial and error mechanism, it can be computationally expensive and insecure with low training efficiency. In this paper, we propose a heuristically accelerated deep Q network (DQN) algorithm to safely preoperatively plan a needle insertion path in a neurosurgical environment. Furthermore, a fuzzy inference system is integrated into the framework as a balance of the heuristic policy and the RL algorithm. Simulations are conducted to test the proposed method in comparison to the traditional greedy heuristic searching algorithm and DQN algorithms. Tests showed promising results of our algorithm in saving over 50 training episodes, calculating path lengths of 0.35 after normalization, which is 0.61 and 0.39 for DQN and traditional greedy heuristic searching algorithm, respectively. Moreover, the maximum curvature during planning is reduced to 0.046 from 0.139 mm−1 using the proposed algorithm compared to DQN.

The diameter at breast height (DBH) of trees and stands is not only a widely used plant functional trait in ecology and biodiversity but also one of the most fundamental measurements in managing forests. However, systematically measuring the DBH of individual trees over large areas using conventional ground-based approaches is labour-intensive and costly. Here, we present an improved area-based approach to estimate plot-level tree DBH from airborne LiDAR data using the relationship between tree height and DBH, which is widely available for most forest types and many individual tree species. We first determined optimal functional forms for modelling height-DBH relationships using field-measured tree height and DBH. Then we estimated plot-level mean DBH by inverting the height-DBH relationships using the tree height predicted by LiDAR. Finally, we compared the predictive performance of our approach with a classical area-based method of DBH. The results showed that our approach significantly improved the prediction accuracy of tree DBH (R2 ​= ​0.85–0.90, rRMSE ​= ​9.57%–11.26%) compared to the classical area-based approach (R2 ​= ​0.80–0.83, rRMSE ​= ​11.98%–14.97%). Our study demonstrates the potential of using height-DBH relationships to improve the estimation of the plot-level DBH from airborne LiDAR data.

Abstract Background Stimulator of IFN genes (STING) is highly expressed in the livers of non-alcoholic fatty liver disease (NAFLD) patients and high fat diet (HFD) induced NAFLD mice model. The STING signaling-mediated inflammation has been shown to play a critical role in metabolic disorders. Lingguizhugan decoction (LGZG), a Traditional Chinese herbal decoction, has been applied to treat metabolic disorders for many years. However, whether LGZG can alleviate the progression of NAFLD through inhibiting inflammation remains unclear. This study was to determine the role of STING-mediated inflammation in the HFD-induced hepatic-lipid deposition treated with LGZG. Methods The anti-inflammatory and anti-steatotic effects of LGZG in vivo were detected by H&E staining, immunofluorescence and immuno-chemistry. Mice bone-marrow-derived macrophages (BMDMs) and primary liver macrophages were treated with STING-specific agonist (DMXAA), LGZG and its critical components respectively. The treated culture supernatant of BMDMs and primary liver macrophages from each group was co-cultured with palmitic acid-treated mouse primary hepatocytes or mouse liver cell line AML-12 respectively to detect whether the activation of STING-mediated pathway is involved in the anti-steatotic effect of LGZG. The hepatocyte lipid deposition in vivo and in vitro were detected by oil red staining. Mitochondrial DNA release of mouse liver extracts were detected by real time PCR. The expression of proteins and inflammatory cytokines related to STING-TBK1-NF-κB pathway was detected by western blotting and ELISA. Results LGZG significantly ameliorated HFD induced hepatic steatosis, oxidative stress, hepatic mitochondrial damage and mitochondrial DNA release, which was correlated with reduction of the expression level of STING as well as the infiltration of STING-positive macrophages in the livers of HFD fed mice. The critical components of LGZG directly inhibited the activation of STING-TBK1-NF-κB pathway in liver macrophages induced by DMXAA, LPS, thereby reducing the release of IFNβ and TNFα. Co-incubating the culture supernatant of LGZG treated liver macrophages and PA-stimulated hepatocytes significantly inhibited the PA-induced lipid deposition. Conclusion This study demonstrates that LGZG can ameliorate HFD-induced hepatic-lipid deposition through inhibiting STING-TBK1-NF-κB pathway in liver macrophages, which provides novel insight for elucidating the molecular mechanism of LGZG alleviating HFD induced hepatic steatosis.

Due to green development in recent years, water-borne epoxy resins (WBE) have become increasingly popular since they generate the lowest level of volatile organic compounds (VOC) during curing. However, because of the large surface tension of water, it is easy to produce voids and cracks during the curing process of the coating. An electrochemical strategy was used in this study to assess the impact of different SiO2 content on the corrosion performance of a WBE coating, in which micron spherical SiO2 particles were synthesized in a liquid phase reduction. The results showed that the synthesized micron spherical SiO2 particles were about 800 ± 50 nm in diameter and in an amorphous state. By hydrophilizing the surfaces of these SiO2 particles, uniform dispersion in an aqueous solvent and a WBE can be achieved. It is important to note that adding a small or excessive amount of SiO2 to a coating will not improve corrosion resistance and may even reduce corrosion resistance. With the appropriate modification of SiO2, corrosion resistance of composite coatings is greatly enhanced, as is the adhesion between the coatings and the metallic substrates. Because the appropriately modified SiO2 can effectively fill the pores that are formed during the curing process, a corrosive medium is less likely to react with the matrix when the medium comes into contact with the matrix. Based on their incorporation content of 3 wt.%, their corrosion resistance is the best after 16 cycles of AC-DC-AC accelerated corrosion tests.

Lower limb-powered exoskeletons can help rehabilitate patients with lower limb disabilities. However, the changes in the biomechanical load on the human body when exoskeletons are used are still poorly understood. The goal of this study was to investigate the changes in kinematic and biomechanical parameters of the lower extremity exoskeleton when worn by normal subjects and patients with unilateral motor impairment using a virtual prototype. The effect of wearing the exoskeleton on gait was derived, and the basis for exoskeleton optimization was given. Virtual prototyping is a cost-effective method to validate the performance of exoskeleton robots. Therefore, two models, a human-exoskeleton model and an asymmetric movement disorder (SSP) subject-exoskeleton model, were developed in AnyBody software for this study. The human-exoskeleton model was driven by the kinematic data of 20 healthy participants walking in an exoskeleton at normal speed (3.6 km/h). As a comparison, the SSP subject-exoskeleton model was driven by data from five SSP subjects walking in an exoskeleton. The experimental results show that after wearing the lower limb exoskeleton, the walking gait satisfies the normal human gait characteristics, but some of the muscle forces suddenly increase. The maximum activation level does not exceed 1, which means that the exoskeleton does not cause muscle damage or fatigue in a short period of time. In both models, the vertical ground reaction force (GRF) Z correlation was the strongest (R > 0.90). The center of pressure (COP) X trajectory correlation was the weakest (R < 0.35). These findings will support the study of the effects of exoskeletal optimization. Also, some gait characteristics of exoskeletons worn by patients with unilateral dyskinesia can be initially explored.

As an important technology in 3D vision, point-cloud registration has broad development prospects in the fields of space-based remote sensing, photogrammetry, robotics, and so on. Of the available algorithms, the Iterative Closest Point (ICP) algorithm has been used as the classic algorithm for solving point cloud registration. However, with the point cloud data being under the influence of noise, outliers, overlapping values, and other issues, the performance of the ICP algorithm will be affected to varying degrees. This paper proposes a global structure and adaptive weight aware ICP algorithm (GSAW-ICP) for image registration. Specifically, we first proposed a global structure mathematical model based on the reconstruction of local surfaces using both the rotation of normal vectors and the change in curvature, so as to better describe the deformation of the object. The model was optimized for the convergence strategy, so that it had a wider convergence domain and a better convergence effect than either of the original point-to-point or point-to-point constrained models. Secondly, for outliers and overlapping values, the GSAW-ICP algorithm was able to assign appropriate weights, so as to optimize both the noise and outlier interference of the overall system. Our proposed algorithm was extensively tested on noisy, anomalous, and real datasets, and the proposed method was proven to have a better performance than other state-of-the-art algorithms.

Abstract Background In the existing application of beam-matched multiroom proton therapy system, the model based on the commissioning data from the leading treatment room was used as the shared model. The purpose of this study is to investigate the ability of independently-tuned room-specific beam models of beam-matched gantries to reproduce the agreement between gantries’ performance when considering the errors introduced by the modeling process. Methods Raw measurements of two gantries’ dosimetric characteristics were quantitatively compared to ensure their agreement after initially beam-matched. Two gantries’ beam model parameters, as well as the model-based computed dosimetric characteristics, were analyzed to study the introduced errors and gantries’ post-modeling consistency. We forced two gantries to share the same beam model. The model-sharing patient-specific quality assurance (QA) tasks were retrospectively performed with 36 cancer patients to study the clinical impact of beam model discrepancies. Results Intra-gantry comparisons demonstrate that the modeling process introduced the errors to a certain extent indeed, which made the model-based reproduced results deviate from the raw measurements. Among them, the deviation introduced to the IDD curves was generally larger than that to the beam spots during modeling. Cross-gantry comparisons show that, from the beam model perspective, the introduced deviations deteriorated the high agreement of the dosimetric characteristics originally shown between two beam-matched gantries, but the cross-gantry discrepancy was still within the clinically acceptable tolerance. In model-sharing patient-specific QA, for the particular gantry, the beam model usage for intensity-modulated proton therapy (IMPT) QA plan generation had no significant effect on the actual delivering performance. All reached a high level of 95.0% passing rate with a 3 mm/3% criterion. Conclusions It was preliminary recognized that among beam-matched gantries, the independently-tuned room-specific beam model from any gantry is reasonable to be chosen as the shared beam model without affecting the treatment efficacy.

The rapid growth of human civilizations has led to imminent pressures to develop new food products with increased nutritional characteristics and decreased environmental footprints. Filamentous fungi, a class of microorganisms that have been utilized in a wide variety of foods for thousands of years, have recently garnered widespread attention in research communities and commercial ventures seeking to explore new and innovative applications in a diverse array of food products including, but not limited to, their more established applications as alternative proteins. Technological advances in the cultivation and processing of filamentous fungi have created new frontiers in the control of textures, flavors, and nutritional properties of fungi-based foods. This review highlights technological advances in the production of fungi-based foods from cultivation to product manufacturing, presents the current state of the art in fungi-based food products, and offers thoughts on their future trajectories. Emphasis is given to circular bioprocessing concepts for the sustainable utilization of agricultural and food processing byproducts.

Abstract Seed predators have the potential to act as agents of natural selection that influence seed traits and seed fates, which in turn affect the whole plant population dynamic. Accordingly, plants deploy a variety of mechanisms (e.g., resistance and tolerance strategies) to lessen the impact of predation on seed crop or on an individual seed. In this study, we described a novel mechanism, seed cloning strategy, in a tropical plant species in countering animal predation. By conducting field‐ and laboratory‐based germination experiments, we found that both rodent damaged and artificially damaged seed fragments of a large‐seeded tree Garcinia xanthochymus (Clusiaceae) could successfully germinate and establish as seedlings. Tissue culture experiments revealed that G. xanthochymus has no endosperm in seeds, and its seed fragments own strong capacity of differentiation and cloning. Seed damage negatively affected seedling growth and germination, but the seed germination rate was remarkably high. Our study suggests that, seed cloning capacity, adopted by the large‐seeded tree G. xanthochymus may act as a novel strategy counteract for seed predation and would play a significant role in stabilizing the mutualism between plant and animals.