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Factory recirculating aquaculture system (RAS) is facing in a stage of continuous research and technological innovation. Intelligent aquaculture is an important direction for the future development of aquaculture. However, the RAS nowdays still has poor self-learning and optimal decision-making capabilities, which leads to high aquaculture cost and low running efficiency. In this paper, a precise aeration strategy based on deep learning is designed for improving the healthy growth of breeding objects. Firstly, the situation perception driven by computer vision is used to detect the hypoxia behavior. Then combined with the biological energy model, it is constructed to calculate the breeding objects oxygen consumption. Finally, the optimal adaptive aeration strategy is generated according to hypoxia behavior judgement and biological energy model. Experimental results show that the energy consumption of proposed precise aeration strategy decreased by 26.3% compared with the manual control and 12.8% compared with the threshold control. Meanwhile, stable water quality conditions accelerated breeding objects growth, and the breeding cycle with the average weight of 400 g was shortened from 5 to 6 months to 3–4 months.

Acute lymphoblastic leukemia (ALL) is a malignant disease of the hematologic system. The current treatment is based on chemotherapeutic drugs, which are becoming less effective due to drug resistance. TNF-related apoptosis-inducing ligand (TRAIL) is an apoptotic protein used to treat cancer that does not affect healthy cells. In recent years, however, ALL cells (e.g., U937) have become more resistant to TRAIL. A novel andrographolide derivative (AND7) with high efficiency and low toxicity was synthesized and combined with TRAIL after the optimal combination ratio was screened using U937 cells. We used peripheral blood mononuclear cells (PBMCs) from patients before the initial treatment of ALL as a model and PBMCs from healthy subjects as a control to determine the mechanism underlying ALL treatment. AND7/TRAIL combination treatment was shown to prevent the original TRAIL-resistant cells from activating the caspase-8/caspase-3 pathway through DR4/DR5 and promote apoptosis via expression of ROS and the apoptotic gene, P53, to achieve an anti-cancer effect. Notably, this study demonstrated that the AND7/TRAIL combination enhanced the anti-cancer effect of AND7 and improved TRAIL resistance. Therefore, the AND7/TRAIL combination is promising for treating ALL and lays the foundation for clinical research.

Abstract Global riverine nitrous oxide (N2O) emissions have increased more than 4-fold in the last century. It has been estimated that the hyporheic zones in small streams alone may contribute approximately 85% of these N2O emissions. However, the mechanisms and pathways controlling hyporheic N2O production in stream ecosystems remain unknown. Here, we report that ammonia-derived pathways, rather than the nitrate-derived pathways, are the dominant hyporheic N2O sources (69.6 ± 2.1%) in agricultural streams around the world. The N2O fluxes are mainly in positive correlation with ammonia. The potential N2O metabolic pathways of metagenome-assembled genomes (MAGs) provides evidence that nitrifying bacteria contain greater abundances of N2O production-related genes than denitrifying bacteria. Taken together, this study highlights the importance of mitigating agriculturally derived ammonium in low-order agricultural streams in controlling N2O emissions. Global models of riverine ecosystems need to better represent ammonia-derived pathways for accurately estimating and predicting riverine N2O emissions.

Glioma is a kind of brain disease with high incidence, high recurrence rate, high mortality, and low cure rate. To obtain accurate diagnosis results of brain glioma, doctors need to manually compare the imaging results of different modalities many times, which will increase the diagnosis time and reduce the diagnostic efficiency. Image fusion technology has been widely used in recent years to obtain information on multimodal medical images. This paper proposes a novel image fusion framework, target information enhanced image fusion network (TIEF), using cross-modal learning and information enhancement techniques. The framework consists of a multi-sequence feature extraction block, a feature selection block, and a fusion block. The multi-sequence feature extraction block consists of multiple sobel dense conv leaky ReLu block (SDCL-block). SDCL-block mainly realizes the extraction of edge features, shallow features, and deep features. The feature selection block identifies the feature channels with rich texture information and strong discrimination ability through the effective combination of global information entropy criterion and feature jump connection. The feature fusion block mainly comprises multi-head and spatial attention mechanisms, which can realize the fusion of intra-modality and inter-modality features. On this basis, considering the influence of tumor spatial location and structure information on the fusion results, a loss function is designed, which is a weighted combination of texture loss, structure loss, and saliency loss so that texture information from multimodal magnetic resonance imaging (MMRI) and saliency information from different anatomical structures of the brain can be fused at the same time to improve the expression ability of features. In this paper, the TIEF algorithm is trained and validated on the MMRI and (Single-Photon Emission Computed Tomography-MRI) SPECT-MRI datasets of glioma and generalized on the (Computed Tomography-MRI) CT-MRI dataset of meningioma to verify the performance of the TIEF algorithm. In the image fusion task, quantitative results showed that TIEF exhibited optimal or suboptimal performance in information entropy, spatial frequency, and average gradient metrics. Qualitative results indicate that the fused images can highlight tumor and edematous features. A downstream image segmentation task was used for evaluation to further verify TIEF’s effectiveness. TIEF achieved the best results in both (Dice similarity coefficient) Dice and (Hausdorff distance 95%) HD95 segmentation metrics. In the generalization task, quantitative results indicated that TIEF obtained more information in the meningioma dataset. In conclusion, TIEF can effectively achieve cross-domain information acquisition and fusion and has robustness and generalization ability.

Abstract Post-consumer plastic waste in the environment has driven the scientific community to develop deconstruction methods that yield valued substances from these synthetic macromolecules. Electrocatalysis is a well-established method for achieving challenging transformations in small molecule synthesis. Here we present the first electro-chemical depolymerization of polyoxymethylene—a highly crystalline engineering thermoplastic (Delrin®)—into its repolymerizable monomer, formaldehyde/1,3,5-trioxane, under ambient conditions. We investigate this electrochemical deconstruction by employing solvent screening, cyclic voltammetry, divided cell studies, electrolysis with redox mediators, small molecule model studies, and control experiments. Our findings determine that the reaction proceeds via a heterogeneous electro-mediated acid depolymerization mechanism. The bifunctional role of the co-solvent 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP) is also revealed. This study demonstrates the potential of electromediated depolymerization serving as an important role in sustainable chemistry by merging the concepts of renewable energy and circular plastic economy.

Arhopalus rusticus is a significant forestry pest known for its destructive impact on various host plants. This species, commonly found in coniferous forests across the Northern Hemisphere, has successfully spread to regions like New Zealand, Australia, and South America. This research is based on the known distribution sites of A. rusticus. Projections are made for the potential global distribution of A. rusticus under historical climatic conditions (1970–2000) and future climatic conditions (2081–2100) for the four forcing scenarios of the Coupled Model International Comparison Program 6 (CMIP6). The aim was to analyze the effects of climate change on the distribution range of this pest and its invasion trend in the southern hemisphere, and to support relevant departments in enhancing the effectiveness of forestry pest control strategies. The study utilized the Biomod2 software package in R to compare six models: generalized linear models (GLMs), generalized additive models (GAMs), multivariate adaptive regression splines (MARSs), artificial neural networks (ANNs), classification and regression trees (CTAs), and random forests (RFs) for modeling species distributions. The optimal model was selected based on evaluation indexes such as AUC and TSS. Projections of A. rusticus distribution under historical and future climate scenarios were created. The prediction results were visualized using ArcGIS software (version 10.2) to classify fitness levels and calculate distribution areas. Based on evaluation metrics, random forests (RFs) demonstrated the highest average assessment index scores, indicating high prediction accuracy (AUC = 0.99, TSS = 0.91, Kappa = 0.93). Model predictions revealed that, under historical climatic conditions, A. rusticus was predominantly found in northern Europe, eastern Asia, eastern and southwestern coastal regions of North America, and there were also highly suitable regions in parts of the southern hemisphere, including central and southwestern Argentina, southern Australia, New Zealand, and South Africa. Among these models, each of the CMIP6’s different climate prediction scenarios had a significant impact on the predicted distribution of A. rusticus. The SSP126 scenario depicted the broadest range of suitability, while the SSP585 scenario presented the narrowest and, overall, the extent of highly suitable regions was contracting. Multi-model predictions suggested that the potential distribution area of A. rusticus during the period of 2081–2100 would likely expand compared to that of 1970–2000, ranging from an increase of 1.13% (SSP126) up to 6.61% (SSP585), positively correlating with the level of radiative forcing. Notably, the most substantial growth was observed in potentially low-suitability region, escalating from 1.17% (SSP126) to 5.55% (SSP585). The distribution of A. rusticus shows decreasing trends from coastal areas to inland areas and from high to low level suitability of regions, and further expansion into the southern hemisphere under future climate conditions. Therefore, quarantine efforts at ports of entry should be strengthened in areas that are not currently infested but are at risk of invasion, and precise preventive measures should be strengthened in areas that are at risk of further expansion under future climatic conditions to prevent its spread to inland areas.

The Global Investment Report 2023 revealed that after a sharp decline in 2020 and a strong rebound in 2021, global foreign direct investment (FDI) declined by 12 percent to $1.3 trillion in 2022. However, in developing countries, FDI increased by 4% to $916 billion, a record share of more than 70% of global flows. The number of greenfield investment projects in developing countries increased by 37 percent and international project finance transactions by 5 percent. Foreign investment from China, the second largest recipient of foreign investment globally, increased by 5 percent. The service industry has become the mainstream industry in the global FDI structure. The global industry is accelerating its transformation to a "service-based economy," international FDI in productive service industries has become an essential means of industrial transfer in developed countries and a meaningful way to upgrade the industrial structure and high-quality development in emerging economies. As a representative province in central China, Hubei Province has unique advantages in human capital, factor cost, and market potential, which provide preferential conditions to attract foreign investment. This paper first introduced the concept of the productive service industry, based on the relevant statistical data from 2011 to 2022, focused on the current situation of foreign investment utilization in five major sub-sectors of the productive service industry in Hubei Province in the past ten years, and empirically investigated the impact of foreign investment utilization in five major sub-sectors of the productive service industry on the economic growth of Hubei Province, and obtained that the level of foreign investment attraction varied significantly among the regions in Hubei Province. The three productive service industries, namely transportation, storage and postal services, information transmission, software and information technology services, and financial services, played a significant role in the active attraction and optimal utilization of foreign capital and the economic development of Hubei Province. Based on this, it was proposed to build a market-oriented rule of law and internationalized business environment, improve the infrastructure construction in different regions of the province, focus on the training of professional talents for the development of productive service industries, and pay attention to the improvement of independent innovation capacity.

Magnetic microrobots are ideal for medical applications owing to their deep tissue penetration, precise control, and flexible movement. After decades of development, various magnetic microrobots have been used to achieve medical functions such as targeted delivery, cell manipulation, and minimally invasive surgery. This review introduces the research status and latest progress in the design and control systems of magnetic medical microrobots from a system integration perspective and summarizes the advantages and limitations of the research to provide a reference for developers. Finally, the future development direction of magnetic medical microrobot design and control systems are discussed.

Tripartite motif (TRIM) family is assigned to RING-finger-containing ligases harboring the largest number of proteins in E3 ubiquitin ligating enzymes. E3 ubiquitin ligases target the specific substrate for proteasomal degradation via the ubiquitin–proteasome system (UPS), which seems to be a more effective and direct strategy for tumor therapy. Recent advances have demonstrated that TRIM genes associate with the occurrence and progression of hepatocellular carcinoma (HCC). TRIMs trigger or inhibit multiple biological activities like proliferation, apoptosis, metastasis, ferroptosis and autophagy in HCC dependent on its highly conserved yet diverse structures. Remarkably, autophagy is another proteolytic pathway for intracellular protein degradation and TRIM proteins may help to delineate the interaction between the two proteolytic systems. In depth research on the precise molecular mechanisms of TRIM family will allow for targeting TRIM in HCC treatment. We also highlight several potential directions warranted further development associated with TRIM family to provide bright insight into its translational values in hepatocellular carcinoma.

Abstract The effects and underlying mechanisms of gastrodin treatment on hypertensive vascular dysfunction and proliferation of vascular smooth muscle cells (VSMCs) were determined in vitro and in vivo. Using a pharmacological target network interaction analysis, 151 common targets and a PPI network were identified containing the top 10 hub genes. Kyoto encyclopedia of genes and genomes (KEGG) analysis identified the PI3K/AKT pathway as a significantly enriched pathway. Both spontaneous hypertensive rats (SHRs) and Wistar Kyoto rats were used to assess the therapeutic effects of gastrodin on hypertension. Gastrodin treatment of the SHRs resulted in a marked attenuation of elevated blood pressure, pulse wave velocity, and pathological changes in the abdominal aorta. Moreover, gastrodin treatment significantly inhibited cell growth and downregulated the expression of PCNA as well as the p-PI3K/PI3K and p-AKT/AKT levels in angiotensin II-stimulated VSMCs. Taken together, gastrodin treatment attenuates blood pressure elevation, vascular dysfunction, and proliferation of VSMCs and inhibits the activation of the PI3K/AKT pathway.

Abstract The goal of sound event detection and localization (SELD) is to identify each individual sound event class and its activity time from a piece of audio, while estimating its spatial location at the time of activity. Conformer combines the advantages of convolutional layers and Transformer, which is effective in tasks such as speech recognition. However, it achieves high performance relying on complex network structure and a large number of computations. In the SELD task of this paper, we propose to use an encoder with a simpler network structure, called the dual-branch attention module (DBAM). The module is improved based on the conformer using two parallel branches of attention and convolution, which can model both global and local contextual information. We also blend low-level and high-level features of the localization task. In addition, we add soft parameter sharing to the joint SELD network, which can efficiently exploit the potential relationship between the two subtasks, SED and DOA. The proposed method can effectively detect two sound events with overlapping occurrence in the same time period. We experimented with the open dataset DCASE 2020 task 3 proving that the proposed method achieves better SELD performance than the baseline model. Furthermore, we conducted ablation experiments for verifying the effectiveness of the dual-branch attention module and soft parameter sharing.

Abstract All the time, pancreatic cancer is a problem worldwide because of its high degree of malignancy and increased mortality. Neural network model analysis is an efficient and accurate machine learning method that can quickly and accurately predict disease feature genes. The aim of our research was to build a neural network model that would help screen out feature genes for pancreatic cancer diagnosis and prediction of prognosis. Our study confirmed that the neural network model is a reliable way to predict feature genes of pancreatic cancer, and immune cells infiltrating play an essential role in the development of pancreatic cancer, especially neutrophils. ANO1, AHNAK2, and ADAM9 were eventually identified as feature genes of pancreatic cancer, helping to diagnose and predict prognosis. Neural network model analysis provides us with a new idea for finding new intervention targets for pancreatic cancer.

Aquatic vegetation is an important component of aquatic ecosystems; therefore, the classification and mapping of aquatic vegetation is an important aspect of lake management. Currently, the decision tree (DT) classification method based on spectral indices has been widely used in the extraction of aquatic vegetation data, but the disadvantage of this method is that it is difficult to fix the threshold value, which, in turn, affects the automatic classification effect. In this study, Sentinel-2 MSI data were used to produce a sample set (about 930 samples) of aquatic vegetation in four inland lakes (Lake Taihu, Lake Caohai, Lake Honghu, and Lake Dongtinghu) using the visual interpretation method, including emergent, floating-leaved, and submerged vegetation. Based on this sample set, a DL model (Res-U-Net) was used to train an automatic aquatic vegetation extraction model. The DL model achieved a higher overall accuracy, relevant error, and kappa coefficient (90%, 8.18%, and 0.86, respectively) compared to the DT method (79%, 23.07%, and 0.77) and random forest (78%,10.62% and 0.77) when utilizing visual interpretation results as the ground truth. When utilizing measured point data as the ground truth, the DL model exhibited accuracies of 59%, 78%, and 91% for submerged, floating-leaved, and emergent vegetation, respectively. In addition, the model still maintains good recognition in the presence of clouds with the influence of water bloom. When applying the model to Lake Honghu from January 2017 to October 2023, the obtained temporal variation patterns in the aquatic vegetation were consistent with other studies. The study in this paper shows that the proposed DL model has good application potential for extracting aquatic vegetation data.

Cladophora qinghaiensis, an endemic species of Cladophora in saltwater lakes, was scientifically named in 2021 (hereafter referred to as Cladophora). Cladophora exists in different morphologies, including attached submerged Cladophora (AC), grown floating Cladophora (GFC), and death floating Cladophora (DFC). Previous satellite remote sensing has mainly focused on identifying floating algae. In this study, Qinghai Lake served as a case study, and a classification decision tree model (CDTM) was proposed. The model employed the chlorophyll spectral index (CSI) and the normalized difference vegetation index (NDVI) to differentiate AC, Floating Cladophora (FC), and water. Additionally, the floating Cladophora index (FCI) was introduced to further distinguish GFC and DFC within FC. The method was applicable to Sentinel-2 images from 2016–2023. Visual interpretation methods were used for Landsat series images from the summer months (July to September) to obtain the AC and FC. The results demonstrate that over the past 30 years, the areas inhabited by AC and FC have increased gradually. The three morphologies of Cladophora also exhibited seasonal variations, with growth observed annually in May–June, reaching peaks in August–September, and gradually declining in October. In addition, by combining factors such as water surface area and climatic factors, we analyzed the driving forces influencing the changes in Cladophora. In this research, AC and FC showed significant correlations with the water surface area, with correlation coefficients (r) of approximately 0.9 and 0.7, respectively. These new findings provide valuable insights regarding the spatiotemporal changes and underlying causes for different morphologies of Cladophora in global saline lakes.

Chronic obstructive pulmonary disease (COPD) is one of the most common chronic diseases in the elderly population and is characterized by persistent respiratory symptoms and airflow obstruction. During COPD progression, a variety of pulmonary and extrapulmonary complications develop, with sarcopenia being one of the most common extrapulmonary complications. Factors that contribute to the pathogenesis of coexisting COPD and sarcopenia include systemic inflammation, hypoxia, hypercapnia, oxidative stress, protein metabolic imbalance, and myocyte mitochondrial dysfunction. These factors, individually or in concert, affect muscle function, resulting in decreased muscle mass and strength. The occurrence of sarcopenia severely affects the quality of life of patients with COPD, resulting in increased readmission rates, longer hospital admission, and higher mortality. In recent years, studies have found that oral supplementation with protein, micronutrients, fat, or a combination of nutritional supplements can improve the muscle strength and physical performance of these patients; some studies have also elucidated the possible underlying mechanisms. This review aimed to elucidate the role of nutrition among patients with coexisting COPD and sarcopenia.

Deep vein thrombosis (DVT) is a common complication in hematologic malignancies and immunologic disorders. Endothelial cell injury and dysfunction comprise the critical contributor for the development of DVT. A disintegrin and metalloproteinase with thrombospondin motifs 13 (ADAMTS13), a plasma metalloprotease that cleaves von Willebrand factor, acts as a critical regulator in normal hemostasis. This study was aimed to explore the role of ADAMTS13 in endothelial cell injury during DVT and the possible mechanism. First, human umbilical vein endothelial cells (HUVECs) were exposed to hydrogen peroxide (H2O2). Then, the mRNA and protein expressions of ADAMTS13 were evaluated with the reverse transcription-quantitative polymerase chain reaction and western blot. After treatment with recombinant ADAMTS13 (rADAMTS13; rA13), the viability and apoptosis of H2O2-induced HUVECs were assessed by cell counting kit-8 assay and terminal-deoxynucleoitidyl transferase-mediated nick end labeling staining. In addition, the levels of prostaglandin F1-alpha, endothelin-1, and reactive oxygen species were detected using the enzyme-linked immunosorbent assay and dichloro-dihydro-fluorescein diacetate assay. The expressions of proteins related to p38/extracellular signal-regulated kinase (ERK) signaling pathway were estimated with the western blot. Then, p79350 (p38 agonist) was used to pretreat cells to analyze the regulatory effects of rA13 on p38/ERK signaling in H2O2-induced HUVEC injury. The results revealed that ADAMTS13 expression was significantly downregulated in H2O2-induced HUVECs. The reduced viability and increased apoptosis of HUVECs induced by H2O2 were revived by ADAMTS13. ADAMTS13 also suppressed the oxidative stress in HUVECs after H2O2 treatment. Besides, ADAMTS13 was found to block p38/ERK signaling pathway, and p79350 reversed the impacts of ADAMTS13 on the damage of HUVECs induced by H2O2. To sum up, ADAMTS13 could alleviate H2O2-induced HUVEC injury through the inhibition of p38/ERK signaling pathway.

A 3D manipulation technique based on two optothermally generated and actuated surface-bubble robots is proposed. A single laser beam can be divided into two parallel beams and used for the generation and motion control of twin bubbles. The movement and spacing control of the lasers and bubbles can be varied directly and rapidly. Both 2D and 3D operations of micromodules were carried out successfully using twin bubble robots. The cooperative manipulation of twin bubble robots is superior to that of a single robot in terms of stability, speed, and efficiency. The operational technique proposed in this study is expected to play an important role in tissue engineering, drug screening, and other fields.

The longicorn beetle Monochamus saltuarius (Coleoptera; Cerambycidae) is an insect vector that transmits the pine wood nematode Bursaphelenchus xylophilus, a serious issue in pine forests in China, Japan, and South Korea. Dastarcus helophoroides and Scleroderma guani are effective in controlling M. saltuarius. However, the application of these parasites for the direct control of vector insects requires detailed analyses of the suitability of the selected natural enemies in potential pest areas, particularly under climate change. Based on species distribution records as well as the current and projected climate change trends up to 2050, we utilized an optimized Maxent algorithm to predict the potential distribution patterns of M. saltuarius and its natural enemies in China. Furthermore, we estimated Schoener’s D to quantify the overlap in adaptability between M. saltuarius and its natural enemies. The potentially suitable areas predicted under the current climate conditions were concentrated mainly in northeast and north China. Additional suitable areas were detected in central, eastern, northwestern, and southwestern China. Under future climate conditions, the suitable range of M. saltuarius gradually shifted southward, with a tendency to expand into high-altitude and colder regions, such as Sichuan, Qinghai, and Tibet. The potential distribution of the natural enemies D. helophoroides and S. guani continued to expand under climate change. A comparison of the appropriate habitats of M. saltuarius and natural enemy insects indicated that D. helophoroides is more suitable for initial biological control in north, northeast, and central China as well as certain northwest and coastal regions. S. guani could be prioritized for nationwide release, while joint control using both species may be effective in Liaoning, Hebei, and Tianjin provinces. These analyses demonstrate that future climate change is likely to exert adverse effects on the potential distribution of M. saltuarius but is favorable for the potential distributions of natural enemies. This study offers important insights into the effective control of M. saltuarius and provides an experimental basis for the utilization of natural enemies according to the characteristics of different regions.

The European Space Agency (ESA) developed the Sentinel-2 Multispectral Imager (MSI), which offers a higher spatial resolution and shorter repeat coverage, making it an important source for the remote-sensing monitoring of water bodies. Atmospheric correction is crucial for the monitoring of water quality. To compare the applicability of seven publicly available atmospheric correction processors (ACOLITE, C2RCC, C2XC, iCOR, POLYMER, SeaDAS, and Sen2Cor), we chose complex and diverse lakes in Qinghai Province, China, as the research area. The lakes were divided into three types based on the waveform characteristics of Rrs: turbid water bodies (class I lakes) represented by the Dabusun Lake (DBX), clean water bodies (class II lakes) represented by the Qinghai Lake (QHH), and relatively clean water bodies (class III lakes) represented by the Longyangxia Reservoir (LYX). Compared with the in situ Rrs, it was found that for the DBX, the Sen2Cor processor performed best. The POLYMER processor exhibited a good performance in the QHH. The C2XC processor performed well with the LYX. Using the Sen2Cor, POLYMER, and C2XC processors for classes I, II, and III, respectively, compared with the Sentinel-3 OLCI Level-2 Water Full Resolution (L2-WFR) products, it was found that the estimated Rrs from the POLYMER had the highest consistency. Slight deviations were observed in the estimation results for both the Sen2Cor and C2XC.

The main application scenario for wearable sensors involves the generation of data and monitoring metrics. fNIRS (functional near-infrared spectroscopy) allows the nonintrusive monitoring of human visual perception. The quantification of visual perception by fNIRS facilitates applications in engineering-related fields. This study designed a set of experimental procedures to effectively induce visible alterations and to quantify visual perception in conjunction with the acquisition of Hbt (total hemoglobin), Hb (hemoglobin), and HbO2 (oxygenated hemoglobin) data obtained from HfNIRS (high-density functional near-infrared spectroscopy). Volunteers completed the visual task separately in response to different visible changes in the simulated scene. HfNIRS recorded the changes in Hbt, Hb, and HbO2 during the study, the time point of the visual difference, and the time point of the task change. This study consisted of one simulated scene, two visual variations, and four visual tasks. The simulation scene featured a car driving location. The visible change suggested that the brightness and saturation of the car operator interface would change. The visual task represented the completion of the layout, color, design, and information questions answered in response to the visible change. This study collected data from 29 volunteers. The volunteers completed the visual task separately in response to different visual changes in the same simulated scene. HfNIRS recorded the changes in Hbt, Hb, and HbO2 during the study, the time point of the visible difference, and the time point of the task change. The data analysis methods in this study comprised a combination of channel dimensionality reduction, feature extraction, task classification, and score correlation. Channel downscaling: This study used the data of 15 channels in HfNIRS to calculate the mutual information between different channels to set a threshold, and to retain the data of the channels that were higher than those of the mutual information. Feature extraction: The statistics derived from the visual task, including time, mean, median, variance, extreme variance, kurtosis, bias, information entropy, and approximate entropy were computed. Task classification: This study used the KNN (K-Nearest Neighbors) algorithm to classify different visual tasks and to calculate the accuracy, precision, recall, and F1 scores. Scoring correlation: This study matched the visual task scores with the fluctuations of Hbt, Hb, and HbO2 and observed the changes in Hbt, Hb, and HbO2 under different scoring levels. Mutual information was used to downscale the channels, and seven channels were retained for analysis under each visual task. The average accuracy was 96.3% ± 1.99%; the samples that correctly classified the visual task accounted for 96.3% of the total; and the classification accuracy was high. By analyzing the correlation between the scores on different visual tasks and the fluctuations of Hbt, Hb, and HbO2, it was found that the higher the score, the more obvious, significant, and higher the fluctuations of Hbt, Hb, and HbO2. Experiments found that changes in visual perception triggered changes in Hbt, Hb, and HbO2. HfNIRS combined with Hbt, Hb, and HbO2 recorded by machine learning algorithms can effectively quantify visual perception. However, the related research in this paper still needs to be further refined, and the mathematical relationship between HfNIRS and visual perception needs to be further explored to realize the quantitative study of subjective and objective visual perception supported by the mathematical relationship.

Vast amounts of monitoring data can be obtained through various optical sensors, and mask detection based on deep learning integrates neural science into a variety of applications in everyday life. However, mask detection poses technical challenges such as small targets, complex scenes, and occlusions, which necessitate high accuracy and robustness in multi-scene target detection networks. Considering that multi-scale features can increase the receptive field and attention mechanism can improve the detection effect of small targets, we propose the YOLO-MSM network based on the multi-scale residual (MSR) block, multi-scale residual cascaded channel-spatial attention (MSR-CCSA) block, enhanced residual CCSA (ER-CCSA) block, and enhanced residual PCSA (ER-PCSA) block. Considering the performance and parameters, we use YOLOv5 as the baseline network. Firstly, for the MSR block, we construct hierarchical residual connections in the residual blocks to extract multi-scale features and obtain finer features. Secondly, to realize the joint attention function of channel and space, both the CCSA block and PCSA block are adopted. In addition, we construct a new dataset named Multi-Scene-Mask, which contains various scenes, crowd densities, and mask types. Experiments on the dataset show that YOLO-MSM achieves an average precision of 97.51%, showing better performance than other detection networks. Compared with the baseline network, the mAP value of YOLO-MSM is increased by 3.46%. Moreover, we propose a module generalization improvement strategy (GIS) by training YOLO-MSM on the dataset augmented with white Gaussian addition noise to improve the generalization ability of the network. The test results verify that GIS can greatly improve the generalization of the network and YOLO-MSM has stronger generalization ability than the baseline.

Biomass conversion holds promise as a more sustainable source of platform chemicals, but limitations in the ways in which lignin can be broken down is a current bottleneck. Here the authors report an electrocatalytic hydrogenolysis over skeletal Ni that cleaves diaryl ethers, chemically resistant moieties in both renewable carbon sources and persistent organic pollutants.

Abstract Design procedure of a novel transmission line low‐pass filter is presented. The filter has a flexible rejection response for a prescribed cut‐off frequency and an in‐band ripple factor. It is compact by using a Z‐shaped coupled‐line section, which is loaded with open‐circuited stubs. All of the transmission line elements are identical in electrical length, and the impedances can be calculated by solving certain specific equations according to prescribed in‐band specifications. The simulated results show that the circuit can generate low‐pass responses with third‐, fifth‐, and seventh‐order filtering functions. The filter order depends on the number and position of the loading stubs. For verification, two sample filters with cut‐off frequency of 0.5/0.3 GHz are fabricated using a three‐/five‐layer PCB process, which generate seventh‐order low‐pass filtering responses with good selectivity. The experimental results agree well with the predictions.

Polymers from biomass waste including plant/forest waste, biological industrial process waste, municipal solid waste, algae, and livestock are potential sources for renewable and sustainable resources. Converting biomass-derived polymers to functional biochar materials via pyrolysis is a mature and promising approach as these products can be widely utilized in many areas such as carbon sequestration, power production, environmental remediation, and energy storage. With abundant sources, low cost, and special features, the biochar derived from biological polymeric substances exhibits great potential to be an alternative electrode material of high-performance supercapacitors. To extend this scope of application, synthesis of high-quality biochar will be a key issue. This work systematically reviews the char formation mechanisms and technologies from polymeric substances in biomass waste and introduces energy storage mechanisms of supercapacitors to provide overall insight into the biological polymer-based char material for electrochemical energy storage. Aiming to enhance the capacitance of biochar-derived supercapacitor, recent progress in biochar modification approaches including surface activation, doping, and recombination is also summarized. This review can provide guidance for valorizing biomass waste to functional biochar materials for supercapacitor to meet future needs.

Seeking sensitive, large-scale, and low-cost substrates is highly important for practical applications of surface-enhanced Raman scattering (SERS) technology. Noble metallic plasmonic nanostructures with dense hot spots are considered an effective construction to enable sensitive, uniform, and stable SERS performance and thus have attracted wide attention in recent years. In this work, we reported a simple fabrication method to achieve wafer-scale ultradense tilted and staggered plasmonic metallic nanopillars filled with numerous nanogaps (hot spots). By adjusting the etching time of the PMMA (polymethyl methacrylate) layer, the optimal SERS substrate with the densest metallic nanopillars was obtained, which possessed a detection limit down to 10−13 M by using crystal violet as the detected molecules and exhibited excellent reproducibility and long-term stability. Furthermore, the proposed fabrication approach was further used to prepare flexible substrates; for example, a SERS flexible substrate was proven to be an ideal platform for analyzing low-concentration pesticide residues on curved fruit surfaces with significantly enhanced sensitivity. This type of SERS substrate possesses potential in real-life applications as low-cost and high-performance sensors.

Climate, human disturbances, and management practices jointly control the spatial and temporal patterns of land surface phenology. However, most studies solely focus on analyzing the climatic controls on the inter-annual variability and trends in vegetation phenology. Investigating the main and interacting effects of management practices and climate might be crucial in determining vegetation phenology and productivity. This study examined the impacts of climate and management practices on vegetation phenology and productivity in adjacent native and introduced prairie pastures, which have detailed long-term management records, by combining climate, management, and satellite remote sensing data (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat). Modeled gross primary production (GPP) using vegetation photosynthesis model (VPM) was also included to investigate the dynamics of productivity. When comparing the impacts of the same management practices on different pastures, we used paired comparison, namely, comparing the native and introduced prairies side by side in the same year. The interactions of management practices and climate were investigated through comparing years with similar management but different climate (e.g., years with rainfall or not following baling events) in the same pasture. Results showed that air temperature (Ta) was an important factor in determining the start of the season (SOS) and the length of the season (LOS). Total rainfall (RF) during the annual growing season (AGS, derived from vegetation indices (VIs)) had the largest explanatory power (R2 = 0.53) in explaining the variations in the seasonal sums of VIs. The variations in GPP were better explained by RF (R2 = 0.43) than Ta (R2 = 0.14). Using the thermal growing season (March–October) or AGS climate factors did not show large differences in determining the relationships between phenology, GPP, and climate factors. Drought shortened the LOS and decreased GPP. In terms of management practices, grazing generally reduced the VIs and burning induced early greening-up and enhanced vegetation growth. Drought plus other management practices (e.g., grazing or baling) greatly affected vegetation phenology and suppressed GPP. The negative impacts (i.e., removal of biomass) of grazing on vegetation was compensated by enhanced vegetation growth after good RF. This study demonstrated that the interactions of climate and management practices could be positive (burning plus baling in a good RF year) or negative (grazing/baling plus drought), and can significantly affect vegetation phenology and production.

Direct bioprinting in vivo has the potential to enable new clinical strategies but is currently limited by printing techniques. Here, the authors report on the development of a ferromagnetic soft catheter for magnetic computer controlled minimally invasive bioprinting and demonstrate the printing potential in vivo.

Modern urban integrated energy systems are usually large in scale and consist of several subsystems located in different areas with various types of users. The design and operation optimization of multi-area integrated energy systems (IES) faces challenges in integrating user engagement, operational independence of subsystems, and the combination of long-term objectives and operation optimization. To solve these problems, the present study proposes a supply-demand coordinated optimization method for multi-area IES to balance the long-term overall objectives with the independence of participants such as users and subsystems. Increasing overall benefits and ensuring fairness can be achieved by using the proposed methods. In the case study, considering long-term objectives, the carbon emissions of the system operation are reduced by 9.43% compared to the case without the long-term objectives. Meanwhile, the results show an approximately 25% reduction in the total cost and a 65% reduction in carbon emission, compared to the baseline. Moreover, the cost of different users decreases by 13%–17% from the baseline at the optimal agreed price. This optimization method provides a holistic framework for the design and operation, supply-demand coordination, and pricing of transactions for multi-area IES involving long-term planning and construction with multiple interests.

BackgroundPrevious studies have investigated the prognostic value of the systemic immune-inflammation index (SII) in small cell lung cancer (SCLC). However, the results have been inconsistent. The study aimed to investigate the prognostic and clinicopathological significance of SII in SCLC through a meta-analysis.MethodsThe PubMed, Web of Science, Embase, Cochrane Library, and China National Knowledge Infrastructure databases were thoroughly searched. The pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated to evaluate the prognostic value of the SII for survival outcomes. The combined odds ratios (ORs) and 95% CIs were used to evaluate the correlation between SII and clinicopathological features.ResultsEight studies comprising 2,267 patients were included in the meta-analysis. Pooled analyses indicated that a high SII was significantly associated with worse overall survival (OS) (HR=1.52, 95% CI=1.15–2.00, p=0.003) but not progression-free survival (HR=1.38, 95% CI=0.81–2.35, p=0.238) in patients with SCLC. Moreover, a high SII was associated with extensive-stage SCLC (OR=2.43, 95% CI=1.86–3.17, p

With increasing livestock production due to high demand for consumption, the planted area of green fodder, an essential livestock supplement, has grown rapidly and will continue to grow in China. However, the climate feedback of this rapid land cover conversion is still unclear. Using multisource data (e.g. remote sensing observation and meteorological data), we compared the land surface temperature of green fodder plantation areas and native grassland in the northeastern Tibetan Plateau. The green fodder area was detected to be cooler than the native grassland by −0.54 ± 0.98 °C in the daytime throughout the growing season. The highest magnitude (−1.20 ± 1.68 °C) of cooling was observed in August. A nonradiative process, indicated by the energy redistribution factor, dominated the cooling effects compared to the radiative process altered by albedo variation. The results indicate the potential cooling effects of increasing green fodder area on native grassland, highlighting the necessity of investigating climate feedback from anthropogenic land use change, including green fodder expansion.

Alfalfa (Medicago sativa L.), referred to as the “Queen of Forages” because of its importance among forage crops, provides high quality forage for the livestock industry. The timing and frequency of alfalfa hay harvesting have implications on its quality and quantity. With ever-increasing capability, it is possible to use satellite remote sensing data to monitor alfalfa harvests. This study investigated the potential of using satellite remote sensing to capture frequent harvesting events on an alfalfa field in central Oklahoma. Both passive remote sensing data, namely Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat-7 and -8, Sentinel-2, Harmonized Landsat and Sentinel-2 (HLS), and active remote sensing data, namely Sentinel-1, were included. Our results indicate that good quality optical remote sensing datasets (i.e., cloud and cloud shadow free) with both fine spatial (≤100 m) and high temporal (effective observation at 8-day intervals or better) resolutions are necessary to detect frequent alfalfa harvesting events, challenged by possible adverse weather conditions and quick regrowth of vegetation after harvest. Landsat (7 and 8) and Sentinel-2 were more sensitive to changes in vegetation indices after harvest than MODIS due to their higher spatial resolutions, which helped avoid the mixed pixel issue in MODIS caused by its coarser spatial resolution (∼500 m). Combining Landsat (7 and 8) with Sentinel-2 imageries through linear regression between the Normalized Difference Vegetation Index (NDVI) values, up to one week apart, increased the accuracy of detecting frequent alfalfa harvesting events. The responses of HLS to alfalfa harvesting events were similar with fused Landsat and Sentinel-2 data using their linear relationship of NDVI values. However, the high noise level in the HLS data needs to be minimized before it can be used to detect alfalfa harvests at the regional scale. In most cases, both Sentinel-1 radar backscatter coefficients (vertical transmit and vertical receive, VV + vertical transmit and horizontal receive, VH) and interferometric coherence from Sentinel-1 Simple Look Complex (SLC) data were decreased by harvesting events in small incident angle observations (34.31°). No consistent relationships existed between backscatter or coherence and alfalfa harvests in larger incident angle observations (45.11°). Future studies should focus on small incident angle observations instead of processing all of the radar data, which has big data volume and is time-consuming. Overall, active radar has the potential to detect alfalfa harvesting events. However, it is visually less intuitive than optical data with incident angles, quantity harvested, and soil moisture being the compounding factors. This study illustrates that combining multiple optical sensors with a fine spatial resolution (e.g., Landsat-7, 8, and Sentinel-2) and/or fusing radar with optical remote sensing to increase the temporal resolution are promising approaches to detect frequent alfalfa harvesting events and other hay harvesting activities.

Some scholars have suggested that the clinical application of exosomes derived from human umbilical cord mesenchymal stem cells (hucMSCs-exo) might represent a novel strategy to improve diabetic wound healing. However, the mechanisms underlying the effects of hucMSCs-exo on wound healing remain poorly understood. This study aimed to identify the mechanism of hucMSCs-exo in treating diabetic wounds. HucMSCs-exo were isolated from human umbilical cord mesenchymal stem cells (hucMSCs) and subcutaneously injected into full-thickness wounds in diabetic rats. Wound healing closure rates and histological analysis were performed. The levels of tumor necrosis factor-α (TNF-α), macrophage mannose receptor (MMR/CD206), platelet endothelial cell adhesion molecule-1 (PECAM-1/CD31), and vascular endothelial growth factor (VEGF) were detected by immunohistochemistry. The degree of collagen deposition was examined using Masson’s trichrome staining. Gross evaluation of wound healing was carried out from day 0 to 14 post-surgery, and the wound site was harvested for histology on days 3, 7, and 14 post-wounding. HucMSCs-exo transplantation increased diabetic wound healing. In vitro, hucMSCs-exo promoted the proliferation of human umbilical vein endothelial cells (HUVECs) and NIH-3T3 cells. In vivo, hucMSCs-exo reduced wound area and inflammatory infiltration and increased collagen fibers. In addition, wound tissues in the hucMSCs-exo group had higher CD206, CD31, and VEGF expressions and lower TNF-α levels than those in the control group on day 14. Our results demonstrated that hucMSCs-exo facilitated diabetic wound repair by inducing anti-inflammatory macrophages and promoting angiogenesis and collagen deposition.

Shape-memory polymers (SMPs) have gradually emerged in the mechanism and biomedical fields and facilitate the upgrading of industrial mechanisms and the breakthrough of technical bottlenecks. However, most of the SMPs are infeasible in harsh environments, such as aerospace, due to the low glass transition temperature. There are still some works that remain in creating truly portable or non-contacting actuators that can match the performances and functions of traditional metal structures. Polyether-ether-ketone (PEEK) with a high glass transition temperature of 143 °C is endowed with outstanding high-temperature resistance and radiation-resistant properties and shape memory behavior. Thus, we explore the shape-memory properties and actuation performances of high-temperature PEEK in bending behaviors. The shape-recovery ratio, actuation speed and force under different programming conditions and structure parameters are summarized to complete the actuation capacities. Meanwhile, a metallic ball transported by shape-memory PEEK and deployed drag sail with thermo-responsive composite joints were shown to verify the potential in aerospace.

The thermal-mechanical process (TMP) plays an important role in the industrial production of 7050 Al alloys for aircraft components. In this work, the microstructural evolution and influence on hardness of a 7050 alloy ingot, during the process from preheating, deformation and cooling to final heat treatment, have been investigated considering the effect of temperature and the post-deformation cooling path. The results showed that an increasing temperature and decreasing cooling rate during TMP can lead to enhanced hardness of the alloy after heat treatment. Moreover, the variation of recrystallization before and after heat treatment was strongly dependent on the cooling path after deformation. Finally, this study provided a general understanding on the relationship between microstructural evolution and harness of the 7050 alloy.

In recent years, microbubbles have been widely used in the field of microrobots due to their unique properties. Microbubbles can be easily produced and used as power sources or tools of microrobots, and the bubbles can even serve as microrobots themselves. As a power source, bubbles can propel microrobots to swim in liquid under low-Reynolds-number conditions. As a manipulation tool, microbubbles can act as the micromanipulators of microrobots, allowing them to operate upon particles, cells, and organisms. As a microrobot, microbubbles can operate and assemble complex microparts in two- or three-dimensional spaces. This review provides a comprehensive overview of bubble applications in microrobotics including propulsion, micromanipulation, and microassembly. First, we introduce the diverse bubble generation and control methods. Then, we review and discuss how bubbles can play a role in microrobotics via three functions: propulsion, manipulation, and assembly. Finally, by highlighting the advantages and current challenges of this progress, we discuss the prospects of microbubbles in microrobotics.

Lithium niobate (LN) is a promising optical material, its micro–nano structures have been applied to fields such as photonic crystals, nonlinear optics, optical waveguides, and so on. At present, lithium niobate structural colors are rarely studied. Although the nanograting structure was researched, it has such large full width at half-maximum (fwhm) that it cannot achieve red, green, or blue pixels or other high-saturation structural colors, thus, its color printing quality is poor. In this paper, we design and simulate lithium niobate nanodisk metasurface resonators (LNNDMRs), which are based on Mie magnetic dipole (MD) and electric dipole (ED) resonances. In addition, the resonators yield very narrow reflection peaks and high reflection efficiencies with over 80%, especially the reflection peaks of red, green, and blue pixels with fwhm around 11 nm, 9 nm, and 6 nm, respectively. Moreover, output colors of different array cells composed of single nanodisk in finite size are displayed, which provides a theoretical basis for their practical applications. Therefore, LNNDMRs pave the way for high-efficiency, compact photonic display devices based on lithium niobate.

Although litter can regulate the global climate by influencing soil N2O fluxes, there is no consensus on the major drivers or their relative importance and how these impact at the global scale. In this paper, we conducted a meta-analysis of 21 global studies to quantify the impact of litter removal and litter doubling on soil N2O fluxes from forests. Overall, our results showed that litter removal significantly reduced soil N2O fluxes (−19.0%), while a doubling of the amount of litter significantly increased soil N2O fluxes (30.3%), based on the results of a small number of studies. Litter removal decreased the N2O fluxes from tropical forest and temperate forest. The warmer the climate, the greater the soil acidity, and the larger the soil C:N ratio, the greater the impact on N2O emissions, which was particularly evident in tropical forest ecosystems. The decreases in soil N2O fluxes associated with litter removal were greater in acid soils (pH < 6.5) or soils with a C:N > 15. Litter removal decreased soil N2O fluxes from coniferous forests (−21.8%) and broad-leaved forests (−17.2%) but had no significant effect in mixed forests. Soil N2O fluxes were significantly reduced in experiments where the duration of litter removal was 2O fluxes due to changes in the ground litter vary with forest type and need to be considered when evaluating current and future greenhouse gas budgets.

Fibrinolytic enzymes are the most effective agents for the treatment of thrombotic diseases. In the present study, we purified and characterized an extracellular fibrinolytic serine metalloprotease (named Velefibrinase) that is produced by marine Bacillus velezensis Z01 and assessed its thrombolysis in vivo. SDS-PAGE and MALDI-TOF-MS analyses showed that the molecular mass of Velefibrinase was 32.3 KDa and belonged to the peptidase S8 family. The optimal fibrinolytic activity conditions of Velefibrinase were 40 °C and pH 7.0. Moreover, Velefibrinase exhibited high substrate specificity to fibrin, and a higher ratio of fibrinolytic/caseinolytic (1.48) values, which indicated that Velefibrinase had excellent fibrinolytic properties. Based on the degradation pattern of fibrin and fibrinogen, Velefibrinase could be classified as α/β-fibrinogenase. In vitro, Velefibrinase demonstrated efficient thrombolytic ability, anti-platelet aggregation, and amelioration of blood coagulation (APTT, PT, TT, and FIB), which were superior to those of commercial anticoagulant urokinase. Velefibrinase showed no hemolysis for erythrocyte in vitro and no hemorrhagic activity in vivo. Finally, Velefibrinase effectively prevented mouse tail thrombosis in a dose-dependent (0.22–0.88 mg/kg) manner. These findings suggested that Velefibrinase has the potential to becoming a new thrombolytic agent.

Chlorophyll-a concentrations in water bodies are one of the most important environmental evaluation indicators in monitoring the water environment. Small water bodies include headwater streams, springs, ditches, flushes, small lakes, and ponds, which represent important freshwater resources. However, the relatively narrow and fragmented nature of small water bodies makes it difficult to monitor chlorophyll-a via medium-resolution remote sensing. In the present study, we first fused Gaofen-6 (a new Chinese satellite) images to obtain 2 m resolution images with 8 bands, which was approved as a good data source for Chlorophyll-a monitoring in small water bodies as Sentinel-2. Further, we compared five semi-empirical and four machine learning models to estimate chlorophyll-a concentrations via simulated reflectance using fused Gaofen-6 and Sentinel-2 spectral response function. The results showed that the extreme gradient boosting tree model (one of the machine learning models) is the most accurate. The mean relative error (MRE) was 9.03%, and the root-mean-square error (RMSE) was 4.5 mg/m3 for the Sentinel-2 sensor, while for the fused Gaofen-6 image, MRE was 6.73%, and RMSE was 3.26 mg/m3. Thus, both fused Gaofen-6 and Sentinel-2 could estimate the chlorophyll-a concentrations in small water bodies. Since the fused Gaofen-6 exhibited a higher spatial resolution and Sentinel-2 exhibited a higher temporal resolution.