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Intercropping with legume forages is recognized as an effective strategy for enhancing nitrogen levels in agroforestry, while mowing may influence nitrogen fixation capacity and yield. This study investigated the rooting, nitrogen fixation, nutritive value, and yield of alfalfa (Medicago sativa L.) under intercropping and varying mowing frequencies (CK, 2, and 3) from 2021 to 2023, using walnut (Juglans regia L.) and alfalfa as experimental subjects. The results indicated that intercropping suppressed root growth, whereas increased mowing frequency stimulated root development in the topsoil (0–20 cm). Specifically, the average root length density, root surface area, and root volume from the twice- and thrice-mowed treatments increased by 18.26, 17.45, and 4.15%, respectively, in comparison to the control. The δ15N values of the intercropped alfalfa were significantly lower than those of the monocropped alfalfa (p < 0.05), with the δ15N values of the mowing-thrice treatment increasing by an average of 38.61% compared to the control. Intercropping suppressed alfalfa yield but did not affect the total nitrogen content in the leaves or the nutritive value, and all mowing treatments resulted in land equivalent ratios (LERs) greater than 1. Furthermore, increased mowing frequency enhanced both the nutritive value and yield of alfalfa. Our study suggests that intercropping with walnut can improve biological nitrogen fixation in alfalfa, and that adopting a mowing-thrice regime can optimize yield and nutritive value.

Liver cancer is one of the most prevalent cancers worldwide. The first-line therapeutic drug sorafenib offers only a moderate improvement in patients’ conditions. Therefore, an approach to enhancing its therapeutic efficacy is urgently needed. It has been revealed that hepatocellular carcinoma (HCC) cells with heightened intracellular quiescin sulfhydryl oxidase 1 (QSOX1) exhibit increased sensitivity to sorafenib. QSOX1 is a secreted disulfide catalyst, and it is widely recognized that extracellular QSOX1 promotes the growth, invasion, and metastasis of cancer cells through its participation in the establishment of extracellular matrix. Inhibiting QSOX1 secretion can increase intracellular QSOX1 and decrease extracellular QSOX1. Such an approach would sensitize HCC cells to sorafenib but remains to be established. Since (−)-epigallocatechin-3-gallate (EGCG) has been demonstrated to be an effective inhibitor of α-fetal protein secretion from HCC cells, we screened QSOX1 secretion inhibition using polyphenolic compounds. We examined eight dietary polyphenols (EGCG, quercetin, fisetin, myricetin, caffeic acid, chlorogenic acid, resveratrol, and theaflavin) and found that EGCG and quercetin effectively inhibited QSOX1 secretion from human HCC cells (HepG2 or Huh7), resulting in high intracellular QSOX1 and low extracellular QSOX1. The combination of EGCG or quercetin, both of which change the cellular distribution of QSOX1, with sorafenib, which has no influence on the cellular distribution of QSOX1, exhibited multiple synergistic effects against the HCC cells, including the induction of apoptosis and inhibition of invasion and metastasis. In conclusion, our current results suggest that dietary EGCG and quercetin have the potential to be developed as adjuvants to sorafenib in the treatment of HCC by modulating the cellular distribution of QSOX1.

Mixed forests often increase their stability and species richness in comparison to pure stands. However, a comprehensive understanding of the effects of mixed forests on soil properties, bacterial community diversity, and soil nitrogen cycling remains elusive. This study investigated soil samples from pure Robinia pseudoacacia stands, pure Quercus variabilis stands, and mixed stands of both species in the southern foothills of the Taihang Mountains. Utilizing high-throughput sequencing and real-time fluorescence quantitative PCR, this study analyzed the bacterial community structure and the abundance of nitrogen-cycling functional genes within soils from different stands. The results demonstrated that Proteobacteria, Acidobacteria, and Actinobacteria were the dominant bacterial groups across all three forest soil types. The mixed-forest soil exhibited a higher relative abundance of Firmicutes and Bacteroidetes, while Nitrospirae and Crenarchaeota were most abundant in the pure R. pseudoacacia stand soils. Employing FAPROTAX for predictive bacterial function analysis in various soil layers, this study found that nitrogen-cycling processes such as nitrification and denitrification were most prominent in pure R. pseudoacacia soils. Whether in surface or deeper soil layers, the abundance of AOB amoA, nirS, and nirK genes was typically highest in pure R. pseudoacacia stand soils. In conclusion, the mixed forest of R. pseudoacacia and Q. variabilis can moderate the intensity of nitrification and denitrification processes, consequently reducing soil nitrogen loss.

The authors of this Comment are longstanding selenium investigators with a total of 200 or more published articles on selenium; the corresponding author (Margaret P [...]

Monitoring drought stress is crucial for estimating productivity and assessing the health status of forest ecosystems under global climate change. Solar-induced chlorophyll fluorescence (SIF) is mechanistically coupled to photosynthesis and has advantages over greenness-based vegetation indices in detecting drought. In recent years, SIF has commonly been used in monitoring drought stress in crop ecosystems. However, the response of tower-based SIF to drought stress in forest ecosystems remains unclear. In this study, we investigated the potential of tower-based SIF to monitor drought, which was quantified using the plant water stress index (PWSI) in a Chinese cork oak plantation. The results show the negative effect of drought on SIF, and afternoon depression of SIF emission under drought stress was observed. Canopy SIF (F) exhibited a nonlinear relationship with PWSI, while the quantum yield of SIF (ΦF) exhibited a significant linear relationship with PWSI at 687 nm and 760 nm (ΦF687: R2 = 0.90; ΦF760: R2 = 0.85). Incident radiation (PAR) and canopy structure affected the response of SIF to drought stress, with PAR as the main factor causing the nonlinear relationship between F and PWSI. Afternoon depression was described as the afternoon/morning ratio (AMR). AMRF and AMRΦF exhibited a negative linear response to PWSI. AMRF was less affected than F by PAR and canopy structures, and AMRΦF was more physiologically representative than ΦF. Moreover, AMRΦF was sensitive to VPD and REW, and it might be a good indicator of drought. Red SIF was more sensitive to drought than far-red SIF, as the R2 of PWSI with AMRΦF687 (R2 = 0.89) was higher than that with AMRΦF687 (R2 = 0.84). These results highlight the potential of tower-based SIF, especially red SIF, for drought monitoring in a plantation, and consideration of the physiological diurnal variation in SIF under drought stress is crucial for improving the accuracy of drought stress monitoring.

Smoking is a pathogenic factor for pulmonary hypertension (PH). Our previous study showed that serum miR-21 levels are elevated in smokers. miR-21 is considered as engaged in the PH process; however, its mechanisms remain unclear. In this investigation, we found that in the lung tissue of smoking-induced PH patients, the levels of miR-21 and aging markers (p21 and p16) were upregulated, and the function of pulmonary vascular endothelial cells was also impaired. Exposure of mice to cigarette smoke (CS) for four months caused similar changes in lung tissues and increased pulmonary arterial pressure, which were attenuated by knockout of miR-21. Further, human umbilical vein endothelial cells (HUVECs) exposed to cigarette smoke extract (CSE) revealed upregulation of miR-21 levels, depression of PTEN, activation of PI3K/AKT/mTOR signaling, an increase in senescence indexes, and enhanced dysfunction. Inhibiting miR-21 overexpression reversed the PTEN-mTOR signaling pathway and prevented senescence and dysfunction of HUVECs. In sum, our data indicate that miR-21-mediated endothelial senescence and dysfunction are involved in CS-induced PH through the activation of PI3K/AKT/mTOR signaling, which suggests that selective miR-21 inhibition offers the potential to attenuate PH.

Point cloud registration is an important task in computer vision and robotics which is widely used in 3D reconstruction, target recognition, and other fields. At present, many registration methods based on deep learning have better registration accuracy in complete point cloud registration, but partial registration accuracy is poor. Therefore, a partial point cloud registration network, HALNet, is proposed. Firstly, a feature extraction network consisting mainly of adaptive graph convolution (AGConv), two-dimensional convolution, and convolution block attention (CBAM) is used to learn the features of the initial point cloud. Then the overlapping estimation is used to remove the non-overlapping points of the two point clouds, and the hybrid attention mechanism composed of self-attention and cross-attention is used to fuse the geometric information of the two point clouds. Finally, the rigid transformation is obtained by using the fully connected layer. Five methods with excellent registration performance were selected for comparison. Compared with SCANet, which has the best registration performance among the five methods, the RMSE(R) and MAE(R) of HALNet are reduced by 10.67% and 12.05%. In addition, the results of the ablation experiment verify that the hybrid attention mechanism and fully connected layer are conducive to improving registration performance.

Improving the landscape- and village-integrated green governance (LVIGE) is currently a problem faced by various countries. China has also put forward the revitalization strategy of “rural development, the environment is the background color”. How to judge and evaluate the landscape- and village-integrated green governance (LVIGE) is the main research purpose of this study. This study is based on the rural data from 2012 to 2021 in China to construct an evaluation system, which mainly includes three dimensions: economic production construction, social and cultural construction, and ecological environment construction. This study uses the relevant entropy method to calculate the landscape- and village-integrated green governance (LVIGE), and the following conclusions are drawn: There is a significant difference in the data on the ecological factors among rural areas in various provinces of China, and there is a gap in green governance. Many provinces still do not pay sufficient attention to rural development, especially the problem of rural green governance. Northeast China ranks at the bottom in terms of economic production construction, social and cultural construction, and ecological environment construction with a large gap. The rapid improvement of the landscape- and village-integrated green governance (LVIGE) is inseparable from the national policies. Therefore, to improve the development of the landscape- and village-integrated green governance (LVIGE), not only national policy support is needed, but also the local government should pay full attention to the development of the rural economy, social culture, and ecology, especially the construction of an ecological environment. The construction of the evaluation system of the LVIGE not only corresponds to the national policy, but also promotes the provinces’ attention to the environment.

Medicinal plants accommodated by understory habitats can easily suffer over-exploitation in the heavy harvest of natural products. It is necessary to develop a sustainable cultural protocol to provide high-quality stocks for efficient regeneration. Drought places stress on medicinal plants during their culture by limiting new sprout growth and reducing the quality of medicinal extracts. Artificial mediating approaches should be considered in a sustainable regime of medicinal plant culture to test the potential tradeoff between resistance to drought and production ability. In this study, Rabdosia rubescens seedlings were raised in three light-emitting diode (LED) spectra from red (71.7% red, 14.6% green, 13.7% blue), green (26.2% red, 17.4% green, 56.4% blue), and blue (17.8% red, 33.7% green, 48.5% blue) lights. Mown seedlings were subjected to a simulated drought event. Drought stressed the seedlings by reducing the growth, dry mass, nitrogen (N) uptake, and oridonin content. Mowing increased the oridonin content but decreased total C and N accumulation and the δ13C level. The red light benefitted starch accumulation only under the well-watered condition, and the green light induced an upregulation of δ13C but decreased antioxidant activity. Oridonin content was negatively associated with combined δ13C and catalase activity. Overall, either mowing or blue light can be recommended for the culture of R. rubescens to increase oridonin content, alleviating some of the negative consequences of drought.

With the development of spectrum observation technology, solar-induced chlorophyll fluorescence (SIF)—an effective substitute for photosynthesis—has been widely used to monitor crop stress, vegetation phenology and ecosystem productivity. The relationship between fluorescence and photosynthesis is complicated because they are sensitive to environmental changes. Understanding the response of SIF to environmental factors is of great significance for clarifying the variation dynamic and relationship between SIF and photosynthesis under different conditions. In this study, the canopy SIF and the environmental factors of a Quercus variabilis BI. plantation were observed simultaneously, and the response of SIF to environmental factors at a daily scale and at a half-hour scale was analyzed. The results showed that SIF had obvious seasonal and diurnal dynamics and was mainly driven by photosynthetically active radiation (PAR). The influence of PAR, air temperature (Ta), vapor pressure deficit (VPD), soil moisture (SM) and wind speed (Ws) on SIF varied with the lapse of the growing season. After eliminating the covariant effect of PAR on the Ta and VPD during the whole growing season, the relationship between VPD and SIF was found to be negative, and the effect of Ta on SIF disappeared. This study enriched the ground observation dataset and provided support for understanding the variations in the relationship between SIF and photosynthesis under different conditions.

As an indispensable module of computer-aided pronunciation training (CAPT) systems, mispronunciation detection and diagnosis (MDD) techniques have attracted a lot of attention from academia and industry over the past decade. To train robust MDD models, this technique requires massive human-annotated speech recordings which are usually expensive and even hard to acquire. In this study, we propose to use transfer learning to tackle the problem of data scarcity from two aspects. First, from audio modality, we explore the use of the pretrained model wav2vec2.0 for MDD tasks by learning robust general acoustic representation. Second, from text modality, we explore transferring prior texts into MDD by learning associations between acoustic and textual modalities. We propose textual modulation gates that assign more importance to the relevant text information while suppressing irrelevant text information. Moreover, given the transcriptions, we propose an extra contrastive loss to reduce the difference of learning objectives between the phoneme recognition and MDD tasks. Conducting experiments on the L2-Arctic dataset showed that our wav2vec2.0 based models outperformed the conventional methods. The proposed textual modulation gate and contrastive loss further improved the F1-score by more than 2.88% and our best model achieved an F1-score of 61.75%.

The two-phase flow in a microchannel consists of liquid–liquid and gas–liquid material components. The automatic recognition of flow patterns using deep learning approaches has been emerging. This study aimed to improve the recognition accuracy of flow patterns in the two-phase flow images. The different convolutional kernels in the GoogLeNet algorithm extracted the image features with different scales. In order to strengthen the important channel and spatial features, this paper proposes the combined five-layer Coord attention and GoogLeNet algorithm to enhance the accuracy of the new algorithm. The optimized algorithm model was derived from image datasets with different liquid–liquid two-phase flows (NaAlg–Oil, GaInSn–Water), and its accuracy was 95.09% in training and 98.12% in testing. This new model was also applied to predict the flow patterns, with a recognition accuracy of more than 97% in both the liquid–liquid and gas–liquid two-phase flows (water–soybean oil, water–lubricating oil, and argon–water).

Diquat (DQ), a widely used bipyridyl herbicide, is associated with significantly higher rates of kidney injuries compared to other pesticides. However, the underlying molecular mechanisms are largely unknown. In this study, we identified the molecular changes in the early stage of DQ-induced kidney damage in a mouse model through transcriptomic, proteomic and metabolomic analyses. We identified 869 genes, 351 proteins and 96 metabolites that were differentially expressed in the DQ-treated mice relative to the control mice (p < 0.05), and showed significant enrichment in the PPAR signaling pathway and fatty acid metabolism. Hmgcs2, Cyp4a10, Cyp4a14 and Lpl were identified as the major proteins/genes associated with DQ-induced kidney damage. In addition, eicosapentaenoic acid, linoleic acid, palmitic acid and (R)-3-hydroxybutyric acid were the major metabolites related to DQ-induced kidney injury. Overall, the multi-omics analysis showed that DQ-induced kidney damage is associated with dysregulation of the PPAR signaling pathway, and an aberrant increase in Hmgcs2 expression and 3-hydroxybutyric acid levels. Our findings provide new insights into the molecular basis of DQ-induced early kidney damage.

Light use efficiency (LUE) models have been widely used to estimate terrestrial gross primary production (GPP). However, the estimation of GPP still has large uncertainties owing to an insufficient understanding of the complex relationship between water availability and photosynthesis. The plant water stress index (PWSI), which is based on canopy temperature, is very sensitive to the plant stomatal opening and has been regarded as a good indicator for monitoring plant water status at the regional scale. In this study, we selected a cork oak plantation in northern China with an obvious seasonal drought as the research object. Using the ground-observed data, we evaluated the applicability of the LUE models with typical water stress scalars (MOD17, MODTEM, EC-LUE, ECM-LUE, SM-LUE, GLO-PEM, and Wang) in a GPP simulation of the cork oak plantation and explored whether the model’s accuracy can be improved by applying PWSI to modify the above models. The results showed that among the seven LUE models, the water stress scalar had a greater impact on the model’s performance than the temperature stress scalar. On sunny days, the daily GPP simulated by the seven LUE models was poorly matched with the measured GPP, and all models explained only 23–52% of the GPP variation in the cork oak plantation. The modified LUE models can significantly improve the prediction accuracy of the GPP and explain 49–65% of the variation in the daily GPP. On cloudy days, the performance of the modified LUE models did not improve, and the evaporative fraction was more suitable for defining the water stress scalar in the LUE models. The ECM-LUE and the modified GLO-PEM based on PWSI had optimal model structures for simulating the GPP of the cork oak plantation under cloudy and sunny days, respectively. This study provides a reference for the accurate prediction of GPP in terrestrial ecosystems in the future.

In recent years, gene therapy has made remarkable achievements in tumor treatment. In a successfully cancer gene therapy, a smart gene delivery system is necessary for both protecting the therapeutic genes in circulation and enabling high gene expression in tumor sites. Magnetic iron oxide nanoparticles (IONPs) have demonstrated their bright promise for highly efficient gene delivery target to tumor tissues, partly due to their good biocompatibility, magnetic responsiveness, and extensive functional surface modification. In this review, the latest progress in targeting cancer gene therapy is introduced, and the unique properties of IONPs contributing to the efficient delivery of therapeutic genes are summarized with detailed examples. Furthermore, the diagnosis potentials and synergistic tumor treatment capacity of IONPs are highlighted. In addition, aiming at potential risks during the gene delivery process, several strategies to improve the efficiency or reduce the potential risks of using IONPs for cancer gene therapy are introduced and addressed. The strategies and applications summarized in this review provide a general understanding for the potential applications of IONPs in cancer gene therapy.

From a very brief speech, human listeners can estimate the pitch range of the speaker and normalize pitch perception. Spectral features which inherently involve both articulatory and phonatory characteristics were speculated to play roles in this process, but few were reported to directly correlate with speaker’s F0 range. To mimic this human auditory capability and validate the speculation, in a preliminary study we proposed an LSTM-based method to estimate speaker’s F0 range from a 300 ms-long speech input, which turned out to outperform the conventional method. By two more experiments, this study further improved the method and verified its validity in estimating the speaker-specific underlying F0 range. After incorporating a novel measurement of F0 range and a multi-task training approach, Experiment 1 showed that the refined model gave more accurate estimates than the initial model. Based on a Japanese-Chinese bilingual parallel speech corpus, Experiment 2 found that the F0 ranges estimated with the model from the Chinese speech and the model from the Japanese speech produced by the same set of speakers had no significant difference, whereas the conventional method showed significant difference. The results indicate that the proposed spectrum-based method captures the speaker-specific underlying F0 range which is independent of the linguistic content.

Scientific papers published in journals or conferences, also considered academic publications, are the manifestation of scientific research achievements. Lots of scientific papers published in digital form bring new challenges for academic evaluation and information retrieval. Therefore, research on the ranking method of scientific papers is significant for the management and evaluation of academic resources. In this paper, we first identify internal and external factors for evaluating scientific papers and propose a publication ranking method based on an analysis of a heterogeneous academic network. We use four types of metadata (i.e., author, venue (journal or conference), topic, and title) as vertexes for creating the network; in there, the topics are trained by the SentenceLDA algorithm with the metadata of the abstract. We then use the Gibbs sampling method to create a heterogeneous academic network and apply the ConNetClus algorithm to calculate the probability value of publication ranking. To evaluate the significance of the method proposed in this paper, we compare the ranking results with BM25, PageRank, etc., and homogeneous networks in MAP and NDCG. As shown in our evaluation results, the performance of the method we propose in this paper is better than other baselines for ranking publications.

It is now generally accepted that an article written by influential authors often deserves a higher ranking in information retrieval. However, it is a challenging task to determine an author’s relative influence since information about the author is, much of the time, inaccessible. Actually, in scientific publications, the author is an important metadata item, which has been widely used in previous studies. In this paper, we bring an optimized AuthorRank, which is a topic-sensitive algorithm calculated by citation context, into citation analysis for testing whether and how topical AuthorRank can replace or enhance classical PageRank for publication ranking. For this purpose, we first propose a PageRank with Priors (PRP) algorithm to rank publications and authors. PRP is an optimized PageRank algorithm supervised by the Labeled Latent Dirichlet Allocation (Labeled-LDA) topic model with full-text information extraction. We then compared four methods of generating an AuthorRank score, looking, respectively, at the first author, the last author, the most famous author, and the “average” author (of a publication). Additionally, two combination methods (Linear and Cobb–Douglas) of AuthorRank and PRP were compared with several baselines. Finally, as shown in our evaluation results, the performance of AuthorRank combined with PRP is better (p < 0.001) than other baselines for publication ranking.

To solve the location-routing problem of maritime emergency materials distribution (MEMD-LRP) of multi-agent joint decision, a bi-level programming model of MEMD-LRP is constructed. The model is established on the premise of considering the rescue time window and emergency materials distribution priority, aiming at the strong regional characteristics of maritime accidents, strong timeliness of maritime rescue, great difficulty of rescue, high cost of rescue, and so on. From the point of view of the emergency management department, the upper level makes the location decision to minimize the construction cost of the shore-based emergency materials reserves and the time satisfaction loss cost of the accident point. From the point of view of the commercial rescue unit, the lower level plans the routes by minimizing the cost of emergency material distribution, ship transportation, ship dispatch, and time penalty. A hybrid algorithm of the ant colony and tabu search is designed to solve the model, and an example is designed and analyzed based on the case of the Bohai Sea to verify the effectiveness of the model and algorithm. Modeling and algorithm design based on bi-level programming can help decision-makers at different levels in maritime emergency logistics systems to make more scientific and reasonable decisions to improve the service capability of the maritime emergency logistics system.

Canopy temperature (Tc) is used to characterize plant water physiology, and thermal infrared (TIR) remote sensing is a convenient technology for measuring Tc in forest ecosystems. However, the images produced through this method contain background pixels of forest gaps, thereby reducing the accuracy of Tc observations. Extracting Tc data from TIR images is of great significance for understanding changes in ecosystem water status. In this study, a temperature threshold method was developed to rapidly, accurately, and automatically extract forest canopy pixels for Tc data obtention. Specifically, this method takes the temperature corresponding to the point with a slope of 0.5 in the curve composed of the normalized average temperature and the normalized cumulative number of pixels as the segmentation threshold to separate the forest gap pixels from the forest canopy pixels in the TIR images and extract the separated forest canopy pixels based on the pixel coordinates for Tc data obtention. Taking the Tc values, measured using a thermocouple, as the standard, Tc extraction using the new temperature threshold method and traditional methods (the Otsu algorithm and direct extraction) was compared in cork oak plantations. The results showed that the temperature threshold method offered the highest extraction accuracy, followed by the direct extraction method and the Otsu algorithm. The temperature threshold method was determined to be the most suitable for extracting Tc data from the TIR images of cork oak plantations.

This study analyzed the engine operating condition curve of the corn kernel harvester. Field experiments identified the feed rate, concave clearance, and cylinder speed as the main factors affecting operating quality and efficiency. A ternary quadratic regression orthogonal center-of-rotation combined optimization test method was used to determine the feed rate, cylinder speed, and concave clearance as the influencing factors, and the engine speed variation rate, crushing rate, impurity rate, loss rate, and cylinder speed variation rate as the objective functions. A mathematical regression model was developed for the combination of operating quality indicators, efficiency indicators, and operating parameters of the corn kernel harvester. A non-linear optimization method was used to optimize the parameters of each influencing factor. The results showed that with a feed rate of 12 kg/s, a forward speed of 5 km/h, a cylinder speed of 360 r/min, and a concave clearance of 30 mm, the average crushing rate was 3.91%, the average impurity rate was 1.71%, and the kernel loss rate was 3.1%. This model could be used for the design and development of intelligent control systems.

This study proposed a design method for forced-vibration subsoiling to reduce resistance and consumption. The vibration digging parameters of the Antlion larvae were obtained by high-speed camera, and a forced-vibration subsoiling shovel handle and six different shovel tips were designed by the non-smooth resistance-reducing surface design method. The resistance-reducing and consumption-reducing effects were verified in the field subsoiling experiment. The results showed that the resistance of subsoiling gradually decreased with the vibration frequency increasing. This vibration subsoiling method could not only reduce resistance but also reduce the total energy consumption, the resistance reduction rate reached 14.2–21.2%, and the total energy consumption was reduced by 11.2–16.5%. It could achieve the comprehensive subsoiling effect and create the soil conditions with the combination of loose and firmness. The non-smooth surface contributed to the reduction in resistance, and the forward speed had a more negligible effect on the resistance of subsoiling. The results had demonstrated that the combination of high-frequency vibration and non-smooth surfaces helped to reduce the cohesion of the soil, reduce the angle of internal friction in the soil, destroy the water film layer on the contact surface between the soil-touching components and the soil, change the soil flow state, and reduce the adhesion of the interface layer. The design method in this study could not only be applied to the design of vibration resistance reduction in the subsoiler but also had significance for the design of the other soil-contacting components.

In response to the poor adaptability of existing harvesters to complex operating conditions in the field, this study took a three-row four-wheel-drive (4WD) corn harvester as the research object, designed a traveling transmission system layout, proposed a control strategy of driving torque distribution, simulated, and analyzed each of the four states of harvester drive wheels slippage. The results showed that under the driving wheels slipping condition, after applying torque control, the adjustment time was 43.3% shorter than that without control in the case of single wheel slipping, 11.1% shorter than that without control in the case of two wheels slipping on the same axle, 41.4% shorter than that without control in the case of two wheels slipping on different axles, and 36.6% shorter than that without control in the case of three driving wheels slipping. The application of drive torque distribution control could significantly improve the traction and passing ability of the corn harvesters during operation, as well as made the harvester travel more smoothly, thus improving the harvest quality. The drive torque distribution control can be applied not only to the three-row corn harvester, but also to other types of harvesters, and self-propelled agricultural machinery to enhance their adaptability, improving their operation quality. It has a significant reference value for the development of the driving system on walking agricultural machinery.

The scientific rationality of farmers’ grain storage technology and equipment is crucial for the biosecurity of grain in the main grain-producing areas represented by Northeast China. In this paper, four farmer grain storage mock silos of different widths were used as a means to track an experimental cycle of grain storage. The absolute water potential of corn in all four silos at the beginning of the experiment was greater than the absolute water potential of air, prompting moisture migration from the grain interior to the air and down to about 14%. Moisture was influenced by wind direction, and moisture decreased faster with better ventilation on both sides of the grain silos. Therefore, grain silo width has a significant effect on the drying effect under naturally ventilated conditions of maize ears. This research focused on the determination and assessment of mycotoxin contamination under farmers’ storage grain conditions and analyzed the effect of silo structure on the distribution of mycotoxin contamination. When the width was too large, areas of high mycotoxin infection existed in the middle of the grain silo, and ventilation and tipping could be used to reduce the risk of toxin production. This study proved that reasonable farmer grain storage techniques and devices in Northeast China can effectively protect grain from mycotoxin contamination.

Neurotransmitter catecholamines (dopamine, epinephrine, and norepinephrine) are liable to undergo oxidation, which copper is deeply involved in. Catecholamine oxidation-derived neurotoxicity is recognized as a pivotal pathological mechanism in neurodegenerative diseases. Glutamate, as an excitatory neurotransmitter, is enriched in the brain at extremely high concentrations. However, the chemical biology relationship of these two classes of neurotransmitters remains largely unknown. In the present study, we assessed the influences of glutamate on the autoxidation of catecholamines, the copper- and copper-containing ceruloplasmin-mediated oxidation of catecholamines, the catecholamine-induced formation of quinoprotein, catecholamine/copper-induced hydroxyl radicals, and DNA damage in vitro. The results demonstrate that glutamate, at a physiologically achievable molar ratio of glutamate/catecholamines, has a pronounced inhibitory effect on catecholamine oxidation, catecholamine oxidation-evoked hydroxyl radicals, quinoprotein, and DNA damage. The protective mechanism of glutamate against catecholamine oxidation could be attributed to its restriction of the redox activity of copper via chelation. This previously unrecognized link between glutamate, catecholamines, and copper suggests that neurodegenerative disorders may occur and develop once the built-in equilibrium is disrupted and brings new insight into developing more effective prevention and treatment strategies for neurodegenerative diseases.

The concentration of PM2.5 is an important index to measure the degree of air pollution. When it exceeds the standard value, it is considered to cause pollution and lower the air quality, which is harmful to human health and can cause a variety of diseases, i.e., asthma, chronic bronchitis, etc. Therefore, the prediction of PM2.5 concentration is helpful to reduce its harm. In this paper, a hybrid model called CNN-BiLSTM-Attention is proposed to predict the PM2.5 concentration over the next two days. First, we select the PM2.5 concentration data in hours from January 2013 to February 2017 of Shunyi District, Beijing. The auxiliary data includes air quality data and meteorological data. We use the sliding window method for preprocessing and dividing the corresponding data into a training set, a validation set, and a test set. Second, CNN-BiLSTM-Attention is composed of the convolutional neural network, bidirectional long short-term memory neural network, and attention mechanism. The parameters of this network structure are determined by the minimum error in the training process, including the size of the convolution kernel, activation function, batch size, dropout rate, learning rate, etc. We determine the feature size of the input and output by evaluating the performance of the model, finding out the best output for the next 48 h. Third, in the experimental part, we use the test set to check the performance of the proposed CNN-BiLSTM-Attention on PM2.5 prediction, which is compared by other comparison models, i.e., lasso regression, ridge regression, XGBOOST, SVR, CNN-LSTM, and CNN-BiLSTM. We conduct short-term prediction (48 h) and long-term prediction (72 h, 96 h, 120 h, 144 h), respectively. The results demonstrate that even the predictions of the next 144 h with CNN-BiLSTM-Attention is better than the predictions of the next 48 h with the comparison models in terms of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2).

Solar-induced chlorophyll fluorescence (SIF) is a hopeful indicator, which along with remote sensing, is used to measure the photosynthetic efficiency and gross primary production (GPP) of vegetation in regional terrestrial ecosystems. Studies have found a significant linear correlation between SIF and GPP in a variety of ecosystems. However, this relationship has mainly been established using SIF and GPP data derived from satellite remote sensing and continuous ground-based observations, respectively, which are difficult to accurately match. To overcome this, some studies have begun to use tower-based automatic observation instruments to study the changes of near-surface SIF and GPP. This study conducts continuous simultaneous observation of SIF, carbon flux, and meteorological factors on the forest canopy of a cork oak plantation during the growing season to explore how meteorological factors impact on canopy SIF and its relationship with GPP. This research found that the canopy SIF has obvious diurnal and day-to-day variations during the growing season but overall is relatively stable. Furthermore, SIF is greatly affected by incident radiation in different weather conditions and can change daily. Meteorological factors have a major role in the relationship between SIF and GPP; overall, the relationship shows a significant linear regression on the 30 min scale, but weakens when aggregating to the diurnal scale. Photosynthetically active radiation (PAR) drives SIF on a daily basis and changes the relationship between SIF and GPP on a seasonal timescale. As PAR increases, the daily slopes of the linear regressions between SIF and GPP decrease. On the 30 min timescale, both SIF and GPP increase with PAR until it reaches 1250 μmol·m−2·s−1; subsequently, SIF continues to increase while GPP decreases and they show opposite trends. Soil moisture and vapor pressure deficit influence SIF and GPP, respectively. Our findings demonstrate that meteorological factors affect the relationship between SIF and GPP, thereby enhancing the understanding of the mechanistic link between chlorophyll fluorescence and photosynthesis.

In the digital microfluidic experiments, the droplet characteristics and flow patterns are generally identified and predicted by the empirical methods, which are difficult to process a large amount of data mining. In addition, due to the existence of inevitable human invention, the inconsistent judgment standards make the comparison between different experiments cumbersome and almost impossible. In this paper, we tried to use machine learning to build algorithms that could automatically identify, judge, and predict flow patterns and droplet characteristics, so that the empirical judgment was transferred to be an intelligent process. The difference on the usual machine learning algorithms, a generalized variable system was introduced to describe the different geometry configurations of the digital microfluidics. Specifically, Buckingham’s theorem had been adopted to obtain multiple groups of dimensionless numbers as the input variables of machine learning algorithms. Through the verification of the algorithms, the SVM and BPNN algorithms had classified and predicted the different flow patterns and droplet characteristics (the length and frequency) successfully. By comparing with the primitive parameters system, the dimensionless numbers system was superior in the predictive capability. The traditional dimensionless numbers selected for the machine learning algorithms should have physical meanings strongly rather than mathematical meanings. The machine learning algorithms applying the dimensionless numbers had declined the dimensionality of the system and the amount of computation and not lose the information of primitive parameters.

Inventory modes classification can reduce the workload of grain depot management and it is time-saving, not labor-intensive. This paper proposed a method of using a temperature contour map converted from digital temperature data to classify stored grain inventory modes in a large bulk grain warehouse, which mainly included detection of inventory changes and routine operations performed (aeration). The back propagation (BP) neural network was used in this method to identify and classify grain storage inventory modes based on the temperature contour map for helping grain depot management work. The method extracted and combined color coherence vector (CCV), texture feature vector (TFV) and smoothness feature vector (SFV) of temperature contour maps as the input vector of the BP neural network, and used inventory modes as the output vector. The experimental results indicated that the accuracy of the BP neural network with vector (CCV and TFV and SFV) as the input vector was about 93.9%, and its training time and prediction time were 320 and 0.12 s, respectively.

Optimization design of an impeller is critical for the energy performance of a fan. This paper takes the promotion of fan efficiency and pressure rise as the optimization objectives to carry out multiparameter and multiobjective optimization research. Firstly, an experimental test bench is built to measure the energy performance of the original fan and verify the accuracy of the numerical method. Then, the hub outlet angle of impeller β1, the impeller outlet angle increment Δβ1, the wrap angle φ, the hub outlet angle of diffuser β2, and the diffuser outlet angle increment Δβ2 are set as the optimal parameters to conduct orthogonal optimal design. The results show that the efficiency of the optimal fan increases by 11.71%, and the pressure rise increases by 50.15%. The pressure and velocity distributions in an optimal fan are uniform, the internal flow separation is weakened, and the influence of tip leakage flow is reduced, which makes for the improvement of energy performance for the fan.

Ground-based synthetic aperture radar interferometry (GB-InSAR) is a valuable tool for deformation monitoring. The 2D interferograms obtained by GB-InSAR can be integrated with a 3D terrain model to visually and accurately locate deformed areas. The process has been preliminarily realized by geometric mapping assisted by terrestrial laser scanning (TLS). However, due to the line-of-sight (LOS) deformation monitoring, shadow and layover often occur in topographically rugged areas, which makes it difficult to distinguish the deformed points on the slope between the ones on the pavement. The extant resampling and interpolation method, which is designed for solving the scale difference between the point cloud and radar pixels, does not consider the local scattering characteristics difference of slope. The scattering difference information of road surface and slope surface in the terrain model is deeply weakened. We propose a differentiated method with integrated GB-InSAR and terrain surface point cloud. Local geometric and scattering characteristics of the slope were extracted, which account for pavement and slope differentiating. The geometric model is based on a GB-InSAR system with linear repeated-pass and the topographic point cloud relative observation geometry. The scattering model is based on k-nearest neighbor (KNN) points in small patches varies as radar micro-wave incident angle changes. Simulation and a field experiment were conducted in an open-pit mine. The results show that the proposed method effectively distinguishes pavement and slope surface deformation and the abnormal area boundary is partially relieved.

Melatonin is a well-documented antioxidant. Physicochemical analysis using the density functional theory suggests that melatonin is a copper chelating agent; however, experimental evidence is still in demand. The present study investigated the influence of melatonin on reactive oxygen species (ROS) generated from polyphenol autoxidation in the presence of copper. Surprisingly, we found that melatonin paradoxically enhanced ROS formation in a redox system containing low concentrations of copper and quercetin (Que) or (−)-epigallocatechin-3-gallate (EGCG), due to reduction of cupric to cuprous ion by melatonin. Addition of DNA to this system inhibited ROS production, because DNA bound to copper and inhibited copper reduction by melatonin. When melatonin was added to a system containing high concentrations of copper and Que or EGCG, it diminished hydroxyl radical formation as expected. Upon addition of DNA to high concentrations of copper and Que, this pro-oxidative system generated ROS and caused DNA damage. The DNA damage was not prevented by typical scavengers of hydroxyl radical DMSO or mannitol. Under these conditions, melatonin or bathocuproine disulfonate (a copper chelator) protected the DNA from damage by chelating copper. When melatonin was administered intraperitoneally to mice, it inhibited hepatotoxicity and DNA damage evoked by EGCG plus diethyldithiocarbamate (a copper ionophore). Overall, the present study demonstrates the pro-oxidant and antioxidant activities of melatonin in the redox system of copper and polyphenols. The pro-oxidant effect is inhibited by the presence of DNA, which prevents copper reduction by melatonin. Interestingly, in-vivo melatonin protects against copper/polyphenol-induced DNA damage probably via acting as a copper-chelating agent rather than a hydroxyl radical scavenger. Melatonin with a dual function of scavenging hydroxyl radical and chelating copper is a more reliable DNA guardian than antioxidants that only have a single function of scavenging hydroxyl radical.

How to explore the interaction between an individual researcher and others in scientific research, find out the degree of association among individual researchers, and evaluate the contribution of researchers to the whole according to the mechanism and law of interaction, is of great significance to grasp the overall trend of the field. Scholars mostly use bibliometrics to solve these problems and analyze the citation and cooperation among academic achievements from the dimension of “quantity”. However, there is still no mature method for scholars to explore the evolution of knowledge and the relationship between authors; this paper tries to fill this gap. We narrow down the scope of research and focus the research content on the literature in biology and chemistry, collect all the papers from PubMed system (a very comprehensive authoritative database of biomedical papers) during 2014−2018, and take year as a specific analysis unit so as to improve the accuracy of the analysis. Then, we construct the author cooperation networks. Finally, through the above methods and steps, we identify the core authors of each year, analyze the recent cooperative relationships among authors, and predict some changes in the cooperative relationship among the authors based on the networks’ analytical data, evaluating and estimating the role that authors play in the overall field. Therefore, we expect that the cooperative authorship networks supported by the complex network theory can better explain the author’s cooperative relationship.

We have demonstrated previously that melatonin attenuates hepatotoxicity triggered by high doses of (−)-epigallocatechin-3-gallate (EGCG) in mice. The current work investigated the influence of melatonin on the oncostatic activity of EGCG in two cancer cell lines, wherein melatonin induced an opposite response of p21. In human tongue cancer TCA8113 cells, melatonin-induced p21 and EGCG-mediated formation of quinoproteins were positively associated with the oncostatic effects of melatonin and EGCG. Melatonin-stimulated an increase in p21 which was correlated with a pronounced nuclear translocation of thioredoxin 1 and thioredoxin reductase 1, both of which are known to induce p21 via promoting p53 trans-activation. Melatonin did not influence the EGCG-mediated increase of quinoprotein formation nor did EGCG impair melatonin-induced p21 up-regulation. Co-treatment with both agents enhanced the cell-killing effect as well as the inhibitory activities against cell migration and colony formation. It is known that p21 also plays a powerful anti-apoptotic role in some cancer cells and confers these cells with a survival advantage, making it a target for therapeutic suppression. In human hepatocellular carcinoma HepG2 cells, melatonin suppressed p21 along with the induction of pro-survival proteins, PI3K and COX-2. However, EGCG prevented against melatonin-induced PI3K and COX-2, and melatonin probably sensitized HepG2 cells to EGCG cytotoxicity via down-regulating p21, Moreover, COX-2 and HO-1 were significantly reduced only by the co-treatment, and melatonin aided EGCG to achieve an increased inhibition on Bcl2 and NFκB. These events occurring in the co-treatment collectively resulted in an enhanced cytotoxicity. In addition, the co-treatment also enhanced the inhibitory activities against cell migration and colony formation. Overall, the results gathered from these two cancer cell lines with a divergent p21 response to melatonin show that the various oncostatic activities of melatonin and EGCG together are more robust than each agent alone, suggesting that they may be useful partners in fighting cancer.

Ionic liquids (ILs) are recognized as a possible replacement of traditional organic solvents, and ILs have been widely applied to extract various compounds. The present work aims to extract ginsenosides from Panax ginseng flower buds using aqueous ionic liquid based ultrasonic assisted extraction (IL-UAE). The extraction yields of 1-alkyl-3-methylimidazolium ionic liquids with different anions and alkyl chains were evaluated. The extraction parameters of eight ginsenosides were optimized by utilizing response surface methodology (RSM). The model demonstrated that a high yield of total ginsenosides could be obtained using IL-UAE, and the optimum extraction parameters were 0.23 M [C4mim][BF4], ultrasonic time of 23 min, temperature of extraction set to 30 °C, and liquid-solid ratio of 31:1. After that, an aqueous biphasic system (ABS) was used to separate ginsenosides further. The nature and concentration of salt, as well as the value of pH in ionic liquid were evaluated, and the optimal conditions (6.0 mL IL extract, 3 g NaH2PO4, and pH 5.0) were obtained. The preconcentration factor was 2.58, and extraction efficiency reached 64.53%. The results indicate that as a simple and efficient method, an IL-UAE-ABS can be considered as a promising method for extracting and separating the natural active compounds from medicinal herbs.

In recent years, terahertz imaging systems and techniques have been developed and have gradually become a leading frontier field. With the advantages of low radiation and clothing-penetrable, terahertz imaging technology has been widely used for the detection of concealed weapons or other contraband carried on personnel at airports and other secure locations. This paper aims to detect these concealed items with deep learning method for its well detection performance and real-time detection speed. Based on the analysis of the characteristics of terahertz images, an effective detection system is proposed in this paper. First, a lots of terahertz images are collected and labeled as the standard data format. Secondly, this paper establishes the terahertz classification dataset and proposes a classification method based on transfer learning. Then considering the special distribution of terahertz image, an improved faster region-based convolutional neural network (Faster R-CNN) method based on threshold segmentation is proposed for detecting human body and other objects independently. Finally, experimental results demonstrate the effectiveness and efficiency of the proposed method for terahertz image detection.

Large petroleum resources in deep sea, and huge market demands for petroleum need advanced petroleum extraction technology. The multiphase pump, which can simultaneously transport oil and gas with considerable efficiency, has been a crucial technology in petroleum extraction. A numerical approach with mesh generation and a Navier-Stokes equation solution is employed to evaluate the effects of gas volume fraction on energy performance and pressure fluctuations of a multiphase pump. Good agreement of experimental and calculation results indicates that the numerical approach can accurately simulate the multiphase flow in pumps. The pressure rise of a pump decreases with the increasing of flow rate, and the pump efficiency decreases with the increasing of GVF (the ratio of the gas volume to the whole volume). Results show that the dominant frequencies of pressure fluctuation in the impeller and diffuser are eleven and three times those of the impeller rotational frequency, respectively. Due to the larger density of water and centrifugal forces, the water aggregates to the shroud and the gas gathers to the hub, which renders the distribution of GVF in the pump uneven. A vortex develops at the blade suction side, near the leading edge, induced by the leakage flow, and further affects the pressure fluctuation in the impeller. The obvious vortex in the diffuser indicates that the design of the divergence angle of the diffuser is not optimal, which induces flow separation due to large diffusion ratio. A uniform flow pattern in the impeller indicates good hydraulic performance of the pump.

The energy performance and radial force of a mixed flow pump with symmetrical and unsymmetrical tip clearance are investigated in this paper. As the tip clearance increases, the pump head and efficiency both decrease. The center of the radial force on the principal axis is located at the coordinate origin when the tip clearance is symmetrical, and moves to the third quadrant when the tip clearance is unsymmetrical. Analysis results show that the total radial force on the principal axis is closely related to the fluctuation of mass flow rate in each single flow channel. Unsteady simulations show that the dominant frequencies of radial force on the hub and blade correspond to the blade number, vane number, or double blade number because of the rotor stator interaction. The radial force on the blade pressure side decreases with the tip clearance increase because of leakage flow. The unsymmetrical tip clearances in an impeller induce uneven leakage flow rate and then result in unsymmetrical work ability of each blade and flow pattern in each channel. Thus, the energy performance decreases and the total radial force increases for a mixed flow pump with unsymmetrical tip clearance.