Search

Objective To investigate the effect of neurite outgrowth inhibitor extracellular peptide residues 1-40 (NEP1-40) combined with poly (lactic-co-glycolic acid) (PLGA) and gelatin electrospun fiber membrane on facial nerve repair in rats. Methods According to the principle of random grouping, 108 male SD rats were divided into four groups (n = 27 in each group, approved by the ethics committee), namely, the sham group, control group, PLGA group, and NEP1-40 + PLGA group. A facial nerve fracture model was established for all of the groups except for the sham group. The control group received no further treatment, the PLGA group and the NEP1-40+PLGA group were supported by PLGA membrane, and the NEP1-40+PLGA group received one immediate local injection of NEP1-40 (5 μg/μL) at a dose of 10 μL. Facial nerve function analysis, electrophysiological examination, transmission electron microscope observation, HE staining, and immunohistochemical staining of myelin marker S100β and axonal marker β3-tubulin were used to evaluate the recovery of injured facial nerves of rats at 2, 4 and 8 weeks. Results At 8 weeks, the facial nerve function score of the NEP1-40+PLGA group was better than that of the control group and PLGA group (P < 0.001), and facial nerve function was significantly restored. Electrophysiological examination of nerve action potentials at the injured facial nerve showed that the amplitude in the NEP1-40+PLGA group was higher than that of the control group and PLGA group (P < 0.001), but there was no significant difference in latency and conduction velocity results between the groups (P > 0.05). At 2, 4, and 8 weeks, transmission electron microscopy showed that the number of myelinated nerve fibers and myelin sheath thickness in the cross-section of the injured facial nerve in the NEP1-40+PLGA group were greater than those in the other groups (P < 0.05). At 8 weeks, HE staining showed that the facial nerves in the control group had partially recovered, but the overall cell distribution was uneven and the boundary with surrounding tissues was slightly blurred. In contrast, the NEP1-40+PLGA group had a relatively uniform cell distribution and a clearer boundary with surrounding tissues. At 2, 4, and 8 weeks, the immunohistochemical results showed that in the cross-section of the injuried facial nerve, NEP1-40 increased the expression of neural markers S100 β and β3-tubulin, especially β3-tubulin, which was close to normal levels (P > 0.05). Conclusion NEP1-40 is beneficial for the generation of new myelin sheaths and axons at the site of injury, and it can promote the repair and regeneration of injured facial nerves to a certain extent, thus accelerating the recovery of injured nerve function.

Abstract Background Soybean (Glycine max (L.) Merr.) is a crucial crop due to its high plant protein and oil content. Previous studies have shown that soybeans exhibit significant heterosis in terms of yield and protein content However, the practical application of soybean heterosis remains difficult, as the molecular mechanisms underlying photoperiod-sensitive genic male sterile (PGMS) is still unclear. Results This study characterized the PGMS line 88-428BY, which is sterile under short-day (SD) conditions and fertile under long-day (LD) conditions. To elucidate the genetic basis for this trait, we collected anthers, from 88-428BY under SD and LD conditions at three developmental stages, resulting in the identification of differentially expressed genes (DEGs) (2333, 2727 and 7282 DEGs, respectively) using Illumina transcriptome analysis. Using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses, we fund that among the DEGs, enriched genes were associated with photoperiod stress, light stimulus, oxidation-reduction processes, multicellular organism development and protein phosphorylation. Additionally, weighted correlation network analysis identified four modules (blue, brown, red, and yellow) that were significantly correlated with PGMS, revealing co-expressed hub genes with potential regulatory roles. Functional annotation of 224 DEGs with|KME| >0.9 across the four modules in seven databases highlighted their involvement in light stimulus, oxidation-reduction processes, multicellular organism development, and protein phosphorylation, suggesting their importance in soybean PGMS. By integrating fertility-related genes previously identified by other studies with the DEGs from our analysis, we identified eight candidate genes associated with the photosensitive sterility in soybeans. Conclusions This study enhances the understanding of PGMS in soybean and establishes the genetic basis for a two-line hybrid seed production system in soybean.

Abstract Dopamine (DA) neurons play a crucial role in the development and manifestation of depression, as well as in response to antidepressant treatments. While the function of the predominantly distributed DA neurons in the ventral tegmental area (VTA) is well established, the contribution of a small fraction of DA neurons in the dorsal raphe nucleus (DRN) during depression remains unclear. In this study, we found that chronic unpredictable stress (CUS) induces depression-related behaviors and decreases spontaneous firing rates, excitatory and inhibitory postsynaptic currents of DA neurons in the DRN associated with reduced excitatory synaptic transmission in male and female mice. The chemogenetic inhibition of DA neurons in the DRN produces depressive phenotypes. Conversely, their activation completely reversed the anhedonic and despair behaviors induced by CUS. Furthermore, we showed that a DRN dopaminergic projecting to the dorsal bed nucleus of the stria terminalis (dBNST) selectively controls depressive behaviors by influencing the neural activity and N-methyl-D-aspartate receptor (NMDAR) mediating EPSC of calcium/calmodulin-dependent protein kinase II+ (CaMKII+) target neurons by regulating dopamine neurotransmitter and dopamine receptor 2 (DR2) in the dBNST. Overall, these findings highlight the essential role of the DRNDA → dBNSTCaMKII+ neural circuit in bi-directionally mediating stress-induced depression-related behaviors. Our findings indicate that DRN DA neurons are a key component of the neural circuitry involved in regulating depression-related behaviors, making them a potential therapeutic target for depression.

Increasing temperatures and severe droughts threaten forest vitality globally. Prediction of forest response to climate change requires knowledge of the spatiotemporal patterns of monthly or seasonal climatic impacts on the growth of tree species, likely driven by local climatic aridity, climate trends, edaphic conditions, and the climatic adaption of tree species. The ability of tree species to cope with changing climate and the effects of environmental variables on growth trends and growth-climate relationships across diverse bioclimatic regions are still poorly understood for many species. This study investigated radial growth trends, interannual growth variability, and growth-climate sensitivity of two dominant tree species, Pinus tabulaeformis (PT) and Pinus sylvestris var. mongolica (PS), across a broad climatic gradient with a variety of soil properties in temperate Northern China. Using a network of 83 tree ring chronologies (54 for PT and 29 for PS) from 1971 to 2010, we documented that both species maintained constant growth trends at wet sites, while both displayed rapid declines at dry sites. We reported the species-specific drivers of spatial heterogeneity in growth trends, interannual growth variability, and growth-climate relationships. Calculated climatic variables and soil properties were identified as the most critical factors affecting the growth trends and growth-climate relationships. However, climatic variables play more essential roles than soil properties in determining the spatial heterogeneity of the growth-climate relationship. Lower clay content and higher soil nutrient regimes can exacerbate the moisture-related susceptibility of tree growth. Our findings highlight that soil properties emerged as important modulating factors to predict the drought vulnerability of forests in addition to climatic variables. Considering the continued climate warming-drying trend in the future, both pines will face a more severe growth decline and increase in drought vulnerability at drier sites with lower clayed soil or higher nutrient regimes.

Anxiety is a prevalent mental illness known for its high incidence, comorbidity, and tendency to recur, posing significant societal and individual burdens. Studies have highlighted Interleukin-19 (IL-19) as having potential relevance in neuropsychiatric disorders. Our previous research revealed that IL-19 overexpression in colonies exacerbated anxiety-related behaviors induced by dextran sodium sulfate/stress. However, the precise role and molecular mechanisms of IL-19 in anxiety regulation remain uncertain. In this study, we initiated an acute restraint stress (ARS)-induced anxious mouse model and identified heightened expression of IL-19 and IL-20Rα in the medial prefrontal cortex (mPFC) of ARS mice. Notably, IL-19 and IL-20Rα were predominantly present in the excitatory pyramidal neurons of the mPFC under both basal and ARS conditions. Utilizing the adeno-associated virus (AAV) strategy, we demonstrated that IL-19 overexpression in the mPFC induced anxiety-related behaviors and elevated stress susceptibility. Additionally, we observed decreased protein levels of brain-derived neurotrophic factor (BDNF) and postsynaptic density protein 95 (PSD95) in the mPFC of IL-19 overexpression mice, accompanied by reduced phosphorylation of in the p38, JNK, and Erk signaling pathways. These findings emphasize the role of IL-19 in modulating anxiety-related behaviors within the mPFC and suggest its potential as a pathological gene and therapeutic target for anxiety.

Abstract Background Although programmed cell death protein 1 (PD-1)/ programmed cell death-ligand protein 1 (PD-L1) checkpoint blockade immunotherapy demonstrates great promise in cancer treatment, poor infiltration of T cells resulted from tumor immunosuppressive microenvironment (TIME) and insufficient accumulation of anti-PD-L1 (αPD-L1) in tumor sites diminish the immune response. Herein, we reported a drug-loaded microbubble delivery system to overcome these obstacles and enhance PD-L1 blockade immunotherapy. Methods Docetaxel (DTX) and imiquimod (R837)-loaded microbubbles (RD@MBs) were synthesized via a typical rotary evaporation method combined with mechanical oscillation. The targeted release of drugs was achieved by using the directional "bursting" capability of ultrasound-targeted microbubble destruction (UTMD) technology. The antitumor immune response by RD@MBs combining αPD-L1 were evaluated on 4T1 and CT26 tumor models. Results The dying tumor cells induced by DTX release tumor-associated antigens (TAAs), together with R837, promoted the activation, proliferation and recruitment of T cells. Besides, UTMD technology and DTX enhanced the accumulation of αPD-L1 in tumor sites. Moreover, RD@MBs remolded TIME, including the polarization of M2-phenotype tumor-associated macrophages (TAMs) to M1-phenotype, and reduction of myeloid-derived suppressor cells (MDSCs). The RD@MBs + αPD-L1 synergistic therapy not only effectively inhibited the growth of primary tumors, but also significantly inhibited the mimic distant tumors as well as lung metastases. Conclusion PD-L1 blockade immunotherapy was enhanced by RD@MBs delivery system.

Tree transpiration contributes to the forest water budget and its environmental responses differ between daytime and nighttime. Elucidating these divergent responses separately from their biophysical controls is crucial for ascertaining the forest-water relationship in changing environments. To investigate the biophysical controls on daytime and nighttime sap flow (Qd and Qn), the sap flux density, meteorological conditions, soil moisture, and canopy leaf area index (LAI) were simultaneously monitored in a Larix principis-rupprechtii plantation in the Liupan Mountains of Northwest China during the growing seasons (June to September) of three hydrological years (2019, 2021, and 2022). The results showed that the main control factors for Qd and Qn varied depending on the timescale. The variation in Qd was mainly controlled by daytime solar radiation (Rd), vapour pressure deficit (VPDd), relative extractable soil water (REWd) and LAI at a daily scale, but only by Rd at a monthly scale and only by VPDd at an interannual scale. The variation in Qn was mainly controlled by the nighttime vapour pressure deficit (VPDn) and relative extractable soil water (REWn), LAI, and Qd at a daily scale but only by VPDn at monthly and interannual scales. The responses of Qd and Qn to soil water deficit were different and varied by timescale. At the daily scale, the effect of VPDd on Qd became weak, while the effect of VPDn on Qn became strong in drier years (2021 and 2022). At the monthly scale, as the main controlling factor of the monthly variation in Qd (Qn), Rd (VPDn) played a decisive role in the wet (dry) months. At the interannual scale, VPD limited Qd but drove Qn, leading to a decrease in Qd and an increase in Qn with increasing soil water deficit. Overall, our findings revealed the different responses of Qd and Qn to biophysical factors and underscored that Qd and Qn should be explored both separately and synchronously; additionally, our results indicated that the proportion of Qn might increase if soil water stress is increased due to future climate change.

Traditional fish farming methods suffer from backward production, low efficiency, low yield, and environmental pollution. As a result of thorough research using deep learning technology, the industrial aquaculture model has experienced gradual maturation. A variety of complex factors makes it difficult to extract effective features, which results in less-than-good model performance. This paper proposes a fish detection method that combines a triple attention mechanism with a You Only Look Once (TAM-YOLO)model. In order to enhance the speed of model training, the process of data encapsulation incorporates positive sample matching. An exponential moving average (EMA) is incorporated into the training process to make the model more robust, and coordinate attention (CA) and a convolutional block attention module are integrated into the YOLOv5s backbone to enhance the feature extraction of channels and spatial locations. The extracted feature maps are input to the PANet path aggregation network, and the underlying information is stacked with the feature maps. The method improves the detection accuracy of underwater blurred and distorted fish images. Experimental results show that the proposed TAM-YOLO model outperforms YOLOv3, YOLOv4, YOLOv5s, YOLOv5m, and SSD, with a mAP value of 95.88%, thus providing a new strategy for fish detection.

An accurate environment model is a fundamental issue in greenhouses control to improve the energy consumption efficiency and to increase the crop yield. With the increase of agricultural data generated by the Internet of Things (IoT), more feasible models are necessary to get full usage of such information. In this research, a Gradient Boost Decision Tree (GBDT) model based on the newly-developed Light Gradient Boosting Machine algorithm (LightGBM or LGBM) was proposed to model the internal temperature of a greenhouse. Features including climate variables, control variables and additional temporal information collected within five years were used to construct a suitable dataset to train and validate the LGBM model. An adaptive cross-validation method was developed as a novelty to improve the LGBM model performance and self-adaptive ability. For comparison of the predictive accuracy, a Back-Propagation (BP) Neural Network model and a Recurrent Neural Network (RNN) model were built under the same process. Another two GBDT algorithms, Extreme Gradient Boosting (Xgboost) and Stochastic Gradient Boosting (SGB), were also introduced to compare the predictive accuracy with LGBM model. Results suggest that the LGBM has best fitting ability for the temperature curves with RMSE value at 0.645℃, as well as the fastest training speed among all algorithms with 60 times faster than the other two neural network algorithms. The LGBM has strongly potential application prospect on both greenhouse environment prediction and real-time predictive control.

Immunotherapy shows great promise on treating tumors. However, insufficient antigen exposure and immunosuppressive tumor microenvironment (TME) caused by hypoxia impose a serial of constraints on the therapeutic efficacy. In this study, we developed an oxygen-carrying nanoplatform loaded with perfluorooctyl bromide (PFOB, a second-generation of perfluorocarbon-based blood substitute), IR780 (a photosensitizer) and imiquimod (R837, an immune adjuvant) to reprogram immunosuppressive TME and reinforce photothermal-immunotherapy. The obtained oxygen-carrying nanoplatforms (abbreviated as IR-R@LIP/PFOB) show highly efficient oxygen release behavior and excellent hyperthermia performance upon laser irradiation, thus achieving the attenuation of the inherent tumor hypoxia and the exposure of tumor associated antigens in situ, and transforming the immunosuppressive TME to an immunosupportive one. We found that the photothermal therapy of IR-R@LIP/PFOB together with anti-programmed cell death protein-1 (anti-PD-1) would elicit a robust antitumor immunity by increasing the tumor-infiltrating frequencies of cytotoxic CD8+ T cells and tumoricidal M1-phenotype macrophages, while reducing immunosuppressive M2-phenotype macrophages and regulatory T cells (Tregs). This study presents these oxygen-carrying IR-R@LIP/PFOB nanoplatforms are potent in removing some negative impacts of immunosuppressive TME caused by hypoxia, and suppressing tumor growth by initiating antitumor immune responses, especially in combination with anti-PD-1 immunotherapy.

Agricultural tractors are subject to lateral forces when traveling on slopes, making it difficult to accurately follow a set course. In this paper, a steering compensation method is first proposed based on the force analysis of a tractor traveling on slopes, which compensates the steering angle according to the friction force and gravity force imposed on the tractor. Further, when traveling on slopes, acceleration and a load applied to the tractor are usually time-varying. To address this problem, this paper proposes a steering compensator that can automatically adjust a compensation coefficient, as well as a design for a model predictive controller with the steering compensator for a tractor. Simulation results show that under different traveling speeds, turning radius, and slope angles, the steering compensator allows the tractor to travel more smoothly, i.e., the distance between the actual traveling route and the reference route fluctuates within a smaller range. Further, during straight-line traveling, the static error can be effectively reduced, i.e., the distance between the actual traveling route and the reference route is closer to zero. Overall, the steering compensator enables the tractor to track the reference route more accurately.

In order to meet the obstacle avoidance requirements of unmanned agricultural machinery in operation, it is necessary to plan a path to avoid obstacles in real time after obstacles are detected. However, the traditional path planning algorithm does not consider kinematic constraints, which makes it difficult to realize the plan, thus affecting the performance of the path tracking controller. In this paper, a real-time path planning algorithm based on particle swarm optimization for an agricultural machinery parametric kinematic model is proposed. The algorithm considers the agricultural machinery kinematic model, defines the path satisfying the kinematic model through a parametric equation, and solves the initial path through the analytic method. Then, considering the constraints of obstacles, acceleration, and turning angle, two objective functions are proposed. The particle swarm optimization algorithm is used to search the path near the initial path which satisfies the obstacle avoidance condition and has a better objective function value. In addition, the influence of the algorithm parameters on the running time is analyzed, and the method of compensating the radius of the obstacle is proposed to compensate the influence of the discrete time on the obstacle collision detection. Finally, experimental results show that the algorithm can plan a path in real time that avoids any moving obstacles and has a better objective function value.

Abstract Gonadotropin-releasing hormone agonist (GnRH-a) is generally added to the improve pregnancy outcomes of hormone replacement therapy cycles among patients with adenomyosis. We aimed to investigate whether adding GnRH-a can result in better pregnancy outcomes. This retrospective analysis included 341 patients with adenomyosis who underwent frozen embryo transfer (FET) after in vitro fertilization (IVF). The control group was treated only with hormone replacement therapy cycles to prepare the endometrium, and GnRH-a was added to the study group before hormone administration to adjust the menstruation cycle. Based on the similar baseline values and embryological data, there was no significant difference in the clinical pregnancy rates (40.63% vs. 42.54%, P = 0.72) and live birth rates (23.75% vs. 23.75%, P = 0.74) of the control and study groups. Other secondary outcomes, including the rates of clinical miscarriage, ectopic pregnancy, preterm birth and term birth, were not significantly different between the two groups. Compared with the hormone replacement therapy cycle alone, GnRH-a downregulation based on a hormone replacement therapy cycle may not increase the rate of clinical pregnancy or live birth of IVF-ET with FET among infertile patients with adenomyosis.

Background. Infection with syphilis is still a major public health problem. The precise data for syphilis seroprevalence in the populations will help to develop a strategy for prevention and treatment of it. However, the data for syphilis prevalence in continuous years among volunteer blood donors in China is rare. Methods. A retrospective study for Treponema pallidum (TP) antibody in blood donors was conducted from January 2010 to December 2019 at the Blood Center of Zhejiang Province, China. TP antibody was detected with two different reagents using enzyme-linked immunosorbent assay and the only sample which was reactive in the two reagents was defined as seropositive. Results. A total of 992,646 volunteer blood donors were analyzed and the positive rate of TP antibody in the blood donors was 0.43%. From 2010 to 2019, the positive rates of TP antibody were 0.53%, 0.51%, 0.51%, 0.43%, 0.36%, 0.18%, 0.11%, 0.12%, 0.11%, and 0.10%, respectively. The positive rates of TP antibody were significantly different among blood donor age group (p

Due to the complex coupling of greenhouse environments, a number of challenges have been encountered in the research of automatic control in Venlo greenhouses. Most algorithms are only concerned with accuracy, yet energy-saving control is of great importance for improving economic benefits. Reinforcement learning, as an unsupervised machine learning method with a framework similar to that of feedback control, is a powerful tool for autonomous decision making in complex environments. However, the loss of benefits and increased time cost in the exploration process make it difficult to apply it to practical scenarios. This work proposes an energy-saving control algorithm for Venlo greenhouse skylights and wet curtain fan based on Reinforcement Learning with Soft Action Mask (SAM), which establishes a trainable SAM network with artificial rules to achieve sub-optimal policy initiation, safe exploration, and efficient optimization. Experiments in a simulated Venlo greenhouse model show that the approach, which is a feasible solution encoding human knowledge to improve the reinforcement learning process, can start with a safe, sub-optimal level and effectively and efficiently achieve reductions in the energy consumption, providing a suitable environment for crops and preventing frequent operation of the facility during the control process.

As essential environmental parameters in the greenhouse, appropriate light and CO2 will improve agricultural productivity and quality. Although many related studies have been carried out on the intelligent regulation of these environmental factors, the regulation of light and CO2 is usually controlled separately, and energy consumption is rarely considered. This paper proposed a coordinated control strategy for greenhouse light and CO2 based on the multi-objective optimization model. Firstly, the experiments on the net photosynthetic rate of blueberry under different temperatures, photon flux density, and CO2 concentration nesting were carried out to establish a blueberry net photosynthetic rate prediction model based on Support Vector Regression (SVR). Secondly, a model for calculating the energy cost of both light and CO2 was constructed. Thirdly, taking the maximum net photosynthetic rate and the minimum energy cost as the objective functions, the Non-dominated Sorting Genetic Algorithm (NSGA-II) was leveraged to obtain the Pareto optimal solutions of the target regulation values of light and CO2 concentration in different temperature ranges. Then, the optimal values were selected based on two different strategies. Finally, the multi-objective optimal control strategy proposed in this paper was compared with both the classical threshold control strategy and the Gaussian curvature maximization control strategy. The results indicated that the strategy which prioritized energy saving could reduce the energy cost by about 22.33% and 19.08%, respectively, under the premise that the net photosynthetic rate was consistent. Meanwhile, the strategy that prioritized production efficiency could increase the net photosynthetic rate by about 8.40% and 4.42%, respectively, with the same energy cost. In conclusion, the proposed multi-objective optimization control can improve the greenhouse climate control performance and reduce cost compared with other mentioned strategies.

Cancer and cancer pain processes a major clinical challenge and the underlined mechanisms of pathogenesis remain elusive. We examined the specific changes in the transcriptomic profiles in the dorsal root ganglion (DRG) neurons of rats with bone cancer and bone cancer pain (BCP) using RNA sequencing technology. The bone cancer and BCP was induced by tumor cells implantation (TCI) into the tibia bone cavity in adult female rats. One week after treatment, TCI caused up- and down-regulation of thousands of genes in DRG. These genes were mainly involved in the immune process, inflammatory response, and intracellular signaling transduction of carbohydrate and cytokine. The cAMP and calcium signaling pathways were the major processes in the initial responses. Differentially expressed gene (DEG) analysis further showed that the genes for ion channels increased during day 1-7, while the genes for cytokine signaling pathways sustainedly increased during day 7-14 after TCI. The time courses of gene expression for ion channels and cytokines support their distinct roles in the early induction and late maintenance of BCP development. In addition, among the top 500 up- and down-regulated genes, 80-90% were unique for bone cancer pain as well as neuropathic and inflammatory pain, while less than 2% were shared among the three different forms of pain. This study reveals the uniqueness of mechanisms underlying bone cancer with pain, which is, to a large extent, differently from pain after acute inflammatory and nerve injury and provides novel potential targets of DEGs for bone cancer with pain.

Optimal operation and control, which can result in the global optimal operation performance of industrial processes, has been a hot topic in recent control strategy designs. However, existing control strategies, such as real-time optimization (RTO), dynamic real-time optimization (DRTO), and economic model predictive control (EMPC), have their own limitations, and they can only generate sub-optimal operation performance. In order to further improve online global operation performance, a new kind of control strategy named efficiency-oriented model predictive control (EfiMPC) is proposed in this paper. The aim of the EfiMPC is discussed first, and then, the ideal EfiMPC strategy with a nested structure is proposed, where the inner layer is the offline construction of an efficiency-oriented terminal region, and the outer layer is the direct optimization of the transient operation performance. This efficiency-oriented terminal region can guarantee a dynamic operation performance in the closed-loop perspective, and a better global operation performance can thus be obtained. A practical EfiMPC strategy, which replaces the offline construction of the efficiency-oriented terminal region with the online optimization of the average dynamic operation performance in the inner layer, is also proposed, and the recursive feasibility as well as the closed-loop stability of practical EfiMPC are discussed. Finally, a CSTR application was used to test the superiority of the proposed EfiMPC strategy, and the simulation results show that EfiMPC can obtain the best global operation performance compared with the other three control strategies; thus, the effectiveness of EfiMPC is demonstrated.

Path-tracking control algorithms in agriculture typically focus on how to improve the trajectory-tracking performance of autonomous agricultural machinery, and the agricultural productivity is optimized in a two-layer way. The upper operational layer optimizes an optimal tracking trajectory with the best agricultural productivity, and the lower control layer—such as Nonlinear Model Predictive Control (NMPC)—receives this optimized tracking trajectory first, and then steers the vehicle to track this trajectory with high accuracy. However, this two-layer structure cannot improve the agricultural productivity at the control layer online, which makes the agricultural operation sub-optimal. In this paper, we focus on agricultural machinery operational efficiency, to represent agricultural productivity; in order to realize optimizing control to further improve agricultural machinery operational efficiency, a new path-tracking control algorithm, named Efficiency-oriented Model Predictive Control (EfiMPC), is proposed. EfiMPC is intrinsically a nested structure, which can consider the global performance of the whole system defined in the operational layer—like the agricultural machinery operational efficiency considered in this paper—in the control layer online; thus, the agricultural machinery operational efficiency can be improved during the farming operation. An unreachable tracking point, denoted as the pseudo-point, has been proposed, to indicate the agricultural machinery operational efficiency objective in a receding horizon fashion; EfiMPC can utilize this pseudo-point to realize the optimizing control online. A simulation case study was used to test the superiority of the proposed EfiMPC algorithm, and the results show that, compared with the traditional NMPC algorithm, the agricultural machinery operational efficiency realized by EfiMPC was improved by 8.56%; thus, the effectiveness of the EfiMPC has been demonstrated.