Suppression of AGRN enhances CD8+ T cell recruitment and inhibits breast cancer progression.

Breast cancer (BC) stands as a prominent contributor to global cancer-related mortality, with an increasing incidence annually. This study aims to investigate AGRN gene expression in BC, as well as explore its influence on the tumor immune microenvironment. AGRN displayed a pronounced upregulation in BC tissues relative to paracancerous tissues. Single-cell RNA analysis highlighted AGRN-specific elevation within cancer cell clusters and also showed expression expressed in stromal as well as immune cell clusters. AGRN upregulation was positively correlated with clinicopathological stage and negatively correlated with BC prognosis. As revealed by the in vitro experiment, AGRN knockdown effectively hinders BC cells in terms of proliferation, invasion as well as migration. AGRN protein, which may interact with EXT1, LRP4, RAPSN, etc., was primarily distributed in the cell cytoplasm. Notably, immune factors might interact with AGRN in BC, evidenced by its discernible associations with immunofactors like IL10, CD274, and PVRL2. Mass spectrometry and immunohistochemistry revealed that the reduction of AGRN led to an increase in CD8+ T cells with triple-negative breast cancer (TNBC). Mechanistically, the connection between TRIM7 and PD-L1 is improved by AGRN, acting as a scaffold, thereby facilitating the accelerated degradation of PD-L1 by TRIM7. Downregulation of AGRN inhibits BC progression and increases CD8+ T cell recruitment. Targeting AGRN may contribute to BC treatment. The biomarker AGRN, serving as a therapeutic target for BC, emerges as a prospective avenue for enhancing both diagnosis and prognosis in BC cases.

Design and experiments with a SLAM system for low-density canopy environments in greenhouses based on an improved Cartographer framework.

To address the problem that the low-density canopy of greenhouse crops affects the robustness and accuracy of simultaneous localization and mapping (SLAM) algorithms, a greenhouse map construction method for agricultural robots based on multiline LiDAR was investigated. Based on the Cartographer framework, this paper proposes a map construction and localization method based on spatial downsampling. Taking suspended tomato plants planted in greenhouses as the research object, an adaptive filtering point cloud projection (AF-PCP) SLAM algorithm was designed. Using a wheel odometer, 16-line LiDAR point cloud data based on adaptive vertical projections were linearly interpolated to construct a map and perform high-precision pose estimation in a greenhouse with a low-density canopy environment. Experiments were carried out in canopy environments with leaf area densities (LADs) of 2.945-5.301 m2/m3. The results showed that the AF-PCP SLAM algorithm increased the average mapping area of the crop rows by 155.7% compared with that of the Cartographer algorithm. The mean error and coefficient of variation of the crop row length were 0.019 m and 0.217%, respectively, which were 77.9% and 87.5% lower than those of the Cartographer algorithm. The average maximum void length was 0.124 m, which was 72.8% lower than that of the Cartographer algorithm. The localization experiments were carried out at speeds of 0.2 m/s, 0.4 m/s, and 0.6 m/s. The average relative localization errors at these speeds were respectively 0.026 m, 0.029 m, and 0.046 m, and the standard deviation was less than 0.06 m. Compared with that of the track deduction algorithm, the average localization error was reduced by 79.9% with the proposed algorithm. The results show that our proposed framework can map and localize robots with precision even in low-density canopy environments in greenhouses, demonstrating the satisfactory capability of the proposed approach and highlighting its promising applications in the autonomous navigation of agricultural robots.

Crosstalk of RNA methylation writers defines tumor microenvironment and alisertib resistance in breast cancer.

Background: The five major RNA methylation modifications (m6A, m1A, m6Am, m5C, and m7G) exert biological roles in tumorigenicity and immune response, mediated mainly by "writer" enzymes. Here, the prognostic values of the "writer" enzymes and the TCP1 role in drug resistance in breast cancer (BC) were explored for further therapeutic strategies. Methods: We comprehensively characterized clinical, molecular, and genetic features of subtypes by consensus clustering. RNA methylation modification "Writers" and related genes_risk (RMW_risk) model for BC was constructed via a machine learning approach. Moreover, we performed a systematical analysis for characteristics of the tumor microenvironment (TME), alisertib sensitivity, and immunotherapy response. A series of experiments in vitro were carried out to assess the association of TCP1 with drug resistance. Results: One "writer" (RBM15B) and two related genes (TCP1 and ANKRD36) were identified for prognostic model construction, validated by GSE1456, GSE7390, and GSE20685 cohorts and our follow-up data. Based on the patterns of the genes related to prognosis, patients were classified into RMW_risk-high and RMW_risk-low subtypes. Lower RMW_Score was associated with better overall survival and the infiltration of immune cells such as memory B cells. Further analysis revealed that RMW_Score presented potential values in predicting drug sensitivity and response for chemo- and immunotherapy. In addition, TCP1 was confirmed to promote BC alisertib-resistant cell proliferation and migration in vitro. Conclusion: RMW_Score could function as a robust biomarker for predicting BC patient survival and therapeutic benefits. This research revealed a potential TCP1 role regarding alisertib resistance in BC, providing new sights into more effective therapeutic plans.

Research on decoupled air speed and air volume adjustment methods for air-assisted spraying in orchards.

Different fruit tree canopies have different requirements for air speed and air volume. Due to the strong relationship between air speed and air volume, the decoupled control of air speed and air volume cannot be achieved using the existing sprayers. In this study, an innovative air-assisted sprayer that supports the independent adjustment of fan speed (0-2940 r/min) and air outlet area (1022.05-2248.51 cm2) is developed, and the maximum air speed and air volume of the sprayer outlet are 45.98 m/s and 37239.94 m3/h, respectively. An independent adjustment test of the fan speed and air outlet area was carried out. The results indicated that the fan speed and air outlet area have opposing adjustment effects on air speed and air volume; decoupled control of the outlet air speed and air volume can thus be achieved through combined control of the fan speed and air outlet area. A test was carried out on combined fan speed and air outlet area control. Two decoupled air speed and air volume adjustment models were established, one with a constant air speed and variable air volume and the other with a constant air volume and variable air speed. The test results show that the air volume adjustment model with constant air speed had a maximum mean error of 1.13%, and the air speed adjustment model with constant air volume had a maximum mean error of 1.67%. The results will provide theoretical and methodological support for the development of airflow adjustment systems for orchard air-assisted sprayer.

Computational model of pesticide deposition distribution on canopies for air-assisted spraying.

The deposited pesticide distribution in fruit tree canopies is crucial for evaluating the efficacy of air-assisted spraying in orchards. Most studies have determined the impact of pesticide application on pesticide deposition on canopies without a quantitative computational model. In this study, an air-assisted orchard sprayer with airflow control was used to perform spraying experiments on artificial and peach trees. In the spraying experiment on an artificial tree, a canopy with leaf areas ranging from 2.54~5.08 m2 was found to require an effective air speed of 18.12~37.05 m/s. The canopy leaf area, air speed at the sprayer fan outlet and spray distance were used as test factors in a three-factor five-level quadratic general rotational orthogonal test to develop a computational model for pesticide deposition at the inner, outer and middle regions of a fruit tree canopy with R 2 values of 0.9042, 0.8575 and 0.8199, respectively. A significance analysis was used to rank the influencing factors for the deposited pesticide distribution in decreasing order of significance as follows: the spray distance, leaf area and air speed for the inner region of the canopy, followed by the spray distance, air speed and leaf area for the middle and outer regions of the canopy. The results of the verification test conducted in a peach orchard showed that the computational errors of the pesticide deposition model for the inner, middle and outer regions of the canopy were 32.62%, 22.38% and 23.26%, respectively. The results provide support for evaluating the efficacy of an air-assisted orchard sprayer and optimizing the sprayer parameters.

Sprayer boom height measurement in wheat field using ultrasonic sensor: An exploratory study.

In order to explore the influencing factors and laws of ultrasonic sensor detecting wheat canopy height, designed an ultrasonic sensor detection height test platform with speed adjustable function. Taking step surface, bare soil and wheat canopy as the research objects, a canopy height calculation method based on K-mean clustering is proposed, and the response characteristics of ultrasonic detection to three media under different operating speeds are explored. Firstly, the step detection test results show that the average detection error of ultrasonic sensor is 1.35%. When the sensor detection distance is switched at the step, with the increase of detection distance, the actual offset at the step increases first and then tends to be stable, and the maximum offset is 10.4cm. The test results of bare soil slope show that the relative error between the detection distance and the manual measurement distance is 1.4% under quasi-static conditions. The leading or lagging of detection under moving conditions is affected by multiple factors such as terrain undulation, speed and detection range. The detection test results of wheat canopy showed that the detection distance was larger than the manual measurement distance, and the smaller the canopy density, the greater the detection error and error variance. When the moving speed is 0.3m/s-1.2m/s, the average detection deviation of the ultrasonic sensor for five kinds of wheat canopy density is 0.14m, and the maximum variance of the detection deviation is 0.07cm2. In this paper, the research on the response characteristics of ultrasonic to the detection of bare soil and sparse canopy in wheat field can provide technical support for the detection of crop canopy in the field.

Wind loss model for the thick canopies of orchard trees based on accurate variable spraying.

Variable application by wind is an efficient application technology recommended by the Food and Agriculture Organization (FAO) of the United Nations that can effectively improve the deposition effect of liquid medicine in a canopy and reduce droplet drift. In view of the difficulty of modelling wind forces in orchard tree canopies and the lack of a wind control model, the wind loss model for a canopy was studied. First, a three-dimensional wind measurement test platform was built for an orchard tree canopy. The orchard tree was located in three-dimensional space, and the inner leaf areas of the orchard tree canopy and the wind force in different areas were measured. Second, light detection and ranging (LiDAR) point cloud data of the orchard tree canopy were obtained by LiDAR scanning. Finally, classic regression, partial least squares regression (PLSR), and back propagation (BP) neural network algorithms were used to build wind loss models in the canopy. The research showed that the BP neural network algorithm can significantly improve the fitting accuracy of the model. Under different fan speeds of 1,381 r/min, 1,502 r/min, and 1,676 r/min, the coefficient of determination (R2) of the model were 81.78, 72.85, and 69.20%, respectively, which were 19.38, 7.55, and 12.3% higher than those of the PLSR algorithm and 21.48, 22.25, and 24.3% higher than those of multiple regression analysis. The comparison showed that the BP neural network algorithm obtains the highest model accuracy, but because the model is not intuitive, PLSR has the advantages of intuitive and simple models in the three algorithms. In practical applications, the wind loss model based on a BP neural network or PLSR can be selected according to the operational requirements and software and hardware conditions. This study can provide a basis for wind control in precise variable spraying and promote the development of wind control technologies.

Cabbage and Weed Identification Based on Machine Learning and Target Spraying System Design.

The complexity of natural elements seriously affects the accuracy and stability of field target identification, and the speed of an identification algorithm essentially limits the practical application of field pesticide spraying. In this study, a cabbage identification and pesticide spraying control system based on an artificial light source was developed. With the image skeleton point-to-line ratio and ring structure features of support vector machine classification and identification, a contrast test of different feature combinations of a support vector machine was carried out, and the optimal feature combination of the support vector machine and its parameters were determined. In addition, a targeted pesticide spraying control system based on an active light source and a targeted spraying delay model were designed, and a communication protocol for the targeted spraying control system based on electronic control unit was developed to realize the controlled pesticide spraying of targets. According to the results of the support vector machine classification test, the feature vector comprised of the point-to-line ratio, maximum inscribed circle radius, and fitted curve coefficient had the highest identification accuracy of 95.7%, with a processing time of 33 ms for a single-frame image. Additionally, according to the results of a practical field application test, the average identification accuracies of cabbage were 95.0%, average identification accuracies of weed were 93.5%, and the results of target spraying at three operating speeds of 0.52 m/s, 0.69 m/s and 0.93 m/s show that the average invalid spraying rate, average missed spraying rate, and average effective spraying rate were 2.4, 4.7, and 92.9%, respectively. Moreover, it was also found from the results that with increasing speeds, the offset of the centre of the mass of the target increased and reached a maximum value of 28.6 mm when the speed was 0.93 m/s. The void rate and pesticide saving rate were 65 and 33.8% under continuous planting conditions and 76.6 and 53.3% under natural seeding deficiency conditions, respectively.

CMPC: An Innovative Lidar-Based Method to Estimate Tree Canopy Meshing-Profile Volumes for Orchard Target-Oriented Spray.

Canopy characterization detection is essential for target-oriented spray, which minimizes pesticide residues in fruits, pesticide wastage, and pollution. In this study, a novel canopy meshing-profile characterization (CMPC) method based on light detection and ranging (LiDAR)point-cloud data was designed for high-precision canopy volume calculations. First, the accuracy and viability of this method were tested using a simulated canopy. The results show that the CMPC method can accurately characterize the 3D profiles of the simulated canopy. These simulated canopy profiles were similar to those obtained from manual measurements, and the measured canopy volume achieved an accuracy of 93.3%. Second, the feasibility of the method was verified by a field experiment where the canopy 3D stereogram and cross-sectional profiles were obtained via CMPC. The results show that the 3D stereogram exhibited a high degree of similarity with the tree canopy, although there were some differences at the edges, where the canopy was sparse. The CMPC-derived cross-sectional profiles matched the manually measured results well. The CMPC method achieved an accuracy of 96.3% when the tree canopy was detected by LiDAR at a moving speed of 1.2 m/s. The accuracy of the LiDAR system was virtually unchanged when the moving speeds was reduced to 1 m/s. No detection lag was observed when comparing the start and end positions of the cross-section. Different CMPC grid sizes were also evaluated. Small grid sizes (0.01 m × 0.01 m and 0.025 m × 0.025 m) were suitable for characterizing the finer details of a canopy, whereas grid sizes of 0.1 m × 0.1 m or larger can be used for characterizing its overall profile and volume. The results of this study can be used as a technical reference for the development of a LiDAR-based target-oriented spray system.

A LiDAR Sensor-Based Spray Boom Height Detection Method and the Corresponding Experimental Validation.

Sprayer boom height (Hb) variations affect the deposition and distribution of droplets. An Hb control system is used to adjust Hb to maintain an optimum distance between the boom and the crop canopy, and an Hb detection sensor is a key component of the Hb control system. This study presents a new, low-cost light detection and ranging (LiDAR) sensor for Hb detection developed based on the principle of single-point ranging. To examine the detection performance of the LiDAR sensor, a step height detection experiment, a field ground detection experiment, and a wheat stubble (WS) height detection experiment as well as a comparison with an ultrasonic sensor were performed. The results showed that the LiDAR sensor could be used to detect Hb. When used to detect the WS height (HWS), the LiDAR sensor primarily detected the WS roots and the inside of the WS canopy. HWS and movement speed of the LiDAR sensor (VLiDAR) has a greater impact on the detection performance of the LiDAR sensor for the WS canopy than that for the WS roots. The detection error of the LiDAR sensor for the WS roots is less than 5.00%, and the detection error of the LiDAR sensor for the WS canopy is greater than 8.00%. The detection value from the LiDAR sensor to the WS root multiplied by 1.05 can be used as a reference basis for adjusting Hb, and after the WS canopy height is added to the basis, the value can be used as an index for adjusting Hb in WS field spraying. The results of this study will promote research on the boom height detection method and autonomous Hb control system.

Dual-Therapeutics-Loaded Mesoporous Silica Nanoparticles Applied for Breast Tumor Therapy.

A material that possesses high loading efficiency (in terms of delivering small molecular drugs, nucleic acids, peptides, and proteins) has various medical applications, such as in tumor diagnosis and gene therapy or chemotherapy of tumors. Mesoporous silica nanoparticles are ideal nanocarriers for constructing drug delivery systems because of the unique mesoporous channels for encapsulation and the sustainable release of anticancer drugs. Herein, we demonstrate a doxorubicin (DOX)-peptides double-loaded and -response nanodrug (DMK nanoplatforms) as a multifunctional nanoplatform for chemotherapy of tumors. The nanoparticles are prepared by a surface modification strategy. The KLAK and DOX release in an acidic/reductive tumor microenvironment, which efficiently penetrate cell nuclei and generate the antitumor effect. Our study provides a new approach for developing a smart drug delivery nanosystem, particularly for peptides-guided pH-sensitive chemotherapy.

Circ-TFCP2L1 Promotes the Proliferation and Migration of Triple Negative Breast Cancer through Sponging miR-7 by Inhibiting PAK1.

CircRNAs are essential factors that have been verified to regulate various forms of carcinogenesis. However, the role of circRNAs in triple negative breast cancer (TNBC) tumourigenesis is not well clarified. In this study, we explored the circRNA expression profiles and possible modulation mechanism of circRNAs on triple negative breast cancer tumourigenesis. We used three pairs of triple negative breast cancer tissues and adjacent noncancerous tissues to perform a human circRNA microarray for screening of circRNA expression patterns in TNBC. The results showed that circ-TFCP2L1 was significantly up-regulated in TNBC tissues and cells, tending to have a shorter disease-free survival of TNBC patients. In vitro loss-of-function experiments showed that knockdown of circ-TFCP2L1 significantly suppressed the proliferation and migration of TNBC cells. Moreover, the results showed that the proliferation and migration capabilities and PAK1 expression in TNBC cells treated with si-circ-TFCP2L1 + miR-7 mimics were significantly suppressed compared with the normal group. Therefore, circ-TFCP2L1 was identified as a sponge of miR-7 functionally targeting PAK1 and further promoting the proliferation and migration of TNBC cells. Taken together, the results from our study reveal a novel regulatory mechanism and offer novel insight into the role of circ-TFCP2L1 in progression of triple negative breast cancer.

A Canopy Density Model for Planar Orchard Target Detection Based on Ultrasonic Sensors.

Orchard target-oriented variable rate spraying is an effective method to reduce pesticide drift and excessive residues. To accomplish this task, the orchard targets' characteristic information is needed to control liquid flow rate and airflow rate. One of the most important characteristics is the canopy density. In order to establish the canopy density model for a planar orchard target which is indispensable for canopy density calculation, a target density detection testing system was developed based on an ultrasonic sensor. A time-domain energy analysis method was employed to analyze the ultrasonic signal. Orthogonal regression central composite experiments were designed and conducted using man-made canopies of known density with three or four layers of leaves. Two model equations were obtained, of which the model for the canopies with four layers was found to be the most reliable. A verification test was conducted with different layers at the same density values and detecting distances. The test results showed that the relative errors of model density values and actual values of five, four, three and two layers of leaves were acceptable, while the maximum relative errors were 17.68%, 25.64%, 21.33% and 29.92%, respectively. It also suggested the model equation with four layers had a good applicability with different layers which increased with adjacent layers.

Two-dimensional automatic measurement for nozzle flow distribution using improved ultrasonic sensor.

Spray deposition and distribution are affected by many factors, one of which is nozzle flow distribution. A two-dimensional automatic measurement system, which consisted of a conveying unit, a system control unit, an ultrasonic sensor, and a deposition collecting dish, was designed and developed. The system could precisely move an ultrasonic sensor above a pesticide deposition collecting dish to measure the nozzle flow distribution. A sensor sleeve with a PVC tube was designed for the ultrasonic sensor to limit its beam angle in order to measure the liquid level in the small troughs. System performance tests were conducted to verify the designed functions and measurement accuracy. A commercial spray nozzle was also used to measure its flow distribution. The test results showed that the relative error on volume measurement was less than 7.27% when the liquid volume was 2 mL in trough, while the error was less than 4.52% when the liquid volume was 4 mL or more. The developed system was also used to evaluate the flow distribution of a commercial nozzle. It was able to provide the shape and the spraying width of the flow distribution accurately.

[Study on spectral detection of green plant target].

Weeds grow scatteredly in fields, where many insentient objects exist, for example, withered grasses, dry twig and barriers. In order to improve the precision level of spraying, it is important to study green plant detecting technology. The present paper discussed detecting method of green plant by using spectral recognizing technology, because of the real-time feature of spectral recognition. By analyzing the reflectivity difference between each of the two sides of the "red edge" of the spectrum from plants and surrounding environment, green plant discriminat index (GPDI) is defined as the value which equals the reflectivity ratio at the wavelength of 850 nm divided by the reflectivity ratio at the wavelength of 650 nm. The original spectral data of green plants and the background were measured by using the handhold FieldSpec 3 Spectroradiometer manufactured by ASD Inc. in USA. The spectral data were processed to get the reflectivity of each measured objects and to work out the GPDI thereof as well. The classification model of green plant and its background was built up using decision tree method in order to obtain the threshold of GPDI to distinguish green plants and the background. The threshold of GPDI was chosen as 5.54. The detected object was recognized as green plant when it is GPDI>GPDITH, and vice versa. Through another test, the accuracy rate was verified which was 100% by using the threshold. The authors designed and developed the green plant detector based on single chip microcomputer (SCM) "AT89S51" and photodiode "OPT101" to realize detecting green plants from the background. After passing through two optical filters, the center wavelengths of which are 650 and 850 nm respectively, the reflected light from measured targets was detected by two photodiodes and converted into electrical signals. These analog signals were then converted to digital signals via an analog-to-digital converter (ADS7813) after being amplified by a signal amplifier (OP400). The converted digital signal of reflected light was eventually sent to the SCM (AT89S51) and was calculated and processed there. The processing results and the control signals were given out to actuate executive device to open or close the solenoid valve. The test results show that the level of detectivity of the designed detector was affected by the species, size, and density of weeds. The detectivity of broad-leaf species is higher than that of narrow-leaf species. Broad-leaf species are more easily detected than those narrow-leaf ones; the bigger the plants and the denser the leaves are, the higher the level of detectivity is.