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The probability analysis of ground deformation is becoming a trend to estimate and control the risk brought by shield tunnelling. The gap parameter is regarded as an effective tool to estimate the ground loss of tunnelling in soft soil. More specifically, ω, which is a gap parameter component defined as the over (or insufficient) excavation due to the change in the posture of the shield machine, may contribute more to the uncertainty of the ground loss. However, the existing uncertainty characterization methods for ωhave several limitations and cannot explain the uncertain correlations between the relevant parameters. Along these lines, to better characterize the uncertainty of ω, the multivariate probability distribution was developed in this work and a dynamic prediction was proposed for it. To attain this goal, 1 523 rings of the field data coming from the shield tunnel between Longqing Road and Baiyun Road in Kunming Metro Line 5 were utilized and 44 parameters including the construction, stratigraphic, and posture parameters were collected to form the database. According to the variance filter method, the mutual information method, and the value of the correlation coefficients, the original 44 parameters were reduced to 10 main parameters, which were unit weight, the stoke of the jacks (A, B, C, and D groups), the pressure of the pushing jacks (A, C groups), the chamber pressure, the rotation speed, and the total force. The multivariate probability distribution was constructed based on the Johnson system of distributions. Moreover, the distribution was satisfactorily verified in explaining the pairwise correlation between ωand other parameters through 2 million simulation cases. At last, the distribution was used as a prior distribution to update the marginal distribution of ωwith any group of the relevant parameters known. The performance of the dynamic prediction was further validated by the field data of 3 shield tunnel cases.

The mortality of patients with severe pneumonia caused by H1N1 infection is closely related to viral replication and cytokine storm. However, the specific mechanisms triggering virus replication and cytokine storm are still not fully elucidated. Here, we identified hypoxia inducible factor-1α (HIF-1α) as one of the major host molecules that facilitates H1N1 virus replication followed by cytokine storm in alveolar epithelial cells. Specifically, HIF-1α protein expression is upregulated after H1N1 infection. Deficiency of HIF-1α attenuates pulmonary injury, viral replication and cytokine storm in vivo. In addition, viral replication and cytokine storm were inhibited after HIF-1α knockdown in vitro. Mechanistically, the invasion of H1N1 virus into alveolar epithelial cells leads to a shift in glucose metabolism to glycolysis, with rapid production of ATP and lactate. Inhibition of glycolysis significantly suppresses viral replication and inflammatory responses. Further analysis revealed that H1N1-induced HIF-1α can promote the expression of hexokinase 2 (HK2), the key enzyme of glycolysis, and then not only provide energy for the rapid replication of H1N1 virus but also produce lactate, which reduces the accumulation of the MAVS/RIG-I complex and inhibits IFN-α/β production. In conclusion, this study demonstrated that the upregulation of HIF-1α by H1N1 infection augments viral replication and cytokine storm by cellular metabolic reprogramming toward glycolysis mainly through upregulation of HK2, providing a theoretical basis for finding potential targets for the treatment of severe pneumonia caused by H1N1 infection.

•Two leukemic evolutionary patterns, “clonal shift” and “clonal drift” are unveiled in relapsed T-ALL via single-cell multiomics profiling.•High RNA-binding protein MSI2 level accounts for persistent clones at relapse through the posttranscriptional regulation of MYC in T-ALL.

Mobile robots extensively employ multiple sensors, including RGBD cameras, LiDAR, and thermal sensors. Sensor fusion plays a vital role in localization and environment perception tasks. However, traditional manual target-based methods for achieving consistent alignment among multiple sensors, especially thermal cameras, are laborious and lack adaptability. This work introduces an autonomous–targetless framework for calibrating LiDAR–RGB and LiDAR–thermal sensors in mobile robots. In our proposed framework, we examine the characteristics of thermal images, identify suitable calibration scenarios, and employ the thermal bridge and line-based PnL algorithm to enable autonomous and targetless calibration. Experimental results demonstrate the efficiency of our method, with overall translation errors of 2.77 and 3.86 cm, and overall rotation errors of 0.21° and 0.46°, respectively, in LiDAR–RGB and LiDAR–thermal calibrations. These results are comparable to state-of-the-art techniques and traditional target-based manual methods. The analysis of different thermal scenes highlights the importance of well-aligned and distinguishable edges across thermal–RGB–LiDAR modalities for optimal calibration results. Simulation tests using synthetic data and validation tests using real-world data showcase the robustness of our model in executing targetless extrinsic calibrations.

In the realm of crack inspection for complex infrastructures, traditional methods have primarily relied on expensive structural health monitoring instruments and labor-intensive procedures. The emergence of unmanned aerial vehicle (UAV) technology brings about effective and innovative solutions for bridge inspection. To advance the technology, this study presents a novel crack inspection system that employs light detection and ranging (LiDAR) scanning to construct a 3-D model of the target structure. A path planner is then developed to ensure complete coverage of all crack points on the structure being inspected. Through extensive testing, the proposed system demonstrates successful detection and localization of various types of cracks. Furthermore, our improved deep crack segmentation model, U-Net with spectral block and self-attention module, surpasses the performance of the original U-Net model, exhibiting a 3.2% higher Dice coefficient and a 3.3% higher mean intersection over union (mIoU) evaluation metric on our self-established crack dataset. In the case of the Crack500 public dataset, our model outperforms the original U-Net model by 10% in Dice coefficient and 14% in mIoU. Moreover, our U-Net with spectral block and self-attention module (USSA-Net) outperforms other latest state-of-the-art (SOTA) models on the DeepCrack500 dataset, surpassing the progressive and adaptive fusion (PAF)-Net and progressive and hierarchical context fusion (PHCF)-Net by approximately 5% in Dice coefficient and 2.7% in mIoU. For crack size estimations, our proposed system accurately estimates the horizontal and vertical dimensions of cracks, achieving a root-mean-square error (RMSE) of 9.9 and 6.2 mm, respectively. Overall, the system achieves millimeter-level crack size estimation accuracy. Moreover, our system is characterized by its low-cost nature and lightweight design. Experimental results showcase the system’s robustness and effectiveness in executing real-world crack inspection tasks, even within complex environments.

[Purpose] The maize soybean relay strip intercropping systems (MS) can improve nitrogen (N) uptake. N uptake mechanisms require further study to better understand how different interspecific row spacing affect root physiological activity and to determine crop’s N utilization. [Methods] A two-year field experiment was conducted with two N application levels referred to as no N application (NN) and conventional N application (CN) were paired with different interspecific distance, including 30, 45, 60, 75 cm (MS30/45/60/75), and 100 cm of monoculture maize and soybean (MM/SS100). [Results] Compared to MM100, MS30 increased root activity and respiration intensity by 35.8%, 36.6%, antioxidant enzyme activity, and the relative expression levels of genes encoding antioxidant enzymes markedly improved. The NO3−-N and NH4+-N uptake increased 83.5% and 39.4%, the N uptake rate improved and the maximum achievable total N uptake (Nmax) increased 49.4% under NN, 20.7% under CN. Relay intercropping soybean in MS60 increased root activity by 63.5%, enhanced antioxidant properties, and root N uptake, shoot N accumulation notably increased than SS100. The N uptake rate of MS60 exceeded SS100 at 70 days after sowing (DAS), and the Nmaxincreased 39.8% than SS100. The correlation analysis indicated aboveground N uptake were positively correlated with root physiological activity. [Conclusions] Therefore, optimizing crop configuration achieved N accumulation increase, N uptake rate accelerate and better N balance by promoting root physiological activity in MS. The suitable interspecific row spacing (45–60 cm) can coordinate the advantages of N uptake to achieve optimal N use and land productivity in relay intercropping system.

Forecasting NOx concentration in fluid catalytic cracking (FCC) regeneration flue gas can guide the real-time adjustment of treatment devices, and then furtherly prevent the excessive emission of pollutants. The process monitoring variables, which are usually high-dimensional time series, can provide valuable information for prediction. Although process features and cross-series correlations can be captured through feature extraction techniques, they are commonly linear transformation, and conducted or trained separately from forecasting model. This process is inefficient and might not be an optimal solution for the following forecasting modeling. Therefore, we propose a time series encoding temporal convolutional network (TSE-TCN). By parameterizing the hidden representation of the encoding–decoding structure with the temporal convolutional network (TCN), and combining the reconstruction error and the prediction error in the objective function, the encoding–decoding procedure and the temporal predicting procedure can be trained by a single optimizer. The effectiveness of the proposed method is verified through an industrial reaction and regeneration process of an FCC unit. Results demonstrate that TSE-TCN outperforms some state-of-art methods with lower root mean square error (RMSE) by 2.74% and higher ${R}^{2}$ score by 3.77%.

Relay cropping of Poaceae and Fabaceae promotes high yield and land-use efficiency by allowing a double harvest. However, it is difficult to increase yield synergistically because of the reduced photosynthetic abilities of legume leaves under the shade of graminoids. Leaf photosynthetic capacity in relay cropping systems is associated with ecological niche differentiation and photosynthetic compensation after restoration of normal light. We conducted a field experiment in southwest China in 2020–2021 to evaluate the effects of three cropping patterns: maize–soybean relay cropping (IMS), monoculture maize (MM), and monoculture soybean (SS), and N application levels: no N application (NN:0 kg N ha−1), reduced N (RN: 180 kg N ha−1), and conventional N (CN: 240 kg N ha−1). Compared to monocropping, relay cropping increased the stay-green traits of maize and soybean by 13% and 89%, respectively. Relay cropping prolonged the leaf stay-green duration in the maize and soybean lag phase by almost 4 and 8 days, respectively. Relay cropping maize (IM) increased the leaf area index (LAI) by 79.4% to 88.5% under NN and 55.5% to 148% under RN. Relay cropping soybean (IS) increased the LAI from 115% to 437% at days 40 to 50 after anthesis. IM increased yield by 65.6%. IS increased yield by 9.7%. HI and system yield were at their highest values under RN. In the relay cropping system, reduced N application extended green leaf duration, increased photosynthesis inside the canopy at multiple levels, ultimately increases soybean yield synergistically.

In existing online multiple object tracking algorithms, schemes that combine object detection and re‐identification (ReID) tasks in a single model for simultaneous learning have drawn great attention due to their balanced speed and accuracy. However, different tasks require to focus different features. Learning two different tasks in the same model extracted features can lead to competition between the two tasks, making it difficult to achieve optimal performance. To reduce this competition, a task‐related attention network, which uses a self‐attention mechanism to allow each branch to learn on feature maps related to its task is proposed. Besides, a smooth gradient‐boosting loss function, which improves the quality of the extracted ReID features by gradually shifting the focus to the hard negative samples of each object during training is introduced. Extensive experiments on MOT16, MOT17, and MOT20 datasets demonstrate the effectiveness of the proposed method, which is also competitive in current mainstream algorithm. We propose a task‐related attention network to reduce the competition between object detection and ReID. we introduce a smooth gradient‐boosting loss function that improves the quality of the extracted ReID features by gradually shifting the focus to the hard negative samples of each object during training.

The Laplacian regularization has been widely used in neural networks due to its ability to improve generalization performance, which enforces adjacent samples with the same labels to share similar features. However, most existing methods only consider the global structure of the data with the same labels, but neglect samples in boundary areas with different labels. To address this limitation and improve performance, this brief proposes a novel regularization method that enhances the hidden structure of deep neural networks. Our proposed method imposes a double Laplacian regularization on the objective function and leverages full data information to capture its hidden structure in the manifold space. The double Laplacian regularization applies both attraction and repulsion effects on the hidden layer, which encourages the hidden features of instances with the same label to be closer, and forces those of different categories to be further away. Extensive experiments demonstrate the proposed method leads to significant improvements in accuracy on different types of deep neural networks.

Forest height and underlying terrain reconstruction is one of the main aims in dealing with forested areas. Theoretically, synthetic aperture radar tomography (TomoSAR) offers the possibility to solve the layover problem, making it possible to estimate the elevation of scatters located in the same resolution cell. This article describes a deep learning approach, named tomographic SAR neural network (TSNN), which aims at reconstructing forest and ground height using multipolarimetric multibaseline (MPMB) SAR data and light detection and ranging (LiDAR)-based data. The reconstruction of the forest and ground height is formulated as a classification problem, in which TSNN, a feedforward network, is trained using covariance matrix elements as input vectors and quantized LiDAR-based data as the reference. In our work, TSNN is trained and tested with P-band MPMB data acquired by ONERA over Paracou region of French Guiana in the frame of the European Space Agency’s campaign TROPISAR and LiDAR-based data provided by the French Agricultural Research Center. The novelty of the proposed TSNN is related to its ability to estimate the height with a high agreement with LiDAR-based measurement and actual height with no requirement for phase calibration. Experimental results of different covariance window sizes are included to demonstrate that TSNN conducts height measurement with high spatial resolution and vertical accuracy outperforming the other two TomoSAR methods. Moreover, the conducted experiments on the effects of phase errors in different ranges show that TSNN has a good tolerance for small errors and is still able to precisely reconstruct forest heights.

Biotic or abiotic stresses reduce leaf area of soybean plants in the intercropping system, especially during critical reproductive growth phase (from pod-initiation to physiological-maturity) of soybean, which finally influences yield and yield components. However, total yield loss due to reduction in soybean leaf area under maize/soybean intercropping system is still unclear. In a three-year field study, an experiment consisted of four treatments: no removal of trifoliate (CK, 100% leaf area), removal of three-trifoliate (SI, 85% leaf area), removal of six-trifoliate (SII, 70% leaf area), and removal of nine-trifoliate (SIII, 55% leaf area) from the top of the soybean canopy under maize/soybean intercropping. These defoliation treatments were applied at the pod initiation (R3) stage by removing the different number of fully developed trifoliate from the top of the soybean canopy in maize/soybean intercropping system. Results revealed that defoliation significantly decreased total dry matter accumulation and partitioning to vegetative and reproductive organs. Compared with CK (no defoliation), treatments SI, SII, and SIII reduced crop growth rate (by 25%, 46%, and 75%), reproductive growth rate (by 21%, 44%, and 63%), pod-initiation (by 11%, 23%, and 32%), while increased pod-abscission (by 11%, 20%, and 37%) and photosynthetic-rate (by 8%, 19%, and 28%), respectively at physiological-maturity. These negative responses reduced pods plant−1by 16%, 32%, and 49% and seeds plant−1by 20%, 34%, and 46% in SI, SII, and SIII, respectively, compared to non-defoliated. Overall, in SI, SII, and SIII, soybean produced 80%, 67%, and 55% of CK yield. Results implied that any change in leaf area of intercropped-soybean, especially during reproductive phase, will directly affect the availability of photoassimilates and nutrients for developing pods and seeds. Thus, more attention should be paid to improve leaf area of intercropped soybean for the high productivity of intercropping systems via appropriate variety selection or planting arrangement. Furthermore, breeders can evolve new soybean varieties, particularly for the intercropping systems, which can cope with the shading effects of tall crops in intercropping systems. Future studies are needed to understand the internal signaling and the molecular mechanism controlling in soybean in intercropping system.

Maize (Zea mayL.)–soybean [Glycine max(L.) Merr.] intercropping is popular in many countries because of its high productivity. However, no studies have explored maize–soybean intercropping via bibliometric methods. Taking the Web of Science database, the visualization applications of CiteSpace and VOSviewer were used to visually analyze the literature related to research in the field of maize–soybean intercropping, with the aim of studying keyword hotspots and evolving trends, and analyzing the future research directions. The results showed that the annual number of publications on maize–soybean intercropping showed rapid growth from 2010 to 2021. The top three publishing countries were China, the United States, and India, and the top three institutions were Sichuan Agricultural University, the Ministry of Agriculture and Rural Affairs of China, and China Agricultural University. Th three most popular journals were Field Crops Research, Agronomy Journal, and Indian Journal of Agronomy. Agronomy, multidisciplinary agriculture and plant science were the most popular research categories. A keyword analysis indicated that “maize” was the most popular study area. Crop root coupling processes, biological nitrogen fixation, efficient utilization of resources, carbon sequestration and emission reduction, microbial communities, and interspecific relationships are the current hot research topics. This study review and analyzed the current research hotspots and future research trends to provide an important reference for scientists to better respond to food security issues. There has been a rapid increase in maize–soybean intercropping research.China, Chinese institutions, and Chinese scholars have conducted much of this research.Maize–soybean intercropping has shown an interdisciplinary distribution.Keywords showed the heterogeneity in maize–soybean intercropping publications across years.

Assessing canopy nitrogen content (CNC) and canopy carbon content (CCC) of maize by hyperspectral remote sensing data permits estimating cropland productivity, protecting farmland ecology, and investigating the nitrogen and carbon cycles in the atmosphere. This study aimed to assess maize CNC and CCC using canopy hyperspectral information and uninformative variable elimination (UVE). Vegetation indices (VIs) and wavelet functions were adopted for estimating CNC and CCC under varying water and nitrogen regimes. Linear, nonlinear, and partial least squares (PLS) regression models were fitted to VIs and wavelet functions to estimate CNC and CCC, and were evaluated for their prediction accuracy. UVE was used to eliminate uninformative variables, improve the prediction accuracy of the models, and simplify the PLS regression models (UVE-PLS). For estimating CNC and CCC, the normalized difference vegetation index (NDVI, based on red edge and NIR wavebands) yielded the highest correlation coefficients (r > 0.88). PLS regression models showed the lowest root mean square error (RMSE) among all models. However, PLS regression models required nine VIs and four wavelet functions, increasing their complexity. UVE was used to retain valid spectral parameters and optimize the PLS regression models. UVE-PLS regression models improved validation accuracy and resulted in more accurate CNC and CCC than the PLS regression models. Thus, canopy spectral reflectance integrated with UVE-PLS can accurately reflect maize leaf nitrogen and carbon status.

The leaf area index (LAI) is crucial for assessing and monitoring maize (Zea maysL.) vegetation status and photosynthetic ability. Predicting maize LAI by hyperspectral remote sensing technology is significant for managing agricultural production. In this study, three N rates (0, 120, and 240 kg N ha−1) and four drought stress treatments (60–70%, 45–55%, 30–40%, and 15–25% of field capacity), were imposed to provide the different environments for maize. The canopy spectral reflectance and LAI of maize were measured at the V6 and V12 stages. In this study, the main objectives were to investigate the performance of a new statistical method for monitoring LAI from canopy spectral reflectance. We used the canopy spectral reflectance to estimate the LAI and compared several methods of spectral analysis, including vegetation indices, wavelet functions, and the combination of continuous wavelet transform (CWT)–uninformative variable elimination (UVE)–partial least squares (PLS) (i.e., CWT–UVE–PLS), for spectral reflectance data analysis and model construction of maize LAI. Results showed that the model using the combination of db3–UVE–PLS achieved the best performance (with the highest coefficient of determination [R2] and lowest RMSE for the calibration [R2= .979, RMSEC = .172] and validation [R2= .861, RMSEP = .533] datasets, respectively) in estimating the maize LAI. The CWT–UVE–PLS exhibited a considerable advantage in avoiding redundant or noise information interaction and achieving excellent correlations with maize LAI. The CWT‐UVE‐PLS method was developed to estimate maize leaf area index.The vegetation indices and wavelet functions show a strong correlation with the leaf area index.The CWT‐UVE‐PLS method achieves the best R2and the lowest root mean square error.The CWT‐UVE‐PLS method can overcome the underestimation problem and saturation effect.The leaf area index can be accurately monitored with the CWT‐UVE‐PLS method.

ABSTRACTPorous titanium nitride coatings are prepared on 310 stainless steel by the powder immersion reaction-assisted coating (PIRAC) titanisation-nitriding process. For titanisation below 950°C for 30 min, the detected phases on 310 steel are FeTi, Fe2Ti and TiN. Titanisation at 1000°C for 30 min, the detected phases on 310 steel are FeTi, Fe2Ti, TiN and Ti. After further nitriding at 800°C for 8 h, titanium nitride coating composed of TiN, Ti2N and TiN0.3can be formed. Pore size and porosity of the prepared coating are decreased with increasing titanisation temperature, while the uniformity of the pores in the coating and hardness of the coating is increased with increasing titanisation temperature. Microhardness of the porous PIRAC TiN coating prepared by titanisation at 900°C + nitriding at 800 °C, titanisation at 950°C + nitriding at 800 °C and titanisation at 1000°C + nitriding at 800°C is 8.65, 8.98 and 9.66 GPa, respectively.

Piezoelectric energy harvesters (PEHs) fabricated using piezoceramics could convert directly the mechanical vibration energy in the environment into electrical energy. The high piezoelectric charge coefficient (d33) and large piezoelectric voltage coefficient (g33) are key factors for the high-performance PEHs. However, high d33and large g33are difficult to simultaneously achieve with respect to g33= d33/(ε0εr) and d33= 2Qε0εrPr. Herein, the energy harvesting performance is optimized by tailoring the CaZrO3content in (0.964−x)(K0.52Na0.48)(Nb0.96Sb0.04)O3−0.036(Bi0.5Na0.5)ZrO3−xCaZrO3ceramics. First, the doping CaZrO3could enhance the dielectric relaxation due to the compositional fluctuation and structural disordering, and thus reduce the domain size to ∼30 nm for x= 0.006 sample. The nanodomains switch easily to external electric field, resulting in large polarization. Second, the rhombohedral–orthorhombic–tetragonal phases coexist in x= 0.006 sample, which reduces the polarization anisotropy and thus improves the piezoelectric properties. The multiphase coexistence structures and miniaturized domains contribute to the excellent piezoelectric properties of d33(354 pC/N). Furthermore, the dielectric relative permittivity (εr) reduces monotonously as the CaZrO3content increases due to the relatively low ion polarizability of Ca2+and Zr4+. As a result, the optimized energy conversion coefficient (d33× g33, 5508 × 10−15m2/N) is achieved for x= 0.006 sample. Most importantly, the assembled PEH with the optimal specimen shows the excellent output power (∼48 µW) and lights up 45 red commercial light-emitting diodes (LEDs). This work demonstrates that tailoring ferroelectric/relaxor behavior in (K,Na)NbO3-based piezoelectric ceramics could effectively enhance the electrical output of PEHs.

By analyzing the composite effects of the plastic shear strain and confining stress according to the laboratory test results, a model adopting a deteriorating elastic modulus for the intact rock is proposed. Soft rock such as coal and the strong rock such as granite with different quality are investigated. Geological Strength Index (GSI) is adopted to represent the integrity of rock, to be specific, from intact rock to the rock mass. Specifically, for the strain-softening rock mass, the elastic modulus is gained by introducing GSI into the deterioration model. The strength parameters are obtained by fitting GSI into Hoek-Brown failure criterion. Then, the elastic modulus and strength parameters of the rock mass are employed in the numerical procedure for solving out the rock deformation, the radii of the plastic softening and residual zones of the tunnels. The rationality of the numerical procedure is verified with the existing semi-analytical and analytical procedures. In the discussion, the influences of the critical plastic parameters and GSI, on the elastic modulus, rock deformation, radii of the plastic softening and residual zones are investigated. Five models for the variation of the modulus including the proposed deterioration model are defined. The rock mass deformation behaviours of the five models are compared. The results indicate that, in the plastic softening zone that is far away from the tunnel periphery, the elastic modulus is more sensitive to the plastic shear strain, whereas near the tunnel periphery, the elastic modulus is primarily affected by the confining stress. Regarding a linear decrease of the elastic modulus versus the plastic shear strain overestimates the elastic modulus to some extent, especially for the soft rock mass.

Ivermectin, an effective treatment for scabies, is not licensed for children weighing <15 kg. Pharmacokinetic modelling has shown a 3 mg dose in young children (2–4 years, weighing 10–14 kg) achieves comparable drug exposure to a 200 μg/kg dose in children aged ≥5 years. This trial evaluated a 3 mg dose in young children.

Plant photosynthetic capacity directly determines crop yield. Light quality regulates photosynthetic capacity. This review discusses plant responses to far-red light from the phenotypic to the molecular level, focusing specifically on the improvement of photosynthetic capacity by adjustment of photosynthetic electron transport and the path of light energy. Far-red light can also regulate leaf angle and increase plant height and leaf area, via expression of associated genes, to capture more light energy. Thus, far-red light regulates plant morphology and photosynthetic capacity. Identifying the mechanism of this regulation may lead to increased crop yields.

Minimal residual disease that persists after chemotherapy is the most valuable prognostic marker for haematological malignancies and solid cancers. Unfortunately, our understanding of the resistance elicited in minimal residual disease is limited due to the rarity and heterogeneity of the residual cells. Here we generated 161,986 single-cell transcriptomes to analyse the dynamic changes of B-cell acute lymphoblastic leukaemia (B-ALL) at diagnosis, residual and relapse by combining single-cell RNA sequencing and B-cell-receptor sequencing. In contrast to those at diagnosis, the leukaemic cells at relapse tended to shift to poorly differentiated states, whereas the changes in the residual cells were more complicated. Differential analyses highlighted the activation of the hypoxia pathway in residual cells, resistant clones and B-ALL with MLL rearrangement. Both in vitro and in vivo models demonstrated that inhibition of the hypoxia pathway sensitized leukaemic cells to chemotherapy. This single-cell analysis of minimal residual disease opens up an avenue for the identification of potent treatment opportunities for B-ALL.

Paraquat (PQ) and diquat (DQ), two highly efficient herbicides sharing similar chemical backbone, both induce reactive oxygen species and are highly toxic to humans and livestock, however, PQ but not DQ poisoning result in pulmonary fibrosis, the leading cause of high mortality rate in patients suffering PQ toxicity. Understanding the unique mechanism of PQ different from DQ therefore would provide potential strategies to reduce PQ-induced pulmonary fibrosis. Here, we identified that PQ but not DQ continuously upregulates TGF-β expression in alveolar type II (AT II) cells. Importantly, such high expression of TGF-β increases cytosolic calcium levels and further promotes the activation of calcineurin-NFAT axis. TGF-β mainly activates NFATc1 and NFATc2, but not NFATc3 or NFATc4. Administration of the inhibitors targeting cytosolic calcium or calcineurin largely reverses PQ-induced epithelial-mesenchymal transition (EMT), whereas DQ has little effects on activation of NFAT and EMT. Ultimately, PQ poisoned patients exhibit significantly reduced blood calcium levels compared to DQ poisoning, possibly via the large usage of calcium by AT II cells. All in all, we found a vicious cycle that the upregulated TGF-β in PQ-induced EMT further aggravates EMT via promotion of the calcium-calcineurin axis, which could be potential drug targets for treating PQ-induced pulmonary fibrosis.

Autonomous robots have garnered extensive utilization in diverse fields. Among the critical concerns for autonomous systems, path planning holds paramount importance. Notwithstanding considerable efforts in its development over the years, path planning for autonomous systems continues to grapple with challenges related to low planning efficiency and inadequate obstacle avoidance response in a timely manner. This study proposes a novel and systematic solution to the path planning problem within intricate office buildings. The solution consists of a global planner and a local planner. To handle the global planning aspect, an adaptive clustering-based dynamic programming rapidly exploring random tree (ACDP-RRT) algorithm is proposed. ACDP-RRT effectively identifies obstacles on the map by leveraging geometric features. These obstacles are then represented as a collection of sequentially arranged convex polygons, optimizing the sampling region and significantly enhancing sampling efficiency. For local planning, a network decoupling actor-critic (ND-AC) algorithm is employed. The proposed ND-AC simplifies the local planner design process by integrating planning and control loops into a neural network (NN) trained via an end-to-end model-free deep reinforcement learning (DRL) framework. Moreover, the adoption of network decoupling (ND) techniques leads to an improved obstacle avoidance success rate when compared to conventional actor-critic (AC)-based methods. Extensive simulations and experiments are conducted to demonstrate the effectiveness and robustness of the proposed approach.

Influences of planting density and nitrogen rate have been investigated frequently in targeted wheat (Triticum aestivumL.) research. Few studies have investigated interactions between these inputs. The objective was to determine the combine effect of N and seeding rates on culm morph‐physiological traits for lodging tolerance and grain yield. The experiment used a split‐split randomized block design using two wheat varieties ‘AnNong0711’ and ‘YanNong19’, split by four seeding (180, 240, 300, and 375 × 104ha−1) and four N rates (0, 180, 240, and 300 kg ha−1). Lodging traits of plant height, culm height center of gravity, and internode length, increased (p< .05) however, stem diameter, wall thickness, and stem breaking strength decreased with increasing N and seeding rate. Stem breaking strength was negatively correlated with culm height center of gravity (r= −.869, p= .01), internode length (r = −.872, p< .01), and lignin (r= −.746, p< .01) but positively correlated with internode diameter (r= .715, p< .05) and wall thickness (r= .696, p< .05). Culm lodging index and cellulose showed positive correlation (r= .807 and .913 respectively) with lignin. Compared to YanNong19, AnNong0711 showed higher grain yield and culm lodging index of 9 and 20.49%, respectively. For improved grain yield, 180 plants m−2was optimal in surface combinations with 210 kg N ha−1for AnNong0711 and 200 kg N ha−1for YanNong19. These combinations of seeding and N rates could successfully mitigate lodging and improve grain yield.

Limited arable land area and deteriorating soil health in smallholder farmers’ fields of subtropical regions in China have led to an urgent demand for sustainable production practices with greater land productivity and nutrients use efficiency. A group of field experiments at three locations (Yaan, Lezhi and Renshou) in two consecutive years of 2012–2013 were conducted to study crop production and nutrients recovery of maize (Zea maysL.) and soybean (Glycine max(L.) Merr.) in monocropping and additive relay intercropping systems. The results showed that the total crop yields of the 2:2 maize-to-soybean wide-narrow row spacing planting pattern (RIwn) were significantly higher than that of the 1:1 maize-to-soybean equal row spacing planting pattern (RIe), and the average land equivalent ratios (LER) of the grain yield were 1.79 and 1.49 for the RIwnand RIe, respectively. The nitrogen (N), phosphorus (P) and potassium (K) recovery efficiency calculated by the ratios of crop nutrients removed and fertilizer input indicated that the RIwnhad a higher nutrients recovery than the RIedue to proper spacing between maize and soybean rows and higher soybean yield in RIwn. In terms of the amount of fertilizer applied, based on this experiment, P should be reduced in maize plantings and increased in soybean plantings to maintain the balance of soil P. Considering the higher temperatures during the soybean vegetative growth phase, N fertilizer inputs should be controlled to prevent excessive soybean growth. In addition, excess K was taken up in the crop biomass, maybe the straw should be returned to the field to maintain soil K fertility sustainable for the long term.

The accumulation of soybean seed constituents such as anthocyanin, proanthocyanidin (PA), and sucrose is affected by various environmental stresses. Considerable information is available on the effects of different environmental stresses; however, the effect of shade at various development stages on the accumulation of the various seed constituents remains elusive. We investigated the effect of shade application on anthocyanin, PA, and sucrose contents at different seed development stages of two black soybean [Glycine max(L.) Merr.] varieties. This study comprised two separate trials: maize–soybean relay intercropping (IC) and soybean monoculture. The shade treatments in the soybean monoculture trial comprised shade applications at whole growth stage, at vegetative stage (SV), and at reproductive stage (SR) and a no‐shade control. Anthocyanin, PA, and sucrose contents were analyzed from seeds obtained at development stages of full‐size seed (R6), physiological maturity (R7), full maturity, 95% mature pods on the plant (R8), and natural air dry (AD). Genotype, shade, and planting season and their interactions had a strong influence on anthocyanin, PA, and sucrose accumulation in soybean seeds at the different seed development stages. Among all the shade treatments, relay IC recorded the highest anthocyanin contents in 2017 (1.88 mg g−1) and 2018 (1.76 mg g−1) in AD seeds. In addition, the application of shade increased PA, and maximum PA (27.14 mg g−1) was obtained in the SV treatment at R6 stage. The overall best sucrose contents (6.14 mg g−1in 2017 and 7.04 mg g−1in 2018) were obtained in soybean seeds harvested at R8 under SV treatment.

Optimized nitrogen (N) management can increase N-use efficiency in intercropping systems. Legume-nonlegume intercropping systems can reduce N input by exploiting biological N fixation by legumes. Measurement of N utilization can help in dissecting the mechanisms underlying N uptake and utilization in legume-nonlegume intercropping systems. An experiment was performed with three planting patterns: monoculture maize (MM), monoculture soybean (SS), and maize-soybean relay intercropping (IMS), and three N application levels: zero N (NN), reduced N (RN), and conventional N (CN) to investigate crop N uptake and utilization characteristics. N recovery efficiency and 15N recovery rate of crops were higher under RN than under CN, and those under RN were higher under intercropping than under the corresponding monocultures. Compared with MM, IMS showed a lower soil N-dependent rate (SNDR) in 2012. However, the SNDR of MM rapidly declined from 86.8% in 2012 to 49.4% in 2014, whereas that of IMS declined slowly from 75.4% in 2012 to 69.4% in 2014. The interspecific N competition rate (NCRms) was higher under RN than under CN, and increased yearly. Soybean nodule dry weight and nitrogenase activities were respectively 34.2% and 12.5% higher under intercropping than in monoculture at the beginning seed stage. The amount (Ndfa) and ratio (%Ndfa) of soybean N2fixation were significantly greater under IS than under SS. In conclusion, N fertilizer was more efficiently used under RN than under CN; in particular, the relay intercropping system promoted N fertilizer utilization in comparison with the corresponding monocultures. An intercropping system helps to maintain soil fertility because interspecific N competition promotes biological N fixation by soybean by reducing N input. Thus, a maize-soybean relay intercropping system with reduced N application is sustainable and environmentally friendly.

The seepage of groundwater and the strain-softening of rock mass in a submarine tunnel expand the plastic region of rock, thereby affecting its overall stability. It is therefore essential to study the stress and strain fields in the rocks surrounding the submarine tunnel by considering the coupled effect of strain-softening and seepage. However, the evolution equation for the hydro-mechanical parameters in the existing fully coupled solution is a uniform equation that is unable to reproduce the characteristics of rock mass in practice. In this study, an updated numerical procedure for the submarine tunnel is derived by coupling strain-softening and seepage effect based on the experimental results. According to the hydro-mechanical coupling theory, the hydro-mechanical parameters such as elastic modulus, Poisson's ratio, Biot's coefficient and permeability coefficient of rocks are characterized by the fitting equations derived from the experimental data. Then, the updated numerical procedure is deduced with the governing equations, boundary conditions, seepage equations and fitting equations. The updated numerical procedure is verified accurately compared with the previous analytical solution. By utilizing the updated numerical procedure, the characteristics of stress field and the influences of initial pore water pressure, Biot's coefficient, and permeability coefficient on the stress, displacement and water-inflow of the surrounding rocks are discussed. Regardless of the variations in hydro-mechanical parameters, the stress distribution has a similar trend. The initial permeability coefficient exerts the most significant influence on the stress field. With the increases in initial pore water pressure and Biot's coefficient, the plastic region expands, and the water-inflow and displacement increase accordingly. Given the fact that the stability of the tunnel is more sensitive to the seepage force controlled by the hydraulic parameters, it is suggested to dewater the ground above the submarine tunnel to control the initial pore water pressure.

The flavonoids produced by legume roots are signal molecules that induce nod genes for symbiotic rhizobium. Nevertheless, the promoting effects of flavonoids in root exudates in intercropping system on soybean nodulation are still unknown. A two years of field experiments was carried with maize soybean strip intercropping, i.e., the interspecific row spacing of 30 cm (MS30), 45 cm (MS45), 60 cm (MS60), and sole soybean/maize:SS/MM, and root interaction, i.e., root no barrier (NB) and root polythene-plastic barrier (PB), to evaluate relationships between flavonoids in root exudates and nodulation. We found that root-root interaction between soybean and maize enhances the nodules number and fresh weight in intercropped soybean. This enhancement increase gradually with expansion of interspecific distance. Proportion of nodules with diameter greater than 0.4cm was higher in intercropped soybean with NB than with PB. The expressions of nodules-related genes (GmENOD40, GmNIN2band GmEXPB2) were up-regulated. Furthermore, compared with monocropping, isoflavones secretion of soybean roots reduced, flavonoids and flavonols secretion of maize and soybean roots increased under intercropping. The secretion of differential metabolites of flavonoids in the rhizosphere of maize and soybean declined with root barrier. The expressions of GmCHS8and GmIFS1in soybean roots were up-regulated and GmICHGwas down-regulated under root interaction. The most of the flavonoids and flavonol compounds were positively correlated with nodule diameter. The nodules number, the nodules fresh weight and the proportion of nodules with a diameter greater than 0.2 cm increased in different genotypes of soybean treated with maize root exudate, which promoted the improvement of nitrogen fixation capacity. Therefore, maize-soybean strip intercropping combined with reasonable spacing to enhance the positive effect of underground root interaction, and improve the nodulation and nitrogen fixation capacity of intercropping soybean.

Plants experience dynamic light environments in the field, and the mechanisms for physiological and biochemical acclimation to fluctuating light (FL) vary among species. How soybean (Glycine max(L.) Merr.) integrates multiple physiological changes to acclimate to FL remains unclear. This study evaluated the impact of FL conditions on soybean morphology and photosynthetic characteristics by analyzing changes in photosynthetic gas exchange parameters and chlorophyll (Chl) afluorescence parameters under alternating high and low light conditions. Results showed that soybeans subjected to FL conditions had low dry matter mass, small and thin leaves, and a low Chl ato Chl bratio, resembling the traits of soybeans grown in low-light environments. However, their photosynthetic gas exchange rates and photosynthetic capacity remained constant, which was not the case under consistent low-light conditions. The adaptation processes for fluctuating and lowlight conditions are distinct. Correlation analyses indicated that the drop in carbon assimilation under FL primarily resulted from two aspects: the speed of recovery in stomatal conductance when transitioning to bright light and the slow relief of nonphotochemical quenching as light levels decreased. Thus, the decrease in carbon assimilation under FL conditions cannot be ascribed to adjustments during low-light phases but is due to a lag in photosynthetic response.

In order to develop and utilize polyhalite (K2SO4·MgSO4·2CaSO4·2H2O), solid–liquid phase equilibria of the quaternary system K+, Mg2+, Ca2+// SO42––H2O and the ternary systems K+, Ca2+// SO42––H2O and Mg2+, Ca2+// SO42––H2O were studied by the isothermal dissolution method at 323.2 K. The results show that only one double salt, K2SO4·CaSO4·H2O, is formed in the ternary system K+, Ca2+// SO42––H2O at 323.2 K; the ternary system of Mg2+, Ca2+// SO42––H2O has no double salt formation at 323.2 K; the solid–liquid phase diagram of the quaternary system K+, Mg2+, Ca2+// SO42––H2O (323.2 K) consists of five invariant points, ten univariable curves, and six crystal regions (two single salts K2SO4, MgSO4·6H2O, and CaSO4·2H2O and three double salts K2SO4·CaSO4·H2O, K2SO4·MgSO4·4H2O, and K2SO4·MgSO4·2CaSO4·2H2O). The results comparing the quaternary system at 308.2 and 323.2 K are as follows: when the temperature increases, the crystal region of the double salt K2SO4·CaSO4·H2O shrinks, while the double salts K2SO4·MgSO4·2CaSO4·2H2O and K2SO4·MgSO4·4H2O increase; and the double salt K2SO4·MgSO4·6H2O transforms into the double salt K2SO4·MgSO4·4H2O. This implies that increasing temperature is conducive to the crystallization of polyhalite.