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The contribution of spike photosynthesis to grain yield (GY) has been overlooked in the accurate spectral prediction of yield. Thus, it’s essential to construct and estimate a yield-related phenotypic trait considering spike photosynthesis. Based on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years, our objectives were to (i) construct a yield-related phenotypic trait (spike–leaf composite indicator, SLI) accounting for the contribution of the spike to photosynthesis, (ii) develop a novel spectral index (enhanced triangle vegetation index, ETVI3) sensitive to SLI, and (iii) establish and evaluate SLI estimation models by integrating spectral indices and machine learning algorithms. The results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area index. ETVI3 maintained a strong correlation with SLI throughout the growth stage, whereas the correlations of other spectral indices with SLI were poor after spike emergence. Integrating spectral indices and machine learning algorithms improved the estimation accuracy of SLI, with the most accurate estimates of SLI showing coefficient of determination, root mean square error (RMSE), and relative RMSE values of 0.71, 0.047, and 26.93%, respectively. These results provide new insights into the role of fruiting organs for the accurate spectral prediction of GY. This high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.

Abstract Background The Daur people are one of the 55 minority ethnic groups in China and have lived in Northern China for 300 years. In traditional Daur medicine, medicinal and edible plants (MEPs) are utilised for health benefits and therapeutic purposes; however, related ethnobotanical knowledge is rarely reported, which is disadvantageous for the sustainable development of these MEPs. Methods Semi-structured interviews with 122 informants, six focus group discussions, and a resource survey were conducted in a Daur minority nationality area in Inner Mongolia from 2015 to 2020, and the data statistics were analysed. In this study, we simulated a system dynamics model aimed at understanding the multiple feedback mechanisms involved in the relationships between the cultural influences and socioeconomic factors, sustainable environment, and development of MEPs. Results A total of 52 species of MEPs were identified and relevant ethnobotanical knowledge was assessed using Daur medicinal species data from Inner Mongolia and the Xinjiang region, with the literature and Ewenki ethnic group data used for comparison. The most commonly used medicinal plant species by the Daur were found to be Betula pendula subsp. mandshurica, Artemisia integrifolia, Crataegus pinnatifida, Saposhnikovia divaricata, Artemisia argyi, and Jacobaea cannabifolia. The MEPs most frequently targeted the digestive and rheumatic immunity systems, as well as infectious diseases or parasitic infections and other common diseases and basic health issues. MEP knowledge was primarily limited to older generations; thus, the valuable ethnobotanical knowledge on traditional medicines must be protected from future losses. Conclusions Our findings provide insights for future research aimed at exploiting the rich phytochemical diversity in traditional medicine and promote its use in modern lifestyles. Effective assessment and management of plant resources will lead to their application for the improvement of dietary diversity, nutrition, and health care.

ABSTRACTVoltage-gated calcium (Cav) channels mediate Ca2+ influx in response to membrane depolarization, playing critical roles in diverse physiological processes. Dysfunction or aberrant regulation of Cav channels can lead to life-threatening consequences. Cav-targeting drugs have been clinically used to treat cardiovascular and neuronal disorders for several decades. This review aims to provide an account of recent developments in the structural dissection of Cav channels. High-resolution structures have significantly advanced our understanding of the working and disease mechanisms of Cav channels, shed light on the molecular basis for their modulation, and elucidated the modes of actions (MOAs) of representative drugs and toxins. The progress in structural studies of Cav channels lays the foundation for future drug discovery efforts targeting Cav channelopathies.

The timely and accurate acquisition of crop-growth information is a prerequisite for implementing intelligent crop-growth management, and portable multispectral imaging devices offer reliable tools for monitoring field-scale crop growth. To meet the demand for obtaining crop spectra information over a wide band range and to achieve the real-time interpretation of multiple growth characteristics, we developed a novel portable snapshot multispectral imaging crop-growth sensor (PSMICGS) based on the spectral sensing of crop growth. A wide-band co-optical path imaging system utilizing mosaic filter spectroscopy combined with dichroic mirror beam separation is designed to acquire crop spectra information over a wide band range and enhance the device’s portability and integration. Additionally, a sensor information and crop growth monitoring model, coupled with a processor system based on an embedded control module, is developed to enable the real-time interpretation of the aboveground biomass (AGB) and leaf area index (LAI) of rice and wheat. Field experiments showed that the prediction models for rice AGB and LAI, constructed using the PSMICGS, had determination coefficients (R²) of 0.7 and root mean square error (RMSE) values of 1.611 t/ha and 1.051, respectively. For wheat, the AGB and LAI prediction models had R² values of 0.72 and 0.76, respectively, and RMSE values of 1.711 t/ha and 0.773, respectively. In summary, this research provides a foundational tool for monitoring field-scale crop growth, which is important for promoting high-quality and high-yield crops.

High-throughput phenotypic analysis plays an increasingly important role in crop breeding. In such research, the breeder usually establishes hundreds to thousands of plots, with each plot having its independent genetic breeding sources. The breeding plot extraction of genetic sources is usually outlined manually on RGB UAV imagery, which is time-consuming and subject to human bias. Therefore, a rapid method to extract the breeding plot for each genetic source in high-throughput phenotypic analysis would be very significant. In this paper, we propose a transferable method for extracting breeding plots from UAV RGB imagery. We utilized the fully convolutional neural network model A-UNet with an attention gate. After obtaining binary raster data from deep learning, we introduced post-processing. Subsequently, the raster data after post-processing were converted to vector data to obtain geographical coordinates. Finally, the UAV imagery was masked by the vector data to obtain the extraction results for each plot. The results showed that A-UNet achieved accuracies of over 90 % in precision, recall, and F1 score. Post-processing resulted in a 93 % average IoU in breeding plot extraction in the main study area. The average IOU achieved over 86 % in different spatial resolutions (1.6 cm and 0.4 cm), plot sizes (1 m × 1.5 and 2 m × 5 m), and crop types (rice). In summary, this study developed a method for extracting breeding plots in high-throughput phenotype analysis, which would help to be used as a high-throughput screening technique for accelerating crop breeding.

Abstract The number of seedlings is an important indicator that reflects the size of the wheat population during the seedling stage. Researchers increasingly use deep learning to detect and count wheat seedlings from unmanned aerial vehicle (UAV) images. However, due to the small size and diverse postures of wheat seedlings, it can be challenging to estimate their numbers accurately during the seedling stage. In most related works in wheat seedling detection, they label the whole plant, often resulting in a higher proportion of soil background within the annotated bounding boxes. This imbalance between wheat seedlings and soil background in the annotated bounding boxes decreases the detection performance. This study proposes a wheat seedling detection method based on a local annotation instead of a global annotation. Moreover, the detection model is also improved by replacing convolutional and pooling layers with the Space-to-depth Conv module and adding a micro-scale detection layer in the YOLOv5 head network to better extract small-scale features in these small annotation boxes. The optimization of the detection model can reduce the number of error detections caused by leaf occlusion between wheat seedlings and the small size of wheat seedlings. The results show that the proposed method achieves a detection accuracy of 90.1%, outperforming other state-of-the-art detection methods. The proposed method provides a reference for future wheat seedling detection and yield prediction.

Leaf chlorophyll content (LCC) is an important indicator of nitrogen content, and therefore, the accurate monitoring of LCC will benefit agronomists in guiding fertilizer applications. Remote sensing techniques have been widely used to estimate LCC from canopy reflectance spectra. However, there is no sensor specific to the estimation of LCC from canopy reflectance spectra, and it is unclear whether LCC estimation is sensitive to the reflectance quality (such as the noise level) or sensor settings (such as spectral resolution). To help design a sensor specific to the estimation of LCC, this study calibrated a semiempirical model based on the leaf area index-insensitive chlorophyll index (LICI) and evaluated its sensitivity to reflectance quality and sensor settings using simulated, measured, and artificial datasets. Our results indicated that random Gaussian noise in reflectance has limited effects on LCC estimation when the level of Gaussian noise is less than 2%, while the negative systematic bias in reflectance has clear effects on the LCC estimation. The LCC estimation accuracy is nearly independent of the spectral resolution (

Abstract Background The metrics for assessing the yield of crops in the field include the number of ears per unit area, the grain number per ear, and the thousand-grain weight. Typically, the ear number per unit area contributes the most to the yield. However, calculation of the ear number tends to rely on traditional manual counting, which is inefficient, labour intensive, inaccurate, and lacking in objectivity. In this study, two novel extraction algorithms for the estimation of the wheat ear number were developed based on the use of terrestrial laser scanning (TLS) in conjunction with the density-based spatial clustering (DBSC) algorithm based on the normal and the voxel-based regional growth (VBRG) algorithm. The DBSC involves two steps: (1) segmentation of the point clouds using differences in the normal vectors and (2) clustering of the segmented point clouds using a density clustering algorithm to calculate the ear number. The VBRG involves three steps: (1) voxelization of the point clouds, (2) construction of the topological relationships between the voxels as a connected region using the k-dimensional tree, and (3) detection of the wheat ears in the connected areas using a regional growth algorithm. Results The results demonstrated that DBSC and VBRG were promising in estimating the number of ears for different cultivars, planting densities, N fertilization rates, and growth stages of wheat (RMSE = 76 ~ 114 ears/m2, rRMSE = 18.62 ~ 27.96%, r = 0.76 ~ 0.84). Comparing the performance of the two algorithms, the overall accuracy of the DBSC (RMSE = 76 ears/m2, rRMSE = 18.62%, r = 0.84) was better than that of the VBRG (RMSE = 114 ears/m2, rRMSE = 27.96%, r = 0.76). It was found that with the DBSC, the calculation in points as units permitted more detailed information to be retained, and this method was more suitable for estimation of the wheat ear number in the field. Conclusions The algorithms adopted in this study provide new approaches for non-destructive measurement and efficient acquisition of the ear number in the assessment of the wheat yield phenotype.

The rapid and accurate estimation of leaf area index (LAI) through remote sensing holds significant importance for precise crop management. However, the direct construction of a vegetation index model based on multi-spectral data lacks robustness and spatiotemporal expansibility, making its direct application in practical production challenging. This study aimed to establish a simple and effective method for LAI estimation to address the issue of poor accuracy and stability that is encountered by vegetation index models under varying conditions. Based on seven years of field plot trials with different varieties and nitrogen fertilizer treatments, the Kalman filter (KF) fusion method was employed to integrate the estimated outcomes of multiple vegetation index models, and the fusion process was investigated by comparing and analyzing the relationship between fixed and dynamic variances alongside the fusion accuracy of optimal combinations during different growth stages. A novel multi-model integration fusion method, KF-DGDV (Kalman Filtering with Different Growth Periods and Different Vegetation Index Models), which combines the growth characteristics and uncertainty of LAI, was designed for the precise monitoring of LAI across various growth phases of rice. The results indicated that the KF-DGDV technique exhibits a superior accuracy in estimating LAI compared with statistical data fusion and the conventional vegetation index model method. Specifically, during the tillering to booting stage, a high R2 value of 0.76 was achieved, while at the heading to maturity stage, it reached 0.66. In contrast, within the framework of the traditional vegetation index model, the red-edge difference vegetation index (DVIREP) model demonstrated a superior performance, with an R2 value of 0.65, during tillering to booting stage, and 0.50 during the heading to maturity stage, respectively. The multi-model integration method (MME) yielded an R2 value of 0.67 for LAI estimation during the tillering to booting stage, and 0.53 during the heading to maturity stage. Consequently, KF-DGDV presented an effective and stable real-time quantitative estimation method for LAI in rice.

Objective To explore the effect of splicing factor 3B subunit (SF3B1) on apoptosis, proliferation and invasion of human lung cancer cells. Methods Non-small cell lung cancer(NSCLC) patients in the General Hospital of Western Theater Command PLA from June 2017 to June 2020 were selected as the research objects to detect the level of SF3B1 in cancer and adjacent tissues, and to analyze the relationship between SF3B1 and the pathological characteristics, survival and prognosis of patients. In addition, A549 cells were cultured and divided into control group, transfected si-NC group and si-SF3B1 group. Cell apoptosis was detected by flow cytometry, cell proliferation was detected by CCK-8, cell invasion was detected by Transwell, and the expression of Ras/Raf/ERK signal protein was detected by Western blot. Results The level of SF3B1 mRNA in cancer tissues was significantly higher than that in adjacent tissues(P＜0.05), and its level in A549 cells was significantly higher than that in normal lung epithelial cell line BEAS-2B(P＜0.05). The high level of SF3B1 was related to lymph node metastasis, tumor size and differentiation(P＜0.05). The median overall survival(OS) and progression free survival(PFS) of patients with low expression of SF3B1 were significantly higher than those with high expression of SF3B1(P＜0.05). After silencing SF3B1, the apoptosis of A549 cells was significantly increased but the proliferation and invasion were significantly decreased. The silence of SF3B1 suppressed the expression of Ras, Raf and p-ERK in A549 cells. Conclusions SF3B1 is highly expressed in NSCLC. Silencing SF3B1 can promote lung cancer cell apoptosis, inhibit cell proliferation and invasion.

The impact-rubbing dynamic characteristics of the power turbine rotor with the hollow shaft and offset discs for aircraft engine are investigated, and the impact-rubbing analytical method for the complex rotor based on MDOP Timoshenko beam theory is proposed in this paper. Compared with the traditional approach, the novel method can obtain more data to satisfy the need of engineering. The Lagrange equation is adopted to derive the equations of motion for the rotor system, and the Newmark-β method is applied to solve the equations. The diagrams such as the bifurcation, axis trajectory, spectrum, and Poincaré map are obtained to research on the effect of the rotating speed, gap, and eccentricity on the vibration response. The finite element analysis was carried out to validate the correctness of the theoretical modeling method. The research results indicate that the power turbine rotor with the hollow shaft on operation shows the various nonlinear dynamic behaviors including the multiperiod, quasi-period, jumping phenomenon, and chaotic motions; there exists an optimal gap between the rotor and the stator from the perspective of the efficiency and the dynamics; the optimal gap should make system avoid the resulting chaos or the quasi-period motion for the stability and safety of the machinery.

Efficient and accurate acquisition of the rice grain protein content (GPC) is important for selecting high-quality rice varieties, and remote sensing technology is an attractive potential method for this task. However, the majority of multispectral sensors are poor predictors of GPC due to their broad spectral bands. Hyperspectral technology provides a new analytical technology for bridging the gap between phenomics and genomics. However, the small size of typical datasets is a constraint for model construction for estimating GPC, limiting their accuracy and reducing their ability to generalize to a wide range of varieties. In this study, we used hyperspectral data of rice grains from 515 japonica varieties and deep convolution generative adversarial networks (DCGANs) to generate simulated data to improve the model accuracy. Features sensitive to GPC were extracted after applying a continuous wavelet transform (CWT), and the estimated GPC model was constructed by partial least squares regression (PLSR). Finally, a genome-wide association study (GWAS) was applied to the measured and generated datasets to detect GPC loci. The results demonstrated that the simulated GPC values generated after 8,000 epochs were closest to the measured values. The wavelet feature (WF1743, 2), obtained from the data with the addition of 200 simulated samples, exhibited the highest GPC estimation accuracy (R2 = 0.58 and RRMSE = 6.70%). The GWAS analysis showed that the estimated values based on the simulated data detected the same loci as the measured values, including the OsmtSSB1L gene related to grain storage protein. This study provides a new technique for the efficient genetic study of phenotypic traits in rice based on hyperspectral technology.

As a typical 2-dimensional material, molybdenum disulfide (MoS2) has atomic thickness in longitudinal size, showing unique optical and electrical properties. MoS2 has become a research hotspot in the field of photodetection. The properties of MoS2 devices are highly dependent on their material characteristics, device structures, and fabrication techniques. Therefore, their photoresponse characteristics may be determined by multiple physical effects, which contribute to the development of MoS2-based broadband photodetectors. Here, we present an experimental study on the broadband MoS2 photodetector ranging from 410 to 1,550 nm, obviously wider than that of conventionally reported MoS2 photodetectors. Our results indicate that the performance of the MoS2 device is dependent on the fabrication procedures. Under the optimal process, the maximum responsivity is 33.75 A W−1 and the corresponding specific detectivity is 6.1 × 1011 cm Hz1/2 W−1 at 480-nm illumination. Through a series of electrical and optoelectronic experimental analysis, the working mechanisms of multiband photoresponse of the MoS2 device are clarified.

Objective: Bispecific antibodies (BsAbs) have demonstrated significant therapeutic impacts for the treatment of a broad spectrum of diseases that include oncology, auto-immune, and infectious diseases. However, the large-scale production of clinical batches of bispecific antibodies still has many challenges that include having low yield, poor stability, and laborious downstream purification processes. To address such challenges, we describe the optimization of the controlled Fab arm exchange (cFAE) process for the efficient generation of BsAbs.Methods: The process optimization of a large-scale good manufacturing practice (GMP) cFAE strategy to prepare BsAbs was based on screening the parameters of temperature, reduction, oxidation, and buffer exchange. We include critical quality standards for the reducing agent cysteamine hydrochloride.Results: This large-scale production protocol enabled the generation of bispecific antibodies with >90% exchange yield and at >95% purity. The subsequent downstream processing could use typical mAb procedures. Furthermore, we demonstrated that the bispecific generation protocol can be scaled up to ∼60 L reaction scale using parental monoclonal antibodies that were expressed in a 200 L bioreactor.Conclusion: We presented a robust development strategy for the cFAE process that can be used for a larger scale GMP BsAb production.

Abstract Background Detecting and counting wheat spikes is essential for predicting and measuring wheat yield. However, current wheat spike detection researches often directly apply the new network structure. There are few studies that can combine the prior knowledge of wheat spike size characteristics to design a suitable wheat spike detection model. It remains unclear whether the complex detection layers of the network play their intended role. Results This study proposes an interpretive analysis method for quantitatively evaluating the role of three-scale detection layers in a deep learning-based wheat spike detection model. The attention scores in each detection layer of the YOLOv5 network are calculated using the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm, which compares the prior labeled wheat spike bounding boxes with the attention areas of the network. By refining the multi-scale detection layers using the attention scores, a better wheat spike detection network is obtained. The experiments on the Global Wheat Head Detection (GWHD) dataset show that the large-scale detection layer performs poorly, while the medium-scale detection layer performs best among the three-scale detection layers. Consequently, the large-scale detection layer is removed, a micro-scale detection layer is added, and the feature extraction ability in the medium-scale detection layer is enhanced. The refined model increases the detection accuracy and reduces the network complexity by decreasing the network parameters. Conclusion The proposed interpretive analysis method to evaluate the contribution of different detection layers in the wheat spike detection network and provide a correct network improvement scheme. The findings of this study will offer a useful reference for future applications of deep network refinement in this field.

The timely and robust prediction of wheat yield is very significant for grain trade and food security. In this study, the yield prediction model was developed by coupling an ensemble model with multi-source data, including vegetation indices (VIs) and meteorological data. The results showed that green chlorophyll vegetation index (GCVI) is the optimal remote sensing (RS) variable for predicting wheat yield compared with other VIs. The accuracy of the adaptive boosting- long short-term memory (AdaBoost-LSTM) ensemble model was higher than the LSTM model. AdaBoost-LSTM coupled with optimal input data had the best performance. The AdaBoost-LSTM model had strong robustness for predicting wheat yield under different irrigation and extreme weather events in general. Additionally, the accuracy of AdaBoost-LSTM for rainfed counties was higher than that for irrigation counties in most years except extreme years. The yield prediction model developed with the characteristic variables of the window from February to April had higher accuracy and smaller data requirements, which was the best prediction window. Therefore, wheat yield can be accurately predicted by the AdaBoost-LSTM model one to two months of lead time before maturity in the HHHP. Overall, the AdaBoost-LSTM model can achieve accurate and robust yield prediction in large-scale regions.

Vegetation phenology changes caused by urbanization could lead to shifts in ecosystem services in urban areas and impact on human health. The characteristics of urbanization affect vegetation phenology need to be emphasized, especially in China with a complex natural environment and rapid urbanization background. In this study, we used remote sensing-based phenological data (MODIS MCD12Q2) to analyze the spatial heterogeneity of vegetation phenology caused by urbanization between urban and non-urban areas in 320 cities across China. We found a significant difference between vegetation phenology in urban and its corresponding non-urban area at national and the regional scale. For national scale, the start of the growing season (SOS) was significantly advanced by 2.53 days (P

Traditional cloud computing is developed from a high-performance cluster. Every server in the high-performance cluster has its own resources, including a CPU, memory, a network, I/O (Input/Output), a power system, and a heat dissipation system. Using software virtualization technologies such as the kernel-based virtual machine (KVM), Xen, VMware, and Hyper-V, these exclusive resources can be shared among these servers to improve the utilization rate. Although these technologies provide a great improvement in the resource utilization rate, some overhead in the process of software virtualization is inevitable. Server architecture and virtualization technology are the two factors that mainly affect cloud computing efficiency. With the rapid development of internet services, big data, and cloud computing, the cloud server has become mainstream instead of the traditional server. On the other hand, hardware virtualization technology has gradually developed. Compared with the traditional cloud computing solutions based on virtual machines, the cloud server based on hardware virtualization can achieve much higher efficiency to better meet cloud computing requirements by removing the software overhead. The cloud server’s design concept of configuration on demand, distributed sharing of hardware resource architecture, and construction method of hardware resource virtualization are presented. A three-level interconnection architecture of the cloud server is designed. In Level-1, the computing pool and the memory pool are built, while Level-2 is for the network pool, and Level-3 is for all resource pools. Different applications in these levels can be realized in the cloud server: Level-1 for computing-intensive applications, Level-2 for transactional applications, and Level-3 for virtual applications. A prototype system of a 16-processor cloud server using hardware virtualization architecture is designed and implemented. In this system, there are sixteen physical nodes. Every physical node is composed of a CPU and two DIMMs (dual inline memory modules). Different types of CPUs may be used in these physical nodes. Every four physical nodes form a computing module. In every computing module, a field-programmable gate array (FPGA)-based interconnection fabric controller (IFC) integrated network, storage, and general I/O resources is designed to interconnect these processors. All IFCs are interlinked. All the processors in this prototype system can share the network, storage, and general I/O resources to realize hardware resource virtualization through these IFCs. For the prototyping system, evaluation experiments on network performance tests by the Netperf program and storage performance tests by the FIO program are performed. The test results show that the prototyping system not only keeps the traditional cloud server’s advantages but also provides better scalability and performance. The advantages of this cloud server are in providing a high-density, high performance-to-cost ratio, a high performance-to-Watt ratio, and high scalability compared with the existing traditional cloud server.

Voltage-gated Ca2+ channel subtype Cav2.3 represents a potential drug target for neurological diseases. Here, authors report cryo-EM structures of Cav2.3 and its mutant to reveal the crosstalk of intracellular segments, which may facilitate future drug discovery.

Abstract Objective The present study aims to explore the correlation of the transforming growth factor β (TGF-β), drosophila mothers against decapentaplegic protein gene (SMAD) 2/3/4, and leukemia inhibitory factors (LIF) with the cyst formation of hepatic Echinococcus granulosus in young children. Methods A total of 40 patients who met the diagnostic criteria for children's hydatid disease in people's Hospital of Xinjiang Uygur Autonomous Region between January 2020 and June 2021 were enrolled a s the study subjects. The cystic fluid of these children was collected as the case group and the corresponding infected viscera or pericystic tissue as the control group, with 40 cases in each group. In vitro cultured protoscolice of hydatid cyst, four groups including control group, LIF siRNA group, LIF factor group and SMAD4 siRNA group were divided by inhibiting TGF-β/SMADs signal pathway. Each assay was performed in triplicate. The expression of TGF-β, SMAD2/3/4 and LIF were detected. Results The results of the clinical trial showed that the contents of SMAD2 and SMAD3 were increased in the case group compared with the control group; the differences were statistically significant (P < 0.05). The expression levels of TGF-β, Smad4, and LIF increased in the case group compared with the control group; however, the differences were not statistically significant. The results of further in vitro experiments, the expression levels of TGF-β, SMAD 2/3/4, and LIF after adding siRNA to interfere with Smad4 decreased in the case group compared with the control group; the differences were statistically significant (P < 0.05). Compared with the control group, the expression levels of TGF-β, SMAD2/3/4, and LIF increased after treatment with added LIF in the case group, and the expression levels of TGF-β, SMAD2/3/4, and LIF decreased after adding siRNA to interfere with LIF in the case group; the differences were all statistically significant (P < 0.05). Conclusion SMAD2 and SMAD3 have a certain clinical relevance with hydatidosis in young children. The LIF expression level may be related to the cystic transformation of protoscoleces. It has been suggested that the TGF-β/Smads/LIF signaling pathway may be present in the process of protoscoleces cyst formation; this provides a research basis for the prevention and treatment of post-infection parasitism of E. multilocularis eggs in young children.

Summary: Hedgehog (Hh) signaling mediated by transcription factor Ci/Gli plays a vital role in embryonic development and adult tissue homeostasis in invertebrates and vertebrates, whose dysregulation leads to many human disorders, including cancer. However, till now, cofactors of Ci/Gli which can affect tumorigenesis are not well known. Here, through genetic screen, we find overexpression of active Ci alone is not sufficient to generate tumor-like eye phenotype in Drosophila, however, its overexpression combined with knockdown of hib causes a striking tumor-like big eye phenotype. Mechanistically, HIB/SPOP inhibits Ci/Gli-mediated tumorigenesis by modulating the RNA polymerase II (RNAPII) components Rpb3/Rpb7 stabilities in E3 ligase dependent manner. In addition, Ci/Gli can promote HIB/SPOP-mediated Rpb7/Rpb3 degradation. Taken together, our results indicate Ci/Gli needs to hook up with suitable RNAPII together to achieve the tumor-like eye phenotype and HIB/SPOP plays dual roles through controlling Ci/Gli and Rpb3/Rpb7 protein stabilities to temper Ci/Gli/RNAPII-mediated tumorigenesis.

Mapping crop distribution using satellite technology is an effective approach for gaining information about food production over broad, regional scales. However, crop classification in high altitude regions from satellite platforms remains challenging, due to the spatial heterogeneity caused by the complex planting patterns. Moreover, the frequent cloud cover makes it difficult to collect time-series imagery for these regions. Thus, this study used a mosaic of single images of Gaofen-6 data to map the crop distribution in high altitude regions of Xining City and Haidong City prefectures of Qinghai Province, China. To improve the accuracy of the crop classification, random forest-recursive feature elimination (RF-RFE) was used to determine an optimal feature subset from existing spectral, texture and topographic features. Then, a two-layer stacking generalization ensemble model, incorporating Random Forest, XGBoost and AdaBoost, was trained. The results reveal that the stacking algorithm outperformed the other single classifiers, with overall accuracy higher than 85% (87.89% for the optimal feature subset and 85.38% for the original spectral band subset). In addition, the user’s and producer’s accuracies for wheat, rape and maize field all exceeded 90%. Elevation was the variable with the highest importance score, illustrating its importance in crop classification of high altitude regions. Overall, the framework, combining RF-RFE and a stacking algorithm, can improve the accuracy of the crop classification in high altitude regions.

This study presents an electrolysis system utilizing a novel self-circulation process of sulfate (SO42−) and persulfate (S2O82−) ions based on a boron-doped diamond (BDD) anode and an activated carbon fiber (ACF) cathode, which is designed to enable electrochemical remediation of environmental contaminants with reduced use of chemical reagents and minimized residues. The production of S2O82− and hydrogen peroxide (H2O2) on the BDD anode and ACF cathode, respectively, is identified as the source of active radicals for the contaminant degradation. The initiator, sulfate, is identified by comparing the degradation efficiency in NaSO4 and NaNO3 electrolytes. Quenching experiments and electron paramagnetic resonance (EPR) spectroscopy confirmed that the SO4−· and ·OH generated on the ACF cathode are the main reactive radicals. A comparison of the degradation efficiency and the generated S2O82−/H2O2 of the divided/undivided electrolysis system is used to demonstrate the superiority of the synergistic effect between the BDD anode and ACF cathode. This work provides evidence of the effectiveness of the philosophy of “catalysis in lieu of supplementary chemical agents” and sheds light on the mechanism of the generation and transmission of reactive species in the BDD and ACF electrolysis system, thereby offering new perspectives for the design and optimization of electrolysis systems.

Respiratory syncytial virus (RSV) is a significant viral pathogen that causes respiratory infections in infants, the elderly, and immunocompromised individuals. RSV-related illnesses impose a substantial economic burden worldwide annually. The molecular structure, function, and in vivo interaction mechanisms of RSV have received more comprehensive attention in recent times, and significant progress has been made in developing inhibitors targeting various stages of the RSV replication cycle. These include fusion inhibitors, RSV polymerase inhibitors, and nucleoprotein inhibitors, as well as FDA-approved RSV prophylactic drugs palivizumab and nirsevimab. The research community is hopeful that these developments might provide easier access to knowledge and might spark new ideas for research programs.

Platygaster robiniae is economically important as a highly specific parasitoid of the invasive pest Obolodiplosis robiniae which was introduced into the Euro-Asia region in the last decade. Despite being a critical and specific parasitoid of the invasive pest O. robiniae and its use as an effective biocontrol agent, the absence of sequence information from P. robiniae have limited its genetic applications for pest management in forests. Mitochondrial (mt) genomes generally contain abundant nucleotide information and thus are helpful for understanding species history. Here, we sequenced the complete mt genome of P. robiniae using next generation sequencing, and annotated 13 protein-coding, 22 tRNA, and 2 rRNA genes and a 702 bp noncoding region. Comparative analysis indicated that this mt genome has a normal A + T content and codons use, however possessed both the expected and unique rearrangements. Ten tRNAs at four gene blocks COII-ATP8, COIII-ND3, ND3-ND5 and the A + T-rich region-ND2 were rearranged, including gene shuffles, transpositions and inversions. Notably, two genes tRNASer(UCN) and tRNALeu(CUN) had undergone long-range inversions, which is the first record of this rearrangement type in the superfamily Platygastroidea. The D-loops of both tRNAIle and tRNALeu(CUN) were absent from the tRNA secondary structure, which has not been reported from hymenopteran previously. Phylogenetic analysis based with the maximum likelihood and Bayesian methods showed that P. robiniae grouped with other species of Platygastridae, and that the superfamily Platygastridea is sister to the other Proctotrupomorpha superfamilies. Our tree strongly supports the monophyly of the five superfamilies of Proctotrupomorpha. This study discovered some unique characters of P. robiniae, and contributes to our understanding of genome rearrangements in the order Hymenoptera.

Abstract Background Abnormal activation of immune system is an important pathogenesis of Parkinson’s disease, but the relationship between peripheral inflammation, central microglia activation and dopaminergic degeneration remains unclear. Objectives To evaluate the brain regional microglia activation and its relationship with clinical severity, dopaminergic presynaptic function, and peripheral inflammatory biomarkers related to adaptive immunity. Methods In this case–control study, we recruited 23 healthy participants and 24 participants with early-stage Parkinson’s disease. 18F-PBR06 PET/MR for microglia activation, 18F-FP-DTBZ for dopaminergic denervation, total account of T cells and subpopulations of T helper (Th1/Th2/Th17) cells, and the levels of serum inflammatory cytokines were assessed. Sanger sequencing was used to exclude the mix-affinity binders of 18F-PBR06-PET. Results Compared to healthy controls, patients with Parkinson’s disease had an increased 18F-PBR06-PET standardized uptake value ratio (SUVR) in the putamen, particularly in the ipsilateral side of the motor onset. 18F-PBR06-PET SUVR was positively associated with 18F-FP-DTBZ-PET SUVR in the brainstem and not associated with disease severity measured by Hoehn and Yahr stage, MDS-UPDRS III scores. Patients with Parkinson’s disease had elevated frequencies of Th1 cells and serum levels of IL10 and IL17A as compared to healthy controls. No significant association between peripheral inflammation markers and microglia activation in the brain of PD was observed. Conclusion Parkinson’s disease is associated with early putaminal microglial activation and peripheral phenotypic Th1 bias. Peripheral adaptive immunity might be involved in microglia activation in the process of neurodegeneration in PD indirectly, which may be a potential biomarker for the early detection and the target for immunomodulating therapy.

The CPU is the core part of all integrated circuits. Although some homemade CPUs of proprietary intellectual property rights are rapidly developed, few high-performance chipsets are available, especially in server domains, to match them. Thus, the total systems designed using these CPUs and low-performance chipsets do not have proper performance. The Loongson CPU faces the same problem. To seek better chipsets for it, certain architecture and some methods are designed and implemented to adapt different types of chipsets. In this architecture, a field-programmable gate array (FPGA) is linked between a CPU and these chipsets. An FPGA is divided into three domains: an HT (hyper transport) bus domain, a processing domain for important but temporarily indeterminate signals, and a CPLD (complex programmable logic device) function domain. In these adaption processes, HT bus signals, the temporarily indeterminate signals, and power signals in CPUs and chipsets are respectively linked into three domains in an FPGA and treated by a programming FPGA to perform all types of possible signal combinations. The power sequence between the CPU and chipsets is coordinated to the right order using an FPGA. The signal integrity difference between them is avoided and trimmed to the right state by amending their signals in the FPGA. In this system, the experimental results show that this architecture and these methods simultaneously make more chipsets work together to be adapted than before in a single motherboard. This combination avoids researching and developing many different motherboards for every type of possible chipset and greatly reduces costs. High-performance server chipsets can be found to properly match the Loongson CPU and have better specifications and higher performance than those currently used for the Loongson CPU. A prototype system composed of the Loongson CPU and five types of chipsets is designed and implemented. Using the above architecture and methods, a type of optimal server chipsets SR5690 + SP5100 has been found, and the matching principles or correct settings for the signal connection and power sequence have been concluded. The Loongson 3B4000 two-way SMP motherboard with SR5690 + SP5100 chipsets is also produced. On this motherboard, the results of evaluation experiments on computing performance tests by the SPEC CPU 2006 program, storage performance tests by the IO zone program, and network performance tests by the Netperf program are performed. Compared with the current Loongson 3B4000 server with a 7A1000 chipset, the test results show the performance on three items is improved by approximately 10%. The combination of the Loongson CPU and this type of server chipset provides wider applications in the server market and promotes the development of the Loongson CPU in its ecosystem.

The purpose of this study is to examine the influence of different parameters on the legitimacy rate and effective efficiency of crowd decision-making and to guide decision-making in real life. In this paper, a crowd decision representation method based on the preference domain is proposed for the large-scale simulation implementation of crowd decision in a crowd intelligence network, a simulation modeling is performed for the members participating in the decision, and a formal propulsion algorithm is perfected. Lastly, the influence of key parameters on the decision results is analyzed through a large-scale simulation experiment. This study analyzes the influence of key parameters, such as the number of candidates, number of voters, and voting legitimacy rate reference value, on the decision-making results and summarizes the selection range of key parameters under different results. Through the simulation experiment of crowd decision-making, this paper provides inspiration for researchers to explore the parameter sensitivity of crowd decision-making and provides guidance for crowd decision-making in social life.

Yield prediction is essential in food security, food trade, and field management. However, due to the associated complex formation mechanisms of yield, accurate and timely yield prediction remains challenging in remote sensing-based crop monitoring domains. In this study, a framework of soybean yield prediction integrating extreme gradient boosting (XGBoost) and multidimensional feature engineering was developed at the county level in the United States using publicly available datasets. Excellent accuracy values were obtained for over 959 counties in 12 states throughout the midwestern U.S., with a test coefficient of determination (R2) of 0.82 and a root-mean-square error (RMSE) of 0.246 t/ha, using our approach. Following a “train–validate–test” assessment strategy, our study shows that XGBoost outperforms other county-level soybean yield prediction models with identical inputs, including linear regression (LR), random forest (RF), k-nearest neighbor (KNN), artificial neural network (ANN), support vector regression (SVR), long short-term memory (LSTM), and deep neural network (DNN). The results show that accurate results of soybean yield prediction can be obtained as early as the pod-setting stage. We implemented the feature importance and Shapley additive explanations (SHAP) algorithms to quantify the impact of input features on the XGBoost model in the training and prediction stages, respectively. The enhanced vegetation index (EVI) at the pod-setting period is the most crucial factor, but the yield prediction is not dependent on only a few key features. Yields were detrended using longer-term historical yield data, and R2 increased from 0.58 to 0.82 while RMSE decreased from 0.374 t/ha to 0.246 t/ha. We employed multidimensional feature engineering to generate phenology-based features, and R2 improved from 0.79 to 0.82 while RMSE decreased from 0.268 t/ha to 0.246 t/ha using this approach. The framework can be easily implemented and extended in the future in combination with early crop identification.

Abstract Background Blood biomarkers that can be used for preclinical Alzheimer’s disease (AD) diagnosis would enable trial enrollment at a time when the disease is potentially reversible. Here, we investigated plasma neuronal-derived extracellular vesicle (nEV) cargo in patients along the Alzheimer’s continuum, focusing on cognitively normal controls (NCs) with high brain β-amyloid (Aβ) loads (Aβ+). Methods The study was based on the Sino Longitudinal Study on Cognitive Decline project. We enrolled 246 participants, including 156 NCs, 45 amnestic mild cognitive impairment (aMCI) patients, and 45 AD dementia (ADD) patients. Brain Aβ loads were determined using positron emission tomography. NCs were classified into 84 Aβ− NCs and 72 Aβ+ NCs. Baseline plasma nEVs were isolated by immunoprecipitation with an anti-CD171 antibody. After verification, their cargos, including Aβ, tau phosphorylated at threonine 181, and neurofilament light, were quantified using a single-molecule array. Concentrations of these cargos were compared among the groups, and their receiver operating characteristic (ROC) curves were constructed. A subset of participants underwent follow-up cognitive assessment and magnetic resonance imaging. The relationships of nEV cargo levels with amyloid deposition, longitudinal changes in cognition, and brain regional volume were explored using correlation analysis. Additionally, 458 subjects in the project had previously undergone plasma Aβ quantification. Results Only nEV Aβ was included in the subsequent analysis. We focused on Aβ42 in the current study. After normalization of nEVs, the levels of Aβ42 were found to increase gradually across the cognitive continuum, with the lowest in the Aβ− NC group, an increase in the Aβ+ NC group, a further increase in the aMCI group, and the highest in the ADD group, contributing to their diagnoses (Aβ− NCs vs. Aβ+ NCs, area under the ROC curve values of 0.663; vs. aMCI, 0.857; vs. ADD, 0.957). Furthermore, nEV Aβ42 was significantly correlated with amyloid deposition, as well as longitudinal changes in cognition and entorhinal volume. There were no differences in plasma Aβ levels among NCs, aMCI, and ADD individuals. Conclusions Our findings suggest the potential use of plasma nEV Aβ42 levels in diagnosing AD-induced cognitive impairment and Aβ+ NCs. This biomarker reflects cortical amyloid deposition and predicts cognitive decline and entorhinal atrophy.

Accurate wheat spike detection is crucial in wheat field phenotyping for precision farming. Advances in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat spikes. However, wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the background. Existing models for wheat spike detection are typically designed for a specific growth stage. Their adaptability to other growth stages or field scenes is limited. Such models cannot detect wheat spikes accurately caused by the difference in color, size, and morphological features between growth stages. This paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity stage. WheatNet constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection accuracy. Moreover, a Detection Network is designed to improve wheat spike detection capability. A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery. A new micro-scale detection layer is added to the network to extract the features of small spikes. Localization loss is improved by Complete Intersection over Union to reduce the impact of the background. The results show that WheatNet can achieve greater accuracy than classical detection methods. The detection accuracy with average precision of spike detection at the filling stage is 90.1%, while it is 88.6% at the maturity stage. It suggests that WheatNet is a promising tool for detection of wheat spikes.

Rapid and accurate estimation of panicle number per unit ground area (PNPA) in winter wheat before heading is crucial to evaluate yield potential and regulate crop growth for increasing the final yield. The accuracies of existing methods were low for estimating PNPA with remotely sensed data acquired before heading since the spectral saturation and background effects were ignored. This study proposed a spectral-textural PNPA sensitive index (SPSI) from unmanned aerial vehicle (UAV) multispectral imagery for reducing the spectral saturation and improving PNPA estimation in winter wheat before heading. The effect of background materials on PNPA estimated by textural indices (TIs) was examined, and the composite index SPSI was constructed by integrating the optimal spectral index (SI) and TI. Subsequently, the performance of SPSI was evaluated in comparison with other indices (SI and TIs). The results demonstrated that green-pixel TIs yielded better performances than all-pixel TIs apart from TI[HOM], TI[ENT], and TI[SEM] among all indices from 8 types of textural features. SPSI, which was calculated by the formula DATT[850,730,675] + NDTICOR[850,730], exhibited the highest overall accuracies for any date in any dataset in comparison with DATT[850,730,675], TINDRE[MEA], and NDTICOR[850,730]. For the unified models assembling 2 experimental datasets, the RV2 values of SPSI increased by 0.11 to 0.23, and both RMSE and RRMSE decreased by 16.43% to 38.79% as compared to the suboptimal index on each date. These findings indicated that the SPSI is valuable in reducing the spectral saturation and has great potential to better estimate PNPA using high-resolution satellite imagery.

Two-dimensional (2D) materials have become more advantageous compared with traditional semiconductor materials for fabrication of modern photodetectors operating at room temperature and possessing small volume and low power consumption. However, the weak absorption caused by atomic thickness severely limits the performance of photodetectors employing 2D materials as active channels. Plasmonic nanomaterials can manipulate light at subwavelength scale and have been viewed as a powerful tool to achieve enhanced photoresponse in semiconductor devices. In this review, the rational design strategies of plasmon-enhanced 2D material photodetectors are comprehensively introduced, where the hybrid nanostructures are classified based on different coupling modes between plasmonic nanostructures and 2D materials. This review has a great chance to provide an instructive reference for understanding and engineering plasmonic effects toward high-performance 2D material photodetectors.

The organ-specific critical nitrogen (Nc) dilution curves are widely thought to represent a new approach for crop nitrogen (N) nutrition diagnosis, N management, and crop modeling. The Nc dilution curve can be described by a power function (Nc = A1·W−A2), while parameters A1 and A2 control the starting point and slope. This study aimed to investigate the uncertainty and drivers of organ-specific curves under different conditions. By using hierarchical Bayesian theory, parameters A1 and A2 of the organ-specific Nc dilution curves for wheat were derived and evaluated under 14 different genotype × environment × management (G × E × M) N fertilizer experiments. Our results show that parameters A1 and A2 are highly correlated. Although the variation of parameter A1 was less than that of A2, the values of both parameters can change significantly in response to G × E × M. Nitrogen nutrition index (NNI) calculated using organ-specific Nc is in general consistent with NNI estimated with overall shoot Nc, indicating that a simple organ-specific Nc dilution curve may be used for wheat N diagnosis to assist N management. However, the significant differences in organ-specific Nc dilution curves across G × E × M conditions imply potential errors in Nc and crop N demand estimated using a general Nc dilution curve in crop models, highlighting a clear need for improvement in Nc calculations in such models. Our results provide new insights into how to improve modeling of crop nitrogen–biomass relations and N management practices under G × E × M.

The growth of the fusarium head blight (FHB) pathogen at the grain formation stage is a deadly threat to wheat production through disruption of the photosynthetic processes of wheat spikes. Real-time nondestructive and frequent proxy detection approaches are necessary to control pathogen propagation and targeted fungicide application. Therefore, this study examined the ch\lorophyll-related phenotypes or features from spectral and chlorophyll fluorescence for FHB monitoring. A methodology is developed using features extracted from hyperspectral reflectance (HR), chlorophyll fluorescence imaging (CFI), and high-throughput phenotyping (HTP) for asymptomatic to symptomatic disease detection from two consecutive years of experiments. The disease-sensitive features were selected using the Boruta feature-selection algorithm, and subjected to machine learning-sequential floating forward selection (ML-SFFS) for optimum feature combination. The results demonstrated that the biochemical parameters, HR, CFI, and HTP showed consistent alterations during the spike–pathogen interaction. Among the selected disease sensitive features, reciprocal reflectance (RR=1/700) demonstrated the highest coefficient of determination (R2) of 0.81, with root mean square error (RMSE) of 11.1. The multivariate k-nearest neighbor model outperformed the competing multivariate and univariate models with an overall accuracy of R2 = 0.92 and RMSE = 10.21. A combination of two to three kinds of features was found optimum for asymptomatic disease detection using ML-SFFS with an average classification accuracy of 87.04% that gradually improved to 95% for a disease severity level of 20%. The study demonstrated the fusion of chlorophyll-related phenotypes with the ML-SFFS might be a good choice for crop disease detection.