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Isolated superior mesenteric artery dissection without aortic involvement is an exceptionally rare event. Nonoperative management remains the first-line therapy. However, surgical interventions can be indicated in the event of bowel ischemia. In the present report, we describe a case of complicated isolated superior mesenteric artery dissection treated with a hybrid approach.

Abstract Climate warming affects global livestock productivity. The meat yield from cattle farming (cattle meat per animal) represents livestock productivity at the individual level. However, the impact of warming on cattle meat yield at a global scale is not well understood. In this study, we combine country-level data on the annual meat yield from cattle farming and socioeconomic data from 1961 to 2020 with climate projections from General Circulation Models. The findings show that cattle meat yield increases as temperatures rise from low to medium and then decreases when annual average temperatures exceed 7 °C; this repose is pronounced in the grassland-based livestock system. Further, we show that warming creates unequal impacts between high- and low-income countries due to the divergent baseline temperature conditions. Future warming aggravates these unequal burdens between countries, with the most pronounced effects observed under the upper-middle emissions scenario.

Phlorizin, as a flavonoid from a wide range of sources, is gradually becoming known for its biological activity. Phlorizin can exert antioxidant effects by regulating the IL-1β/IKB-α/NF-KB signaling pathway. At the same time, it exerts its antibacterial activity by reducing intracellular DNA agglutination, reducing intracellular protein and energy synthesis, and destroying intracellular metabolism. In addition, phlorizin also has various pharmacological effects such as antiviral, antidiabetic, antitumor, and hepatoprotective effects. Based on domestic and foreign research reports, this article reviews the plant sources, extraction, and biological activities of phlorizin, providing a reference for improving the clinical application of phlorizin.

Over the past few years, there has been extensive exploration of machine learning (ML), especially deep learning (DL), for crop yield prediction, resulting in impressive levels of accuracy. However, such models are highly dependent on training samples with ground truth labels (i.e., crop yield records), which are not available in some regions. Additionally, due to the existence of domain shifts between different spatial regions, DL models trained within one region (i.e., source domain) tend to have poor performance when directly applied to other regions (i.e., target domain). Unsupervised domain adaptation (UDA) has become a promising strategy to improve the transferability of DL models by aligning the feature distributions in the source domain and the target domain. Despite the success, existing UDA models generally assume an identical label space across different domains. This assumption can be invalid in crop yield prediction scenarios, as crop yields can vary significantly in heterogeneous regions. Due to the mismatch between label spaces, negative transfer may occur if the entire source and target domains are forced to align. To address this issue, we proposed a novel partial domain adversarial neural network (PDANN), which relaxes the assumption of fully, equally shared label spaces across domains by downweighing the outlier source samples. Specifically, during model training, the PDANN weighs each labeled source sample based on the likelihood of its yield value given the expected target yield distribution. Instead of aligning the target domain to the entire source domain, the PDANN model downweighs the outlier source samples and performs partial weighted alignment of the target domain to the source domain. As a result, the negative transfer caused by source samples in the outlier label space would be alleviated. In this study, we assessed the model’s performance on predicting yields for two main commodities in the U.S., including corn and soybean, using the U.S. corn belt as the study region. The counties under study were divided into two distinct ecological zones and alternatively used as the source and target domains. Feature variables, including time-series vegetation indices (VIs) and sequential meteorological variables, were collected and aggregated at the county level. Next, the PDANN model was trained with the extracted features and corresponding crop yield records from the U.S. Department of Agriculture (USDA). Finally, the trained model was evaluated for three testing years from 2019 to 2021. The experimental results showed that the developed PDANN model had achieved a mean coefficient of determination (R2) of 0.70 and 0.67, respectively, in predicting corn and soybean yields, outperforming three other ML and UDA models by a large margin from 6% to 46%. As the first study performing partial domain adaptation for crop yield prediction, this research demonstrates a novel solution for addressing negative transfer and improving DL models’ transferability on crop yield prediction.

Potato is one of the most significant food crops globally due to its essential role in the human diet. The growing demand for potato, coupled with severe environmental losses caused by extensive farming activities, implies the need for better crop protection and management practices. Precision agriculture is being well recognized as the solution as it deals with the management of spatial and temporal variability to improve agricultural returns and reduce environmental impact. As the initial step in precision agriculture, the traditional methods of crop and field characterization require a large input in labor, time, and cost. Recent developments in remote sensing technologies have facilitated the process of monitoring crops and quantifying field variations. Successful applications have been witnessed in the area of precision potato farming. Thus, this review reports the current knowledge on the applications of remote sensing technologies in precision potato trait characterization. We reviewed the commonly used imaging sensors and remote sensing platforms with the comparisons of their strengths and limitations and summarized the main applications of the remote sensing technologies in potato. As a result, this review could update potato agronomists and farmers with the latest approaches and research outcomes, as well as provide a selective list for those who have the intentions to apply remote sensing technologies to characterize potato traits for precision agriculture.

Potato growth depends largely on nitrogen (N) availability in the soil. However, the shallow-root crop coupled with its common cultivation in coarse-textured soils leads to its poor N use efficiency. Fast and accurate estimations of potato tissue N concentrations are urgently needed to assist the decision making in precision fertilization management. Remote sensing has been utilized to evaluate the potato N status by correlating spectral information with lab tests on leaf N concentrations. In this study, a systematic comparison was conducted to quantitatively evaluate the performance of hyperspectral and multispectral images in estimating the potato N status, providing a reference for the trade-off between sensor costs and performance. In the experiment, two potato varieties were planted under four fertilization rates with replicates. UAV images were acquired multiple times during the season with a narrow-band hyperspectral imager. Multispectral reflectance was simulated by merging the relevant narrow bands into broad bands to mimic commonly used multispectral cameras. The whole leaf total N concentration and petiole nitrate-N concentration were obtained from 160 potato leaf samples. A partial least square regression model was developed to estimate the two N status indicators using different groups of image features. The best estimation accuracies were given by reflectance of the full spectra with 2.2 nm narrow, with the coefficient of determination (R2) being 0.78 and root mean square error (RMSE) being 0.41 for the whole leaf total N concentration; while, for the petiole nitrate-N concentration, the 10 nm bands had the best performance (R2 = 0.87 and RMSE = 0.13). Generally, the model performance decreased with an increase of the spectral bandwidth. The hyperspectral full spectra largely outperformed all three multispectral cameras, but there was no significant difference among the three brands of multispectral cameras. The results also showed that spectral bands in the visible regions (400–700 nm) were the most highly correlated with potato N concentrations.

Genomic alterations are commonly found in the signaling pathways of fibroblast growth factor receptors (FGFRs). Although there is no selective FGFR inhibitors in market, several promising inhibitors have been investigated in clinical trials, and showed encouraging efficacies in patients. By designing a hybrid between the FGFR-selectivity-enhancing motif dimethoxybenzene group and our previously identified novel scaffold, we discovered a new series of potent FGFR inhibitors, with the best one showing sub-nanomolar enzymatic activity. After several round of optimization and with the solved crystal structure, detailed structureactivity relationship was elaborated. Together with in vitro metabolic stability tests and in vivo pharmacokinetic profiling, a representative compound (35) was selected and tested in xenograft mouse model, and the result demonstrated that inhibitor 35 was effective against tumors with FGFR genetic alterations, exhibiting potential for further development. KEY WORDS: Fibroblast growth factor, Tyrosine kinase receptor, Structure-based, Crystallography, Selectivity, Hybrid, 5-Hydrosulfonyl-5H-pyrrolo[2,3-b]pyrazine, Inhibitor

Spatially explicit crop yield datasets with continuous long-term series are essential for understanding the spatiotemporal variation of crop yield and the impact of climate change on it. There are several spatial disaggregation methods to generate gridded yield maps, but these either use an oversimplified approach with only a couple of ancillary data or an overly complex approach with limited flexibility and scalability. This study developed a spatial disaggregation method using improved spatial weights generated from machine learning. When applied to Chinese maize yield, extreme gradient boosting (XGB) derived the best prediction results, with a cross-validation coefficient of determination (R2) of 0.81 at the municipal level. The disaggregated yield at 1 km grids could explain 54% of the variance of the county-level statistical yield, which is superior to the existing gridded maize yield dataset in China. At the site level, the disaggregated yields also showed much better agreement with observations than the existing gridded maize yield dataset. This lightweight method is promising for generating spatially explicit crop yield datasets with finer resolution and higher accuracy, and for providing necessary information for maize production risk assessment in China under climate change.

Potato is the largest non-cereal food crop in the world. Timely estimation of end-of-season tuber production using in-season information can inform sustainable agricultural management decisions that increase productivity while reducing impacts on the environment. Recently, unmanned aerial vehicles (UAVs) have become increasingly popular in precision agriculture due to their flexibility in data acquisition and improved spatial and spectral resolutions. In addition, compared with natural color and multispectral imagery, hyperspectral data can provide higher spectral fidelity which is important for modelling crop traits. In this study, we conducted end-of-season potato tuber yield and tuber set predictions using in-season UAV-based hyperspectral images and machine learning. Specifically, six mainstream machine learning models, i.e., ordinary least square (OLS), ridge regression, partial least square regression (PLSR), support vector regression (SVR), random forest (RF), and adaptive boosting (AdaBoost), were developed and compared across potato research plots with different irrigation rates at the University of Wisconsin Hancock Agricultural Research Station. Our results showed that the tuber set could be better predicted than the tuber yield, and using the multi-temporal hyperspectral data improved the model performance. Ridge achieved the best performance for predicting tuber yield (R2 = 0.63) while Ridge and PLSR had similar performance for predicting tuber set (R2 = 0.69). Our study demonstrated that hyperspectral imagery and machine learning have good potential to help potato growers efficiently manage their irrigation practices.

Alfalfa is a valuable and intensively produced forage crop in the United States, and the timely estimation of its yield can inform precision management decisions. However, traditional yield assessment approaches are laborious and time-consuming, and thus hinder the acquisition of timely information at the field scale. Recently, unmanned aerial vehicles (UAVs) have gained significant attention in precision agriculture due to their efficiency in data acquisition. In addition, compared with other imaging modalities, hyperspectral data can offer higher spectral fidelity for constructing narrow-band vegetation indices which are of great importance in yield modeling. In this study, we performed an in-season alfalfa yield prediction using UAV-based hyperspectral images. Specifically, we firstly extracted a large number of hyperspectral indices from the original data and performed a feature selection to reduce the data dimensionality. Then, an ensemble machine learning model was developed by combining three widely used base learners including random forest (RF), support vector regression (SVR) and K-nearest neighbors (KNN). The model performance was evaluated on experimental fields in Wisconsin. Our results showed that the ensemble model outperformed all the base learners and a coefficient of determination (R2) of 0.874 was achieved when using the selected features. In addition, we also evaluated the model adaptability on different machinery compaction treatments, and the results further demonstrate the efficacy of the proposed ensemble model.

In this paper, we present a model-free detection-based tracking approach for detecting and tracking moving objects in street scenes from point clouds obtained via a Doppler LiDAR that can not only collect spatial information (e.g., point clouds) but also Doppler images by using Doppler-shifted frequencies. Using our approach, Doppler images are used to detect moving points and determine the number of moving objects followed by complete segmentations via a region growing technique. The tracking approach is based on Multiple Hypothesis Tracking (MHT) with two extensions. One is that a point cloud descriptor, Oriented Ensemble of Shape Function (OESF), is proposed to evaluate the structure similarity when doing object-to-track association. Another is to use Doppler images to improve the estimation of dynamic state of moving objects. The quantitative evaluation of detection and tracking results on different datasets shows the advantages of Doppler LiDAR and the effectiveness of our approach.

One of the intrinsic properties of conventional terrestrial laser scanning technology is the unevenness of its point density over the scene where objects rendered closer to the scanner are more densely covered than the ones far away. This uneven distribution can be amplified as the working range of a laser scanner gets longer. In such case a higher pulse repetition rate (PRR) is applied to the whole scanning area and the scanning time will be dramatically increased. To improve the efficiency of the conventional laser scanning technology, a prototype of adaptive scanning technology, the HRS3D-AS scanner has been developed by Blackmore Sensors and Analytics, Inc. This paper briefly describes the working principles of the adaptive scanner and presents a thorough evaluation on the distributions of the point density in comparison to the conventional scanning. Based on this study, we show that such a new technology can produce a point cloud of more uniform density and less data volume. The overall field scanning time can be reduced by several times compared to the conventional, PRR-fixed scanning. Such properties are expected to significantly simplify the algorithmic development and increase the productivity in data acquisition and processing. The limitations of this new adaptive scanning technology are also discussed in terms of redundant and unresolved details. Finally, recommendations related to the practicing of such adaptive scan are discussed.

Fibroblast growth factor receptors (FGFRs), a subfamily of receptor tyrosine kinases, are aberrant in various cancer types, and considered to be promising targets for cancer therapy. We started with a weak-active compound that was identified from our internal hepatocyte growth factor receptor (also called c-Met) inhibitor project, and optimized it with the guidance of a co-crystal structure of compound 8 with FGFR1. Through rational design, synthesis, and the biological evaluation of a series of 5H-pyrrolo[2,3-b]pyrazine derivatives, we discovered several potent FGFR kinase inhibitors. Among them, compound 13 displayed high selectivity and favorable metabolic properties, demonstrating a promising lead for further development.

Catenary theory is recognized as the most effective suspension theory which can reflect the real situation of suspension. Catenary equation belongs to transcendental function, so there are some difficulties in calculation and application. Parabola theory, suspension curve theory and perturbation method are gradually formed as the theoretical basis for the study of suspension cables. This paper takes the 500m spherical radio telescope project as the background. The modeling method of suspension cable under multiple concentrated loads is analyzed, and the mathematical model of supporting cable after load is derived. The research in this paper provides a reference for the establishment of mathematical model of suspension cable in practice.

Abnormality of fibroblast growth factor receptor (FGFR)-mediated signaling pathways were frequently found in various human malignancies, making FGFRs hot targets for cancer treatment. To address the consistent need for a new chemotype of FGFR inhibitors, here, we started with a hit structure identified from our internal hepatocyte growth factor receptor (also called c-Met) inhibitor project, and conducted a chemical optimization. After exploring three parts of the hit compound, we finally discovered a new series of pyrrolo[2,3-b]pyrazine FGFR inhibitors, which contain a novel scaffold and unique molecular shape. We believe that our findings can help others to further develop selective FGFR inhibitors.

Tyrosine kinase fibroblast growth factor receptor (FGFR), which is aberrant in various cancer types, is a promising target for cancer therapy. Here we reported the design, synthesis, and biological evaluation of a new series of 6-(2,6-dichloro-3,5-dimethoxyphenyl)-4-substituted-1H-indazole derivatives as potent FGFR inhibitors. The compound 6-(2,6-dichloro-3,5-dimethoxyphenyl)-N-phenyl-1H-indazole-4-carboxamide (10a) was identified as a potent FGFR1 inhibitor, with good enzymatic inhibition. Further structure-based optimization revealed that 6-(2,6-dichloro-3,5-dimethoxyphenyl)-N-(3-(4-methylpiperazin-1-yl)phenyl)-1H-indazole-4-carboxamide (13a) is the most potent FGFR1 inhibitor in this series, with an enzyme inhibitory activity IC50 value of about 30.2 nM.