Prediction of acute coronary syndrome in patients with myeloproliferative neoplasms.

Background: Patients with myeloproliferative neoplasms (MPN) are exposed to a higher risk of cardiovascular disease, especially cardiovascular calcification. The present research aimed to analyze the clinical features and coronary artery calcium score (CACS) in MPN patients, and construct an effective model to predict acute coronary syndrome (ACS) in MPN patients. Materials and methods: A total of 175 MPN patients and 175 controls were recruited from the First Affiliated Hospital of Ningbo University. Based on cardiovascular events, the MPN patients were divided into the ACS group and the non-ACS group. Multivariate Cox analysis was completed to explore ACS-related factors. Furthermore, ROC curves were plotted to assess the predictive effect of CACS combined with white blood cells (WBC) and platelet for ACS in MPN patients. Results: The MPN group exhibited a higher CACS than the control group (133 vs. 55, P < 0.001). A total of 16 patients developed ACS in 175 MPN patients. Compared with non-ACS groups, significant differences in age, diabetes, smoking history, WBC, percentage of neutrophil, percentage of lymphocyte, neutrophil count, hemoglobin, hematocrit, platelet, lactate dehydrogenase, ß 2-microglobulin, and JAK2V617F mutation were observed in the ACS groups. In addition, the CACS in the ACS group was also significantly higher than that in the non-ACS group (374.5 vs. 121, P < 0.001). The multivariable Cox regression analysis identified WBC, platelet, and CACS as independent risk factors for ACS in MPN patients. Finally, ROC curves indicated that WBC, platelet, and CACS have a high predictive value for ACS in MPN patients (AUC = 0.890). Conclusion: CACS combined with WBC and platelet might be a promising model for predicting ACS occurrence in MPN patients.

Global convergence in terrestrial gross primary production response to atmospheric vapor pressure deficit.

Atmospheric vapor pressure deficit (VPD) increases with climate warming and may limit plant growth. However, gross primary production (GPP) responses to VPD remain a mystery, offering a significant source of uncertainty in the estimation of global terrestrial ecosystems carbon dynamics. In this study, in-situ measurements, satellite-derived data, and Earth System Models (ESMs) simulations were analysed to show that the GPP of most ecosystems has a similar threshold in response to VPD: first increasing and then declining. When VPD exceeds these thresholds, atmospheric drought stress reduces soil moisture and stomatal conductance, thereby decreasing the productivity of terrestrial ecosystems. Current ESMs underscore CO2 fertilization effects but predict significant GPP decline in low-latitude ecosystems when VPD exceeds the thresholds. These results emphasize the impacts of climate warming on VPD and propose limitations to future ecosystems productivity caused by increased atmospheric water demand. Incorporating VPD, soil moisture, and canopy conductance interactions into ESMs enhances the prediction of terrestrial ecosystem responses to climate change.

Evaluation of Coronary Artery Diffuse Calcification Stenosis by Corrected Coronary Opacification Difference.

OBJECTIVES: The artifacts produced by calcification on coronary computed tomographic angiography (CCTA) have a great influence on the diagnosis of coronary stenosis. The purpose of this study is to investigate the value of corrected coronary opacification (CCO) difference in the diagnosis of stenosis in diffusely calcified coronary arteries (DCCAs). METHODS: A total of 84 patients were enrolled. The CCO difference across the diffuse calcification was measured through CCTA. Coronary arteries were grouped according to the extent of stenosis obtained by invasive coronary angiography (ICA). The Kruskal-Wallis H test was used to compare the CCO differences between different groups and a receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the CCO difference. RESULTS: Among the 84 patients, 58 patients had one DCCA, 14 patients had 2 DCCAs, and 12 patients had 3 DCCAs. A total of 122 coronary arteries were examined, 16 showed no significant stenosis, 42 had <70% stenosis, and 64 had 70-99% stenosis. The median CCO differences among the 3 groups were 0.064, 0.117, and 0.176, respectively. There were significant differences between the group without stenosis and the group with 70-99% stenosis (H = -3.581, P = 0.001), and between the group with <70% stenosis and the group with 70-99% stenosis (H = -2.430, P = 0.045). The area under the ROC curve was 0.681 and the optimal cut-off point was 0.292. Taking the ICA results as the gold standard, the sensitivity and specificity for the diagnosis of ≥70% coronary stenosis with a cut-off point of 0.292 were 84.4% and 44.8%, respectively. CONCLUSION: CCO difference could be useful in the diagnosis of ≥70% severe coronary stenosis in DCCA. Through this non-invasive examination, the CCO difference could be a reference for clinical treatment.

Daily activity patterns and body temperature of the Oriental migratory locust, Locusta migratoria manilensis (Meyen), in natural habitat.

Current pest management techniques would benefit from understanding the behavioural rhythms of the target pest and its body temperature, a critical aspect not well studied and potentially limiting the effectiveness of biopesticides under natural conditions. This study aims 1) to understand under natural conditions the behavioural patterns of different stages of hoppers and adults of Locusta migratoria manilensis and 2) to identify the environmental factors modulating their body temperature through field observation. We carried out an intensive field sampling in two of the main locust breeding regions in China, recording the body temperature (day and night), morphological traits (stage, sex and size) and microhabitat of 953 individuals. The results revealed that locusts preferred the ground as their main activity subhabitat, particularly for hoppers. Adults tended to move upper in the reed canopy at two peaks (10-11 h and 14-15 h). Locusts body temperature during daytime increased with development stage and size, while the opposite pattern occurred during night time. Entompathogenic fungi are more effective if the body temperature of the target pest is in a proper range without too high or too low. Application of biopesticides should focus on younger locusts spraying in the morning or at dusk as the locusts have lower body temperatures.

Detect and attribute the extreme maize yield losses based on spatio-temporal deep learning.

Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food security. While the data-driven deep learning approach has shown great capacity in predicting yield patterns, its capacity to detect and attribute the impacts of climatic extremes on yields remains unknown. In this study, we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018, with a special focus on the extreme yield loss in 2012. We found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018 (R2 = 0.81) and well reproduced the extreme yield anomalies in 2012 (R2 = 0.79). Further attribution analysis indicated that extreme heat stress was the major cause for yield loss, contributing to 72.5% of the yield loss, followed by anomalies of vapor pressure deficit (17.6%) and precipitation (10.8%). Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in 2012. Our results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era.

Meiosis resumption in human primordial germ cells from induced pluripotent stem cells by in vitro activation and reconstruction of ovarian nests.

BACKGROUND: The differentiation of human induced pluripotent stem cells (iPSCs) into oocytes, which involves the transformation from mitosis to meiosis, has been a hotspot of biological research for many years and represents a desirable experimental model and potential strategy for treating infertility. At present, studies have shown that most cells stagnate in the oogonium stage after differentiation into primordial germ cells (PGCs) from human iPSCs. METHODS: iPSCs carrying a SYCP3-mkate2 knock-in reporter were generated by the CRISPR/Cas9 strategy to monitor meiosis status during induced differentiation from iPSCs into oocytes. These induced PGCs/oogonia were activated by small molecules from the Wnt signaling pathway and then cocultured with reconstructed human ovarian nests in vivo for further development. RESULTS: First, human PGCs and oogonia were efficiently induced from iPSCs. Second, induced dormant PGCs resumed meiosis and then differentiated into primary oocytes through the in vitro activation of the Wnt signaling pathway. Finally, a new coculture system involving the reconstruction of ovarian nests in vitro could facilitate the differentiation of oocytes. CONCLUSIONS: Human PGCs/oogonia induced from iPSCs can be activated and used to resume meiosis by molecules of the Wnt signaling pathway. The coculture of activated PGCs and reconstruction of ovarian nests facilitated differentiation into primary oocytes and the generation of haploid human oocytes in vivo. These findings established a new strategy for germline competence in primary oocytes and provided a keystone for human gametogenesis in vitro and in vivo.

Clinical and Pulmonary CT Characteristics of Patients Infected With the SARS-CoV-2 Omicron Variant Compared With Those of Patients Infected With the Alpha Viral Strain.

Background: Omicron has become the dominant variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) globally. We aimed to compare the clinical and pulmonary computed tomography (CT) characteristics of the patients infected with SARS-CoV-2 Omicron with those of patients infected with the Alpha viral strain. Methods: Clinical profiles and pulmonary CT images of 420 patients diagnosed with coronavirus disease-2019 (COVID-19) at Ningbo First Hospital between January 2020 and April 2022 were collected. Demographic characteristics, symptoms, and imaging manifestations of patients infected with the SARS-CoV-2 Omicron variant were compared with those of patients infected with the Alpha strain. Results: A total of 38 patients were diagnosed to be infected with the Alpha strain of SARS-CoV-2, whereas 382 patients were thought to be infected with the Omicron variant. Compared with patients infected with the Alpha strain, those infected with the Omicron variant were younger, and a higher proportion of men were infected (P < 0.001). Notably, 93 (24.3%) of the patients infected with Omicron were asymptomatic, whereas only two (5.3%) of the patients infected with the Alpha strain were asymptomatic. Fever (65.8%), cough (63.2%), shortness of breath (21.1%), and diarrhea (21.1%) were more common in patients infected with the SARS-CoV-2 Alpha strain, while runny nose (24.1%), sore throat (31.9%), body aches (13.6%), and headache (12.3%) were more common in patients with the Omicron variant. Compared with 33 (86.84%) of 38 patients infected with the Alpha strain, who had viral pneumonia on pulmonary CT images, only 5 (1.3%) of 382 patients infected with the Omicron variant had mild foci. In addition, the distribution of opacities in the five patients was unilateral and centrilobular, whereas most patients infected with the Alpha strain had bilateral involvement and multiple lesions in the peripheral zones of the lung. Conclusion: The SARS-CoV-2 Alpha strain mainly affects the lungs, while Omicron is confined to the upper respiratory tract in patients with COVID-19.

Exploration on Scientific Research Data-Targeted Intelligent Recommendation System Using Machine Learning Under the Background of Sustainable Development.

The purpose is to provide researchers with reliable Scientific Research Data (SRD) from the massive amounts of research data to establish a sustainable Scientific Research (SR) environment. Specifically, the present work proposes establishing an Intelligent Recommendation System (IRS) based on Machine Learning (ML) algorithm and SRD. Firstly, the IRS is established over ML technology. Then, based on user Psychology and Collaborative Filtering (CF) recommendation algorithm, a hybrid algorithm [namely, Content-Based Recommendation-Collaborative Filtering (CBR-CF)] is established to improve the utilization efficiency of SRD and Sustainable Development (SD) of SR. Consequently, the present work designs literature and SRD-targeted IRS using the hybrid recommendation under the background of SD. The proposed system's feasibility is analyzed through experiments. Additionally, the system performance is analyzed and verified from accuracy, diversity, coverage, novelty, and recommendation efficiency. The results show that the hybrid algorithm can make up for the shortcomings of a single algorithm and improve the recommendation efficiency. Experiments show that the accuracy of the proposed CBR-CF algorithm is the highest. In particular, the recommendation accuracy for the single-user system can reach 82-93%, and the recall of all recommended algorithms falls between 60 and 91%. The recall of the hybrid algorithm is higher than that of a single algorithm, and the highest recall is 91%. Meanwhile, the hybrid algorithm has comprehensive coverage, good applicability, and diversity. Therefore, SD-oriented SRD-targeted IRS is of great significance to improve the SRD utilization and the accuracy of IRS, and expand the achievement value of SR. The research content provides a reference for establishing a sustainable SR environment and improving SR efficiency.

Tear Cytokines as Biomarkers for Acute Ocular Graft-Versus-Host Disease.

PURPOSE: The purpose of this study was to analyze tear cytokine and complement levels in patients diagnosed with acute ocular graft-versus-host disease (oGVHD) and examine the consistency of these levels with the severity of clinical manifestations. METHODS: Ten patients with acute oGVHD (20 eyes) were enrolled for the assessment of tear cytokine levels and ocular surface parameters, and 18 healthy people (36 eyes) were selected as the control group. The tear cytokine and complement levels were measured using microsphere-based immunoassay analysis. RESULTS: The main clinical manifestations of acute oGVHD include eye redness, a large amount of purulent exudate, eye pain, and even false membranes. The levels of intercellular cell adhesion molecule-1, interleukin 6 (IL-6), interleukin 1 beta (IL-1ß), interleukin 8, epidermal growth factor (EGF), interleukin 7 (IL-7), B-cell activating factor, granulocyte-macrophage colony-stimulating factor (GM-CSF), and complement in patients with acute oGVHD showed significant differences compared with those in normal people. Furthermore, the levels of IL-6, IL-1ß, EGF, GM-CSF, IL-7, and C3a showed a stronger correlation with ocular surface parameters. CONCLUSIONS: Our study was the first to enroll patients with acute oGVHD to assess tear cytokine levels as a method contributing to the diagnosis of acute oGVHD. In addition, it has been demonstrated that certain tear cytokines, including intercellular cell adhesion molecule-1, IL-6, IL-1ß, interleukin 8, B-cell activating factor, GM-CSF, IL-7, EGF, and complement, may be new diagnostic biomarkers for acute oGVHD.

The predictive value of coronary artery calcification score combined with bone mineral density for the 2-year risk of cardiovascular events in maintenance hemodialysis patients.

PURPOSE: Cardiovascular disease is the leading cause of death in maintenance hemodialysis (MHD) patients. The aim of this study is to investigate the predictive value of coronary artery calcification score (CACs) combined with bone mineral density (BMD) for the risk of cardiovascular diseases in MHD patients. METHODS: From January 2017 to January 2019, we enrolled 112 MHD patients and 112 controls in Ningbo First Hospital, and retrospectively counted the cardiovascular events in the next 2 years after enrollment. According to the occurrence of cardiovascular events, the MHD patients were divided into CVD group and non-CVD group. The differences of vertebral BMD and CACs between the two groups were compared. ROC curve, Kaplan-Meier curve and Cox regression analyses were used for assess the predictive value of 2-year cardiovascular events in MHD patients. RESULTS: Among 112 MHD patients, 49 (43.75%) patients had cardiovascular events. The results showed that the average value of BMD in MHD patients was significantly lower than that in the control group (99.88 ± 30.99 VS. 108.35 ± 23.98, P = 0.0231). The CACs in MHD patients were significantly higher than that in the control group (317.81 ± 211.53 VS. 190.03 ± 100.50, P < 0.001). The results between CVD group and the non-CVD group were to the same direction (BMD: 81.12 ± 31.28 VS. 114.48 ± 21.61, P < 0.001; CACs: 447.16 ± 234.11 VS. 217.21 ± 119.03, P < 0.001). Besides, CACs combined with BMD yield an AUC of 0.875 with a sensitivity of 79.60%, a specificity of 82.50%. Kaplan-Meier curve and Cox regression analyses indicated that CACs and BMD were independently associated with high risk of cardiovascular events in MHD patients. CONCLUSION: The combination of CACs and vertebral BMD could predict the occurrence of cardiovascular events in MHD patients to some extent.

Transverse myelitis associated with primary biliary cirrhosis: clinical, laboratory, and neuroradiological features.

PURPOSE: Only five patients diagnosed with transverse myelitis (TM) associated with primary biliary cirrhosis (PBC) have been reported in the literature to date. We report two additional patients with TM associated with PBC at our hospital and review all seven cases. MATERIALS AND METHODS: An association between neuromyelitis optic spectrum disease (NMOSD) and PBC is reported for the first time in one of our patients. The second patient was diagnosed with TM associated with PBC without Sjögren's syndrome (SS). A literature review was performed using the PubMed database. RESULTS: All patients diagnosed with TM associated with PBC were female with a median age of 53 years. TM was associated with SS in 71.4% of the patients. Complete TM and incomplete TM were diagnosed in 71.4% and 28.6% of the patients. The erythrocyte sedimentation rate was increased in 83.3% of patients. All patients were positive for anti-mitochondrial antibodies. Other autoantibodies, including anti-nuclear antibodies, rheumatoid factor, anti-SSA antibody, were detected in some patients. Cerebrospinal fluid analysis was abnormal in 83.3% of patients. The spinal cord lesions involved more than three vertebral segments in 85.7% of patients. Glucocorticoids were administered in 85.7% of patients, and good responses were observed. CONCLUSIONS: The association between TM and PBC may be missed by neurologists. More attention should be paid to the association between NMOSD and PBC. Most patients show SS and may experience relapse, and there is a good rationale for early commencement of immunosuppressive therapy.

[GIS-based measurement of Haloxylon ammodendron tree ring]. / 基于GIS的梭梭年轮测量方法.

Tree ring data is of significance for reconstructing climate and predicting environmental dynamics. In order to accurately measure spacing and other parameters of Haloxylon ammodendron tree ring, we first assigned coordinate system to the scanned H. ammodendron disc PS images based on GIS, and then completed the H. ammodendron tree ring spacing measurement by using ENVI image classification software and GIS measuring tool. The measurement accuracy was proved by WinDENDRO tree ring analysis system. The results showed that there was no significant difference between those two methods (P=0.63), and that the difference of paired mean value was 0.87 µm, indicating that the measured results were accurate and reliable. The constructed method in this study could be used to measure the spacing of H. ammodendron tree ring, which lay the foundation for the automatic measurement of tree ring parameters such as area and perimeter. Our method could replace the current professional tree ring analysis system for some ring parameter measurement. The study would contribute to the dendroclimatology analysis and the investigation on age structure of H. ammodendron population.

Leaf Anthocyanin Content Retrieval with Partial Least Squares and Gaussian Process Regression from Spectral Reflectance Data.

Leaf pigment content retrieval is an essential research field in remote sensing. However, retrieval studies on anthocyanins are quite rare compared to those on chlorophylls and carotenoids. Given the critical physiological significance of anthocyanins, this situation should be improved. In this study, using the reflectance, partial least squares regression (PLSR) and Gaussian process regression (GPR) were sought to retrieve the leaf anthocyanin content. To our knowledge, this is the first time that PLSR and GPR have been employed in such studies. The results showed that, based on the logarithmic transformation of the reflectance (log(1/R)) with 564 and 705 nm, the GPR model performed the best (R2/RMSE (nmol/cm2): 0.93/2.18 in the calibration, and 0.93/2.20 in the validation) of all the investigated methods. The PLSR model involved four wavelengths and achieved relatively low accuracy (R2/RMSE (nmol/cm2): 0.87/2.88 in calibration, and 0.88/2.89 in validation). GPR apparently outperformed PLSR. The reason was likely that the non-linear property made GPR more effective than the linear PLSR in characterizing the relationship for the absorbance vs. content of anthocyanins. For GPR, selected wavelengths around the green peak and red edge region (one from each) were promising to build simple and accurate two-wavelength models with R2 > 0.90.

Coffee Flower Identification Using Binarization Algorithm Based on Convolutional Neural Network for Digital Images.

Crop-type identification is one of the most significant applications of agricultural remote sensing, and it is important for yield estimation prediction and field management. At present, crop identification using datasets from unmanned aerial vehicle (UAV) and satellite platforms have achieved state-of-the-art performances. However, accurate monitoring of small plants, such as the coffee flower, cannot be achieved using datasets from these platforms. With the development of time-lapse image acquisition technology based on ground-based remote sensing, a large number of small-scale plantation datasets with high spatial-temporal resolution are being generated, which can provide great opportunities for small target monitoring of a specific region. The main contribution of this paper is to combine the binarization algorithm based on OTSU and the convolutional neural network (CNN) model to improve coffee flower identification accuracy using the time-lapse images (i.e., digital images). A certain number of positive and negative samples are selected from the original digital images for the network model training. Then, the pretrained network model is initialized using the VGGNet and trained using the constructed training datasets. Based on the well-trained CNN model, the coffee flower is initially extracted, and its boundary information can be further optimized by using the extracted coffee flower result of the binarization algorithm. Based on the digital images with different depression angles and illumination conditions, the performance of the proposed method is investigated by comparison of the performances of support vector machine (SVM) and CNN model. Hence, the experimental results show that the proposed method has the ability to improve coffee flower classification accuracy. The results of the image with a 52.5° angle of depression under soft lighting conditions are the highest, and the corresponding Dice (F1) and intersection over union (IoU) have reached 0.80 and 0.67, respectively.

Forensic applicability of autosomal insertion/deletion loci in Chinese Daur ethnic group and genetic affinity evaluations between Daur group and reference populations.

In present study, we evaluated the genetic diversities of 30 insertion/deletion (InDel) loci and analyzed the genetic relationships between Daur and other comparison populations. In the studied Daur group, any two InDel loci showed no linkage disequilibrium, and all loci showed no deviations from exact tests of Hardy-Weinberg equilibrium. Insertion allele frequencies at 30 InDel loci ranged from 0.1459 (HLD39) to 0.8774 (HLD118). The observed heterozygosity and expected heterozygosity values were ranged from 0.1984 (HLD118) to 0.5564 (HLD6) and 0.2155 (HLD118) to 0.5000 (HLD92 and HLD6), respectively. The combined power of discrimination and power of exclusion values were 0.999999999993428 and 0.9878, respectively, which indicated that this panel of 30 InDels could be used for individual identifications in Daur group. Population genetic analyses including pairwise fixation index, STRUCTURE analysis, principal component analysis, genetic distance, multidimensional scaling analysis and phylogenetic analysis demonstrated that the Daur group had the closer genetic relationships with the groups from western China in comparison with other continental populations.

Estimating reed loss caused by Locusta migratoria manilensis using UAV-based hyperspectral data.

Locusta migratoria manilensis has caused major damage to vegetation and crops. Quantitative evaluation studies of vegetation loss estimation from locust damage have seldom been found in traditional satellite-remote-sensing-based research due to insufficient temporal-spatial resolution available from most current satellite-based observations. We used remote sensing data acquired from an unmanned aerial vehicle (UAV) over a simulated Locusta migratoria manilensis damage experiment on a reed (Phragmites australis) canopy in Kenli District, China during July 2017. The experiment was conducted on 72 reed plots, and included three damage duration treatments with each treatment including six locust density levels. To establish the appropriate loss estimation models after locust damage, a hyperspectral imager was mounted on a UAV to collect reed canopy spectra. Loss components of six vegetation indices (RVI, NDVI, SAVI, MSAVI, GNDVI, and IPVI) and two "red edge" parameters (Dr and SDr) were used for constructing the loss estimation models. Results showed that: (1) Among the six selected vegetation indices, loss components of NDVI, MSAVI, and GNDVI were more sensitive to the variation of dry weight loss of reed green leaves and produced smaller estimation errors during the model test process, with RMSEs ranging from 8.8 to 9.1 g/m;. (2) Corresponding model test results based on loss components of the two selected red edge parameters yielded RMSEs of 27.5 g/m2 and 26.1 g/m2 for Dr and SDr respectively, suggesting an inferior performance of red edge parameters compared with vegetation indices for reed loss estimation. These results demonstrate the great potential of UAV-based loss estimation models for evaluating and quantifying degree of locust damage in an efficient and quantitative manner. The methodology has promise for being transferred to satellite remote sensing data in the future for better monitoring of locust damage of larger geographical areas.

[Spatiotemporal variation of growing season length of vegetation in China.] / 中国植被生长期的时空变化.

We analyzed the variation trend of growing season length (GSL) of different periods in provinces (regions) of China and the corresponding movement velocity of GSL isolines at 150, 200, 250, 300 and 350 days, based on daily mean temperature data of 822 meteorological stations from 1951 to 2017. In this study, the definition of GSL given by the world meteorological organization was adopted, together with Slope, Hurst and Mann-Kendall indices. The results showed that the GSL in northern China changed significantly during 1951-2017. The extension of GSL was faster in the north than the south, and faster in high-altitude areas than low-altitude ones. The trend of future GSL change in most regions of China converged with the current extension trend. The extension of GSL in northern provinces (regions) was generally 0.1-0.2 d·a-1, of which the fastest was Tibet with a speed of 0.44 d·a-1. The period 1981-2000 was the most changeable time of GSL in Chinese provinces (regions). The growing season start (GSS) of all provinces (regions) contributed more to the GSL extension, except for Xinjiang, whose GSL extension was dominated by the growing season end (GSE). In the high-latitude or high-altitude provinces, GSL was more sensitive to the change of mean annual temperature. The higher the mean annual temperature, the longer the GSL. Since 1951, China's GSL isolines of 150, 200, 250, 300 and 350 days showed notable variations. The fastest movement velocity was the 200 days isoline in Northeast China with an average northward movement velocity of 6.11 km·a-1. The general principle of the movement of China's GSL isoline was that the higher the value of the isoline, the slower the northward movement, with even a southward shift in part of the 350 days isoline. The extension of GSL in China would result in the northward shift of crop planting boundary and the extension of natural vegetation growth period. However, the specific impacts of this change on the quality, crop yield, and ecosystem carbon sequestration need further research.

A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level.

Understanding large-scale crop growth and its responses to climate change are critical for yield estimation and prediction, especially under the increased frequency of extreme climate and weather events. County-level corn phenology varies spatially and interannually across the Corn Belt in the United States, where precipitation and heat stress presents a temporal pattern among growth phases (GPs) and vary interannually. In this study, we developed a long short-term memory (LSTM) model that integrates heterogeneous crop phenology, meteorology, and remote sensing data to estimate county-level corn yields. By conflating heterogeneous phenology-based remote sensing and meteorological indices, the LSTM model accounted for 76% of yield variations across the Corn Belt, improved from 39% of yield variations explained by phenology-based meteorological indices alone. The LSTM model outperformed least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) approaches for end-of-the-season yield estimation, as a result of its recurrent neural network structure that can incorporate cumulative and nonlinear relationships between corn yield and environmental factors. The results showed that the period from silking to dough was most critical for crop yield estimation. The LSTM model presented a robust yield estimation under extreme weather events in 2012, which reduced the root-mean-square error to 1.47 Mg/ha from 1.93 Mg/ha for LASSO and 2.43 Mg/ha for RF. The LSTM model has the capability to learn general patterns from high-dimensional (spectral, spatial, and temporal) input features to achieve a robust county-level crop yield estimation. This deep learning approach holds great promise for better understanding the global condition of crop growth based on publicly available remote sensing and meteorological data.

Rice Yield Estimation Using Parcel-Level Relative Spectral Variables From UAV-Based Hyperspectral Imagery.

Time-series Vegetation Indices (VIs) are usually used for estimating grain yield. However, multi-temporal VIs may be affected by different background, illumination, and atmospheric conditions, so the absolute differences among time-series VIs may include the effects induced from external conditions in addition to vegetation changes, which will pose a negative effect on the accuracy of crop yield estimation. Therefore, in this study, the parcel-based relative vegetation index (ΔVI) and the parcel-based relative yield are proposed and further used to estimate rice yield. Hyperspectral images at key growth stages, including tillering stage, jointing stage, booting stage, heading stage, filling stage, and ripening stage, as well as rice yield, were obtained with Rikola hyperspectral imager mounted on Unmanned Aerial Vehicle (UAV) in 2017 growing season. Three types of parcel-level relative vegetation indices, including Relative Normalized Difference Vegetation Index (RNDVI), Relative Ratio Vegetation Index (RRVI), and Relative Difference Vegetation Index (RDVI) are created by using all possible two-band combinations of discrete channels from 500 to 900 nm. The optimal VI type and its band combinations at different growth stages are identified for rice yield estimation. Furthermore, the optimal combinations of different growth stages for yield estimation are determined by F-test and validated using leave-one-out cross validation (LOOCV) method. The comparison results show that, for the single-growth-stage model, RNDVI[880,712] at booting stage has the best correlation with rice yield with a R 2-value of 0.75. For the multiple-growth-stage model, RNDVI[808,744] at jointing stage, RNDVI[880,712] at booting stage and RNDVI[808,744] at filling stage gain a higher R 2-value of 0.83 with the mean absolute percentage error of estimated rice yield of 3%. The study demonstrates that the proposed method with parcel-level relative vegetation indices and relative yield can achieve higher yield estimation accuracy because it can make full use of the advantage that remote sensing can monitor relative changes accurately. The new method will further enrich the technology system for crop yield estimation based on remotely sensed data.

Imatinib-induced irreversible interstitial lung disease: A case report.

RATIONALE: Imatinib mesylate (imatinib) is a classic tyrosine kinase inhibitor used to treat chronic myeloid leukemia. Although it is well tolerated by most patients and helps in the achievement of complete remission, a few rare imatinib-associated adverse effects such as pulmonary interstitial fibrosis have been reported. Because of its rareity, the clinical features of imatinib-induced interstitial lung disease (ILD) remain unclear. PATIENT CONCERNS: A 49-year-old Chinese man with chronic myeloid leukemia received oral treatment with imatinib and initially exhibited a good response. However, he presented with cough and fever 9 months after treatment initiation. DIAGNOSES: Pulmonary computed tomography indicated diffuse interstitial fibrosis in both lungs. All tests for possible infectious pathologies provided negative results. INTERVENTIONS: The patient was diagnosed with interstitial pneumonia and treated with antibiotics; however, there was no improvement. On the basis of a suspicion of imatinib-induced ILD, imatinib was discontinued and prednisone treatment was initiated. OUTCOMES: The patient's symptoms ameliorated with treatment, and imatinib was reintroduced. However, he developed cough and dyspnea again, and his treatment was switched to nilotinib as a second-line regimen. He was regularly monitored, and although his clinical symptoms ameliorated, computed tomography performed 29 months after he was diagnosed with ILD showed irreversible pulmonary interstitial fibrosis without progression. LESSONS: Clinicians should consider the possibility of severe irreversible ILD and carefully monitor patients receiving imatinib treatment.