Machine Learning Based Crop Drought Mapping System by UAV Remote Sensing RGB Imagery

Water stress has adverse effects on crop growth and yield, where its monitoring plays a vital role in precision crop management. This paper aims at initially exploiting the potentials of UAV aerial RGB image in crop water stress assessment by developing a simple but effective supervised learning system. Various techniques are seamlessly integrated into the system including vegetation segmentation, feature engineering, Bayesian optimization and Support Vector Machine (SVM) classifier. In particular, wheat pixels are first segmented from soil background by using the classical vegetation index thresholding. Rather than performing pixel-wise classification, pixel squares of appropriate dimension are defined as samples, from which various features for pure vegetation pixels are extracted including spectral and color index (CI) features. SVM with Bayesian optimization is adopted as the classifier. To validate the developed system, a Unmanned Aerial Vehicle (UAV) survey is performed to collect high-resolution atop canopy RGB imageries by using DJI S1000 for the experimental wheat fields of Gucheng town, Heibei Province, China. Two levels of soil moisture were designed after seedling establishment for wheat plots by using intelligent irrigation and rain shelter, where field measurements were to obtain ground soil water ratio for each wheat plot. Comparative experiments by three-fold cross-validation demonstrate that pixel-wise classification, with a high computation load, can only achieve an accuracy of 82.8% with poor F1 score of 71.7%; however, the developed system can achieve an accuracy of 89.9% with F1 score of 87.7% by using only spectral intensities, and the accuracy can be further improved to 92.8% with F1 score of 91.5% by fusing both spectral intensities and CI features. Future work is focused on incorporating more spectral information and advanced feature extraction algorithms to further improve the performance

Expression of the phosphorylated MEK5 protein is associated with TNM staging of colorectal cancer

<p>Abstract</p> <p>Background</p> <p>Activation of MEK5 in many cancers is associated with carcinogenesis through aberrant cell proliferation. In this study, we determined the level of phosphorylated MEK5 (pMEK5) expression in human colorectal cancer (CRC) tissues and correlated it with clinicopathologic data.</p> <p>Methods</p> <p>pMEK5 expression was examined by immunohistochemistry in a tissue microarray (TMA) containing 335 clinicopathologic characterized CRC cases and 80 cases of nontumor colorectal tissues. pMEK5 expression of 19 cases of primary CRC lesions and paired with normal mucosa was examined by Western blotting. The relationship between pMEK5 expression in CRC and clinicopathologic parameters, and the association of pMEK5 expression with CRC survival were analyzed respectively.</p> <p>Results</p> <p>pMEK5 expression was significantly higher in CRC tissues (185 out of 335, 55.2%) than in normal tissues (6 out of 80, 7.5%; <it>P </it>< 0.001). Western blotting demonstrated that pMEK5 expression was upregulated in 12 of 19 CRC tissues (62.1%) compared to the corresponding adjacent nontumor colorectal tissues. Overexpression of pMEK5 in CRC tissues was significantly correlated to the depth of invasion (<it>P </it>= 0.001), lymph node metastasis (<it>P </it>< 0.001), distant metastasis (<it>P </it>< 0.001) and high preoperative CEA level (<it>P </it>< 0.001). Consistently, the pMEK5 level in CRC tissues was increased following stage progression of the disease (<it>P </it>< 0.001). Analysis of the survival curves showed a significantly worse 5-year disease-free (<it>P </it>= 0.002) and 5-year overall survival rate (<it>P </it>< 0.001) for patients whose tumors overexpressed pMEK5. However, in multivariate analysis, pMEK5 was not an independent prognostic factor for CRC (DFS: <it>P </it>= 0.139; OS: <it>P </it>= 0.071).</p> <p>Conclusions</p> <p>pMEK5 expression is correlated with the staging of CRC and its expression might be helpful to the TNM staging system of CRC.</p

Critical Leaf Water Content for Maize Photosynthesis under Drought Stress and Its Response to Rewatering

Crop photosynthesis is closely related to leaf water content (LWC), and clarifying the LWC conditions at critical points in crop photosynthesis has great theoretical and practical value for accurately monitoring drought and providing early drought warnings. This experiment was conducted to study the response of LWC to drought and rewatering and to determine the LWC at which maize photosynthesis reaches a maximum and minimum and thus changes from a state of stomatal limitation (SL) to non-stomatal limitation (NSL). The effects of rehydration were different after different levels of drought stress intensity at different growth stages, and the maize LWC recovered after rewatering following different drought stresses at the jointing stage; however, the maize LWC recovered more slowly after rewatering following 43 days and 36 days of drought stress at the tasselling and silking stages, respectively. The LWC when maize photosynthesis changed from SL to NSL was 75.4% ± 0.38%, implying that the maize became rehydrated under physiologically impaired conditions. The LWCs at which the maize Vcmax25 reached maximum values and zero differed between the drought and rewatering periods. After exposure to drought stress, the maize exhibited enhanced drought stress tolerance, an obviously reduced suitable water range, and significantly weakened photosynthetic capacity. These results provide profound insight into the turning points in maize photosynthesis and their responses to drought and rewatering. They may also help to improve crop water management, which will be useful in coping with the increased frequency of drought and extreme weather events expected under global climate change

Dynamic Characteristics of Canopy and Vegetation Water Content during an Entire Maize Growing Season in Relation to Spectral-Based Indices

A variety of spectral vegetation indices (SVIs) have been constructed to monitor crop water stress. However, their abilities to reflect dynamic canopy water content (CWC) and vegetation water content (VWC) during the growing season have not been concurrently examined, and the underlying mechanisms remain unclear, especially in relation to soil drying. In this study, a field experiment was conducted and designed with various irrigation regimes applied during two consecutive growing seasons of maize. The results showed that CWC, VWC, and the SVIs exhibited obvious trends of first increasing and then decreasing within a growing season. In addition, VWC was allometrically related to CWC across the two growing seasons. A linear relationship between the five SVIs and CWC occurred within a certain CWC range (0.01&ndash;0.41 kg m&minus;2), while the relationship between these SVIs and VWC was nonlinear. Furthermore, the five SVIs indicated critical values for VWC, and these values were 1.12 and 1.15 kg m&minus;2 for the water index (WI) and normalized difference water index (NDWI), respectively; however, the normalized difference infrared index (NDII), normalized difference vegetation index (NDVI), and optimal soil-adjusted vegetation index (OSAVI) had the same critical value of 0.55 kg m&minus;2. Therefore, in comparison to the NDII, NDVI, and OSAVI, the WI and NDWI better reflected the crop water content based on their sensitives to CWC and VWC. Moreover, CWC was the most important direct biotic driver of the dynamics of SVIs, while leaf area index (LAI) was the most important indirect biotic driver. VWC was a critical indirect regulator of WI, NDWI, NDII, and OSAVI dynamics, whereas vegetation dry mass (VDM) was the critical indirect regulator of NDVI dynamics. These findings may provide additional information for estimating agricultural drought and insights on the impact mechanism of soil water deficits on SVIs

A Simulation Study on Optimization of Sowing Time of Maize (<i>Zea mays</i> L.) for Maximization of Growth and Yield in the Present Context of Climate Change under the North China Plain

Adjusting the sowing dates of crops is an effective measure for adapting them to climate change, but very few studies have explained how the optimum sowing dates can be determined. In this study, we used the sowing date field data from 2018 to 2021 from Hebei Gucheng Agricultural Meteorology National Observation and Research Station to analyze the effects of the sowing date on growth, development, and yield of maize, and to quantify the impact of light-temperature potential productivity on different stages of the yield formation. The results showed that delayed sowing decreased the vegetative growth period (VGP) and increased the reproductive growth period (RGP) of maize. The light-temperature potential productivity of the whole growth (WG) period had an exponential relationship with the theoretical yield. At least 14,614.95 kg ha−1 of light-temperature potential productivity was needed to produce grain yield. The maximum theoretical yield was approximately 18,052.56 kg ha−1, as indicated by the curve simulation results. The influence of light-temperature potential productivity on theoretical yield was as follows: VGP > RGP > vegetative and reproductive period (VRP). Accordingly, a method for determining the sowing time window based on VGP was established, and the optimal sowing dates were estimated for 1995–2021 and the SSP2-4.5 scenario in CMIP6 in the middle of this century (2030–2060). The simulation results showed that the optimum sowing date of maize “Lianyu 1” at the study site was 20–25 May in 1995–2021. In the middle of this century, the optimal sowing time of maize was ahead of schedule and the suitable sowing window was increased slightly. We conclude that advancing the sowing date of maize is a practical strategy for enhancing yield in the context of climate warming, and this strategy will provide a meaningful reference for scientific optimization of sowing dates to adapt maize to climate change

A Simulation Study on Optimization of Sowing Time of Maize (Zea mays L.) for Maximization of Growth and Yield in the Present Context of Climate Change under the North China Plain

Adjusting the sowing dates of crops is an effective measure for adapting them to climate change, but very few studies have explained how the optimum sowing dates can be determined. In this study, we used the sowing date field data from 2018 to 2021 from Hebei Gucheng Agricultural Meteorology National Observation and Research Station to analyze the effects of the sowing date on growth, development, and yield of maize, and to quantify the impact of light-temperature potential productivity on different stages of the yield formation. The results showed that delayed sowing decreased the vegetative growth period (VGP) and increased the reproductive growth period (RGP) of maize. The light-temperature potential productivity of the whole growth (WG) period had an exponential relationship with the theoretical yield. At least 14,614.95 kg ha&minus;1 of light-temperature potential productivity was needed to produce grain yield. The maximum theoretical yield was approximately 18,052.56 kg ha&minus;1, as indicated by the curve simulation results. The influence of light-temperature potential productivity on theoretical yield was as follows: VGP &gt; RGP &gt; vegetative and reproductive period (VRP). Accordingly, a method for determining the sowing time window based on VGP was established, and the optimal sowing dates were estimated for 1995&ndash;2021 and the SSP2-4.5 scenario in CMIP6 in the middle of this century (2030&ndash;2060). The simulation results showed that the optimum sowing date of maize &ldquo;Lianyu 1&rdquo; at the study site was 20&ndash;25 May in 1995&ndash;2021. In the middle of this century, the optimal sowing time of maize was ahead of schedule and the suitable sowing window was increased slightly. We conclude that advancing the sowing date of maize is a practical strategy for enhancing yield in the context of climate warming, and this strategy will provide a meaningful reference for scientific optimization of sowing dates to adapt maize to climate change

GABAergic neurons in the dorsomedial hypothalamus regulate states of consciousness in sevoflurane anesthesia

Summary: The neural inhibitory gamma-aminobutyric acid (GABA) system in the regulation of anesthetic consciousness is heterogeneous, and the medial hypothalamus (MH), consisting of ventromedial hypothalamus (VMH) and dorsomedial hypothalamus (DMH), plays an important role in sleep and circadian rhythm. However, the role of MH GABAergic neurons (MHGABA) in anesthesia remains unclear. In this study, we used righting reflex, electroencephalogram (EEG), and arousal behavioral score to evaluate the sevoflurane anesthesia. Activation of MHGABA or DMHGABA neurons prolonged the anesthesia induction time, shortened the anesthesia emergence time, and induced EEG arousal and body movement during anesthesia; meanwhile, VMHGABA neurons activated only induced EEG changes during 1.5% sevoflurane anesthesia. Furthermore, inhibition of DMHGABA neurons significantly deepened sevoflurane anesthesia. Therefore, DMHGABA neurons exert a strong emergence-promoting effect on induction, maintenance, and arousal during sevoflurane general anesthesia, which helps to reveal the mechanism of anesthesia

Revealing underlying regulatory mechanisms of LINC00313 in Osimertinib-resistant LUAD cells by ceRNA network analysis

Background: Osimertinib, a third-generation epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI), is the preferred treatment for EGFR-mutated lung cancer. However, acquired resistance inevitably develops. While non-coding RNAs have been implicated in lung cancer through various functions, the molecular mechanisms responsible for osimertinib resistance remain incompletely elucidated. Methods: RNA-sequencing technology was employed to determine differentially expressed lncRNAs (DE-lncRNAs) and mRNAs (DE-mRNAs) between H1975 and H1975OR cell lines. Starbase 2.0 was utilized to predict DE-lncRNA and DE-mRNA interactions, constructing ceRNA networks. Subsequently, functional and pathway enrichment analysis were performed on target DE-mRNAs to identify pathways associated with osimertinib resistance. Key target DE-mRNAs were then selected as potential risk signatures for lung adenocarcinoma (LUAD) prognostic modeling using multivariate Cox regression analyses. The Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and immunohistochemistry staining were used for result validation. Results: Functional analysis revealed that the identified DE-mRNAs primarily enriched in EGFR-TKI resistance pathways, especially in the PI3K/Akt signaling pathway, where their concerted actions may lead to osimertinib resistance. Specifically, upregulation of LINC00313 enhanced COL1A1 expression by acting as a miR-218-5p sponge, triggering an upstream response that activates the PI3K/Akt pathway, potentially contributing to osimertinib resistance. Furthermore, the expressions of LINC00313 and COL1A1 were validated by qRT-PCR, and the activation of the PI3K/Akt pathway was confirmed by immunohistochemistry staining. Conclusions: Our results suggest that the LINC00313/miR-218-5p/COL1A1 axis potentially contributes to osimertinib resistance through the PI3K/Akt signaling pathway, providing novel insights into the molecular mechanisms underlying acquired osimertinib resistance in LUAD. Additionally, our study may aid in the identification of potential therapeutic targets for overcoming resistance to osimertinib