The arabidopsis RCC1 family protein TCF1 regulates freezing tolerance and cold acclimation through modulating lignin biosynthesis

Cell water permeability and cell wall properties are critical to survival of plant cells during freezing, however the underlying molecular mechanisms remain elusive. Here, we report that a specifically cold-induced nuclear protein, Tolerant to Chilling and Freezing 1 (TCF1), interacts with histones H3 and H4 and associates with chromatin containing a target gene, BLUE-COPPER-BINDING PROTEIN (BCB), encoding a glycosylphosphatidylinositol-anchored protein that regulates lignin biosynthesis. Loss of TCF1 function leads to reduced BCB transcription through affecting H3K4me2 and H3K27me3 levels within the BCB gene, resulting in reduced lignin content and enhanced freezing tolerance. Furthermore, plants with knocked-down BCB expression (amiRNA-BCB) under cold acclimation had reduced lignin accumulation and increased freezing tolerance. The pal1pal2 double mutant (lignin content reduced by 30% compared with WT) also showed the freezing tolerant phenotype, and TCF1 and BCB act upstream of PALs to regulate lignin content. In addition, TCF1 acts independently of the CBF (C-repeat binding factor) pathway. Our findings delineate a novel molecular pathway linking the TCF1-mediated cold-specific transcriptional program to lignin biosynthesis, thus achieving cell wall remodeling with increased freezing tolerance

An electro-pneumatic force tracking system using fuzzy logic based volume flow control

In this paper, a fuzzy logic based volume flow control method is proposed to precisely control the force of a pneumatic actuator in an electro-pneumatic system including four on-off valves. The volume flow feature, which is the relationship between the duty cycle of the pulse width modulation (PWM) period, pressure difference, and volume flow of an on-off valve, is based on the experimental data measured by a high-precision volume flow meter. Through experimental data analysis, the maximum and minimum duty cycles are acquired. A new volume flow control method is introduced for the pneumatic system. In this method, the raw measured data are innovatively processed by a segmented, polynomial fitting method, and a newly designed procedure for calculating the duty cycle is adopted. This procedure makes it possible to combine the original data with fuzzy logic control (FLC). Additionally, the method allows us to accurately control the minimum and maximum opening pulse width of the valve. Several experiments are performed based on the experimental data, instead of the traditional theoretical models. Only 0.141 N (1.41%) overshoot and 0.03 N (0.03%) steady-state error are observed in the step response experiment, and 0.123 N average error is found while tracking the sine wave reference

Prediction of Corn Yield in the USA Corn Belt Using Satellite Data and Machine Learning: From an Evapotranspiration Perspective

The reliable prediction of corn yield for the United States of America is essential for effective food and energy management of the world. Three satellite-derived variables were selected, namely enhanced vegetation index (EVI), leaf area index (LAI) and land surface temperature (LST). The least absolute shrinkage and selection operator (LASSO) was used for regression, while random forest (RF), support vector regression (SVR) and long short-term memory (LSTM) methods were selected for machine learning. The three variables serve as inputs to these methods, and their efficacy in predicting corn yield was assessed in relation to evapotranspiration (ET). The results confirmed that a high level of performance can be achieved for yield prediction (mean predicted R2 = 0.63) by combining EVI + LAI + LST with the four methods. Among them, the best results were obtained by using LSTM (mean predicted R2 = 0.67). EVI and LST provided extra and unique information in peak and early growth stages for corn yield, respectively, and the usefulness of including LAI was not readily apparent across the whole season, which was consistent with the field growing conditions affecting the ET of corn. The satellite-derived data and the methods used in this study could be used for predicting the yields of other crops in different regions

Production of Bioflocculants Prepared from Wastewater Supernatant of Anaerobic Co-Digestion of Corn Straw and Molasses Wastewater Treatment

Novel bioflocculants (BS-MBF) were prepared using the wastewater supernatant from anaerobic co-digestion of corn straw and molasses wastewater as a nutrient resource. Acetic acid and ethanol were the dominant fermentation products during the anaerobic digestion process and were estimated to be 50.5% and 30.0%, respectively, after 150 d of operation. Equal volumes of bioflocculant producing bacteria F2 (Rhizobium radiobacter) and F6 (Bacillus sphaericus) were mixed to form F+, which was inoculated to wastewater supernatant at different times. A maximum flocculation activity of 91.3% was achieved, and 2.32 g/L of purified bioflocculant was extracted when a compound medium from 110-d wastewater supernatant was used. The removal efficiencies of heavy metals from simulated electroplating wastewater were tested by using these prepared bioflocculants. The optimal conditions for heavy metal removal to BS-MBF were found to be at 374 mg/L at an initial pH of 6.0 and a contact time of 40 min. The adsorption capacities for Cu2+ and Zn2+ reached more than 90%, while for Cr6+ it reached approximately 30%. Overall, the study showed for the first time that wastewater supernatant from anaerobic co-digestion of corn straw and molasses wastewater can be used for producing bioflocculants, which can be effectively used to remove heavy metals from electroplating wastewater

Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Phenology is an indicator of crop growth conditions, and is correlated with crop yields. In this study, a phenological approach based on a remote sensing vegetation index was explored to predict the yield in 314 counties within the US Corn Belt, divided into semi-arid and non-semi-arid regions. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product MOD09Q1 was used to calculate the normalized difference vegetation index (NDVI) time series. According to the NDVI time series, we divided the corn growing season into four growth phases, calculated phenological information metrics (duration and rate) for each growth phase, and obtained the maximum correlation NDVI (Max-R2). Duration and rate represent crop growth days and rate, respectively. Max-R2 is the NDVI value with the most significant correlation with corn yield in the NDVI time series. We built three groups of yield regression models, including univariate models using phenological metrics and Max-R2, and multivariate models using phenological metrics, and multivariate models using phenological metrics combined with Max-R2 in the whole, semi-arid, and non-semi-arid regions, respectively, and compared the performance of these models. The results show that most phenological metrics had a statistically significant (p 2 = 0.44). Models established with phenological metrics realized yield prediction before harvest in the three regions with R2 = 0.64, 0.67, and 0.72. Compared with the univariate Max-R2 models, the accuracy of models built with Max-R2 and phenology metrics improved. Thus, the phenology metrics obtained from MODIS-NDVI accurately reflect the corn characteristics and can be used for large-scale yield prediction. Overall, this study showed that phenology metrics derived from remote sensing vegetation indexes could be used as crop yield prediction variables and provide a reference for data organization and yield prediction with physical crop significance

Multi-channel high-order network representation learning research

The existing network representation learning algorithms mainly model the relationship between network nodes based on the structural features of the network, or use text features, hierarchical features and other external attributes to realize the network joint representation learning. Capturing global features of the network allows the obtained node vectors to retain more comprehensive feature information during training, thereby enhancing the quality of embeddings. In order to preserve the global structural features of the network in the training results, we employed a multi-channel learning approach to perform high-order feature modeling on the network. We proposed a novel algorithm for multi-channel high-order network representation learning, referred to as the Multi-Channel High-Order Network Representation (MHNR) algorithm. This algorithm initially constructs high-order network features from the original network structure, thereby transforming the single-channel network representation learning process into a multi-channel high-order network representation learning process. Then, for each single-channel network representation learning process, the novel graph assimilation mechanism is introduced in the algorithm, so as to realize the high-order network structure modeling mechanism in the single-channel network representation learning. Finally, the algorithm integrates the multi-channel and single-channel mechanism of high-order network structure joint modeling, realizing the efficient use of network structure features and sufficient modeling. Experimental results show that the node classification performance of the proposed MHNR algorithm reaches a good order on Citeseer, Cora, and DBLP data, and its node classification performance is better than that of the comparison algorithm used in this paper. In addition, when the vector length is optimized, the average classification accuracy of nodes of the proposed algorithm is up to 12.24% higher than that of the DeepWalk algorithm. Therefore, the node classification performance of the proposed algorithm can reach the current optimal order only based on the structural features of the network under the condition of no external feature supplementary modeling

Experimental and Eulerian-Lagrangian-Lagrangian study of binary gas-solid flow containing particles of significantly different sizes

The coexistence of large particles (such as biomass or coal particles) and fine particles in gas-solid flow is common. In this study, an Eulerian-Lagrangian-Lagrangian method (EMMS-DPM-DEM) was developed to simulate the binary gas-solid flow containing particles of significantly different sizes, where fine particles were simulated using coarse grained discrete element method and the motion of large particles was captured using discrete element method. Experiments on density segregation of large particles were also carried out to validate the developed simulation method. It was shown that EMMS-DPM-DEM can predict the density segregation process of large particles reasonably well. Furthermore, the density segregation mechanism of large particles in a dense fluidized bed was explained at different scales, i.e., the Archimedes principle at macroscale, the entrainment and the global circulation pattern due to bubble motions at mesoscale and the particle-particle and gas-particle interactions at microscale. The method proposed here can be directly used to study the hydrodynamics of biomass thermochemical conversion in fluidized beds, although it was validated using segregation experiments of coal particles. (C) 2019 Elsevier Ltd. All rights reserved

Extreme Temperature Switches Eliminate Root-Knot Nematodes: A Greenhouse Study

Root-knot nematodes (RKNs) severely affect the yield and quality of vegetable crops. While some chemical pesticides can effectively eliminate RKNs, they leave behind significant pesticide residues in vegetables and soil that are potentially harmful for humans. Research suggests that dormant RKNs in the soil become active in the presence of a host, upon which they become sensitive to extreme temperature changes. Here, we tested a novel method to eliminate RKNs (Meloidogyne incognita) in a greenhouse setting. RKNs were first activated by water spinach, at which point the soil was heated by natural sunlight and suddenly cooled by the addition of dry ice. This rapid change in temperature eliminated >90% of activated RKNs. After the temperature treatment, the physical features of soil did not change; however, soil porosity, available potassium content and soil invertase activity increased markedly. The treated soil was then used for cucumber planting to test its viability. Compared with cucumber plants grown in untreated soil, plants in treated soil had higher, thicker, and stronger shoots, and higher photosynthetic ability. Cucumber plants grown in untreated soil were severely infested with nematodes while few plants grown in treated soil had root knots containing nematodes. Based on these findings, we suggest that host plant induction followed by switched temperature treatment is an effective, easy, safe, and cost-effective method for eliminating RKNs