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1. Nematocidal Potential of Phenolic Acids: A Phytochemical Seed-Coating Approach to Soybean Cyst Nematode Management

   https://doi.org/10.3390/plants13020319  

   Yates, Ping; Janiol, Juddy; Li, Changbao; Song, Bao-Hua (January 2024, Plants)

   Soybeans, one of the most valuable crops worldwide, are annually decimated by the soybean cyst nematode (SCN), Heterodera glycines, resulting in massive losses in soybean yields and economic revenue. Conventional agricultural pesticides are generally effective in the short term; however, they pose growing threats to human and environmental health; therefore, alternative SCN management strategies are urgently needed. Preliminary findings show that phenolic acids are significantly induced during SCN infection and exhibit effective nematocidal activities in vitro. However, it is unclear whether these effects occur in planta or elicit any negative effects on plant growth traits. Here, we employed a phytochemical-based seed coating application on soybean seeds using phenolic acid derivatives (4HBD; 2,3DHBA) at variable concentrations and examined SCN inhibition against two SCN types. Moreover, we also examined plant growth traits under non-infected or SCN infected conditions. Notably, 2,3DHBA significantly inhibited SCN abundance in Race 2-infected plants with increasingly higher chemical doses. Interestingly, neither compound negatively affected soybean growth traits in control or SCN-infected plants. Our findings suggest that a phytochemical-based approach could offer an effective, more environmentally friendly solution to facilitate current SCN management strategies and fast-track the development of biopesticides to sustainably manage devastating pests such as SCN. 

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2. Structural and functional analysis of two SHMT8 variants associated with soybean cyst nematode resistance

   https://doi.org/10.1111/febs.16971  

   Korasick, David A.; Owuocha, Luckio F.; Kandoth, Pramod K.; Tanner, John J.; Mitchum, Melissa G.; Beamer, Lesa J. (October 2023, The FEBS Journal)

   Two amino acid variants in soybean serine hydroxymethyltransferase 8 (SHMT8) are associated with resistance to the soybean cyst nematode (SCN), a devastating agricultural pathogen with worldwide economic impacts on soybean production. SHMT8 is a cytoplasmic enzyme that catalyzes the pyridoxal 5‐phosphate‐dependent conversion of serine and tetrahydrofolate (THF) to glycine and 5,10‐methylenetetrahydrofolate. A previous study of the P130R/N358Y double variant of SHMT8, identified in the SCN‐resistant soybean cultivar (cv.) Forrest, showed profound impairment of folate binding affinity and reduced THF‐dependent enzyme activity, relative to the highly active SHMT8 in cv. Essex, which is susceptible to SCN. Given the importance of SCN‐resistance in soybean agriculture, we report here the biochemical and structural characterization of the P130R and N358Y single variants to elucidate their individual effects on soybean SHMT8. We find that both single variants have reduced THF‐dependent catalytic activity relative to Essex SHMT8 (10‐ to 50‐fold decrease ink_cat/K_m) but are significantly more active than the P130R/N368Y double variant. The kinetic data also show that the single variants lack THF‐substrate inhibition as found in Essex SHMT8, an observation with implications for regulation of the folate cycle. Five crystal structures of the P130R and N358Y variants in complex with various ligands (resolutions from 1.49 to 2.30 Å) reveal distinct structural impacts of the mutations and provide new insights into allosterism. Our results support the notion that the P130R/N358Y double variant in Forrest SHMT8 produces unique and unexpected effects on the enzyme, which cannot be easily predicted from the behavior of the individual variants. 

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3. Movement and Motion of Soybean Cyst Nematode Heterodera glycines Populations and Individuals in Response to Abamectin

   https://doi.org/10.1094/PHYTO-10-17-0339-R  

   Jensen, Jared P.; Beeman, Augustine Q.; Njus, Zach L.; Kalwa, Upender; Pandey, Santosh; Tylka, Gregory L. (July 2018, Phytopathology®)

   Two new in vitro methods were developed to analyze plant-parasitic nematode behavior, at the population and the individual organism levels, through time-lapse image analysis. The first method employed a high-resolution flatbed scanner to monitor the movement of a population of nematodes over a 24-h period at 25°C. The second method tracked multiple motion parameters of individual nematodes on a microscopic scale, using a high-speed camera. Changes in movement and motion of second-stage juveniles (J2) of the soybean cyst nematode Heterodera glycines Ichinohe were measured after exposure to a serial dilution of abamectin (0.1 to 100 μg/ml). Movement and motion of H. glycines were significantly reduced as the concentration of abamectin increased. The effective range of abamectin to inhibit movement and motion of H. glycines J2 was between 1.0 and 10 μg/ml. Proof-of-concept experiments for both methods produced one of the first in vitro sensitivity studies of H. glycines to abamectin. The two methods developed allow for higher-throughput analysis of nematode movement and motion and provide objective and data-rich measurements that are difficult to achieve from conventional microscopic laboratory methods. 

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4. Classification of Pancreatic Cysts in Computed Tomography Images Using a Random Forest and Convolutional Neural Network Ensemble

   Dmitriev, Konstantin (January 2017, Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017)

   There are many different types of pancreatic cysts. These range from completely benign to malignant, and identifying the exact cyst type can be challenging in clinical practice. This work describes an automatic classification algorithm that classifies the four most common types of pancreatic cysts using computed tomography images. The proposed approach utilizes the general demographic information about a patient as well as the imaging appearance of the cyst. It is based on a Bayesian combination of the random forest classifier, which learns subclass-specific demographic, intensity, and shape features, and a new convolutional neural network that relies on the fine texture information. Quantitative assessment of the proposed method was performed using a 10-fold cross validation on 134 patients and reported a classification accuracy of 83.6%. 

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5. Dorsal-ventral patterned neural cyst from human pluripotent stem cells in a neurogenic niche

   https://doi.org/10.1126/sciadv.aax5933  

   Zheng, Y.; Xue, X.; Resto-Irizarry, A. M.; Li, Z.; Shao, Y.; Zheng, Y.; Zhao, G.; Fu, J. (December 2019, Science Advances)

   Despite its importance in central nervous system development, development of the human neural tube (NT) remains poorly understood, given the challenges of studying human embryos, and the developmental divergence between humans and animal models. We report a human NT development model, in which NT-like tissues, neuroepithelial (NE) cysts, are generated in a bioengineered neurogenic environment through self-organization of human pluripotent stem cells (hPSCs). NE cysts correspond to the neural plate in the dorsal ectoderm and have a default dorsal identity. Dorsal-ventral (DV) patterning of NE cysts is achieved using retinoic acid and/or sonic hedgehog and features sequential emergence of the ventral floor plate, P3, and pMN domains in discrete, adjacent regions and a dorsal territory progressively restricted to the opposite dorsal pole. This hPSC-based, DV patterned NE cyst system will be useful for understanding the self-organizing principles that guide NT patterning and for investigations of neural development and neural disease. 

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