Brain functional connectivity alterations in patients with anterior cruciate ligament injury.

Recent advancements in neuroimaging have illustrated that anterior cruciate ligament (ACL) injuries could impact the central nervous system (CNS), causing neuroplastic changes in the brain beyond the traditionally understood biomechanical consequences. While most of previous functional magnetic resonance imaging (fMRI) studies have focused on localized cortical activity changes post-injury, emerging research has suggested disruptions in functional connectivity across the brain. However, these prior investigations, albeit pioneering, have been constrained by two limitations: a reliance on small-sample participant cohorts, often limited to two to three patients, potentially limiting the generalizability of findings, and an adherence to region of interest based analysis, which may overlook broader network interactions. To address these limitations, our study employed resting-state fMRI to assess whole-brain functional connectivity in 15 ACL-injured patients, comparing them to matched controls using two distinct network analysis methods. Using Network-Based Statistics, we identified widespread reductions in connectivity that spanned across multiple brain regions. Further modular connectivity analysis showed significant decreases in inter-modular connectivity between the sensorimotor and cerebellar modules, and intra-modular connectivity within the default-mode network in ACL-injured patients. Our results thus highlight a shift from localized disruptions to network-wide dysfunctions, suggesting that ACL injuries induce widespread CNS changes. This enhanced understanding has the potential to stimulate the development of strategies aiming to restore functional connectivity and improve recovery outcomes.

Rice kinase OsMRLK63 contributes to drought tolerance by regulating reactive oxygen species production.

Drought is a major adverse environmental factor that plants face in nature but the molecular mechanism by which plants transduce stress signals and further endow themselves with tolerance remains unclear. Malectin/malectin-like domains containing receptor-like kinases (MRLKs) have been proposed to act as receptors in multiple biological signaling pathways, but limited studies show their roles in drought-stress signaling and tolerance. In this study, we demonstrate OsMRLK63 in rice (Oryza sativa L.) functions in drought tolerance by acting as the receptor of 2 rapid alkalization factors, OsRALF45 and OsRALF46. We show OsMRLK63 is a typical receptor-like kinase that positively regulates drought tolerance and reactive oxygen species (ROS) production. OsMRLK63 interacts with and phosphorylates several nicotinamide adenine dinucleotide phosphate (NADPH) oxidases with the primarily phosphorylated site at Ser26 in the N-terminal of RESPIRATORY BURST OXIDASE HOMOLOGUE A (OsRbohA). The application of the 2 small signal peptides (OsRALF45/46) on rice can greatly alleviate the dehydration of plants induced by mimic drought. This function depends on the existence of OsMRLK63 and the NADPH oxidase-dependent ROS production. The 2 RALFs interact with OsMRLK63 by binding to its extracellular domain, suggesting they may act as drought/dehydration signal sensors for the OsMRLK63-mediated process. Our study reveals a OsRALF45/46-OsMRLK63-OsRbohs module which contributes to drought-stress signaling and tolerance in rice.

Rapid alkalinization factor: function, regulation, and potential applications in agriculture.

Rapid alkalinization factor (RALF) is widespread throughout the plant kingdom and controls many aspects of plant life. Current studies on the regulatory mechanism underlying RALF function mainly focus on Arabidopsis, but little is known about the role of RALF in crop plants. Here, we systematically and comprehensively analyzed the relation between RALF family genes from five important crops and those in the model plant Arabidopsis thaliana. Simultaneously, we summarized the functions of RALFs in controlling growth and developmental behavior using conservative motifs as cues and predicted the regulatory role of RALFs in cereal crops. In conclusion, RALF has considerable application potential in improving crop yields and increasing economic benefits. Using gene editing technology or taking advantage of RALF as a hormone additive are effective way to amplify the role of RALF in crop plants.

Rice Carbohydrate-Binding Malectin-Like Protein, OsCBM1, Contributes to Drought-Stress Tolerance by Participating in NADPH Oxidase-Mediated ROS Production.

Carbohydrate-binding malectin/malectin-like domain-containing proteins (CBMs) are a recently identified protein subfamily of lectins that participates various functional bioprocesses in the animal, bacterial, and plant kingdoms. However, little is known the roles of CBMs in rice development and stress response. In this study, OsCBM1, which encodes a protein containing only one malectin-like domain, was cloned and characterized. OsCBM1 is localized in both the endoplasmic reticulum and plasma membrane. Its transcripts are dominantly expressed in leaves and could be significantly stimulated by a number of phytohormone applications and abiotic stress treatments. Overexpression of OsCBM1 increased drought tolerance and reactive oxygen species production in rice, whereas the knockdown of the gene decreased them. OsCBM1 physically interacts with OsRbohA, a NADPH oxidase, and the expression of OsCBM1 in osrbohA, an OsRbohA-knockout mutant, is significantly downregulated under both normal growth and drought stress conditions. Meanwhile, OsCBM1 can also physically interacts with OsRacGEF1, a specific guanine nucleotide exchange factor for the Rop/Rac GTPase OsRac1, and transient coexpression of OsCBM1 with OaRacGEF1 significantly enhanced ROS production. Further transcriptome analysis showed that multiple signaling regulatory mechanisms are involved in the OsCBM1-mediated processes. All these results suggest that OsCBM1 participates in NADPH oxidase-mediated ROS production by interacting with OsRbohA and OsRacGEF1, contributing to drought stress tolerance of rice. Multiple signaling pathways are likely involved in the OsCBM1-mediated stress tolerance in rice.

Rapid analysis of adulterations in Chinese lotus root powder (LRP) by near-infrared (NIR) spectroscopy coupled with chemometric class modeling techniques.

This paper develops a rapid analysis method for adulteration identification of a popular traditional Chinese food, lotus root powder (LRP), by near-infrared spectroscopy and chemometrics. 85 pure LRP samples were collected from 7 main lotus producing areas of China to include most if not all of the significant variations likely to be encountered in unknown authentic materials. To evaluate the model specificity, 80 adulterated LRP samples prepared by blending pure LRP with different levels of four cheaper and commonly used starches were measured and predicted. For multivariate quality models, two class modeling methods, the traditional soft independent modeling of class analogy (SIMCA) and a recently proposed partial least squares class model (PLSCM) were used. Different data preprocessing techniques, including smoothing, taking derivative and standard normal variate (SNV) transformation were used to improve the classification performance. The results indicate that smoothing, taking second-order derivatives and SNV can improve the class models by enhancing signal-to-noise ratio, reducing baseline and background shifts. The most accurate and stable models were obtained with SNV spectra for both SIMCA (sensitivity 0.909 and specificity 0.938) and PLSCM (sensitivity 0.909 and specificity 0.925). Moreover, both SIMCA and PLSCM could detect LRP samples mixed with 5% (w/w) or more other cheaper starches, including cassava, sweet potato, potato and maize starches. Although it is difficult to perform an exhaustive collection of all pure LRP samples and possible adulterations, NIR spectrometry combined with class modeling techniques provides a reliable and effective method to detect most of the current LRP adulterations in Chinese market.

Combining local wavelength information and ensemble learning to enhance the specificity of class modeling techniques: Identification of food geographical origins and adulteration.

Class modeling techniques are required to tackle various one-class problems. Because the training of class models is based on the target class and the origins of future test objects usually cannot be exactly predefined, the criteria for feature selection of class models are not very straightforward. Although feature reduction can be expected to improve class models performance, more features retained can provide a sufficient description of the sought-for class. This paper suggests a strategy to balance class description and model specificity by ensemble learning of sub-models based on separate local wavelength intervals. The acceptance or rejection of a future object can be explicitly determined by examining its acceptance frequency by sub-models. Considering the lack of information about sub-model independence, we propose to use a data-driven method to control the sensitivity of the ensemble model by cross validation. In this way, all the wavelength intervals are used for class description and the local wavelength intervals are highlighted to enhance the ability to detect out-of-class objects. The proposed strategy was performed on one-class partial least squares (OCPLS) and soft independent modeling of class analogy (SIMCA). By analysis of two infrared spectral data sets, one for geographical origin identification of white tea and the other for discrimination of adulterations in pure sesame oil, the proposed ensemble class modeling method was demonstrated to have similar sensitivity and better specificity compared with total-spectrum SIMCA and OCPLS models. The results indicate local spectral information can be extracted to enhance class model specificity.