Effectiveness of moxibustion treatment as adjunctive therapy in osteoarthritis of the knee: a randomized, double-blinded, placebo-controlled clinical trial

INTRODUCTION: Our objective was to compare the effectiveness and safety of traditional Chinese moxibustion to that of sham moxibustion in patients with chronic knee osteoarthritis (KOA) pain. METHODS: We conducted a randomized placebo-controlled trial involving 110 patients with KOA who met the inclusion criteria. These patients randomly received either active moxibustion (n = 55) or sham moxibustion control (n = 55) at acupoints Dubi (ST 35), extra-point Neixiyan (EX-LE 4), and an Ashi (tender) point three times a week for 6 weeks. Effects were evaluated with Western Ontario and McMaster Universities’ Osteoarthritis Index (WOMAC VA 3.1) criteria at the end of the course of treatment and 3, 12, and 24 weeks after the initial treatment. RESULTS: The WOMAC pain scores showed greater improvement in the active treatment group than in control at weeks 3 (P = 0.012), 6 (P <0.001), 12 (P = 0.002), and 24 (P = 0.002) as did WOMAC physical function scores of the active treatment group at week 3 (P = 0.002), 6 (P = 0.015), and 12 (P <0.001) but not 24 (P = 0.058). Patients and practitioners were blinded successfully, and no significant adverse effects were found during the trial. CONCLUSIONS: A 6-week course of moxibustion seems to relieve pain effectively and improve function in patients with KOA for up to 18 weeks after the end of treatment. Moxibustion treatment appears to be safe, and the usefulness of the novel moxa device was validated. TRIAL REGISTRATION: Current controlled trial: ISRCTN68475405. Registered 4 April 2014

Identification of protein complexes from multi-relationship protein interaction networks

BACKGROUND: Protein complexes play an important role in biological processes. Recent developments in experiments have resulted in the publication of many high-quality, large-scale protein-protein interaction (PPI) datasets, which provide abundant data for computational approaches to the prediction of protein complexes. However, the precision of protein complex prediction still needs to be improved due to the incompletion and noise in PPI networks. RESULTS: There exist complex and diverse relationships among proteins after integrating multiple sources of biological information. Considering that the influences of different types of interactions are not the same weight for protein complex prediction, we construct a multi-relationship protein interaction network (MPIN) by integrating PPI network topology with gene ontology annotation information. Then, we design a novel algorithm named MINE (identifying protein complexes based on Multi-relationship protein Interaction NEtwork) to predict protein complexes with high cohesion and low coupling from MPIN. CONCLUSIONS: The experiments on yeast data show that MINE outperforms the current methods in terms of both accuracy and statistical significance

The Effect of Different Laser Irradiation on Cyclophosphamide-Induced Leucopenia in Rats

Objective. To assess the effect of different lasers on cyclophosphamide- (CTX-) induced leucopenia in rats. Methods. 11 rats were normal control and 55 rats were injected with a dose of 80 mg/kg CTX for the first time and 40 mg/kg on the 6th and the 11th days to establish a leucopenia model. Rats of the irradiation groups received a 5-minute laser irradiation with either single 10.6 μm or 650 nm laser or alternatively 10.6 μm–650 nm laser irradiation, besides a sham treatment on acupoint Dazhui (DU 14) and acupoint Zusanli (ST 36) of both sides, 8 times for 16 days. Normal and model control group received no treatment. Results. On day 16 after the first CTX injection, the WBC counts from all the laser irradiation groups were significantly higher than those from the model control and the sham group (P<0.05), while there were no significant differences compared with the normal control (P>0.05). The TI of 10.6 μm–650 nm laser irradiation group was significantly higher than that of the model control group (P<0.05). Conclusions. The single and combined 10.6 μm and 650 nm laser irradiation on ST36 and DU14 accelerated the recovery of the WBC count in the rats with leucopenia

DPS1 regulates cuticle development and leaf senescence in rice

AbstractLeaves are the primary food‐producing organs for a plant that carry out photosynthesis and contribute to biomass and grain yield. Leaf senescence is a developmentally regulated physiological process but early leaf senescence is known to negatively affect plant yield. The cuticle is an outer waxy protective layer on the leaf surface which protects plants from pathogens attack as well as dehydration. Our understanding of the molecular mechanisms underlying cuticle development and leaf senescence is still immature. The present study reports the role of the DEGENERATED PANICLE AND PARTIAL STERILITY 1 (DPS1) gene encoding a cystathionine β‐synthase (CBS) domain‐containing protein in cuticle development and leaf senescence in rice. The dps1 loss‐of‐function mutant showed leaf senescence phenotype with twisted leaves, significantly reduced chlorophyll content and degenerated chloroplasts characterized by a reduced number of starch granules and an abundance of osmiophilic bodies. Furthermore, dps1 leaves displayed defective cuticle development, reduced wax and cutin compounds, and lower relative water content as compared with wild type. Physiological assays showed significantly higher accumulation of reactive oxygen species (ROS) accompanied by enhanced DNA fragmentation in dps1 leaves, which could be associated with chloroplast degeneration and defective cuticle development. Transcriptome analysis revealed altered expression of several critical genes related to photosynthesis and wax/cutin pathway. This study revealed a crucial role of DPS1 in regulating leaf cuticle development and senescence by affecting the expression of several genes. Thus, a moderate expression of DPS1 is necessary for better plant growth and productivity

Efficient residual network using hyperspectral images for corn variety identification

Corn seeds are an essential element in agricultural production, and accurate identification of their varieties and quality is crucial for planting management, variety improvement, and agricultural product quality control. However, more than traditional manual classification methods are needed to meet the needs of intelligent agriculture. With the rapid development of deep learning methods in the computer field, we propose an efficient residual network named ERNet to identify hyperspectral corn seeds. First, we use linear discriminant analysis to perform dimensionality reduction processing on hyperspectral corn seed images so that the images can be smoothly input into the network. Second, we use effective residual blocks to extract fine-grained features from images. Lastly, we detect and categorize the hyperspectral corn seed images using the classifier softmax. ERNet performs exceptionally well compared to other deep learning techniques and conventional methods. With 98.36% accuracy rate, the result is a valuable reference for classification studies, including hyperspectral corn seed pictures

Silver nanoparticle-induced enhancement of light extraction in two-dimensional light-emitting diodes

Monolayer transition metal dichalcogenides (TMDCs) with direct bandgaps are considered promising candidates for building light-emitting diodes (LEDs). One crucial indicator of their performance is the brightness of electroluminescence (EL). In this study, we fabricate WS2-based LEDs that make full use of the assistance of effective transient-mode charge injection. By introducing self-assembled silver nanoparticles (NPs) on top of the LED, the extraction efficiency is significantly improved, with a 2.9-fold EL enhancement observed in the experiment. Full-wave simulations further confirm that the improvement comes from the scattering capability of silver NPs, with results qualitatively fitting the experiment. This approach, with its compatibility with van der Waals heterostructures, can be further promoted to enhance the brightness of 2D monolayer TMDC-based LEDs.</p

Effects of nitrogen addition and plant litter manipulation on soil fungal and bacterial communities in a semiarid sandy land

The plant and soil microbial communities are influenced by variability in environmental conditions (e.g., nitrogen addition); however, it is unclear how long-term nitrogen addition and litter manipulation affect soil microbial communities in a semiarid sandy grassland. Therefore, we simulated the impact of N addition and litter manipulation (litter removal, litter doubling) on plant and soil microbial communities in Horqin grassland, northern China through an experiment from 2014 to 2019. Our results revealed that in the case of non-nitrogen (N0), litter manipulation significantly reduced vegetation coverage (V) (p &lt; 0.05); soil bacterial communities have higher alpha diversity than that of the fungi, and the beta diversity of soil fungi was higher than that of the bacteria; soil microbial alpha diversity was significantly decreased by nitrogen addition (N10) (p &lt; 0.05); N addition and litter manipulation had significantly interactive influences on soil microbial beta diversity, and litter manipulation (C0 and C2) had significantly decreased soil microbial beta diversity (p &lt; 0.05) in the case of nitrogen addition (N10) (p &lt; 0.05). Moreover, bacteria were mostly dominated by the universal phyla Proteobacteria, Actinobacteria, and Acidobacteria, and fungi were only dominated by Ascomycota. Furthermore, the correlation analysis, redundancy analysis (RDA), and variation partitioning analysis indicated that the soil fungi community was more apt to be influenced by plant community diversity. Our results provide evidence that plant and soil microbial community respond differently to the treatments of the 6-year N addition and litter manipulation in a semiarid sandy land

Plant Functional Traits Modulate Effects of Drought on C:N:P Stoichiometry of Plant, Litter, and Soil Microbe in an Arid Grassland

The carbon (C), nitrogen (N) and phosphorus (P) stoichiometry of plant-soil systems is crucial to drive nutrient cycling in arid ecosystems. Although precipitation changes are increasingly affecting nutrient cycling with important feedbacks to ecosystem function, however, a mechanistic understanding of how the stoichiometry in plant-soil systems responds to precipitation changes in arid grasslands is still lacking. We conducted a 7-year field experiment with manipulative precipitation (ambient, ± 20%, ± 40% and ± 60%) to examine the effect of increased precipitation and drought on the stoichiometry of plant, litter, soil and soil microbe in an arid grassland in northern China. We also quantify the roles of plant composition, diversity and functional traits, soil properties and soil microbial diversity in determining the stoichiometry in plant-soil systems. Increased precipitation or drought did not affect soil stoichiometry. Increased precipitation increased litter C but drought decreased plant and litter C:N. Further, drought increased plant and litter N and microbial C content, as well as plant C:P and N:P, litter N:P and microbial C:N and C:P. Drought effects on C:P and N:P in plant and litter and on microbial C:N were mediated by leaf N content, while the effects on plant C:P and N:P were mediated by leaf C content. Additionally, plant height regulated the drought effects on microbial C:P. C:N:P stoichiometry in plant, litter and soil microbe is more sensitive to drought than increased precipitation and plant functional traits are critical for predicting nutrient cycling in arid grasslands under future drought scenarios.This research was supported by the Major Science and Technology Project of Inner Mongolia Autonomous Region (2024JBGS0011-02), National Natural Science Foundation of China (42071140), Youth Innovation Promotion Association CAS (2022437), and CAS “Light of West China” Program (E129050301).Peer reviewe