Genome Wide Identification and Expression Profiling of Ethylene Receptor Genes during Soybean Nodulation

It has long been known that the gaseous plant hormone ethylene plays a key role in nodulation in legumes. The perception of ethylene by a family of five membrane-localized receptors is necessary to trigger the ethylene signaling pathway, which regulates various biological responses in Arabidopsis. However, a systematic analysis of the ethylene receptors in leguminous plants and their roles in nodule development is lacking. In this study, we performed a characterization of ethylene receptor genes based on the latest Glycine max genome sequence and a public microarray database. Eleven ethylene receptor family genes were identified in soybean through homology searches, and they were divided into two subgroups. Exon–intron analysis showed that the gene structures are highly conserved within each group. Further analysis of their expression patterns showed that these ethylene receptor genes are differentially expressed in various soybean tissues and organs, including functional nodules. Notably, the ethylene receptor genes showed different responses to rhizobial infection and Nod factors, suggesting a possible role for ethylene receptors and ethylene signaling in rhizobia–host cell interactions and nodulation in soybean. Together, these data indicate the functional divergence of ethylene receptor genes in soybean, and that some of these receptors mediate nodulation, including rhizobial infection, nodule development, and nodule functionality. These findings provide a foundation for further elucidation of the molecular mechanism by which the ethylene signaling pathway regulates nodulation in soybean, as well as other legumes

Gattini 2010: Cutting Edge Science at the Bottom of the World

The high altitude Antarctic sites of Dome A and the South Pole offer intriguing locations for future large scale optical astronomical Observatories. The Gattini project was created to measure the optical sky brightness, large area cloud cover and aurora of the winter-time sky above such high altitude Antarctic sites. The Gattini-DomeA camera was installed on the PLATO instrument module as part of the Chinese-led traverse to the highest point on the Antarctic plateau in January 2008. This single automated wide field camera contains a suite of Bessel photometric filters (B, V, R) and a long-pass red filter for the detection and monitoring of OH emission. We have in hand one complete winter-time dataset (2009) from the camera that was recently returned in April 2010. The Gattini-South Pole UV camera is a wide-field optical camera that in 2011 will measure for the first time the UV properties of the winter-time sky above the South Pole dark sector. This unique dataset will consist of frequent images taken in both broadband U and B filters in addition to high resolution (R similar to 5000) long slit spectroscopy over a narrow bandwidth of the central field. The camera is a proof of concept for the 2m-class Antarctic Cosmic Web Imager telescope, a dedicated experiment to directly detect and map the redshifted lyman alpha fluorescence or Cosmic Web emission we believe possible due to the unique geographical qualities of the site. We present the current status of both projects

An estimate of carbon emissions from 2004 wildfires across Alaskan Yukon River Basin

© 2007 Tan et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Flash Nanoprecipitation: Prediction and Enhancement of Particle Stability via Drug Structure

Flash nanoprecipitation (FNP) can generate hydrophobic drug nanoparticles in ∼100 nm with a much higher drug loading (e.g., > 40 wt %) than traditional nanocarriers (e.g., < 20 wt %). This paper studies the effects of drug molecules on nanoparticle stability made via FNP and demonstrates that chemically bonding a drug compound (e.g., paclitaxel) with a cleavable hydrophobic moiety of organosilicate (e.g., triethoxysilicate) is able to enhance the particle size stability. A nonionic amphiphilic diblock copolymer, poly­(lactic-<i>co</i>-glycolic acid)-<i>block</i>-poly­(ethylene glycol) (PLGA-<i>b</i>-PEG), is used as a model surfactant to provide steric stabilization. The experiments here show that the lower the drug solubility in the aqueous medium, the more stable the particles in terms of Ostwald ripening, which are consistent with the prediction by the LSW theory. The initial particle size distribution is sufficiently narrow and of insignificance to Ostwald ripening. To correlate the particle stability with hydrophobicity, this study introduces the <i>n</i>-octanol/water partition coefficient (Log<i>P</i>), a hydrophobicity indication, into the FNP technique. A comparison of various drugs and their analogues shows that Log<i>P</i> of a drug is a better hydrophobicity indication than the solubility parameter (δ) and correlates well with the particle stability. Empirically, with ACDLog<i>P</i> > ∼12, nanoparticles have good stability; with ∼2 < ACDLog<i>P</i> < ∼9, nanoparticles show fast Ostwald ripening and interparticle recrystallization; with ACDLog<i>P</i> < ∼2, the drug is very likely difficult to form nanoparticles. This rule creates a quick way to predict particle stability for a randomly selected drug structure and helps to enable a fast preclinical drug screen

Are patients with preoperative synovitis suitable for unicompartmental knee arthroplasty? Magnetic resonance imaging evidence from a retrospective cohort study

Abstract Background The use of unicompartmental knee arthroplasty (UKA) in patients with preoperative synovitis is controversial. This study aimed to investigate the association between synovitis detected by magnetic resonance imaging (MRI) and prognosis after UKA. Methods Synovitis was graded using the MRI Osteoarthritis Knee Score criteria based on preoperative MRI findings of 132 UKAs performed between June 2020 and August 2021. The Knee Society Knee Score (KS-KS) and the Knee Society Function Score were collected preoperatively and 1 year postoperatively. The relationship between synovitis and the changes in the Knee Society score was analyzed using logistic regression. Results Univariate logistic regression showed that patients with higher preoperative synovitis scores (odds ratio (OR) = 1.925, 95% confidence interval (CI): 1.482–2.500, P < 0.001) had higher KS-KS changes. After adjusting for confounding variables, synovitis was proven to be an independent factor for KS-KS improvement after UKA in multivariate logistic regression (OR = 1.814, 95% CI: 1.354–2.430, P < 0.001). Before UKA, patients with synovitis had lower pain scores (PS) than patients without synovitis (95% CI: -17.159 – -11.160, t = -9.347, P < 0.001). There was no difference in PS between the two groups after UKA (95% CI: -6.559 – 0.345, t = -1.782, P = 0.077). Conclusions Patients with synovitis can achieve good improvement of pain symptoms, and the efficacy is not inferior to that of non-synovitis patients after UKA

Porous Polystyrene-<i>Block</i>-Poly(Acrylic Acid)/Hemoglobin Membrane Formed by Dually Driven Self-Assembly and Electrochemical Application

This study demonstrated a facile method to form a porous polymeric membrane, immobilizing a biocatalyst. A polyelectrolyte-based amphiphilic diblock copolymer, i.e., polystyrene-<i>block</i>-poly­(acrylic acid) (PS-<i>b</i>-PAA), self-assembled with hemoglobin (Hb) dually driven by charge and amphiphilicity during solution-casting and evaporation. XPS and contact angle measurements suggested that the PS block enriched on the membrane surface. The PAA block pointed toward the internal membrane as well as ordered the Hb arrangement at the interface of the polymer and electrode. The obtained PS-<i>b</i>-PAA/Hb electrode showed a remarkably enhanced direct electron transfer (ET), which was revealed to be a surface-controlled process accompanied by single-proton transfer. The membrane was tested to catalyze the reduction of hydrogen peroxide, and exhibited an excellent reproducibility and stability. This method with a charge and amphiphilicity dually driven (CADD) self-assembly opened up a new way to construct a third-generation electrochemical biosensor

Bone Fracture Pre-Ischemic Stroke Exacerbates Ischemic Cerebral Injury in Mice.

Ischemic stroke is a devastating complication of bone fracture. Bone fracture shortly after stroke enhances stroke injury by augmenting inflammation. We hypothesize that bone fracture shortly before ischemic stroke also exacerbates ischemic cerebral injury. Tibia fracture was performed 6 or 24 hours before permanent middle cerebral artery occlusion (pMCAO) on C57BL/6J mice or Ccr2RFP/+Cx3cr1GFP/+ mice that have the RFP gene knocked into one allele of Ccr2 gene and GFP gene knocked into one allele of Cx3cr1 gene. Behavior was tested 3 days after pMCAO. Infarct volume, the number of CD68+ cells, apoptotic neurons, bone marrow-derived macrophages (RFP+), and microgila (GFP+) in the peri-infarct region were quantified. Compared to mice subjected to pMCAO only, bone fracture 6 or 24 hours before pMCAO increased behavioral deficits, the infarct volume, and the number of CD68+ cells and apoptotic neurons in the peri-infarct area. Both bone marrow-derived macrophages (CCR2+) and microglia (CX3CR1+) increased in the peri-infarct regions of mice subjected to bone fracture before pMCAO compared to stroke-only mice. The mice subjected to bone fracture 6 hours before pMCAO had more severe injury than mice that had bone fracture 24 hours before pMCAO. Our data showed that bone fracture shortly before stroke also increases neuroinflammation and exacerbates ischemic cerebral injury. Our findings suggest that inhibition of neuroinflammation or management of stroke risk factors before major bone surgery would be beneficial for patients who are likely to suffer from stroke

Correction: Bone Fracture Pre-Ischemic Stroke Exacerbates Ischemic Cerebral Injury in Mice

Ischemic stroke is a devastating complication of bone fracture. Bone fracture shortly after stroke enhances stroke injury by augmenting inflammation. We hypothesize that bone fracture shortly before ischemic stroke also exacerbates ischemic cerebral injury. Tibia fracture was performed 6 or 24 hours before permanent middle cerebral artery occlusion (pMCAO) on C57BL/6J mice or Ccr2RFP/+Cx3cr1GFP/+ mice that have the RFP gene knocked into one allele of Ccr2 gene and GFP gene knocked into one allele of Cx3cr1 gene. Behavior was tested 3 days after pMCAO. Infarct volume, the number of CD68+ cells, apoptotic neurons, bone marrow-derived macrophages (RFP+), and microgila (GFP+) in the peri-infarct region were quantified. Compared to mice subjected to pMCAO only, bone fracture 6 or 24 hours before pMCAO increased behavioral deficits, the infarct volume, and the number of CD68+ cells and apoptotic neurons in the peri-infarct area. Both bone marrow-derived macrophages (CCR2+) and microglia (CX3CR1+) increased in the peri-infarct regions of mice subjected to bone fracture before pMCAO compared to stroke-only mice. The mice subjected to bone fracture 6 hours before pMCAO had more severe injury than mice that had bone fracture 24 hours before pMCAO. Our data showed that bone fracture shortly before stroke also increases neuroinflammation and exacerbates ischemic cerebral injury. Our findings suggest that inhibition of neuroinflammation or management of stroke risk factors before major bone surgery would be beneficial for patients who are likely to suffer from stroke

Data Modeling of Sewage Treatment Plant Based on Long Short-Term Memory with Multilayer Perceptron Network

As wastewater treatment usually involves complicated biochemical reactions, leading to strong coupling correlation and nonlinearity in water quality parameters, it is difficult to analyze and optimize the control of the wastewater treatment plant (WWTP) with traditional mathematical models. This research focuses on how deep learning techniques can be used to model the data from a specific WWTP so as to optimize the required energy consumption. In the operation of a wastewater treatment plant, various sensors are used to record the treatment process data; these data are used to train deep neural networks (DNNs). A long short-term memory with multilayer perceptron network (LMPNet) model is proposed to model the water quality parameters and site control parameters, such as COD, pH, NH3-N, et al., and the LMPNet model prediction error is then measured by criteria such as the MSE, MAE, and R2. The experimental results show that the LMPNet model demonstrates great accuracy in the modeling of the control of WWTPs. A life-long learning strategy is also developed for the LMPNet in order to adapt to the environment that may change over time. By developing performance evaluation metrics, the purification performance can be analyzed, and the prediction reference can be provided for the subsequent control optimization and energy saving plan