Transcriptional regulatory network controlling secondary cell wall biosynthesis and biomass production in vascular plants

Secondary wall is an abundant component of plant biomass and has a potential to be a renewable resource of bioenergy and biomaterials. It is important to unravel the molecular mechanism underlying secondary wall formation and how it contributes to plant biomass production. In this review, we summarized the potential role of transcription factors (TFs) in secondary wall formation, and prospected to design the future bioenergy crops with high density biomass, low cellulose recalcitrance and lignin content.Keywords: Transcription factors (TFs), secondary cell wall, plant biomass, NACs, mining yeast binding sites (MYBs

Using improved support vector regression to predict the transmitted energy consumption data by distributed wireless sensor network

AbstractMassive energy consumption data of buildings was generated with the development of information technology, and the real-time energy consumption data was transmitted to energy consumption monitoring system by the distributed wireless sensor network (WSN). Accurately predicting the energy consumption is of importance for energy manager to make advisable decision and achieve the energy conservation. In recent years, considerable attention has been gained on predicting energy use of buildings in China. More and more predictive models appeared in recent years, but it is still a hard work to construct an accurate model to predict the energy consumption due to the complexity of the influencing factors. In this paper, 40 weather factors were considered into the research as input variables, and the electricity of supermarket which was acquired by the energy monitoring system was taken as the target variable. With the aim to seek the optimal subset, three feature selection (FS) algorithms were involved in the study, respectively: stepwise, least angle regression (Lars), and Boruta algorithms. In addition, three machine learning methods that include random forest (RF) regression, gradient boosting regression (GBR), and support vector regression (SVR) algorithms were utilized in this paper and combined with three feature selection (FS) algorithms, totally are nine hybrid models aimed to explore an improved model to get a higher prediction performance. The results indicate that the FS algorithm Boruta has relatively better performance because it could work well both on RF and SVR algorithms, the machine learning method SVR could get higher accuracy on small dataset compared with the RF and GBR algorithms, and the hybrid model called SVR-Boruta was chosen to be the proposed model in this paper. What is more, four evaluate indicators were selected to verify the model performance respectively are the mean absolute error (MAE), the mean squared error(MSE), the root mean squared error (RMSE), and the R-squared (R2), and the experiment results further verified the superiority of the recommended methodology

An experimental study on the rotational accuracy of variable preload spindle-bearing system

The rotational performance of the spindle-bearing system has critical influence upon the geometric shape and surface roughness of the machined parts. The effects of preload and preload method on the rotational performance of the spindle-bearing system is explored experimentally to reveal the role of preload and preload method in spindle rotational performances under different speeds. A test rig on which both the rigid preload and elastic preload can be realized, equipped with variable preload spindle-bearing system, is developed. Based on the mechanical model, the relationship of the axial preload and negative axial clearance of the spindle-bearing system is provided. Rotating sensitive radial error motion tests are conducted for evaluating synchronous and asynchronous radial errors of the variable preload spindle-bearing system under different rotating speeds and preload methods. The change regularity of synchronous and asynchronous radial errors with preloads under different rotating speeds are given. The results show that the preload plays an important role on the rotational performance of spindle-bearing system. The rigid preload is more efficient in achieving better rotational performance than elastic preload under the same rotating speed. Furthermore, this article significantly guides the preload designing and assembling of the new spindle-bearing system

One-step Iterative Estimation of Effective Atomic Number and Electron Density for Dual Energy CT

Dual-energy computed tomography (DECT) is a promising technology that has shown a number of clinical advantages over conventional X-ray CT, such as improved material identification, artifact suppression, etc. For proton therapy treatment planning, besides material-selective images, maps of effective atomic number (Z) and relative electron density to that of water ($\rho_e$) can also be achieved and further employed to improve stopping power ratio accuracy and reduce range uncertainty. In this work, we propose a one-step iterative estimation method, which employs multi-domain gradient $L_0$-norm minimization, for Z and $\rho_e$ maps reconstruction. The algorithm was implemented on GPU to accelerate the predictive procedure and to support potential real-time adaptive treatment planning. The performance of the proposed method is demonstrated via both phantom and patient studies

Recent advances in mass spectrometry-based proteomics and metabolomics in chronic rhinosinusitis with nasal polyps

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a complex and heterogeneous disease, typically diagnosed through endoscopy and computed tomography and treated with glucocorticoid or surgery. There is an urgent need to develop molecular-level diagnostic or prognostic tools to better understand the pathophysiology of CRSwNP. Proteomics and metabolomics, emerging fields, offer significant potential in elucidating the mechanisms underlying CRSwNP. Mass spectrometry, a powerful and sensitive tool for trace substance detection, is broadly applied for proteomics and metabolomics analysis in CRSwNP research. While previous literature has summarized the advancement of mass spectrometry-based CRSwNP proteomics from 2004 to 2018, recent years have seen new advances in this field, particularly about non-invasive samples and exosomes. Furthermore, mass spectrometry-based CRSwNP metabolomics research has opened new avenues for inquiry. Therefore, we present a comprehensive review of mass spectrometry-based proteomics and metabolomics studies on CRSwNP conducted between 2019 and 2022. Specifically, we highlight protein and metabolic biomarkers that have been utilized as diagnostic or prognostic markers for CRSwNP. Lastly, we conclude with potential directions for future mass spectrometry-based omics studies of CRSwNP

Influence of external heat sources on volumetric thermal errors of precision machine tools

Volumetric accuracy is susceptible to thermal gradient caused by internal heat source (IHS) and external heat source (EHS). A temperature-structure multi-step calculation method is presented to investigate the influences of EHSs on volumetric thermal errors of precision machine tools. The temperature and structure of the machine tool are simulated first, and then, the volumetric thermal errors are calculated using multi-body theory method. Simulations are completed to study the effects of different EHSs on a machine tool, and series of validating experiments are carried out to verify the modeling method. The test results in specific position and working condition revealed that EHSs contribute 53, 21, and 68% of thermal deviations in X, Y, and Z directions individually. It is illustrated that the EHS is an important factor affecting the volumetric accuracy of precision machine tools. The methods provided in this paper are valuable for machine tool designers to evaluate the EHS effects on volumetric thermal errors during designing process; furthermore, some insulating measures are suggested to improve the accuracy and accuracy stability of precision machine tools by reducing the EHS influences

Gridded inventories of historical usage for selected organochlorine pesticides in Gansu Province, China

HCHs and DDTs were banned in 1983 in China; however, they are still remaining in various environmental media. Since endosulfan was introduced in China in 1994, it is widely used in agriculture. In this study, temporal and spatial uses of endosulfan, HCHs, and DDTs in Gansu province of China have been presented. It is estimated that the total usage is 701 tons for endosulfan between 1994 and 2007, 1,712 tons for HCHs between 1952 and 1983, and 462 tons for DDTs between 1951 and 1983, respectively. Endosulfan usage increased dramatically in 1998 due to its application on other crops except on cotton. The HCH and DDT usage displayed a rapid increase after 1972, reaching the peak in 1976 and in 1975, respectively; since then, they declined until being banned in 1983. The gridded usage inventories of these three kinds of organochlorine pesticides in Gansu province, with a 1/4A degrees longitude by 1/6A degrees latitude resolution, have been created by using different crops for endosulfan and the area of dry farmland for HCHs and DDTs as surrogate data. The most intensive use was in northwestern regions for endosulfan and southeastern regions for HCHs and DDTs in Gansu province

PO-047 Expression of Aromatase and Synthesis of Sex Steroid Hormones in Skeletal Muscle Following Exercise Training in Ovariectomized Rats

Objective Age-related muscle wasting (sarcopenia) is accompanied by a decrease in estrogen levels which can compromise the health of aging women. Recent studies have shown that the key enzyme of estrogen synthesis (aromatase) is detected in the skeletal muscle. The purpose of this study was to investigate the effects of exercise on the expression of aromatase and the synthesis of sex steroid hormones in skeletal muscle following exercise training. Methods Fourteen female ovariectomized rats were divided into two groups, treadmill running (n=7) and sedentary (n=7) group. Exercise training on a treadmill (25 m/min, 60 min/day, 6 days/week) for 5 weeks. Immunofluorescence assay was used to detect estradiol and aromatase levels in soleus muscle and plantar muscle. Detected the expression of AKT, Aromatase, FoxO1, MyoD protein level by Western blotting. Results We found that in ovariectomized rats, exercise training significantly increased the soleus and plantar muscles mass. The level of aromatase expression and 17-b-estradiol (E2) were increased significantly in skeletal muscle following exercise training(P < 0.05). In addition, the down-stream Akt-FoxO1-MyoD signaling pathway was significantly regulated in both soleus and plantaris muscles following exercise(P< 0.05). Conclusions These results demonstrate that exercise training increased the expression of aromatase and local estrogen production in skeletal muscle, which potentially influences skeletal muscle in ovariectomized rats through activation of Akt-FoxO1-MyoD signaling pathway

New Superhard Carbon Phases Between Graphite and Diamond

Two new carbon allotropes (H-carbon and S-carbon) are proposed, as possible candidates for the intermediate superhard phases between graphite and diamond obtained in the process of cold compressing graphite, based on the results of first-principles calculations. Both H-carbon and S-carbon are more stable than previously proposed M-carbon and W-carbon and their bulk modulus are comparable to that of diamond. H-carbon is an indirect-band-gap semiconductor with a gap of 4.459 eV and S-carbon is a direct-band-gap semiconductor with a gap of 4.343 eV. The transition pressure from cold compressing graphite is 10.08 GPa and 5.93 Gpa for H-carbon and S-carbon, respectively, which is in consistent with the recent experimental report.Comment: 5pages,4figures,submitted to Phys.Rev.Lett on 18Jan12, transfer to Phys.Rev.B on 25Mar12; Solid State Communications(2012), http://dx.doi.org/10.1016/j.ssc.2012.05.02

Red blood cell distribution width combined with age as a predictor of acute ischemic stroke in stable COPD patients

AimThis retrospective study aimed to investigate the independent clinical variables associated with the onset of acute cerebral ischemic stroke (AIS) in patients with stable chronic obstructive pulmonary disease (COPD).MethodA total of 244 patients with COPD who had not experienced a relapse within 6 months were included in this retrospective study. Of these, 94 patients hospitalized with AIS were enrolled in the study group, and the remaining 150 were enrolled in the control group. Clinical data and laboratory parameters were collected for both groups within 24 h after hospitalization, and the data of the two groups were statistically analyzed.ResultsThe levels of age, white blood cell (WBC), neutrophil (NEUT), glucose (GLU), prothrombin time (PT), albumin (ALB), and red blood cell distribution width (RDW) were different in the two groups (P < 0.01). Logistic regression analysis showed that age, WBC, RDW, PT, and GLU were independent risk factors for the occurrence of AIS in patients with stable COPD. Age and RDW were selected as new predictors, and the receiver operating characteristic curves (ROC) were plotted accordingly. The areas under the ROC curves of age, RDW, and age + RDW were 0.7122, 0.7184, and 0.7852, respectively. The sensitivity was 60.5, 59.6, and 70.2%, and the specificity was 72.4, 86.0, and 60.0%, respectively.ConclusionThe combination of RDW and age in patients with stable COPD might be a potential predictor for the onset of AIS