miR-17-5p and miR-106a are involved in the balance between osteogenic and adipogenic differentiation of adipose-derived mesenchymal stem cells

AbstractMesenchymal stem cells (MSCs) can differentiate into several distinct cell types, including osteoblasts and adipocytes. The balance between osteogenic and adipogenic differentiation is disrupted in several osteogenic-related disorders, such as osteoporosis. So far, little is known about the molecular mechanisms that drive final lineage commitment of MSCs. In this study, we revealed that miR-17-5p and miR-106a have dual functions in the modulation of human adipose-derived mesenchymal stem cells (hADSCs) commitment by gain- and loss-of-function assays. They could promote adipogenesis and inhibit osteogenesis. Luciferase reporter assay, western blot and ELISA suggested BMP2 was a direct target of miR-17-5p and miR-106a. Downregulation of endogeneous BMP2 by RNA interference suppressed osteogenesis and increased adipogenesis, similar to the effect of miR-17-5p and miR-106a upregulation. Moreover, the inhibitory effects of miR-17-5p on osteogenic and adipogenic differentiation of hADSCs could be reversed by BMP2 RNA interference. In conclusion, miR-17-5p and miR-106a regulate osteogenic and adipogenic lineage commitment of hADSCs by directly targeting BMP2, and subsequently decreased osteogenic TAZ, MSX2 and Runx2, and increased adipogenic C/EBPα and PPARγ

Theoretical Insight into the Spectral Characteristics of Fe(II)-Based Complexes for Dye-Sensitized Solar Cells—Part I: Polypyridyl Ancillary Ligands

The design of light-absorbent dyes with cheaper, safer, and more sustainable materials is one of the key issues for the future development of dye-sensitized solar cells (DSSCs). We report herein a theoretical investigation on a series of polypyridyl Fe(II)-based complexes of FeL2(SCN)2, [FeL3]2+, [FeL′(SCN)3]-, [FeL′2]2+, and FeL′′(SCN)2 (L = 2,2′-bipyridyl-4,4′-dicarboxylic acid, L′ = 2,2′,2″-terpyridyl-4,4′,4″-tricarboxylic acid, L″ = 4,4‴-dimethyl-2,2′ : 6′,2″ :6″,2‴-quaterpyridyl-4′,4″-biscarboxylic acid) by density functional theory (DFT) and time-dependent DFT (TD-DFT). Molecular geometries, electronic structures, and optical absorption spectra are predicted in both the gas phase and methyl cyanide (MeCN) solution. Our results show that polypyridyl Fe(II)-based complexes display multitransition characters of Fe → polypyridine metal-to-ligand charge transfer and ligand-to-ligand charge transfer in the range of 350–800 nm. Structural optimizations by choosing different polypyridyl ancillary ligands lead to alterations of the molecular orbital energies, oscillator strength, and spectral response range. Compared with Ru(II) sensitizers, Fe(II)-based complexes show similar characteristics and improving trend of optical absorption spectra along with the introduction of different polypyridyl ancillary ligands

Pseudomonas aeruginosa Quorum-Sensing and Type VI Secretion System Can Direct Interspecific Coexistence During Evolution

It is reported that a wide range of bacterial infections are polymicrobial, and the members in a local microcommunity can influence the growth of neighbors through physical and chemical interactions. Pseudomonas aeruginosa is an important opportunistic pathogen that normally causes a variety of acute and chronic infections, and clinical evidences suggest that P. aeruginosa can be frequently coisolated with other pathogens from the patients with chronic infections. However, the interspecific interaction and the coexisting mechanism of P. aeruginosa with coinfecting bacterial species during evolution still remain largely unclear. In this study, the relationships of P. aeruginosa with other Gram-positive (Staphylococcus aureus) and Gram-negative (Klebsiella pneumoniae) are investigated by using a series of on-plate proximity assay, in vitro coevolution assay, and RNA-sequencing. We find that although the development of a quorum-sensing system contributes P. aeruginosa a significant growth advantage to compete with S. aureus and K. pneumoniae, the quorum-sensing regulation of P. aeruginosa will be decreased during evolution and thus provides a basis for the formation of interspecific coexistence. The results of comparative transcriptomic analyses suggest that the persistent survival of S. aureus in the microcommunity has no significant effect on the intracellular transcriptional pattern of P. aeruginosa, while a more detailed competition happens between P. aeruginosa and K. pneumoniae. Specifically, the population of P. aeruginosa with decreased quorum-sensing regulation can still restrict the proportion increase of K. pneumoniae by enhancing the type VI secretion system-elicited cell aggressivity during further coevolution. These findings provide a general explanation for the formation of a dynamic stable microcommunity consisting of more than two bacterial species, and may contribute to the development of population biology and clinical therapy

Forecasting Electricity Demand Using a New Grey Prediction Model with Smoothness Operator

A stable electricity supply is the basis for ensuring the healthy and sustained development of a regional economy. Reasonable electricity prediction is the key to guaranteeing the stability and efficiency of electricity supply. To this end, we used a reformative grey prediction model to forecast electricity demand. In order to effectively improve the smoothness of a raw modelling sequence, we employed an existing smoothing algorithm that significantly compressed the amplitude of the random oscillation sequence. Then, an improved grey forecasting model with three parameters (IGFM_TP) was deduced. In the end, a new model was used to forecast the demand for electricity of one city in the western region of China, and comparisons of simulation values and errors with those of GFM_TP, GM(1,1), DGM(1,1) and SAIGM were conducted. The findings show that the mean absolute simulation percentage error of IGFM_TP was 7.8%, and those of the other four models were 12.1%, 12.3%, 11.1%, and 10.1%, respectively. Therefore, the simulation precision of the new model achieved an optimal effect. The proposed new grey model provides is an effective method for electricity demand prediction

Design Considerations for 60 GHz CMOS Power Amplifiers

This paper describes design considerations for millimeter-wave CMOS power amplifiers (PA). Solutions are presented from device level to circuit level and demonstrated by a measured 60 GHz PA prototype in 65 nm bulk CMOS technology. The proposed PA achieves a peak output power of 14 dBm with a peak power-added efficiency (PAE) of 7.2%. The small signal gain is 10.2 dB and 1-dB compression output power is 10.8 dBm. The transformer-based passives employed in the design enable a compact layout with an active area of 0.3 mm2. The PA consumes a quiescent current of 143 mA from a 1.6 V supply voltage.status: publishe

Two Water Stable Copper Metal-Organic Frameworks with Performance in the Electrocatalytic Activity for Water Oxidation

Two novel water stable metal-organic frameworks, [Cu(L)·(4,4′-bipy)·(ClO4)]n (1), [Cu(L)·(phen)·(ClO4)·(H2O)]2 (2), have been constructed by HL=[5-Mercapto-1-methyl] tetrazole acetic acid and Cu (II) salt in the presence of assistant N-containing ligands. MOF 1 and MOF 2 with open CuII sites, resulting the framework 1 and 2 show electrocatalytic activity for water oxidation in alkaline solution. The electrochemical properties of complex for oxygen evolution reaction (OER) were evaluated by linear sweep voltammetry (LSV) and the Tafel slopes. Complex 1 has a higher LSV activity with a lower over potential of 1.54 V and a much higher increase in current density. Meanwhile, the Tafel slope of complex 1 (122.0 mV dec-1) is much lower than complex 2 (243.5 mV dec-1). This phenomenon makes complex 1 a promising porous material for electrocatalytic activity

Deep Convolutional Neural Network for Detection and Prediction of Waxy Corn Seed Viability Using Hyperspectral Reflectance Imaging

This paper aimed to combine hyperspectral imaging (378–1042 nm) and a deep convolutional neural network (DCNN) to rapidly and non-destructively detect and predict the viability of waxy corn seeds. Different viability levels were set by artificial aging (aging: 0 d, 3 d, 6 d, and 9 d), and spectral data for the first 10 h of seed germination were continuously collected. Bands that were significantly correlated (SC) with moisture, protein, starch, and fat content in the seeds were selected, and another optimal combination was extracted using a successive projection algorithm (SPA). The support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and deep convolutional neural network (DCNN) approaches were used to establish the viability detection and prediction models. During detection, with the addition of different levels, the recognition effect of the first three methods decreased, while the DCNN method remained relatively stable (always above 95%). When using the previous 2.5 h data, the prediction accuracy rate was generally higher than the detection model. Among them, SVM + full band increased the most, while DCNN + full band was the highest, reaching 98.83% accuracy. These results indicate that the combined use of hyperspectral imaging technology and the DCNN method is more conducive to the rapid detection and prediction of seed viability

Deep Convolutional Neural Network for Detection and Prediction of Waxy Corn Seed Viability Using Hyperspectral Reflectance Imaging

This paper aimed to combine hyperspectral imaging (378–1042 nm) and a deep convolutional neural network (DCNN) to rapidly and non-destructively detect and predict the viability of waxy corn seeds. Different viability levels were set by artificial aging (aging: 0 d, 3 d, 6 d, and 9 d), and spectral data for the first 10 h of seed germination were continuously collected. Bands that were significantly correlated (SC) with moisture, protein, starch, and fat content in the seeds were selected, and another optimal combination was extracted using a successive projection algorithm (SPA). The support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and deep convolutional neural network (DCNN) approaches were used to establish the viability detection and prediction models. During detection, with the addition of different levels, the recognition effect of the first three methods decreased, while the DCNN method remained relatively stable (always above 95%). When using the previous 2.5 h data, the prediction accuracy rate was generally higher than the detection model. Among them, SVM + full band increased the most, while DCNN + full band was the highest, reaching 98.83% accuracy. These results indicate that the combined use of hyperspectral imaging technology and the DCNN method is more conducive to the rapid detection and prediction of seed viability