Objective
To investigate the effect of hypoxia inducible factor 1α (HIF1α)-kidney injury molecule 1 (KIM1) pathway on extracellular matrix degradation in human tubular epithelial cells under high glucose, and to explore the possible mechanism of this pathway participated in renal interstitial fibrosis of DN.
Methods
The human tubular epithelial cells (HK2) were cultured in vitro and divided into the following groups: (1)Normal control Group (D-glucose 5.6 mmol/L); (2) Mannitol group (D-glucose 5.6 mmol/L+D-mannitol 24.4 mmol/L); (3) High glucose group (D-glucose 30 mmol/L); (4) Control siRNA group; (5) HIF1α siRNA group; (6) KIM1 siRNA group. The corresponding indexes were measured at 12th, 24th and 36th hours. Western blotting, immunofluorescence and qRT-PCR were used to examine the expression of HIF1α, KIM1, matrix metalloproteinase-9 (MMP9), tissue inhibitor of metalloproteinases-1 (TIMP1), fibronectin (FN) and type I collage (COL-I) in protein and mRNA.
Results
Compared with the control group, the protein and mRNA expression of HIF1α, KIM1, TIMP1, FN and COL-I in the high glucose group were increased in a time-dependent manner (P<0.01), and MMP9 was decreased in time-dependent manner (P<0.01). Compared with the high glucose group, the protein and mRNA expression of HIF1α, KIM1, TIMP1, FN and COL-I in HIF1α siRNA group was decreased (P<0.01), and MMP9 was increased (P<0.01); However, the protein and mRNA expressions of KIM1, TIMP1, FN and COL-I in KIM1 siRNA group was decreased (P<0.01), MMP9 was increased (P<0.01), and the change of HIF1α was of no significance.
Conclusions
Down-regulation of HIF1α can significantly inhibit the expression of KIM1 in HK2 and decrease the expression of extracellular matrix, and down-regulation of KIM1 can also decrease the extracellular matrix under high glucose, which suggests that HIF1α may regulate the expression of KIM1 in human tubular epithelial cells under high glucose condition, and this pathway may participate in renal interstitial fibrosis of DN.
Key words:
Diabetic nephropathies; Extracellular matrix; Hypoxia inducible factor 1, alpha subunit; Kidney injury molecule 1
Complex interactions between plants and microorganisms form the basis of constructed wetlands (CWs) for pollutant removal. In the rhizosphere, radial oxygen loss (ROL) plays a key role in the activity and abundance of functional microorganisms. However, little has been done to explore how ROL would influence the niche differentiation of microbial communities at different vertical spatial scales. We demonstrate that ROL decreases with depth, promoting an oxidation-reduction rhizosphere microecosystem in CWs. The high level of ROL in the upper layer could support the oxygen supply for aerobic bacteria (Haliangium), facilitating the COD (60%) and NH4+-N (50%) removal, whereas the enrichment of denitrifiers (e.g., Hydrogenophaga and Ralstonia) and methanotrophs (Methanobaterium) in the lower layer could stimulate denitrification. The function prediction results further certified that the abundance of genes catalyzing nitrifying and denitrification processes were significantly enhanced in the upper and bottom layers, respectively, which was attributed to the oxygen concentration gradient in the rhizosphere. This study contributes to further unraveling the rhizosphere effect and enables an improved understanding of the decontamination mechanisms of CWs.
We observe $n$ pairs of independent (but not necessarily i.i.d.) random variables $X_{1}=(W_{1},Y_{1}),\ldots,X_{n}=(W_{n},Y_{n})$ and tackle the problem of estimating the conditional distributions $Q_{i}^{\star}(w_{i})$ of $Y_{i}$ given $W_{i}=w_{i}$ for all $i\in\{1,\ldots,n\}$. Even though these might not be true, we base our estimator on the assumptions that the data are i.i.d.\ and the conditional distributions of $Y_{i}$ given $W_{i}=w_{i}$ belong to a one parameter exponential family $\bar{\mathscr{Q}}$ with parameter space given by an interval $I$. More precisely, we pretend that these conditional distributions take the form $Q_{{\boldsymbol{\theta}}(w_{i})}\in \bar{\mathscr{Q}}$ for some ${\boldsymbol{\theta}}$ that belongs to a VC-class $\bar{\boldsymbol{\Theta}}$ of functions with values in $I$. For each $i\in\{1,\ldots,n\}$, we estimate $Q_{i}^{\star}(w_{i})$ by a distribution of the same form, i.e.\ $Q_{\hat{\boldsymbol{\theta}}(w_{i})}\in \bar{\mathscr{Q}}$, where $\hat {\boldsymbol{\theta}}=\hat {\boldsymbol{\theta}}(X_{1},\ldots,X_{n})$ is a well-chosen estimator with values in $\bar{\boldsymbol{\Theta}}$. We show that our estimation strategy is robust to model misspecification, contamination and the presence of outliers. Besides, we provide an algorithm for calculating $\hat{\boldsymbol{\theta}}$ when $\bar{\boldsymbol{\Theta}}$ is a VC-class of functions of low or moderate dimension and we carry out a simulation study to compare the performance of $\hat{\boldsymbol{\theta}}$ to that of the MLE and median-based estimators.
Understanding the pattern of species extinction risk is key to biodiversity conservation. Previous studies showed extinction risk correlates strongly with taxon species richness. However, there is no consistent conclusion to this hypothesis, and patterns differ among different taxonomic groups. Here, we collated lists of vascular plant and terrestrial vertebrate species information on their threatened status in Yunnan Province and performed the first systematical analysis to test the relationship between the proportion of threatened or extinct species and species richness at the family-level of the two above taxa in Yunnan Province, China. We found that extinction risk was not randomly distributed among families and the estimated extinction risk was higher among closer phylogenetic families than expected by chance. Moreover, there were significant negative correlations between extinction risk and family size in vascular plants and terrestrial vertebrates. Our results suggest that family size is a good predictor of extinction risk and extinction risk is related to evolutionary history at family-level among vascular plants and terrestrial vertebrates in Yunnan.
To investigate the role of kidney injury molecular 1 (KIM-1) in high glucose-induced autophagy and apoptosis in renal tubular epithelial cells.Human renal tubular epithelial cells (HK2) were treated with normal glucose (NG, D -glucose 5.6 mmol/L), high glucose (HG, 30 mmol/L), high osmotic (HO, D-glucose 5.6 mmol/L + D-mannitol 24.4 mmol/L), HG + KIM-1 siRNA, HG + siRNA control. The expressions of KIM-1 and microtubule-associated protein 1 light chain 3II (LC3II) were measured by western blot as well as real time PCR; the number of autophagosome was detected by electron microscopy; and the level of apoptosis was analyzed by flow cytometry.In the HG group, the expressions of KIM-1 and LC3II were increased markedly, which was accompanied by more autophagosome and higher level of apoptosis compared with NG group. Silencing of KIM-1 by siRNA inhibited the increases in the levels of LC3II, autophagosome and apoptosis.KIM-1 may mediate high glucose-induced autophagy and apoptosis in renal tubular epithelial cells.
In-memory computing (IMC) is an effectual solution for energy-efficient artificial intelligence applications. Analog IMC amortizes the power consumption of multiple sensing amplifiers with analog-to-digital converter (ADC), and simultaneously completes the calculation of multi-line data with high parallelism degree. Based on a universal one-transistor one-magnetic tunnel junction (MTJ) spin transfer torque magnetic RAM (STT-MRAM) cell, this paper demonstrates a novel tunneling magnetoresistance (TMR) ratio magnifying method to realize analog IMC. Previous concerns include low TMR ratio and analog calculation nonlinearity are addressed using device-circuit interaction. Peripheral circuits are minimally modified to enable in-memory matrix-vector multiplication. A current mirror with feedback structure is implemented to enhance analog computing linearity and calculation accuracy. The proposed design maximumly supports 1024 2-bit input and 1-bit weight multiply-and-accumulate (MAC) computations simultaneously. The 2-bit input is represented by the width of the input (IN) pulses, while the 1-bit weight is stored in STT-MRAM and the x7500 magnified TMR (m-TMR) ratio is obtained by latching. The proposal is simulated using 28-nm CMOS process and MTJ compact model. The integral nonlinearity is reduced by 57.6% compared with the conventional structure. 9.47-25.4 TOPS/W is realized with 2-bit input, 1-bit weight and 4-bit output convolution neural network (CNN).
Music and visual arts are essential in children's arts education, and their integration has garnered significant attention. Existing data analysis methods for exploring audio-visual correlations are limited. Yet, relevant research is necessary for innovating and promoting arts integration courses. In our work, we collected substantial volumes of music-inspired doodles created by children and interviewed education experts to comprehend the challenges they encountered in the relevant analysis. Based on the insights we obtained, we designed and constructed an interactive visualization system DoodleTunes. DoodleTunes integrates deep learning-driven methods for automatically annotating several types of data features. The visual designs of the system are based on a four-level analysis structure to construct a progressive workflow, facilitating data exploration and insight discovery between doodle images and corresponding music pieces. We evaluated the accuracy of our feature prediction results and collected usage feedback on DoodleTunes from five domain experts.
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