Decreasing mitochondrial fission ameliorates HIF-1α-dependent pathological retinal angiogenesis.

Angiogenesis plays a critical role in many pathological processes, including irreversible blindness in eye diseases such as retinopathy of prematurity. Endothelial mitochondria are dynamic organelles that undergo constant fusion and fission and are critical signalling hubs that modulate angiogenesis by coordinating reactive oxygen species (ROS) production and calcium signalling and metabolism. In this study, we investigated the role of mitochondrial dynamics in pathological retinal angiogenesis. We showed that treatment with vascular endothelial growth factor (VEGF; 20 ng/ml) induced mitochondrial fission in HUVECs by promoting the phosphorylation of dynamin-related protein 1 (DRP1). DRP1 knockdown or pretreatment with the DRP1 inhibitor Mdivi-1 (5 µM) blocked VEGF-induced cell migration, proliferation, and tube formation in HUVECs. We demonstrated that VEGF treatment increased mitochondrial ROS production in HUVECs, which was necessary for HIF-1α-dependent glycolysis, as well as proliferation, migration, and tube formation, and the inhibition of mitochondrial fission prevented VEGF-induced mitochondrial ROS production. In an oxygen-induced retinopathy (OIR) mouse model, we found that active DRP1 was highly expressed in endothelial cells in neovascular tufts. The administration of Mdivi-1 (10 mg·kg-1·d-1, i.p.) for three days from postnatal day (P) 13 until P15 significantly alleviated pathological angiogenesis in the retina. Our results suggest that targeting mitochondrial fission may be a therapeutic strategy for proliferative retinopathies and other diseases that are dependent on pathological angiogenesis.

[Decomposition of Interference Hyperspectral Images Using Improved Morphological Component Analysis].

As the special imaging principle of the interference hyperspectral image data, there are lots of vertical interference stripes in every frames. The stripes' positions are fixed, and their pixel values are very high. Horizontal displacements also exist in the background between the frames. This special characteristics will destroy the regular structure of the original interference hyperspectral image data, which will also lead to the direct application of compressive sensing theory and traditional compression algorithms can't get the ideal effect. As the interference stripes signals and the background signals have different characteristics themselves, the orthogonal bases which can sparse represent them will also be different. According to this thought, in this paper the morphological component analysis (MCA) is adopted to separate the interference stripes signals and background signals. As the huge amount of interference hyperspectral image will lead to glow iterative convergence speed and low computational efficiency of the traditional MCA algorithm, an improved MCA algorithm is also proposed according to the characteristics of the interference hyperspectral image data, the conditions of iterative convergence is improved, the iteration will be terminated when the error of the separated image signals and the original image signals are almost unchanged. And according to the thought that the orthogonal basis can sparse represent the corresponding signals but cannot sparse represent other signals, an adaptive update mode of the threshold is also proposed in order to accelerate the computational speed of the traditional MCA algorithm, in the proposed algorithm, the projected coefficients of image signals at the different orthogonal bases are calculated and compared in order to get the minimum value and the maximum value of threshold, and the average value of them is chosen as an optimal threshold value for the adaptive update mode. The experimental results prove that whether LASIS and LAMIS image data, the traditional MCA algorithm can separate the interference stripes signals and background signals very well, and make the interference hyperspectral image decomposition perfectly, and the improved MCA algorithm not only keep the perfect results of the traditional MCA algorithm, but also can reduce the times of iteration and meet the iterative convergence conditions much faster than the traditional MCA algorithm, which will also provide a very good solution for the new theory of compressive sensing.

[A New Spectral-Spatial Algorithm Method for Hyperspectral Image Target Detection].

With high-resolution spatial information and continuous spectrum information, hyperspectral remote sensing image -has a unique advantage in the field of target detection. Traditional hyperspectral remote sensing image target detection methods emphasis on using spectral information to determine deterministic algorithm and statistical algorithms. Deterministic algorithms find the target by calculating the distance between the target spectrum and detected spectrum however, they are unable to detect sub-pixel target and are easily affected by noise. Statistical methods which calculate background statistical characteristics to detect abnormal point as target. It can detect subpixel target targets and small targets better thanbig size target,. With the spatial resolution increasing, subpixel target detection target has gradually grown to a single pixel and multi-pixel target. At this point, hyperspectral image usually has large homogeneous regions where the neighboring pixels wihin the regions consist of the same type of materials and have a similar spectral characteristics, therefore, the spatial information should be needed to incorporate into the algorithm for targe detection. This paper proposes an algorithm for hyperspectral target detection combined spectrum characteristics and spatial characteristics. The algorithm is based on traditional target detection operator and combined neighborhood clustering statistics. Firstly, the algorithm uses target detection operator to divided hyperspectral image into a potential target region and background region. Then, it calculates the centroid of the potential target area. Finally, as the centroid for neighborhood clustering center to clust data in order to exclud background from potential target area, through iterative calculation to obtain the final results of the target detection. The traditional statistics algorithms defines the total image as background area in order to extract background statistics features, and the algorithm propsed devided the total image into background part and potential target part, which cut off the target interference for background statistics feature extraction. Compared with CEM operators and ACE operators, the algorithm proposed outperforms than traditional operators in big target detection .

[A Novel Spatial-Spectral Sparse Representation for Hyperspectral Image Classification Based on Neighborhood Segmentation].

Traditional hyperspectral image classification algorithms focus on spectral information application, however, with the increase of spatial resolution of hyperspectral remote sensing images, hyperspectral imaging presents clustering properties on spatial domain for the same category. It is critical for hyperspectral image classification algorithms to use spatial information in order to improve the classification accuracy. However, the marginal differences of different categories display more obviously. If it is introduced directly into the spatial-spectral sparse representation for image classification without the selection of neighborhood pixels, the classification error and the computation time will increase. This paper presents a spatial-spectral joint sparse representation classification algorithm based on neighborhood segmentation. The algorithm calculates the similarity with spectral angel in order to choose proper neighborhood pixel into spatial-spectral joint sparse representation model. With simultaneous subspace pursuit and simultaneous orthogonal matching pursuit to solve the model, the classification is determined by computing the minimum reconstruction error between testing samples and training pixels. Two typical hyperspectral images from AVIRIS and ROSIS are chosen for simulation experiment and results display that the classification accuracy of two images both improves as neighborhood segmentation threshold increasing. It concludes that neighborhood segmentation is necessary for joint sparse representation classification.

The secondary visual cortex mediated the enhancement of associative learning on methamphetamine self-administration behaviors.

RATIONALE: Methamphetamine addiction is a persistent and intractable pathological learning and memory, whereas no approved therapeutics is available. However, few attentions have been paid to how associative learning participates in the formation of intractable memory related to drug addiction OBJECTIVES AND METHODS: To investigate the role of associative learning in methamphetamine addiction and the underlying neurobiological mechanism, methamphetamine self-administration, oral sucrose self-administration, chemogenetic neuromanipulation, and fiber photometry in mice were performed in this study. RESULTS: We reported that associative learning increased methamphetamine-induced self-administration, but not oral sucrose self-administration. In addition, the enhancement of methamphetamine-induced self-administration was independent of more methamphetamine consumption, and remained with higher drug-taking and motivation in the absence of visual cues, suggesting the direct effects of the associative learning that enhanced methamphetamine-induced self-administration. Moreover, chemogenetic inactivation of the secondary visual cortex (V2) reduced the enhancement of the drug-taking induced by associative learning but did not alter sucrose-taking. Further fiber photometry of V2 neurons demonstrated that methamphetamine-associative learning elicits V2 neuron excitation, and sucrose-associative learning elicits V2 neuron inhibition. CONCLUSIONS: Therefore, this study reveals the neurobiological mechanism of V2 excitability underlying how associative learning participates in the formation of intractable memory related to drug addiction, and gives evidence to support V2 as a promising target for stimulation therapy for methamphetamine addiction.

Conserved residues in the subunit interface of tau glutathione s-transferase affect catalytic and structural functions.

The tau class glutathione S-transferases (GSTs) have important roles in stress tolerance and the detoxification of herbicides in crops and weeds. Structural investigations of a wheat tau GST (TaGSTU4) show two subunit interactions: a hydrogen bond between the Tyr93 and Pro65 from another subunit of the dimer, and two salt bridges between residues Glu78 and side chains of Arg95 and Arg99 in the opposite subunit. By investigating enzyme activities, kinetic parameters and structural characterizations, this study showed the following results: (i) the hydrogen bond interaction between the Tyr93 and Pro65 was not essential for dimerization, but contributed to the enzyme's catalytic activity, thermal stability and affinity towards substrates glutathione and 1-chloro-2, 4-dinitrobenzene; and (ii) two salt bridges mainly contributed to the protein structure stability and catalysis. The results of this study form a structural and functional basis for rational design of more selective and environmentally friendly herbicides.

[Spatial domain display for interference image dataset].

The requirements of imaging interferometer visualization is imminent for the user of image interpretation and information extraction. However, the conventional researches on visualization only focus on the spectral image dataset in spectral domain. Hence, the quick show of interference spectral image dataset display is one of the nodes in interference image processing. The conventional visualization of interference dataset chooses classical spectral image dataset display method after Fourier transformation. In the present paper, the problem of quick view of interferometer imager in image domain is addressed and the algorithm is proposed which simplifies the matter. The Fourier transformation is an obstacle since its computation time is very large and the complexion would be even deteriorated with the size of dataset increasing. The algorithm proposed, named interference weighted envelopes, makes the dataset divorced from transformation. The authors choose three interference weighted envelopes respectively based on the Fourier transformation, features of interference data and human visual system. After comparing the proposed with the conventional methods, the results show the huge difference in display time.

Molecular characterization of a dehydroascorbate reductase from Pinus bungeana.

Abstract Dehydroascorbate reductase (DHAR) plays a critical role in the ascorbate-glutathione recycling reaction for most higher plants. To date, studies on DHAR in higher plants have focused largely on Arabidopsis and agricultural plants, and there is virtually no information on the molecular characteristics of DHAR in gymnosperms. The present study reports the cloning and characteristics of a DHAR (PbDHAR) from a pine, Pinus bungeana Zucc. ex Endl. The PbDHAR gene encodes a protein of 215 amino acid residues with a calculated molecular mass of 24.26 kDa. The predicted 3-D structure of PbDHAR showed a typical glutathione S-transferase fold. Reverse transcription-polymerase chain reaction revealed that the PbDHAR was a constitutive expression gene in P. bungeana. The expression level of PbDHAR mRNA in P. bungeana seedlings did not show significant change under high temperature stress. The recombinant PbDHAR was overexpressed in Escherichia coli following purification with affinity chromatography. The recombinant PbDHAR exhibited enzymatic activity (19.84 micromol/min per mg) and high affinity (a K(m) of 0.08 mM) towards the substrates dehydroascorbate (DHA). Moreover, the recombinant PbDHAR was a thermostable enzyme, and retained 77% of its initial activity at 55 degrees C. The present study is the first to provide a detailed molecular characterization of the DHAR in P. bungeana.

[Analysis of mitochondrial DNA gene tRNALeu(UUR) A3243G mutation in diabetic pedigrees].

OBJECTIVE: To investigate the clinical characteristics and the prevalence of mitochondrial gene A3243G mutation in diabetic pedigrees. METHODS: Nineteen suspected mitochondrial DNA diabetic family members from three families were recruited. The gene fragment was amplified by PCR, and mutation was detected by direct sequencing. RESULTS: In three pedigrees, the three probands and their mothers were found carrying the most common nt3243A>G mutation. Most of diabetic patients in these families were deaf and diabetes was developed at early age, characterized by impaired beta cell function and low body mass index (BMI). CONCLUSION: The mitochondrial gene A3243G mutation may cause diabetes mellitus and deaf.

Discrete 1 : 1 complexes and higher order assemblies formed from aminobenzene sulphonate anions and a tetraimidazolium "molecular box".

Use of isomeric aminobenzene sulphonate anions in conjunction with a tetraimidazolium "molecular box" leads to self-assembled embedded structures. Simple 1 : 1 complexes are formed at low concentrations in DMSO when the host : guest ratio is 1.0. Higher order species are seen as the concentration is increased or the host-guest ratio is perturbed. The assembly and disassembly of the supramolecular aggregates can be controlled by application of various external stimuli, including changes in concentration, temperature, and protonation state of the guest species.

Molecular evolution of trehalose-6-phosphate synthase (TPS) gene family in Populus, Arabidopsis and rice.

Trehalose-6-phosphate synthase (TPS) plays important roles in trehalose metabolism and signaling. Plant TPS proteins contain both a TPS and a trehalose-6-phosphate phosphatase (TPP) domain, which are coded by a multi-gene family. The plant TPS gene family has been divided into class I and class II. A previous study showed that the Populus, Arabidopsis, and rice genomes have seven class I and 27 class II TPS genes. In this study, we found that all class I TPS genes had 16 introns within the protein-coding region, whereas class II TPS genes had two introns. A significant sequence difference between the two classes of TPS proteins was observed by pairwise sequence comparisons of the 34 TPS proteins. A phylogenetic analysis revealed that at least seven TPS genes were present in the monocot-dicot common ancestor. Segmental duplications contributed significantly to the expansion of this gene family. At least five and three TPS genes were created by segmental duplication events in the Populus and rice genomes, respectively. Both the TPS and TPP domains of 34 TPS genes have evolved under purifying selection, but the selective constraint on the TPP domain was more relaxed than that on the TPS domain. Among 34 TPS genes from Populus, Arabidopsis, and rice, four class I TPS genes (AtTPS1, OsTPS1, PtTPS1, and PtTPS2) were under stronger purifying selection, whereas three Arabidopsis class I TPS genes (AtTPS2, 3, and 4) apparently evolved under relaxed selective constraint. Additionally, a reverse transcription polymerase chain reaction analysis showed the expression divergence of the TPS gene family in Populus, Arabidopsis, and rice under normal growth conditions and in response to stressors. Our findings provide new insights into the mechanisms of gene family expansion and functional evolution.

Trifluoromethylation of flavonoids and anti-tumor activity of the trifluoromethylated flavonoid derivatives.

3-Trifluoromethylflavonoid derivatives were prepared for the first time by trifluoromethylation of 3-iodoflavonoid derivatives. Other C ring and B ring trifluoromethylated flavonoid derivatives were also prepared. All the compounds were tested for their effect on the U2OS cell cycle in vitro. Bistrifluoromethylated apigenin derivative 13 showed the strongest activity against the cell growth.