To explore the regulation mechanism of the quorum sensing regulator AphA on the functional activity of type Ⅵ secretion system VflT6SS2 in
.
Western Blot analysis was used to detect the relative ...expression and secretion of VflT6SS2 signature component hemolysin-coregulated protein (Hcp) in wild type (WT), Δ
and corresponding complementary strains. Quantitative reverse transcription PCR and luminescence activity assay of the promoter-
fusion system was used to measure the mRNA expression levels and promoter activity of the VflT6SS2 core and accessory gene-cluster representative genes
2,
(
2) and
(
2), and the quorum sensing regulator HapR in WT and Δ
strains. A point mutation experiment combined with a luminescence activity assay was used to verify the regulatory binding site of AphA in the
2b promoter region. Electrophoretic mobility shift assay (EMSA) was used to determine AphA binding to the
promoter.
The mRNA expression levels of
2,
(
2),
(
2), and
as well as the protein expression and secretion levels of
Aim To detect the β-amyloid plaques (Aβ) in a rat model of Alzheimer's disease (AD) using superparamagnetic iron oxide nanoparticles coated with 1,1-dicyano-2-6-(dimethylamino)-naphthalene-2-yl ...propene carboxyl derivative (DDNP-SPIO). Materials and methods DDNP-SPIO was prepared in a previous trial. The binding affinity of DDNP-SPIO to Aβ was tested using fluorescence spectrophotometry in vitro. In vivo , five AD rats and five non-AD rats were intravenously injected with DDNP-SPIO at a dose of 76 μmol Fe/kg. Coronal T2 *-weighted images were collected at baseline and repeated at 10, 30, and 60 min post-injection. Enhancement features of the two groups were analysed. After imaging, brain specimens were resected for Congo red and Prussian blue staining to assess the binding of DDNP-SPIO to Aβ deposits. Results In vitro experiments indicated that the DDNP-SPIO nanoparticles displayed high binding affinities towards Aβ with a Kd value of 29.4 nmol/l. A significant decrease in SI was detected in the hippocampal area of AD rats after intravenous injection of the nanoparticles, but not in non-AD rats. The measurement of the percentage signal loss decreased to 52% in AD rats. In non-AD rats, only 10% signal loss was observed. There was a significant difference between the two groups ( t = 4.533, p < 0.05). The signal decrease resulted from the binding of the DDNP-SPIO nanoparticles to the Aβ plaques, which was identified with Congo red and Prussian blue staining. Conclusion The DDNP-SPIO nanoparticles could potentially be used for visualizing Aβ plaques, which may be helpful for diagnosing the early stages of AD and monitoring the effects of drug therapy.
A highly efficient light-trapping structure, consisting of a diffractive grating, a distributed Bragg reflector (DBR) and a metal reflector was proposed. As an example, the proposed light-trapping ...structure with an indium tin oxide (ITO) diffraction grating, an a-Si:H/ITO DBR and an Ag reflector was optimized by the simulation via rigorous coupled-wave analysis (RCWA) for a 2.0-μm-thick c-Si solar cell with an optimized ITO front antireflection (AR) layer under the air mass 1.5 (AM1.5) solar illumination. The weighted absorptance under the AM1.5 solar spectrum (
A
AM1.5) of the solar cell can reach to 69%, if the DBR is composed of 4 pairs of a-Si:H/ITOs. If the number of a-Si:H/ITO pairs is up to 8, a larger
A
AM1.5 of 72% can be obtained. In contrast, if the Ag reflector is not adopted, the combination of the optimized ITO diffraction grating and the 8-pair a-Si:H/ITO DBR can only result in an
A
AM1.5 of 68%. As the reference,
A
AM1.5
=
31% for the solar cell only with the optimized ITO front AR layer. So, the proposed structure can make the sunlight highly trapped in the solar cell. The adoption of the metal reflector is helpful to obtain highly efficient light-trapping effect with less number of DBR pairs, which makes that such light-trapping structure can be fabricated easily.
The use of multi-branch architectures in off-the-shelf light-weight residual series neural networks can significantly improve their performance in remote sensing scene classification tasks. However, ...such architectures come at the expense of an increased number of parameters and calculations. In this paper, we propose the Decoupling Multi-branch Pointwise Convolutions (DMPConv), which works without a corresponding increase in parameters and calculations during inferencing, and at the same time, can maintain the same performance improvement ability as the multi-branch architectures. DMPConv can be decoupled into two states, the training-time DMPConv and the inferencing-time DMPConv. The training-time DMPConv enhances the expressivity of the network by using weighted multi-branch 1×1 convolutions. After training, we use structural reconstruction to convert the training-time DMPConv to the inferencing-time DMPConv, which has the same form as vanilla 1×1 convolution, so as to realize the inferencing-free. Extensive experiments were conducted on multiple remote sensing scene classification benchmarks, including Aerial Image data set and NWPU-RESISC45 data set to demonstrate the superiority of DMPConv.
Abstract Purpose To examine the effect of diabetes, duration of diabetes, and blood glucose on speech-, low/mid-, and high-frequency hearing loss. Methods In this cross-sectional study, 2821 ...participants aged 20–87 years in the China National Health Survey were included. Diabetes was defined as valid fasting blood glucose (FBG) of ≥ 7.0 mmol/L, a self-reported history of diabetes or the use of anti-diabetic medications. Speech-(500, 1000, 2000, and 4000 Hz), low/mid- (500, 1000 and 2000 Hz), and high-frequency (4000, 6000, and 8000 Hz) hearing loss was defined as pure tone average of responding frequencies > 20 dB HL in the better ear, respectively. Results In fully adjusted models, for speech-, low/mid-, and high-frequency hearing loss, compared with no diabetes, those with diabetes (OR95%CI: 1.44 1.12, 1.86, 1.23 0.94, 1.61, and 1.75 1.28, 2.41, respectively) and with diabetes for > 5 years duration (OR95%CI: 1.63 1.09, 2.42, and 1.63 1.12, 2.36, 2.15 1.25, 3.70, respectively) were at higher risk. High FBG level was associated with a higher risk of speech-, low/ mid-, and high-frequency hearing loss. And there were stronger associations between HL and diabetes, longer duration and higher in “healthier population” (no hypertension, no dyslipidemia and younger age). Conclusion Diabetes, longer duration, and higher FBG level were independently associated with hearing loss for speech-, low/mid- and high-frequency hearing loss, particularly in higher frequency and “healthier population”. Paying more attention to hearing loss in those populations could lower the burden of hearing loss.
People's demand for the decision-making space of opinion expression is getting higher, and the methods to determine the threshold value of current consensus still remain elusive. To deal with large ...and diverse information of users and discuss deeply the threshold in social networks, we establish a new consistency model with a new preference structure. In this paper, the Pythagorean fuzzy numbers (PFNs) are introduced into social network group decision-making for the expression of decision-makers' preference (DMs) and the concepts definition of the distance measurements, consensus index, and threshold indifference curves, respectively. In addition, we establish a Pythagorean fuzzy group consensus model with minimum adjustment through determining the setting rule of threshold value before reaching the consensus. Finally, we use the proposed model to solve the selection of square cabin hospitals.
The performance of semantic segmentation in high-resolution aerial imagery has been improved rapidly through the introduction of deep fully convolutional neural network (FCN). However, due to the ...complexity of object shapes and sizes, the labeling accuracy of small-sized objects and object boundaries still need to be improved. In this paper, we propose a neighboring pixel affinity loss (NPALoss) to improve the segmentation performance of these hard pixels. Specifically, we address the issues of how to determine the classifying difficulty of one pixel and how to get the suitable weight margin between well-classified pixels and hard pixels. Firstly, we convert the first problem into a problem that the pixel categories in the neighborhood are the same or different. Based on this idea, we build a neighboring pixel affinity map by counting the pixel-pair relationships for each pixel in the search region. Secondly, we investigate different weight transformation strategies for the affinity map to explore the suitable weight margin and avoid gradient overflow. The logarithm compression strategy is better than the normalization strategy, especially the common logarithm. Finally, combining the affinity map and logarithm compression strategy, we build NPALoss to adaptively assign different weights for each pixel. Comparative experiments are conducted on the ISPRS Vaihingen dataset and several commonly-used state-of-the-art networks. We demonstrate that our proposed approach can achieve promising results.
With the development of deep learning, remote sensing image scene classification technology has been greatly improved. However, current deep networks used for scene classification usually introduce ...ingenious extra modules to fit the characteristics of remote sensing images. It causes a high labor cost and brings more parameters, which makes the network more complicated and poses new intractable problems. In this paper, we rethink this popular “add module” pattern and propose a more lightweight model, called ProbDenseNet (PDN). PDN is obtained via a random search strategy in Neural Architecture Search (NAS) which is an automated network design manner. In our method, all topological connections are assigned importance degrees which subject to a uniform distribution. And we set a regulator to adjust the sparsity of the network. By this way, the design procedure is more automated and the network structure becomes more lightweight. Experimental results on AID benchmark demonstrate that the proposed PDN model can achieve competitive performance even with much fewer parameters. And we also find that excessive connections do not always improve the network’s performance while they can drag down the network’s behavior as well. Furthermore, we conduct experiments on Vaihingen dataset with classical Fully Convolutional Network (FCN) framework. Quantitative and qualitative results both indicate that the features learned by PDN can also transfer in semantic segmentation task.
For HIT (heterojunction with intrinsic thin-layer) solar cell with Al back surface field on p-type Si substrate, the impacts of substrate resistivity on the solar cell performance were investigated ...by utilizing AFORS-HET software as a numerical computer simulation tool. The results show that the optimized substrate resistivity (
R
op) to obtain the maximal solar cell efficiency is relative to the bulk defect density, such as oxygen defect density (
D
od), in the substrate and the interface defect density (
D
it) on the interface of amorphous/crystalline Si heterojunction. The larger
D
od or
D
it is, the higher
R
op is. The effect of
D
it is more obvious.
R
op is about 0.5
Ω
cm for
D
it
=
1.0
×
10
11/cm
2, but is higher than 1.0
Ω
cm for
D
it
=
1.0
×
10
12/cm
2. In order to obtain very excellent solar cell performance, Si substrate, with the resistivity of 0.5
Ω
cm,
D
od lower than 1.0
×
10
10/cm
3, and
D
it lower than 1.0
×
10
11/cm
2, is preferred, which is different to the traditional opinion that 1.0
Ω
cm resistivity is the best.
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