The pleiotropic functions of intracellular hydrophobins in aerial hyphae and fungal spores.

Higher fungi can rapidly produce large numbers of spores suitable for aerial dispersal. The efficiency of the dispersal and spore resilience to abiotic stresses correlate with their hydrophobicity provided by the unique amphiphilic and superior surface-active proteins-hydrophobins (HFBs)-that self-assemble at hydrophobic/hydrophilic interfaces and thus modulate surface properties. Using the HFB-enriched mold Trichoderma (Hypocreales, Ascomycota) and the HFB-free yeast Pichia pastoris (Saccharomycetales, Ascomycota), we revealed that the rapid release of HFBs by aerial hyphae shortly prior to conidiation is associated with their intracellular accumulation in vacuoles and/or lipid-enriched organelles. The occasional internalization of the latter organelles in vacuoles can provide the hydrophobic/hydrophilic interface for the assembly of HFB layers and thus result in the formation of HFB-enriched vesicles and vacuolar multicisternal structures (VMSs) putatively lined up by HFBs. These HFB-enriched vesicles and VMSs can become fused in large tonoplast-like organelles or move to the periplasm for secretion. The tonoplast-like structures can contribute to the maintenance of turgor pressure in aerial hyphae supporting the erection of sporogenic structures (e.g., conidiophores) and provide intracellular force to squeeze out HFB-enriched vesicles and VMSs from the periplasm through the cell wall. We also show that the secretion of HFBs occurs prior to the conidiation and reveal that the even spore coating of HFBs deposited in the extracellular matrix requires microscopic water droplets that can be either guttated by the hyphae or obtained from the environment. Furthermore, we demonstrate that at least one HFB, HFB4 in T. guizhouense, is produced and secreted by wetted spores. We show that this protein possibly controls spore dormancy and contributes to the water sensing mechanism required for the detection of germination conditions. Thus, intracellular HFBs have a range of pleiotropic functions in aerial hyphae and spores and are essential for fungal development and fitness.

Dimethyl Carbonate Synthesis from CO2 over CeO2 with Electron-Enriched Lattice Oxygen Species.

Direct synthesis of dimethyl carbonate (DMC) from CO2 plays an important role in carbon neutrality, but its efficiency is still far from the practical application, due to the limited understanding of the reaction mechanism and rational design of efficient catalyst. Herein, abundant electron-enriched lattice oxygen species were introduced into CeO2 catalyst by constructing the point defects and crystal-terminated phases in the crystal reconstruction process. Benefitting from the acid-base properties modulated by the electron-enriched lattice oxygen, the optimized CeO2 catalyst exhibited a much higher DMC yield of 22.2 mmol g-1 than the reported metal-oxide-based catalysts at the similar conditions. Mechanistic investigations illustrated that the electron-enriched lattice oxygen can provide abundant sites for CO2 adsorption and activation, and was advantageous of the formation of the weakly adsorbed active methoxy species. These were facilitating to the coupling of methoxy and CO2 for the key *CH3OCOO intermediate formation. More importantly, the weakened adsorption of *CH3OCOO on the electron-enriched lattice oxygen can switch the rate-determining-step (RDS) of DMC synthesis from *CH3OCOO formation to *CH3OCOO dissociation, and lower the corresponding activation barriers, thus giving rise to a high performance. This work provides insights into the underlying reaction mechanism for DMC synthesis from CO2 and methanol and the design of highly efficient catalysts.

Insights into the Diffusion Behaviors of Water over Hydrophilic/Hydrophobic Catalysts During the Conversion of Syngas to High-Quality Gasoline.

Although considerable efforts towards directly converting syngas to liquid fuels through Fischer-Tropsch synthesis have been made, developing catalysts with low CO2 selectivity for the synthesis of high-quality gasoline remains a big challenge. Herein, we designed a bifunctional catalyst composed of hydrophobic FeNa@Si-c and HZSM-5 zeolite, which exhibited a low CO2 selectivity of 14.3 % at 49.8 % CO conversion, with a high selectivity of 62.5 % for gasoline in total products. Molecular dynamic simulations and model experiments revealed that the diffusion of water molecules through hydrophilic catalyst was bidirectional, while the diffusion through hydrophobic catalyst was unidirectional, which were crucial to tune the water-gas shift reaction and control CO2 formation. This work provides a new fundamental understanding about the function of hydrophobic modification of catalysts in syngas conversion.

Impact of Paenarthrobacter ureafaciens ZF1 on the soil enzyme activity and microbial community during the bioremediation of atrazine-contaminated soils.

Bioremediation of atrazine-contaminated soil is considered a safe and effective approach in removing contaminates from the soil. However, the effects of adding foreign organisms to assist bioremediation on soil environmental quality and ecosystem are unclear. Here, the ecological remediation potential of strain Paenarthrobacter ureafaciens ZF1 on atrazine-contaminated soil was investigated through miniature experiments using variations in soil enzymes and bacterial communities as indicators. The results showed that strain ZF1 accelerated atrazine degradation, which could completely degrade atrazine at concentrations of 100 mg·L- 1 atrazine within 2 h in liquid medium and could remove up to 99.3% of atrazine (100 mg·kg- 1 in soil) within 6 days. During soil bioremediation, atrazine promoted the activities of urease and cellulase, and inhibited the activities of sucrase and catalase, while the strain ZF1 significantly promoted the activities of these four enzymes. High-throughput sequencing of 16S rRNA genes showed that ZF1 affected the relative abundance and bacterial community structure, and promoted bacterial diversity and evenness. Furthermore, redundancy analysis revealed a certain correlation among the strain ZF1, atrazine residue, soil enzyme activity, and soil bacterial community. The strain ZF1 in this work demonstrated remarkable potential for ecological restoration, and can be an effective and environmentally friendly alternative in remediating atrazine-contaminated soil.

Unsupervised despeckling of optical coherence tomography images by combining cross-scale CNN with an intra-patch and inter-patch based transformer.

Optical coherence tomography (OCT) has found wide application to the diagnosis of ophthalmic diseases, but the quality of OCT images is degraded by speckle noise. The convolutional neural network (CNN) based methods have attracted much attention in OCT image despeckling. However, these methods generally need noisy-clean image pairs for training and they are difficult to capture the global context information effectively. To address these issues, we have proposed a novel unsupervised despeckling method. This method uses the cross-scale CNN to extract the local features and uses the intra-patch and inter-patch based transformer to extract and merge the local and global feature information. Based on these extracted features, a reconstruction network is used to produce the final denoised result. The proposed network is trained using a hybrid unsupervised loss function, which is defined by the loss produced from Nerighbor2Neighbor, the structural similarity between the despeckled results of the probabilistic non-local means method and our method as well as the mean squared error between their features extracted by the VGG network. Experiments on two clinical OCT image datasets show that our method performs better than several popular despeckling algorithms in terms of visual evaluation and quantitative indexes.

At least three families of hyphosphere small secreted cysteine-rich proteins can optimize surface properties to a moderately hydrophilic state suitable for fungal attachment.

The secretomes of filamentous fungi contain a diversity of small secreted cysteine-rich proteins (SSCPs) that have a variety of properties ranging from toxicity to surface activity. Some SSCPs are recognized by other organisms as indicators of fungal presence, but their function in fungi is not fully understood. We detected a new family of fungal surface-active SSCPs (saSSCPs), here named hyphosphere proteins (HFSs). An evolutionary analysis of the HFSs in Pezizomycotina revealed a unique pattern of eight single cysteine residues (C-CXXXC-C-C-C-C-C) and a long evolutionary history of multiple gene duplications and ancient interfungal lateral gene transfers, suggesting their functional significance for fungi with different lifestyles. Interestingly, recombinantly produced saSSCPs from three families (HFSs, hydrophobins and cerato-platanins) showed convergent surface-modulating activity on glass and on poly(ethylene-terephthalate), transforming their surfaces to a moderately hydrophilic state, which significantly favoured subsequent hyphal attachment. The addition of purified saSSCPs to the tomato rhizosphere had mixed effects on hyphal attachment to roots, while all tested saSSCPs had an adverse effect on plant growth in vitro. We propose that the exceptionally high diversity of saSSCPs in Trichoderma and other fungi evolved to efficiently condition various surfaces in the hyphosphere to a fungal-beneficial state.

Isolation and Identification of Pseudomonas sp. Strain DY-1 from Agricultural Soil and Its Degradation Effect on Prometryne.

Prometryne is a widely used herbicide in China to control annual grasses and broadleaf weeds. However, the stability of prometryne makes it difficult to be degraded, which poses a threat to human health. This study presents a bacterial strain isolated from soil samples with a prometryne application history, designated strain DY-1. Strain DY-1, identified as Pseudomonas sp., is capable of utilizing prometryne as a sole carbon source for growth and degrading 100% of prometryne within 48 h from an initial concentration of 50 mg L-1. To further optimize the degradation of prometryne, the prometryne concentration, temperature, pH, and salt concentration were examined. The optimal conditions for degradation of prometryne by strain DY-1 were an initial prometryne concentration of 50 mg L-1, 30 °C, pH 7-8, and NaCl concentration of 200 mg L-1. The same strain also degraded other s-triazine herbicides, including simetryne, ametryne, desmetryne, and metribuzin, under the same conditions. The biodegradation pathway of prometryne was established by isolating sulfoxide prometryne as the first metabolite and by the identification of sulfone prometryne and 2-hydroxy prometryne by liquid chromatography-mass spectrometry (LC-MS/MS). The results illustrated that strain DY-1 achieved the removal of prometryne by gradually oxidizing and hydrolyzing the methylthio groups. A bioremediation trial with contaminated soil and pot experiments showed that after treating the prometryne-contaminated soil with strain DY-1, the content of prometryne was significantly reduced (P < 0.05). This study provides an efficient bacterial strain and approach that could be potentially useful for detoxification and bioremediation of prometryne analogs.

Cloning and characterization of the Cry79Aa1 gene from a lepidopteran active strain of Bacillus thuringiensis.

Bacillus thuringiensis (Bt) has been used globally as a biopesticide for effective and environmentally friendly pest control. Research has intensified following the development of resistance by lepidopteran species to Bt insecticidal crystal proteins. Discovering new Bt strains with novel toxin properties which can overcome resistance is one of the strategies to improve pesticide sustainability. The genome of the Bacillus thuringiensis LTS290 strain was sequenced and assembled in 252 contigs containing a total of 6,391,328 bp. The novel cry79Aa1 gene from this strain was identified and cloned. Cry79Aa1 contains 729 amino acid residues and a molecular mass of 84.8 kDa by SDS-PAGE analysis. Cry79Aa1 was found to be active against the lepidopteran larvae of Spodoptera exigua, Helicoverpa armigera, and Plutella xylostella with LC50 values of 13.627 µg/mL, 42.8 µg/mL, and 38.086 µg/mL, respectively. However, Cry79Aa1 protein showed almost no insecticidal activity against Leguminivora glycinivorella, although some degree of growth retardation was observed.

The Evolutionary and Functional Paradox of Cerato-platanins in Fungi.

Cerato-platanins (CPs) form a family of fungal small secreted cysteine-rich proteins (SSCPs) and are of particular interest not only because of their surface activity but also their abundant secretion by fungi. We performed an evolutionary analysis of 283 CPs from 157 fungal genomes with the focus on the environmental opportunistic plant-beneficial and mycoparasitic fungus Trichoderma Our results revealed a long evolutionary history of CPs in Dikarya fungi that have undergone several events of lateral gene transfer and gene duplication. Three genes were maintained in the core genome of Trichoderma, while some species have up to four CP-encoding genes. All Trichoderma CPs evolve under stabilizing natural selection pressure. The functional genomic analysis of CPs in Trichoderma guizhouense and Trichoderma harzianum revealed that only epl1 is active at all stages of development but that it plays a minor role in interactions with other fungi and bacteria. The deletion of this gene results in increased colonization of tomato roots by Trichoderma spp. Similarly, biochemical tests of EPL1 heterologously produced by Pichia pastoris support the claims described above. Based on the results obtained, we conclude that the function of CPs is probably linked to their surfactant properties and the ability to modify the hyphosphere of submerged mycelia and, thus, facilitate the nutritional versatility of fungi. The effector-like functions do not sufficiently describe the diversity and evolution of these proteins in fungi, as they are also maintained, duplicated, or laterally transferred in the genomes of nonherbivore fungi.IMPORTANCE Cerato-platanins (CPs) are surface-active small proteins abundantly secreted by filamentous fungi. Consequently, immune systems of plants and other organisms recognize CPs and activate defense mechanisms. Some CPs are toxic to plants and act as virulence factors in plant-pathogenic fungi. Our analysis, however, demonstrates that the interactions with plants do not explain the origin and evolution of CPs in the fungal kingdom. We revealed a long evolutionary history of CPs with multiple cases of gene duplication and events of interfungal lateral gene transfers. In the mycoparasitic Trichoderma spp., CPs evolve under stabilizing natural selection and hamper the colonization of roots. We propose that the ability to modify the hydrophobicity of the fungal hyphosphere is a key to unlock the evolutionary and functional paradox of these proteins.

Guided filtering-based nonlocal means despeckling of optical coherence tomography images.

This Letter presents a guided filtering (GF)-based nonlocal means (NLM) method for despeckling of optical coherence tomography (OCT) images. Unlike existing NLM methods that determine weights using image intensities or features, the proposed method first uses the GF to capture both grayscale information and features of the input image and then introduces them into the NLM for accurate weight computation. The boosting and iterative strategies are further incorporated to ensure despeckling performance. Experiments on the real OCT images demonstrate that our method outperforms the compared methods by delivering sufficient noise reduction and preserving image details well.

Image Deblurring Using Multi-Stream Bottom-Top-Bottom Attention Network and Global Information-Based Fusion and Reconstruction Network.

Image deblurring has been a challenging ill-posed problem in computer vision. Gaussian blur is a common model for image and signal degradation. The deep learning-based deblurring methods have attracted much attention due to their advantages over the traditional methods relying on hand-designed features. However, the existing deep learning-based deblurring techniques still cannot perform well in restoring the fine details and reconstructing the sharp edges. To address this issue, we have designed an effective end-to-end deep learning-based non-blind image deblurring algorithm. In the proposed method, a multi-stream bottom-top-bottom attention network (MBANet) with the encoder-to-decoder structure is designed to integrate low-level cues and high-level semantic information, which can facilitate extracting image features more effectively and improve the computational efficiency of the network. Moreover, the MBANet adopts a coarse-to-fine multi-scale strategy to process the input images to improve image deblurring performance. Furthermore, the global information-based fusion and reconstruction network is proposed to fuse multi-scale output maps to improve the global spatial information and recurrently refine the output deblurred image. The experiments were done on the public GoPro dataset and the realistic and dynamic scenes (REDS) dataset to evaluate the effectiveness and robustness of the proposed method. The experimental results show that the proposed method generally outperforms some traditional deburring methods and deep learning-based state-of-the-art deblurring methods such as scale-recurrent network (SRN) and denoising prior driven deep neural network (DPDNN) in terms of such quantitative indexes as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) and human vision.

A Novel Reconstruction Method of K-Distributed Sea Clutter with Spatial-Temporal Correlation.

The reconstruction of sea clutter plays an important role in target detection and recognition in a maritime environment. Reproducing the temporal and spatial correlations of real data simultaneously is always a problem in the reconstruction of sea clutter due to the complex coupling between them. In this paper, the spatial-temporal correlated proportional method (STCPM), based on a compound model, is proposed to reconstruct K-distributed sea clutter with correlation characteristics obtained from the real data. The texture component with spatial-temporal correlation is generated by the proportional method and the speckle component with temporal correlation is generated by matrix transformation. Compared with previous methods, the biggest innovation of the STCPM is that it can more accurately generate K-distributed sea clutter with both temporal and spatial correlations. The comparison of the reconstructed and real data demonstrates that the method can reproduce the characteristics of real sea clutter well.

Zone-Shrinking Fresnel Zone Travel-Time Tomography for Sound Speed Reconstruction in Breast USCT.

Many studies have been carried out on ultrasound computed tomography (USCT) for its potential application in breast imaging. The sound speed (SS) image modality in USCT can help doctors diagnose the breast cancer, as the tumor usually has a higher sound speed than normal tissues. Travel time is commonly used to reconstruct SS image. Raypath travel-time tomography (RTT) assumes that the sound wave travels through a raypath. RTT is computationally efficient but with low contrast to noise ratio (CNR). Fresnel zone travel-time tomography (FZTT) is based on the assumption that the sound wave travels through an area called the Fresnel zone. FZTT can provide SS image with high CNR but low accuracy due to the wide Fresnel zone. Here, we propose a zone-shrinking Fresnel zone travel-time tomography (ZSFZTT), where a weighting factor is adopted to shrink the Fresnel zone during the inversion process. Numerical phantom and in vivo breast experiments were performed with ZSFZTT, FZTT, and RTT. In the numerical experiment, the reconstruction biases of size by ZSFZTT, FZTT, and RTT were 0.2%~8.3%, 2.3%~31.7%, and 1.8%~25%; the reconstruction biases of relative SS value by ZSFZTT, FZTT, and RTT were 24.7%~42%, 53%~60.8%, and 30.3%~47.8%; and the CNR by ZSFZTT, FZTT, and RTT were 67.7~96.6, 68.5~98, and 1.7~2.7. In the in vivo breast experiment, ZSFZTT provided the highest CNR of 8.6 compared to 8.1 by FZTT and 1.9 by RTT. ZSFZTT improved the reconstruction accuracy of size and the relative reconstruction accuracy of SS value compared to FZTT and RTT while maintaining a high CNR similar to that of FZTT.

Non-Rigid Multi-Modal 3D Medical Image Registration Based on Foveated Modality Independent Neighborhood Descriptor.

The non-rigid multi-modal three-dimensional (3D) medical image registration is highly challenging due to the difficulty in the construction of similarity measure and the solution of non-rigid transformation parameters. A novel structural representation based registration method is proposed to address these problems. Firstly, an improved modality independent neighborhood descriptor (MIND) that is based on the foveated nonlocal self-similarity is designed for the effective structural representations of 3D medical images to transform multi-modal image registration into mono-modal one. The sum of absolute differences between structural representations is computed as the similarity measure. Subsequently, the foveated MIND based spatial constraint is introduced into the Markov random field (MRF) optimization to reduce the number of transformation parameters and restrict the calculation of the energy function in the image region involving non-rigid deformation. Finally, the accurate and efficient 3D medical image registration is realized by minimizing the similarity measure based MRF energy function. Extensive experiments on 3D positron emission tomography (PET), computed tomography (CT), T1, T2, and PD weighted magnetic resonance (MR) images with synthetic deformation demonstrate that the proposed method has higher computational efficiency and registration accuracy in terms of target registration error (TRE) than the registration methods that are based on the hybrid L-BFGS-B and cat swarm optimization (HLCSO), the sum of squared differences on entropy images, the MIND, and the self-similarity context (SSC) descriptor, except that it provides slightly bigger TRE than the HLCSO for CT-PET image registration. Experiments on real MR and ultrasound images with unknown deformation have also be done to demonstrate the practicality and superiority of the proposed method.

Laplacian Eigenmaps Network-Based Nonlocal Means Method for MR Image Denoising.

Magnetic resonance (MR) images are often corrupted by Rician noise which degrades the accuracy of image-based diagnosis tasks. The nonlocal means (NLM) method is a representative filter in denoising MR images due to its competitive denoising performance. However, the existing NLM methods usually exploit the gray-level information or hand-crafted features to evaluate the similarity between image patches, which is disadvantageous for preserving the image details while smoothing out noise. In this paper, an improved nonlocal means method is proposed for removing Rician noise in MR images by using the refined similarity measures. The proposed method firstly extracts the intrinsic features from the pre-denoised image using a shallow convolutional neural network named Laplacian eigenmaps network (LEPNet). Then, the extracted features are used for computing the similarity in the NLM method to produce the denoised image. Finally, the method noise of the denoised image is utilized to further improve the denoising performance. Specifically, the LEPNet model is composed of two cascaded convolutional layers and a nonlinear output layer, in which the Laplacian eigenmaps are employed to learn the filter bank in the convolutional layers and the Leaky Rectified Linear Unit activation function is used in the final output layer to output the nonlinear features. Due to the advantage of LEPNet in recovering the geometric structure of the manifold in the low-dimension space, the features extracted by this network can facilitate characterizing the self-similarity better than the existing NLM methods. Experiments have been performed on the BrainWeb phantom and the real images. Experimental results demonstrate that among several compared denoising methods, the proposed method can provide more effective noise removal and better details preservation in terms of human vision and such objective indexes as peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).

PCANet-Based Structural Representation for Nonrigid Multimodal Medical Image Registration.

Nonrigid multimodal image registration remains a challenging task in medical image processing and analysis. The structural representation (SR)-based registration methods have attracted much attention recently. However, the existing SR methods cannot provide satisfactory registration accuracy due to the utilization of hand-designed features for structural representation. To address this problem, the structural representation method based on the improved version of the simple deep learning network named PCANet is proposed for medical image registration. In the proposed method, PCANet is firstly trained on numerous medical images to learn convolution kernels for this network. Then, a pair of input medical images to be registered is processed by the learned PCANet. The features extracted by various layers in the PCANet are fused to produce multilevel features. The structural representation images are constructed for two input images based on nonlinear transformation of these multilevel features. The Euclidean distance between structural representation images is calculated and used as the similarity metrics. The objective function defined by the similarity metrics is optimized by L-BFGS method to obtain parameters of the free-form deformation (FFD) model. Extensive experiments on simulated and real multimodal image datasets show that compared with the state-of-the-art registration methods, such as modality-independent neighborhood descriptor (MIND), normalized mutual information (NMI), Weber local descriptor (WLD), and the sum of squared differences on entropy images (ESSD), the proposed method provides better registration performance in terms of target registration error (TRE) and subjective human vision.

The Design and Analysis of Split Row-Column Addressing Array for 2-D Transducer.

For 3-D ultrasound imaging, the row-column addressing (RCA) with 2N connections for an N × N 2-D array makes the fabrication and interconnection simpler than the fully addressing with N² connections. However, RCA degrades the image quality because of defocusing in signal channel direction in the transmit event. To solve this problem, a split row-column addressing scheme (SRCA) is proposed in this paper. Rather than connecting all the elements in the signal channel direction together, this scheme divides the elements in the signal channel direction into several disconnected blocks, thus enables focusing beam access in both signal channel and switch channel directions. Selecting an appropriate split scheme is the key for SRCA to maintaining a reasonable tradeoff between the image quality and the number of connections. Various split schemes for a 32 × 32 array are fully investigated with point spread function (PSF) analysis and imaging simulation. The result shows the split scheme with five blocks (4, 6, 12, 6, and 4 elements of each block) can provide similar image quality to fully addressing. The splitting schemes for different array sizes from 16 × 16 to 96 × 96 are also discussed.

[Research on Reconstruction of Ultrasound Diffraction Tomography Based on Compressed Sensing].

Ultrasound diffraction tomography (UDT) possesses the characteristics of high resolution, sensitive to dense tissue, and has high application value in clinics. To suppress the artifact and improve the quality of reconstructed image, classical interpolation method needs to be improved by increasing the number of projections and channels, which will increase the scanning time and the complexity of the imaging system. In this study, we tried to accurately reconstruct the object from limited projection based on compressed sensing. Firstly, we illuminated the object from random angles with limited number of projections. Then we obtained spatial frequency samples through Fourier diffraction theory. Secondly, we formulated the inverse problem of UDT by exploring the sparsity of the object. Thirdly, we solved the inverse problem by conjugate gradient method to reconstruct the object. We accurately reconstructed the object using the proposed method. Not only can the proposed method save scanning time to reduce the distortion by respiratory movement, but also can reduce cost and complexity of the system. Compared to the interpolation method, our method can reduce the reconstruction error and improve the structural similarity.

Electrodeposition technique-dependent photoelectrochemical and photocatalytic properties of an In2S3/TiO2 nanotube array.

Electrodeposition is a very versatile tool to fabricate multicomponent TiO2 nanotube array (NTA) composites. However, the understanding of the correlation between the component structure and the fabrication technique has not been clearly investigated yet, though it has been observed that the performance of composites is bound up with the component structure. In this work, the photoelectrochemical properties of In2S3-TiO2 NTA composites prepared by CV electrodeposition, potentiostatic electrodeposition and pulse electrodeposition, respectively, were investigated. The results revealed that the as-prepared photoelectrodes exhibited electrodeposition technique-dependent properties, and the pulse prepared In2S3-TiO2 yielded the highest and stable photocurrent response, consequently exhibiting a superior photocatalytic activity in the degradation of p-nitrophenol (PNP). This may be attributed to the homogeneous, ultra-fine structure of In2S3 nanoparticles (NPs), which brings about a high charge separation efficiency. Furthermore, the trapping tests showed that both radicals and holes were the main active species in the photocatalytic degradation of PNP. This work not only provided a firm basis for maximizing photocatalytic activity via tuning fabrication techniques but also gave a deep insight into the photocatalytic mechanism.

[A three-dimensional transrectal ultrasound imaging system].

A three-dimensional (3D) transrectal ultrasound (TRUS) imaging system is presented in this paper. The 3D imaging system is used for diagnosing diseases of prostate. The 3D image is reconstructed by a series of two-dimensional image data which is obtained through rectum. It can be a guide to prostate needle biopsies. The system is built by two parts: hardware and software. In the hardware, the mechanical device, stepper motor, control circuit, B Mode TRUS and personal computer (PC) workshop are presented. The software includes the firmware of micro control unit and software of the PC workshop. In order to evaluate the performance of the 3D imaging system, we did experiments with water and agar phantoms, and the results demonstrated the system's ability of 3D imaging with high-precision.