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Chalcogenide nanostructures and nanocomposites have been the focus of semiconductor nanomaterial research due to their remarkable optoelectronic and photocatalytic properties and potential application in photodegrading enviromental pollutions. However, currently available synthesizing methods tend to be costly and inefficient. In this paper, we propose a facile two-step solution-phase method to synthesize well-defined monodisperse ZnS-CdS nanocomposites. The morphology and size of ZnS nanoparticles can be easily controlled by adjusting the amount of the source of sulfur. After surface modification with tiny CdS nanoparticles through natural electrostatic attraction, uniform ZnS-CdS nanocomposites are obtained, which has been further confirmed by transmission electron microscopy (TEM) and energy dispersive spectrometry (EDS). The photocatalytic activities of various ZnS samples and ZnS-CdS nanocomposites have been investigated by degrading Rhodamine B under UV-light. Compared with pure ZnS nanoparticles and ZnS powders, the as-obtained ZnS-CdS nanocomposites exhibit excellent photocatalytic performances due to the effective charge separation and increased specific surface area by the attachment of CdS. Moreover, resulting from the effective passivation of surface electronic states, the photoluminescence intensity of the ZnS-CdS nanocomposites is also significantly improved relative to plain ZnS. [ABSTRACT FROM AUTHOR]

Current automation-assisted technologies for screening cervical cancer mainly rely on automated liquid-based cytology slides with proprietary stain. This is not a cost-efficient approach to be utilized in developing countries. In this article, we propose the first automation-assisted system to screen cervical cancer in manual liquid-based cytology (MLBC) slides with hematoxylin and eosin (H&E) stain, which is inexpensive and more applicable in developing countries. This system consists of three main modules: image acquisition, cell segmentation, and cell classification. First, an autofocusing scheme is proposed to find the global maximum of the focus curve by iteratively comparing image qualities of specific locations. On the autofocused images, the multiway graph cut (GC) is performed globally on the a* channel enhanced image to obtain cytoplasm segmentation. The nuclei, especially abnormal nuclei, are robustly segmented by using GC adaptively and locally. Two concave-based approaches are integrated to split the touching nuclei. To classify the segmented cells, features are selected and preprocessed to improve the sensitivity, and contextual and cytoplasm information are introduced to improve the specificity. Experiments on 26 consecutive image stacks demonstrated that the dynamic autofocusing accuracy was 2.06 μm. On 21 cervical cell images with nonideal imaging condition and pathology, our segmentation method achieved a 93% accuracy for cytoplasm, and a 87.3% F-measure for nuclei, both outperformed state of the art works in terms of accuracy. Additional clinical trials showed that both the sensitivity (88.1%) and the specificity (100%) of our system are satisfyingly high. These results proved the feasibility of automation-assisted cervical cancer screening in MLBC slides with H&E stain, which is highly desirable in community health centers and small hospitals. © 2013 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

Recently, virtual staining techniques have attracted more and more attention, which can help bypass the chemical staining process of traditional histopathological examination, saving time and resources. Meanwhile, as an emerging tool to characterize specific tissue structures in a label-free manner, the Mueller matrix microscopy can supplement more structural information that may not be apparent in bright-field images. In this Letter, we propose the Mueller matrix guided generative adversarial networks (MMG-GAN). By integrating polarization information provided by the Mueller matrix microscopy, the MMG-GAN enables the effective transformation of input H&E-stained images into corresponding Masson trichrome (MT)-stained images. The experimental results demonstrate the accuracy of the generated images by MMG-GAN and reveal the potential for more stain transformation tasks by incorporating the Mueller matrix polarization information, laying the foundation for future polarimetry-assisted digital pathology.

Supported metal pair sites have sparked interest due to their tremendous potential as bifunctional catalysts. Here, we report the structural Ni 0 -Ni δ+ pair sites constructed in a well-defined nanocrystal phase of Ni 3 P. These Ni 0 -Ni δ+ pair sites exhibited a remarkable product formation rate of 123 mol BA /mol metal /h for the hydrogenation of benzonitrile (BN) to benzylamine (BA). The heterogeneity of surface Ni atoms over the Ni 3 P crystal created two types of metal centers, Ni 0 and Ni δ+ , with a specific spatial distance of 4-5 Å. The Ni 0 site acted as the center for H 2 activation, while the Ni δ+ site served as the adsorption and activation center for the C ≡ N group. The highly efficient cooperation effect of Ni 0 -Ni δ+ pair sites resulted in a TOF of 2915 h -1 in BN hydrogenation, which is 2.4 and 9.7 times higher than that over the mono-Ni 0 and -Ni δ+ sites, respectively.

For in-vivo polarimetry such as Mueller matrix endoscopy of human internal organ cavities, the complicated undulating tissue surfaces deliver an inescapable occurrence of oblique incidence, which induce a prominent aberration to backscattering tissue polarimetry. In this Letter, we quantitatively analyze such polarimetric aberration on polarization basic parameters derived from the Mueller matrix. A correlation heatmap is obtained as applicable criteria to select an appropriate incident angle for different polarization basic parameters. Based on the analyzing results, we propose two aberration optimization strategies of parameter selection and azimuth rotation, which are suitable for tissue samples with randomly and well-aligned fiber textures, respectively. Both strategies are demonstrated to be effective in the ex-vivo human gastric muscularis tissue experiment. The findings presented in this Letter can be useful to provide accurate polarization imaging results, widely applied on in-vivo polarimetric endoscopy for tissues with complicated surface topography.

Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into "abnormal" and "normal" categories. However, the success of most traditional classification methods relies on the presence of accurate cell segmentations. Despite sixty years of research in this field, accurate segmentation remains a challenge in the presence of cell clusters and pathologies. Moreover, previous classification methods are only built upon the extraction of hand-crafted features, such as morphology and texture. This paper addresses these limitations by proposing a method to directly classify cervical cells-without prior segmentation-based on deep features, using convolutional neural networks (ConvNets). First, the ConvNet is pretrained on a natural image dataset. It is subsequently fine-tuned on a cervical cell dataset consisting of adaptively resampled image patches coarsely centered on the nuclei. In the testing phase, aggregation is used to average the prediction scores of a similar set of image patches. The proposed method is evaluated on both Pap smear and LBC datasets. Results show that our method outperforms previous algorithms in classification accuracy (98.3%), area under the curve (0.99) values, and especially specificity (98.3%), when applied to the Herlev benchmark Pap smear dataset and evaluated using five-fold cross validation. Similar superior performances are also achieved on the HEMLBC (H&E stained manual LBC) dataset. Our method is promising for the development of automation-assisted reading systems in primary cervical screening.