Simple method for sub-diffraction resolution imaging of cellular structures on standard confocal microscopes by three-photon absorption of quantum dots

This study describes a simple technique that improves a recently developed 3D sub-diffraction imaging method based on three-photon absorption of commercially available quantum dots. The method combines imaging of biological samples via tri-exciton generation in quantum dots with deconvolution and spectral multiplexing, resulting in a novel approach for multi-color imaging of even thick biological samples at a 1.4 to 1.9-fold better spatial resolution. This approach is realized on a conventional confocal microscope equipped with standard continuous-wave lasers. We demonstrate the potential of multi-color tri-exciton imaging of quantum dots combined with deconvolution on viral vesicles in lentivirally transduced cells as well as intermediate filaments in three-dimensional clusters of mouse-derived neural stem cells (neurospheres) and dense microtubuli arrays in myotubes formed by stacks of differentiated C2C12 myoblasts.

Joint multiple dictionary learning for tensor sparse coding

Traditional dictionary learning algorithms are used for finding a sparse representation on high dimensional data by transforming samples into a one-dimensional (1D) vector. This 1D model loses the inherent spatial structure property of data. An alternative solution is to employ Tensor Decomposition for dictionary learning on their original structural form —a tensor— by learning multiple dictionaries along each mode and the corresponding sparse representation in respect to the Kronecker product of these dictionaries. To learn tensor dictionaries along each mode, all the existing methods update each dictionary iteratively in an alternating manner. Because atoms from each mode dictionary jointly make contributions to the sparsity of tensor, existing works ignore atoms correlations between different mode dictionaries by treating each mode dictionary independently. In this paper, we propose a joint multiple dictionary learning method for tensor sparse coding, which explores atom correlations for sparse representation and updates multiple atoms from each mode dictionary simultaneously. In this algorithm, the Frequent-Pattern Tree (FP-tree) mining algorithm is employed to exploit frequent atom patterns in the sparse representation. Inspired by the idea of K-SVD, we develop a new dictionary update method that jointly updates elements in each pattern. Experimental results demonstrate our method outperforms other tensor based dictionary learning algorithms.

Cancer patients taking herbal medicines: a review of clinical purposes, associated factors, and perceptions of benefit or harm

Ethnopharmacological relevance: Cancer patients in all cultures are high consumers of herbal medicines (HMs) usually as part of a regime consisting of several complementary and alternative medicine (CAM) modalities, but the type of patient, the reasons for choosing such HM-CAM regimes, and the benefits they perceive from taking them are poorly understood. There are also concerns that local information may be ignored due to language issues. This study investigates aspects of HM-CAM use in cancer patients using two different abstracting sources: Medline, which contains only peer-reviewed studies from SCI journals, and in order to explore whether further data may be available regionally, the Thai national databases of HM and CAM were searched as an example. Materials and methods: the international and Thai language databases were searched separately to identify relevant studies, using key words chosen to include HM use in all traditions. Analysis of these was undertaken to identify socio-demographic and clinical factors, as well as sources of information, which may inform the decision to use HMs. Results: Medline yielded 5,638 records, with 49 papers fitting the criteria for review. The Thai databases yielded 155, with none relevant for review. Factors associated with HM-CAM usage were: a younger age, higher education or economic status, multiple chemotherapy treatment, late stage of disease. The most common purposes for using HM-CAM cited by patients were to improve physical symptoms, support emotional health, stimulate the immune system, improve quality of life, and relieve side-effects of conventional treatment. Conclusions: Several indicators were identified for cancer patients who are most likely to take HM-CAM. However, interpreting the clinical reasons why patients decide to use HM-CAM is hampered by a lack of standard terminology and thematic coding, because patients' own descriptions are too variable and overlapping for meaningful comparison. Nevertheless, fears that the results of local studies published regionally are being missed, at least in the case of Thailand, appeared to be unfounded.

Understanding complexities in international accounting standard setting: agenda entry and the case of IFRS for SMEs

Earlier accounting works have shown that an understanding of agenda entry is critical to better understanding the accounting standards setting process. Consider Walker and Robinson (1993; 1994) and Ryan (1998); and more generally agenda entrance as theorized in Kingdon (2011). In 2003, the IASB placed on its agenda a project to promulgate a standard for small and medium-sized entities (SMEs). This provides our focus. It seemed to be a departure from the IASB’s constitutional focus on capital market participants. Kingdon’s three-streams model of agenda entry helps to identify some of the complexities related to politics and decision making messiness that resulted in a standard setting project for simplified IFRS, misleadingly titled IFRS for SMEs. Complexities relate to the broader international regulatory context, including the boundaries of the IASB’s standard-setting jurisdiction, the role of board members in changing those boundaries, and such sensitivities over the language that the IASB could not agree on a suitably descriptive title. The paper shows similarities with earlier agenda entrance studies by Walker and Robinson (1994) and Ryan (1998). By drawing on interviewees’ recollections and other material it especially reinforces the part played by the nuanced complexities that influence what emerges as an international accounting standard.

Tensor LRR and sparse coding-based subspace clustering

Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data, and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established state-of-the-art methods.