Multivariable Scaling for the Anomalous Hall Effect

We derive a general scaling relation for the anomalous Hall effect in ferromagnetic metals involving multiple competing scattering mechanisms, described by a quadratic hypersurface in the space spanned by the partial resistivities. We also present experimental findings, which show strong deviation from previously found scaling forms when different scattering mechanism compete in strength but can be nicely explained by our theory

The Influence Mechanism of Overseas Investment Bank Rating On Stock Fluctuation of Chinese Internet Enterprises in a Credit Crisis

Whether the efficiency information of China\u27s Internet enterprises which are listed overseas can be effectively transferred to capital market during a credit crisis, the rating information provided by investment banks should be a crucial bridge for listed firms and investors. In order to probe the influence mechanism of the rating information provided by investment bank, we choose Chinese concept stocks related to a credit crisis in the United States capital market in 2011 to do our empirical research. Our study found that, the timing of release of rating reports, target stock price and enterprise target market play significant influence on the fluctuation of stock prices, and the ranking of investment bank has played an important moderating role

Spectroscopic data de-noising via training-set-free deep learning method

De-noising plays a crucial role in the post-processing of spectra. Machine learning-based methods show good performance in extracting intrinsic information from noisy data, but often require a high-quality training set that is typically inaccessible in real experimental measurements. Here, using spectra in angle-resolved photoemission spectroscopy (ARPES) as an example, we develop a de-noising method for extracting intrinsic spectral information without the need for a training set. This is possible as our method leverages the self-correlation information of the spectra themselves. It preserves the intrinsic energy band features and thus facilitates further analysis and processing. Moreover, since our method is not limited by specific properties of the training set compared to previous ones, it may well be extended to other fields and application scenarios where obtaining high-quality multidimensional training data is challenging

Local generation of hydrogen for enhanced photothermal therapy.

By delivering the concept of clean hydrogen energy and green catalysis to the biomedical field, engineering of hydrogen-generating nanomaterials for treatment of major diseases holds great promise. Leveraging virtue of versatile abilities of Pd hydride nanomaterials in high/stable hydrogen storage, self-catalytic hydrogenation, near-infrared (NIR) light absorption and photothermal conversion, here we utilize the cubic PdH0.2 nanocrystals for tumour-targeted and photoacoustic imaging (PAI)-guided hydrogenothermal therapy of cancer. The synthesized PdH0.2 nanocrystals have exhibited high intratumoural accumulation capability, clear NIR-controlled hydrogen release behaviours, NIR-enhanced self-catalysis bio-reductivity, high NIR-photothermal effect and PAI performance. With these unique properties of PdH0.2 nanocrystals, synergetic hydrogenothermal therapy with limited systematic toxicity has been achieved by tumour-targeted delivery and PAI-guided NIR-controlled release of bio-reductive hydrogen as well as generation of heat. This hydrogenothermal approach has presented a cancer-selective strategy for synergistic cancer treatment

Creating hospital-specific customized clinical pathways by applying semantic reasoning to clinical data

AbstractObjectiveClinical pathways (CPs) are widely studied methods to standardize clinical intervention and improve medical quality. However, standard care plans defined in current CPs are too general to execute in a practical healthcare environment. The purpose of this study was to create hospital-specific personalized CPs by explicitly expressing and replenishing the general knowledge of CPs by applying semantic analysis and reasoning to historical clinical data.MethodsA semantic data model was constructed to semantically store clinical data. After querying semantic clinical data, treatment procedures were extracted. Four properties were self-defined for local ontology construction and semantic transformation, and three Jena rules were proposed to achieve error correction and pathway order recognition. Semantic reasoning was utilized to establish the relationship between data orders and pathway orders.ResultsA clinical pathway for deviated nasal septum was used as an example to illustrate how to combine standard care plans and practical treatment procedures. A group of 224 patients with 11,473 orders was transformed to a semantic data model, which was stored in RDF format. Long term order processing and error correction made the treatment procedures more consistent with clinical practice. The percentage of each pathway order with different probabilities was calculated to declare the commonality between the standard care plans and practical treatment procedures. Detailed treatment procedures with pathway orders, deduced pathway orders, and orders with probability greater than 80% were provided to efficiently customize the CPs.ConclusionsThis study contributes to the practical application of pathway specifications recommended by the Ministry of Health of China and provides a generic framework for the hospital-specific customization of standard care plans defined by CPs or clinical guidelines

HPLC/QTOF-MS metabolomics analysis applied to identify skin biomarkers of UVC-induced skin injury in mice and preventive effects of abietic acid

Abietic acid (AA) is a main constituent from pine resin, which has definite therapeutical effects for treating skin ulcers and tumor. Here, we explored the metabolome changes in skin tissues of mice with UVC-induced skin injury treated with AA by a HPLC-QTOF-MS/MS method. Model mice were induced with UVC irradiation. Skin histopathological changes were examined by routine HE staining. Metabolomic analysis technology and pattern recognition statistical method were applied to analyze the metabolites in the skin tissues of mice to study the therapeutic effect of AA on UVC-induced skin injury in mice. Ceramides, sphingosines, glycyl-L-glutamine, dihydroorotic acid, adenosine, dCMP and lyso-phosphatidylcholines can be used as biomarkers of UVC-induced skin injury. AA can improve the pathological tissue from the pathway of skin lipid and purine pyrimidine metabolism to achieve the therapeutic effect. AA can effectively treat UVC-induced skin injury in mice