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Kerr-type left-handed metamaterial (LHM) slab is proved to have an effect of focusing paraxial Gaussian beams and changing their waist radius, as conventional lens can do. The expressions for the focusing distance and the spot radius at the focal point are derived by the variational approach. We show that the incident Gaussian beams can be compressed or expanded by a single Kerr LHM slab, according to the sign of the Kerr nonlinearity and the divergence of the incident beam. Especially, it is demonstrated the focusing properties are significantly tuned by the slab thickness, the beam power and the divergence of the incident Gaussian beam.

We propose a scheme for hierarchical quantum information splitting with the recently realized six-photon cluster state (Lu et al. in Nat. Phys. 3:91, 2007), where a Boss distributes a quantum secret (quantum state) to five distant agents who are divided into two grades. Two agents are in the upper grade and three agents are in the lower grade. An agent of the upper grade only needs the collaboration of two of the other four agents for getting the secret, while an agent of the lower grade needs the collaboration of all the other four agents. In other words, the agents of two grades have different authorities to recover Boss's secret.Keywords Quantum secret · Hierarchical splitting · Photonic qubit · Cluster statesThe rapidly growing field of quantum information science is the fruit of the combination of information theory and quantum mechanics. Quantum information processing mainly involves the manipulation and transmission of information with the principle of quantum mechanics. The unique and useful properties of quantum mechanics is the inner reason for that why quantum information theory can implement many information processing tasks that classical information theory cannot achieve. In quantum information science, information is encoded in quantum states, and the information processing is in fact the manipulation and transfer of quantum states. Entanglement, the most intriguing property of quantum mechanics, is the center resource of quantum information science, and plays a powerful role in the transfer of quantum states. One well-known example is the quantum teleportation [1, 2] which utilizes the bipartite or multipartite entangled states to transport an unknown quantum state from one site to another one. However, not all entangled states can be used to implement perfect teleportation, and that whether or not an entangled state can implement teleportation is determined by its entanglement properties [3]. Thus teleportation can also reveal some properties of entangled states, especially multipartite entangled states [4,5]. On the

AimAccurate severity grading of lumbar spine disease by magnetic resonance images (MRIs) plays an important role in selecting appropriate treatment for the disease. However, interpreting these complex MRIs is a repetitive and time-consuming workload for clinicians, especially radiologists. Here, we aim to develop a multi-task classification model based on artificial intelligence for automated grading of lumbar disc herniation (LDH), lumbar central canal stenosis (LCCS) and lumbar nerve roots compression (LNRC) at lumbar axial MRIs.MethodsTotal 15254 lumbar axial T2W MRIs as the internal dataset obtained from the Fifth Affiliated Hospital of Sun Yat-sen University from January 2015 to May 2019 and 1273 axial T2W MRIs as the external test dataset obtained from the Third Affiliated Hospital of Southern Medical University from June 2016 to December 2017 were analyzed in this retrospective study. Two clinicians annotated and graded all MRIs using the three international classification systems. In agreement, these results served as the reference standard; In disagreement, outcomes were adjudicated by an expert surgeon to establish the reference standard. The internal dataset was randomly split into an internal training set (70%), validation set (15%) and test set (15%). The multi-task classification model based on ResNet-50 consists of a backbone network for feature extraction and three fully-connected (FC) networks for classification and performs the classification tasks of LDH, LCCS, and LNRC at lumbar MRIs. Precision, accuracy, sensitivity, specificity, F1 scores, confusion matrices, receiver-operating characteristics and interrater agreement (Gwet k) were utilized to assess the model’s performance on the internal test dataset and external test datasets.ResultsA total of 1115 patients, including 1015 patients from the internal dataset and 100 patients from the external test dataset [mean age, 49 years ± 15 (standard deviation); 543 women], were evaluated in this study. The overall accuracies of grading for LDH, LCCS and LNRC were 84.17% (74.16%), 86.99% (79.65%) and 81.21% (74.16%) respectively on the internal (external) test dataset. Internal and external testing of three spinal diseases showed substantial to the almost perfect agreement (k, 0.67 - 0.85) for the multi-task classification model.ConclusionThe multi-task classification model has achieved promising performance in the automated grading of LDH, LCCS and LNRC at lumbar axial T2W MRIs.

BackgroundThyroid cancer becomes the most common endocrine cancer with the greatest growing incidence in this decade. Sorafenib is a multikinase inhibitor for the treatment of progressive radioactive iodine-refractory differentiated thyroid cancer (DTC), while the off-target toxicity effect is usually inconvenient for patients taking.MethodsIn this study, hollow mesoporous silica nanoparticles (HMSNs) with transferrin modification (Tf-HMSNs) were loaded with sorafenib (sora@Tf-HMSNs) to help targeted delivery of sorafenib. Due to the biocompatible Tf shell, Tf-HMSNs exhibited excellent bio-compatibility and increased intracellular accumulation, which improved the targeting capability to cancer cells in vitro and in vivo.ResultsSora@Tf-HMSNs treatment exhibited the strongest inhibition effect of res-TPC-1 cells and res-BCPAP cells compared with sora@HMSNs and sorafenib groups and induced more cancer cell apoptosis. Finally, Western blot analysis was conducted to check the expression of RAF/MEK/ERK signaling pathway after sorafenib encapsulated Tf-HMSNs treatment.ConclusionOverall, sora@Tf-HMSNs can significantly increase the effective drug concentration in cancer cells and thus enhance the anticancer effect, which are expected to be promising nanocarriers to deliver anticancer drugs for effective and safe therapy for RAI-refractory DTC.