Intentional islanding describes the condition in which a microgrid or a portion of the power grid, which consists of a load and a distributed generation (DG) system, is isolated from the remainder of the utility system. In this situation, it is important for the microgrid to continue to provide adequate power to the load. Under normal operation, each DG inverter system in the microgrid usually works in constant current control mode in order to provide a preset power to the main grid. When the microgrid is cut off from the main grid, each DG inverter system must detect this islanding situation and must switch to a voltage control mode. In this mode, the microgrid will provide a constant voltage to the local load. This paper describes a control strategy that is used to implement grid-connected and intentional-islanding operations of distributed power generation. This paper proposes an intelligent load-shedding algorithm for intentional islanding and an algorithm of synchronization for grid reconnection.

Inhibition of immune checkpoints, including cytotoxic T-lymphocyte antigen-4 (CTLA-4), programmed death-1 (PD-1), and its ligand PD-L1, has demonstrated exciting and durable remissions across a spectrum of malignancies. Combinatorial regimens blocking complementary immune checkpoints further enhance the therapeutic benefit. The activity of these agents for patients with glioblastoma, a generally lethal primary brain tumor associated with significant systemic and microenvironmental immunosuppression, is not known. We therefore systematically evaluated the antitumor efficacy of murine antibodies targeting a broad panel of immune checkpoint molecules, including CTLA-4, PD-1, PD-L1, and PD-L2 when administered as single-agent therapy and in combinatorial regimens against an orthotopic, immunocompetent murine glioblastoma model. In these experiments, we observed long-term tumor-free survival following single-agent anti–PD-1, anti–PD-L1, or anti–CTLA-4 therapy in 50%, 20%, and 15% of treated animals, respectively. Combination therapy of anti–CTLA-4 plus anti–PD-1 cured 75% of the animals, even against advanced, later-stage tumors. In long-term survivors, tumor growth was not seen upon intracranial tumor rechallenge, suggesting that tumor-specific immune memory responses were generated. Inhibitory immune checkpoint blockade quantitatively increased activated CD8+ and natural killer cells and decreased suppressive immune cells in the tumor microenvironment and draining cervical lymph nodes. Our results support prioritizing the clinical evaluation of PD-1, PD-L1, and CTLA-4 single-agent targeted therapy as well as combination therapy of CTLA-4 plus PD-1 blockade for patients with glioblastoma. Cancer Immunol Res; 4(2); 124–35. ©2015 AACR.

We report a pilot study of performing classification of motor imagery for brain-computer interface applications, by means of source analysis of scalp-recorded EEGs. Independent component analysis (ICA) was used as a spatio-temporal filter extracting signal components relevant to left or right motor imagery (MI) tasks. Source analysis methods including equivalent dipole analysis and cortical current density imaging were applied to reconstruct equivalent neural sources corresponding to MI, and classification was performed based on the inverse solutions. The classification was considered correct if the equivalent source was found over the motor cortex in the corresponding hemisphere. A classification rate of about 80% was achieved in the human subject studied using both the equivalent dipole analysis and the cortical current density imaging analysis. The present promising results suggest that the source analysis approach could manifest a clearer picture on the cortical activity, and thus facilitate the classification of MI tasks from scalp EEGs.

In MRI of the human brain, subject motion is a major cause of magnetic resonance image quality degradation. To compensate for the effects of head motion during data acquisition, an in‐bore optical motion tracking system is proposed. The system comprises two MR‐compatible infrared cameras that are fixed on a holder right above and in front of the head coil. The resulting close proximity of the cameras to the object allows precise tracking of its movement. During image acquisition, the MRI scanner uses this tracking information to prospectively compensate for head motion by adjusting the gradient field direction and radio frequency (RF) phases and frequencies. Experiments performed on subjects demonstrate robust system performance with translation and rotation accuracies of 0.1 mm and 0.15°, respectively. Magn Reson Med, 2009.

Electroencephalogram (EEG) recordings during motor imagery tasks are often used as input signals for brain-computer interfaces (BCIs). The translation of these EEG signals to control signals of a device is based on a good classification of various kinds of imagination. We have developed a wavelet-based time-frequency analysis approach for classifying motor imagery tasks. Time-frequency distributions (TFDs) were constructed based on wavelet decomposition and event-related (de)synchronization patterns were extracted from symmetric electrode pairs. The weighted energy difference of the electrode pairs was then compared to classify the imaginary movement. The present method has been tested in nine human subjects and reached an averaged classification rate of 78%. The simplicity of the present technique suggests that it may provide an alternative method for EEG-based BCI applications.

BACKGROUND AND PURPOSE: Patient survival in high-grade glioma remains poor, despite the recent developments in cancer treatment. As new chemo-, targeted molecular, and immune therapies emerge and show promising results in clinical trials, image-based methods for early prediction of treatment response are needed. Deep learning models that incorporate radiomics features promise to extract information from brain MR imaging that correlates with response and prognosis. We report initial production of a combined deep learning and radiomics model to predict overall survival in a clinically heterogeneous cohort of patients with high-grade gliomas. MATERIALS AND METHODS: Fifty patients with high-grade gliomas from our hospital and 128 patients with high-grade glioma from The Cancer Genome Atlas were included. For each patient, we calculated 348 hand-crafted radiomics features and 8192 deep features generated by a pretrained convolutional neural network. We then applied feature selection and Elastic Net-Cox modeling to differentiate patients into long- and short-term survivors. RESULTS: In the 50 patients with high-grade gliomas from our institution, the combined feature analysis framework classified the patients into long- and short-term survivor groups with a log-rank test P value < .001. In the 128 patients from The Cancer Genome Atlas, the framework classified patients into long- and short-term survivors with a log-rank test P value of .014. For the mixed cohort of 50 patients from our institution and 58 patients from The Cancer Genome Atlas, it yielded a log-rank test P value of .035. CONCLUSIONS: A deep learning model combining deep and radiomics features can dichotomize patients with high-grade gliomas into long- and short-term survivors.