IoT and information processing in smart energy applications.

The articles in this special section address smart energy applications from the perspective of the Internet of Things (IoT). For smart grid applications, we need to predict the electrical load so that the underlying smart grid can effectively balance the power supply and demand. In general, predictions are made based on the data obtained using IoT and smart meter technologies. The (IoT) could accelerate establishment of such infrastructures. With IoT technologies, many more devices could be controlled and managed through the Internet; data pertaining to the grid, commercial buildings, and residential premises can readily be collected and utilized. To derive valuable information from the data, further information and data processing become essential

Clinical, biological, thoracolumbar X ray and genetic characteristics of 232 patients with PsA, serum tested for ANA at 1: 100.

<p>Clinical, biological, thoracolumbar X ray and genetic characteristics of 232 patients with PsA, serum tested for ANA at 1: 100.</p

Mean EBV load in RA patients treated with Tocilizumab.

<p>Epstein Barr virus copy number per 500ng PBMC DNA was assayed in 35 patients under tocilizumab. For every patient, EBV load, median and standard deviation are given every 6 months from the start till 36 months. Four patients accepted to be followed for one more year.</p

Mean EBV load in RA patients treated with Abatacept.

<p>Epstein Barr virus copy number per 500ng PBMC DNA was assayed in 55 patients under abatacept. For all patients, EBV load, median and standard deviation are given every 6 months from the beginning till 36 months. Four patients accepted to be followed for one more year.</p

EBV load evolution under abatacept treatment.

<p>Each black circle represents a patient’s EBV load. Red dots indicate mean EBV load every 6 months. Mean EBV load evolution is drawn as a blue line +/- 1 standard deviation (dark grey surface) displaying the LOESS (LOcally wEighted Scatter-plot Smoother) regression.</p

EBV load evolution under tocilizumab treatment.

<p>Each black circle indicates EBV load in one patient. Red dots indicate mean EBV load. Mean EBV load evolution is drawn as a blue line +/- 1 standard deviation (dark grey surface) indicating the LOESS (LOcally wEighted Scatter-plot Smoother) regression.</p

Patients characteristics.

<p>Patients characteristics.</p

Patients' and controls' characteristics.

<p>RF: rheumatoid factor, pos: positive, SE: Shared Epitope, ACPA: anti citrullinated protein antibodies, NT: not tested</p

HLA-DRB1 genotype risk for ACPA positive RA.

<p>Red boxes: OR significantly higher than 1. Green boxes: OR significantly less than 1.</p

HLA-DRB1 allelic frequencies in patients and controls.

*<p>statistically significant.</p><p>ORs and p values by Fisher's test.</p