Yellow laser light generation by frequency doubling of the output from a master oscillator fiber power amplifier system

We present a power-scalable approach for yellow laser-light generation based on standard Ytterbium (Yb) doped fibers. To force the cavity to lase at 1154 nm, far above the gain-maximum, measures must be taken to fulfill lasing condition and to suppress competing amplified spontaneous emission (ASE) in the high-gain region. To prove the principle we built a fiber-laser cavity and a fiber-amplifier both at 1154 nm. In between cavity and amplifier we suppressed the ASE by 70 dB using a fiber Bragg grating (FBG) based filter. Finally we demonstrated efficient single pass frequency doubling to 577 nm with a periodically poled lithium niobate crystal (PPLN). With our linearly polarized 1154 nm master oscillator power fiber amplifier (MOFA) system we achieved slope efficiencies of more than 15 % inside the cavity and 24 % with the fiber-amplifier. The frequency doubling followed the predicted optimal efficiency achievable with a PPLN crystal. So far we generated 1.5 W at 1154nm and 90 mW at 577 nm. Our MOFA approach for generation of 1154 nm laser radiation is power-scalable by using multi-stage amplifiers and large mode-area fibers and is therefore very promising for building a high power yellow laser-light source of several tens of Watt.

Characterization of the dose distribution in the halo region of a clinical proton pencil beam using emulsion film detectors

Proton therapy is a high precision technique in cancer radiation therapy which allows irradiating the tumor with minimal damage to the surrounding healthy tissues. Pencil beam scanning is the most advanced dose distribution technique and it is based on a variable energy beam of a few millimeters FWHM which is moved to cover the target volume. Due to spurious effects of the accelerator, of dose distribution system and to the unavoidable scattering inside the patient's body, the pencil beam is surrounded by a halo that produces a peripheral dose. To assess this issue, nuclear emulsion films interleaved with tissue equivalent material were used for the first time to characterize the beam in the halo region and to experimentally evaluate the corresponding dose. The high-precision tracking performance of the emulsion films allowed studying the angular distribution of the protons in the halo. Measurements with this technique were performed on the clinical beam of the Gantry1 at the Paul Scherrer Institute. Proton tracks were identified in the emulsion films and the track density was studied at several depths. The corresponding dose was assessed by Monte Carlo simulations and the dose profile was obtained as a function of the distance from the center of the beam spot.

Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)

The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.