Cryogenic micro-calorimeters for mass spectrometric identification of neutral molecules and molecular fragments

We have systematically investigated the energy resolution of a magnetic micro-calorimeter (MMC) for atomic and molecular projectiles at impact energies ranging from $E\approx13$ to 150 keV. For atoms we obtained absolute energy resolutions down to $\Delta E \approx 120$ eV and relative energy resolutions down to $\Delta E/E\approx10^{-3}$. We also studied in detail the MMC energy-response function to molecular projectiles of up to mass 56 u. We have demonstrated the capability of identifying neutral fragmentation products of these molecules by calorimetric mass spectrometry. We have modeled the MMC energy-response function for molecular projectiles and conclude that backscattering is the dominant source of the energy spread at the impact energies investigated. We have successfully demonstrated the use of a detector absorber coating to suppress such spreads. We briefly outline the use of MMC detectors in experiments on gas-phase collision reactions with neutral products. Our findings are of general interest for mass spectrometric techniques, particularly for those desiring to make neutral-particle mass measurements

Characterization of the 163^{163}Ho Electron Capture Spectrum: A Step Towards the Electron Neutrino Mass Determination

The isotope $^{163}$Ho is in many ways the best candidate to perform experiments to investigate the value of the electron neutrino mass. It undergoes an electron capture process to $^{163}$Dy with an energy available to the decay, Q$_{EC}$, of about 2.8 keV. According to the present knowledge, this is the lowest Q$_{EC}$ value for such transitions. Here we discuss a newly obtained spectrum of $^{163}$Ho, taken by cryogenic metallic magnetic calorimeters with $^{163}$Ho implanted in the absorbers and operated in anticoincident mode for background reduction. For the first time, the atomic deexcitation of the $^{163}$Dy daughter atom following the capture of electrons from the 5s shell in $^{163}$Ho, the OI line, was observed with a calorimetric measurement. The peak energy is determined to be 48 eV. In addition, a precise determination of the energy available for the decay Q$_{EC}$=(2.858±0.010$_{stat}$±0.05$_{syst}$)  keV was obtained by analyzing the intensities of the lines in the spectrum. This value is in good agreement with the measurement of the mass difference between $^{163}$Ho and $^{163}$Dy obtained by Penning-trap mass spectrometry, demonstrating the reliability of the calorimetric technique

Multichannel read-out for arrays of metallic magnetic calorimeters

Metallic magnetic micro-calorimeters (MMCs) operated at millikelvin temperature offer the possibility to achieve eV-scale energy resolution with high stopping power for X-rays and massive particles in an energy range up to several tens of keV. This motivates their use in a wide range of applications in fields as particle physics, atomic and molecular physics. Present detector systems consist of MMC arrays read out by 32 two-stage SQUID read-out channels. In contrast to the design of the detector array and consequently the design of the front-end SQUIDs, which need to be optimised for the physics case and the particles to be detected in a given experiment, the read-out chain can be standardised. We present our new standardised 32-channel parallel read-out for the operation of MMC arrays to be operated in a dilution refrigerator. The read-out system consists of a detector module, whose design depends on the particular application, an amplifier module, ribbon cables from room temperature to the millikelvin platform and a data acquisition system. In particular, we describe the realisation of the read-out system prepared for the ECHo-1k experiment for the operation of two 64-pixel arrays. The same read-out concept is also used for the maXs detector systems, developed for the study of the de-excitation of highly charged heavy ions by X-rays, as well as for the MOCCA system, developed for the energy and position sensitive detection of neutral molecular fragments for the study of fragmentation when molecular ions recombine with electrons. The choice of standard modular components for the operation of 32-channel MMC arrays offer the flexibility to upgrade detector modules without the need of any changes in the read-out system and the possibility to individually exchange parts in case of damages or failures

Role of the progesterone receptor for paclitaxel resistance in primary breast cancer

Paclitaxel plays an important role in the treatment of primary breast cancer. However, a substantial proportion of patients treated with paclitaxel does not appear to derive any benefit from this therapy. We performed a prospective study using tumour cells isolated from 50 primary breast carcinomas. Sensitivity of primary tumour cells to paclitaxel was determined in a clinically relevant range of concentrations (0.85–27.2 μg ml−1 paclitaxel) using an ATP assay. Chemosensitivity data were used to study a possible association with immunohistochemically determined oestrogen and progesterone receptor (ER and PR) status, as well as histopathological parameters. Progesterone receptor (PR) mRNA expression was also determined by quantitative RT–PCR. We observed a clear association of the PR status with chemosensitivity to paclitaxel. Higher levels of immunohistochemically detected PR expression correlated with decreased chemosensitivity (P=0.008). Similarly, high levels of PR mRNA expression were associated with decreased paclitaxel chemosensitivity (P=0.007). Cells from carcinomas with T-stages 3 and 4 were less sensitive compared to stages 1 and 2 (P=0.013). Multiple regression analysis identified PR receptor status and T-stage as independent predictors of paclitaxel chemosensitivity, whereas the ER, N-stage, grading and age were not influential. In conclusion, in vitro sensitivity to paclitaxel was higher for PR-negative compared with PR-positive breast carcinoma cells. Thus, PR status should be considered as a possible factor of influence when designing new trials and chemotherapy protocols

Algorithm for J-Integral Measurements by Digital Image Correlation

The work is devoted to the testing of the algorithm for calculating J-integral based on the construction of vector fields by digital image correlation (DIC) method. A comparative analysis of J-integral values calculated using DIC and instrumental data obtained in accordance with ASTM E 1820 "Standard Test Method for Measurement of Fracture Toughness" has made. It is shown that this approach can be used for cases when the standard technique for measuring the J-integral cannot be applied, or the standard technique does not allow achieving the required accuracy for the integral determination in local areas of the loaded material

Long-term outcome prediction by clinicopathological risk classification algorithms in node-negative breast cancer--comparison between Adjuvant!, St Gallen, and a novel risk algorithm used in the prospective randomized Node-Negative-Breast Cancer-3 (NNBC-3) trial.

Defining risk categories in breast cancer is of considerable clinical significance. We have developed a novel risk classification algorithm and compared its prognostic utility to the Web-based tool Adjuvant! and to the St Gallen risk classification. After a median follow-up of 10 years, we retrospectively analyzed 410 consecutive node-negative breast cancer patients who had not received adjuvant systemic therapy. High risk was defined by any of the following criteria: (i) age <35 years, (ii) grade 3, (iii) human epithelial growth factor receptor-2 positivity, (iv) vascular invasion, (v) progesterone receptor negativity, (vi) grade 2 tumors >2 cm. All patients were also characterized using Adjuvant! and the St Gallen 2007 risk categories. We analyzed disease-free survival (DFS) and overall survival (OS). The Node-Negative-Breast Cancer-3 (NNBC-3) algorithm enlarged the low-risk group to 37% as compared with Adjuvant! (17%) and St Gallen (18%), respectively. In multivariate analysis, both Adjuvant! [P = 0.027, hazard ratio (HR) 3.81, 96% confidence interval (CI) 1.16-12.47] and the NNBC-3 risk classification (P = 0.049, HR 1.95, 95% CI 1.00-3.81) significantly predicted OS, but only the NNBC-3 algorithm retained its prognostic significance in multivariate analysis for DFS (P < 0.0005). The novel NNBC-3 risk algorithm is the only clinicopathological risk classification algorithm significantly predicting DFS as well as OS