Measurement of the Neutron-Neutron Scattering Length

The ¹S₀ neutron-neutron scattering length aₙₙ characterizes the two-neutron interaction at low energies and therefore is a fundamental quantity in many broad research fields such as nuclear structure physics. There were numerous attempts to determine the scattering length in the past decades, some of them with contradicting results, also including more recent ones. The precise and accurate measurement of aₙₙ still remains challenging and to this day, in contrast to the proton-proton scattering length, no direct measurement via n-n scattering is feasible. In this work, a new approach to measure aₙₙ is presented that makes use of relativistic radioactive ion beams created at high energies, in order to investigate n-n scattering at low energies. The experiment will be conducted at the "Radioactive Ion Beam Factory" of the research institute RIKEN in Japan and aims at the investigation of the free two-neutron system using the knockout reactions ⁶He(p,pα)2n and t(p,2p)2n. Furthermore, single-neutron events resulting from the d(p,2p)n reaction will serve for calibration and validation purposes. In the case of the ⁶He(p,pα)2n reaction, the n-n scattering length is accessible by comparison of the experimentally determined two-neutron relative-energy spectrum to calculations using the effective field theory for halo nuclei, called "Halo EFT". For the t(p,2p)2n reaction, the corresponding calculations will instead be based on pionless EFT. For the purpose of this experiment, the new high-resolution neutron detector HIME is currently developed and constructed at the "Institut für Kernphysik" in Darmstadt, Germany. A prototype of that detector has already been built at RIKEN. In this work, it was taken in operation and tested with electronics from "Gesellschaft für Schwerionenforschung" in Darmstadt. All reactions will take place in inverse kinematics with a beam energy of about 200 MeV/nucleon, resulting in two-neutron systems that move with relativistic velocity in the laboratory system. Thereby, a nearly constant neutron-detection efficiency in the relative-energy region of interest can be achieved. The measurements will be kinematically complete, which allows for a strong background suppression. In order to reach sufficient resolution, the relative-energy spectrum will be reconstructed by direct invariant-mass measurement, requiring coincident two-neutron detection. The analysis methods for the reconstruction of the primary interaction points in the neutron detector, which have been developed in this work, are tested and discussed with simulated data. Due to the limited resolution, efficiency and acceptance of the experimental setup, the measured relative-energy distribution cannot be compared directly to theoretical calculations. Different approaches of solving this issue are presented and discussed with simulated data as well

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice

Measuring HCC Tumor Size in MRI—The Sequence Matters!

Background: The aim of this paper was to assess and compare the accuracy of common magnetic resonance imaging (MRI) pulse sequences in measuring the lesion sizes of hepatocellular carcinomas (HCCs) with respect to the Milan criteria and histopathology as a standard of reference. Methods: We included 45 patients with known HCC who underwent contrast-enhanced MRI of the liver prior to liver transplantation or tumor resection. Tumor size was assessed pathologically for all patients. The MRI protocol contained axial T2-weighted images as well as T1-weighted imaging sequences before and after application of Gd-EOB-DTPA. Tumor diameters, the sharpness of lesions, and the presence of artifacts were evaluated visually on all available MRI sequences. MRI measurements and pathologically assessed tumor dimensions were correlated using Pearson’s correlation coefficient and Bland–Altman plots. The rate of misclassifications following Milan criteria was assessed. Results: The mean absolute error (in cm) of MRI size measurements in comparison to pathology was the smallest for the hepatobiliary phase T1-weighted acquisition (0.71 ± 0.70 cm, r = 0.96) and largest for the T2w turbo-spin-echo (TSE) sequence (0.85 ± 0.78 cm, r = 0.94). The misclassification rate regarding tumor size under the Milan criteria was lowest for the T2w half-Fourier acquisition single-shot turbo spin-echo sequence and the hepatobiliary phase T1w acquisition (each 8.6%). The highest rate of misclassification occurred in the portal venous phase T1w acquisition and T2w TSE sequence (each 14.3%). Conclusions: The hepatobiliary phase T1-weighted acquisition seems to be most accurate among commonly used MRI sequences for measuring HCC tumor size, resulting in low rates of misclassification with respect to the Milan criteria

Diagnostic Value of CEUS Prompting Liver Biopsy: Histopathological Correlation of Hepatic Lesions with Ambiguous Imaging Characteristics

Background: Contrast-enhanced ultrasound (CEUS) allows for dynamic analysis of vascularization patterns of unclear hepatic lesions. Our study aimed to evaluate the diagnostic performance of CEUS for further characterizing suspicious liver lesions by comparing findings from CEUS examinations with corresponding histopathology. Methods: Between 2005 and 2016, 160 patients with unclear liver lesions underwent CEUS followed by liver biopsy. All examinations were performed by an experienced consultant radiologist (EFSUMB Level 3) and included native B-mode ultrasound, Color Doppler, and CEUS. A second-generation blood pool contrast agent was applied for CEUS. Results: CEUS was successfully performed in all patients without occurrence of any adverse side effects. CEUS showed a sensitivity of 94.5%, a specificity of 70.6%, a true positive rate of 87.3%, and a true negative rate of 85.7% compared to histopathological results as the reference standard. Conclusions: CEUS represents a safe imaging modality with a high diagnostic accuracy in assessing both—benign and malignant—liver lesions compared to corresponding histopathological results

α\alpha-clustering in Heavy Nuclei 112–124^{112–124}Sn Probed with (p,pα)(p,p\alpha ) Reaction

International audienceWe measured the α-clustering strength at the surface of tin isotopes ^112,116,120,124Sn by using quasi-free $(p,p\alpha )$ reaction at RCNP. Formation of α clusters at the surface of tin isotopes was clearly evidenced from our results. Surface α-clustering in heavy nuclei provides a natural explanation for the origin of α particles in α decay, and may also impact the neutron-skin thickness which plays a critical role in constraining the nuclear symmetry energy

α\alpha-clustering at the Surface of Tin Isotopes 112−124^{112−124}Sn Studied with (p,pα)(p, p\alpha) Reaction

International audienceα-clustering strength at the surface of tin isotopes ^112,116,120,124Sn was measured by using quasi-free (p, pα) reaction at RCNP. By measuring the scattered protons and α particles in coincidence, formation of α clusters at the surface of tin isotopes was clearly evidenced. Surface α-clustering in heavy nuclei provides a natural explanation for the origin of α particles in α decay, and may also impact the neutron-skin thickness which plays an important role in constraining the nuclear symmetry energy

Formation of α clusters in dilute neutron-rich matter

International audienceThe surface of neutron-rich heavy nuclei, with a neutron skin created by excess neutrons, provides an important terrestrial model system to study dilute neutron-rich matter. By using quasi-free α cluster–knockout reactions, we obtained direct experimental evidence for the formation of α clusters at the surface of neutron-rich tin isotopes. The observed monotonous decrease of the reaction cross sections with increasing mass number, in excellent agreement with the theoretical prediction, implies a tight interplay between α-cluster formation and the neutron skin. This result, in turn, calls for a revision of the correlation between the neutron-skin thickness and the density dependence of the symmetry energy, which is essential for understanding neutron stars. Our result also provides a natural explanation for the origin of α particles in α decay.</jats:p

NetRank feature selection outperforms standard feature selection methods.

<p>(<b>A</b>) The accuracy of different feature selection methods for predicting patient outcome was tested on the screening dataset. The NetRank feature selection using a transcription factor network is shown in red. For smaller training set sizes, our method is superior to all other feature selection methods, reaching an accuracy of 72% in a Monte Carlo cross-validation. (<b>B</b>) Markers found with NetRank are more accurate than markers described in literature.</p

Clinical characteristics of patients used in this study.

<p>The screening dataset (genome-wide gene expression profiling) comprises 30 samples of surgically resected pancreatic ductal adenocarcinoma from patients without adjuvant chemotherapy. The validation dataset (immunohistochemistry of seven marker candidates) comprises samples from 412 patients, of which 172 had received adjuvant therapy and 240 had not. Significant differences between the adjuvant and no adjuvant therapy subgroups were found for regional lymph nodes status (, Fisher's exact test) and for the stage groupings (, Fisher's exact test). Differences in all other variables were not significant.</p>†<p>Based on postsurgical histopathological assessment (indicated by the p prefix).</p>‡<p>Stage was assessed by the American Joint Committee on Cancer 2006 guidelines.</p