Element specific atom counting for heterogeneous nanostructures: Combining multiple ADF STEM images for simultaneous thickness and composition determination.

In this paper, a methodology is presented to count the number of atoms in heterogeneous nanoparticles based on the combination of multiple annular dark field scanning transmission electron microscopy (ADF STEM) images. The different non-overlapping annular detector collection regions are selected based on the principles of optimal statistical experiment design for the atom-counting problem. To count the number of atoms, the total intensities of scattered electrons for each atomic column, the so-called scattering cross-sections, are simultaneously compared with simulated library values for the different detector regions by minimising the squared differences. The performance of the method is evaluated for simulated Ni@Pt and Au@Ag core-shell nanoparticles. Our approach turns out to be a dose efficient alternative for the investigation of beam-sensitive heterogeneous materials as compared to the combination of ADF STEM and energy dispersive X-ray spectroscopy.

Optimal experiment design for element specific atom counting using multiple annular dark field scanning transmission electron microscopy detectors.

This paper investigates the possible benefits for counting atoms of different chemical nature when analysing multiple 2D scanning transmission electron microscopy (STEM) images resulting from independent annular dark field (ADF) detector regimes. To reach this goal, the principles of statistical detection theory are used to quantify the probability of error when determining the number of atoms in atomic columns consisting of multiple types of elements. In order to apply this theory, atom-counting is formulated as a statistical hypothesis test, where each hypothesis corresponds to a specific number of atoms of each atom type in an atomic column. The probability of error, which is limited by the unavoidable presence of electron counting noise, can then be computed from scattering-cross sections extracted from multiple ADF STEM images. Minimisation of the probability of error as a function of the inner and outer angles of a specified number of independent ADF collection regimes results in optimal experimental designs. Based on simulations of spherical Au@Ag and Au@Pt core-shell nanoparticles, we investigate how the combination of two non-overlapping detector regimes helps to improve the probability of error when unscrambling two types of atoms. In particular, the combination of a narrow low angle ADF detector with a detector formed by the remaining annular collection regime is found to be optimal. The benefit is more significant if the atomic number Z difference becomes larger. In addition, we show the benefit of subdividing the detector regime into three collection areas for heterogeneous nanostructures based on a structure consisting of three types of elements, e.g., a mixture of Au, Ag and Al atoms. Finally, these results are compared with the probability of error resulting when one would ultimately use a pixelated 4D STEM detector and how this could help to further reduce the incident electron dose.

Enhancing Low-Intensity Coaching in Parent Implemented Early Start Denver Model Intervention for Early Autism: A Randomized Comparison Treatment Trial.

Short-term low intensity parent implemented intervention studies for toddlers with autism spectrum disorder (ASD) have found it difficult to demonstrate significantly improved developmental scores or autism severity compared to community treatment. We conducted a randomized comparative intent-to-treat study of a parent implemented intervention to (1) test the effects of an enhanced version on parent and child learning, and (2) evaluate the sensitivity to change of proximal versus distal measures of child behavior. We randomized 45 children with ASD, 12-30 months of age, into one of two versions of parent-implemented Early Start Denver Model (P-ESDM), the basic model, in which we delivered 1.5 h of clinic-based parent coaching weekly, and an enhanced version that contained three additions: motivational interviewing, multimodal learning tools, and a weekly 1.5-h home visit. We delivered the intervention for 12 weeks and measured child and parent change frequently in multiple settings. We found a time-by-group interaction: parents in the enhanced group demonstrated significantly greater gains in interaction skills than did parents in the non-enhanced group. Both interventions were associated with significant developmental acceleration; however, child outcomes did not differ by group. We found a significant relationship between degree of change in parental interaction skill and rate of children's improvement on our proximal measure. Parents in both groups reported satisfaction with the intervention. These findings suggest that parent skills improved more in the enhanced group than the comparison group. Children in the two groups showed similar improvements. Rate of individual parent learning was associated with greater individual child progress on a measure quite proximal to the treatment, though not on standardized assessments.

Prognostic factors in lung cancer with brain metastasis.

BACKGROUND AND PURPOSE: Metastasis to the brain develops in 25% of all patients with lung cancer. Although the outcome is usually poor, there seems to be a subset of patients with favorable prognostic factors who may live longer. Prognostic factors were analyzed retrospectively in 103 patients with brain metastases from lung carcinoma to identify patients who would benefit from more intensive treatment strategies. MATERIALS AND METHODS: Between October 1991 and December 1994, 103 patients with brain metastasis from lung cancer were irradiated with palliative intent. Palliation was defined as 50% or more regression of neurological signs and symptoms 2 weeks after the completion of cranial radiotherapy. Local (related to the lung tumor) symptom status at the time of brain metastasis, the presence of metastases other than brain, multiplicity of brain metastases on CT scan and time of occurrence of brain metastasis were the factors which were evaluated with multivariate analysis. RESULTS: Palliation was accomplished in 85% of cases. Palliation duration ranged from 0.5 to 54 months (median 3 months). The overall median survival was 5 months. Only one patient is under follow-up without any symptoms related to the brain metastasis. According to the multivariate analysis survival was significantly decreased in the presence of symptoms related to the primary tumor (P = 0.001). CONCLUSION: The presence of symptoms related to the primary tumor at the time of brain metastasis is one of the factors that can be used to distinguish patients with a favorable outcome. In patients with favorable prognostic factors and thus longer survival probability, the role of boost dose after whole brain radiotherapy or surgical resection in suitable cases needs to be investigated.