Radiocarbon dating of modern peat profiles: Pre- and post-bomb C-14 variations in the construction of age-depth models

We present studies of 9 modern (up to 400-yr-old) peat sections from Slovenia, Switzerland, Austria, Italy, and Finland. Precise radiocarbon dating of modern samples is possible due to the large bomb peak of atmospheric 14C concentration in 1963 and the following rapid decline in the 14C level. All the analyzed 14C profiles appeared concordant with the shape of the bomb peak of atmospheric 14C concentration, integrated over some time interval with a length specific to the peat section. In the peat layers covered by the bomb peak, calendar ages of individual peat samples could be determined almost immediately, with an accuracy of 23 yr. In the pre-bomb sections, the calendar ages of individual dated samples are determined in the form of multi-modal probability distributions of about 300 yr wide (about AD 16501950). However, simultaneous use of the post-bomb and pre-bomb 14C dates, and lithological information, enabled the rejection of most modes of probability distributions in the pre-bomb section. In effect, precise age-depth models of the post-bomb sections have been extended back in time, into the wiggly part of the 14C calibration curve.

High depth-resolution laser ablation chemical analysis of additive-assisted Cu electroplating for microchip architectures

Laser ablation/ionisation mass spectrometry with a vertical resolution at a nanometre scale was applied for the quantitative characterisation of the chemical composition of additive-assisted Cu electroplated deposits used in the microchip industry. The detailed chemical analysis complements information gathered by optical techniques and allows new insights into the metal deposition process.

Prestimulus default mode activity influences depth of processing and recognition in an emotional memory task

Low self-referential thoughts are associated with better concentration, which leads to deeper encoding and increases learning and subsequent retrieval. There is evidence that being engaged in externally rather than internally focused tasks is related to low neural activity in the default mode network (DMN) promoting open mind and the deep elaboration of new information. Thus, reduced DMN activity should lead to enhanced concentration, comprehensive stimulus evaluation including emotional categorization, deeper stimulus processing, and better long-term retention over one whole week. In this fMRI study, we investigated brain activation preceding and during incidental encoding of emotional pictures and on subsequent recognition performance. During fMRI, 24 subjects were exposed to 80 pictures of different emotional valence and subsequently asked to complete an online recognition task one week later. Results indicate that neural activity within the medial temporal lobes during encoding predicts subsequent memory performance. Moreover, a low activity of the default mode network preceding incidental encoding leads to slightly better recognition performance independent of the emotional perception of a picture. The findings indicate that the suppression of internally-oriented thoughts leads to a more comprehensive and thorough evaluation of a stimulus and its emotional valence. Reduced activation of the DMN prior to stimulus onset is associated with deeper encoding and enhanced consolidation and retrieval performance even one week later. Even small prestimulus lapses of attention influence consolidation and subsequent recognition performance. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc.

Usability and Acceptability of ASSESS MS: Assessment of Motor Dysfunction in Multiple Sclerosis Using Depth-Sensing Computer Vision

Background: Sensor-based recordings of human movements are becoming increasingly important for the assessment of motor symptoms in neurological disorders beyond rehabilitative purposes. ASSESS MS is a movement recording and analysis system being developed to automate the classification of motor dysfunction in patients with multiple sclerosis (MS) using depth-sensing computer vision. It aims to provide a more consistent and finer-grained measurement of motor dysfunction than currently possible. Objective: To test the usability and acceptability of ASSESS MS with health professionals and patients with MS. Methods: A prospective, mixed-methods study was carried out at 3 centers. After a 1-hour training session, a convenience sample of 12 health professionals (6 neurologists and 6 nurses) used ASSESS MS to capture recordings of standardized movements performed by 51 volunteer patients. Metrics for effectiveness, efficiency, and acceptability were defined and used to analyze data captured by ASSESS MS, video recordings of each examination, feedback questionnaires, and follow-up interviews. Results: All health professionals were able to complete recordings using ASSESS MS, achieving high levels of standardization on 3 of 4 metrics (movement performance, lateral positioning, and clear camera view but not distance positioning). Results were unaffected by patients’ level of physical or cognitive disability. ASSESS MS was perceived as easy to use by both patients and health professionals with high scores on the Likert-scale questions and positive interview commentary. ASSESS MS was highly acceptable to patients on all dimensions considered, including attitudes to future use, interaction (with health professionals), and overall perceptions of ASSESS MS. Health professionals also accepted ASSESS MS, but with greater ambivalence arising from the need to alter patient interaction styles. There was little variation in results across participating centers, and no differences between neurologists and nurses. Conclusions: In typical clinical settings, ASSESS MS is usable and acceptable to both patients and health professionals, generating data of a quality suitable for clinical analysis. An iterative design process appears to have been successful in accounting for factors that permit ASSESS MS to be used by a range of health professionals in new settings with minimal training. The study shows the potential of shifting ubiquitous sensing technologies from research into the clinic through a design approach that gives appropriate attention to the clinic environment.

Automatic identification of discourse markers in dialogues: An in-depth study of like and well

The lexical items like and well can serve as discourse markers (DMs), but can also play numerous other roles, such as verb or adverb. Identifying the occurrences that function as DMs is an important step for language understanding by computers. In this study, automatic classifiers using lexical, prosodic/positional and sociolinguistic features are trained over transcribed dialogues, manually annotated with DM information. The resulting classifiers improve state-of-the-art performance of DM identification, at about 90% recall and 79% precision for like (84.5% accuracy, κ = 0.69), and 99% recall and 98% precision for well (97.5% accuracy, κ = 0.88). Automatic feature analysis shows that lexical collocations are the most reliable indicators, followed by prosodic/positional features, while sociolinguistic features are marginally useful for the identification of DM like and not useful for well. The differentiated processing of each type of DM improves classification accuracy, suggesting that these types should be treated individually.

Zwei neue Birnen : Lieffer Seeding u. Directeur Alphand

von F. C. Binz

Design, Calibration, and Evaluation of Depth-of-Interaction-Capable PET Detector Modules

High-resolution, small-bore PET systems suffer from a tradeoff between system sensitivity, and image quality degradation. In these systems long crystals allow mispositioning of the line of response due to parallax error and this mispositioning causes resolution blurring, but long crystals are necessary for high system sensitivity. One means to allow long crystals without introducing parallax errors is to determine the depth of interaction (DOI) of the gamma ray interaction within the detector module. While DOI has been investigated previously, newly available solid state photomultipliers (SSPMs) well-suited to PET applications and allow new modules for investigation. Depth of interaction in full modules is a relatively new field, and so even if high performance DOI capable modules were available, the appropriate means to characterize and calibrate the modules are not. This work presents an investigation of DOI capable arrays and techniques for characterizing and calibrating those modules. The methods introduced here accurately and reliably characterize and calibrate energy, timing, and event interaction positioning. Additionally presented is a characterization of the spatial resolution of DOI capable modules and a measurement of DOI effects for different angles between detector modules. These arrays have been built into a prototype PET system that delivers better than 2.0 mm resolution with a single-sided-stopping-power in excess of 95% for 511 keV g's. The noise properties of SSPMs scale with the active area of the detector face, and so the best signal-to-noise ratio is possible with parallel readout of each SSPM photodetector pixel rather than multiplexing signals together. This work additionally investigates several algorithms for improving timing performance using timing information from multiple SSPM pixels when light is distributed among several photodetectors.

THE MOLECULAR INTERACTION BETWEEN TYPE II DIABETES AND ALZHEIMER’S DISEASE THROUGH CROSS-SEEDING OF PROTEIN MISFOLDING

With the population of the world aging, the prominence of diseases such as Type II Diabetes (T2D) and Alzheimer’s disease (AD) are on the rise. In addition, patients with T2D have an increased risk of developing AD compared to age-matched individuals, and the number of AD patients with T2D is higher than among aged-matched non-AD patients. AD is a chronic and progressive dementia characterized by amyloid-beta (Aβ) plaques, neurofibrillary tangles (NFTs), neuronal loss, brain inflammation, and cognitive impairment. T2D involves the dysfunctional use of pancreatic insulin by the body resulting in insulin resistance, hyperglycemia, hyperinsulinemia, pancreatic beta cell (β-cell) death, and other complications. T2D and AD are considered protein misfolding disorders (PMDs). PMDs are characterized by the presence of misfolded protein aggregates, such as in T2D pancreas (islet amyloid polypeptide - IAPP) and in AD brain (amyloid– Aβ) of affected individuals. The misfolding and accumulation of these proteins follows a seeding-nucleation model where misfolded soluble oligomers act as nuclei to propagate misfolding by recruiting other native proteins. Cross-seeding occurs when oligomers composed by one protein seed the aggregation of a different protein. Our hypothesis is that the pathological interactions between T2D and AD may in part occur through cross-seeding of protein misfolding. To test this hypothesis, we examined how each respective aggregate (Aβ or IAPP) affects the disparate disease pathology through in vitro and in vivo studies. Assaying Aβ aggregates influence on T2D pathology, IAPP+/+/APPSwe+/- double transgenic (DTg) mice exhibited exacerbated T2D-like pathology as seen in elevated hyperglycemia compared to controls; in addition, IAPP levels in the pancreas are highest compared to controls. Moreover, IAPP+/+/APPSwe+/- animals demonstrate abundant plaque formation and greater plaque density in cortical and hippocampal areas in comparison to controls. Indeed, IAPP+/+/APPSwe+/- exhibit a colocalization of both misfolded proteins in cerebral plaques suggesting IAPP may directly interact with Aβ and aggravate AD pathology. In conclusion, these studies suggest that cross-seeding between IAPP and Aβ may occur, and that these protein aggregates exacerbate and accelerate disease pathology, respectively. Further mechanistic studies are necessary to determine how these two proteins interact and aggravate both pancreatic and brain pathologies.