The last 15 years have seen a surge of new multi-chip optical and near-IR imagers. While some of them are accompanied by specific reduction pipelines, user-friendly and generic reduction tools are ...uncommon. In this paper I introduce THELI, an easy-to-use graphical interface driving an end-to-end pipeline for the reduction of any optical, near-IR, and mid-IR imaging data. The advantages of THELI when compared to other approaches are highlighted. Combining a multitude of processing algorithms and third party software, THELI provides researchers with a single, homogeneous tool. A short learning curve ensures quick success for new and more experienced observers alike. All tasks are largely automated, while at the same time a high level of flexibility and alternative reduction schemes ensure that widely different scientific requirements can be met. Over 90 optical and infrared instruments at observatories world-wide are pre-configured, while more can be added by the user. The Appendices contain three walk-through examples using public data (optical, near-IR, and mid-IR). Additional extensive documentation for training and troubleshooting is available online.
Starch-rich raw materials are widely used in the food industry. Their functionality and end-use applications are markedly influenced by starch characteristics. Starches with varying amylose (AM) and ...amylopectin (AP) content are of particular interest due to their ability to influence and modify the texture, quality and stability of starch-based food products. The present study shows the influence of the AM/AP content on physicochemical and morphological properties of a range of starches (Maize = 3%, 23%, 71%; Potato = 2%, 21%; Wheat = 28; Barley = 3%, 25% AM content w/w of starch).
Starches have been analyzed in terms of their chemical composition, water retention capacity, morphological characteristics, and pasting/thermal properties. The changes in starch granule morphology during gelatinization were monitored by confocal laser scanning microscopy (CLSM). The different analysis revealed that waxy-starches (AP>90%) had a high water retention capacity (1.2–1.5 times higher) and developed higher paste viscosities (up to 40% for maize; 43% for barley). The swollen granules were highly susceptible to mechanical breakdown and solubilized faster. Higher AM contents showed inhibition of an extensive granule swelling and lowered the paste viscosity. The exceptional integrity of the high-AM starch even prevented its gelatinization at atmospheric pressure. Significant differences in physicochemical and morphological properties between the starches from regular, high-AM and waxy strains have become evident, no direct relationship between the AM/AP contents and the internal growth ring structures of the starch granules could be identified by CLSM. The waxy starches had a higher gelatinization temperature (up to 2 °C) and enthalpy (up to 20%), which indicates a higher crystalline and molecular order.
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► Relationships of the AM/AP content and the chemical, visual, pasting, thermal characteristics. ► Gelatinization behavior of starch granules visualized and analyzed by a micrograph processing. ► Morphological characterization by micrographs analyzes of different starches. ► CLSM micrographs combined with pasting properties and thermal changes starches.
Terrestrial and airborne laser scanning (TLS and ALS) techniques have only recently developed to the point where they allow wide-area measurements of snow distribution in varying terrain. In this ...paper we present multiple TLS measurements showing the snow depth development for a series of precipitation events. We observe that the pattern of maximum accumulation is similar for the two years presented here (correlation up to r = 0.97). Storms arriving from the northwest show persistent snow depth distributions and contribute most to the final accumulation pattern. Snow depth patterns of maximum accumulation for the two years are more similar than the distribution created by any two pairs of individual storms. Based on the strong link between accumulation patterns and terrain, we investigated the ability of a model based on terrain and wind direction to predict accumulation patterns. This approach of Winstral et al. (2002), which describes wind exposure and shelter, was able to predict the general accumulation pattern over scales of slopes but failed to match observed variance. Furthermore, a high sensitivity to the local wind direction was demonstrated. We suggest that Winstral et al.'s model could form a useful tool for application from hydrology and avalanche risk assessment to glaciology.
•Diverse versions of a stochastic model compared in terms of high flow statistics.•Key role of non-linear storage–discharge to reproduce frequencies of high flows.•Importance of studying individual ...recessions to portray the basin hydrologic response.•Emergence of heavy tails linked to non-linearity of catchment response.•Implications for floods, riparian vegetation dynamics and sediment transport.
The right tail of streamflow distributions quantifies the occurrence probability of high flows, which play an important role in the dynamics of many eco-hydrological processes and eventually contribute to shape riverine environments. In this paper, the ability of a mechanistic analytical model for streamflow distributions to capture the statistical features of high flows has been investigated. The model couples a stochastic description of soil moisture dynamics with a simplified hydrologic response based on a catchment-scale storage–discharge relationship. Different types of relations between catchment water storage and discharge have been investigated, and alternative methods for parameter estimation have been compared using informal performance metrics and formal model selection criteria. The study highlights the pivotal role of non-linear storage–discharge relations in reproducing observed frequencies of high flows, and reveals the importance of analyzing the behavior of individual events for a reliable characterization of recession parameters. The emergence of heavy-tailed streamflow distributions is mechanistically linked to the degree of non-linearity of the catchment hydrologic response, with implications for the understanding of rivers’ flooding potential and related ecologic and morphological processes.
Over the past decades, advances in data collection and machine learning have paved the way for the development of autonomous simulation frameworks. Among these, many are capable not only of ...assimilating real‐time data to correct their predictive shortcomings but also of improving their future performance through self‐optimization. In hydrogeology, such techniques harbor great potential for informing sustainable management practices. Simulating the intricacies of groundwater flow requires an adequate representation of unknown, often highly heterogeneous geology. Unfortunately, it is difficult to reconcile the structural complexity demanded by realistic geology with the simplifying assumptions introduced in many calibration methods. The particle filter framework would provide the necessary versatility to retain such complex information but suffers from the curse of dimensionality, a fundamental limitation discouraging its use in systems with many unknowns. Due to the prevalence of such systems in hydrogeology, the particle filter has received little attention in groundwater modeling so far. In this study, we explore the combined use of dimension‐reducing techniques and artificial parameter dynamics to enable a particle filter framework for a groundwater model. Exploiting freedom in the design of the dimension‐reduction approach, we ensure consistency with a predefined geological pattern. The performance of the resulting optimizer is demonstrated in a synthetic test case for three such geological configurations and compared to two Ensemble Kalman Filter setups. Favorable results even for deliberately misspecified settings make us hopeful that nested particle filters may constitute a useful tool for geologically consistent real‐time parameter optimization.
Key Points
This study employs the nested particle filter, an ensemble‐based method for online parameter optimization and data assimilation
Hyperparameterization alleviates the curse of dimensionality, and artificial parameter dynamics rejuvenate the particles
The algorithm allows for fully nonlinear online optimization while honoring predefined geological characterizations
The COVID-19 pandemic has disrupted established care paths worldwide. Patient awareness of the pandemic and executive limitations imposed on public life have changed the perception of when to seek ...care for acute conditions in some cases. We sought to study whether there is a delay in presentation for acute ischemic stroke patients in the first month of the pandemic in the US.
The interval between last-known-well (LKW) time and presentation of 710 consecutive patients presenting with acute ischemic strokes to 12 stroke centers across the US were extracted from a prospectively maintained quality database. We analyzed the timing and severity of the presentation in the baseline period from February to March 2019 and compared results with the timeframe of February and March 2020.
There were 320 patients in the 2-month baseline period in 2019, there was a marked decrease in patients from February to March of 2020 (227 patients in February, and 163 patients in March). There was no difference in the severity of the presentation between groups and no difference in age between the baseline and the COVID period. The mean interval from LKW to the presentation was significantly longer in the COVID period (603±1035 min) compared with the baseline period (442±435 min, P<0.02).
We present data supporting an association between public awareness and limitations imposed on public life during the COVID-19 pandemic in the US and a delay in presentation for acute ischemic stroke patients to a stroke center.
The future of pathology is digital Pallua, J.D.; Brunner, A.; Zelger, B. ...
Pathology, research and practice,
September 2020, 2020-09-00, 20200901, Letnik:
216, Številka:
9
Journal Article
Recenzirano
Odprti dostop
Information, archives, and intelligent artificial systems are part of everyday life in modern medicine. They already support medical staff by mapping their workflows with shared availability of ...cases’ referral information, as needed for example, by the pathologist, and this support will be increased in the future even more.
In radiology, established standards define information models, data transmission mechanisms, and workflows. Other disciplines, such as pathology, cardiology, and radiation therapy, now define further demands in addition to these established standards. Pathology may have the highest technical demands on the systems, with very complex workflows, and the digitization of slides generating enormous amounts of data up to Gigabytes per biopsy. This requires enormous amounts of data to be generated per biopsy, up to the gigabyte range.
Digital pathology allows a change from classical histopathological diagnosis with microscopes and glass slides to virtual microscopy on the computer, with multiple tools using artificial intelligence and machine learning to support pathologists in their future work.
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