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In this article, we report on the heat-transfer resistance at interfaces as a novel, denaturation-based method to detect single-nucleotide polymorphisms in DNA. We observed that a molecular brush of double-stranded DNA grafted onto synthetic diamond surfaces does not notably affect the heat-transfer resistance at the solid-to-liquid interface. In contrast to this, molecular brushes of single-stranded DNA cause, surprisingly, a substantially higher heat-transfer resistance and behave like a thermally insulating layer. This effect can be utilized to identify ds-DNA melting temperatures via the switching from low- to high heat-transfer resistance. The melting temperatures identified with this method for different DNA duplexes (29 base pairs without and with built-in mutations) correlate nicely with data calculated by modeling. The method is fast, label-free (without the need for fluorescent or radioactive markers), allows for repetitive measurements, and can also be extended toward array formats. Reference measurements by confocal fluorescence microscopy and impedance spectroscopy confirm that the switching of heat-transfer resistance upon denaturation is indeed related to the thermal on-chip denaturation of DNA.
Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-supervised learning, have been suggested, but they typically rely on extensive empirical datasets. Inspired by recent advances in computer vision, we propose a pretraining task termed "frequency pretraining" to pretrain a neural network for sleep staging by predicting the frequency content of randomly generated synthetic time series. Our experiments demonstrate that our method surpasses fully supervised learning in scenarios with limited data and few subjects, and matches its performance in regimes with many subjects. Furthermore, our results underline the relevance of frequency information for sleep stage scoring, while also demonstrating that deep neural networks utilize information beyond frequencies to enhance sleep staging performance, which is consistent with previous research. We anticipate that our approach will be advantageous across a broad spectrum of applications where EEG data is limited or derived from a small number of subjects, including the domain of brain-computer interfaces.
Recently, we introduced and mathematically analysed a new method for grid deformation (Grajewski et al., 2009) [15] we call basic deformation method (BDM) here. It generalises the method proposed by Liao et al. (Bochev et al., 1996; Cai et al., 2004; Liao and Anderson, 1992) [4], [6], [20]. In this article, we employ the BDM as core of a new multilevel deformation method (MDM) which leads to vast improvements regarding robustness, accuracy and speed. We achieve this by splitting up the deformation process in a sequence of easier subproblems and by exploiting grid hierarchy. Being of optimal asymptotic complexity, we experience speed-ups up to a factor of 15 in our test cases compared to the BDM. This gives our MDM the potential for tackling large grids and time-dependent problems, where possibly the grid must be dynamically deformed once per time step according to the user's needs. Moreover, we elaborate on implementational aspects, in particular efficient grid searching, which is a key ingredient of the BDM.
After a short introduction of a new nonconforming linear finite element on quadrilaterals recently developed by Park, we derive a dual weighted residual-based a posteriori error estimator (in the sense of Becker and Rannacher) for this finite element. By computing a corresponding dual solution we estimate the error with respect to a given target error functional. The reliability and efficiency of this estimator is analyzed in several numerical experiments.
Background/Aims: Common systems for the quantification of cellular contraction rely on animal-based models, complex experimental setups or indirect approaches. The herein presented CellDrum technology for testing mechanical tension of cellular monolayers and thin tissue constructs has the potential to scale-up mechanical testing towards medium-throughput analyses. Using hiPS-Cardiac Myocytes (hiPS-CMs) it represents a new perspective of drug testing and brings us closer to personalized drug medication. Methods: In the present study, monolayers of self-beating hiPS-CMs were grown on ultra-thin circular silicone membranes and deflect under the weight of the culture medium. Rhythmic contractions of the hiPS-CMs induced variations of the membrane deflection. The recorded contraction-relaxation-cycles were analyzed with respect to their amplitudes, durations, time integrals and frequencies. Besides unstimulated force and tensile stress, we investigated the effects of agonists and antagonists acting on Ca²⁺ channels (S-Bay K8644/verapamil) and Na⁺ channels (veratridine/lidocaine). Results: The measured data and simulations for pharmacologically unstimulated contraction resembled findings in native human heart tissue, while the pharmacological dose-response curves were highly accurate and consistent with reference data. Conclusion: We conclude that the combination of the CellDrum with hiPS-CMs offers a fast, facile and precise system for pharmacological, toxicological studies and offers new preclinical basic research potential.
Trace metal determination by dc resistance changes of microstructured thin gold film electrodes
(1999)
Experience has shown that a priori created static resource allocation plans are vulnerable to runtime deviations and hence often become uneconomic or highly exceed a predefined soft deadline. The assumption of constant task execution times during allocation planning is even more unlikely in a cloud environment where virtualized resources vary in performance. Revising the initially created resource allocation plan at runtime allows the scheduler to react on deviations between planning and execution. Such an adaptive rescheduling of a many-task application workflow is only feasible, when the planning time can be handled efficiently at runtime. In this paper, we present the static low-complexity resource allocation planning algorithm (LCP) applicable to efficiently schedule many-task scientific application workflows on cloud resources of different capabilities. The benefits of the presented algorithm are benchmarked against alternative approaches. The benchmark results show that LCP is not only able to compete against higher complexity algorithms in terms of planned costs and planned makespan but also outperforms them significantly by magnitudes of 2 to 160 in terms of required planning time. Hence, LCP is superior in terms of practical usability where low planning time is essential such as in our targeted online rescheduling scenario.
An increasing number of applications target their executions on specific hardware like general purpose Graphics Processing Units. Some Cloud Computing providers offer this specific hardware so that organizations can rent such resources. However, outsourcing the whole application to the Cloud causes avoidable costs if only some parts of the application benefit from the specific expensive hardware. A partial execution of applications in the Cloud is a tradeoff between costs and efficiency. This paper addresses the demand for a consistent framework that allows for a mixture of on- and off-premise calculations by migrating only specific parts to a Cloud. It uses the concept of workflows to present how individual workflow tasks can be migrated to the Cloud whereas the remaining tasks are executed on-premise.