skip to main content

Search for: All records

Creators/Authors contains: "Schmidt, M."

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract The sleep disorder narcolepsy is associated with symptoms related to either boundary state control that include excessive daytime sleepiness and sleep fragmentation, or rapid eye movement (REM) sleep features including cataplexy, sleep paralysis, hallucinations, and sleep-onset REM sleep events (SOREMs). Although the loss of Hypocretin/Orexin (Hcrt/Ox) peptides or their receptors have been associated with the disease, here we propose a circuit perspective of the pathophysiological mechanisms of these narcolepsy symptoms that encompasses brain regions, neuronal circuits, cell types, and transmitters beyond the Hcrt/Ox system. We further discuss future experimental strategies to investigate brain-wide mechanisms of narcolepsy that will bemore »essential for a better understanding and treatment of the disease.« less
  2. An efficient application of a material is only possible if we know its physical and chemical properties, which is frequently obstructed by the presence of micro‐ or macroscopic inclusions of secondary phases. While sometimes a sophisticated synthesis route can address this issue, often obtaining pure material is not possible. One example is TaGeIr, which has highly sample‐dependent properties resulting from the presence of several impurity phases, which influence electronic transport in the material. The effect of these minority phases was avoided by manufacturing, with the help of focused‐ion‐beam, a μm‐scale device containing only one phase—TaGeIr. This work provides evidence formore »intrinsic semiconducting behavior of TaGeIr and serves as an example of selective single‐domain device manufacturing. This approach gives a unique access to the properties of compounds that cannot be synthesized in single‐phase form, sparing costly and time‐consuming synthesis efforts.« less
  3. Nucleic acid kinetic simulators aim to predict the kinetics of interacting nucleic acid strands. Many simulators model the kinetics of interacting nucleic acid strands as continuous-time Markov chains (CTMCs). States of the CTMCs represent a collection of secondary structures, and transitions between the states correspond to the forming or breaking of base pairs and are determined by a nucleic acid kinetic model. The number of states these CTMCs can form may be exponentially large in the length of the strands, making two important tasks challenging, namely, mean first passage time (MFPT) estimation and parameter estimation for kinetic models based onmore »MFPTs. Gillespie’s stochastic simulation algorithm (SSA) is widely used to analyze nucleic acid folding kinetics, but could be computationally expensive for reactions whose CTMC has a large state space or for slow reactions. It could also be expensive for arbitrary parameter sets that occur in parameter estimation. Our work addresses these two challenging tasks, in the full state space of all non-pseudoknotted secondary structures of each reaction. In the first task, we show how to use a reduced variance stochastic simulation algorithm (RVSSA), which is adapted from SSA, to estimate the MFPT of a reaction’s CTMC. In the second task, we estimate model parameters based on MFPTs. To this end, first, we show how to use a generalized method of moments (GMM) approach, where we minimize a squared norm of moment functions that we formulate based on experimental and estimated MFPTs. Second, to speed up parameter estimation, we introduce a fixed path ensemble inference (FPEI) approach, that we adapt from RVSSA. We implement and evaluate RVSSA and FPEI using the Multistrand kinetic simulator. In our experiments on a dataset of DNA reactions, FPEI speeds up parameter estimation compared to inference using SSA, by more than a factor of three for slow reactions. Also, for reactions with large state spaces, it speeds up parameter estimation by more than a factor of two.« less