Auxiliary subunit regulation of high-voltage activated calcium channels expressed in mammalian cells

The effects of auxiliary calcium channel subunits on the expression and functional properties of high-voltage activated (HVA) calcium channels have been studied extensively in the Xenopus oocyte expression system, but are less completely characterized in a mammalian cellular environment. Here, we provide the first systematic analysis of the effects of calcium channel beta and alpha(2)-delta subunits on expression levels and biophysical properties of three different types (Ca(v)1.2, Ca(v)2.1 and Ca(v)2.3) of HVA calcium channels expressed in tsA-201 cells. Our data show that Ca(v)1.2 and Ca(v)2.3 channels yield significant barium current in the absence of any auxiliary subunits. Although calcium channel beta subunits were in principle capable of increasing whole cell conductance, this effect was dependent on the type of calcium channel alpha(1) subunit, and beta(3) subunits altogether failed to enhance current amplitude irrespective of channel subtype. Moreover, the alpha(2)-delta subunit alone is capable of increasing current amplitude of each channel type examined, and at least for members of the Ca(v)2 channel family, appears to act synergistically with beta subunits. In general agreement with previous studies, channel activation and inactivation gating was regulated both by beta and by alpha(2)-delta subunits. However, whereas pronounced regulation of inactivation characteristics was seen with the majority of the auxiliary subunits, effects on voltage dependence of activation were only small (< 5 mV). Overall, through a systematic approach, we have elucidated a previously underestimated role of the alpha(2)-delta(1) subunit with regard to current enhancement and kinetics. Moreover, the effects of each auxiliary subunit on whole cell conductance and channel gating appear to be specifically tailored to subsets of calcium channel subtypes

Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

BACKGROUND: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. FOCUS: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. DATA: We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., 'control') or group 2 (e.g., 'treated'). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. METHODS: We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data

Observation of a low energy nuclear recoil peak in the neutron calibration data of the CRESST-III Experiment

New-generation direct searches for low mass dark matter feature detection thresholds at energies well below 100 eV, much lower than the energies of commonly used X-ray calibration sources. This requires new calibration sources with sub-keV energies. When searching for nuclear recoil signals, the calibration source should ideally cause mono-energetic nuclear recoils in the relevant energy range. Recently, a new calibration method based on the radiative neutron capture on $^{182}$W with subsequent de-excitation via single $\gamma$-emission leading to a nuclear recoil peak at 112 eV was proposed. The CRESST-III dark matter search operated several CaWO$_{4}$-based detector modules with detection thresholds below 100 eV in the past years. We report the observation of a peak around the expected energy of 112 eV in the data of three different detector modules recorded while irradiated with neutrons from different AmBe calibration sources. We compare the properties of the observed peaks with Geant-4 simulations and assess the prospects of using this for the energy calibration of CRESST-III detectors.Comment: 8 pages, 4 figures; submitted to Phys. Rev.

Testing spin-dependent dark matter interactions with lithium aluminate targets in CRESST-III

In the past decades, numerous experiments have emerged to unveil the nature of dark matter, one of the most discussed open questions in modern particle physics. Among them, the CRESST experiment, located at the Laboratori Nazionali del Gran Sasso, operates scintillating crystals as cryogenic phonon detectors. In this work, we present first results from the operation of two detector modules which both have 10.46 g LiAlO$_2$ targets in CRESST-III. The lithium contents in the crystal are $^6$Li, with an odd number of protons and neutrons, and $^7$Li, with an odd number of protons. By considering both isotopes of lithium and $^{27}$Al, we set the currently strongest cross section upper limits on spin-dependent interaction of dark matter with protons and neutrons for the mass region between 0.25 and 1.5 GeV/c$^2$.Comment: 9 pages, 8 figure

Dietary Supplements and Sports Performance: Introduction and Vitamins

Sports success is dependent primarily on genetic endowment in athletes with morphologic, psychologic, physiologic and metabolic traits specific to performance characteristics vital to their sport. Such genetically-endowed athletes must also receive optimal training to increase physical power, enhance mental strength, and provide a mechanical advantage. However, athletes often attempt to go beyond training and use substances and techniques, often referred to as ergogenics, in attempts to gain a competitive advantage. Pharmacological agents, such as anabolic steroids and amphetamines, have been used in the past, but such practices by athletes have led to the establishment of anti-doping legislation and effective testing protocols to help deter their use. Thus, many athletes have turned to various dietary strategies, including the use of various dietary supplements (sports supplements), which they presume to be effective, safe and legal

Results on sub-GeV Dark Matter from a 10 eV Threshold CRESST-III Silicon Detector

We present limits on the spin-independent interaction cross section of dark matter particles with silicon nuclei, derived from data taken with a cryogenic calorimeter with 0.35 g target mass operated in the CRESST-III experiment. A baseline nuclear recoil energy resolution of $(1.36\pm 0.05)$ eV$_{\text{nr}}$, currently the lowest reported for macroscopic particle detectors, and a corresponding energy threshold of $(10.0\pm 0.2)$ eV$_{\text{nr}}$ have been achieved, improving the sensitivity to light dark matter particles with masses below 160 MeV/c$^2$ by a factor of up to 20 compared to previous results. We characterize the observed low energy excess, and we exclude noise triggers and radioactive contaminations on the crystal surfaces as dominant contributions.Comment: 8 pages, 5 figures; precised the position of the calibration source in Fig. 1; extended the discussion about the observed energy spectrum; added the DM limit curve to ancillary files. Published in Phys. Rev.

Towards an automated data cleaning with deep learning in CRESST

The CRESST experiment employs cryogenic calorimeters for the sensitive measurement of nuclear recoils induced by dark matter particles. The recorded signals need to undergo a careful cleaning process to avoid wrongly reconstructed recoil energies caused by pile-up and read-out artefacts. We frame this process as a time series classification task and propose to automate it with neural networks. With a data set of over one million labeled records from 68 detectors, recorded between 2013 and 2019 by CRESST, we test the capability of four commonly used neural network architectures to learn the data cleaning task. Our best performing model achieves a balanced accuracy of 0.932 on our test set. We show on an exemplary detector that about half of the wrongly predicted events are in fact wrongly labeled events, and a large share of the remaining ones have a context-dependent ground truth. We furthermore evaluate the recall and selectivity of our classifiers with simulated data. The results confirm that the trained classifiers are well suited for the data cleaning task.Comment: 12 pages, 8 figures, 6 table

Latest observations on the low energy excess in CRESST-III

The CRESST experiment observes an unexplained excess of events at low energies. In the current CRESST-III data-taking campaign we are operating detector modules with different designs to narrow down the possible explanations. In this work, we show first observations of the ongoing measurement, focusing on the comparison of time, energy and temperature dependence of the excess in several detectors. These exclude dark matter, radioactive backgrounds and intrinsic sources related to the crystal bulk as a major contribution.Comment: 10 pages, 5 figures; to be published in IDM2022 proceeding