Precise wavelength determination of the 4s24p 2P3/2 - 2P1/2 transition in Mo11+ and Ru13+ ions

The 4s24p 2P3/2 – 2P1/2 magnetic dipole transition in Ga-like ions is interested in developing of high precise highly charged ion clock [Phys. Rev. A, 99, 02213(2019)]. In this work, we present direct observations of the transition in Mo11+ and Ru13+ ions at an electron beam ion trap. Internal and external calibration methods are used for determining the wavelength of the Mo11+ and Ru13+ lines, respectively. Both measurements reach precision levels of a few ppm. Compared with the available values, the current results significantly improve the experimental uncertainty.

ILLUMINANCE DISTRIBUTION MEASUREMENT OF MUSEUM EXHIBITS USING DIGITAL IMAGING LUMINANCE METER

The method for estimating the illuminance distribution in the vertical plane of museum objects (paintings) using a digital imaging luminance meters (ILMD) is considered. In order to pass from the luminance distribution to the illuminance distribution, a screen with reflective properties close to diffuse (Lambert) reflection is used. The theoretical and experimental uncertainty estimation of the measurement method done.

Thermodynamic Modeling of Multicomponent Rare Earth Nitrates Aqueous Systems

Solubility constants of rare earth (RE) nitrates crystalline hydrates are determined in a wide temperature range (−30 to 120°C), salts solubilities and phase diagrams of water–RE nitrate systems are calculated. For multicomponent (n > 5) solutions of RE nitrates the assessment of solution properties as well as phase diagrams are shown to be feasible within experimental uncertainty. In case of mixtures of RE nitrates with similar hydrodynamic radii of ions, the parameters of RE1–RE2 interparticle interaction can be ignored without losing accuracy of thermodynamic modeling.

Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty

Measurements of protein–ligand interactions have reproducibility limits due to experimental errors. Any model based on such assays will consequentially have such unavoidable errors influencing their performance which should ideally be factored into modelling and output predictions, such as the actual standard deviation of experimental measurements (σ) or the associated comparability of activity values between the aggregated heterogenous activity units (i.e., Ki versus IC50 values) during dataset assimilation. However, experimental errors are usually a neglected aspect of model generation. In order to improve upon the current state-of-the-art, we herein present a novel approach toward predicting protein–ligand interactions using a Probabilistic Random Forest (PRF) classifier. The PRF algorithm was applied toward in silico protein target prediction across ~ 550 tasks from ChEMBL and PubChem. Predictions were evaluated by taking into account various scenarios of experimental standard deviations in both training and test sets and performance was assessed using fivefold stratified shuffled splits for validation. The largest benefit in incorporating the experimental deviation in PRF was observed for data points close to the binary threshold boundary, when such information was not considered in any way in the original RF algorithm. For example, in cases when σ ranged between 0.4–0.6 log units and when ideal probability estimates between 0.4–0.6, the PRF outperformed RF with a median absolute error margin of ~ 17%. In comparison, the baseline RF outperformed PRF for cases with high confidence to belong to the active class (far from the binary decision threshold), although the RF models gave errors smaller than the experimental uncertainty, which could indicate that they were overtrained and/or over-confident. Finally, the PRF models trained with putative inactives decreased the performance compared to PRF models without putative inactives and this could be because putative inactives were not assigned an experimental pXC50 value, and therefore they were considered inactives with a low uncertainty (which in practice might not be true). In conclusion, PRF can be useful for target prediction models in particular for data where class boundaries overlap with the measurement uncertainty, and where a substantial part of the training data is located close to the classification threshold.

Reproducibility and Instruction Following in the Shop Floor Laboratory Work: The Case of a TMS Experiment

The article addresses the production of reproducibility as a topic that has become acutely relevant in the recent discussions on the replication crisis in science. It brings the ethnomethodological stance on reproducibility into the discussions, claiming that reproducibility is necessarily produced locally, on the shop floor, with methodological guidelines serving as references to already established practices rather than their origins. The article refers to this argument empirically, analyzing how a group of novice neuroscientists performs a series of measurements in a transcranial magnetic stimulation experiment. Based on ethnography and video analysis, the article traces a history of the local measurement procedure invented by the researchers in order to overcome the experimental uncertainty. The article aims to demonstrate (1) how reproducibility of the local procedure is achieved in the shop floor work of the practitioners and (2) how the procedure becomes normalized and questioned as incorrect in the course of experimental practice. It concludes that the difference between guidelines and practical actions is not problematic per se; what may be problematic is that researchers can be engaged in different working projects described by the same instruction.

Probabilistic Random Forest Improves Bioactivity Predictions Close to the Classification Threshold by Taking into Account Experimental Uncertainty

<p>In the context of small molecule property prediction, experimental errors are usually a neglected aspect during model generation. The main caveat to binary classification approaches is that they weight minority cases close to the threshold boundary equivalently in distinguishing between activity classes. For example, a pXC50 activity value of 5.1 or 4.9 are treated equally important in contributing to the opposing activity (e.g., classification threshold of 5), even though experimental error may not afford such discriminatory accuracy. This is detrimental in practice and therefore it is equally important to evaluate the presence of experimental error in databases and apply methodologies to account for variability in experiments and uncertainty near the decision boundary.<br></p><p></p><p> </p><p>In order to improve upon this, we herein present a novel approach toward predicting protein-ligand interactions using a Probabilistic Random Forest (PRF) classifier. The PRF comprises a modification to the long-established Random Forest (RF), to take into account uncertainties in the assigned classes (i.e., activity labels). This enables representing the activity in a framework in-between the classification and regression architecture, with philosophical differences from either approach. Compared to classification, this approach enables better representation of factors increasing/decreasing inactivity. Conversely, one can utilize all data (even delimited/operand/censored data far from a cut-off) at the same time as taking into account the granularity around the cut-off, compared to a classical regression framework. The algorithm was applied toward ~550 target prediction tasks from ChEMBL and PubChem. The largest benefit in incorporating the experimental deviation in PRF was observed for data points close to the binary threshold boundary, when such information is not considered in any way in the original RF algorithm. In comparison, the baseline RF outperformed PRF for cases with high confidence to belong to the active class (far from the binary decision threshold). The RF models gave errors smaller than the experimental uncertainty, which could indicate that they are <i>overtrained</i> and/or <i>over-confident</i>. Overall, we show that PRF can be useful for target prediction models in particular for data where class boundaries overlap with the measurement uncertainty, and where a substantial part of the training data is located close to the classification threshold. With this approach, we present, to our knowledge, for the first time an application of probabilistic modelling of activity data for target prediction using the PRF algorithm.</p>