PIPP: Improving peptide identity propagation using neural networks

Peptide identity propagation (PIP) can substantially reduce missing values in label-free mass spectrometry quantification by transferring peptides identified by tandem mass (MS/MS) spectra in one run to experimentally related runs where the peptides are not identified by MS/MS. The existing frameworks for matching identifications between runs perform peak tracing and propagation based on similarity of precursor features using only a limited number of dimensions available in MS1 data. These approaches do not produce accompanying confidence estimates and hence cannot filter probable false positive identity transfers. We introduce an embedding based PIP that uses a higher dimensional representation of MS1 measurements that is optimized to capture peptide identities using deep neural networks. We developed a propagation framework that works entirely on MaxQuant results. Current PIP workflows typically perform propagation mainly using two feature dimensions, and rely on deterministic tolerances for identification transfer. Our framework overcomes both these limitations while additionally assigning probabilities to each transferred identity. The proposed embedding approach enables quantification of the empirical false discovery rate (FDR) for peptide identification, while also increasing depth of coverage through coembedding the runs from the experiment with experimental libraries. In published datasets with technical and biological variability, we demonstrate that our method reduces missing values in MaxQuant results, maintains high quantification precision and accuracy, and low false transfer rate.

Confidence Learning for Semi-Supervised Acoustic Event Detection

In recent years, the involvement of synthetic strongly labeled data, weakly labeled data, and unlabeled data has drawn much research attention in semi-supervised acoustic event detection (SAED). The classic self-training method carries out predictions for unlabeled data and then selects predictions with high probabilities as pseudo-labels for retraining. Such models have shown its effectiveness in SAED. However, probabilities are poorly calibrated confidence estimates, and samples with low probabilities are ignored. Hence, we introduce a confidence-based semi-supervised Acoustic event detection (C-SAED) framework. The C-SAED method learns confidence deliberately and retrains all data distinctly by applying confidence as weights. Additionally, we apply a power pooling function whose coefficient can be trained automatically and use weakly labeled data more efficiently. The experimental results demonstrate that the generated confidence is proportional to the accuracy of the predictions. Our C-SAED framework achieves a relative error rate reduction of 34% in contrast to the baseline model.

Two-year-olds’ eye movements reflect confidence in their understanding of words

We study the fundamental issue of whether children evaluate the reliability of their language interpretation, i.e., their confidence in understanding words. In two experiments, two-year- olds (n1 = 50; n2 = 60) saw two objects and heard one of them being named; both objects were then hidden behind screens and children were asked to look towards the named object, which was eventually revealed. When children knew the label used, they showed increased post-decision persistence after a correct compared to an incorrect anticipatory look, a marker of decision confidence in word comprehension (experiment 1). When interacting with an unreliable speaker, children showed accurate word comprehension, but reduced confidence in the accuracy of their own choice, indicating that children’s confidence estimates are influenced by social information (experiment 2). Thus, by 2 years, children can estimate their confidence during language comprehension, long before they can reflect upon and talk about their linguistic skills.

An improved method for evaluating inverted encoding models

Inverted encoding models have recently become popular as a method for decoding stimuli and investigating neural representations. Here we present a novel modification to inverted encoding models that improves the flexibility and interpretability of stimulus reconstructions, addresses some key issues inherent in the standard inverted encoding model procedure, and provides trial-by-trial stimulus predictions and goodness-of-fit estimates. The standard inverted encoding model approach estimates channel responses (or reconstructions), which are averaged and aligned across trials and then typically evaluated using a metric such as slope, amplitude, etc.). We discuss how this standard procedure can produce spurious results and other interpretation issues. Our modifications are not susceptible to these methodological issues and are further advantageous due to our decoding metric taking into account the choice of population-level tuning functions and employing a prediction error-based metric directly comparable across experiments. Our modifications also allow researchers to obtain trial-by-trial confidence estimates independent of prediction error which can be used to threshold reconstructions and increase statistical power. We validate and demonstrate the improved utility of our modified inverted encoding model procedure across three real fMRI datasets, and additionally offer a Python package for easy implementation of our approach.

Why Do We Step on the Same Rake? The Occurrence of Recurring Errors in the Learning Process

The present study investigated the occurrence of recurring errors in the learning process. Our goal was to determine the reasons for recurring errors. In considering these reasons we were trying to find the characteristic features of incorrect responses at the beginning of a learning process in order to predict the occurrence of recurring errors in the learning process. Response times and confidence measures were used as the predictors of error repetition. The results of two experiments have shown that response times and confidence levels for the recurring and singular errors at the beginning of learning are different. Response times were shorter for recurring errors than for singular errors at the beginning of a learning process. This information can be used to predict the repetition of such errors further along during learning. In addition, in both experiments, the correct responses were slower for stimuli that would cause recurring errors in the future. This allowed us to predict the repetition of such errors. Participants’ confidence estimates also varied with different types of erroneous responses and allowed the repetition of errors during the learning process to be predicted. Thus, our results allow the prediction of error repetition and also can be used to make suggestions about the phenomenon of recurring errors and the causes of their occurrence.

Radiation Power Control of the Industrial CO2 Laser Excited by а Nonself-Sustained Glow Discharge by Changing the Frequency of Ionization Pulses

The article describes radiation power control of industrial CO2 lasers of Lantan series excited by а nonself-sustained glow discharge in the automatic mode. These lasers are closed-cycle fast gas-transport lasers excited by a nonself-sustained glow discharge with ionization by periodic-pulsed capacitively coupled auxiliary discharge. In this case, ionization and conductivity are provided by periodic-pulsed capacitively coupled discharge. The energy contribution to molecular oscillations is provided by the passage of the main discharge current through the plasma with electron density given by ionization. This permits easy laser power control, provides excellent optical homogeneity and stability of an active volume together with high laser efficiency. A system of a nonself-sustained glow discharge with ionization by periodic-pulsed capacitively coupled auxiliary discharge, the stages of creation and brief characteristics of the Lantan series lasers is presented. The method of controlling the power of laser radiation by changing the frequency of the ionization pulses is determined. This control method allows operating of the laser in continuous and in pulse-periodic modes with adjustable pulse ratio and pulse duration, and also provides switching from one mode to another. In the continuous mode, the radiation power is controlled by changing the frequency of ionization pulses, which are high voltage pulses with duration of 100 ns, given with the frequency of 1-5 kHz. Pulse-periodic radiation control is performed by modulating ionization pulses that consists of pulses being delivered in batches. The frequency of the pulses in a batch determines the radiation power in a pulse. The frequency of the batches following is the frequency of the pulse mode, and the length of the batch determines the pulses duration. Based on the experimental data, the dependence of the radiation power on the ionization pulses frequency was determined. An experimental system is presented and the measuring accuracy of the laser radiation power and the frequency of ionization pulses is determined. Data acquiring and processing of experimental results were performed using the NI 6008 USB data acquisition device in the LabVIEW programs of National Instruments. To study the dependence of the laser power on Мехатроника, автоматизация, управление, Том 21, № 4, 2020 231 the frequency of the ionization pulses, a regression analysis method was applied. Studies have shown that the dependence of the laser power on the ionization pulses frequency is linear in a wide range of parameters. The equation of the direct regression is calculated. The confidence estimates of the coefficients of the direct regression and the confidence estimates of the deviation of the theoretical direct regression from the empirical one are calculated with a confidence level of 95%.

Room for improvement: A study of overconfidence in numerical skills among British graduates

The experiment tested overconfidence in number skills among British graduates and non-graduates. The data were collected at a residential management training programme for part-time professional students. The aim of the research was to test whether graduate professionals, due to their higher qualifications, overstated their numeracy abilities compared to non-graduates. The experiment, conducted using E-prime, showed a significant interaction between the level of qualification and the overstatement of numerical abilities. The results support the hypotheses and showed that graduates rated themselves higher than their actual abilities: their test performance was not consistent with their confidence estimates. The findings are significant in relation to rethinking higher education curricula, which are currently under pressure to align with the needs of the economy. The authors advocate more inclusive and interpretive research for a greater understanding of the relevant issues to offer useful policy data and help higher education institutions prepare their graduates for task fulfilment and decision-making in a dynamic workplace. To date, few experiments have tested the numeracy level of graduates to corroborate the narrative communicated by employers. This study, despite the limited sample, is a first attempt and will serve as a reference for future, wider studies.