Machine Learning-Assisted Identification of Factors Contributing to the Technical Variability Between Bulk and Single-Cell RNA-Seq Experiments

Background: Recent studies in the area of transcriptomics performed on single-cell and population levels reveal noticeable variability in gene expression measurements provided by different RNA sequencing technologies. Due to increased noise and complexity of single-cell RNA-Seq (scRNA-Seq) data over the bulk experiment, there is a substantial number of variably-expressed genes and so-called dropouts, challenging the subsequent computational analysis and potentially leading to false positive discoveries. In order to investigate factors affecting technical variability between RNA sequencing experiments of different technologies, we performed a systematic assessment of single-cell and bulk RNA-Seq data, which have undergone the same pre-processing and sample preparation procedures. Results: Our analysis indicates that variability between gene expression measurements as well as dropout events are not exclusively caused by biological variability, low expression levels, or random variation. Furthermore, we propose FAVSeq, a machine learning-assisted pipeline for detection of factors contributing to gene expression variability in matched RNA-Seq data provided by two technologies. Based on the analysis of the matched bulk and single-cell dataset, we found the 3'-UTR and transcript lengths as the most relevant effectors of the observed variation between RNA-Seq experiments, while the same factors together with cellular compartments were shown to be associated with dropouts. Conclusions: Here, we investigated the sources of variation in RNA-Seq profiles of matched single-cell and bulk experiments. In addition, we proposed the FAVSeq pipeline for analyzing multimodal RNA sequencing data, which allowed to identify factors affecting quantitative difference in gene expression measurements as well as the presence of dropouts. Hereby, the derived knowledge can be employed further in order to improve the interpretation of RNA-Seq data and identify genes that can be affected by assay-based deviations. Source code is available under the MIT license at https://github.com/slipnitskaya/FAVSeq.

Machine learning-assisted identification of factors contributing to the technical variability between bulk and single-cell RNA-seq experiments

Background: Recent studies in the area of transcriptomics performed on single-cell and population levels reveal noticeable variability in gene expression measurements provided by different RNA sequencing technologies. Due to increased noise and complexity of single-cell RNA-Seq (scRNA-Seq) data over the bulk experiment, there is a substantial number of variably-expressed genes and so-called dropouts, challenging the subsequent computational analysis and potentially leading to false positive discoveries. In order to investigate factors affecting technical variability between RNA sequencing experiments of different technologies, we performed a systematic assessment of single-cell and bulk RNA-Seq data, which have undergone the same pre-processing and sample preparation procedures. Results: Our analysis indicates that variability between gene expression measurements as well as dropout events are not exclusively caused by biological variability, low expression levels, or random variation. Furthermore, we propose FAVSeq, a machine learning-assisted pipeline for detection of factors contributing to gene expression variability in matched RNA-Seq data provided by two technologies. Based on the analysis of the matched bulk and single-cell dataset, we found the 3'-UTR and transcript lengths as the most relevant effectors of the observed variation between RNA-Seq experiments, while the same factors together with cellular compartments were shown to be associated with dropouts. Conclusions: Here, we investigated the sources of variation in RNA-Seq profiles of matched single-cell and bulk experiments. In addition, we proposed the FAVSeq pipeline for analyzing multimodal RNA sequencing data, which allowed to identify factors affecting quantitative difference in gene expression measurements as well as the presence of dropouts. Hereby, the derived knowledge can be employed further in order to improve the interpretation of RNA-Seq data and identify genes that can be affected by assay-based deviations. Source code is available under the MIT license at https://github.com/slipnitskaya/FAVSeq.

Voltage stabilization of a controlled autonomous magnetoelectric generator with a magnetic shunt and permanent magnet excitation

The paper presents the results of testing and research of the characteristics of a controlled autonomous magnetoelectric synchronous generator with a magnetic shunt. Structurally, the studied generator is a modified asynchronous machine in which the rotor is made with permanent magnets and an additional system in the form of a magnetic shunt. By adjusting the winding current of the magnetic shunt, the output voltage of the generator is regulated. The following characteristics were investigated: the no-load characteristic during operation with permanent magnets and when the winding current of the magnetic shunt changes with forward and reverse polarity. Also, the external characteristic for active and active-inductive loads; the control characteristic when the load current changes at a constant generator voltage. Analysis of the obtained characteristics makes it possible to determine the limits of regulation of the external characteristic, which is ≈40 % relative to the main magnetic flux. The obtained regulation depth allows maintaining the stability of the external characteristic for power factors not exceeding 0.9, which is the usual passport value for autonomous power plants based on synchronous generators. Comparison of the data of research conducted on the experimental setup shows sufficient convergence for engineering and practical tasks. The maximum quantitative difference is 9.3 %, which suggests the adequacy of the previously developed mathematical model. The control characteristic, constructed experimentally at constant generator voltage, is the control law of the magnetic shunt winding for the studied generator. The investigated version of a synchronous generator with a magnetic shunt should be used for autonomous power plants, renewable energy systems, and autonomous power supply systems.

Opposing regulation of short-term memory by basal ganglia direct and indirect pathways that are coactive during behavior

The basal ganglia direct and indirect pathways are viewed to mediate opposing functions in movement. However, this classic model is challenged by recent findings that both pathways are coactive during behavior. We examined the roles of direct (dSPNs) and indirect (iSPNs) pathway spiny projection neurons in a decision-making task with a short-term memory (STM) component. Optogenetic stimulation of cortical-input-defined dSPNs and iSPNs during STM oppositely biased upcoming licking choice, without affecting licking execution. Optogenetically identified dSPNs and iSPNs showed similar response patterns, although with quantitative difference in spatiotemporal organization. To understand how coactive dSPNs and iSPNs play opposing roles, we recorded population activity in frontal cortex and the basal ganglia output nucleus SNr. Stimulation of dSPNs and iSPNs bidirectionally regulated cortical decision variable through the differential modulation of SNr ramping activity. These results reconcile different views by demonstrating that coactive dSPNs and iSPNs precisely shape cortical activity in a push-pull balance.

Association of Equine Herpesvirus 5 with Mild Respiratory Disease in a Survey of EHV1, -2, -4 and -5 in 407 Australian Horses

Equine herpesviruses (EHVs) are common respiratory pathogens in horses; whilst the alphaherpesviruses are better understood, the clinical importance of the gammaherpesviruses remains undetermined. This study aimed to determine the prevalence of, and any association between, equine respiratory herpesviruses EHV1, -2, -4 and -5 infection in horses with and without clinical signs of respiratory disease. Nasal swabs were collected from 407 horses in Victoria and included clinically normal horses that had been screened for regulatory purposes. Samples were collected from horses during Australia’s equine influenza outbreak in 2007; however, horses in Victoria required testing for proof of freedom from EIV. All horses tested in Victoria were negative for EIV, hence archived swabs were available to screen for other pathogens such as EHVs. Quantitative PCR techniques were used to detect EHVs. Of the 407 horses sampled, 249 (61%) were clinically normal, 120 (29%) presented with clinical signs consistent with mild respiratory disease and 38 (9%) horses had an unknown clinical history. Of the three horses detected shedding EHV1, and the five shedding EHV4, only one was noted to have clinical signs referable to respiratory disease. The proportion of EHV5-infected horses in the diseased group (85/120, 70.8%) was significantly greater than those not showing signs of disease (137/249, 55%). The odds of EHV5-positive horses demonstrating clinical signs of respiratory disease were twice that of EHV5-negative horses (OR 1.98, 95% CI 1.25 to 3.16). No quantitative difference between mean loads of EHV shedding between diseased and non-diseased horses was detected. The clinical significance of respiratory gammaherpesvirus infections in horses remains to be determined; however, this survey adds to the mounting body of evidence associating EHV5 with equine respiratory disease.

Modelling aerosol-based exposure to SARS-CoV-2 by an agent based Monte Carlo method: Risk estimates in a shop and bar

Present day risk assessment on the spreading of airborne viruses is often based on the classical Wells-Riley model assuming immediate mixing of the aerosol into the studied environment. Here, we improve on this approach and the underlying assumptions by modeling the space-time dependency of the aerosol concentration via a transport equation with a dynamic source term introduced by the infected individual(s). In the present agent-based methodology, we study the viral aerosol inhalation exposure risk in two scenarios including a low/high risk scenario of a “supermarket”/“bar”. The model takes into account typical behavioral patterns for determining the rules of motion for the agents. We solve a diffusion model for aerosol concentration in the prescribed environments in order to account for local exposure to aerosol inhalation. We assess the infection risk using the Wells-Riley model formula using a space-time dependent aerosol concentration. The results are compared against the classical Wells-Riley model. The results indicate features that explain individual cases of high risk with repeated sampling of a heterogeneous environment occupied by non-equilibrium concentration clouds. An example is the relative frequency of cases that might be called superspreading events depending on the model parameters. A simple interpretation is that averages of infection risk are often misleading. They also point out and explain the qualitative and quantitative difference between the two cases—shopping is typically safer for a single individual person.

What Can Neural Activity Tell Us About Cognitive Resources in Aging?

A reduction in cognitive resources has been originally proposed to account for age-related decrements in several cognitive domains. According to this view, aging limits the pool of available cognitive supplies: Compared to younger adults, elderly exhaust the resources more rapidly as task difficulty increases, hence a dramatic performance drop. Neurophysiological indexes (e.g., BOLD response and EEG activity) may be instrumental to quantify the amount of such cognitive resources in the brain and to pinpoint the stage of stimulus processing where the decrement in age-related resources is evident. However, as we discuss in this mini-review, the most recent studies on the neurophysiological markers of age-related changes lack a consistent coupling between neural and behavioral effects, which casts doubt on the advantage of measuring neural indexes to study resource deployment in aging. For instance, in the working memory (WM) domain, recent cross-sectional studies found varying patterns of concurrent age-related brain activity, ranging from equivalent to reduced and increased activations of old with respect to younger adults. In an attempt to reconcile these seemingly inconsistent findings of brain-behavior coupling, we focus on the contribution of confounding sources of variability and propose ways to control for them. Finally, we suggest an alternative perspective to explain age-related effects that implies a qualitative (instead of or along with a quantitative) difference in the deployment of cognitive resources in aging.

An ERP Study on Attraction Effects in Advanced L2 Learners

In English, the rule of agreement is quite simple: verbs must agree with their subject head nouns in terms of number features. Despite this simplicity, agreement processing is always interrupted when the subject phrase of the sentence “The key to the cabinets is on the table,” contains two nouns with a mismatch in number features commonly known as attraction effects. This study used event-related potentials (ERPs) to examine whether late advanced second language (L2) learners can acquire native-like sensitivity of attraction effects. The results revealed that L2 learners showed ERP patterns qualitatively similar to native English speakers: ungrammatical verbs following singular attractors elicited a P600 effect relative to their grammatical counterparts, whereas this positivity was replaced by an N400 effect when plural attractors intervened between the subject head nouns and the verbs. Of particular interest, given that, compared to native speakers, the amplitude of the P600 effect elicited by L2 learners was smaller, there was a quantitative difference between native speakers and L2 learners. We proposed that these two ERP components represented the two processing routes of agreement: the P600 effect indexed a full, combinatorial process, which parsed morphosyntactic features between agreement controllers and targets, whereas the N400 effect indexed a shallow, heuristic process, which evaluated lexical associations between agreeing elements. Moreover, similar to native speakers, advanced L2 learners showed an asymmetrical pattern of attraction effects, in that plural attractors were interfered with ungrammaticality at disagreeing verbs, but they did not cause any difficulties in processing grammatical sentences at agreeing verbs. The overall results suggested that compared to native processing, L2 processing of complex agreement with attractor interference was shallower and therefore late advanced L2 learners could not achieve native-like attraction effects.

Studying changes in climate normals in long-term forecasting of the Sea of Okhotsk ice cover.

There is a similarity in dynamics and a quantitative difference in the ice cover regime in four consecutive 30-year periods: 1961–1990, 1971–2000, 1981–2010, 1991–2020 are noted. The greatest differences are observed in the regime characteristics of the first and the last periods. The absolute maximum or minimum recorded in at least one of the months from January to May determines the nature of the ice cover of the entire ice season. The sensitivity of the predictive physical-statistical model to the replacement of climatic norms has been investigated. Estimates of the quality of forecasts of the average monthly ice cover are obtained. Keywords: base period, long-term forecast, physical and statistical model, ice cover, climate characteristics, typification, forecast skill scores