Spectrotemporal cortical dynamics and semantic control during sentence completion.

OBJECTIVE: To investigate cortical oscillations during a sentence completion task (SC) using magnetoencephalography (MEG), focusing on the semantic control network (SCN), its leftward asymmetry, and the effects of semantic control load. METHODS: Twenty right-handed adults underwent MEG while performing SC, consisting of low cloze (LC: multiple responses) and high cloze (HC: single response) stimuli. Spectrotemporal power modulations as event-related synchronizations (ERS) and desynchronizations (ERD) were analyzed: first, at the whole-brain level; second, in key SCN regions, posterior middle/inferior temporal gyri (pMTG/ITG) and inferior frontal gyri (IFG), under different semantic control loads. RESULTS: Three cortical response patterns emerged: early (0-200 ms) theta-band occipital ERS; intermediate (200-700 ms) semantic network alpha/beta-band ERD; late (700-3000 ms) dorsal language stream alpha/beta/gamma-band ERD. Under high semantic control load (LC), pMTG/ITG showed prolonged left-sided engagement (ERD) and right-sided inhibition (ERS). Left IFG exhibited heightened late (2500-2550 ms) beta-band ERD with increased semantic control load (LC vs. HC). CONCLUSIONS: SC involves distinct cortical responses and depends on the left IFG and asymmetric engagement of the pMTG/ITG for semantic control. SIGNIFICANCE: Future use of SC in neuromagnetic preoperative language mapping and for understanding the pathophysiology of language disorders in neurological conditions.

EPIC-TRACE: predicting TCR binding to unseen epitopes using attention and contextualized embeddings.

MOTIVATION: T cells play an essential role in adaptive immune system to fight pathogens and cancer but may also give rise to autoimmune diseases. The recognition of a peptide-MHC (pMHC) complex by a T cell receptor (TCR) is required to elicit an immune response. Many machine learning models have been developed to predict the binding, but generalizing predictions to pMHCs outside the training data remains challenging. RESULTS: We have developed a new machine learning model that utilizes information about the TCR from both α and ß chains, epitope sequence, and MHC. Our method uses ProtBERT embeddings for the amino acid sequences of both chains and the epitope, as well as convolution and multi-head attention architectures. We show the importance of each input feature as well as the benefit of including epitopes with only a few TCRs to the training data. We evaluate our model on existing databases and show that it compares favorably against other state-of-the-art models. AVAILABILITY AND IMPLEMENTATION: https://github.com/DaniTheOrange/EPIC-TRACE.

TSignal: a transformer model for signal peptide prediction.

MOTIVATION: Signal peptides (SPs) are short amino acid segments present at the N-terminus of newly synthesized proteins that facilitate protein translocation into the lumen of the endoplasmic reticulum, after which they are cleaved off. Specific regions of SPs influence the efficiency of protein translocation, and small changes in their primary structure can abolish protein secretion altogether. The lack of conserved motifs across SPs, sensitivity to mutations, and variability in the length of the peptides make SP prediction a challenging task that has been extensively pursued over the years. RESULTS: We introduce TSignal, a deep transformer-based neural network architecture that utilizes BERT language models and dot-product attention techniques. TSignal predicts the presence of SPs and the cleavage site between the SP and the translocated mature protein. We use common benchmark datasets and show competitive accuracy in terms of SP presence prediction and state-of-the-art accuracy in terms of cleavage site prediction for most of the SP types and organism groups. We further illustrate that our fully data-driven trained model identifies useful biological information on heterogeneous test sequences. AVAILABILITY AND IMPLEMENTATION: TSignal is available at: https://github.com/Dumitrescu-Alexandru/TSignal.

TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs.

MOTIVATION: T cells use T cell receptors (TCRs) to recognize small parts of antigens, called epitopes, presented by major histocompatibility complexes. Once an epitope is recognized, an immune response is initiated and T cell activation and proliferation by clonal expansion begin. Clonal populations of T cells with identical TCRs can remain in the body for years, thus forming immunological memory and potentially mappable immunological signatures, which could have implications in clinical applications including infectious diseases, autoimmunity and tumor immunology. RESULTS: We introduce TCRconv, a deep learning model for predicting recognition between TCRs and epitopes. TCRconv uses a deep protein language model and convolutions to extract contextualized motifs and provides state-of-the-art TCR-epitope prediction accuracy. Using TCR repertoires from COVID-19 patients, we demonstrate that TCRconv can provide insight into T cell dynamics and phenotypes during the disease. AVAILABILITY AND IMPLEMENTATION: TCRconv is available at https://github.com/emmijokinen/tcrconv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Proactive and Reactive Inhibitory Control Strategies: Exploring the Impact of Interindividual Variables on an ERP Continuous Performance Task (AX-CPT).

According to the Dual Mechanisms of Control (DMC) framework, cognitive control can be divided into two strategies: proactive cognitive control, which relies mainly on the active maintenance of contextual information relevant to the ongoing task; and reactive cognitive control, which is a form of transient control triggered by an external cue. Although cognitive control has been studied extensively, little is known about the specificities of inhibition within the framework of the DMC model and the influence of interindividual variables on inhibitory control.Thanks to an inhibitory version of the continuous performance task (CPT), we studied behavioral performances and Event-Related Potentials (ERPs) related to proactive and reactive inhibition, and their links to psychological profile and cognitive performances. One hundred and five young adults underwent the task, along with a short clinical and cognitive evaluation.We were able to observe ERPs related to proactive (cue-N1, cue-N2, cue-P3, and the contingent negative variation) and reactive inhibitory control (target-N2 and target-P3). Our results showed that proactive strategies appeared to be linked with impulsivity, working memory abilities, dominant response inhibition, gender, and the consumption pattern of nicotine. Reactive strategies appeared to be linked with attentional and working memories abilities.Overall, the inhibitory AX-CPT allowed a specific investigation of cognitive control within the framework of the DMC based on behavioral and ERP variables. This provided us an opportunity to investigate the principal ERP components related to proactive and reactive inhibitory control strategies as well as to link them with specific clinical and cognitive variables.

Local fresh- and sea-water effects on fog occurrence.

Fog is an important atmospheric phenomenon highly relevant to ecosystems and/or the environment. Two essential prerequisites of fog formation are the presence of fog condensation nuclei and water in the atmosphere. The aim of our study was to examine in detail how fog occurrence is influenced by water areas in the immediate vicinity of the fog observation site. We have used as input data long-term observations on fog occurrence measured at 56 professional meteorological stations in Romania in 1981-2017 and GIS-derived information on water areas and on two topographical indices, TWI and TPI, in the neighbourhood of these stations. We formulated three alternative models of different complexity based on a semiparametric generalised additive logistic model for the probability of fog occurrence with potentially nonlinear, smooth effects modelled via penalised splines. A radius of 9 km appeared to be the most influential when considering the water area in a circle around the fog observation station. Based on our results, we concluded that (i) the water area in the vicinity of the station is a factor influencing fog occurrence, (ii) the water's effect differs according to water type (freshwater or seawater proximity), and (iii) GIS-derived topographical indices are informative for the explanation of fog occurrence and their inclusion enhanced the fit of the models substantially. Our findings, based on a reliable long-term data set of fog occurrence and recent GIS-derived data, explored by a relevant statistical approach will enhance further considerations related to fog formation and its environmental consequences.

Terrain and its effects on fog occurrence.

Fog is a very complex phenomenon, relevant to both atmospheric physics and chemistry, contributing to the atmospheric inputs of both nutrients and pollutants to the environment. Fog occurrence is affected by numerous factors. The aim of this study is to examine the effects of terrain on fog occurrence. Namely, we studied in detail how altitude, slope and landform influence the probability of fog occurrence using the generalized additive model. In particular, we investigated how different explanatory variables might modify (deform) the trend and the seasonal component of the probability of fog occurrence. We used long-term records of daily fog occurrence measured in 1981-2017 at 56 professional meteorological stations in Romania, reflecting different environments and geographical areas. The altitude of the sites under review ranged between 13 and 2504 m above sea level, the coverage of localities at different altitudes being highly uneven. Out of the terrain variables considered, the most decisive influence was found to be altitude. We have included information on slope and landform, which refined and bettered the basic model. Our model results indicated a significant decrease in the probability of fog occurrence over the examined period. The behaviour of fog differed according to the altitude, the most profound effects being observed for ground-level fog and fog above flat terrain. The probability of fog occurrence at different altitudes varied mostly in summer and autumn, whereas it was very similar in winter.

Exploratory Analysis of Urban Climate Using a Gap-Filled Landsat 8 Land Surface Temperature Data Set.

The Landsat 8 satellites have retrieved land surface temperature (LST) resampled at a 30-m spatial resolution since 2013, but the urban climate studies frequently use a limited number of images due to the problems related to missing data over the city of interest. This paper endorses a procedure for building a long-term gap-free LST data set in an urban area using the high-resolution Landsat 8 imagery. The study is applied on 94 images available through 2013-2018 over Bucharest (Romania). The raw images containing between 1.1% and 58.4% missing LST data were filled in using the Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm implemented in the sinkr R packages. The resulting high-spatial-resolution gap-filled land surface temperature data set was used to explore the LST climatology over Bucharest (Romania) an urban area, at a monthly, seasonal, and annual scale. The performance of the gap-filling method was checked using a cross-validation procedure, and the results pledge for the development of an LST-based urban climatology.

Mapping monthly rainfall erosivity in Europe.

Rainfall erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall erosivity both spatially and temporally. During winter months, significant rainfall erosivity is present only in part of the Mediterranean countries. A sudden increase of erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall erosivity in summer is almost 4 times higher (315MJmmha-1h-1) compared to winter (87MJmmha-1h-1). The Cubist model has been selected among various statistical models to perform the spatial interpolation due to its excellent performance, ability to model non-linearity and interpretability. The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sum of all months has to be close to annual erosivity. The performance of the Cubist models proved to be generally high, resulting in R2 values between 0.40 and 0.64 in cross-validation. The obtained months show an increasing trend of erosivity occurring from winter to summer starting from western to Eastern Europe. The maps also show a clear delineation of areas with different erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year.

Reply to the comment on "Rainfall erosivity in Europe" by Auerswald et al.

Recently, in the Auerswald et al. (2015) comment on "Rainfall erosivity in Europe", 5 criticisms were addressed: i) the neglect of seasonal erosion indices, ii) the neglect of published studies and data, iii) the low temporal resolution of the data, especially of the maximum rain intensity, iv) the use of precipitation data instead of rain data and the subsequent deviation of the R-factor in Germany and Austria compared with previous studies, and v) the differences in considered time periods between countries. We reply as follows: (i) An evaluation of the seasonal erosion index at the European scale is, to our knowledge, not achievable at present with the available data but would be a future goal. Synchronous publication of the seasonal erosion index is not mandatory, specifically because seasonal soil loss ratios are not available at this scale to date. We are looking forward to the appropriate study by the authors of the comment, who assert that they have access to the required data. (ii) We discuss and evaluate relevant studies in our original work and in this reply; however, we cannot consider what is not available to the scientific community. (iii) The third point of critique was based on a misunderstanding by Auerswald et al. (2015), as we did indeed calculate the maximum intensity with the highest resolution of data available. (iv) The low R-factor values in Germany and the higher values in Austria compared with previous studies are not due to the involvement of snow but are rather due to a Pan-European interpolation. We argue that an interpolation across the borders of Austria creates a more reliable data set. (v) We agree that the use of a short time series or time series from different periods is generally a problem in all large-scale studies and requires improvement in the future. However, because this affects countries with a rather low variability of the R-factor in our study, we are confident that the overall results of the map are not biased. In conclusion, the Pan-European rainfall data compilation (REDES) was a great success and yielded data from 1541 stations with an average length of 17.1years and a temporal resolution of <60min. However, a Pan-European data collection will never be complete without the help and supply of data from its users. Thus, we invite the authors of the comment to share their data in the open REDES to help build even better rainfall-erosivity maps at regional or European scales.

Rainfall erosivity in Europe.

Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most commonly expressed as the R-factor in the USLE model and its revised version, RUSLE. At national and continental levels, the scarce availability of data obliges soil erosion modellers to estimate this factor based on rainfall data with only low temporal resolution (daily, monthly, annual averages). The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets. Data have been collected from 1541 precipitation stations in all European Union (EU) Member States and Switzerland, with temporal resolutions of 5 to 60 min. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 min using linear regression functions. Precipitation time series ranged from a minimum of 5 years to a maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression (GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wettest months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha(-1) h(-1) yr(-1), with the highest values (>1000 MJ mm ha(-1) h(-1) yr(-1)) in the Mediterranean and alpine regions and the lowest (<500 MJ mm ha(-1) h(-1) yr(-1)) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also the highest in Mediterranean regions which implies high risk for erosive events and floods.