Entire Set Research Articles

Implied roughness in the term structure of oil market volatility

This paper analyses the attributes and the significance of the roughness of oil market volatility. We employ unspanned stochastic volatility models driven by rough Brownian motions that yield semi-analytical prices for future options entailing efficient calibration applications. By performing a Monte Carlo simulation study, we show that the semi-analytical pricing performs well thus establishing its efficiency for calibration applications. Thus we calibrate option prices written on oil futures and provide empirical evidence of the roughness in oil volatility. Introducing just one additional parameter, the Hurst parameter, indicating the roughness of the volatility improves the calibration by almost a factor of 10. The calibrated option-implied Hurst parameter varies over time, but the entire set of parameters becomes more stable than in the non-rough case corresponding to a fixed Hurst parameter 1/2. These results underscore the importance to model the time dependency of the roughness of oil market volatility.

A Two-Stage Registration Strategy for Thermal–Visible Images in Substations

The analysis of infrared video images is becoming one of the methods used to detect thermal hazards in many large-scale engineering sites. The fusion of infrared thermal imaging and visible image data in the target area can help people to identify and locate the fault points of thermal hazards. Among them, a very important step is the registration of thermally visible images. However, the direct registration of images with large-scale differences may lead to large registration errors or even failure. This paper presents a novel two-stage thermal–visible-image registration strategy specifically designed for exceptional scenes, such as a substation. Firstly, the original image pairs that occur after binarization are quickly and roughly registered. Secondly, the adaptive downsampling unit partial-intensity invariant feature descriptor (ADU-PIIFD) algorithm is proposed to correct the small-scale differences in details and achieve finer registration. Experiments are conducted on 30 data sets containing complex power station scenes and compared with several other methods. The results show that the proposed method exhibits an excellent and stable performance in thermal–visible-image registration, and the registration error on the entire data set is within five pixels. Especially for multimodal images with poor image quality and many detailed features, the robustness of the proposed method is far better than that of other methods, which provides a more reliable image registration scheme for the field of fire safety.

The sound of silence: Quantification of typical absence seizures by sonifying EEG signals from a custom-built wearable device.

To develop and validate a method for long-term (24-h) objective quantification of absence seizures in the EEG of patients with childhood absence epilepsy (CAE) in their real home environment using a wearable device (waEEG), comparing automatic detection methods with auditory recognition after seizure sonification. The waEEG recording was acquired with two scalp electrodes. Automatic analysis was performed using previously validated software (Persyst® 14) and then fully reviewed by an experienced clinical neurophysiologist. The EEG data were converted into an audio file in waveform format with a 60-fold time compression factor. The sonified EEG was listened to by three inexperienced observers and the number of seizures and the processing time required for each data set were recorded blind to other data. Quantification of seizures from the patient diary was also assessed. Eleven waEEG recordings from seven CAE patients with an average age of 8.18 ± 1.60 years were included. No differences in the number of seizures were found between the recordings using automated methods and expert audio assessment, with significant correlations between methods (ρ > .89, p < .001) and between observers (ρ > .96, p < .001). For the entire data set, the audio assessment yielded a sensitivity of .830 and a precision of .841, resulting in an F1 score of .835. Auditory waEEG seizure detection by lay medical personnel provided similar accuracy to post-processed automatic detection by an experienced clinical neurophysiologist, but in a less time-consuming procedure and without the need for specialized resources. Sonification of long-term EEG recordings in CAE provides a user-friendly and cost-effective clinical workflow for quantifying seizures in clinical practice, minimizing human and technical constraints.

Associations of intrauterine exposure to manganese with fetal and early-childhood growth: a prospective prenatal cohort study.

Prenatal manganese (Mn) exposure may be related to poor birth outcomes; however, there are few relevant epidemiological reports on the effects of intrauterine Mn levels on intrauterine fetal and early childhood growth. From 2013 to 2016, 2082 pairs of mothers and infants were recruited in Wuhan, China, who provided an entire set of urine samples during their first, second, and third trimesters. Fetal head circumference (HC), abdominal circumference (AC), femoral length (FL), and estimated fetal weight (EFW) were obtained by ultrasound at the 16, 24, and 31weeks of pregnancy. When the children were born, 6months old, 12months old, and 24months old, their weight, height, weight-for-height, and BMI were measured. We used generalized linear models, generalized estimated equations, and restricted cubic spline curves (RCS) to investigate the linear and nonlinear relationships between antenatal Mn levels and fetal and early childhood growth. In all fetuses, Mn exposure during the 1st and 2nd gestation was associated with decreased fetal AC, FL, and EFW at 24weeks (e.g., for each doubling of urinary Mn concentrations during the 1st and 2nd gestation, the SD score of EFW at 24weeks decreased by - 4.16% (95% CI, - 6.22%, - 2.10%) and - 3.78% (95% CI, - 5.86%, - 1.70%)). Mn concentrations in the highest tertile group of the 1st and 2nd gestation were related to decreased fetus growth parameters compared to the lowest tertile group. For each doubling of the average Mn concentrations during pregnancy, the z-scores of weight, weight-for-height, and BMI at 12months decreased, with percentage changes of - 2.93% (95% CI, - 5.08%, - 0.79%), - 3.25% (95% CI, - 5.56%, - 0.94%), and - 3.09% (95% CI, - 5.44%, - 0.73%). In the RCS model, we found a reverse U-shaped association between 1st trimester Mn concentration and fetal FL at 16weeks and HC at 31weeks in male fetuses and a non-linear association between mean Mn concentration during pregnancy and girls' weight-for-height and BMI at 6months. Intrauterine exposure to Mn may be related to restricted growth in the fetus and early childhood, especially in fetuses at 24weeks of gestation and children at 12months of age. Also, meaningful curvilinear relationships were found in the sex stratification.

Closing the Organofluorine Mass Balance in Marine Mammals Using Suspect Screening and Machine Learning-Based Quantification.

High-resolution mass spectrometry (HRMS)-based suspect and nontarget screening has identified a growing number of novel per- and polyfluoroalkyl substances (PFASs) in the environment. However, without analytical standards, the fraction of overall PFAS exposure accounted for by these suspects remains ambiguous. Fortunately, recent developments in ionization efficiency (IE) prediction using machine learning offer the possibility to quantify suspects lacking analytical standards. In the present work, a gradient boosted tree-based model for predicting log IE in negative mode was trained and then validated using 33 PFAS standards. The root-mean-square errors were 0.79 (for the entire test set) and 0.29 (for the 7 PFASs in the test set) log IE units. Thereafter, the model was applied to samples of liver from pilot whales (n = 5; East Greenland) and white beaked dolphins (n = 5, West Greenland; n = 3, Sweden) which contained a significant fraction (up to 70%) of unidentified organofluorine and 35 unquantified suspect PFASs (confidence level 2-4). IE-based quantification reduced the fraction of unidentified extractable organofluorine to 0-27%, demonstrating the utility of the method for closing the fluorine mass balance in the absence of analytical standards.

Bias-free estimation of the covariance function and the power spectral density from data with missing samples including extended data gaps

Nonparametric estimation of the covariance function and the power spectral density of uniformly spaced data from stationary stochastic processes with missing samples is investigated. Several common methods are tested for their systematic and random errors under the condition of variations in the distribution of the missing samples. In addition to random and independent outliers, the influence of longer and hence correlated data gaps on the performance of the various estimators is also investigated. The aim is to construct a bias-free estimation routine for the covariance function and the power spectral density from stationary stochastic processes under the condition of missing samples with an optimum use of the available information in terms of low estimation variance and mean square error, and that independent of the spectral composition of the data gaps. The proposed procedure is a combination of three methods that allow bias-free estimation of the desired statistical functions with efficient use of the available information: weighted averaging over valid samples, derivation of the covariance estimate for the entire data set and restriction of the domain of the covariance function in a post-processing step, and appropriate correction of the covariance estimate after removal of the estimated mean value. The procedures abstain from interpolation of missing samples as well as block subdivision. Spectral estimates are obtained from covariance functions and vice versa using Wiener–Khinchin’s theorem.

Species-agnostic transfer learning for cross-species transcriptomics data integration without gene orthology.

Novel hypotheses in biomedical research are often developed or validated in model organisms such as mice and zebrafish and thus play a crucial role. However, due to biological differences between species, translating these findings into human applications remains challenging. Moreover, commonly used orthologous gene information is often incomplete and entails a significant information loss during gene-id conversion. To address these issues, we present a novel methodology for species-agnostic transfer learning with heterogeneous domain adaptation. We extended the cross-domain structure-preserving projection toward out-of-sample prediction. Our approach not only allows knowledge integration and translation across various species without relying on gene orthology but also identifies similar GO among the most influential genes composing the latent space for integration. Subsequently, during the alignment of latent spaces, each composed of species-specific genes, it is possible to identify functional annotations of genes missing from public orthology databases. We evaluated our approach with four different single-cell sequencing datasets focusing on cell-type prediction and compared it against related machine-learning approaches. In summary, the developed model outperforms related methods working without prior knowledge when predicting unseen cell types based on other species' data. The results demonstrate that our novel approach allows knowledge transfer beyond species barriers without the dependency on known gene orthology but utilizing the entire gene sets.

Application of Structural Equation Modelling to Cybersecurity Risk Analysis in the Era of Industry 4.0

In the current digital transformation to Industry 4.0, the demands on the ability of countries to react responsibly and effectively to threats in the field of cyber security (CS) are increasing. Cyber safety is one of the pillars and concepts of Industry 4.0, as digitization brings convergence and integration of information technologies (IT) and operational technologies (OT), IT/OT systems, and data. Collecting and connecting a large amount of data in smart factories and cities poses risks, in a broader context for the entire state. The authors focus attention on the issue of CS, where, despite all digitization, the human factor plays a key role—an actor of risk as well as strengthening the sustainability and resilience of CS. It is obvious that in accordance with how the individuals (decision-makers) perceive the risk, thus they subsequently evaluate the situation and countermeasures. Perceiving cyber threats/risks in their complexity as a part of hybrid threats (HT) helps decision-makers prevent and manage them. Due to the growing trend of HT, the need for research focused on the perception of threats by individuals and companies is increasing. Moreover, the literature review points out a lack of methodology and evaluation strategy. This study presents the results of the research aimed at the mathematical modelling of risk perception of threats to the state and industry through the disruption of CS. The authors provide the developed factor model of cyber security (FMCS), i.e., the model of CS threat risk perception. When creating the FMCS, the researchers applied SEM (structural equation modelling) and confirmatory factor analysis to the data obtained by the implementation of the research tool (a questionnaire designed by the authors). The pillars and sub-pillars of CS defined within the questionnaire enable quantification in the perception of the level of risk of CS as well as differentiation and comparison between the analyzed groups of respondents (students of considered universities in SK and CZ). The convergent and discriminant validity of the research instrument is verified, and its reliability is confirmed (Cronbach’s alpha = 0.95047). The influence of the individual pillars is demonstrated as significant at the significance level of α = 5%. For the entire research set N = 964, the highest share of risk perception of CS threats is achieved by the DISRIT pillar (disruption or reduction of the resistance of IT infrastructure).

TOPICS SURROUNDING THE COMBINATORIAL ANABELIAN GEOMETRY OF HYPERBOLIC CURVES IV: DISCRETENESS AND SECTIONS

Abstract Let $\Sigma $ be a nonempty subset of the set of prime numbers which is either equal to the entire set of prime numbers or of cardinality one. In the present paper, we continue our study of the pro- $\Sigma $ fundamental groups of hyperbolic curves and their associated configuration spaces over algebraically closed fields in which the primes of $\Sigma $ are invertible. The present paper focuses on the topic of comparison between the theory developed in earlier papers concerning pro- $\Sigma $ fundamental groups and various discrete versions of this theory. We begin by developing a theory concerning certain combinatorial analogues of the section conjecture and Grothendieck conjecture. This portion of the theory is purely combinatorial and essentially follows from a result concerning the existence of fixed points of actions of finite groups on finite graphs (satisfying certain conditions). We then examine various applications of this purely combinatorial theory to scheme theory. Next, we verify various results in the theory of discrete fundamental groups of hyperbolic topological surfaces to the effect that various properties of (discrete) subgroups of such groups hold if and only if analogous properties hold for the closures of these subgroups in the profinite completions of the discrete fundamental groups under consideration. These results make possible a fairly straightforward translation, into discrete versions, of pro- $\Sigma $ results obtained in previous papers by the authors. Finally, we discuss a construction that was considered previously by M. Boggi in the discrete case from the point of view of the present paper.

"A Rare Vocabulary Dictionary based on school curriculum literature": theoretical issues of creation and methodological prospects for use

The article describes the principles of compiling the Rare Vocabulary Dictionary based on school curriculum literature (hereinafter referred to as the Dictionary) and its methodological potential for being used as an electronic educational resource in teaching schoolchildren. Moreover, the study examines the specific features of the Dictionary in comparison with similar existing lexicographic publications. The Dictionary creation is relevant as there is a need to make Russian classical literature texts more understandable for the modern reader. As a result, the resource can promote the culture of reading literary texts and the formation of students’ linguistic personality, as well as enhance learning efficiency. The Dictionary materials were comprised of Russian authors’ works included in the school literature curriculum for years 5 to 11. The paper pays particular attention to the innovative solutions concerning the principles of glossary compilation and dictionary entry creation. The research relied on corpus linguistics methodology and its tools for working with text, which enabled us to select vocabulary based on objective criteria (low frequency in modern Russian) and describe the meaning of a particular word relying on the entire set of contexts of its use in the literature under study. The focus of lexicographic interpretation was an extensive layer of rare vocabulary of various semantic groups (including proper names and occasionalisms) and different parts of speech. Semantic description demonstrates the history of word meanings in the language in conjunction with contextual interpretation of a lexical unit. The entries contain encyclopedic information and a detailed cultural and/or philological commentary. Maximal attention is given to authorial neologisms. Some entries are accompanied by images of the item being described. The Dictionary is implemented as a module of the new Russian Classics cross platform mobile application. The article also describes the operating principles and capabilities of the application.

Risk Assessment of Potential Injury during School Chemical Experiments Supported by the Database of Safe and Didactically Validated Procedures e-BEDOX

Considering the importance of the use of experiment as a source of knowledge in chemistry and its teaching, it is necessary to pay attention to the risks of its implementation. The text presents the procedures and results of a risk assessment of 101 selected school chemical experiments that are used in lower secondary school practice, including their inclusion in textbooks, as well as in leisure activities. The actual risk assessment of these chemical experiments has been carried out using the Ishikawa diagram, also known as the cause and effect diagram, in order to highlight the possible hazards involved in their conduct. In relation to the identified risks, the existing instructions for all selected experiments were evaluated and new information and safety instructions were added to meet the requirements for safe and didactically validated procedures according to standard CSN 01 8003. As the processed experiments contain chemicals of virtually all hazard classes, it is very important that all real risks are taken into account when using them in schools. The entire set of produced materials, including the original video recordings, has been processed into an electronic database e‑BEDOX, which is available at: https://ebedox.cz.

Uniaxial ratcheting and ductile damage in structural steel with a stochastic spreading of experimental data

AbstractThis paper provides new experimental data regarding inelastic strain accumulation in structural steel samples. The samples are subjected to stress‐controlled cyclic loading with positive mean stress within each cycle; the loading blocks are combined in a complex pattern to study the effect of load history. A large‐strain ratcheting model is constructed based on a material law with a nonlinear kinematic and isotropic hardening. A new rule of ductile damage accumulation is suggested and a class of models with hardening stagnation is introduced. We show that models which include the hardening stagnation describe the experimental data more accurately. Unfortunately, due to a large spread of mechanical properties from sample to sample, it is impossible to describe the entire set of experiments using a single set of parameters. To address this challenge, we have developed novel mathematical tools for parameter identification in the context of stochastic variations in mechanical properties. These tools include the concept of a collar of model responses and the notion of protrusion. We propose an extended parameter identification problem that rethinks the standard error functional. We demonstrate that the extended identification problem effectively accounts for the large spread observed in experimental data when applied to the developed ratcheting model.

Content analysis of AI-generated (ChatGPT) responses concerning orthodontic clear aligners.

To assess the accuracy of ChatGPT answers concerning orthodontic clear aligners. A cross-sectional content analysis of ChatGPT generated responses to queries related to clear aligner treatment (CAT) was undertaken. A total of 111 questions were generated by three orthodontists based on a set of predefined domains and subdomains. The artificial intelligence (AI)-generated (ChatGPT) answers were extracted and their accuracy was determined independently by five orthodontists. The accuracy of answers was assessed using a prepiloted four-point scale scoring rubric. Descriptive statistics were performed. The total mean accuracy score for the entire set was 2.6 ± 1.1. It was noted that 58% of the AI-generated answers were scored as objectively true, 18% were selected facts, 9% were minimal facts, and 15% were false. False claims included the ability of CAT to reduce the need for orthognathic surgery (4.0 ± 0.0), improve airway function (3.8 ± 0.5), achieve root parallelism (3.6 ± 0.5), alleviate sleep apnea (3.8 ± 0.5), and produce more stable results compared to fixed appliances (3.8 ± 0.5). The overall level of accuracy of ChatGPT responses to questions concerning CAT was suboptimal and lacked citations to relevant literature. Ability of the software to offer current and precise information was limited. Therefore, clinicians and patients must be mindful of false claims and relevant facts omitted in the answers generated by ChatGPT.

A Raman spectroscopy based chemometric approach to predict the derived cetane number of hydrocarbon jet fuels and their mixtures

Fuel ignition quality, measured in the form of Derived Cetane Number (DCN), is an important part of integrating fuels, including sustainable aviation fuels, in compression ignition engines. DCN has been correlated with simulated and/or real spectroscopic measurements as well as other physical and chemical properties, but rarely have these correlations developed into a pathway to application. One application of the correlations is the use of miniaturized onboard fuel sensors that could assist, by using predicted DCN, in real-time feedforward engine control. To aid in the application of developing such DCN fuel sensors, Raman spectra coupled with chemometrics and a selection of influential spectral features were investigated. In this study, the Raman spectra were obtained from a database that included jet fuels, jet fuel mixtures, pure hydrocarbon components, and their weighted mixtures. The resulting Raman spectral database from the experimental measurements included spectra of components that span a wide range of DCNs and covered all the expected chemical functional groups present in a standard jet fuel. Chemometric models were developed to associate Raman spectra with DCN in subsets of the spectral range to aid in sensor miniaturization. The models were tested on jet fuels such as National Jet Fuel Combustion Program fuels designated A-1, A-2, and A-3 along with mixtures of jet fuels that spanned a wide range of DCN, simulating fuels that could represent real-world scenarios. An Artificial Neural Network (ANN) model trained on the fingerprint region (500 cm−1 – 1800 cm−1) of the Raman spectra was able to capture the non-linearity of the association between the Raman spectra and DCN with a test R2 score of 0.926, a test MSE of 3.61, and a test MPE of 3.41. Around 97 % of the unseen test samples were predicted within 10 % of the DCN measured with an Ignition Quality Tester. One hundred features of the fingerprint region influencing DCN predictions in the optimal ANN model were extracted using a Global Surrogate (GS) model. A reduced ANN model trained on only these one hundred features performed slightly better with a test R2 score of 0.935, test MSE of 3.19, test MPE of 3.20 and with the entire set of unseen test samples predicted within 10 % of the measured DCN. For assessing applicability of real-time and online DCN sensing, the Raman spectrometer was integrated with a flow cell capable of allowing measurements of DCN in flowing fuel samples and included the optimal ANN model of the fingerprint region and the 100-feature GS-ANN model on a Raspberry Pi computer. A number of unseen F-24/alcohol-to-jet fuel mixtures composed of unknown volumes were tested using the flow cell for DCN, and all of these samples were predicted within 10 % of the measured DCN.

Structural Shifts in the Soil Prokaryotic Communities Marking the Podzol-Forming Process on Sand Dumps

This work describes the microbial community structure of the continuously revegetated chronosequence of a former sand quarry, which demonstrates a unique example of nearly complete soil restoration in less than 100 years. Samples were collected at five time points (0, 3, 30, 70 years and mature soil) from the entire set of soil horizons, revealing the history of pedogenesis. Real-time PCR was applied to quantitatively describe the bacterial and archaeal communities. High-throughput sequencing of the bacterial and archaeal V4 variable region of the 16S rRNA gene was used to identify abundant microbial taxa. A beta-diversity analysis revealed that the prokaryotic community structure responded strongly to the processes of organic matter accumulation and the corresponding evolution of the soil into discrete horizons. Changes in soil microbiota in the course of soil profile evolution revealed three groups of prokaryotes, which tended to accumulate in the specific soil horizons and might be associated with the certain soil-forming processes, including plant roots growth. This research showed the heuristic potential of soil horizon profiling in microbiological studies as opposed to the formal depth-dependent separation of the soil layers. The results allowed us to trace the relationship between the structure of the soil prokaryotic community and the peculiarities of the evolution of the podzolic soil profile as well as to identify the microbial indicators and drivers of primary pedogenesis.

The negative footprint illusion is exacerbated by the numerosity of environment-friendly additions: unveiling the underpinning mechanisms

ABSTRACT The addition of environmentally friendly items to conventional items sometimes leads people to believe that the carbon footprint of the entire set decreases rather than increases. This negative footprint illusion is supposedly underpinned by an averaging bias: people base environmental impact estimates not on the total impact of items but on their average. Here, we found that the illusion’s magnitude increased with the addition of a greater number of “green” items when the number of conventional items remained constant (Studies 1 and 2), supporting the averaging-bias account. We challenged this account by testing what happens when the number of items in the conventional and “green” categories vary while holding the ratio between the two categories constant (Study 3). At odds with the averaging-bias account, the magnitude of the illusion increased as the category size increased, revealing a category-size bias, and raising questions about the interplay between these biases in the illusion.

Evaluation of artificial intelligence for detecting periapical lesions on panoramic radiographs

Background/Aim: The aim of this study is to evaluate the function of diagnostic computer software designed for the detection of periapical lesions on panoramic images with deep learning methods. Material and Methods: In our study, more than 500 adult retrospective panoramic radiography images obtained randomly were evaluated, and periapical lesions were labeled on the radiographs with the ResultLab.Ai labeling program (ResultLab.AI, Istanbul, Turkey). All images have been rechecked and verified by Oral, Dental and Maxillofacial Radiology experts. The model used is a U-Net based architecture customized for dental radiographs and optimized for fast operation. What is meant by customization is the structures called "Attention Gate" added to the architecture to draw the model's attention to the lesions. Results: Mask estimation was made separately for each image and F1 and IoU scores were calculated by comparing them with the marked masks. A list was created from the calculated F1 and IoU scores. The F1 and IoU score of the entire data set was calculated by taking the average of the values in this list. In IoU score calculation, Keras library's Mean IoU metric was used to calculate for 2 classes. In the F1 score calculation, the F1 score metric of the SKLearn library was used to calculate the unweighted average for 2 classes. As a result, the IoU-Score was obtained as 0.8578 and the F1-Score as 0.8587. Conclusions: Detection of periapical lesions on panoramic radiographs with an artificial intelligence approach will help practitioners reach definitive conclusions even in lesions that are likely to be overlooked. In addition, success rates for deep learning methods improve as data set size grows. As the number of images rises, the training models' success rates will follow.

Voxel-Wise Brain Graphs From Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Goal:</i> Structural brain graphs are conventionally limited to defining nodes as gray matter regions from an atlas, with edges reflecting the density of axonal projections between pairs of nodes. Here we explicitly model the entire set of voxels within a brain mask as nodes of high-resolution, subject-specific graphs. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> We define the strength of local voxel-to-voxel connections using diffusion tensors and orientation distribution functions derived from diffusion MRI data. We study the graphs' Laplacian spectral properties on data from the Human Connectome Project. We then assess the extent of inter-subject variability of the Laplacian eigenmodes via a procrustes validation scheme. Finally, we demonstrate the extent to which functional MRI data are shaped by the underlying anatomical structure via graph signal processing. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> The graph Laplacian eigenmodes manifest highly resolved spatial profiles, reflecting distributed patterns that correspond to major white matter pathways. We show that the intrinsic dimensionality of the eigenspace of such high-resolution graphs is only a mere fraction of the graph dimensions. By projecting task and resting-state data on low-frequency graph Laplacian eigenmodes, we show that brain activity can be well approximated by a small subset of low-frequency components. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusions:</i> The proposed graphs open new avenues in studying the brain, be it, by exploring their organisational properties via graph or spectral graph theory, or by treating them as the scaffold on which brain function is observed at the individual level.

Purification of Circular RNAs Using Poly(A) Tailing Followed by RNase R Digestion.

Thousands of eukaryotic protein-coding genes can be alternatively spliced to yield linear mRNAs and circular RNAs (circRNAs). Some circRNAs accumulate to higher levels than their cognate linear mRNAs, but the vast majority are expressed at low levels. Hence, for most circRNAs, only a handful of sequencing reads, if any, that span the backsplicing junction are observed in standard RNA-seq libraries. It thus has become common to use the 3'-5' exonuclease ribonuclease R (RNase R) to selectively degrade linear RNAs when aiming to prove transcript circularity or biochemically enrich circRNAs. However, RNase R fails to degrade linear RNAs with structured 3' ends or internal G-quadruplex structures. To overcome these shortcomings, we describe an improved protocol for circRNA purification from total RNA that employs a poly(A) tailing step prior to RNase R digestion, which is performed in a Li+ containing buffer (rather than K+) to destabilize G-quadruplexes. This biochemical method enables higher enrichment (two- to threefold) of circRNAs to be obtained compared to standard RNase R protocols due to more efficient removal of linear RNAs. By then performing quantitative RT-PCR (RT-qPCR) or generating RNA-seq libraries, the expression of individual circRNAs can be examined or the entire set of expressed circRNAs defined using established annotation algorithms. We describe step-by-step methods for annotating circRNAs using the CIRI2 and CIRCexplorer2 algorithms. In total, this overall approach can be used to enrich for circRNAs from any total RNA sample, thereby enabling one to quickly identify and validate circRNAs of interest for functional studies.

First-principles evaluation of dopant impact on structural deformability and processability of Li7La3Zr2O12.

Li7La3Zr2O12 (LLZO) and related ceramic solid electrolytes feature excellent stability and reasonable ionic conductivity, but processing remains challenging. High-temperature co-sintering is required for successful integration with the electrode, which is energetically costly and can lead to unacceptable cathode degradation. The introduction of dopants can promote lower-temperature processing by improving deformability and disrupting lattice integrity; however, an unbiased, systematic study correlating these properties to the dopant chemistry and composition is lacking. Here, we rely on a set of static and dynamic metrics derived from first-principles simulations to estimate the impact of doping on LLZO processability by quantifying LLZO structural deformability. We considered three distinct dopants (Al, Ba, and Ta) as representatives of substitutional incorporation on Li, La, and Zr sites. Our descriptors indicate that doping in general positively impacts lattice deformability, although significant sensitivities to dopant identity and concentration are observed. Amongst the tested dopants, Al doping (on the Li site) appears to have the greatest impact, as signaled across nearly the entire set of computed features. We suggest that these proxy descriptors, once properly calibrated against well-controlled experiments, could enable the use of first-principles simulations to computationally screen new ceramic electrolyte compositions with improved processability.