bluuebunny/crossref_metadata_2025_split huggingface dataset:
Determining the Timing of Driver Influences on 1.8–3.5 MeV Electron Flux at Geosynchronous Orbit Using ARMAX Methodology and Stepwise Regression
Authors: Simms L. E. , Engebretson M. J. , Reeves G. D. 
Published: January 2023

AbstractAlthough lagged correlations have suggested influences of solar wind velocity (V) and number density (N), Bz, ultralow frequency (ULF) wave power, and substorms (as measured by the auroral electrojet (AE) index) on MeV electron flux at geosynchronous orbit over an impressive number of hours and days, a satellite's diurnal cycle can inflate correlations, associations between drivers may produce spurious effects, and correlations between all previous time steps may create an appearance of additive influence over many hours. Autoregressive‐moving average transfer function (ARMAX) multiple regressions incorporating previous hours simultaneously can eliminate cycles and assess the impact of parameters, at each hour, while others are controlled. ARMAX influences are an order of magnitude lower than correlations uncorrected for time behavior. Most influence occurs within a few hours, not the many hours suggested by correlation. A log transformation accounts for nonlinearities. Over all hours, solar wind velocity (V) and number density (N) show an initial negative impact, with longer term positive influences over the 9 (V) or 27 (N) hr. Bz is initially a positive influence, with a longer term (6 hr) negative effect. ULF waves impact flux in the first (positive) and second (negative) hour before the flux measurement, with further negative influences in the 12–24 hr before. AE (representing electron injection by substorms) shows only a short term (1 hr) positive influence. However, when only recovery and after‐recovery storm periods are considered (using stepwise regression), there are positive influences of ULF waves, AE, and V, with negative influences of N and Bz.
An Algorithm to Separate Ionospheric Turbulence Radar Echoes From Those of Meteor Trails in Large Data Sets
Authors: Ivarsen Magnus F. , St‐Maurice Jean‐Pierre , Hussey Glenn C. , Galeschuk Draven , Lozinsky Adam , Pitzel Brian , McWilliams Kathryn A. 
Published: January 2023

AbstractCoherent scatter echoes from disintegrating meteors and from the unstable ionospheric E‐region can overlap considerably between 90 and 110 km altitudes. As the physical origin of plasma irregularities produced by meteor trails differs starkly from that of E‐region auroral irregularities, this has consequences for winds as well as electrodynamic studies, thereby introducing a need to distinguish between the two types of echoes. To that goal, we have developed a novel separation algorithm to automatically sort through arbitrarily large data sets in the region of overlap. This proves very useful when the 3D location of echoes is available. The algorithm uses a definition of crowding, or clustering, in both time and space and has been developed and tested with a comprehensive data set obtained from the recently built Canadian icebear 3D radar. We discuss the characteristics belonging to the two classes of echoes, and present statistical results about the location of each type of echo as a function of conditions. Our proposed algorithm can be applied to any coherent scatter echo data with high resolution 3D location information.
OntoCAT - a simpler way to access ontology resources
Authors: Adamusiak Tomasz , Burdett Tony , van der Velde K Joeri , Abeygunawardena Niran , Antonakaki Despoina , Parkinson Helen , Swertz Morris 
Published: January 2023

AbstractOntoCAT is an open source package developed to simplify the task of querying heterogeneous ontology resources. It supports local ontologies in OBO and OWL format as well as public repositories NCBO BioPortal and EBI Ontology Lookup Service (OLS). It is available from "http://ontocat.sourceforge.net":http://ontocat.sourceforge.net
Detection of Nonlinear Effects in Gene Expression Pathways
Authors: Mayr Andreas , Clevert Djork-Arne , Hochreiter Sepp 
Published: January 2023

AbstractOne of the main topics in systems biology is to model genetic pathways. Genes of a pathway, which show linear dependencies of their expression values, are easy to identify to belong to the pathway. However, if feedback loops or signal cascades are present, gene expression values of pathway genes can be nonlinearly dependent on the expression values of other genes in the pathway. In this situation such genes are hard to detect as belonging to the pathway because nonlinearity and noise must be distinguished.We propose an algorithm to infer nonlinear network elements in pathways from microarray data. Our model assumes, that gene expression values, belonging to one pathway, are mainly driven by one single latent factor. We expect that there are two groups of genes in a pathway: genes belonging to the first group are linearly dependent on the hidden factor, genes from the other group show a nonlinear dependence from the latent variable. The goal is to identify the kind of dependence from the hidden factor.Our algorithm for detecting nonlinear effects is an extension of linear Gaussian factor analysis. Nonlinearities are modelled by the square of the latent variable weighted by specific coefficients. We derived a novel model selection method for this generalization of factor analysis. To avoid the interpretation of noise as nonlinearity, we determine p-values that measure the probability of a linear gene being detected by chance as nonlinear. We apply our algorithm to microarray data of breast cancer samples, where we identified nonlinear dependencies of gene expression values in the p53 pathway.
The true story behind the annotation of a pathway
Authors: Dall’Olio Giovanni Marco , Laayouni Hafid 
Published: January 2023

AbstractOn 2010 we worked on the annotation of the N-Glycosylation pathway in the Reactome database. During this process, we found some unclear points and errors in other databases, and we reported and helped fix them. After we finished, we realized that the work of reporting errors to a database is basically not acknowledged by the scientific community: this is unfortunate because if only this process would be a bit more recognized and transparent, we could have better data in the databases and a more active community. Moreover, the fact that many databases tend to keep error reporting private creates great issues to the reproducibility of a work.Another way to look at this talk is: if you dedicate 6 months of your PhD thesis to annotate carefully a set of genes, in this case a pathway I have been studying, how many errors do you expect to find in other databases, or what should you be careful at?
Comments and Suggestions for Improvement of the Archon Genomics X PRIZE Validation Protocol
Authors: Zaranek Alexander Wait , Clegg Tom , Vandewege Ward , Thakuria Joseph 
Published: January 2023

AbstractThis document is a comment on the X PRIZE validation protocol written by Kedes et al. (2011). We propose several modifications which we think will improve the fairness and transparency of the contest while keeping the cost of the validation process under control.
Difference in response reliability predicted by STRFs in the cochlear nuclei of barn owls
Authors: Steinberg Louisa , Pena Jose 
Published: January 2023

AbstractThe brainstem auditory pathway is obligatory for all aural information. Brainstem auditory neurons must encode the level and timing of sounds, as well as their time-dependent spectral properties, the fine structure and envelope, which are essential for sound discrimination. This study focused on envelope coding in the two cochlear nuclei of the barn owl, nucleus angularis (NA) and nucleus magnocellularis (NM). NA and NM receive input from bifurcating auditory nerve fibers and initiate processing pathways specialized in encoding interaural time (ITD) and level (ILD) differences, respectively. We found that NA neurons, though unable to accurately encode stimulus phase, lock more strongly to the stimulus envelope than NM units. The spectrotemporal receptive fields (STRFs) of NA neurons exhibit a pre-excitatory suppressive field. Using multilinear regression analysis and computational modeling, we show that this feature of STRFs can account for enhanced across-trial response reliability, by locking spikes to the stimulus envelope. Our findings indicate a dichotomy in envelope coding between the time and intensity processing pathways as early as at the level of the cochlear nuclei. This allows the ILD processing pathway to encode envelope information with greater fidelity than the ITD processing pathway. Furthermore, we demonstrate that the properties of the neurons’ STRFs can be quantitatively related to spike timing reliability.
Voltage-sensitive dye imaging reveals tonotopic organization of auditory cortex spontaneous activity
Authors: Farley Brandon , Norena Arnaud 
Published: January 2023

AbstractImaging neural activity across a large (several mm) cortical area with high temporal and spatial resolution is desirable, for example in the auditory system to measure cortical processing across a broad frequency spectrum. Voltage-sensitive dye imaging (VSDI) has a unique combination of properties making this possible, but so far studies have been limited to studying simple sparsely-presented sensory stimuli. We demonstrate the feasibility of long-acquisition VSDI (using the dye RH-1691) in auditory cortex while presenting complex time-varying acoustic stimuli or silence. Using a dense array of partially-overlapping 50 ms tone pips (8 frequencies per octave spanning six octaves), we obtained high-resolution spectrotemporal receptive fields (STRFs) simultaneously across the majority of the guinea pig primary auditory cortical fields (A1 and DC). Long epochs of spontaneous activity were also measured, permitting a comparison of spontaneous activity patterns with functional architecture. By grouping all pixels in areas A1 and DC according to sound frequency preference (obtained from STRFs), we reveal that spontaneous activity (such as cortical spindles) show complex spatial patterns, which are organized according to sound frequency preference within and across cortical areas. More specifically, spontaneous activity correlation decreases as frequency preference diverges within A1 or DC; but additionally, pixels in A1 are also highly correlated with (even far-away) pixels in DC sharing similar frequency preference. These properties of patterned cortical spontaneous activity constrain mechanistic hypotheses regarding their genesis. Beyond these observations, the feasibility of VSDI with continuous stimulation or silence permits measuring population activity during long-lasting sound patterns, which is necessary for examining cortical dynamics and sensory-context dependent processing.
Optimal decision bounds for probabilistic population codes and time varying evidence
Authors: Drugowitsch Jan , Moreno-Bote Rubén , Pouget Alexandre 
Published: January 2023

AbstractDecision making under time constraints requires the decision maker to trade off between making quick, inaccurate decisions and gathering more evidence for more accurate, but slower decisions. We have previously shown that, under rather general settings, optimal behavior can be described by a time-dependent decision bound on the decision maker’s belief of being correct (Drugowitsch, Moreno-Bote, Pouget, 2009). In cases where the reliability of sensory information remains constant over time, we have shown how to design diffusion models (DMs) with time-changing boundaries that feature such behavior. Such theories can be easily mapped onto simple neural models of decision making with two perfectly anti-correlated neurons, where they predict the existence of a stopping bound on the most active neurons. It is unclear however how the stopping bound would be implemented with more realistic neural population codes, particularly when the reliability of the evidence changes over time.Here we show that, under certain realistic conditions, we can apply the theory of optimal decision making to the biologically more plausible probabilistic population codes (PPCs; Ma et al. 2006). Our analysis shows that, with population codes, the optimal decision bounds are a function of the neural activity of all neurons in the population, rather than a previously postulated bound on its maximum activity. This theory predicts that the bound on the most active neurons would appear to shift depending on the firing rate of other neurons in the population, a puzzling behavior under the drift diffusion model as it would wrongly suggest that subjects change their stopping rule across conditions. This theory also applies to the case of time varying evidence, a case that cannot be handled by drift diffusion models without unrealistic assumptions.
Learning complex tasks with probabilistic population codes
Authors: Turner Richard , Berkes Pietro , Fiser Jozsef 
Published: January 2023

AbstractRecent psychophysical experiments imply that the brain employs a neural representation of the uncertainty in sensory stimuli and that probabilistic computations are supported by the cortex. Several candidate neural codes for uncertainty have been posited including Probabilistic Population Codes (PPCs). PPCs support various versions of probabilistic inference and marginalisation in a neurally plausible manner. However, in order to establish whether PPCs can be of general use, three important limitations must be addressed. First, it is critical that PPCs support learning. For example, during cue combination, subjects are able to learn the uncertainties associated with the sensory cues as well as the prior distribution over the stimulus. However, previous modelling work with PPCs requires these parameters to be carefully set by hand. Second, PPCs must be able to support inference in non-linear models. Previous work has focused on linear models and it is not clear whether non-linear models can be implemented in a neurally plausible manner. Third, PPCs must be shown to scale to high-dimensional problems with many variables. This contribution addresses these three limitations of PPCs by establishing a connection with variational Expectation Maximisation (vEM). In particular, we show that the usual PPC update for cue combination can be interpreted as the E-Step of a vEM algorithm. The corresponding M-Step then automatically provides a method for learning the parameters of the model by adapting the connection strengths in the PPC network in an unsupervised manner. Using a version of sparse coding as an example, we show that the vEM interpretation of PPC can be extended to non-linear and multi-dimensional models and we show how the approach scales with the dimensionality of the problem. Our results provide a rigorous assessment of the ability of PPCs to capture the probabilistic computations performed in the cortex.
Critical functional lung volumes in neonatal intensive care: evidence and clinical applications
Authors: Dassios Theodore 
Published: January 2023

Abstract
Respiratory disease is common in premature and sick newborn infants and can often necessitate the initiation of intensive care. Newborn infants often suffer from conditions that are associated with decreased lung volumes that occur as a result of abnormal or incomplete lung development. Such conditions are prematurity and respiratory distress syndrome, preterm premature rupture of membranes and the ensuing pulmonary hypoplasia and congenital lung anomalies such as congenital diaphragmatic hernia. These diseases have a structural component manifesting with lower lung volumes and a functional component that can present with increased oxygen and ventilatory requirements. The corresponding decreased functional lung volume is possibly responsible for some unfavourable pulmonary outcomes. Some infants are unable to wean off invasive respiratory support and, in extreme cases, unable to sustain independent breathing that can lead to long-term invasive ventilation or subsequent death. The aim of this review is to summarise the available evidence behind the concept of a critical functional lung volume in neonatal intensive care and describe the clinical implications that arise from decreased functional lung volumes in the main high-risk populations of newborn infants.

Impact

Newborn infants suffer from diseases such as respiratory distress syndrome, pulmonary hypoplasia and congenital diaphragmatic hernia that are associated with a decrease in the total lung volume and impaired lung function.

Critically decreased functional lung volumes during neonatal care are associated with failure to wean off invasive respiratory support, increased mortality and possibly longer-term respiratory complications.
Circular RNA circPOFUT1 enhances malignant phenotypes and autophagy-associated chemoresistance via sequestrating miR-488-3p to activate the PLAG1-ATG12 axis in gastric cancer
Authors: Luo Ming , Deng Xiaofeng , Chen Zonglin , Hu Yongjun 
Published: January 2023

AbstractCircular RNAs are key regulators in regulating the progression and chemoresistance of gastric cancer (GC), suggesting circular RNAs as potential therapeutic targets for GC. The roles of a novel circular RNA circPOFUT1 in GC are unknown. Here, we found that circPOFUT1 was upregulated in GC tissues and cells, and increased circPOFUT1 expression indicated poor prognosis. Overexpression of circPOFUT1 enhanced cell proliferation, migration, invasion and autophagy-associated chemoresistance in GC, which were suppressed by miR-488-3p overexpression. CircPOFUT1 reduced miR-488-3p expression via sponging miR-488-3p in GC cells. PLAG1 interacted with ATG12 and promoted its expression. MiR-488-3p bound to PLAG1 and suppressed the expression of PLAG1 and ATG12 in GC cells. Overexpression of circPOFUT1 enhanced autophagy-associated chemoresistance of GC cells in vivo, but it was inhibited by overexpression of miR-488-3p. Collectively, circPOFUT1 directly sponged miR-488-3p to activate the expression of PLAG1 and ATG12, thus enhancing malignant phenotypes and autophagy-associated chemoresistance in GC. Our findings show the potential of circPOFUT1 as biomarkers and targeting circPOFUT1 as a therapeutic strategy for GC.
Biophysical impacts of earth greening can substantially mitigate regional land surface temperature warming
Authors: Li Yitao , Li Zhao-Liang , Wu Hua , Zhou Chenghu , Liu Xiangyang , Leng Pei , Yang Peng , Wu Wenbin , Tang Ronglin , Shang Guo-Fei , Ma Lingling 
Published: January 2023

AbstractVegetation change can alter surface energy balance and subsequently affect the local climate. This biophysical impact has been well studied for forestation cases, but the sign and magnitude for persistent earth greening remain controversial. Based on long-term remote sensing observations, we quantify the unidirectional impact of vegetation greening on radiometric surface temperature over 2001–2018. Here, we show a global negative temperature response with large spatial and seasonal variability. Snow cover, vegetation greenness, and shortwave radiation are the major driving factors of the temperature sensitivity by regulating the relative dominance of radiative and non-radiative processes. Combined with the observed greening trend, we find a global cooling of −0.018 K/decade, which slows down 4.6 ± 3.2% of the global warming. Regionally, this cooling effect can offset 39.4 ± 13.9% and 19.0 ± 8.2% of the corresponding warming in India and China. These results highlight the necessity of considering this vegetation-related biophysical climate effect when informing local climate adaptation strategies.
The diverse meteorology of Jezero crater over the first 250 sols of Perseverance on Mars
Authors: Rodriguez-Manfredi J. A. , de la Torre Juarez M. , Sanchez-Lavega A. , Hueso R. , Martinez G. , Lemmon M. T. , Newman C. E. , Munguira A. , Hieta M. , Tamppari L. K. , Polkko J. , Toledo D. , Sebastian E. , Smith M. D. , Jaakonaho I. , Genzer M. , De Vicente-Retortillo A. , Viudez-Moreiras D. , Ramos M. , Saiz-Lopez A. , Lepinette A. , Wolff M. , Sullivan R. J. , Gomez-Elvira J. , Apestigue V. , Conrad P. G. , Del Rio-Gaztelurrutia T. , Murdoch N. , Arruego I. , Banfield D. , Boland J. , Brown A. J. , Ceballos J. , Dominguez-Pumar M. , Espejo S. , Fairén A. G. , Ferrandiz R. , Fischer E. , Garcia-Villadangos M. , Gimenez S. , Gomez-Gomez F. , Guzewich S. D. , Harri A.-M. , Jimenez J. J. , Jimenez V. , Makinen T. , Marin M. , Martin C. , Martin-Soler J. , Molina A. , Mora-Sotomayor L. , Navarro S. , Peinado V. , Perez-Grande I. , Pla-Garcia J. , Postigo M. , Prieto-Ballesteros O. , Rafkin S. C. R. , Richardson M. I. , Romeral J. , Romero C. , Savijärvi H. , Schofield J. T. , Torres J. , Urqui R. , Zurita S. ,   the MEDA team
Published: January 2023

AbstractNASA’s Perseverance rover’s Mars Environmental Dynamics Analyzer is collecting data at Jezero crater, characterizing the physical processes in the lowest layer of the Martian atmosphere. Here we present measurements from the instrument’s first 250 sols of operation, revealing a spatially and temporally variable meteorology at Jezero. We find that temperature measurements at four heights capture the response of the atmospheric surface layer to multiple phenomena. We observe the transition from a stable night-time thermal inversion to a daytime, highly turbulent convective regime, with large vertical thermal gradients. Measurement of multiple daily optical depths suggests aerosol concentrations are higher in the morning than in the afternoon. Measured wind patterns are driven mainly by local topography, with a small contribution from regional winds. Daily and seasonal variability of relative humidity shows a complex hydrologic cycle. These observations suggest that changes in some local surface properties, such as surface albedo and thermal inertia, play an influential role. On a larger scale, surface pressure measurements show typical signatures of gravity waves and baroclinic eddies in a part of the seasonal cycle previously characterized as low wave activity. These observations, both combined and simultaneous, unveil the diversity of processes driving change on today’s Martian surface at Jezero crater.
Laplacian renormalization group for heterogeneous networks
Authors: Villegas Pablo , Gili Tommaso , Caldarelli Guido , Gabrielli Andrea 
Published: January 2023

AbstractThe renormalization group is the cornerstone of the modern theory of universality and phase transitions and it is a powerful tool to scrutinize symmetries and organizational scales in dynamical systems. However, its application to complex networks has proven particularly challenging, owing to correlations between intertwined scales. To date, existing approaches have been based on hidden geometries hypotheses, which rely on the embedding of complex networks into underlying hidden metric spaces. Here we propose a Laplacian renormalization group diffusion-based picture for complex networks, which is able to identify proper spatiotemporal scales in heterogeneous networks. In analogy with real-space renormalization group procedures, we first introduce the concept of Kadanoff supernodes as block nodes across multiple scales, which helps to overcome detrimental small-world effects that are responsible for cross-scale correlations. We then rigorously define the momentum space procedure to progressively integrate out fast diffusion modes and generate coarse-grained graphs. We validate the method through application to several real-world networks, demonstrating its ability to perform network reduction keeping crucial properties of the systems intact.