Miracle-dz/UniFAR-FTUs huggingface dataset:
Inadequacy of common stochastic neural networks for reliable clinical decision support

Widespread adoption of AI for medical decision making is still hindered due to ethical and safety-related concerns. For AI-based decision support systems in healthcare settings it is paramount to be reliable and trustworthy. Common deep learning approaches, however, have the tendency towards overconfidence under data shift. Such inappropriate extrapolation beyond evidence-based scenarios may have dire consequences. This highlights the importance of reliable estimation of local uncertainty and its communication to the end user. While stochastic neural networks have been heralded as a potential solution to these issues, this study investigates their actual reliability in clinical applications. We centered our analysis on the exemplary use case of mortality prediction for ICU hospitalizations using EHR from MIMIC3 study. For predictions on the EHR time series, Encoder-Only Transformer models were employed. Stochasticity of model functions was achieved by incorporating common methods such as Bayesian neural network layers and model ensembles. Our models achieve state of the art performance in terms of discrimination performance (AUC ROC: 0.868+-0.011, AUC PR: 0.554+-0.034) and calibration on the mortality prediction benchmark. However, epistemic uncertainty is critically underestimated by the selected stochastic deep learning methods. A heuristic proof for the responsible collapse of the posterior distribution is provided. Our findings reveal the inadequacy of commonly used stochastic deep learning approaches to reliably recognize OoD samples. In both methods, unsubstantiated model confidence is not prevented due to strongly biased functional posteriors, rendering them inappropriate for reliable clinical decision support. This highlights the need for approaches with more strictly enforced or inherent distance-awareness to known data points, e.g., using kernel-based techniques.
The golf model on $\mathbb{Z}/n\mathbb{Z}$ and on $\mathbb{Z}$

We introduce a particle model, that we call the $\textit{golf model}$. Initially, on a graph $G$, balls and holes are placed at random on some distinct vertices. Balls then move one by one, doing a random walk on $G$, starting from their initial vertex and stopping at the first empty hole they encounter, filling it. On finite graphs, under reasonable assumptions (if there are more holes than balls, and if the Markov chain characterizing the random walks is irreducible) a final configuration is reached almost surely. In the paper, we are mainly interested in ${\bf H}^1$, the set of remaining holes. We give the distribution of ${\bf H}^1$ on $\mathbb{Z}/n\mathbb{Z}$, and describe a phase transition on the largest distance between two consecutive holes when the number of remaining holes has order $\sqrt{n}$. We show that the model on $\mathbb{Z}$ is well-defined when every vertex contains either a ball with probability $d_{\sf b}$, a hole with probability $d_{\sf h}$, or nothing, independently from the other vertices, as long as $d_{\sf b} \leq d_{\sf h}$, and we describe the law of ${\bf H}^1$ in this case.
Microwave transitions in atomic sodium: Radiometry and polarimetry using the sodium layer

We calculate, via variational techniques, single- and two-photon Rydberg microwave transitions, as well as scalar and tensor polarizabilities of sodium atom using the parametric one-electron valence potential, including the spin-orbit coupling. The trial function is expanded in a basis set of optimized Slater-type orbitals, resulting in highly accurate and converged eigen-energies up to $n=60$. We focus our studies on the microwave band 90-150 GHz, due to its relevance to laser excitation in the Earth's upper-atmospheric sodium layer for wavelength-dependent radiometry and polarimetry, as precise microwave polarimetry in this band is an important source of systematic uncertainty in searches for signatures of primordial gravitational waves within the anisotropic polarization pattern of photons from the cosmic microwave background. We present the most efficient transition coefficients in this range, as well as the scalar and tensor polarizabilities compared with available experimental and theoretical data.
Analysis of BOSS Galaxy Data with Weighted Skew-Spectra

We present the first application of the weighted skew-spectra to analyze non-Gaussian information in galaxy survey data. Using the tree-level galaxy skew-spectra together with the one-loop power spectrum multipoles, we analyze the Sloan Digital Sky Survey (SDSS)-III Baryon Oscillation Spectroscopic Survey (BOSS) galaxy clustering data, and target our search towards the equilateral bispectrum shape of primordial non-Gaussianity. We use the Effective Field Theory model for the galaxy power spectrum and bispectrum, and account for systematic effects, such as the survey geometry. From our likelihood analysis, we find $f_{\rm NL}^{\rm equil}=-34^{+296}_{-334}$ at $68\%$ CL, consistent with previous works, while systematic errors from our treatment of the survey geometry lead to an unreliable estimation of $f_{\rm NL}^{\rm ortho}$. We further constrain the bias and counterterm parameters, while keeping the cosmology fixed to $\textit{Planck }2018$ values. As a check, we also validate our analysis pipeline using the ${\tt Nseries}$ simulation suite.
Field measurements reveal insights into the impact of turbulent wind on loads experienced by parabolic trough solar collectors

Insights into dynamic wind loading on parabolic trough solar collectors are presented in this study. The novel conclusions derived in this study were generated by analyzing a first-of-a-kind measurement campaign of wind and structural loads performed at an operational power plant. Previous research primarily relied on wind tunnel tests and simulations, leaving uncertainty about wind loading effects in operational settings. We demonstrate that the parabolic trough field significantly alters the turbulent wind field within the plant, especially under winds perpendicular to the trough rows. Our measurements within the trough field demonstrate reduced wind speeds, altered wind directions, breakup of larger eddies into smaller ones, and vortex shedding from the trough assemblies. These modifications to the wind field directly impact both static and dynamic support structure loads, with our measurements revealing higher loads on trough assemblies compared to those observed in wind tunnel tests. The insights gleaned from this study offer a fresh perspective on our understanding of wind-driven loads on solar collectors. By informing the development of next-generation design tools and models, this research paves the way for enhanced structural integrity and improved optical performance in future parabolic trough systems.
Single NV in nanodiamond for quantum sensing of protein dynamics in an ABEL trap

Enzymes are cellular protein machines using a variety of conformational changes to power fast biochemical catalysis. Our goal is to exploit the single-spin properties of the luminescent NV (nitrogen-vacancy) center in nanodiamonds to reveal the dynamics of an active enzyme complex at physiological conditions with the highest spatio-temporal resolution. Specifically attached to the membrane enzyme FoF1-ATP synthase, the NV sensor will report the adenosine triphosphate (ATP)-driven full rotation of Fo motor subunits in ten consecutive 36° steps. Conformational dynamics are monitored using either a double electron-electron resonance scheme or NV- magnetometry with optical readout or using NV- relaxometry with a superparamagnetic nanoparticle as the second marker attached to the same enzyme. First, we show how all photophysical parameters like individual size, charge, brightness, spectral range of fluorescence and fluorescence lifetime can be determined for the NV- center in a single nanodiamond held in aqueous solution by a confocal anti-Brownian electrokinetic trap (ABEL trap). Stable photon count rates of individual nanodiamonds and the absence of blinking allow for observation times of single nanodiamonds in solution exceeding hundreds of seconds. For the proposed quantum sensing of nanometer-sized distance changes within an active enzyme, we show that local magnetic field fluctuations can be detected all-optically by analyzing fluorescence lifetime changes of the NV- center in each nanodiamond in solution.
Investigating the Star Formation Rates of Active Galactic Nucleus Hosts Relative to the Star-forming Main Sequence

A fundamental question in galaxy and black hole evolution remains how galaxies and their supermassive black holes have evolved together over cosmic time. Specifically, it is still unclear how the position of X-ray active galactic nucleus (AGN) host galaxies with respect to the star-forming main sequence (MS) may change with the X-ray luminosity (L X) of the AGN or the stellar mass (M ⋆) of the host galaxy. We use data from the XMM-Spitzer Extragalactic Representative Volume Survey (XMM-SERVS) to probe this issue. XMM-SERVS is covered by the largest medium-depth X-ray survey (with superb supporting multiwavelength data) and thus contains the largest sample to date for study. To ensure consistency, we locally derive the MS from a large reference galaxy sample. In our analysis, we demonstrate that the turnover of the galaxy MS does not allow reliable conclusions to be drawn for high-mass AGNs, and we establish a robust safe regime where the results do not depend upon the choice of MS definition. Under this framework, our results indicate that less massive AGN host galaxies ( log M ⋆ ∼ 9.5 – 10.5 M ⊙ ) generally possess enhanced star formation rates compared to their normal-galaxy counterparts while the more massive AGN host galaxies ( log M ⋆ ∼ 10.5 – 11.5 M ⊙ ) lie on or below the star-forming MS. Further, we propose an empirical model for how the placement of an AGN with respect to the MS (SFRnorm) evolves as a function of both M ⋆ and L X.
(CA) n oligonucleotides correct RNA mis-splicing in TDP-43 pathology

The occurrence of extensive cryptic splicing following the depletion of nuclear TDP-43 results in impaired neuronal function and degeneration. Here, we designed oligonucleotides containing CA-repeat sequences which mimic TDP-43 pre-mRNA binding. Treating TDP-43-depleted neurons with (CA) n oligonucleotide can broadly repress cryptic splicing events, subsequently reverse protein loss, and restore neuronal activity. Importantly, (CA) n ASO did not induce major gene expression changes or cytotoxicity in healthy neurons. Our results demonstrate that (CA) n oligonucleotides are potential therapeutic agents to address global mis-splicing in TDP-43 proteinopathies.
Multivariate Time-Lagged Multidimensional Pattern Connectivity (mvTL-MDPC) for EEG/MEG Functional Connectivity Analysis

Multidimensional connectivity methods are critical to reveal the full pattern of complex interactions between brain regions over time. However, to date only bivariate multidimensional methods are available for time-resolved EEG/MEG data, which may overestimate connectivity due to the confounding effects of spurious and indirect dependencies. Here, we introduce a novel functional connectivity method which is both multivariate and multidimensional, Multivariate Time-lagged Multidimensional Pattern Connectivity (mvTL-MDPC), to address this issue in time-resolved EEG/MEG applications. This novel method extends its bivariate counterpart TL-MDPC to estimate how well patterns in an ROI 1 at time point t1 can be linearly predicted from patterns of an ROI 2 at time point t2 while partialling out the multivariate contributions from other brain regions. We compared the performance of mvTL-MDPC and TL-MDPC on simulated data designed to test their ability to identify true direct connections, using the Euclidean distance to the ground truth to measure goodness-of-fit. These simulations demonstrate that mvTL-MDPC produces more reliable and accurate results than the bivariate method. We therefore applied this method to an existing EEG/MEG dataset contrasting words presented in more or less demanding semantic tasks, to identify the dynamic brain network underlying controlled semantic cognition. As expected, mvTL-MDPC was more selective than TL-MDPC, identifying fewer connections, likely due to a reduction in the detection of spurious or indirect connections. Dynamic connections were identified between bilateral anterior temporal lobes, posterior temporal cortex and inferior frontal gyrus, in line with recent neuroscientific models of semantic cognition.
OCT4 is expressed in extraembryonic endoderm stem (XEN) cell progenitors during somatic cell reprogramming

During development, progenitors of embryonic stem (ES) and extraembryonic endoderm stem (XEN) cells are concomitantly specified within the inner cell mass (ICM) of the mouse blastocyst. Similarly, XEN cells are induced (iXEN cells) alongside induced pluripotent stem (iPS) cells following overexpression of Oct4, Sox2, Klf4 and Myc (OSKM) during somatic cell reprogramming. It is unclear how or why this cocktail produces both stem cell types, but OCT4 has been associated with non-pluripotent outcomes. In this report, we show that, during OSKM reprogramming, many individual Oct4-GFP-expressing cells are fated to become iXEN cells. Interestingly, SKM alone was also sufficient to induce iXEN cell formation, likely via activation of endogenous Oct4. These observations indicate that iXEN cell formation is not strictly an artifact of Oct4 overexpression. Moreover, our results suggest that a pathway to XEN may be an integral feature of establishing pluripotency during reprogramming, as in early embryo development.
A dynamic functional connectivity toolbox for multiverse analysis

Various methods for estimating dynamic functional connectivity from fMRI data and subsequent analyses through graph theoretic approaches have been introduced in recent years. But with many of the ground truths unknown, researchers are often faced with arbitrary yet defensible decisions for their analyses, which raises concerns about the reproducibility of results. We here aim to address this issue through the implementation of a wide array of dynamic functional connectivity methods in a unified Python software package, facilitating a diverse exploration of brain dynamics. Anchored in the framework of multiverse analysis, the present work further introduces a workflow for the systematic examination of various methodological decisions. The developed toolbox is supplemented by a graphical user interface for easy usability and accessibility if usage outside of a script-based Python analysis pipeline is desired. Further, extensive demo scripts are provided for researchers to easily adapt this approach for their own analyses.
Evaluation of Deep Learning for predicting rice traits using structural and single-nucleotide genomic variants

Structural variants (SVs) such as deletions, inversions, duplications, and Transposable Element (TE) Insertion Polymorphisms (TIPs) are prevalent in plant genomes and have played an important role in evolution and domestication, as they constitute a significant source of genomic and phenotypic variability. Nevertheless, most methods in quantitative genetics focusing on crop improvement, such as genomic prediction, consider Single Nucleotide Polymorphisms (SNPs) as the only type of genetic marker. Here, we used rice to investigate whether combining the structural and nucleotide genome-wide variation can improve prediction ability of traits when compared to using only SNPs. Moreover, we also examine the potential advantage of Deep Learning (DL) networks over Bayesian Linear models, which have been widely applied in genomic prediction. Specifically, the performance of BayesC and a Bayesian Reproducible Kernel Hilbert space regressions were compared to two different DL architectures, the Multilayer Perceptron, and the Convolution Neural Network. We further explore their prediction ability by using various marker input strategies and found that exploiting structural and nucleotide variation improves prediction ability on complex traits in rice. Also, DL models outperformed Bayesian models in 75% of the studied cases. Finally, DL systematically improved prediction ability of binary traits against the Bayesian models.
Giant pyramidal neurons of the primary motor cortex express vasoactive intestinal polypeptide (VIP), a known marker of cortical interneurons

Vasoactive intestinal polypeptide (VIP) is known to be present in a subclass of cortical interneurons that are involved in the disinhibition of excitatory pyramidal neurons. Here, using three different antibodies, we demonstrate that VIP is also present in the giant layer 5 pyramidal (Betz) neurons which are characteristic of the limb and axial representations of the marmoset primary motor cortex (cytoarchitectural area 4ab). No VIP staining was observed in smaller layer 5 pyramidal cells present in the primary motor facial representation (cytoarchitectural area 4c), or in premotor cortex (e.g. the caudal subdivision of the dorsal premotor cortex, A6DC), indicating the selective expression of VIP in Betz cells. The most intense VIP staining occurred in the largest Betz cells, located in the medial part of A4ab. VIP in Betz cells was colocalized with neuronal specific marker (NeuN) and a calcium-binding protein parvalbumin (PV). PV also intensely labelled axon terminals surrounding Betz cell somata. Whereas Betz cells bodies were located in layer 5, VIP-positive (VIP+) interneurons were more abundant in the superficial cortical layers. They constituted about 5-7% of total cortical neuronal population, with the highest density observed in area 4c. Our results demonstrate the expression of VIP in the largest excitatory neurons of the primate cortex, which may offer new functional insights into the role of VIP in the brain, and also provide opportunities for genetic manipulation of Betz cells.
Concurrent processing of the prosodic hierarchy is supported by cortical entrainment and phase-amplitude coupling

Models of phonology posit a hierarchy of prosodic units that are relatively independent from syntactic structure, requiring its own parsing. Surprisingly, it remains unexplored how this prosodic hierarchy is represented in the brain. We investigated this foundational question by means of an EEG study. Thirty young adults listened to German sentences containing manipulations at different levels of the prosodic hierarchy. Evaluating speech-to-brain cortical entrainment and phase-amplitude coupling revealed that prosody’s hierarchical structure is maintained at the neural level. The faithfulness of this tracking varied as a function of the hierarchy’s degree of intactness as well as systematic inter-individual differences in audio-motor synchronization. The results underscore the role of complex oscillatory mechanisms in configuring the continuous and hierarchical nature of the speech signal and firmly situate prosody as a structure indispensable from theoretical perspectives on spoken language comprehension in the brain.
Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics

Higher brain functions require flexible integration of information across widely distributed brain regions depending on the task context. Resting-state functional magnetic resonance imaging (fMRI) has provided substantial insight into large-scale intrinsic brain network organisation, yet the principles of rapid context-dependent reconfiguration of that intrinsic network organisation are much less understood. A major challenge for task connectome mapping is the absence of a gold standard for deriving whole-brain task-modulated functional connectivity matrices. Here, we performed biophysically realistic simulations to control the ground-truth task-modulated functional connectivity over a wide range of experimental settings. We revealed the best-performing methods for different types of task designs and their fundamental limitations. Importantly, we found that rapid (100 ms) modulations of oscillatory neuronal synchronisation can be recovered from sluggish haemodynamic fluctuations even at typically low fMRI temporal resolution (2 s). Finally, we provide practical recommendations on task design and statistical analysis to foster task connectome mapping.
Sociality shapes the life histories of animals along the fast-slow continuum, but does not affect their senescence

The role of sociality on the demography of animal species has become an intense focus of research in recent decades. However, efforts to understand the sociality-demography nexus have focused on single species or isolated taxonomic groups. Consequently, we lack generality regarding how sociality shapes demographic traits along the Animal Kingdom. Here, I propose a continuum of sociality, from solitary to tightly social, and test whether it predicts key demographic properties of 152 species, from jellyfish to humans. After correction for body mass and phylogenetic relationships, I show that the sociality continuum predicts key life history traits: more social species live longer, postpone maturity, have greater generation time, and greater probability of achieving reproduction than solitary, gregarious, communal, or colonial species. The sociality continuum is positively correlated with the fast-slow continuum. However, contrary to predictions, sociality does not result in more buffered populations: more social species have a lower ability to benefit from disturbances, although display greater resistance than more solitary species. Finally, I also show that sociality does not shape reproductive or actuarial senescence rates. This cross-taxonomic examination of sociality across the demography of 13 taxonomic classes highlights keyways in which individual interactions shape most aspects of animal demography.
Learned response dynamics reflect stimulus timing and encode temporal expectation violations in superficial layers of mouse V1

The ability to recognize ordered event sequences is a fundamental component of sensory cognition and underlies the capacity to generate temporally specific expectations of future events based on previous experience. Various lines of evidence suggest that the primary visual cortex participates in some form of predictive processing, but many details remain ambiguous. Here we use two-photon calcium imaging in layer 2/3 (L2/3) of the mouse primary visual cortex (V1) to study changes to neural activity under a multi-day sequence learning paradigm with respect to prediction error responses, stimulus encoding, and time. We find increased neural activity at the time an expected, but omitted, stimulus would have occurred but no significant prediction error responses following an unexpected stimulus substitution. Sequence representations became sparser and less correlated with training, although these changes had no effect on decoding accuracy of stimulus identity or timing. Additionally, we find that experience modifies the temporal structure of stimulus responses to produce a bias towards predictive stimulus-locked activity. Finally, we find significant temporal structure during intersequence rest periods that was largely unchanged by training.