Miracle-dz/UniFAR-FTUs huggingface dataset:
Frozen Economy During COVID-19

ABSTRACT This article collects a novel dataset by combining COVID-19 statistics with the measurement of economic activities in different sectors to investigate the “freezing” effect of the pandemic on the economy. We exploit the latest data on COVID-19 from January 2020 to April 2022. Our main findings indicate that there is a significant negative relationship between COVID-19 and a set of economic indicators in the corporate sector, household sector, and financial sector, which implies the economy has become frozen during the COVID-19 pandemic. To be specific, COVID-19 and resultant business closures, labor shortages, logistics blocks, demand reduction, and financial vulnerability inevitably impair the vibrancy and resilience of economic activities.
Convergence of alternate minimization schemes for phase field

We consider time-discrete evolutions for a phase-field model (for fracture and damage) obtained by alternate minimization schemes. First, we characterize their time-continuous limit in terms of parametrized BV-evolutions, introducing a suitable family of “intrinsic energy norms”. Further, we show that the limit evolution satisfies Griffith’s criterion, for a phase-field energy release, and that the irreversibility constraint is thermodynamically consistent.
Systems Age: A single blood methylation test to quantify aging heterogeneity across 11 physiological systems

Individuals, organs, tissues, and cells age in diverse ways throughout the lifespan. Epigenetic clocks attempt to quantify differential aging between individuals, but they typically summarize aging as a single measure, ignoring within-person heterogeneity. Our aim was to develop novel systems-based methylation clocks that, when assessed in blood, capture aging in distinct physiological systems. We combined supervised and unsupervised machine learning methods to link DNA methylation, system-specific clinical chemistry and functional measures, and mortality risk. This yielded a panel of 11 system-specific scores– Heart, Lung, Kidney, Liver, Brain, Immune, Inflammatory, Blood, Musculoskeletal, Hormone, and Metabolic. Each system score predicted a wide variety of outcomes, aging phenotypes, and conditions specific to the respective system, and often did so more strongly than existing epigenetic clocks that report single global measures. We also combined the system scores into a composite Systems Age clock that is predictive of aging across physiological systems in an unbiased manner. Finally, we showed that the system scores clustered individuals into unique aging subtypes that had different patterns of age-related disease and decline. Overall, our biological systems based epigenetic framework captures aging in multiple physiological systems using a single blood draw and assay and may inform the development of more personalized clinical approaches for improving age-related quality of life.
Rewiring the carbon cycle: a theoretical framework for animal-driven ecosystem carbon sequestration

Most carbon cycle models do not consider animal-mediated effects, focusing instead on carbon exchanges among plants, microbes, and the atmosphere. Yet, a growing body of empirical evidence from diverse ecosystems points to pervasive animal effects on ecosystem carbon cycling and shows that ignoring them could lead to misrepresentation of an ecosystem’s carbon cycle. We develop a new theoretical framework to account for animal effects on ecosystem carbon cycling. We combine a classic ecosystem compartment modeling approach with a classic carbon model to account for carbon flux and storage among plant, animal, and soil microbial trophic compartments. We show, by way of numerical analyses of steady state conditions, that herbivore presence alters the dominant pathways of control over carbon storage and capture. This altered control arises via direct, consumptive effects and especially via indirect, non-consumptive pathways by instigating faster nutrient recycling. This leads to a quantitative change in the ecosystem’s carbon balance, increasing the amount of carbon captured and stored in the ecosystem by 2–3 fold. The modeling shows that animals could play a larger role in ecosystem carbon cycle than previously thought. Our framework provides further guidance for empirical research aimed at quantifying animal-mediated control of carbon cycling and to inform the development of nature-based climate change solutions that leverage animal influence on the carbon cycle to help mitigate climate change.
b-physics signals of the lightest CP-odd Higgs boson in the next-to-minimal supersymmetric standard model at large tan β

We investigate the low energy phenomenology of the lighter pseudoscalar $A_1^0$ in the NMSSM. The $A_1^0$ mass can naturally be small due to a global $U(1)_R$ symmetry of the Higgs potential, which is only broken by trilinear soft terms. The $A_1^0$ mass is further protected from renormalization group effects in the large $\tan \beta$ limit. We calculate the $b \to s A_1^0$ amplitude at leading order in $\tan \beta$ and work out the contributions to rare $K$, $B$ and radiative $\Upsilon$-decays and $B -\bar B$ mixing. We obtain constraints on the $A_1^0$ mass and couplings and show that masses down to ${\cal{O}}(10)$ MeV are allowed. The $b$-physics phenomenology of the NMSSM differs from the MSSM in the appearance of sizeable renormalization effects from neutral Higgses to the photon and gluon dipole operators and the breakdown of the MSSM correlation between the $B_s \to \mu^+ \mu^-$ branching ratio and $B_s - \bar B_s$ mixing. For $A_1^0$ masses above the tau threshold the $A_1^0$ can be searched for in $b \to s \tau^+ \tau^-$ processes with branching ratios $\lsim 10^{-3}$.
Pinpoint: trajectory planning for multi-probe electrophysiology and injections in an interactive web-based 3D environment

Targeting deep brain structures during electrophysiology and injections requires intensive training and expertise. Even with experience, researchers often can’t be certain that a probe is placed precisely in a target location and this complexity scales with the number of simultaneous probes used in an experiment. Here, we present Pinpoint, open-source software that allows for interactive exploration of stereotaxic insertion plans. Once an insertion plan is created, Pinpoint allows users to save these online and share them with collaborators. 3D modeling tools allow users to explore their insertions alongside rig and implant hardware and ensure plans are physically possible. Probes in Pinpoint can be linked to electronic micro-manipulators allowing real-time visualization of current brain region targets alongside neural data. In addition, Pinpoint can control manipulators to automate and parallelize the insertion process. Compared to previously available software, Pinpoint’s easy access through web browsers, extensive features, and real-time experiment integration enable more efficient and reproducible recordings.
Explanation of saltatory conduction in myelinated axons and of micro-saltatory conduction in C fibers of pain sensation by a wave-type ion plasmonic mechanism of stimulus kinetics

Ion plasmon-polariton model of stimulus in myelinated axons and in C fibers of pain sensation is developed. This solves a long standing problem in neuroscience of by 2 − 3 orders of magnitude discrepancy between the observed fast speed of the saltatory conduction in myelinated axons or in C fibers with the upper limit of diffusive ion current velocity in these axons. The latter, described in the framework of so-called cable model, is too low in axons because of poor conductivity of neuron inner cytosol. The compliance with observations has been achieved upon plasmonic model of ionic local oscillations synchronized in periodically corrugated axons and propagating with high speed in the form of wave-type plasmon-polariton without any net diffusion current, thus not limited by resistivity. The new model of stimulus in myelinated axons reveals the different controlling role of myelin than previously thought from cable model. The control mechanism in non-myelinated C fibers is also proposed in agreement with observations. Recognition of plasmon model of neural signaling may be important for identifying a new targets for the future treatment at demyelination diseases and for fighting pain.
Genetic dissection of BDNF and TrkB expression in glial cells

The brain-derived neurotrophic factor (BDNF) and its high-affinity receptor tropomyosin-related kinase receptor B (TrkB) are widely expressed in the central nervous system. It is well documented that neurons express BDNF and full-length TrkB (TrkB.FL), and a lower level of truncated TrkB (TrkB.T). With conflicting results, glial cells also have been reported to express BDNF and TrkB. In the current study, we employed a more sensitive and reliable genetic method to characterize the expression of BDNF and TrkB in glial cells in the mouse brain. We utilized three Cre mouse strains in which Cre recombinase is expressed in the same cells as BDNF, TrkB.FL, or all TrkB isoforms, and crossed them to Cre-dependent EGFP reporter mice to label BDNF- or TrkB-expressing cells. We performed immunohistochemistry with glial cell markers to examine the expression of BDNF and TrkB in microglia, astrocytes, and oligodendrocytes. Surprisingly, we found no BDNF- or TrkB-expressing microglia in the brain and spinal cord. Consistent with previous studies, most astrocytes only express TrkB.T in the adult brain. Moreover, there are a small number of astrocytes and oligodendrocytes that express BDNF, the function of which is to be determined. We also found that oligodendrocyte precursor cells, but not mature oligodendrocytes, express both TrkB.FL and TrkB.T in the adult brain. These results not only clarify the expression of BDNF and TrkB in glial cells, but also open opportunities to investigate previously unidentified roles of BDNF and TrkB in glial cells.
Tissue tension permits β-catenin phosphorylation to drive mesoderm specification in human embryonic stem cells

The role of morphogenetic forces in cell fate specification is an area of intense interest. Our prior studies suggested that the development of high cell-cell tension in human embryonic stem cells (hESC) colonies permits the Src-mediated phosphorylation of junctional β-catenin that accelerates its release to potentiate Wnt-dependent signaling critical for initiating mesoderm specification. Using an ectopically expressed nonphosphorylatable mutant of β-catenin (Y654F), we now provide direct evidence that impeding tension-dependent Src-mediated β-catenin phosphorylation impedes the expression of Brachyury (T) and the epithelial-to-mesenchymal transition (EMT) necessary for mesoderm specification. Addition of exogenous Wnt3a or inhibiting GSK3β activity rescued mesoderm expression, emphasizing the importance of force dependent Wnt signaling in regulating mechanomorphogenesis. Our work provides a framework for understanding tension-dependent β-catenin/Wnt signaling in the self-organization of tissues during developmental processes including gastrulation.
MosaiCatcher v2: a single-cell structural variations detection and analysis reference framework based on Strand-seq

Single-cell DNA template strand sequencing (Strand-seq) allows a range of various genomic analysis including chromosome length haplotype phasing and structural variation (SV) calling in individual cells. Here, we present MosaiCatcher v2, a standardised workflow and reference framework for single-cell SV detection using Strand-seq. This framework introduces a range of functionalities, including: an automated upstream Quality Control (QC) and assembly sub-workflow that relies on multiple genome assemblies and incorporates a multistep normalisation module, integration of the scNOVA SV functional characterization and of the ArbiGent SV genotyping modules, platform portability, as well as a user-friendly and shareable web report. These new features of MosaiCatcher v2 enables reproducible computational processing of Strand-seq data, which are increasingly used in human genetics and single cell genomics, towards production environments. Availability and Implementation Mosaicatcher v2 is a standardised workflow, implemented using the Snakemake workflow management system. The pipeline is available on GitHub: https://github.com/friendsofstrandseq/mosaicatcher-pipeline/ and on the snakemake-workflow-catalog: https://snakemake.github. io/snakemake-workflow-catalog/? usage=friendsofstrandseq/mosaicatcher-pipeline. Contact jan.korbel@embl.de Supplementary information Supplementary data are available at Bioinformatics online.
A high-quality reference genome for the common creek chub, Semotilus atromaculatus

Creek chub (Semotilus atromaculatus) are a leuciscid minnow species commonly found in anthropogenically disturbed environments, making them an excellent model organism to study human impacts on aquatic systems. Genomic resources for creek chub and other leuciscid species are currently limited. However, advancements in DNA sequencing now allow us to create genomic resources at a historically low cost. Here, we present a high quality 239 contig reference genome for the common creek chub, created with PacBio HiFi sequencing. We compared the assembly quality of two pipelines: Pacific Biosciences’ Improved Phase Assembly (IPA; 873 contigs) and Hifiasm (239 contigs). Quality and completeness of this genome is comparable to the zebrafish (Danioninae) and fathead minnow (Leuciscidae) genomes. The creek chub genome is highly syntenic to the zebrafish and fathead minnow genomes, and while our assembly does not resolve into the expected 25 chromosomes, synteny with zebrafish suggests that each creek chub chromosome is likely represented by 1-4 large contigs in our assembly. This reference genome is a valuable resource that will enhance genomic bio-diversity studies of creek chub and other non-model leuciscid species common to disturbed environments.
Predicting Disease-gene Associations through Self-supervised Mutual Infomax Graph Convolution Network

Illuminating associations between diseases and genes can help reveal the pathogenesis of syndromes and contributes to treatments, but a large number of associations still remained unexplored. To identify novel disease-gene association, many computational methods have been developed using disease and gene related prior knowledge. However, these methods remain of relatively inferior performance due to the limited external data sources and the inevitable noise among the prior knowledge. In this study, we have developed a new method, Self-Supervised Mutual Infomax Graph Convolution Network (MiGCN), to predict disease-gene associations under the guidance of external disease-disease and gene-gene collaborative graphs. The noises within the collaborative graphs were eliminated by maximizing the mutual information between nodes and neighbors through a graphical mutual infomax layer. In parallel, the node interactions were strengthened by a novel informative message passing layer to improve the learning ability of graph neural network. The extensive experiments showed that our model achieves performance improvement over the state-of-art method by more than 8% on AUC. The datasets, source codes and trained models of MiGCN are available at https://github.com/biomed-AI/MiGCN.
Transcriptomic response in pyroxsulam-resistant and susceptible Bromus sterilis identified three distinct mechanisms of resistance

Bromus sterilis has evolved into a more predominant weed in the Czech Republic’s winter wheat fields, owing largely to the widespread application of pyroxsulam for its management. In this study, we report a biotype that has developed resistance to pyroxsulam and has also shown cross- resistance to other herbicides. Although no differences in ploidy levels or no mutations of acetolactate synthase (ALS) were detected, a significant elevation of ALS enzyme activity was observed in the R biotype. Through combined analysis of enzyme inhibition and total transcript expression (RNA-Seq), we have identified differentially expressed transcripts that potentially contribute to pyroxsulam metabolism. Furthermore, we observed a significant increase in the expression of genes involved in redox mechanisms and transporters that could contribute to enhanced resistance to pyroxulam in the R biotype. Our results present a novel understanding of herbicide resistance in B. sterilis through three distinct resistance mechanisms (ALS gene overexpression, enhanced metabolism and reduced translocation) without mutation in the herbicide target protein. This understanding is the foundation for improving management strategies for herbicide resistant B. sterilis.
What are we really decoding? Unveiling biases in EEG-based decoding of the spatial focus of auditory attention

Objective Spatial auditory attention decoding (Sp-AAD) refers to the task of identifying the direction of the speaker to which a person is attending in a multi-talker setting, based on the listener’s neural recordings, e.g., electroencephalography (EEG). The goal of this study is to thoroughly investigate potential biases when training such Sp-AAD decoders on EEG data, particularly eye-gaze biases and latent trial-dependent confounds, which may result in Sp-AAD models that decode eye-gaze or trial-specific fingerprints rather than spatial auditory attention. Approach We designed a two-speaker audiovisual Sp-AAD protocol in which the spatial auditory and visual attention were enforced to be either congruent or incongruent, and we recorded EEG data from sixteen participants undergoing several trials recorded at distinct timepoints. We trained a simple linear model for Sp-AAD based on common spatial patterns (CSP) filters in combination with either linear discriminant analysis (LDA) or k-means clustering, and evaluated them both across- and within-trial. Main results We found that even a simple linear Sp-AAD model is susceptible to overfitting to confounding signal patterns such as eye-gaze and trial fingerprints (e.g., due to feature shifts across trials), resulting in artificially high decoding accuracies. Furthermore, we found that changes in the EEG signal statistics across trials deteriorate the trial generalization of the classifier, even when the latter is retrained on the test trial with an unsupervised algorithm. Significance Collectively, our findings confirm that there exist subtle biases and confounds that can strongly interfere with the decoding of spatial auditory attention from EEG. It is expected that more complicated non-linear models based on deep neural networks, which are often used for Sp-AAD, are even more vulnerable to such biases. Future work should perform experiments and model evaluations that avoid and/or control for such biases in Sp-AAD tasks.
A data standard for the reuse and reproducibility of any stable isotope probing-derived nucleic acid sequence (MISIP)

DNA/RNA-stable isotope probing (SIP) is a powerful tool to link in situ microbial activity to sequencing data. Every SIP dataset captures distinct information about microbial community metabolism, kinetics, and population dynamics, offering novel insights according to diverse research questions. Data re-use maximizes the information available from the time and resource intensive SIP experimental approach. Yet, a review of publicly available SIP sequencing metadata reveals that critical information necessary for reproducibility and reuse is often missing. Here, we outline the Minimum Information for any Stable Isotope Probing Sequence (MISIP) according to the Minimum Information for any (x) Sequence (MIxS) data standard framework and include examples of MISIP reporting for common SIP approaches. Our objectives are to expand the capacity of MIxS to accommodate SIP-specific metadata and guide SIP users in metadata collection when planning and reporting an experiment. The MISIP standard requires five metadata fields: isotope, isotopolog, isotopolog label and approach, and gradient position, and recommends several fields that represent best practices in acquiring and reporting SIP sequencing data (ex. gradient density and nucleic acid amount). The standard is intended to be used in concert with other MIxS checklists to comprehensively describe the origin of sequence data, such as for marker genes (MISIP-MIMARKS) or metagenomes (MISIP-MIMS), in combination with metadata required by an environmental extension (e.g., soil). The adoption of the proposed data standard will assure the reproducibility and reuse of any sequence derived from a SIP experiment and, by extension, deepen understanding of in situ biogeochemical processes and microbial ecology.
Mindful Design and Operation for High Reliability Autonomous Systems

Autonomous systems (ATS) enabled by blockchain and artificial intelligence are hailed to revolutionize our lives and economy. However, ethical concerns and a lack of trust in machines making decisions on their own may explain why these systems are not yet embraced in large scale implementations. In block-chain-based ATSs, doubts have been fueled by failures of large crypto projects believed to be secure, such as the meltdown of the Terra/Luna algorithmic stablecoin (ASC). In this research, we analyze the failure of this ASC system through the lens of mindfulness using secondary data like the project’s white-paper and an interview with its chief executive officer. Hence, we make two contributions: First, we explore and argue for the possibility to mindfully design and operate ATSs as digital high reliability organizations. Second, we build a punctuated equilibrium model of mindfulness in systems. We thus extend the understanding of the design and management of ATSs.
The Explanation Dialogues: Understanding How Legal Experts Reason About XAI Methods

The Explanation Dialogues project is an expert focus study that aims to uncover expectations, reasoning, and rules of legal experts and practitioners towards explainable artificial intelligence (XAI). We examine legal perceptions and disputes that arise in a fictional scenario that resembles a daily life situation - a bank’s use of an automated decision-making (ADM) system to decide on credit allocation to individuals. Through this simulation, the study aims to provide insights into the legal value and validity of explanations of ADMs, identify potential gaps and issues that may arise in the context of compliance with European legislation, and provide guidance on how to address these shortcomings.
Selecting the Optimal Number of Crowd Workers for Forecasting Tasks

Crowdsourcing platforms enable requesters to elicit information from thousands of workers worldwide. A question that arises when outsourcing a task to a crowd is how many crowd workers a requester should hire. Focusing on forecasting tasks, we provide a methodological way of answering that question by formally describing how the number of crowd workers relates to the expected forecast error. Specifically, we discuss how an error curve associates a number of hired crowd workers with a fixed and a variable error. We then suggest an optimality concept that enables requesters to select an optimal number of crowd workers based on the tolerable variable error. In an illustrative study on Amazon Mechanical Turk, we demonstrate how highly prescriptive our approach is. In particular, we show how requesters can perform ex-ante and ex-post analyses by determining the optimal number of workers before running a task and by determining the fixed error after running the task and collecting forecasts.