Miracle-dz/UniFAR-FTUs huggingface dataset:
Providing a CS unplugged experience at a distance

C Science Unplugged activities are characterized by providing meaningful learning in a physical environment without computers at all, and yet it turns out that the activities can be made engaging even if everyone participating can only communicate through computers! This is the situation that arises when gathering in person isn’t possible, and videoconference tools are used to communicate. To maintain the engagement of in-person activities, we use an idea from the dawn of the information age to focus on what is possible despite the human-tohuman bandwidth being restricted.
Classical simulation of quantum many-body systems with a tree tensor network

We show how to efficiently simulate a quantum many-body system with tree structure when its entanglement (Schmidt number) is small for any bipartite split along an edge of the tree. As an application, we show that any one-way quantum computation on a tree graph can be efficiently simulated with a classical computer.
Tracing Students' Learning Performance on Multiple Skills using Bayesian Methods

Knowledge tracing (KT) is a research field of growing importance in technology enhanced education. It models students’ mastery levels of skills and predicts their performance in question answering through an Intelligent Tutoring System (ITS). Traditionally, Bayesian methods like Bayesian Knowledge Tracing (BKT) and Weakest Knowledge Tracing (WKT) apply Hidden Markov Model to model the latent learning process of individual skills. With the advent of recent advances in Deep Learning (DL), DL based KT methods achieve better prediction performance by exploiting the temporal dependencies between consecutive exercises on different skills. However, Bayesian methods are still valuable with a simpler, more intuitive and interpretable model of learning. This paper proposes a new Bayesian method called Corrigible Knowledge Tracing (CKT) which assumes students can learn from mistakes in answering multi-skill questions. In addition, the combinations of high occurrence skills are considered, so that integrated abilities are more fully recognised. In evaluation, the harmonic mean is suggested for combining the predicted probabilities, and accuracy is chosen to be the performance metric. The proposed method is compared with both Bayesian methods and DL approaches by using the conventional question-solving records from a modern large-scale educational data mining dataset called EdNet. Experiment results show that the performance of CKT is on par with state-of-the-art DL approaches. The overall improvement from WKT to the proposed method is substantial.
Perceived challenges and opportunities of machine learning applications in governmental organisations: an interview-based exploration in the Netherlands

As the application of machine learning (ML) algorithms becomes more widespread, governmental organisations try to benefit from this technology. While ML has the potential to support public services, its application also introduces challenges. Several scholars have described the possible opportunities and challenges of ML applications in the public sector conceptually. However, it is not yet investigated if and how these concepts materialise and are perceived by end-users in the public sector when ML is applied. Therefore, it is neither clear whether these concepts are valid, nor what regulation could be introduced to address them effectively. This empirical study’s objective is to shed light on how challenges and opportunities of governmental use of ML algorithms are perceived by Dutch professionals in the public sector. We attain our objective by conducting interviews with twelve professionals from Dutch executive and supervisory organisations in the public sector that respectively use ML and supervise the use of ML. Results show that ML is used primarily for improvements in the accuracy and speed of public task execution. Furthermore, interviewed professionals experience several barriers for ML implementation as well as risks following from the use of ML. The implications of these findings for practice are discussed, as well as opportunities for further research.
Comparison of automated office blood pressure measurement with 24-hour ambulatory blood pressure measurement

Abstract Purpose Although 24-hour ambulatory blood pressure measurement (24-h ABPM) is the most important method to establish true hypertension, in clinical practice often repeated automated office blood pressure (AOBP) measurements are used because of convenience and lower costs. We aimed to assess the agreement rate between a 30 and 60 min AOBP and 24-h ABPM. Materials and methods Patients with known hypertension (cohort 1) and patients visiting the neurology outpatient clinic after minor stroke or transient ischaemic attack (cohort 2) were selected. We performed AOBP for 30–60 min at 5-min intervals followed by 24-h ABPM and calculated average values of both measurements. Agreement between the two methods was studied with McNemar and Bland–Altman plots with a clinically relevant limit of agreement of ≤10 mm Hg difference in systolic BP. Results Our final cohort consisted of 135 patients from cohort 1 and 72 patients from cohort 2. We found relatively low agreement based on the clinical relevant cut-off value; 64.7% of the measurements were within the limits of agreement for 24-h systolic and 50.2% for 24-h diastolic. This was 61.4% for daytime systolic and 56.6% for daytime diastolic. In 73.5% of the patients, both methods led to the same diagnosis of either being hypertensive or non-hypertensive. This resulted in a significant difference between the methods to determine the diagnosis of hypertension (p < 0.0001). Conclusion We conclude that 30–60 min AOBP measurements cannot replace a 24-h ABPM and propose to perform 24-h ABPM at least on a yearly basis to confirm AOBP measurements.
Beyond CMB cosmic variance limits on reionization with the polarized Sunyaev-Zel'dovich effect

Upcoming cosmic microwave background (CMB) surveys will soon make the first detection of the polarized Sunyaev-Zel'dovich effect, the linear polarization generated by the scattering of CMB photons on the free electrons present in collapsed objects. Measurement of this polarization along with knowledge of the electron density of the objects allows a determination of the quadrupolar temperature anisotropy of the CMB as viewed from the space-time location of the objects. Maps of these remote temperature quadrupoles have several cosmological applications. Here we propose a new application: reconstruction of the cosmological reionization history. We show that with quadrupole measurements out to redshift 3, constraints on the mean optical depth can be improved by an order of magnitude beyond the CMB cosmic variance limit.
Multiplicity and disks within the high-mass core NGC 7538IRS1. Resolving cm line and continuum emission at 0.06″ × 0.05″ resolution

Context: High-mass stars have a high degree of multiplicity and most likely form via disk accretion processes. The detailed physics of the binary and disk formation are still poorly constrained. Methods: Using the VLA in its most extended configuration at ~24GHz toward the prototypical high-mass star-forming region NGC7538IRS1 has allowed us to study the NH3 and thermal CH3OH emission and absorption as well as the cm continuum emission at an unprecedented spatial resolution of 0.06"x0.05", corresponding to a linear resolution of ~150AU at a distance of 2.7kpc. Results: A comparison of these new cm continuum data with previous VLA observations from 23yrs ago reveals no recognizable proper motions. If the emission were caused by a protostellar jet, proper motion signatures should have been easily identified. In combination with the high spectral indices S~nu^{alpha} (alpha between 1 and 2), this allows us to conclude that the continuum emission is from two hypercompact HII regions separated in projection by about 430AU. The NH3 spectral line data reveal a common rotating envelope indicating a bound high-mass binary system. In addition to this, the thermal CH3OH data show two separate velocity gradients across the two hypercompact HII regions. This indicates two disk-like structures within the same rotating circumbinary envelope. Disk and envelope structures are inclined by ~33deg, which can be explained by initially varying angular momentum distributions within the natal, turbulent cloud. Conclusions: Studying high-mass star formation at sub-0.1" resolution allows us to isolate multiple sources as well as to separate circumbinary from disk-like rotating structures.
Absorption and scattering by interstellar dust in the silicon K-edge of GX 5-1

Context. We study the absorption and scattering of X-ray radiation by interstellar dust particles, which allows us to access the physical and chemical properties of dust. The interstellar dust composition is not well understood, especially on the densest sight lines of the Galactic plane. X-rays provide a powerful tool in this study. Aims. We present newly acquired laboratory measurements of silicate compounds taken at the Soleil synchrotron facility in Paris using the Lucia beamline. The dust absorption profiles resulting from this campaign were used in this pilot study to model the absorption by interstellar dust along the line of sight of the low-mass X-ray binary GX 5-1. Methods. The measured laboratory cross-sections were adapted for astrophysical data analysis and the resulting extinction profiles of the Si K-edge were implemented in the SPEX spectral fitting program. We derive the properties of the interstellar dust along the line of sight by fitting the Si K-edge seen in absorption in the spectrum of GX 5-1. Results. We measured the hydrogen column density towards GX 5-1 to be 3.40 ± 0.1 × 10 22 cm -2 . The best fit of the silicon edge in the spectrum of GX 5-1 is obtained by a mixture of olivine and pyroxene. In this study, our modeling is limited to Si absorption by silicates with different Mg:Fe ratios. We obtained an abundance of silicon in dust of 4.0 ± 0.3 × 10 -5 per H atom and a lower limit for total abundance, considering both gas and dust of >4.4 × 10 -5 per H atom, which leads to a gas to dust ratio of >0.22. Furthermore, an enhanced scattering feature in the Si K-edge may suggest the presence of large particles along the line of sight.
Major revisions in pancrustacean phylogeny with recommendations for resolving challenging nodes

The clade Pancrustacea, comprising crustaceans and hexapods, is the most diverse group of animals on earth, containing over 80% of animal species. It has been the subject of several recent phylogenomic analyses, but despite analyzing hundreds of genes, relationships within Pancrustacea show a notable lack of stability. Here, the phylogeny is estimated with expanded taxon sampling, particularly of malacostracans, using a precise tree-based approach to infer orthology. Our results show that small changes in taxon sampling have a large impact on phylogenetic estimation. By analyzing only shared orthologs between two slightly different taxon sets, we show that the differences in the resulting topologies are due to the effects of taxon sampling on the phylogenetic reconstruction method, not on ortholog identification. We compare trees resulting from our phylogenomic analyses with those from the literature to explore the large tree space of pancrustacean phylogenetic hypotheses and find that statistical topology tests reject the previously published trees in favor of the ML trees produced here. Our results reject several clades including Caridoida, Eucarida, Multicrustacea, Vericrustacea, and Syncarida. We recover a novel relationship between decapods, euphausiids, and syncarids that we refer to as the Syneucarida. With denser taxon sampling, we find Stomatopoda sister to this clade, which we name Stomatocaridea, dividing Malacostraca into three clades: Leptostraca, Peracarida, and Stomatocaridea. A new Bayesian divergence time estimation is conducted using 13 vetted fossils. We review our results in the context of other pancrustacean phylogenetic hypotheses and highlight the key taxa to sample in future studies.
Cross-linking mass spectrometry discovers, evaluates, and validates the experimental and predicted structural proteome

Significant recent advances in structural biology, particularly in the field of cryo-electron microscopy, have dramatically expanded our ability to create structural models of proteins and protein complexes. However, many proteins remain refractory to these approaches because of their low abundance, low stability or – in the case of complexes – simply not having yet been analysed. Here, we demonstrate the power of combining cross-linking mass spectrometry (XL-MS) with artificial intelligence-based structure prediction to discover and experimentally substantiate models for protein and protein complex structures at proteome scale. We present the deepest XL-MS dataset to date, describing 28,910 unique residue pairs captured across 4,084 unique human proteins and 2,110 unique protein-protein interactions. We show that integrative models of complexes driven by AlphaFold Multimer and inspired and corroborated by the XL-MS data offer new opportunities to deeply mine the structural proteome and interactome and reveal new mechanisms underlying protein structure and function.
Endothelial Caveolin-1 and CXCL10 promote transcellular migration of autoreactive T cells across the blood-brain barrier

CXCL10 is an interferon-inducible chemokine that can recruit CXCR3+ leukocytes to the central nervous system, leading to neuroinflammation, demyelination, and neuronal losses. How CXCL10 promotes leukocyte extravasation and diapedesis across the blood-brain barrier – formed by brain endothelial cells – is poorly understood. Here, we report that CXCL10 mediates CD4+ T cell migration through the brain endothelial cell cytoplasm (transcellular), but not cell-cell junctions (paracellular), via the vesicular trafficking protein Caveolin-1. Caveolin-1 promotes CXCL10 aggregation into cytoplasmic stores in brain endothelial cells in vitro to provide the local, high concentration necessary for recruitment of CXCR3+ leukocytes. This process also requires LFA-1 activity. In the absence of Caveolin-1, endothelial CXCL10 is secreted, and the local signaling cues are lost. Consistent with our in vitro data, genetic ablation of Caveolin-1 in endothelial cells reduces the severity of active experimental autoimmune encephalomyelitis (EAE), a murine model for multiple sclerosis, by decreasing the infiltration of CXCR3+ T cells into the CNS. Moreover, loss of Caveolin-1 protects against the adoptive transfer of autoreactive T cells. Our findings establish a novel mechanism by which brain endothelial cells utilize Caveolin-1 dependent CXCL10 intracellular stores to license T cells for transcellular migration across the blood-brain barrier.
Haplotype-resolved de novo genome assemblies of four coniferous tree species

Coniferous trees in gymnosperm are an important source of wood production. Because of their long lifecycle, the breeding programs of coniferous tree are time- and labor-consuming. Genomics could accelerate the selection of superior trees or clones in the breeding programs; however, the genomes of coniferous trees are generally giant in size and exhibit high heterozygosity. Therefore, the generation of long contiguous genome assemblies of coniferous species has been difficult. In this study, we optimized the DNA library preparation protocols and employed high-fidelity (HiFi) long-read sequencing technology to sequence and assemble the genomes of four coniferous tree species, Larix kaempferi, Chamaecyparis obtusa, Cryptomeria japonica, and Cunninghamia lanceolata. Genome assemblies of the four species totaled 13.5 Gb (L. kaempferi), 8.5 Gb (C. obtusa), 9.2 Gb (C. japonica), and 11.7 Gb (C. lanceolata), which covered 99.6% of the estimated genome sizes on average. The contig N50 value, which indicates assembly contiguity, ranged from 1.2 Mb in C. obtusa to 16.0 Mb in L. kaempferi, and the assembled sequences contained, on average, 89.2% of the single-copy orthologs conserved in embryophytes. Assembled sequences representing alternative haplotypes covered 70.3–95.1% of the genomes, suggesting that the four coniferous tree genomes exhibit high heterozygosity levels. The genome sequence information obtained in this study represents a milestone in tree genetics and genomics, and will facilitate gene discovery, allele mining, phylogenetics, and evolutionary studies in coniferous trees, and accelerate forest tree breeding programs.
Human and murine neutrophils share core transcriptional programs in both homeostatic and inflamed contexts

Neutrophils are frequently studied in murine models, but the extent to which findings translate to humans remains poorly defined. Here, we performed an integrative transcriptomic analysis of 11 murine and 13 human datasets. In homeostasis, neutrophils exhibited the highest number of lineage-specific genes and the greatest degree of correlated expression among genes with one-to-one orthologs (r = 0.79, P < 2.2 × 10−16) compared to other leukocytes. In inflammation, neutrophils displayed considerable transcriptional diversity, but shared a core inflammation program across a broad range of conditions which was conserved between species. This core program included genes encoding IL-1 family members, CD14, IL-4R, CD69 and PD-L1. Chromatin accessibility of core inflammation genes increased significantly in blood compared to bone marrow and further with migration from blood to tissue. Transcription factor enrichment analysis nominated members of the NF-κB family and AP-1 complex as important drivers of the core inflammation program, and HoxB8 neutrophils with JUNB knockout showed a significantly reduced expression of core inflammation genes at baseline and upon stimulation. In vitro perturbations confirmed surface protein upregulation of core inflammation members in both species. Together, we demonstrate substantial transcriptional conservation in neutrophils in homeostasis and identify a core inflammation program conserved across species. This systems biology approach can be leveraged to improve transitions between the murine and human context. Key Points The transcriptome of resting neutrophils is substantially conserved between humans and mice A core inflammation program in neutrophils is shared across a broad range of conditions and conserved across humans and mice Graphical Abstract
Isolation and Molecular Identification of Endophytic Bacteria from Tylosema esculentum

Tylosema esculentum is a perennial legume that is predominant in Namibia, Botswana, and South Africa. It is rich in nutrients, drought-tolerant, and a climate change contender for future agriculture. The objectives of this study were to isolate and identify culturable bacterial endophytes. Our study assessed the mean CFUs/mL among the tuberous roots, stems, and leaves to determine the density and diversity using ANOVA. It was a quantitatively controlled experiment in which seeds were grown in a greenhouse. The bacteria number varied from 1.29 × 108 to 3.5 × 108 CFU/ml of broth, with the highest in the tuberous roots and the lowest in the leaves. At a 5% significant level, we revealed that there is a difference among the bacterial densities of the plant parts. Shannon-Weiner diversity showed that there is a higher diversity of bacteria in the tuberous roots. Of the isolates that were obtained, 53.8% belonged to Enterobacter, 15.4% to Bacillus, 15.4% to Pantoea, and 7.67% to Burkholderia. These discoveries have made us more aware of the endophytic bacteria we now have. These strains might thus be used in food crops to boost productivity, enabling food security, especially in Africa. The significance of the isolates in biological control, agriculture, and biotechnology for growth promotion will be determined by further research.
A Signal Processing Approach to Voltage-Sensitive Dye Optical Imaging

L’imagerie optique par colorant potentiometrique est une methode d’enregistrement de l’activite corticale prometteuse, mais dont le potentiel reel est limite par la presence d’artefacts et d’interferences dans les acquisitions. A partir de modeles existant dans la litterature, nous proposons un modele generatif du signal base sur un melange additif de composantes, chacune contrainte dans une union d’espaces lineaires determines par son origine biophysique. Motives par le probleme de separation de composantes qui en decoule, qui est un probleme inverse lineaire sous-determine, nous developpons : (1) des regularisations convexes structurees spatialement, favorisant en particulier des solutions parcimonieuses ; (2) un nouvel algorithme proximal de premier ordre pour minimiser efficacement la fonctionnelle qui en resulte ; (3) des methodes statistiques de selection de parametre basees sur l’estimateur non biaise du risque de Stein. Nous etudions ces outils dans un cadre general, et discutons leur utilite pour de nombreux domaines des mathematiques appliques, en particulier pour les problemes inverses ou de regression en grande dimension. Nous developpons par la suite un logiciel de separation de composantes en presence de bruit, dans un environnement integre adapte a l’imagerie optique par colorant potentiometrique. Finalement, nous evaluons ce logiciel sur differentes donnees, synthetiques et reelles, montrant des resultats encourageants quant a la possibilite d’observer des dynamiques corticales complexes.
Deep-learning based bioactive therapeutic peptides generation and screening

Many bioactive peptides demonstrated therapeutic effects over-complicated diseases, such as antiviral, antibacterial, anticancer, etc. Similar to the generating de novo chemical compounds, with the accumulated bioactive peptides as a training set, it is possible to generate abundant potential bioactive peptides with deep learning. Such techniques would be significant for drug development since peptides are much easier and cheaper to synthesize than compounds. However, there are very few deep learning-based peptide generating models. Here, we have created an LSTM model (named LSTM_Pep) to generate de novo peptides and finetune learning to generate de novo peptides with certain potential therapeutic effects. Remarkably, the Antimicrobial Peptide Database has fully utilized in this work to generate various kinds of potential active de novo peptide. We proposed a pipeline for screening those generated peptides for a given target, and use Main protease of SARS-COV-2 as concept-of-proof example. Moreover, we have developed a deep learning-based protein-peptide prediction model (named DeepPep) for fast screening the generated peptides for the given targets. Together with the generating model, we have demonstrated iteratively finetune training, generating and screening peptides for higher predicted binding affinity peptides can be achieved. Our work sheds light on to the development of deep learning-based methods and pipelines to effectively generating and getting bioactive peptides with a specific therapeutic effect, and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target.