Miracle-dz/UniFAR-FTUs huggingface dataset:
Kinetic and Structural Analysis of Ribonucleotide Extension by DNA Polymerase η

DNA polymerases catalyze DNA synthesis with high fidelity, which is essential for all life. Extensive kinetic and structural efforts have been executed in exploring mechanisms of DNA polymerases, surrounding their kinetic pathway, catalytic mechanisms, and factors that dictate polymerase fidelity. Recent time-resolved crystallography studies on DNA polymerase η (Pol η) and β have revealed essential transient events during DNA synthesis reaction, such as mechanisms of primer deprotonation, separated roles of the three metal ions, and conformational changes that disfavor incorporation of the incorrect substrate. DNA-embedded ribonucleotides (rN) are the most common lesion on DNA and a major threat to genome integrity. While kinetics of rN incorporation has been explored and structural studies have revealed that DNA polymerases have a steric gate that destabilizes rNTP binding, mechanism of extension upon rN addition remains poorly characterized. Using steady-state kinetics, static and time-resolved X-ray crystallography with Pol η as a model system, we showed that the extra hydroxyl group on the primer terminus does not reduce the catalytic efficiency of Pol η. However, rN ended primers alter the dynamics of the polymerase active site as well as the catalysis and fidelity of DNA synthesis. During rN extension, Pol η fidelity drops significantly across different sequence context. Systematic structural studies suggest that the rN at the primer end improved primer alignment and reduced barriers in C2’-endo to C3’-endo sugar conformation change. Our work provides important insights for rN extension and implicates a possible mechanism for rN removal.
LncRNA Malat1 represses Th17 effector program by maintaining a critical bivalent super-enhancer and promotes intestinal inflammation

Interleukin IL-17 cytokines are central regulators of mucosal homeostasis and disease. In mouse models of colonic tissue injury, IL-17A promotes epithelial barrier functions and restricts local inflammation. Here, we report that IL-17A production by the diverse T lymphocyte subsets is dynamically regulated at different stages of colitis pathogenesis. During the onset and peak of the disease, Tγδ17 cells are the major IL-17A producers, while Th17 activity is temporally restricted by long non-coding RNA (lncRNA) Malat1. In response to IL-6 and TGFβ signaling, Malat1 is recruited to the Th17-specific cis-regulatory elements, CNS3 and CNS4, of the Il17a locus to fine-tune bivalent super-enhancer activities and repress local transcription. During the resolution phase of inflammation, Malat1 expression is down-regulated to enhance Th17 activities, allowing Th17 cells to emerge as the main producers of IL-17A in the colonic lamina propria. Genetic ablation of Malat1 potentiates IL-17A production in Th17 cells and improves disease outcomes in mouse models of colitis. These findings uncover a surprising role of a chromatin-associated lncRNA in regulating colonic Th17-specific responses to control the timing of inflammation resolution. Significance Statement T cells are critical modulators of mucosal barrier function and inflammation. The function of long-noncoding RNAs (lncRNAs) in T cells and their role in mucosal inflammation remain elusive. Here, we identify an essential role of the lncRNA Malat1 restricting transcription of the Il17a locus in Th17 cells encoding a cytokine implicated in epithelial barrier function post-injury. By controlling the activity of the bivalent super-enhancer at the Il17a locus, Malat1 regulates the timing of inflammation resolution in the intestine. The Malat1-Il17a pathway reveals new targets for combating mucosal diseases. Graphic Abstract
Rhythmic Modulation of Visual Discrimination is Dependent on Individuals’ Spontaneous Motor Tempo

Rhythmic structure in our daily experience originates from various sources. It is generated endogenously and observed in spontaneous fluctuations in behaviour and performance. It can also arise exogenously from everyday stimuli, such as speech, motion and music. Here we examined how individual differences in spontaneous motor rhythms affect the tendency to use external rhythmic structure to guide perception. To measure individual differences in spontaneous rhythms of performance we utilized a spontaneous tapping task. To measure individual differences in perceptual rhythmic modulation we designed a visual discrimination task in which targets can appear either in-phase or out-of-phase with a preceding rhythmic stream of visual stimuli. We manipulated the tempo of the visual stream over different experimental blocks (0.77 Hz, 1.4 Hz, 2 Hz). We found that visual rhythmic stimulation modulates discrimination performance. The modulation was dependent on the tempo of stimulation, with maximal perceptual benefits for the slowest tempo of stimulation (0.77 Hz). Most importantly, the strength of modulation was also affected by individuals’ spontaneous motor tempo. Specifically, individuals with slower spontaneous tempi showed greater rhythmic modulation compared to individuals with faster spontaneous tempi. This discovery suggests that different tempi affect the cognitive system with varying levels of efficiency, and that self-generated rhythms impact our ability to utilize rhythmic structure in the environment for guiding perception and performance.
Immunological landscape of human lymph nodes during ex vivo measles virus infection

Lymph nodes are the primary site of replication for measles virus (MeV). Here, we modeled MeV infection in human tonsil explants, utilizing a clinical strain of MeV that expresses GFP. We show that MeV replicates efficiently in this tissue as measured by increasing infectious virus production and GFP+ cells (>10% of tonsillar cells by day 8). Using scRNA-Seq, we identified 29 cell populations, all of which were susceptible to MeV. While T cells were the most abundant cell type in the lymphoid explants, B cells were the dominant infected population. Flow cytometry analysis revealed that the preferential infection of B cells was associated with higher CD150 expression. We found that while germinal center B cells were the largest population of infected B cells, there were no differences in susceptibility to MeV among individual B cell subsets. Among CD3+ T cells, infection in both the CD4+ and CD8+ compartments displayed bias towards antigen experienced subsets and away from naive cells, consistent with relative CD150 expression. Differential gene expression analysis revealed that the host response to MeV was dominated by the potent induction of interferon stimulated genes within both T and B cells. These data provide new insights into how MeV infection progresses in lymph nodes, a critical launching point for pathogenesis. Author Summary Measles virus (MeV) is a re-emerging pathogen that has dramatic disease outcomes in children. Immunological amnesia is a dangerous disease outcome that is caused by MeV infection of lymphocytes. Here, we infect human tonsil explants with a GFP-expressing clinical isolate of MeV to assess the immunological events that occur during infection of this critical site. Using single-cell RNA sequencing, we identified 29 distinct populations of tonsillar cells that are susceptible to MeV infection. Further immunophenotyping revealed a preferential infection of B cell lineages compared to T cell lineages, and that among T cells, memory cells are preferential targets of infection compared to naïve counterparts. Taken together, our data thoroughly characterize the infectious and immunological events that shape disease progression in lymph nodes and identify cellular susceptibilities to MeV infection which may be critical in the development of antivirals for MeV. CONFLICTS OF INTEREST The authors have declared that no conflict of interest exists.
28 Minutes Later: A behavioral and transcriptomic investigation into the potential role of 1 aflatrem-like compounds in establishing Ophiocordyceps extended phenotypes in zombie 2 ants 3

13 Parasites and their hosts often share intimate coevolutionary relationship that can lead to bizarre 14 phenotypes over time. Ophiocordyceps is one such genus that has evolved to manipulate the 15 behavior of its various insect and arachnid hosts, the clearest examples of which are found in ants 16 of the Camponotus tribe, colloquially known as “zombie ants”. While the unusual behaviors 17 induced during infection are well described, the molecular driving force behind these changes are 18 still unknown. Recent genomic and transcriptomics analyses have identified several candidate 19 secreted proteins and secondary metabolites highly upregulated during infection. Of particular 20 interest amongst these compounds is an aflatrem-like ergot alkaloid related to the mycotoxin 21 aflatrem known to cause neurological issues in other animals, issues collectively called “staggers 22 syndrome”. To test if aflatrem-like compounds play a role in Camponotus floridanus manipulation, healthy ants were injected with purified aflatrem and their behavior was monitored for 30 minutes. RNA-Seq was also performed on the heads of aflatrem-injected ants to compare any changes at 25 the transcriptomic level to data available for ants infected by Ophiocordyceps camponoti-floridani . 26 Behavioral assay footage was analyzed using the machine learning tool MARGO, which showed 27 a negative correlation between activity and speed, and dose of aflatrem. Behaviors were also scored 28 manually, identifying a positive coloration between aflatrem dose and staggering behavioral 29 phenotypes. RNA-seq discovered 262 genes significantly up or down regulated compared to sham 30 injected ants. Of these genes, 108 were also differentially regulated in the heads of late-stage 31 Ophiocordyceps -infected ants. Together, these results indicate that aflatrem-like compounds may 32 play an important role during late-stages of infection by reducing activity in ants, keeping them in 33 elevated microclimates optimal for the production and dispersal of spores.
The SARS-CoV-2 Spike Protein Mutation Explorer: using an interactive application to improve the public understanding of SARS-CoV-2 variants of concern

SARS-CoV-2 is the virus responsible for the COVID-19 pandemic, which began in late 2019 and has resulted in millions of death globally. The need to understand the pandemic means that detailed descriptions of features of this virus are now of interest to non-expert audiences. In particular, there has been much public interest in the spike protein that protrudes from the surface of the SARS-CoV-2 virus particle. The spike is the major determinant of viral infectivity and the main target for protective immune responses, and included in vaccines, and so its properties influence the impact of the pandemic on people’s lives. This protein is rapidly evolving, with mutations that enhance transmissibility or weaken vaccine protection creating new variants of concern (VOCs) and associated sub-lineages. The spread of SARS-CoV-2 VOCs has been tracked by groups such as the COVID-19 Genomics UK consortium (COG-UK). Their online mutation explorer (COG-UK/ME), which analyses and shares SARS-CoV-2 sequence data, contains information about VOCs that is designed primarily for an expert audience but is potentially of general interest during a pandemic. We wished to make this detailed information about SARS-CoV-2 VOCs more widely accessible. Previously work has shown that visualisations and interactivity can facilitate active learning and boost engagement with molecular biology topics, while animations of these topics can boost understanding on protein structure, function, and dynamics. We therefore set out to develop an educational graphical resource, the SARS-CoV-2 Spike Protein Mutation Explorer (SSPME), which contains interactive 3D molecular models and animations explaining SARS-CoV-2 spike protein variants and VOCs. We performed user-testing of the original COG-UK/ME website and of the SSPME, using a within-groups design to measure knowledge acquisition and a between-groups design to contrast the effectiveness and usability. Statistical analysis demonstrated that, when compared to the COG-UK/ME, the SSPME had higher usability and significantly improved participant knowledge confidence and knowledge acquisition. The SSPME therefore provides an example of how 3D interactive visualisations can be used for effective science communication and education on complex biomedical topics, as well as being a resource to improve the public understanding of SARS-CoV-2 VOCs.
Probabilistic Matrix Factorization for Gene Regulatory Network Inference

Gene regulatory network inference methods are often heuristically designed for specific datasets and biological problems, making it challenging to include extra information and compare against other algorithms. Here, we propose the use of probabilistic matrix factorization for gene regulatory network (PMF-GRN) inference from single-cell gene expression datasets of any size, incorporating experimental evidence into prior distributions over latent factors. We use variational inference to infer GRNs, enabling hyperparameter search and direct comparison to other generative models. We evaluate our method using Saccharomyces cerevisiae and Bacillus subtilis , benchmarking against database-derived gold standard GRNs. We find that PMF-GRN infers GRNs more accurately than the current state-of-the-art method, while reducing the need for heuristic model selection. We demonstrate the necessity of incorporating prior information into any matrix factorization approach to GRN inference. Finally, we find that PMF-GRN’s uncertainty estimates are well-calibrated.
Switched Control Based on Takagi-Sugeno Fuzzy Model for Dual Active Bridge DC-DC Converter

The use of the Dual Active Bridge (DAB) DC-DC converter has been increasing in recent years in several applications. This paper proposes the application of a nonlinear control strategy for the DAB converter, which consists of switching linear controllers based on a switching function. In order to achieve this, the Takagi-Sugeno (T-S) fuzzy model of the DAB converter is realised as a convex combination of linear systems considering the load of the converter as an uncertain parameter. The switching function is chosen based on the uncertain parameters of the (T-S) fuzzy model and linear controllers are designed using some theorems based on Lyapunov stability theory with the use of Linear Matrix Inequalities (LMI’s). The results of the control strategy are validated by simulation, verifying an improvement in the dynamic response and robustness against parameter variations with respect to the linear controllers widely used in the DAB converter.
Towards Interval Type-2 Fuzzy-Based PPG Quality Assessment for Physiological Monitoring

Wearable technology is having a profound impact in healthcare applications. In this context, one of the main goals of current wearable systems is to provide new devices capable of delivering a greater amount of more precise information. Amongst the different applications, physiological continuous monitoring is becoming one of the most wanted features. In fact, most of the commercially and research grade wearable systems include cardiac activity acquisition, which is generally performed by means of photoplethysmography sensors (PPG). These optical-based sensors present different challenges related to the prevailing of a good signal quality. Thus, in case of dealing with digital processing algorithms which are responsible for extracting different features from such signal, this fact can lead to erroneous results. On this basis, this paper presents an ongoing work towards the design of an interval type-II fuzzy-based system for the PPG signal quality assessment. Moreover, this initial proposed system is implemented into a constrained 32-bit ARM Cortex-M4 system-on-chip. Specifically, the system uses a reduced set of features together with a low complexity fuzzy rule base Mamdani inference model, and is based on a non-overlapping 3-second signal processing window. Results show that the system achieved overall accuracy of 94.84%. The proposed system has great potential for integrating accurate and reliable continuous health monitoring systems into constrained edge devices.
Machine Learning Algorithms and Internet of Things for Healthcare: A Survey

The Internet of Things (IoT) has recently attracted a lot of attention in numerous applications. In particular, IoT technologies have shown significant improvements and cost reductions when used in healthcare systems. However, there are still many challenges in applying IoT technologies to healthcare systems, such as processing and analyzing huge amounts of health data. This data needs to be transferred to the cloud, securely stored, and properly analyzed for medical decisions. Furthermore, it is important to automate these decisions using machine learning (ML). In this article, we review the ML schemes that provide intelligence integrated with IoT used in healthcare systems. Afterward, we discuss existing types of ML algorithms and provide a number of challenges for ML issues in IoT-based healthcare applications. Our discussions are based on investigating current solutions with future research directions.
APT Malicious Sample Organization Traceability Based on Text Transformer Model

Traditional malicious code identification software relies primarily on the static signature of the program, and matches malicious samples to specific signatures by mapping files. At present, the traceability of APT attack organizations mainly relies on manual analysis of samples, and there are difficulties such as insufficient automatic analysis and difficulty in accurate traceability through a single feature. In order to solve the above problems, this paper proposes the traceability method of APT malicious sample organization based on text Transformer model. Six tissue samples headed by APT-28 reached 93.24% of classified accuracy, which can effectively respond to the security threat of APT attacks. The text feature extraction structure of this method fuses the convolutional structure and the Transformer model, which can be flexible to deal with various malicious code sequence lengths. Compared with other malicious code classification methods based on text sequences, the accuracy of this model is improved by 1.5%.
Event Popularity Prediction Using Influential Hashtags From Social Media

Event popularity prediction over social media is crucial for estimating information propagation scope, decision making, and emergency prevention. However, existing approaches only focus on predicting the occurrences of single attribute such as a message, a hashtag or an image, which are not comprehensive enough for representing complex social event propagation. In this paper, we predict the event popularity, where an event is described as a set of messages containing multiple hashtags. We propose a novel hashtag-influence-based event popularity prediction by mining the impact of an influential hashtag set on the event propagation. Specifically, we first propose a hashtag-influence-based cascade model to select the influential hashtags over an event hashtag graph built by the pairwise hashtag similarity and the topic distribution of event-related hashtags. A novel measurement is proposed to identify the hashtag influence of an event over its content and social impacts. A hashtag correlation-based algorithm is proposed to optimize the seed selection in a greedy manner. Then, we propose an event-fitting boosting model to predict the event popularity by embedding the feature importance over events into the XGBOOST model. Moreover, we propose an event-structure-based method, which incrementally updates the prediction model over social streams. We have conducted extensive experiments to prove the effectiveness and efficiency of the proposed approach.
New way to manage high density signal connections and the small power of sub-K cryo-generators

The small power of cryo-generators at sub-K temperature is a strong bottleneck for large sub-K experiments, such as large spatial spectro-imagers for astrophysics, or qubits arrays for quantic computing. It is even very often the limitation to the number of channels of the experiments.To tackle this, we have developed a very innovative hybridization technology allowing the interconnections, by a large number of links, of two devices (typically a sub-K detector and the first stage of its readout electronics) cooled at different temperatures (typically 50 mK and 0.3 – 1 K). These hybridizations present a very low electrical resistance and an extremely high thermal resistance, unmatched by existing technologies, thanks to a multilayer superconducting metallization technology.The very low thermal conduction (much lower than the phonon conduction in a homogeneous superconducting material) is obtained thanks to an acoustic mismatch between layers. The link is ultra-compact (≃ μm) and with ultra-low electrical impedance thanks to the use of superconducting materials only. It can be used to interconnect devices, for example when associated with ball hybridization, or inside the elementary cells of low temperature detectors, for example to separate the two functions of absorption and thermometry. So this new process could transform drastically, in the future, the architecture of nearly all sub-K instrumentations.We have already realized such device at the Plateforme Technologique Amont (PTA) Grenoble facility. Measurements in the sub-K range demonstrate very interesting thermal performances. In this paper, we present the principle behind this thermal property of this Multi-Layer, the first results, and the perspectives.
An Enhanced Information Sharing Roadside Unit Allocation Scheme for Vehicular Networks

Sharing up-to-date environment information collected by intelligent connected vehicles is critical in achieving travel comfort, convenience, and safety in vehicular networks. Individually collected information should be made available to other vehicular nodes, adjacent or distant, to achieve an informed and well-managed vehicular traffic. The coverage reach of sharing these road data can be maximized by allocating roadside units in strategic positions. In this work, we propose an Enhanced Information SHAring via Roadside Unit Allocation (EISHA–RSU) scheme that strategically determines where RSUs must be deployed from all spatial candidate locations. The urban area is irregularly partitioned into effective regions of movement (ERM) according to vehicular capacity with priority. For each ERM, EISHA–RSU greedily allocates the initial RSU to an effective position and optimally assigns the remaining RSUs to spatial locations that capture the maximum I2V/V2I information sharing based on the area’s average road speed. In effect, the proposed deployment scheme addresses both the issues of coverage and connectivity among vehicles and the infrastructure. We evaluate the proposed RSU allocation scheme by employing three urban empirical mobility datasets and compare its network starvation fairness, effectiveness, and efficiency performance measures with three other deployment benchmarks. Overall, EISHA–RSU reduces the number of required RSUs to cover a certain area, exhibits higher connectivity, and achieves maximum I2V/V2I information sharing among the evaluated schemes.