Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental results pertaining to the performance of thermal igniters

This report summarizes the results of various experimental programs regarding the performance of thermal igniters for the deliberate ignition of hydrogen in light water reactors. Experiments involving both premixed combustion and combustion with continuous hydrogen injection are reviewed. Combustion characteristics examined include flammability limits of hydrogen:air and hydrogen:air:steam mixtures, combustion pressure rises, combustion completeness, flame speeds, and heat transfer aspects. Comparisons of igniter type and igniter reliability under simulated reactor accident conditions are included. The results of the research programs provide a broad data base covering nearly all aspects of hydrogen combustion related to the performance of deliberate ignition systems.
A Method for Mapping the Experience of Engagement in Video Games

related to the player’s willingness to continue playing. Our categorization of engagement is therefore more detailed than the Brown and Cairns (2004) study, and whereas other studies investigate games with virtual game environments (e.g., Calleja, 2011) and multiplayer online role-playing games (e.g., Yee, 2006), we attempt to cover all game genres, so the choice of the following terms was made in order to be as general as possible. We have chosen the terms intellectual, physical, sensory, social, narrative, and emotional engagement to cover the various causes of the desire to continue. This study proposes a method intended to investigate the basal level of engagement through the willingness to continue playing, and it therefore does not attempt to explore the next levels or phases of the player experience such as, for example, engrossment, total immersion (comparable to presence; Brown and Cairns, 2004), flow (Csíkszentmihályi, 1991), incorporation (Calleja, 2011), or enjoyment (Klimmt, 2003). By evaluating the desire to continue aspect of the player experience, we can determine the most fundamental requirement of a successful player experience, as it is can be argued that it is not possible to immerse or involve oneself,
Selection of Superior Planting Stocks and Development of Regeneration Techniques for Coastal Restoration: A Pilot Study

There is an increasing demand in the U.S. for wetland plants to be used for marsh restoration and coastal stability projects. However, physiologically adapted wetland plants suitable for wetland restoration and specific environmental conditions generally are not available. Selecting a physiologically adapted species for a specific wetland restoration project requires careful matching of plant species to that of wetland conditions. Such conditions include degree of flooding soil conditions, and salinity. Wiregrass (Spartina Patens) is a dominant brackish marsh grass in U.S. Gulf Coast marshes which grows over a wide range of salinities ranging from the edge of the freshwater habitats to saline marshes. Although it is considered as a salt-tolerant species, growth of this species is adversely affected by excess salinity. Field observations along the Louisiana gulf coast indicate considerable variations in the performance of populations of this species in response to various salinity regimes suggesting potential differences in response of these populations to changes in salinity. To test this hypothesis, comparative studies were conducted to evaluate the responses of selected populations to elevated salinities.
Attention-based Image Segmentation

Ensemble Learning is widely used mainly in problems involving classiﬁcation and regression tasks, such as [1] and [2], however, still needs some research to use Ensemble Learning with image segmentation. This project proposes a framework to use the concepts of Ensemble Learning with a deep neural attention network to improve results on image segmentation tasks. We propose a new framework based on image segmentation fusion to out-perform single models. This framework is divided into three parts; a set of segmentation models, the PoolNet, an attention model, the Expert, and an aggregation function, F . That aggregation function could be non-trainable, such as weighted arithmetic mean or trainable such as a deep neural network.
A context information manager for pervasive environments

In a pervasive computing environment, heterogeneous devices need to communicate in order to provide services adapted to the situation of users. So, they need to assess this situation as their context. We have developed an extensible context model using semantic web technologies and a context information management component that enable the interaction between context information producer devices and context information consumer devices and as well as their insertion in an open environment.
South Caucasus Food Security Learning Summary: How to support national influencing using a multi-stakeholder approach

In September 2013, Oxfam launched the four-year EC-funded project Improving Regional Food Security through National Strategies and Small Holder Production in the South Caucasus . The aim was to influence national level policies around food security and nutrition, in order to better reflect the needs and interests of smallholder farmers in Georgia, Armenia and Azerbaijan. The project was in line with Oxfam’s economic justice priorities in the region: to challenge inequalities and promote sustainable livelihoods for smallholder farmers. This paper synthesizes experience and learning from the project. The aim of the paper is to support the design of future national policy influencing programmes.
an integrated system for regulon inference in prokaryotes by comparative genomics

: RegPredict web server is designed to provide comparative genomics tools for reconstruction and analysis of microbial regulons using comparative genomics approach. The server allows the user to rapidly generate reference sets of regulons and regulatory motif profiles in a group of prokaryotic genomes. The new concept of a cluster of co-regulated orthologous operons allows the user to distribute the analysis of large regulons and to perform the comparative analysis of multiple clusters independently. Two major workflows currently implemented in RegPredict are: (i) regulon reconstruction for a known regulatory motif and (ii) ab initio inference of a novel regulon using several scenarios for the generation of starting gene sets. RegPredict provides a comprehensive collection of manually curated positional weight matrices of regulatory motifs. It is based on genomic sequences, ortholog and operon predictions from the MicrobesOnline. An interactive web interface of RegPredict integrates and presents diverse genomic and functional information about the candidate regulon members from several web resources. RegPredict is freely accessible at http://regpredict.lbl. gov. ABSTRACT RegPredict web server is designed to provide comparative genomics tools for reconstruction and analysis of microbial regulons using comparative genomics approach. The server allows the user to rapidly generate reference sets of regulons and regulatory motif profiles in a group of prokaryotic genomes. The new concept of a cluster of co-regulated orthologous operons allows the user to distribute the analysis of large regulons and to perform the comparative analysis of multiple clusters independently. Two major workflows currently implemented in RegPredict are: (i) regulon reconstruction for a known regulatory motif and (ii) ab initio inference of a novel regulon using several scenarios for the generation of starting gene sets. RegPredict provides a comprehensive collection of manually curated positional weight matrices of regulatory motifs. It is based on genomic sequences, ortholog and operon predictions from the MicrobesOnline. An interactive web interface of RegPredict integrates and presents diverse genomic and functional information about the candidate regulon members from several web resources. RegPredict is
An e D cient global optimization method for self-potential data inversion using micro-differential evolution

Self-potential (SP) method has many applications, where the interpretation of SP data can be used for qualitative and quantitative interpretation. However, inversion of SP data in this paper is of quantitative interpretation and consists of highly non-linear, multimodal data and deploys global optimum method (GOM). Micro-differential evolution (MDE) is a GOM with small or micro-population size (5 – 8 populations) for each iteration. Consequently, this approach involves small numbers of forward computation in the inversion process. Two MDE variants, including adaptive MDE ( l JADE) and vectorized random mutation factor (MVDE) were tested B rst for different level of noises containing synthetic SP data with single anomaly and applied to synthetic SP data of multiple anomalies. The MDE variants are reliable and e A ective for inverting noisy SP data. Furthermore, in order to check the rationality of MDE variants, the algorithm is applied to seven B eld data from different applications, including groundwater exploration, shear zone tracing, water accumulation in landslides and embankment stability assessment. The model parameters revealed by MDE variants are accurate and show good agreement with the previous results estimated using other approaches. In addition, MDE variants also require fewer forward modelling calculations than other optimization approaches.
MULTI CLASS CLASSIFICATION

Generalization error of classifier can be reduced by larger margin of separating hyperplane. The proposed classification algorithm implements margin in classical perceptron algorithm, to reduce generalized errors by maximizing margin of separating hyperplane. Algorithm uses the same updation rule with the perceptron, to converge in a finite number of updates to solutions, possessing any desirable fraction of the margin. This solution is again optimized to get maximum possible margin. The algorithm can process linear, non-linear and multi class problems. Experimental results place the proposed classifier equivalent to the support vector machine and even better in some cases. Some preliminary experimental results are briefly discussed.
RNA-MobiSeq: Deep mutational scanning and mobility-based selection for RNA structure inference

Tertiary structures of RNAs are increasingly found to be essential for their functions and yet are notoriously challenging to be determined by experimental techniques or predicted by energy-based and AlphaFold2-like deep-learning-based computational methods. Here, we coupled deep mutational scanning with mobility-based selection to separate structurally stable from nonstable RNA variants. The subsequent high-throughput sequencing allows the detection of the covariational signals of key secondary and tertiary base pairs with significantly improved, RNA-specific, unsupervised analysis of covariation-induced deviation of activity (CODA2) and amplification of these signals by Monte-Carlo simulated annealing. The resulting base-pairing structures can serve as the restraints for final energy-based structure predictions. This MobiSeq technique was tested on four structurally different RNAs. The results show that the detection of tertiary base pairs by MobiSeq allows reasonably accurate prediction of RNA tertiary structures. The proposed method should be useful for structural inference and improved mechanistic understanding of all RNAs with detectable changes in mobility due to deleterious mutations.
Symplasmic phloem loading and subcellular transport in storage roots are key factors for carbon allocation in cassava

Cassava is a deciduous woody perennial shrub that stores large amounts of carbon and water in its storage roots. Previous studies have shown that assimilate unloading into storage roots happens symplasmically once secondary anatomy is established. However, mechanisms controlling phloem loading and overall carbon partitioning to different cassava tissues remain unclear. Here we used a combination of histological, transcriptional, and biochemical analyses on different cassava tissues and timepoints to better understand source-sink carbon allocation. We find that cassava likely utilizes a predominantly passive symplasmic phloem loading strategy, indicated by the lack of expression of genes coding for key players of sucrose transport, the existence of branched plasmodesmata in the companion cell/bundle sheath interface of minor leaf veins, and very high leaf sucrose concentrations. Furthermore, we show that tissue-specific changes in anatomy and NSC contents are associated with tissue-specific modification in gene expression for sucrose cleavage/synthesis, as well as subcellular compartmentalization of sugars. Overall, our data suggest that carbon allocation during storage root filling is mostly facilitated symplasmically, and is likely mostly regulated by local tissue demand and subcellular compartmentalization.
A non-linear finite element model for the determination of elastic and thermal properties of nanocomposites

Abstract : This thesis presents a non-linear model for the thermal and elastic properties of single-walled carbon nanotube reinforced polymer composites. Finite Element Analysis (FEA), in conjunction with the Embedded Fiber Method (EFM), is used to calculate the effective stress-strain curve and thermal conductivity of the composite material. First, the geometry of a user-defined volume fraction of nanotubes is randomly generated and their properties are incorporated into the polymer matrix using the EFM. Non-linear FEA is next performed to account for the non-linear properties of the polymer matrix and the carbon nanotubes. Finally, Monte Carlo Analysis of five hundred random microstructures is performed to capture the stochastic nature of the random fiber generation and to derive statistically sound results. The model is validated by comparison with several different experiments reported in the open literature.
Human Alveolar and Monocyte-derived Human Macrophage Responses to Mycobacterium tuberculosis

Rationale Alveolar macrophages (AMs) and recruited monocyte-derived macrophages (MDMs) mediate early lung immune responses to Mycobacterium tuberculosis (Mtb). Differences in the response of these distinct cell types is poorly understood and may provide insight into mechanisms of TB pathogenesis. Objectives To determine whether Mtb induces unique and essential anti-microbial pathways in human AMs compared to MDMs. Methods Using paired human AMs and 5-day MCSF-derived MDMs from 6 healthy volunteers, we infected cells with Mtb H37Rv for 6 hours, isolated RNA, and analyzed transcriptomic profiles with RNASeq. Main Results We found 681 genes that were Mtb-dependent in AMs compared to MDMs and 4538 that were Mtb-dependent in MDMs but not AMs (FDR < 0.05). Using hypergeometric enrichment of DEGs in Broad Hallmark gene sets, we found that Type I and II IFN Response were the only gene sets selectively induced in Mtb-infected AM (FDR <0.05). In contrast, MYC targets, unfolded protein response and MTORC1 signaling, were selectively enriched in MDMs (FDR < 0.05). IFNA1, IFNA8, IFNE, and IFNL1 were specifically and highly upregulated in AMs compared to MDMs at baseline and/or after Mtb infection. IFNA8 modulated Mtb-induced pro-inflammatory cytokines and, compared to other interferons, stimulated unique transcriptomes. Several DNA sensors and Interferon Regulatory Factors had higher expression at baseline and/or after Mtb infection in AMs compared to MDMs. Conclusions These findings demonstrate that Mtb infection induced unique transcriptional responses in human AMs compared to MDMs, including upregulation of the IFN response pathway and specific DNA sensors.
The MAP2Ks can use ADP to phosphorylate and activate their substrate MAPKs in vitro

Summary Kinases are a diverse group of enzymes that use ATP to phosphorylate a variety of substrates. Protein kinases evolved in eukaryotes as important mediators of cell signalling that target specific amino acid side chains to modulate downstream protein function. Among them, the MAPKs (mitogen-activated protein kinases) are a family of intracellular protein kinases that form signalling cascades responding to a number of stimuli, that control fundamental mechanisms such as proliferation, differentiation, inflammation and cell death. Signals propagate through consecutive kinases which eventually phosphorylate and activate a MAPK. Here, we show that the dual specificity threonine/tyrosine MAP kinase kinases (MAP2Ks or MEKs) are able to phosphorylate and activate their substrate MAPKs using ADP as well as ATP in vitro. As the pathways are involved in the stress response, we speculate that it would represent an advantage to be able to maintain signalling under conditions such as hypoxia, that occur under a number of cell stresses, including cancer and atherosclerosis, where the available pool of ATP could be depleted. Highlights The MAP2K dual-specificity protein kinases can phosphorylate their target MAPKs using ADP in vitro The reaction with ADP is less efficient than with ATP First example of an enzyme that can use both ATP and ADP ADP phosphorylation might be a potential mechanism to maintain signal integrity when cell energy resources are constrained, as during ischemia
CDKN1B (p27kip1) enhances drug tolerant persister CTCs by restricting polyploidy following mitotic inhibitors

The mitotic inhibitor docetaxel (DTX) is often used to treat endocrine-refractory metastatic breast cancer, but initial responses are mitigated as patients eventually have disease progression. Using a cohort of ex vivo cultures of circulating tumor cells (CTCs) from patients with heavily pretreated breast cancer (n=18), we find two distinct patterns of DTX susceptibility, independent of clinical treatment history. In CTCs cultured from some patients, treatment with a single dose of DTX results in complete cell killing, associated with accumulation of non-viable polyploid (≥8N) cells arising from endomitosis. In others, a transient viable drug-tolerant persister (DTP) population emerges, ultimately enabling renewed proliferation of CTCs with preserved parental cell ploidy and DTX sensitivity. In these CTC cultures, efficient cell cycle exit generates a ≤4N drug-tolerant state dependent on CDKN1B (p27Kip1). Exposure to DTX triggers stabilization of CDKN1B through AKT-mediated phosphorylation at serine 10. Suppression of CDKN1B reduces the number of persister CTCs, increases ≥8N mitotic cells and abrogates regrowth after DTX exposure. Thus, CDKN1B-mediated suppression of endomitosis contributes to a reversible persister state following mitotic inhibitors in patient-derived treatment refractory breast cancer cells. Summary in bullets Transient DTX tolerant persister cells emerge in some patient-derived cultured CTCs. DTX-tolerant persisters restrict endoreduplication and polyploidy through CDKN1 (p27kip1). DTX exposure induces CDKN1B stabilization through AKT mediated phosphorylation at serine 10. Suppression of polyploidy underlies a drug tolerant persister state specific to mitotic inhibitors.
Augmentation of DNA exonuclease TREX1 in macrophages as a therapy for cardiac ischemic injury

Noncoding RNAs (ncRNAs) are increasingly recognized as bioactive. Here we report the development of TY1, a synthetic ncRNA bioinspired by a naturally-occurring human small Y RNA with immunomodulatory properties. TY1 upregulates TREX1, an exonuclease that rapidly degrades cytosolic DNA. In preclinical models of myocardial infarction (MI) induced by ischemia/reperfusion, TY1 reduced scar size. The cardioprotective effect of TY1 was abrogated by prior depletion of macrophages and mimicked by adoptive transfer of macrophages exposed either to TY1 or TREX1. Inhibition of TREX1 in macrophages blocked TY1 cardioprotection. Consistent with a central role for TREX1, TY1 attenuated DNA damage in the post-MI heart. This novel mechanism—pharmacologic upregulation of TREX1 in macrophages—establishes TY1 as the prototype for a new class of ncRNA drugs with disease-modifying bioactivity. One Sentence Summary Upregulation of three prime exonuclease, TREX1, in macrophages enhances tissue repair post myocardial infarction.
Extracellular electron transfer genes expressed by candidate flocking bacteria in cable bacteria sediment

Cable bacteria, filamentous sulfide oxidizers that live in sediments, are at times associated with large flocks of swimming bacteria. It has been proposed that these flocks of bacteria transport electrons extracellularly to cable bacteria via an electron shuttle intermediate, but the identity and activity of these bacteria in freshwater sediment remains mostly uninvestigated. We coupled metagenomics and metatranscriptomics to 16S rRNA amplicon-based correlations with cable bacteria from two time series experiments up to 155 days. We identified bacteria expressing genes for extracellular electron transfer and motility, including synthesis genes for potential extracellular electron shuttles: phenazines and flavins. Of the 85 high quality MAGs (Metagenome Assembled Genomes >90% complete and <5% contaminated), 56 had genes encoding flagellar proteins, and of these 22 had genes encoding extracellular electron transport proteins. The candidate flockers constituted 21.4% of all MAGs and 42.1% of the proposed flocking bacteria expressed extracellular electron transfer genes. The proposed flockers belonged to a large variety of taxonomic groups: 18 genera spread across 9 phyla. Our data suggest that electric relationships in freshwater sediments between cable bacteria and other microbes likely help to generate and/or sustain cryptic element cycling and ‘deep oxygen breathing’, affecting more element cycles than sulfur, like metal– and in particular iron– and carbon cycles.
Environmental and Historical Determinants of African Horse Sickness: Insights from Predictive Modeling

This study marks a pioneering effort in analyzing the global epidemiological patterns of African Horse Sickness (AHS) across different regions. By employing predictive modeling with a comprehensive set of environmental variables, we uncovered overarching global patterns in AHS dynamics, a first in this field. Our analysis revealed significant regional differences influenced by specific climatic conditions, highlighting the disease’s complexity. The study also identifies new high-risk areas for AHS, underscoring the necessity for regionally tailored disease management strategies. Despite some limitations, such as the exclusion of wild equine data, this research offers critical insights for global AHS intervention and prevention, setting a path for future research incorporating broader datasets and socio-economic factors. Author Summary AHS presents a significant challenge to the global equine industry, impacting both health and economic aspects. Our study highlights the profound effect of climate change, particularly the frequency of extreme climatic events including temperature and humidity variations, on the transmission dynamics of diseases like AHS. In our research, we focused on overcoming the challenges associated with identifying key environmental factors and determining the appropriate geographic scale for a comprehensive global understanding of AHS. Our aim was to bridge existing knowledge gaps and elucidate the fundamental principles governing AHS transmission. This study establishes a solid foundation for understanding the intricate dynamics of AHS and offers practical pathways for conservation efforts. It emphasizes the urgent need for environmentally conscious strategies to protect horse populations and the industries dependent on them. By highlighting the relationship between environmental factors, vector presence, and AHS transmission, our research underscores the importance of a holistic approach to disease mitigation. In conclusion, the findings of our study not only contribute to the scientific understanding of AHS but also serve as a guide for policymakers and practitioners in developing effective strategies for disease management and prevention, tailored to the specific environmental conditions and challenges faced in different regions around the world.