Miracle-dz/UniFAR-FTUs huggingface dataset:
Neodymium budget in the Mediterranean Sea: evaluating the role of atmospheric dusts using a high-resolution dynamical-biogeochemical model

Abstract. The relative importance of river solid discharge, deposited sediment remobilisation, and atmospheric dust as sources of neodymium (Nd) to the ocean is the subject of ongoing debate, the magnitudes of these fluxes being associated with a significant uncertainty. The Mediterranean basin is a specific basin; it receives a vast amount of emissions from different sources and is surrounded by continental margins, with a significant input of dust as compared to the global ocean. Furthermore, it is largely impacted by the Atlantic water inflow via the Strait of Gibraltar. Here, we present the first simulation of dissolved Nd concentration ([Nd]) and Nd isotopic composition (εNd) using a high-resolution regional model (NEMO/MED12/PISCES) with an explicit representation of all Nd inputs, and the internal cycle, i.e. the interactions between the particulate and dissolved phases. The high resolution of the oceanic model (at 1/12∘), essential to the simulation of a realistic Mediterranean circulation in present-day conditions, gives a unique opportunity to better apprehend the processes governing the Nd distribution in the marine environment. The model succeeds in simulating the main features of εNd and produces a realistic distribution of [Nd] in the Mediterranean Sea. We estimated the boundary exchange (BE, which represents the transfer of elements from the margin to the sea and their removal by scavenging) flux at 89.43 × 106 g(Nd) yr−1, representing ∼84.4 % of the total external Nd source to the Mediterranean basin. The river discharge provided 3.66 × 106 g(Nd) yr−1, or 3.5 % of the total Nd flow into the Mediterranean. The flux of Nd from partially dissolved atmospheric dusts was estimated at 5.2 × 106 g(Nd) yr−1, representing 5 % of the total Nd input, and 7.62 × 106 g(Nd) yr−1 comes from the Atlantic across the Strait of Gibraltar, i.e. 7.1 % of the total Nd input. The total quantity of Nd in the Mediterranean Sea was estimated to 7.28 × 109 g(Nd); this leads to a new calculated Nd residence time of ∼68 year. This work highlights that the impact of river discharge on [Nd] is localised near the catchments of the main rivers. In contrast, the atmospheric dust input has a basin-wide influence, correcting for a too-radiogenic εNd when only the BE input is considered and improving the agreement of simulated dissolved Nd concentration with field data. This work also suggests that εNd is sensitive to the spatial distribution of Nd in the atmospheric dust, and that the parameterisation of the vertical cycling (scavenging/remineralisation) considerably constrains the ability of the model to simulate the vertical profile of εNd.
Edinburgh Research Explorer Investigation of the effect of Interleukin-1 receptor antagonist (IL-Ira) on markers of inflammation in non-ST elevation acute coronary syndromes (The MRC-ILA-HEART Study) on in

Background: Acute Coronary Syndromes account for 15% of deaths in the UK, and patients remain at significant risk of re-admission for future complications and death. Pathologically the underlying process of atherosclerosis is driven by inflammatory mechanisms, which are activated in ACS patients. Previous studies have investigated the role of inflammatory markers in this process, including interleukin 1 (IL-1) and C Reactive Protein
Unlocking the Pandora’s Box of Uncultivable Microbes: The “iChip” As A Gateway to Antimicrobial Drug Discovery

A wide range of microorganisms make up the microbial world, the majority of which have not yet been studied or grown. Thus, the term "microbial dark matter". Modifying artificial nutrient medium only results in a marginal increase in cultivability. So, using naturally occurring combinations of growth factors is an alternate method of cultivating species whose requirements are unknown. By putting cells extracted from various environmental samples into diffusion chambers and subsequently reintroducing them into the natural environment for incubation, this technique cultivates microorganisms. Microorganisms can be grown and separated in axenic culture in a single step by resizing the chambers and putting only one to several cells inside each one. It has been demonstrated that using this cultivation platform—also known as the "iChip" or "isolation chip"—uncultivable bacteria can be successfully isolated. Teixobactin and Clovibactin are novel antibiotics that were extracted from organisms isolated using the iChip. Despite the fact that these novel compounds are still in the earliest phases and cannot be guaranteed to reach the market, the iChip's use ought to contribute to the discovery of additional potentially useful novel antibiotics.
PriDe CT: Towards Public Consensus, Private Transactions, and Forward Secrecy in Decentralized Payments

—Anonymous Zether, proposed by B¨unz et al. (FC, 2020) and subsequently improved by Diamond (IEEE S&P, 2021) is an account-based confidential payment mechanism that works by using a smart contract to achieve privacy (i.e. identity of receivers to transactions and payloads are hidden). In this work, we look at simplifying the existing protocol while also achieving batching of transactions for multiple receivers, while ensuring consensus and forward secrecy. To the best of our knowledge, this work is the first to formally study the notion of forward secrecy in the setting of blockchain, borrowing a very popular and useful idea from the world of secure messaging. Specifically, we introduce:
Genome-Wide Identification of Trehalose-6-phosphate Synthase (TPS) Gene Family Reveals the Potential Role in Carbohydrate Metabolism in Peach

Trehalose-6-phosphate synthase (TPS) is essential for plant growth and development, linking trehalose-6-phosphate (T6P) to carbon metabolism. However, little is known about the TPS gene family in peaches and their potential roles in regulating carbohydrates in peach fruit. In this study, nine TPS genes were identified in the peach genome and named according to the homologous genes in Arabidopsis. Phylogenetic analysis showed that three subfamilies were identified, including TPSI, TPSII-1, and TPSII-2, which were also consistent with gene structure analysis. Considerable cis-elements were enriched in the promoters, including plant hormone-related elements. Tissue-specific analysis showed that these TPS genes were mainly expressed in leaves, stems, and fruit, showing different expression patterns for each gene. In addition, during fruit development, the content of trehalose-6-phosphate (T6P) was positively correlated with the expression of PpTPS7a and negatively with sucrose non-fermenting-1-related kinase 1 (SnRK1) activity. Transient overexpression and silencing of PpTPS7a in peach fruit validated its function in regulating T6P content and SnRK1 activity.
Name Disambiguation Scheme Based on Heterogeneous Academic Sites

Academic researchers publish their work in various formats, such as papers, patents, and research reports, on different academic sites. When searching for a particular researcher’s work, it can be challenging to pinpoint the right individual, especially when there are multiple researchers with the same name. In order to handle this issue, we propose a name disambiguation scheme for researchers with the same name based on heterogeneous academic sites. The proposed scheme collects and integrates research results from these varied academic sites, focusing on attributes crucial for disambiguation. It then employs clustering techniques to identify individuals who share the same name. Additionally, we implement the proposed rule-based algorithm name disambiguation method and the existing deep learning-based identification method. This approach allows for the selection of the most accurate disambiguation scheme, taking into account the metadata available in the academic sites, using a multi-classifier approach. We consider various researchers’ achievements and metadata of articles registered in various academic search sites. The proposed scheme showed an exceptionally high F1-measure value of 0.99. In this paper, we propose a multi-classifier that executes the most appropriate disambiguation scheme depending on the inputted metadata. The proposed multi-classifier shows the high F1-measure value of 0.67.
International Journal of Research Publication and Reviews

: This study aims to establish a model to evaluate factors affecting the perceived usefulness of online reviews and the impact of online review reliability on young people's impulsive shopping behavior. At Hanoi. Research has shown the influence of key factors on perceived usefulness of online reviews and the impact of online review credibility on impulsive shopping behavior; Evaluate the influence of different factors in the model on the perceived usefulness of online reviews and the impact of online review reliability on impulsive shopping behavior of young people in Vietnam. Hanoi; Evaluate the influence of uniformity tendency on impulsive shopping behavior of young people in Hanoi.
SELECTION OF POTENTIAL PROBIOTIC LACTOBACILLI FROM PIG FAECES TO BE USED AS ADDITIVES IN PELLETED FEEDING

Abstract Thirty-ﬁve isolates from pig feces were identiﬁed as Lactobacillus reuteri (12 strains), Lactobacillus mucosae (7), Lactobacillus plantarum (6), Lactobacillus kitasatonis (3), Lactobacillus rossiae (2), Lactobacillus ultunensis (2), Lactobacillus crispatus (2), and Lactobacillus intestinalis (1)by partial sequence analysis of the 16S rRNA. All isolates were detected at 8–9 log CFUg −1 . Preliminarily, strains were selected based onresistance to heat treatments (ca. 70 ◦ C for 10 s). The decrease in viability for some L. reuteri , L. mucosae , L. plantarum , L. kitasatonis ,and L. rossiae strains was lower than 1 log cycle. Selected strains were further characterized for acid and bile salt resistance, and antibacterial activity.Except for L. kitasatonis , tolerance to simulated gastric and intestinal conditions was enhanced for all strains by addition of reconstituted skimmedmilk. Antibacterial activity was found against Gram-positive and -negative potential pathogens. L. reuteri
Decadal to centennial fluctuations in the intensity of the eastern tropical North Pacific oxygen minimum zone during the last 1200 years

Oxygen minimum zones (OMZs), located below highly productive marine regions, are sites of microbially mediated denitrification and biogeochemical cycling that have global significance. The intensity of OMZs fluctuates naturally; however, the degree of these fluctuations and a comprehensive understanding of the factors that drive these fluctuations on decadal to centennial time scales is lacking. Our high-resolution (near-annual) record of δ15Nsed from laminated sediments at the Pescadero Slope in the Gulf of California (eastern tropical North Pacific) fluctuates between maximum values of 10.5‰ and minimum values of 8.0‰ over the past 1200 years. An analysis of the relationship between δ15NO3− and [O2] in the water column suggests that the observed range of δ15Nsed values is equivalent to an approximately 8 µM fluctuation in O2 content and that these changes can occur in less than 25 years. Our findings show that the OMZ typically intensifies quickly and contracts gradually; the average rate of OMZ intensification (−0.24 µM O2/yr) is twice as fast as the rate of OMZ reoxygenation. Spectral analyses of the δ15Nsed record and Br/Cl counts, with the latter are used as a proxy for organic carbon preservation, suggest that the Pacific Decadal Oscillation and the Suess (deVries) solar cycle (solar irradiance) may influence the intensity of the OMZ and carbon production/export during the late Holocene. Coherence between δ15Nsed and weight percent organic carbon also suggests that similar mechanisms influence both OMZ fluctuations and variation in organic carbon production/export.
Detecting Communities and Correlated Attribute Clusters on Multi-Attributed Graphs

SUMMARY Multi-attributed graphs, in which each node is characterized by multiple types of attributes, are ubiquitous in the real world. Detection and characterization of communities of nodes could have a signiﬁcant impact on various applications. Although previous studies have attempted to tackle this task, it is still challenging due to di ﬃ culties in the integration of graph structures with multiple attributes and the presence of noises in the graphs. Therefore, in this study, we have focused on clusters of attribute values and strong correlations between communities and attribute-value clusters. The graph clustering methodology adopted in the proposed study involves C ommunity detection, A ttribute-value clustering, and deriving R elationships between communities and attribute-value clusters (CAR for short). Based on these concepts, the proposed multi-attributed graph clustering is modeled as CAR-clustering. To achieve CAR-clustering, a novel algorithm named CARNMF is developed based on non-negative matrix factorization (NMF) that can detect CAR in a cooperative manner. Re-sults obtained from experiments using real-world datasets show that the CARNMF can detect communities and attribute-value clusters more accurately than existing comparable methods. Furthermore, clustering re-sults obtained using the CARNMF indicate that CARNMF can successfully detect informative communities with meaningful semantic descriptions through correlations between communities and attribute-value clusters.
Purinergic Signaling Pathway in Human Olfactory Neuronal Precursor Cells

Extracellular ATP and trophic factors released by exocytosis modulate in vivo proliferation, migration, and differentiation in multipotent stem cells (MpSC); however, the purinoceptors mediating this signaling remain uncharacterized in stem cells derived from the human olfactory epithelium (hOE). Our aim was to determine the purinergic pathway in isolated human olfactory neuronal precursor cells (hONPC) that exhibit MpSC features. Cloning by limiting dilution from a hOE heterogeneous primary culture was performed to obtain a culture predominantly constituted by hONPC. Effectiveness of cloning to isolate MpSC-like precursors was corroborated through immunodetection of specific protein markers and by functional criteria such as self-renewal, proliferation capability, and excitability of differentiated progeny. P2 receptor expression in hONPC was determined by Western blot, and the role of these purinoceptors in the ATP-induced exocytosis and changes in cytosolic Ca2+ ([Ca2+]i) were evaluated using the fluorescent indicators FM1-43 and Fura-2 AM, respectively. The clonal culture was enriched with SOX2 and OCT3/4 transcription factors; additionally, the proportion of nestin-immunopositive cells, the proliferation capability, and functionality of differentiated progeny remained unaltered through the long-term clonal culture. hONPC expressed P2X receptor subtypes 1, 3-5, and 7, as well as P2Y2, 4, 6, and 11; ATP induced both exocytosis and a transient [Ca2+]i increase predominantly by activation of metabotropic P2Y receptors. Results demonstrated for the first time that ex vivo-expressed functional P2 receptors in MpSC-like hONPC regulate exocytosis and Ca2+ signaling. This purinergic-triggered release of biochemical messengers to the extracellular milieu might be involved in the paracrine signaling among hOE cells.
A Knowledge-driven Approach for Human-Robot Collaborative Manufacturing

Human-Robot Collaboration (HRC) is expected to have a broad impact on manufacturing when human workers and robots can work together safely and efficiently. For HRC to be safe, no unintentional contact should occur between the human and robot. In this paper we focus on a human-robot collaborative manufacturing application, that is, composite sheet layup, as a context for presenting a knowledge-driven approach for ensuring safe and effective HRC. A knowledge-based system is developed to offer a single endpoint that fuses multiple sources of information from various sub-systems (i.e.
Machine Learning to Advance Human Genome-Wide Association Studies

Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data are made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity. In this review, we outline some of the main applications of machine learning to assigning human genetic loci to health outcomes. We summarise widely used methods and discuss their advantages and challenges. We also identify several tools, such as Combi, GenNet, and GMSTool, specifically designed to integrate these methods for hypothesis-free analysis of genetic variation data. We elaborate on the additional value and limitations of these tools from a geneticist’s perspective. Finally, we discuss the fast-moving field of foundation models and large multi-modal omics biobank initiatives.
Reconstruction of early Holocene Thermal Maximum temperatures using present vertical distribution of conifers in the Pannon region (SE Central Europe)

Palaeoclimatic reconstruction is a main subject of palaeoecology, clarifying fossil palaeoenvironmental patterns. Our study provides a macroecological approach to reconstruct the mean annual temperature (MAT) of the Pannon region at the early Holocene Thermal Maximum (HTM, warmest period of the Holocene), based on the absence of forest-dwelling conifers in the North Hungarian Mountains and their presence in the surrounding Carpathians on the same altitude. We suppose that the HTM was enough warm to drive conifers to extinction from elevations between 900 and 1100 m a.s. l. in the relatively isolated N-Hungarian Mts. Conversely, HTM still allowed the survival of residual dwarf pine (Pinus mugo) stands on the isolated peaks of the West Transylvanian Mountains between 1600 and 1800 m a.s. l. Our study provides an estimate for the value of MAT of HTM of Pannon region with an interval of 0.4°C, relying on macroecological considerations. We calculate the temperature of the HTM 1.3–1.7°C warmer than the present temperature. This method can be used in a general sense, if conditions meet the requirements of the method even in horizontal cases, with area isolates of climate-sensitive species.
Testing Schwartz’s Model of Cultural Value Orientations in Europe with the European Social Survey An Empirical Comparison of Additive Indexes with Factor Scores

The aim of this study is to compare results of measuring the Schwartz (2004) cultural value orientations us-ing multilevel conﬁrmatory factor analysis (ML CFA) vs. the unweighted aggregated means (simple means) used in previous research. We conduct this comparison with data from the 21-item short version of the Por-trait Values Questionnaire in 6 rounds of the European Social Survey (ESS). An advantage of estimating factor scores via ML CFA vs. simple means is that ML CFA weights the impact of the factors on their indicators empirically. It also permits simultaneously assessing Schwartz’ 10 individual level and 7 cultural level values by decomposing the total variances of the 21 items into a within part (individual values) and a between part (cultural values), while controlling for random measurement errors. High intercorrelations between related values in ML CFA required unifying two pairs of cultural values. Comparing correlations with theoretically relevant macro indicators of the factor scores and of the simple means of the cultural values indicated somewhat higher external validity for the factor scores. Taken together, the ﬁndings suggest that the derived factor scores provide suitable macro indicators of Schwartz’ cultural value orientations for future studies using the ESS data.
Bayesian Deep Q-Learning via Sequential Monte Carlo

Exploration in reinforcement learning remains a difficult challenge. Recently, ensembles with randomized prior functions have been popularized to quantify uncertainty in the value model, in order to drive exploration with success. However these ensembles have no theoretical guarantee to resemble the actual posterior. In this work, we view training ensembles from the perspective of sequential Monte Carlo, and propose an algorithm that exploits both the practical flexibility of ensembles and theory of the Bayesian paradigm. We incorporate this method into a standard DQN agent and experimentally show improved exploration capabilities over a regular ensemble.
Nonlinear Feedback Control of VTOL UAVs

This paper addresses the nonlinear feedback control of Unmanned Aerial Vehicles (UAVs) with Vertical Take-Off and Landing (VTOL) capacities, such as multi-copters, ducted fans, helicopters, convertible UAVs, etc. First, dynamic models of these systems are recalled and discussed. Then, a nonlinear feedback control approach is presented. It applies to a large class of VTOL UAVs and aims at ensuring large stability domains and robustness with respect to unmodeled dynamics. This approach addresses most control objectives encountered in practice, for both remotely operated and fully autonomous flight.
Hyperelastic phase-ﬁeld fracture mechanics modeling of the toughening induced by Bouligand structures in natural materials

Bouligand structures are widely observed in natural materials; elasmoid ﬁsh scales and the exoskeleton of arthropods, such as lobsters, crabs, mantis shrimp and insects, are prime examples. In ﬁsh scales, such as those of the Arapaima gigas , the tough inner core beneath the harder surface of the scale displays a Bouligand structure comprising a layered arrangement of collagen ﬁbrils with an orthogonal or twisted staircase (or plywood) architecture. A much rarer variation of this structure, the double-twisted Bouligand structure, has been discovered in the primitive elasmoid scales of the coelacanth ﬁsh; this architecture is quite distinct from “modern” elasmoid ﬁsh scales yet provides extraordinary resistance to deformation and fracture. Here we examine the toughening mechanisms created by the double-twisted Bouligand structure in comparison to those generated by the more common single Bouligand structures. Speciﬁcally, we have developed an orientation-dependent, hyperelastic, phase-ﬁeld fracture mechanics method to computationally examine the relative fracture toughness of elasmoid ﬁsh scales comprising single vs. double-twisted Bouligand structures of ﬁbrils. The model demonstrates the critical role played by the extra inter-bundle ﬁbrils found in coelacanth ﬁsh scales in enhancing the toughness of Bouligand-type structures. Synthesis and fracture tests of 3-D printed Bouligand-type materials are presented to support the modeling and complement our understanding of the fracture mechanisms in Bouligand-type structures.
Global sensitivity analysis of a techno-socio-economic building stock energy model

Building stock models are increasingly applied for deriving policy recommendations in achieving climate neutrality of the building sector. However, they are associated with significant uncertainties, which usually are not systematically analysed. We apply the elementary effects method on scenarios calculated with the model Invert/EE-Lab. Among others, we identified parameters relevant for the decision making algorithm within the model as most influential. This has implications on the interpretation of results, in particular for the future mix of energy carriers which show very similar costs and attractiveness. The systematic application of global sensitivity analyses can help to improve the understanding of building stock modelling results and better substantiate related policy conclusions.