Miracle-dz/UniFAR-FTUs huggingface dataset:
Insights into the interplay between Epstein‐Barr virus (EBV) and multiple sclerosis (MS): A state‐of‐the‐art review and implications for vaccine development

Abstract Background and Aims Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system (CNS). MS results from an inflammatory process leading to the loss of neural tissue and increased disability over time. The role of Epstein Barr Virus (EBV), as one of the most common global viruses, in MS development has been the subject of several studies. However, many related questions are still unanswered. This study aimed to review the connection between MS and EBV and provide a quick outline of MS prevention using EBV vaccination. Methods For this narrative review, an extensive literature search using specific terms was conducted across online databases, including PubMed/Medline, Scopus, Web of Science, and Google Scholar, to identify pertinent studies. Results Several studies proved that almost 100% of people with MS showed a history of EBV infection, and there was an association between high titers of EBV antibodies and an increased risk of MS development. Various hypotheses are proposed for how EBV may contribute to MS directly and indirectly: (1) Molecular Mimicry, (2) Mistaken Self, (3) Bystander Damage, and (4) Autoreactive B cells infected with EBV. Conclusion Given the infectious nature of EBV and its ability to elude the immune system, EBV emerges as a strong candidate for being the underlying cause of MS. The development of an EBV vaccine holds promise for preventing MS; however, overcoming the challenge of creating a safe and efficacious vaccine presents a significant obstacle.
Computational multigene interactions in virus growth and infection spread

Abstract Viruses persist in nature owing to their extreme genetic heterogeneity and large population sizes, which enable them to evade host immune defenses, escape antiviral drugs, and adapt to new hosts. The persistence of viruses is challenging to study because mutations affect multiple virus genes, interactions among genes in their impacts on virus growth are seldom known, and measures of viral fitness are yet to be standardized. To address these challenges, we employed a data-driven computational model of cell infection by a virus. The infection model accounted for the kinetics of viral gene expression, functional gene–gene interactions, genome replication, and allocation of host cellular resources to produce progeny of vesicular stomatitis virus, a prototype RNA virus. We used this model to computationally probe how interactions among genes carrying up to eleven deleterious mutations affect different measures of virus fitness: single-cycle growth yields and multicycle rates of infection spread. Individual mutations were implemented by perturbing biophysical parameters associated with individual gene functions of the wild-type model. Our analysis revealed synergistic epistasis among deleterious mutations in their effects on virus yield; so adverse effects of single deleterious mutations were amplified by interaction. For the same mutations, multicycle infection spread indicated weak or negligible epistasis, where single mutations act alone in their effects on infection spread. These results were robust to simulation in high- and low-host resource environments. Our work highlights how different types and magnitudes of epistasis can arise for genetically identical virus variants, depending on the fitness measure. More broadly, gene–gene interactions can differently affect how viruses grow and spread.
Gender, smoking, and tobacco cessation with pharmacological treatment in a cluster randomized clinical trial

INTRODUCTION Whether men find it easier to quit smoking than women is still controversial. Different studies have reported that the efficacy of pharmacological treatments could be different between men and women. This study conducted a secondary analysis of ‘Subsidized pharmacological treatment for smoking cessation by the Spanish public health system’ (FTFT-AP study) to evaluate the effectiveness of a drug-funded intervention for smoking cessation by gender. METHODS A pragmatic randomized clinical trial by clusters was used. The population included smokers aged ≥18 years, smoking >10 cigarettes per day, randomly assigned to an intervention group receiving regular practice and financed pharmacological treatment, or to a control group receiving only regular practice. The main outcome was continued abstinence at 12 months, self-reported and validated with CO-oximetry. The percentage, with 95% confidence intervals, of continued abstinence was compared between both groups at 12 months post-intervention, by gender and the pharmacological treatment used. Multilevel logistic regression analysis was performed. RESULTS A total of 1154 patients from 29 healthcare centers were included. The average age was 46 years (SD=11.78) and 51.7% were men. Overall, the self-reported abstinence at 12 months was 11.1% (62) in women and 15.7% (93) in men (AOR=1.4; 95% CI: 1.0–2.0), and abstinence validated by CO-oximetry was 4.6% (26) and 5.9% (35) in women and men, respectively (OR=1.3; 95% CI: 0.7–2.2). In the group of smokers receiving nicotine replacement treatment, self-reported abstinence was higher in men compared to women (29.5% vs 13.5%, OR=2.7; 95% CI: 1.3–5.8). CONCLUSIONS The effectiveness of a drug-financed intervention for smoking cessation was greater in men, who also showed better results in self-reported abstinence with nicotine replacement treatment.
Proteomic Profiling of Dilated Cardiomyopathy Plasma Samples — Searching for Biomarkers with Potential to Predict the Outcome of Therapy

Determination of the prognosis and treatment outcomes of dilated cardiomyopathy is a serious problem due to the lack of valid specific protein markers. Using in-depth proteome discovery analysis, we compared 49 plasma samples from patients suffering from dilated cardiomyopathy with plasma samples from their healthy counterparts. In total, we identified 97 proteins exhibiting statistically significant dysregulation in diseased plasma samples. The functional enrichment analysis of differentially expressed proteins uncovered dysregulation in biological processes like inflammatory response, wound healing, complement cascade, blood coagulation, and lipid metabolism in dilated cardiomyopathy patients. The same proteome approach was employed in order to find protein markers whose expression differs between the patients well-responding to therapy and nonresponders. In this case, 45 plasma proteins revealed statistically significant different expression between these two groups. Of them, fructose-1,6-bisphosphate aldolase seems to be a promising biomarker candidate because it accumulates in plasma samples obtained from patients with insufficient treatment response and with worse or fatal outcome. Data are available via ProteomeXchange with the identifier PXD046288.
DISTRIBUTION OF HEAVY METALS IN FLUTED PUMPKIN (TELFEIRIA OCIDENTALIS) LEAVES PLANTED AT DIFFERENT DISTANCES AWAY FROM THE TRAFFIC CONGESTED HIGHWAYS

This study assessed the level of heavy metals (Pb, Ni, Cr, Mn, Cd, and Zn) accumulation in leaves of fluted pumpkin (Telfeiria ocidentalis) planted at 10, 50 and 100 meters away from three major traffic congested highways, and rural forest farmlands (RFF) in Akwa Ibom and Cross River States in Nigeria. The results obtained from this study showed that the levels of the heavy metals (Pb, Ni, Mn, Cd, and Zn) obtained for the leaves of vegetables planted at 10m from the highways were significantly higher (P ≤ 0.05) compared respectively to the levels obtained for the leaves at 50 and 100m away from the highways, and the RFF. However, the levels of the heavy metals obtained for the leaves planted within 50m, 100m and RFF were observed to fall within the same concentration range, although the levels for 50m were insignificantly higher (P ≥ 0.05). This observation indicated that the emissions of heavy metals from the vehicles on traffic congested roads may be deposited on the leaves of vegetables planted at close proximity to the road. Hence, the risk of heavy metals accumulation in leafy vegetables planted within 10m from traffic congested highways is reported in this study.
Individual Fairness Guarantee in Learning with Censorship

Algorithmic fairness, studying how to make machine learning (ML) algorithms fair, is an established area of ML. As ML technologies expand their application domains, including ones with high societal impact, it becomes essential to take fairness into consideration when building ML systems. Yet, despite its wide range of socially sensitive applications, most work treats the issue of algorithmic bias as an intrinsic property of supervised learning, i.e. , the class label is given as a precondition. Unlike prior fairness work, we study individual fairness in learning with censorship where the assumption of availability of the class label does not hold, while still requiring that similar individuals are treated similarly. We argue that this perspective represents a more realistic model of fairness research for real-world application deployment, and show how learning with such a relaxed precondition draws new insights that better explain algorithmic fairness. We also thoroughly evaluate the performance of the proposed methodology on three real-world datasets, and validate its superior performance in minimizing discrimination while maintaining predictive performance.
Advanced Surface Passivation for High-Sensitivity Studies of Biomolecular Condensates

Biomolecular condensates are cellular compartments that concentrate biomolecules without an encapsulating membrane. In recent years, significant advances have been made in the understanding of condensates through biochemical reconstitution and microscopic detection of these structures. Quantitative visualization and biochemical assays of biomolecular condensates rely on surface passivation to minimize background and artifacts due to condensate adhesion. However, the challenge of undesired interactions between condensates and glass surfaces, which can alter material properties and impair observational accuracy, remains a critical hurdle. Here, we introduce an efficient, generically applicable, and simple passivation method employing self-assembly of the surfactant Pluronic F127 (PF127). The method greatly reduces nonspecific binding across a range of condensates systems for both phase-separated droplets and biomolecules in dilute phase. Additionally, by integrating PF127 passivation with the Biotin-NeutrAvidin system, we achieve controlled multi-point attachment of condensates to surfaces. This not only preserves condensate properties but also facilitates long-time FRAP imaging and high-precision single-molecule analyses. Using this method, we have explored the dynamics of polySIM molecules within polySUMO/polySIM condensates at the single-molecule level. Our observations suggest a potential heterogeneity in the distribution of available polySIM-binding sites within the condensates. Significance Statement The understanding of biomolecular condensates has significantly benefited from biochemical reconstitution with microscopy detection. Here, we present a novel surface passivation method utilizing self-assembly of Pluronic F127 on hydrophobic surfaces. This approach not only effectively minimizes non-specific binding without altering the physical properties of the condensates but also offers universal passivation across a variety of condensate systems. It demonstrates high resistance to different treatments and enables condensate immobilization through controlled anchor points. This allows for highly sensitive analytical techniques, including single-molecule imaging. The simplicity and high-performance of this method, coupled with time and cost efficiencies, could facilitate robustness and throughput of experiments, and could broaden the accessibility of biochemical phase separation studies to a wider scientific community.
In-situ architecture of the human prohibitin complex

Prohibitins are a highly conserved family of proteins that have been implicated in a variety of functions including mitochondrial stress signalling and housekeeping, cell cycle progression, apoptosis, lifespan regulation and many others1, 2. The human prohibitins PHB1 and PHB2 have been proposed to act as scaffolds within the mitochondrial inner membrane, but their molecular organisation remained elusive. Using an integrative structural biology approach combining quantitative Western blotting, cryo-electron tomography, subtomogram averaging and molecular modelling, we determined the molecular organisation of the human prohibitin complex within the mitochondrial inner membrane. The proposed bell-shaped structure consists of eleven alternating PHB1 and PHB2 molecules. This study reveals an average of about 43 prohibitin complexes per crista, covering 1-3 % of the cristae membranes. These findings provide a structural basis for understanding the functional contributions of prohibitins to the integrity and spatial organisation of the mitochondrial inner membrane.
Investigating 3D microbial community dynamics in the rhizosphere using complex-field and fluorescence microscopy

Microbial interactions in the rhizosphere contribute to soil health. Understanding these interactions thus has the potential to advance sustainable agriculture, ecosystem management, and environmental stewardship. Yet it is difficult to understand what we cannot see; amongst the limitations in rhizosphere imaging are challenges associated with rapidly and non-invasively imaging microbial cells over field depths relevant to plant roots. Here, we present a novel bimodal imaging technique called Complex-field and Fluorescence microscopy using the Aperture Scanning Technique (CFAST) that begins to address these limitations by integrating quantitative phase and 3D fluorescence imaging. We showcase CFAST’s practicality and versatility in two ways. First, by harnessing its depth of field of more than 100 microns, we significantly reduce the number of captures required for 3D imaging of plant roots and bacteria in the rhizoplane, thereby minimizing potential photobleaching and phototoxicity. Second, by leveraging CFAST’s phase sensitivity and fluorescence specificity, we track early bacterial aggregate development, bacterial competition, and gene expression under varying environmental conditions. Specifically, we resolve bacterial growth dynamics of mixed populations at the early stages of colonization without the need for genetically labeling environmental isolates. Moreover, we find that the expression of genes of interest to rhizosphere chemistry (e.g. representative genes involved in phosphorus-sensing and antibiotic production) varies spatiotemporally within microbial populations that are surface-attached and appears distinct from their expression in planktonic cultures. Together, CFAST’s attributes overcome commercial imaging platform limitations and enable new insights to be gained into microbial behavioral dynamics in experimental systems of relevance to the rhizosphere.
Wildfire Communication and Climate Risk Mitigation

Throughout the late 19th century and most of the 20th century, risks associated with wildfire were addressed by suppressing fires as quickly as possible. However, by the 1960s, it became clear that fire exclusion policies were having adverse effects on ecological health, as well as contributing to larger and more damaging wildfires over time. Although federal fire policy has changed to allow fire to be used as a management tool on the landscape, this change has been slow to take place, while the number of people living in high-risk wildland–urban interface communities continues to increase. Under a variety of climate scenarios, in particular for states in the western United States, it is expected that the frequency and severity of fires will continue to increase, posing even greater risks to local communities and regional economies. Resource managers and public safety officials are increasingly aware of the need for strategic communication to both encourage appropriate risk mitigation behavior at the household level, as well as build continued public support for the use of fire as a management tool aimed at reducing future wildfire risk. Household decision making encompasses both proactively engaging in risk mitigation activities on private property, as well as taking appropriate action during a wildfire event to protect personal safety. Very little research has directly explored the connection between climate-related beliefs, wildfire risk perception, and action; however, the limited existing research suggests that climate-related beliefs have little direct effect on wildfire-related action. Instead, action appears to depend on understanding the benefits of different mitigation actions and in engaging the public in interactive, participatory communication programs that build trust between the public and natural resource managers. A relatively new line of research focuses on resource managers as critical decision makers in the risk management process, pointing to the need to thoughtfully engage audiences other than the lay public to improve risk management. Ultimately, improving the decision making of both the public and managers charged with mitigating the risks associated with wildfire can be achieved by carefully addressing several common themes from the literature. These themes are to (1) promote increased efficacy through interactive learning, (2) build trust and capacity through social interaction, (3) account for behavioral constraints and barriers to action, and (4) facilitate thoughtful consideration of risk-benefit tradeoffs. Careful attention to these challenges will improve the likelihood of successfully managing the increasing risks that wildfire poses to the public and ecosystems alike in a changing climate.
Developing machine-learning-based amyloid predictors with Cross-Beta DB

Due to shifts in environmental conditions, mutations, or interactions with other biomolecules, some proteins that would normally be soluble can undergo aggregation, resulting in the formation of clumps of amyloid fibrils. Understanding of this phenomenon is of paramount importance due not only to its association with various diseases (including Alzheimer’s disease), but also due to increasingly abundant evidence for its functional roles. Numerous studies have demonstrated that the propensity to form amyloids is coded by the amino acid sequence and this finding has paved the way for the development of several computational predictors of amyloidogenicity. The ultimate objective of computational methods is to accurately predict the formation of disease-related and functionally relevant amyloids that occur in vivo. These amyloid fibrils are known to form very specific “cross-β” structures of protein regions longer than about 15 residues. Remarkably, despite the significance of the naturally occurring amyloids, there has been a lack of datasets specifically dedicated to them. Hence, we built Cross-Beta DB, a database composed of cross-β amyloids formed in natural conditions. This database is expected to be indispensable for benchmarking amyloid predictors. We used the Cross-Beta DB to train and benchmark several such algorithms, using machine learning. The best-performing of these, the random-forest-based Cross-Beta RF Predictor, demonstrated superior performance over the other existing methods, fostering high expectations for an improved prediction of naturally occurring amyloids.
Oncogenic KRAS cells use Wnt signalling and cell dormancy to override homeostatic cell elimination mechanisms in adult pancreas

Epithelial tissues use homeostatic defence mechanisms to actively remove aberrant or poorly functioning cells to maintain tissue health. We show that when present in tissues in low numbers, cells expressing cancer-causing mutations compete with normal cells for survival and are often eliminated. This implies that tumour initiation would require mechanisms whereby mutant cells override tissue homeostasis and remain in a tissue; however, the biology of these initial events is poorly understood. Here, we use an in vivo model of sporadic tumorigenesis in the adult pancreas to show that cell elimination processes are inefficient and a proportion of KrasG12D or p53R172H expressing cells are never eliminated from the epithelium. Using RNA sequencing of non-eliminated mutant populations, we reveal a general activation of the non-canonical Wnt pathway. We demonstrate that non-eliminated KrasG12D cells express cell dormancy and diapause gene signatures, suggesting non-eliminated KRasG12D cells adopt a dormant cell state in vivo. To model cell competition in vitro, we used established MDCK cell systems to show that active Wnt or cell cycle arrest blocks elimination of RAS mutant cells from normal epithelial cell sheets. Importantly, we demonstrate that Wnt signalling is a general mechanism that blocks elimination of some KrasG12D and p53R172H cells from pancreas tissues in vivo. Our results suggest that mutant cells activate Wnt and/or a dormant cell state to avoid homeostatic tissue defence mechanisms and survive in the adult pancreas.
Chlamydomonas cells transition through distinct Fe nutrition stages within 48 h of transfer to Fe-free medium

Low iron (Fe) bioavailability can limit the biosynthesis of Fe-containing proteins, which are especially abundant in photosynthetic organisms, thus negatively affecting global primary productivity. Understanding cellular coping mechanisms under Fe limitation is therefore of great interest. We surveyed the temporal responses of Chlamydomonas (Chlamydomonas reinhardtii) cells transitioning from an Fe-rich to an Fe-free medium to document their short- and long-term adjustments. While slower growth, chlorosis and lower photosynthetic parameters are evident only after one or more days in Fe-free medium, the abundance of some transcripts, such as those for genes encoding transporters and enzymes involved in Fe assimilation, change within minutes, before changes in intracellular Fe content are noticeable, suggestive of a sensitive mechanism for sensing Fe. Promoter reporter constructs indicate a transcriptional component to this immediate primary response. With acetate provided as a source of reduced carbon, transcripts encoding respiratory components are maintained relative to transcripts encoding components of photosynthesis and tetrapyrrole biosynthesis, indicating metabolic prioritization of respiration over photosynthesis. In contrast to the loss of chlorophyll, carotenoid content is maintained under Fe limitation despite a decrease in the transcripts for carotenoid biosynthesis genes, indicating carotenoid stability. These changes occur more slowly, only after the intracellular Fe quota responds, indicating a phased response in Chlamydomonas, involving both primary and secondary responses during acclimation to poor Fe nutrition.