Miracle-dz/UniFAR-FTUs huggingface dataset:
Upscaling Methane Flux From Plot Level to Eddy Covariance Tower Domains in Five Alaskan Tundra Ecosystems

Spatial heterogeneity in methane (CH4) flux requires a reliable upscaling approach to reach accurate regional CH4 budgets in the Arctic tundra. In this study, we combined the CLM-Microbe model with three footprint algorithms to scale up CH4 flux from a plot level to eddy covariance (EC) tower domains (200 m × 200 m) in the Alaska North Slope, for three sites in Utqiaġvik (US-Beo, US-Bes, and US-Brw), one in Atqasuk (US-Atq) and one in Ivotuk (US-Ivo), for a period of 2013–2015. Three footprint algorithms were the homogenous footprint (HF) that assumes even contribution of all grid cells, the gradient footprint (GF) that assumes gradually declining contribution from center grid cells to edges, and the dynamic footprint (DF) that considers the impacts of wind and heterogeneity of land surface. Simulated annual CH4 flux was highly consistent with the EC measurements at US-Beo and US-Bes. In contrast, flux was overestimated at US-Brw, US-Atq, and US-Ivo due to the higher simulated CH4 flux in early growing seasons. The simulated monthly CH4 flux was consistent with EC measurements but with different accuracies among footprint algorithms. At US-Bes in September 2013, RMSE and NNSE were 0.002 μmol m−2 s−1 and 0.782 using the DF algorithm, but 0.007 μmol m−2 s−1 and 0.758 using HF and 0.007 μmol m−2 s−1 and 0.765 using GF, respectively. DF algorithm performed better than the HF and GF algorithms in capturing the temporal variation in daily CH4 flux each month, while the model accuracy was similar among the three algorithms due to flat landscapes. Temporal variations in CH4 flux during 2013–2015 were predominately explained by air temperature (67–74%), followed by precipitation (22–36%). Spatial heterogeneities in vegetation fraction and elevation dominated the spatial variations in CH4 flux for all five tower domains despite relatively weak differences in simulated CH4 flux among three footprint algorithms. The CLM-Microbe model can simulate CH4 flux at both plot and landscape scales at a high temporal resolution, which should be applied to other landscapes. Integrating land surface models with an appropriate algorithm provides a powerful tool for upscaling CH4 flux in terrestrial ecosystems.
Comparison of Rapid Cytokine Immunoassays for Functional Immune Phenotyping

Background Cell-based functional immune-assays may allow for risk stratification of patients with complex, heterogeneous immune disorders such as sepsis. Given the heterogeneity of patient responses and the uncertain immune pathogenesis of sepsis, these assays must first be defined and calibrated in the healthy population. Objective Our objective was to compare the internal consistency and practicality of two immune assays that may provide data on surrogate markers of the innate and adaptive immune response. We hypothesized that a rapid turnaround, microfluidic-based immune assay (ELLA) would be comparable to a dual-color, enzyme-linked immunospot (ELISpot) assay in identifying tumor necrosis factor (TNF) and interferon (IFN)γ production following ex vivo whole blood stimulation. Design This was a prospective, observational cohort analysis. Whole blood samples from ten healthy, immune-competent volunteers were stimulated for either 4 hours or 18 hours with lipopolysaccharide, anti-CD3/anti-CD28 antibodies, or phorbol 12-myristate 13-acetate with ionomycin to interrogate innate and adaptive immune responses, respectively. Measurements and Main Results ELLA analysis produced more precise measurement of TNF and IFNγ concentrations as compared with ELISpot, as well as a four- to five-log10 dynamic range for TNF and IFNγ concentrations, as compared with a two-log10 dynamic range with ELISpot. Unsupervised clustering accurately predicted the ex vivo immune stimulant used for 90% of samples analyzed via ELLA, as compared with 72% of samples analyzed via ELISpot. Conclusions We describe, for the first time, a rapid and precise assay for functional interrogation of the innate and adaptive arms of the immune system in healthy volunteers. The advantages of the ELLA microfluidic platform may represent a step forward in generating a point-of-care test with clinical utility, for identifying deranged immune phenotypes in septic patients.
Optimizing CRISPR/Cas9 Editing of Repetitive Single Nucleotide Variants

CRISPR/Cas9, base editors and prime editors comprise the contemporary genome editing toolbox. Many studies have optimized the use of CRISPR/Cas9, as the original CRISPR genome editing system, in substituting single nucleotides by homology directed repair (HDR), although this remains challenging. Studies describing modifications that improve editing efficiency fall short of isolating clonal cell lines or have not been validated for challenging loci or cell models. We present data from 95 transfections using a colony forming and an immortalized cell line comparing the effect on editing efficiency of donor template modifications, concentration of components, HDR enhancing agents and cold shock. We found that in silico predictions of guide RNA efficiency correlated poorly withactivity in cells. Using NGS and ddPCR we detected editing efficiencies of 5–12% in the transfected populations which fell to 1% on clonal cell line isolation. Our data demonstrate the variability of CRISPR efficiency by cell model, target locus and other factors. Successful genome editing requires a comparison of systems and modifications to develop the optimal protocol for the cell model and locus. We describe the steps in this process in a flowchart for those embarking on genome editing using any system and incorporate validated HDR-boosting modifications for those using CRISPR/Cas9.
Codon Usage Bias Correlates With Gene Length in Neurodegeneration Associated Genes

Codon usage analysis is a crucial part of molecular characterization and is used to determine the factors affecting the evolution of a gene. The length of a gene is an important parameter that affects the characteristics of the gene, such as codon usage, compositional parameters, and sometimes, its functions. In the present study, we investigated the association of various parameters related to codon usage with the length of genes. Gene expression is affected by nucleotide disproportion. In sixty genes related to neurodegenerative disorders, the G nucleotide was the most abundant and the T nucleotide was the least. The nucleotide T exhibited a significant association with the length of the gene at both the overall compositional level and the first and second codon positions. Codon usage bias (CUB) of these genes was affected by pyrimidine and keto skews. Gene length was found to be significantly correlated with codon bias in neurodegeneration associated genes. In gene segments with lengths below 1,200 bp and above 2,400 bp, CUB was positively associated with length. Relative synonymous CUB, which is another measure of CUB, showed that codons TTA, GTT, GTC, TCA, GGT, and GGA exhibited a positive association with length, whereas codons GTA, AGC, CGT, CGA, and GGG showed a negative association. GC-ending codons were preferred over AT-ending codons. Overall analysis indicated that the association between CUB and length varies depending on the segment size; however, CUB of 1,200–2,000 bp gene segments appeared not affected by gene length. In synopsis, analysis suggests that length of the genes correlates with various imperative molecular signatures including A/T nucleotide disproportion and codon choices. In the present study we additionally evaluated various molecular features and their correlation with different indices of codon usage, like the Codon Adaptation Index (CAI) and Relative Dynonymous Codon Usage (RSCU) of codons. We also considered the impact of gene fragment size on different molecular features in genes related to neurodegeneration. This analysis will aid our understanding of and in potentially modulating gene expression in cases of defective gene functioning in clinical settings.
The Outcome of Induction Therapy for EBV-Related Hemophagocytic Lymphohistiocytosis: A Model for Risk Stratification

Background Epstein–Barr virus (EBV)–related hemophagocytic lymphohistiocytosis (HLH) is an abnormal inflammation caused by EBV infection, which has high mortality during induction therapy. Objectives This study is aimed to build a model to predict the risk of death during induction therapy. Methods The patients with EBV-HLH admitted from January 2015 to December 2018 were retrospectively reviewed. The primary outcome was death during induction therapy. The interval from receiving therapy to death or the end of induction therapy was the observing time. The patients admitted from January 2015 to December 2017 were assigned to the primary group, and the patients admitted from January to December 2018 were assigned to the validation group. Results We included 234 patients with EBV-HLH, of whom 65 (27.4%) died during induction therapy. The middle observing time was 25 days. On the basis of the primary group, the multivariate Cox analysis demonstrated age >18 years, blood urea nitrogen, procalcitonin >2 µg/L, serum CD25, and EBV-DNA in peripheral blood mononuclear cell as the risk factors of death during induction therapy. We developed a nomogram integrating the above factors with high predictive accuracy (c-statistic, 0.86) and stratified all patients into the high-risk and the low-risk groups. On the basis of the validation group, the high-risk patients had a higher risk of death (hazard ratio, 4.93; P = 0.012). In the subgroup analysis based on patients receiving etoposide-based strategy, the mortality in high-risk and low-risk patients was 43.9 and 3.1 per 100 person-weeks, respectively. Conclusion We developed a nomogram for risk stratification of patients with EBV-HLH receiving induction therapy.
Six Drivers to Face the XXI Century Challenges and Build the New Healthcare System: “La Salute in Movimento” Manifesto

The aging of the population, the burden of chronic diseases, possible new pandemics are among the challenges for healthcare in the XXI century. To face them, technological innovations and the national recovery and resilience plan within the European Union can represent opportunities to implement changes and renovate the current healthcare system in Italy, in an effort to guarantee equal access to health services. Considering such scenario, a panel of Italian experts gathered in a multidisciplinary Think Tank to discuss possible design of concepts at the basis of a new healthcare system. These ideas were summarized in a manifesto with six drivers for change: vision, governance, competence, intelligence, humanity and relationship. Each driver was linked to an action to actively move toward a new healthcare system based on trust between science, citizens and institutions.
Age Dependent Partitioning Patterns of Essential Nutrients Induced by Copper Feeding Status in Leaves and Stems of Poplar

Copper (Cu) is an essential micronutrient, and its deficiency can cause plants to undergo metabolic changes at several levels of organization. It has been shown that leaf age can play a role in nutrient partitioning along the shoot axis of poplar. In this study, we investigated the effect of Cu deficiency on the altered partitioning of essential macro and micronutrients in leaves and stems of different age. Cu deficiency was associated with higher concentrations of calcium, magnesium, sulfur, iron, zinc, manganese, and molybdenum in leaves and relatively higher concentrations of calcium, phosphorus, iron, and zinc in stems. Leaf and stem age had significant effects on nutrient partitioning. Principal component analyses revealed patterns that point to inverse influences in leaves and stems on nutrient partitioning. Specifically, these analyses revealed that nutrient partitioning in leaves was influenced by Cu feeding status more than developmental stage, whereas nutrient partitioning in stems was influenced by developmental stage more than Cu feeding status. These results suggest that Cu deficiency and developmental stage can significantly influence the partitioning and homeostasis of macro and micronutrients in poplar organs.
Impact of Corticosteroids on the Proportions of Circulating Tfh Cell Subsets in Patients With Systemic Lupus Erythematous

Objectives This study aimed to analyze the distribution of T follicular helper (Tfh) cells in lupus patients, and the effects of steroids on circulating Tfh cells. Methods Circulating Tfh cell subsets were defined by multicolor flow cytometry as Tfh17, Tfh2 or Tfh1 subpopulations of CXCR5+CD45RA–CD4+ T cells in the peripheral blood of SLE patients and healthy controls. To test the effects of corticosteroid on Tfh cells, PBMC harvested from both SLE and healthy controls were cocultured with dexamethasone, and then analyzed by Flow cytometry. Results The proportion of Tfh17 cells in SLE patients was increased significantly compared with healthy controls. Additionally, patients with an active disease had reduced Tfh1 subsets than those with an inactive disease and healthy controls. The frequency of Tfh2 cells was associated with the proportion of circulating plasmablasts and the amount of anti-dsDNA. Dexamethasone reduced the percentage of Tfh2 cells while increased the proportion of Tfh17 subset in gated CXCR5+CD45RA–CD4+ T cells. Conclusion Our study investigated the distribution of circulating Tfh subsets in lupus patients. Corticosteroids treatment not only down-regulated the proportion of circulating Tfh cells, but also altered the distribution of Tfh subsets in vivo and in vitro.
Efficacy of Resveratrol in Experimental Subarachnoid Hemorrhage Animal Models: A Stratified Meta-Analysis

Background: Subarachnoid hemorrhage (SAH) is a serious neurosurgical emergency with extremely high morbidity and mortality rates. Resveratrol (RES), a natural polyphenolic phytoalexin, is broadly presented in a wide variety of plants. Previous research had reasonably revealed its neuroprotective effects on experimental SAH animal models to some extent. But the results were more controversial. Therefore, we conducted a meta-analysis to evaluate the evidence on the effectiveness of RES in improving outcomes in SAH animal models. Methods: A systematic literature review was conducted in PubMed, EMBASE, and Web of Science databases to incorporate experimental control studies on the efficacy of RES on SAH models into our research. The standardized mean difference (SMD) was used to compare the brain water content (BWC) and neurological score (NS) between the treatment and control groups. Results: Overall, 16 articles published from 2014 to 2022 met the inclusion criteria. The meta-analysis of BWC showed a significant difference in favor of RES treatment (SMD: −1.026; 95% CI: −1.380, −0.672; p = 0.000) with significant heterogeneity (Q = 84.97; I2 = 60.0%; p = 0.000). Further stratified analysis was performed for methodological differences, especially dosage, time of treatments, and time-point of outcome assessment. The meta-analysis of NS showed a significant difference in favor of RES treatment (SMD: 1.342; 95% CI: 1.089, 1.595; p = 0.000) with low heterogeneity (Q = 25.58; I2 = 17.9%; p = 0.223). Conclusion: Generally, RES treatment showed an improvement in both pathological and behavioral outcomes in SAH animal models. The results of this study may provide a reference for preclinical and clinical studies in the future to some extent, with great significance for human health.
A Game of Infection – Song of Respiratory Viruses and Interferons

Humanity has experienced four major pandemics since the twentieth century, with the 1918 Spanish flu, the 2002 severe acute respiratory syndrome (SARS), the 2009 swine flu, and the 2019 coronavirus disease (COVID)-19 pandemics having the most important impact in human health. The 1918 Spanish flu caused unprecedented catastrophes in the recorded human history, with an estimated death toll between 50 – 100 million. While the 2002 SARS and 2009 swine flu pandemics caused approximately 780 and 280,000 deaths, respectively, the current COVID-19 pandemic has resulted in > 6 million deaths globally at the time of writing. COVID-19, instigated by the SARS – coronavirus-2 (SARS-CoV-2), causes unprecedented challenges in all facets of our lives, and never before brought scientists of all fields together to focus on this singular topic. While for the past 50 years research have been heavily focused on viruses themselves, we now understand that the host immune responses are just as important in determining the pathogenesis and outcomes of infection. Research in innate immune mechanisms is crucial in understanding all aspects of host antiviral programmes and the mechanisms underpinning virus-host interactions, which can be translated to the development of effective therapeutic avenues. This review summarizes what is known and what remains to be explored in the innate immune responses to influenza viruses and SARS-CoVs, and virus-host interactions in driving disease pathogenesis. This hopefully will encourage discussions and research on the unanswered questions, new paradigms, and antiviral strategies against these emerging infectious pathogens before the next pandemic occurs.
Exposure to Depression Memes on Social Media Increases Depressive Mood and It Is Moderated by Self-Regulation: Evidence From Self-Report and Resting EEG Assessments

This study aimed to investigate the effects of depression memes, spread mainly on social media, on depressive mood, and the moderating role of self-regulation based on self-report and electrophysiological (resting EEG frontal alpha asymmetry) assessments. We conducted a semi-online crossover study; first, we collected brain activity data from healthy young adults (n = 32) who were subsequently provided a link to the online experiment. Each participant participated in both the neutral and meme conditions. We also evaluated their level of depressive mood immediately before and after exposure to the stimuli. We further conducted a series of linear mixed effects model analyses and found that depression memes contributed to an increase in depressive symptoms. Specifically, lack of emotional clarity, difficulties in goal-directed behaviors in emotional distress, and impulse control difficulties were linked to greater depressive mood in the case of exposure to depression memes compared with neutral images. However, time interactions were insignificant. These results mainly indicate the centrality of behavioral problems during times of emotional distress caused by depression memes. Lastly, although frontal alpha asymmetry did not predict a change in depressive mood or significantly differ across conditions, lower inhibitory control may result in increased processing of depression memes as negative stimuli. This result is consistent with our self-report results (e.g., impulsivity) as well as other related studies in the literature. However, further research is needed to verify these frontal alpha asymmetry results.
Mutator-Derived lncRNA Landscape: A Novel Insight Into the Genomic Instability of Prostate Cancer

Background Increasing evidence has emerged to reveal the correlation between genomic instability and long non-coding RNAs (lncRNAs). The genomic instability-derived lncRNA landscape of prostate cancer (PCa) and its critical clinical implications remain to be understood. Methods Patients diagnosed with PCa were recruited from The Cancer Genome Atlas (TCGA) program. Genomic instability-associated lncRNAs were identified by a mutator hypothesis-originated calculative approach. A signature (GILncSig) was derived from genomic instability-associated lncRNAs to classify PCa patients into high-risk and low-risk groups. The biochemical recurrence (BCR) model of a genomic instability-derived lncRNA signature (GILncSig) was established by Cox regression and stratified analysis in the train set. Then its prognostic value and association with clinical features were verified by Kaplan–Meier (K-M) analysis and receiver operating characteristic (ROC) curve in the test set and the total patient set. The regulatory network of transcription factors (TFs) and lncRNAs was established to evaluate TF–lncRNA interactions. Results A total of 95 genomic instability-associated lncRNAs of PCa were identified. We constructed the GILncSig based on 10 lncRNAs with independent prognostic value. GILncSig separated patients into the high-risk (n = 121) group and the low-risk (n = 121) group in the train set. Patients with high GILncSig score suffered from more frequent BCR than those with low GILncSig score. The results were further validated in the test set, the whole TCGA cohort, and different subgroups stratified by age and Gleason score (GS). A high GILncSig risk score was significantly associated with a high mutation burden and a low critical gene expression (PTEN and CDK12) in PCa. The predictive performance of our BCR model based on GILncSig outperformed other existing BCR models of PCa based on lncRNAs. The GILncSig also showed a remarkable ability to predict BCR in the subgroup of patients with TP53 mutation or wild type. Transcription factors, such as FOXA1, JUND, and SRF, were found to participate in the regulation of lncRNAs with prognostic value. Conclusion In summary, we developed a prognostic signature of BCR based on genomic instability-associated lncRNAs for PCa, which may provide new insights into the epigenetic mechanism of BCR.
The Real-World Impact of Vaccination on COVID-19 Cases During Europe’s Fourth Wave

Objectives: Disease control is important to limit the social, economic and health effects of COVID-19 and reduce the risk of novel variants emerging. Evidence suggests vaccines are less effective against the Omicron variant, but their impact on disease control is unclear. Methods: We used a longitudinal fixed effects Poisson regression model to assess the impact of vaccination on COVID-19 case rates across 32 countries in Europe from 13th October to 01st January 2022. We controlled for country and time fixed effects and the severity of public health restrictions. Results: Full vaccination coverage increased by 4.2%, leading to a 54% reduction in case rates across Europe (p < 0.001). This protection decreased over time but remained significant at 5 weeks after the detection of Omicron. Mean booster vaccination rates increased from 2.71% to 24.5% but provided no significant additional benefit. For every one-unit increase in the severity of public health restrictions, case rates fell by a further 2% (p = 0.019). Conclusion: Full vaccination significantly limited the spread of COVID-19 and blunted the impact of the Omicron variant, despite becoming less useful over time.