bluuebunny/crossref_metadata_2025_split huggingface dataset:
Magnitude and Breadth of Antibody Cross-reactivity Induced by Recombinant Influenza Hemagglutinin Trimer Vaccine Is Enhanced by Combination Adjuvants
Authors: Hernandez-Davies Jenny , Dollinger Emmanuel , Pone Egest , Felgner Jiin , Strohmeier Shirin , Jan Sharon , Albin Tyler , Jain Aarti , Nakajima Rie , Jasinskas Algimantas , Krammer Florian , Esser-Kahn Aaron , Felgner Philip , Nie Qing , DAVIES Huw 
Published: December 2021

Abstract
The effects of adjuvants for increasing the immunogenicity of influenza vaccines are well known. However, the effect of adjuvants on increasing the breadth of cross-reactivity is less well understood. In this study we have performed a systematic screen of different toll-like receptor (TLR) agonists, with and without a squalene-in-water emulsion on the immunogenicity of a recombinant trimerized hemagglutinin (HA) vaccine in mice after single-dose administration. Antibody (Ab) cross-reactivity for other variants within and outside the immunizing subtype (homosubtypic and heterosubtypic cross-reactivity, respectively) was assessed using a protein microarray approach. Of all the formulations tested, a combination of CpG, MPLA and AddaVAX (termed “IVAX-1”) yielded the greatest breadth and magnitude of Ab responses, particularly against the HA1 region (which includes the variable head domain) of HA. Antigen-specific plasma cell labeling experiments show the components of IVAX-1 are synergistic. This adjuvant preferentially stimulates CD4 T cells to produce Th1>Th2 type (IgG2c>IgG1) antibodies and cytokine responses. Moreover, IVAX-1 induces identical homo- and heterosubtypic IgG and IgA cross-reactivity profiles when administered intranasally. Consistent with these observations, a single-cell transcriptomics analysis demonstrated significant increases in expression of IgG1, IgG2b and IgG2c genes of B cells in H5/IVAX-1 immunized mice relative to naïve mice, as well as significant increases in expression of the IFNg gene of both CD4 and CD8 T cells. These data support the use of adjuvants for enhancing the breath and durability of antibody responses of influenza virus vaccines.
Structural and Functional Neuroplasticity Following Bilateral Vestibular Loss: A Longitudinal [18F]-UCB-H/[18F]-FDG Dual Tracer Rat Study
Authors: Antons Melissa , Lindner Magdalena , Grosch Maximilian , Oos Rosel , Palumbo Giovanna , Brendel Matthias , Ziegler Sibylle , Bartenstein Peter , Dieterich Marianne , Zwergal Andreas 
Published: December 2021

Abstract
Neuronal lesions trigger mechanisms of structural and functional neuroplasticity, which can support recovery. However, the temporal and spatial appearance of structure-function changes and their interrelation remain unclear. The current study aimed to directly compare serial whole-brain in vivo measurements of functional plasticity (by [18F]-FDG-PET) and structural synaptic plasticity (by [18F]-UCB-H-PET) before and after bilateral labyrinthectomy in rats and investigate the effect of locomotor training. Complex structure-function changes were found after bilateral labyrinthectomy: in brainstem-cerebellar circuits, regional cerebral glucose metabolism (rCGM) decreased early, followed by reduced synaptic density. In the thalamus, increased [18F]-UCB-H binding preceded a higher rCGM uptake. In frontal-basal ganglia loops, an increase in synaptic density was paralleled by a decrease in rCGM. In the group with locomotor training, thalamic rCGM and [18F]-UCB-H binding increased following bilateral labyrinthectomy compared to the no training group. Rats with training had relatively fewer body rotations. In conclusion, combined [18F]-FDG/[18F]-UCB-H dual tracer imaging reveals that adaptive neuroplasticity after bilateral vestibular loss is not a uniform process but is composed of complex spatial and temporal patterns of structure-function coupling in networks for vestibular, multisensory, and motor control, which can be modulated by early physical training.
Blood RNA Sequencing Confirms Upregulated BATF2 and FCGR1A Expression in Children with Autism Spectrum Disorder
Authors: Voinsky Irena , Zoabi Yazeed , Shomron Noam , Harel Moria , Cassuto Hanoch , Tam Joseph , Bennuri Sirish , Rose Shannon , Frye Richard E. , Aran Adi , Gurwitz David 
Published: December 2021

Abstract
Mutations in over 100 genes are implicated in autism spectrum disorder (ASD). DNA mutations and epigenomic modifications also contribute to ASD. Transcriptomics analysis of blood samples may offer clues for pathways dysregulated in ASD. To expand and validate published findings of RNA-sequencing (RNA-seq) studies, we performed RNA-seq of whole blood samples from a discovery cohort of eight children with ASD compared with nine age- and sex-matched neurotypical children. This revealed 10 genes with differential expression. Using real-time PCR, we compared whole blood samples from 35 children with ASD and 21 matched neurotypical children for the 10 dysregulated genes detected by RNA-seq. This revealed higher expression levels of the proinflammatory transcripts BATF2 and FCGR1A, and lower expression levels of the anti-inflammatory transcripts ISG15 and MT2A in the ASD compared to the control group. BATF2 and FCGR1A were recently reported as upregulated in blood samples of Japanese adults with ASD. Coupled with that publication, our findings support involvement of these genes in ASD phenotypes, independent of age and ethnicity. Upregulation of BATF2 and FCGR1A and downregulation of ISG15 and MT2A were reported to reduce cancer risk. Implications of the dysregulated genes for pro-inflammatory phenotypes, immunity, and cancer risk in ASD are discussed.
miRNA-124-3p.1 Inhibits the Osteogenic and Odontogenic Differentiation of SCAPs via MACF1/smad7 Axis
Authors: Li Na , Li Zehan , Yan Ming , Wang Yanqiu , Gu Yongchun , Wu Jintao , Yan Chaoting , Xiao Tong , Fu Lin , Yu Jinhua 
Published: December 2021

Abstract
Background: Previous research has indicated that altered expression of micro-RNAs (miRNAs) is in connection with differentiation of stem cells from apical papillae (SCAPs). We investigated the mechanisms that miR-124-3p.1 inhibited osteogenic and odontogenic differentiation of SCAPs. Methods: SCAPs were isolated from dental apical papilla. MiR-124-3p.1 mimic and inhibitor were used for overexpression and knockdown assays. For overexpression and knockdown of microtubule actin cross‐linking factor 1 (MACF1), lentivirus infection and siRNA transfection were performed. Luciferase reporter assay was performed to determine the relationship between miR-124-3p.1 and MACF1. The osteogenic and odontogenic differentiation potential was analyzed by alkaline phosphatase activity analysis (ALP), alizarin red S (ARS) staining, quantitative real time reverse-transcription polymerase chain reaction (qRT-PCR), western blot and immunofluorescence (IF) staining. Results: We observed a time dependent decrease of miR‐124‐3p.1 level in mineralization induction of SCAPs. Further study found that miR‐124‐3p.1 exhibited an inhibitory effect on SCAPs osteo/odontogenic differentiation. Similarly, we found that the overexpression of miR‐124‐3p.1 dramatically inhibited MACF1 protein level in SCAPs and knockdown of miR‐124‐3p.1 significantly increased MACF1 protein level in SCAPs. Moreover, MACF1 was verified as the targeting of miR‐124‐3p.1. Meanwhile, the expression of MACF1 was related to smad7 nuclear translocation.Conclusion: Collectively, diverse data demonstrated that miR‐124‐3p.1 is a regulator of MACF1/smad7, playing plays an important role in osteogenic and odontogenic differentiation of SCAPs via MACF1/smad7 axis.
Methodological Perspectives on the Study of the Health Effects of Unemployment – Reviewing the Mode of Unemployment, the Statistical Analysis Method and the Role of Confounding Factors
Authors: Norström Fredrik , Hammarström Anne 
Published: December 2021

Abstract
Introduction:

Studying the relationship between unemployment and health raises many methodological challenges. In the current study, the aim was to evaluate how different ways of measuring unemployment and the choice of statistical model affects the effect estimate.
Methods:

The Northern Swedish cohort was used, and two follow-up surveys thereof from 1995 and 2007, as well as register data about unemployment. Self-reported current unemployment, self-reported accumulated unemployment and register-based accumulated unemployment were used to measure unemployment and its effect on self-reported health was evaluated. Analyses were conducted with G-computation, logistic regression and three estimators for the inverse probability weighting propensity scores, and 11 potentially confounding variables were part of the analyses. Results were presented with absolute differences in the proportion with poor self-reported health between unemployed and employed individuals for all estimators but logistic regression.
Results:

Of the initial 1083 pupils in the cohort, 488–693 individuals were defined as employed and 61–214 individuals were defined as unemployed in our different analyses. In the analyses, the deviation was large between the unemployment measures, with a difference of at least 2.5% in effect size when unemployed was compared with employed for the self-reported and register-based unemployment modes. The choice of statistical method only had a small influence on effect estimates and the deviation was in most cases lower than 1%. When models were compared based on the choice of potential confounders in the analytical model, the deviations were rarely above 0.6% when comparing models with 4 and 11 potential confounders. Our variable for health selection was the only one that strongly affected estimates when it was not part of the statistical model.
Conclusions:

Misspecifications of the statistical model or choice of analytical method might not matter much for effect estimates of the relationship between unemployment and health except for the inclusion of a variable measuring earlier health status before becoming unemployed. On the other hand, how unemployment is measured is highly important.
Assessing Integrity Using Vegetation Structure and Composition
Authors: McNellie Megan J , Dorrough Josh , Oliver Ian , Yen Jian DL , Ferrier Simon , Newell Graeme , Gibbons Philip 
Published: December 2021

Abstract
ContextThe draft post-2020 Global Biodiversity Framework aims to achieve a 15% net gain in the area, connectivity and integrity of natural systems by 2050. ObjectivesFirst, we analyse the complexity (foliage cover) and composition (native species richness) of 6 plant functional groups relative to their empirically defined benchmark. Second, we extrapolate the spatial patterns in foliage cover and species richness to predict where different plant functional groups are above or below benchmark as spatially-explicit, continuous characteristics across the landscape.MethodsWe assess the integrity of vegetation relative to a numerical benchmark using the log of the response ratio (LRR) to reflect the proportional change in the response variable. We use ensembles of artificial neural networks to build spatially-explicit, continuous, landscape-scale models of cover and species richness to assess locations where functional groups meet or exceed benchmarks.ResultsModels of vegetation cover LRR performed well (R2 0.79 – 0.88), whereas models of the vegetation richness LRR were more variable (R2 0.57 – 0.80). Predicted patterns show that across the landscape (11.5 million ha), there is a larger area that meets or exceeds the cover benchmarks (approximately 112 000 ha or 1%), and an order of magnitude lower (approximately 10 000 ha or 0.1%) for richness benchmarks. ConclusionsSpatially explicit maps of vegetation integrity can provide important information to complement assessments of area and connectivity. Our results highlight that net gains in the area, connectivity and integrity of ecosystems will require significant investment in restoration.
Host Adaptation of Codon Usage in SARS-CoV-2 From Mammals Indicate Natural Selection
Authors: Fu Yanan , Huang Yanping , Rao Jingjing , Zeng Feng , Yang Ruiping , Liu Zhixin , Du Weixing , Liu Long 
Published: December 2021

Abstract
The outbreak of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, spread across hosts from humans to animals, transmitting particularly effectively in mink. How SARS-CoV-2 selects and evolves in the host, and the differences in the evolution of different animals are still unclear. To analysis the mutation and codon usage bias of SARS-CoV-2 in infected humans and animals. The SARS-CoV-2 sequence in mink (Mink-SARS2) and binding energy with receptor were calculated compared with human. The relative synonymous codon usage of viral encoded gene was analyzed to characterize the differences and the evolutionary characteristics. A synonymous codon usage analysis showed that SARS-CoV-2 is optimized to adapt in the animals in which it is currently reported, and all of the animals showed decreased adaptability relative to that of humans, except for mink. The neutrality plot showed that the effect of natural selection on different SARS-CoV-2 sequences is stronger than mutation pressure. A binding affinity analysis indicated that the spike protein of the SARS-CoV-2 variant in mink showed a greater preference for binding with the mink receptor ACE2 than with the human receptor, especially as the mutation Y453F and N501T in Mink-SARS2 lead to improvement of binding affinity for mink receptor. In summary, mutations Y453F and N501T in Mink-SARS2 lead to improvement of binding affinity with mink receptor, indicating possible natural selection and current host adaptation. Monitoring the variation and codon bias of SARS-CoV-2 provides a theoretical basis for tracing the epidemic, evolution and cross-species spread of SARS-CoV-2.
The genomics of exceptional longevity in rockfishes refine genetic foundations of human lifespan variation
Authors: Treaster Stephen , Deelen Joris , Daane Jacob , Murabito Joanne , Karasik David , Harris Matthew 
Published: December 2021

Abstract
Longevity is a defining, heritable trait that varies dramatically between species. To resolve the genetic regulation of this trait, we have mined genomic variation in rockfishes, ranging in longevity from 11 to over 205 years. Shifts in rockfish longevity occurred multiple times independently, and in a short evolutionary time frame, thus empowering convergence analyses. Our analyses reveal a common network of genes under convergent restricted evolution in long-lived lineages, encompassing established aging regulators such as insulin-signaling, yet also identify flavonoid (aryl-hydrocarbon) metabolism as a novel pathway modulating longevity. Further, these genes were used to refine human longevity GWAS, identifying the aryl-hydrocarbon metabolism pathway to be significantly associated with the 99th percentile of human longevity, independently validating its importance and conservation. This evolutionary intersection highlights a novel, conserved genetic architecture that associates with the evolution of longevity across vertebrates and provides actionable targets for research into lifespan and healthspan modulation.
Vision-Based Melt Pool Monitoring for Wire-Arc Additive Manufacturing Using Deep Learning Method
Authors: Xia Chunyang , Pan Zengxi , Li Yuxing , Li Huijun 
Published: December 2021

Abstract
Wire-arc additive manufacturing (WAAM) technology has been widely recognized as a promising alternative for fabricating large-scale components, due to its advantages of high deposition rate and high material utilization rate. However, some anomalies may occur during the deposition process, such as humping, spattering, and robot suspend. this study proposed to apply Deep Learning in the visual monitoring to diagnose different anomalies during WAAM process. The melt pool images of different anomalies were collected for training and validation by a visual monitoring system. The classification performance of several representative CNN architectures, including ResNet, EfficientNet, VGG-16 and GoogLeNet, were investigated and compared. The classification accuracy of 97.62%, 97.45%, 97.15% and 97.25% was achieved by each model. The results proved that the CNN models are effective in classifying different types of melt pool images of WAAM. Our study is applicable beyond WAAM and should benefit other additive manufacturing or arc welding techniques.
Approaches for Image Reconstruction in Low-Field Magnetic Resonance Imaging
Authors: Obungoloch Johnes , Ahishakiye Emmanuel 
Published: December 2021

Abstract
Background: Magnetic Resonance Imaging (MRI) and spectroscopic techniques are frequently employed for clinical diagnostics as well as basic research in areas like cognitive neuroimaging. MRI is a widely used imaging modality for intracranial diseases. However, conventional MRI is expensive to purchase, maintain and sustain, limiting their use in low-income countries. Low field MRI can provide an economical, long-term, and safe imaging option to high-field MRI and computed tomography (CT) for brain imaging. This paper offers a review of the image reconstruction techniques used in low field magnetic resonance imaging (MRI). It is aimed at familiarizing the readers with the relevant knowledge, literature, and the latest updates on the state-of-art image reconstruction techniques that have been used in low field MRI citing their strengths, and areas for improvement. Methods: An in-depth keyword-based search was undertaken for publications on image reconstruction approaches in low-field MRI in the top scientific databases such as Google Scholar, Wiley, Science Direct, Springer, IEEE, Scopus, Nature, Elsevier, and PubMed throughout this study. This research also contained relevant postgraduate theses. For the selection of relevant research publications, the PRISMA flow diagram and protocol were also used.Results: Studies revealed that Inhomogeneities are present in low field MRI, implying that the traditional method of acquiring the image, using the inverse Fourier Transform, is no longer viable. The image reconstruction techniques reviewed include iterative methods, dictionary learning methods, and deep learning methods. Experimental results from the literature revealed improved image quality of the reconstructed images using data driven and learning based methods (deep learning and dictionary learning methods). Conclusion: The study revealed that there is limited literature on the image reconstruction approaches in low field MRI even if though there are sufficient studies on the subject in high field MRI. Data driven and learning based methods improves image reconstruction quality when compared to analytic and iterative approaches.
Vaccination is reasonably effective in limiting the spread of COVID-19 infections, hospitalizations and deaths with COVID-19
Authors: Damijan Joze , Damijan Sandra , Kostevc Crt 
Published: December 2021

Abstract
This paper uses large cross-country data for 110 countries to examine the effectiveness of COVID vaccination coverage. Our results confirm that vaccines are reasonably effective in both limiting the spread of infections and containing more severe disease progression in symptomatic patients. First, the results show that full vaccination rate is consistently negatively correlated with the number of new COVID cases, whereby a 10 percent increase in vaccination rate is associated with a 1.3 to 1.7 percent decrease in new COVID cases. Second, the magnitude of vaccination is shown to contribute significantly to moderating severe disease progression. On average, a 10 percent increase in the rate of vaccination leads to a reduction of about 5 percent in the number of new hospitalizations, 12 percent decrease in the number of new intensive care patients and 2 percent reduction in the number of new deaths. Finally, by comparing the data for the same period between 2020 and 2021, we also check how good is vaccination as a substitute for lockdowns or other stringent government protection measures. Results suggest that vaccination does not appear to be an effective substitute for more stringent government safety measures to contain the spread of COVID infections until a high vaccination coverage threshold (more than 70 percent) has been achieved. On the other hand, vaccination is shown to be quite effective in limiting the more severe course of the disease in symptomatic patients already at moderate vaccination coverage (between 40 and 70 percent). This suggests that vaccination can also help to reduce pressure on the health system and thus benefit the overall public health of society.
Identification And Expression Analysis of FORMIN Gene Family In Rice (Oryza Sativa)
Authors: Li Bin , Du Zhi-Ye , He Shan , Xiao Kai , Wang Xing , Wang Kai-Shun , Chen Ji , Huang Jin 
Published: December 2021

Abstract
Background: FORMIN proteins, which are composed of proteins containing FH1 and FH2 domains, play crucial roles in the growth and development of organisms. However, the functions of FORMINs in rice (Oryza sativa) remain largely unclear. Results: In this study, a total of 17 FORMIN genes were identified in rice, OsFH17 was the first time identified in this study. In addition, the distribution on chromosomes, gene structure, as well as conserved motifs of rice FORMINs was investigated. According to their protein structural and phylogenetic features, these 17 rice FORMIN genes were classified into two distinct subfamilies. Subcellular localization prediction showed that rice FORMINs were located in cytosol, golgi, endoplasmic reticulum, extracellular, and vacuole. Protein protein interaction (PPI) prediction results shown that FORMIN protein might answer hormone signals and be involved in cytoskeleton dynamics regulation and cell wall morphology regulation. The results of silico analysis and qRT-PCR confirmation of the gene expression showed that the expression of rice FORMINs were related to their tissue distribution. Moreover, OsFH3, OsFH5 and OsFH7 were upregulated under phytohormone treatments. Conclusions: Overall, our research may shed light on the understanding and further investigation of the biological functions of rice FORMINs.