Mesenchymal stem cells (MSCs) were isolated from bone marrow, culture-expanded, and then seeded at 1, 4, and 8 million cells/mL onto collagen gel constructs designed to augment tendon repair in vivo. To investigate the effects of seeding density on the contraction kinetics and cellular morphology, the contraction of the cell/collagen constructs was monitored over time up to 72 h in culture conditions. Constructs seeded at 4 and 8 million cells/mL showed no significant differences in their gross appearance and dimensions throughout the contraction process. By contrast, constructs seeded at 1 million cells/mL initially contracted more slowly and their diameters at 72 h were 62 to 73% larger than those seeded at higher densities. During contraction, MSCs reoriented and elongated significantly with time. Implants prepared at higher seeding densities showed more well aligned and elongated cell nuclei after 72 h of contraction. Changes in nuclear morphology of the MSCs in response to physical constraints provided by the contracted collagen fibrils may trigger differentiation pathways toward the fibroblastic lineage and influence the cell synthetic activity. Controlling the contraction and organization of the cells and matrix will be critical for successfully creating tissue engineered grafts.

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Root system architecture (RSA) is becoming recognized as important for water and nutrient acquisition in plants. This study focuses on finding single nucleotide polymorphisms (SNPs) associated with seedling RSA traits from 300 doubled haploid (DH) lines derived from crosses between Germplasm Enhancement of Maize (GEM) accessions and inbred lines PHB47 and PHZ51. These DH lines were genotyped using 62,077 SNP markers, while root and shoot phenotype data were collected from 14-day old seedlings. Genome-wide association studies (GWAS) were conducted using three models to offset false positives/negatives. Multiple SNPs associated with seedling root traits were detected, some of which were within or linked to gene models that showed expression in seedling roots. Significant trait associations involving the SNP S5_152926936 on Chromosome 5 were detected in all three models, particularly the trait network area. The SNP is within the gene model GRMZM2G021110, which is expressed in roots at seedling stage. SNPs that were significantly associated with seedling root traits, and closely linked to gene models that encode proteins associated with root development were also detected. This study shows that the GEM-DH panel may be a source of allelic diversity for genes controlling seedling root development. KeywordsMaize, Zea mays L., Root system architecture, Germplasm enhancement of maize (GEM), GWAS, Doubled haploids RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. A R T I C L E I N F O Keywords:Maize Zea mays L. Root system architecture Germplasm enhancement of maize (GEM) GWAS Doubled haploids A B S T R A C TRoot system architecture (RSA) is becoming recognized as important for water and nutrient acquisition in plants. This study focuses on finding single nucleotide polymorphisms (SNPs) associated with seedling RSA traits from 300 doubled haploid (DH) lines derived from crosses between Germplasm Enhancement of Maize (GEM) accessions and inbred lines PHB47 and PHZ51. These DH lines were genotyped using 62,077 SNP markers, while root and shoot phenotype data were collected from 14-day old seedlings. Genome-wide association studies (GWAS) were conducted using three models to offset false positives/negatives. Multiple SNPs associated with seedling root traits were detected, some of which were within or linked to gene models that showed expression in seedling roots. Significant trait associations involving the SNP S5_152926936 on Chromosome 5 were detected in all three models, particularly the trait network area. The SNP is within the gene model GRMZM2G021110, which is expressed in roots at seedling stage. SNPs that were significantly associated with seedling root traits, and closely linked to gene models that encode proteins associated with root development were also detected. This study shows that the GEM-DH panel may be a source of allelic diversity for genes controlling seedling root develo...

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Selecting the most discriminative genes/miRNAs has been raised as an important task in bioinformatics to enhance disease classifiers and to mitigate the dimensionality curse problem. Original feature selection methods choose genes/miRNAs based on their individual features regardless of how they perform together. Considering group features instead of individual ones provides a better view for selecting the most informative genes/miRNAs. Recently, deep learning has proven its ability in representing the data in multiple levels of abstraction, allowing for better discrimination between different classes. However, the idea of using deep learning for feature selection is not widely used in the bioinformatics field yet. In this paper, a novel multi-level feature selection approach named MLFS is proposed for selecting genes/miRNAs based on expression profiles. The approach is based on both deep and active learning. Moreover, an extension to use the technique for miRNAs is presented by considering the biological relation between miRNAs and genes. Experimental results show that the approach was able to outperform classical feature selection methods in hepatocellular carcinoma (HCC) by 9%, lung cancer by 6% and breast cancer by around 10% in F1-measure. Results also show the enhancement in F1-measure of our approach over recently related work in [1] and [2].

Long COVID (LC), a type of post-acute sequelae of SARS-CoV-2 infection (PASC), occurs after at least 10% of SARS-CoV-2 infections, yet its etiology remains poorly understood. Here, we used multiple omics assays (CyTOF, RNAseq, Olink) and serology to deeply characterize both global and SARS-CoV-2-specific immunity from blood of individuals with clear LC and non-LC clinical trajectories, 8 months following infection and prior to receipt of any SARS-CoV-2 vaccine. Our analysis focused on deep phenotyping of T cells, which play important roles in immunity against SARS-CoV-2 yet may also contribute to COVID-19 pathogenesis. Our findings demonstrate that individuals with LC exhibit systemic inflammation and immune dysregulation. This is evidenced by global differences in T cell subset distribution in ways that imply ongoing immune responses, as well as by sex-specific perturbations in cytolytic subsets. Individuals with LC harbored increased frequencies of CD4+ T cells poised to migrate to inflamed tissues, and exhausted SARS-CoV-2-specific CD8+ T cells. They also harbored significantly higher levels of SARS-CoV-2 antibodies, and in contrast to non-LC individuals, exhibited a mis-coordination between their SARS-CoV-2-specific T and B cell responses. Collectively, our data suggest that proper crosstalk between the humoral and cellular arms of adaptive immunity has broken down in LC, and that this, perhaps in the context of persistent virus, leads to the immune dysregulation, inflammation, and clinical symptoms associated with this debilitating condition.

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