Training the 21st century immunologist

Immunology, along with other fields of biology, is undergoing a revolution. Here we discuss the challenges and opportunities presented by considering the dynamical systems properties of the immune system, and harnessing the power of data-rich technologies. We present specific recommendations for changing graduate programs to incorporate training that will enable students to actively participate in the analyses of complex data and their biological system, and urge that we move from viewing quantitative and computational biology as interdisciplinary, to recognizing these as intrinsic to the discipline of immunology going forward

Ropes on a line embedded in a Grassmannian variety.

Let L be a line contained in a Grassmannian variety G. A d-rope C _ G supported on L is a locally Cohen-Macaulay curve of degree d with Cred = L and (IL,G)2 _ IC,G. We characterize the d-ropes C supported on L and embedded in G. In some cases we describe also the vector bundles on such a rope C. Finally, we describe the parameter spaces for ropes embedded in G.Let L be a line contained in a Grassmannian variety G. A d-rope C G sup ported on L is a locally Cohen-Macaulay curve of degree d with Cred = L and (IL,G )2 IC,G . We characterize the d-ropes C supported on L and embedded in G. In some cases we describe also the vector bundles on such a rope C. Finally, we describe the parameter spaces for ropes embedded in G

Transfer transcriptomic signatures for infectious diseases

The modulation of the transcriptome is among the earliest responses to infection. However, defining the transcriptomic signatures of disease is challenging because logistic, technical, and cost factors limit the size and representativeness of samples in clinical studies. These limitations lead to a poor performance of signatures when applied to new datasets. Although the study focuses on infection, the central hypothesis of the work is the generalization of sets of signatures across diseases. We use a machine learning approach to identify common elements in datasets and then test empirically whether they are informative about a second dataset from a disease or process distinct from the original dataset. We identify sets of genes, which we name transfer signatures, that are predictive across diverse datasets and/or species (e.g., rhesus to humans). We demonstrate the usefulness of transfer signatures in two use cases: the progression of latent to active tuberculosis and the severity of COVID-19 and influenza A H1N1 infection. This indicates that transfer signatures can be deployed in settings that lack disease-specific biomarkers. The broad significance of our work lies in the concept that a small set of archetypal human immunophenotypes, captured by transfer signatures, can explain a larger set of responses to diverse diseases

On the even Gorenstein liaison classes of ropes on a line

We describe the even Gorenstein liaison classes of ropes supported on a line which are not arithmetically Buchsbaum

Dissecting the regulatory strategies of NF-kB RelA target genes in the inflammatory response reveals differential transactivation logics

Nuclear factor κB (NF-κB) RelA is the potent transcriptional activator of inflammatory response genes. We stringently defined a list of direct RelA target genes by integrating physical (chromatin immunoprecipitation sequencing [ChIP-seq]) and functional (RNA sequencing [RNA-seq] in knockouts) datasets. We then dissected each gene’s regulatory strategy by testing RelA variants in a primary-cell genetic-complementation assay. All endogenous target genes require RelA to make DNA-base-specific contacts, and none are activatable by the DNA binding domain alone. However, endogenous target genes differ widely in how they employ the two transactivation domains. Through model-aided analysis of the dynamic time-course data, we reveal the gene-specific synergy and redundancy of TA1 and TA2. Given that post-translational modifications control TA1 activity and intrinsic affinity for coactivators determines TA2 activity, the differential TA logics suggests context-dependent versus context-independent control of endogenous RelA-target genes. Although some inflammatory initiators appear to require co-stimulatory TA1 activation, inflammatory resolvers are a part of the NF-κB RelA core response

Diagnostic yield and accuracy of image-guided percutaneous core needle biopsy of paediatric solid tumours: An experience from Italy

Abstract Background Percutaneous core needle biopsy (PCNB) has become an accepted method to collect tumour tissue samples given its safety, minimal invasiveness, high accuracy and cost-effectiveness. Procedure It is a single centre, retrospective evaluation of 213 ultrasound (US) or computed tomography (CT) guided PCNBs of paediatric solid tumours performed from 2005 to 2017. Safety, diagnostic yield, accuracy, and efficacy assessments of the PCNB procedure were performed. Univariate logistic models were applied to assess the relation of the diagnostic yield with patient, procedure and lesion features. Results The image-guide was US in 91.08% of biopsies; the needle gauge was ≥16 G in 69.01% of the biopsies. The anatomical site of lesion was deep in 113 biopsies (53.05%). The nature of the lesion was the only factor associated with diagnostic yield (OR: 4.04; 95% CI 1.23–13.28; p: 0.022), with benign lesion as an unfavourable factor. Complication incidence was 1.41%. Overall, the diagnostic yield of PCNB was 93.90% (95% CI: 89.79-96.71%), the diagnostic accuracy was 96.86% (95% CI: 93.29–98.84%) and the diagnostic efficacy was 93.33% (95% CI: 86.75–97.28%). Sensitivity was 97.94% (95% CI: 92.75–99.75%) and specificity 100% (95% CI: 66.37–100%). Conclusion PCNB can be recommended as the first-choice method for solid tumours diagnosis in paediatric, adolescent and young adult patients because of its high diagnostic success, safety and accessibility