Oct-1 regulates IL-17 expression by directing interchromosomal associations in conjunction with CTCF in T cells.

Interchromosomal associations can regulate gene expression, but little is known about the molecular basis of such associations. In response to antigen stimulation, naive T cells can differentiate into Th1, Th2, and Th17 cells expressing IFN-γ, IL-4, and IL-17, respectively. We previously reported that in naive T cells, the IFN-γ locus is associated with the Th2 cytokine locus. Here we show that the Th2 locus additionally associates with the IL-17 locus. This association requires a DNase I hypersensitive region (RHS6) at the Th2 locus. RHS6 and the IL-17 promoter both bear Oct-1 binding sites. Deletion of either of these sites or Oct-1 gene impairs the association. Oct-1 and CTCF bind their cognate sites cooperatively, and CTCF deficiency similarly impairs the association. Finally, defects in the association lead to enhanced IL-17 induction. Collectively, our data indicate Th17 lineage differentiation is restrained by the Th2 locus via interchromosomal associations organized by Oct-1 and CTCF.

RUNX transcription factor-mediated association of Cd4 and Cd8 enables coordinate gene regulation.

T cell fate is associated with mutually exclusive expression of CD4 or CD8 in helper and cytotoxic T cells, respectively. How expression of one locus is temporally coordinated with repression of the other has been a long-standing enigma, though we know RUNX transcription factors activate the Cd8 locus, silence the Cd4 locus, and repress the Zbtb7b locus (encoding the transcription factor ThPOK), which is required for CD4 expression. Here we found that nuclear organization was altered by interplay among members of this transcription factor circuitry: RUNX binding mediated association of Cd4 and Cd8 whereas ThPOK binding kept the loci apart. Moreover, targeted deletions within Cd4 modulated CD8 expression and pericentromeric repositioning of Cd8. Communication between Cd4 and Cd8 thus appears to enable long-range epigenetic regulation to ensure that expression of one excludes the other in mature CD4 or CD8 single-positive (SP) cells.

PCGF homologs, CBX proteins, and RYBP define functionally distinct PRC1 family complexes.

The heterogeneous nature of mammalian PRC1 complexes has hindered our understanding of their biological functions. Here, we present a comprehensive proteomic and genomic analysis that uncovered six major groups of PRC1 complexes, each containing a distinct PCGF subunit, a RING1A/B ubiquitin ligase, and a unique set of associated polypeptides. These PRC1 complexes differ in their genomic localization, and only a small subset colocalize with H3K27me3. Further biochemical dissection revealed that the six PCGF-RING1A/B combinations form multiple complexes through association with RYBP or its homolog YAF2, which prevents the incorporation of other canonical PRC1 subunits, such as CBX, PHC, and SCM. Although both RYBP/YAF2- and CBX/PHC/SCM-containing complexes compact chromatin, only RYBP stimulates the activity of RING1B toward H2AK119ub1, suggesting a central role in PRC1 function. Knockdown of RYBP in embryonic stem cells compromised their ability to form embryoid bodies, likely because of defects in cell proliferation and maintenance of H2AK119ub1 levels.

Close proximity to Igh is a contributing factor to AID-mediated translocations.

Class switch recombination (CSR) has the potential to generate genomic instability in B cells as activation-induced cytidine deaminase (AID), which mediates this process, is known to target many sites outside Igh. Nonetheless we do not fully understand what factors influence AID targeting genome-wide. Given that errors in CSR can lead to dangerous, oncogenic chromosomal translocations it is important to identify the elements that determine which genes are at risk of being "hit" and could be involved in aberrant rearrangements. Here we have investigated the influence of nuclear organization in determining "off-target" activity and the choice of fusion partners. Our studies indicate that the vast majority of known AID-mediated Igh translocation partners are found in chromosomal domains that contact this locus during class switching. Further, these interaction domains can be used to identify other genes that are hit by AID.

Ranking and combining multiple predictors without labeled data.

In a broad range of classification and decision-making problems, one is given the advice or predictions of several classifiers, of unknown reliability, over multiple questions or queries. This scenario is different from the standard supervised setting, where each classifier's accuracy can be assessed using available labeled data, and raises two questions: Given only the predictions of several classifiers over a large set of unlabeled test data, is it possible to (i) reliably rank them and (ii) construct a metaclassifier more accurate than most classifiers in the ensemble? Here we present a spectral approach to address these questions. First, assuming conditional independence between classifiers, we show that the off-diagonal entries of their covariance matrix correspond to a rank-one matrix. Moreover, the classifiers can be ranked using the leading eigenvector of this covariance matrix, because its entries are proportional to their balanced accuracies. Second, via a linear approximation to the maximum likelihood estimator, we derive the Spectral Meta-Learner (SML), an unsupervised ensemble classifier whose weights are equal to these eigenvector entries. On both simulated and real data, SML typically achieves a higher accuracy than most classifiers in the ensemble and can provide a better starting point than majority voting for estimating the maximum likelihood solution. Furthermore, SML is robust to the presence of small malicious groups of classifiers designed to veer the ensemble prediction away from the (unknown) ground truth.

Preanalytical variables and phosphoepitope expression in FFPE tissue: quantitative epitope assessment after variable cold ischemic time.

Individualized targeted therapies for cancer patients require accurate and reproducible assessment of biomarkers to be able to plan treatment accordingly. Recent studies have shown highly variable effects of preanalytical variables on gene expression profiling and protein levels of different tissue types. Several publications have described protein degradation of tissue samples as a direct result of delay of formalin fixation of the tissue. Phosphorylated proteins are more labile and epitope degradation can happen within 30 min of cold ischemic time. To address this issue, we evaluated the change in antigenicity of a series of phosphoproteins in paraffin-embedded samples from breast tumors as a function of time to formalin fixation. A tissue microarray consisting of 93 breast cancer specimens with documented time-to-fixation was used to evaluate changes in antigenicity of 12 phosphoepitopes frequently used in research settings as a function of cold ischemic time. Analysis was performed in a quantitative manner using the AQUA technology for quantitative immunofluorescence. For each marker, least squares univariate linear regression was performed and confidence intervals were computed using bootstrapping. The majority of the epitopes tested revealed changes in expression levels with increasing time to formalin fixation. Some phosphorylated proteins, such as phospho-HSP27 and phospho-S6 RP, involved in post-translational modification and stress response pathways increased in expression or phosphorylation levels. Others (like phospho-AKT, phosphor-ERK1/2, phospho-Tyrosine, phospho-MET, and others) are quite labile and loss of antigenicity can be reported within 1-2 h of cold ischemic time. Therefore specimen collection should be closely monitored and subjected to quality control measures to ensure accurate measurement of these epitopes. However, a few phosphoepitopes (like phospho-JAK2 and phospho-ER) are sufficiently robust for routine usage in companion diagnostic testing.

TrAp: a tree approach for fingerprinting subclonal tumor composition.

Revealing the clonal composition of a single tumor is essential for identifying cell subpopulations with metastatic potential in primary tumors or with resistance to therapies in metastatic tumors. Sequencing technologies provide only an overview of the aggregate of numerous cells. Computational approaches to de-mix a collective signal composed of the aberrations of a mixed cell population of a tumor sample into its individual components are not available. We propose an evolutionary framework for deconvolving data from a single genome-wide experiment to infer the composition, abundance and evolutionary paths of the underlying cell subpopulations of a tumor. We have developed an algorithm (TrAp) for solving this mixture problem. In silico analyses show that TrAp correctly deconvolves mixed subpopulations when the number of subpopulations and the measurement errors are moderate. We demonstrate the applicability of the method using tumor karyotypes and somatic hypermutation data sets. We applied TrAp to Exome-Seq experiment of a renal cell carcinoma tumor sample and compared the mutational profile of the inferred subpopulations to the mutational profiles of single cells of the same tumor. Finally, we deconvolve sequencing data from eight acute myeloid leukemia patients and three distinct metastases of one melanoma patient to exhibit the evolutionary relationships of their subpopulations.

Arpeggio: harmonic compression of ChIP-seq data reveals protein-chromatin interaction signatures.

Researchers generating new genome-wide data in an exploratory sequencing study can gain biological insights by comparing their data with well-annotated data sets possessing similar genomic patterns. Data compression techniques are needed for efficient comparisons of a new genomic experiment with large repositories of publicly available profiles. Furthermore, data representations that allow comparisons of genomic signals from different platforms and across species enhance our ability to leverage these large repositories. Here, we present a signal processing approach that characterizes protein-chromatin interaction patterns at length scales of several kilobases. This allows us to efficiently compare numerous chromatin-immunoprecipitation sequencing (ChIP-seq) data sets consisting of many types of DNA-binding proteins collected from a variety of cells, conditions and organisms. Importantly, these interaction patterns broadly reflect the biological properties of the binding events. To generate these profiles, termed Arpeggio profiles, we applied harmonic deconvolution techniques to the autocorrelation profiles of the ChIP-seq signals. We used 806 publicly available ChIP-seq experiments and showed that Arpeggio profiles with similar spectral densities shared biological properties. Arpeggio profiles of ChIP-seq data sets revealed characteristics that are not easily detected by standard peak finders. They also allowed us to relate sequencing data sets from different genomes, experimental platforms and protocols. Arpeggio is freely available at http://sourceforge.net/p/arpeggio/wiki/Home/.

A tissue quality index: an intrinsic control for measurement of effects of preanalytical variables on FFPE tissue.

While efforts are made to improve tissue quality and control preanalytical variables, pathologists are often confronted with the challenge of molecular analysis of patient samples of unknown quality. Here we describe a first attempt to construct a tissue quality index (TQI) or an intrinsic control that would allow a global assessment of protein status based on quantitative measurement of a small number of selected, informative epitopes. Quantitative immunofluorescence (QIF) of a number of proteins was performed on a series of 93 breast cancer cases where levels of expression were assessed as a function of delayed time to formalin fixation. A TQI was constructed based on the combination of proteins that most accurately reflect increased and decreased levels of expression in proportion to delay time. The TQI, defined by combinations of measurements of cytokeratin, ERK1/2 and pHSP-27 and their relationship to cold ischemic time were validated on a second build of the training series and on two independent breast tissue cohorts with recorded time to formalin fixation. We show an association of negative TQI values (an indicator for loss of tissue quality) with increasing cold ischemic time on both validation cohorts and an association with loss of ER expression levels on all three breast cohorts. Using expression levels of three epitopes, we can begin to assess the likelihood of delayed time to fixation or decreased tissue quality. This TQI represents a proof of concept for the use of epitope expression to provide a mechanism for monitoring tissue quality.

Picking ChIP-seq peak detectors for analyzing chromatin modification experiments.

Numerous algorithms have been developed to analyze ChIP-Seq data. However, the complexity of analyzing diverse patterns of ChIP-Seq signals, especially for epigenetic marks, still calls for the development of new algorithms and objective comparisons of existing methods. We developed Qeseq, an algorithm to detect regions of increased ChIP read density relative to background. Qeseq employs critical novel elements, such as iterative recalibration and neighbor joining of reads to identify enriched regions of any length. To objectively assess its performance relative to other 14 ChIP-Seq peak finders, we designed a novel protocol based on Validation Discriminant Analysis (VDA) to optimally select validation sites and generated two validation datasets, which are the most comprehensive to date for algorithmic benchmarking of key epigenetic marks. In addition, we systematically explored a total of 315 diverse parameter configurations from these algorithms and found that typically optimal parameters in one dataset do not generalize to other datasets. Nevertheless, default parameters show the most stable performance, suggesting that they should be used. This study also provides a reproducible and generalizable methodology for unbiased comparative analysis of high-throughput sequencing tools that can facilitate future algorithmic development.

Genome-wide remodeling of the epigenetic landscape during myogenic differentiation.

We have examined changes in the chromatin landscape during muscle differentiation by mapping the genome-wide location of ten key histone marks and transcription factors in mouse myoblasts and terminally differentiated myotubes, providing an exceptionally rich dataset that has enabled discovery of key epigenetic changes underlying myogenesis. Using this compendium, we focused on a well-known repressive mark, histone H3 lysine 27 trimethylation, and identified novel regulatory elements flanking the myogenin gene that function as a key differentiation-dependent switch during myogenesis. Next, we examined the role of Polycomb-mediated H3K27 methylation in gene repression by systematically ablating components of both PRC1 and PRC2 complexes. Surprisingly, we found mechanistic differences between transient and permanent repression of muscle differentiation and lineage commitment genes and observed that the loss of PRC1 and PRC2 components produced opposing differentiation defects. These phenotypes illustrate striking differences as compared to embryonic stem cell differentiation and suggest that PRC1 and PRC2 do not operate sequentially in muscle cells. Our studies of PRC1 occupancy also suggested a "fail-safe" mechanism, whereby PRC1/Bmi1 concentrates at genes specifying nonmuscle lineages, helping to retain H3K27me3 in the face of declining Ezh2-mediated methyltransferase activity in differentiated cells.

Expression of drug targets in primary and matched metastatic renal cell carcinoma tumors.

BACKGROUND: Targeted therapies in renal cell carcinoma can have different effects on primary and metastatic tumors. To pave the way for predictive biomarker development, we assessed differences in expression of targets of currently approved drugs in matched primary and metastatic specimens from 34 patients. METHODS: Four cores from each site were embedded in tissue microarray blocks. Expression of B-Raf, C-Raf, cKIT, FGF-R1, HIF-2α, mTOR, PDGF-Rß, VEGF-R1, VEGF-R2, VEGF-R3, VEGF, VEGF-B, VEGF-C, VEGF-D, MEK1, and ERK1/2 was studied using a quantitative immunofluorescence method. RESULTS: No significant differences were observed in global expression levels in primary and metastatic renal cell carcinoma tumors, with the exception of MEK, which had higher expression in metastatic than primary specimens. Similarly, more ki67 positive cells were seen in metastatic specimens. Correlations between marker expression in primary and metastatic specimens were variable, with the lowest correlation seen for FGF-R1 and VEGF-D. There were no significant differences in the degree of heterogeneity in primary versus metastatic tumors. CONCLUSIONS: Expression of most of the studied markers was similar in primary and metastatic renal cell carcinoma tumors, suggesting that predictive biomarker testing for these markers can be conducted on either the primary or metastatic tumors for most markers.

LMD: Multiscale Marker Identification in Single-cell RNA-seq Data.

Accurate cell marker identification in single-cell RNA-seq data is crucial for understanding cellular diversity and function. An ideal marker is highly specific in identifying cells that are similar in terms of function and state. Current marker identification methods, commonly based on clustering and differential expression, capture general cell-type markers but often miss markers for subtypes or functional cell subsets, with their performance largely dependent on clustering quality. Moreover, cluster-independent approaches tend to favor genes that lack the specificity required to characterize regions within the transcriptomic space at multiple scales. Here we introduce Localized Marker Detector (LMD), a novel tool to identify "localized genes" - genes with expression profiles specific to certain groups of highly similar cells - thereby characterizing cellular diversity in a multi-resolution and fine-grained manner. LMD's strategy involves building a cell-cell affinity graph, diffusing the gene expression value across the cell graph, and assigning a score to each gene based on its diffusion dynamics. We show that LMD exhibits superior accuracy in recovering known cell-type markers in the Tabula Muris bone marrow dataset relative to other methods for marker identification. Notably, markers favored by LMD exhibit localized expression, whereas markers prioritized by other clustering-free algorithms are often dispersed in the transcriptomic space. We further group the markers suggested by LMD into functional gene modules to improve the separation of cell types and subtypes in a more fine-grained manner. These modules also identify other sources of variation, such as cell cycle status. In conclusion, LMD is a novel algorithm that can identify fine-grained markers for cell subtypes or functional states without relying on clustering or differential expression analysis. LMD exploits the complex interactions among cells and reveals cellular diversity at high resolution.

Immune dysfunction revealed by digital spatial profiling of immuno-oncology markers in progressive stages of renal cell carcinoma and in brain metastases.

BACKGROUND: The tumor microenvironment (TME) contributes to cancer progression and treatment response to therapy, including in renal cell carcinoma (RCC). Prior profiling studies, including single-cell transcriptomics, often involve limited sample sizes and lack spatial orientation. The TME of RCC brain metastases, a major cause of morbidity, also remains largely uncharacterized. METHODS: We performed digital spatial profiling on the NanoString GeoMx platform using 52 validated immuno-oncology markers on RCC tissue microarrays representing progressive stages of RCC, including brain metastases. We profiled 76 primary tumors, 27 adjacent histologically normal kidney samples, and 86 metastases, including 24 brain metastases. RESULTS: We observed lower immune checkpoint (TIM-3 and CTLA-4), cytolytic (GZMA and GZMB), and T cell activation (CD25) protein expression in metastases compared with primary tumors in two separate cohorts. We also identified changes in macrophages in metastases, with brain metastases-susceptible patients showing less M1-like, inflammatory macrophage markers (HLA-DR and CD127) in metastatic samples. A comparison of brain metastases to extracranial metastases revealed higher expression of the anti-apoptotic, BCL-2-family protein BCL-XL and lower expression of the innate immune activator STING in brain metastases. Lower TIM-3 and CD40 in the TME of brain metastases appear to be associated with longer survival, a finding that requires further validation. CONCLUSIONS: Compared with primary tumors, RCC metastases, including brain metastases, express lower levels of numerous markers of immune activation and current or investigational therapeutic targets. Our findings may have important implications for designing future biomarker and treatment studies and may aid in development of brain metastases-specific therapies.

Integrated analysis of tumor samples sheds light on tumor heterogeneity.

The heterogeneity of tumor samples is a major challenge in the analysis of high-throughput profiling of tumor biopsies and cell lines. The measured aggregate signals of multigenerational progenies often represent an average of several tumor subclones with varying genomic aberrations and different gene expression levels. The goal of the present study was to integrate copy number analyses from SNP-arrays and karyotyping, gene expression profiling, and pathway analyses to detect heterogeneity, identify driver mutations, and explore possible mechanisms of tumor evolution. We showed the heterogeneity of the studied samples, characterized the global copy number alteration profiles, and identified genes whose copy number status and expression levels were aberrant. In particular, we identified a recurrent association between two BRAF(V600E) and BRAF(V600K) mutations and changes in DKK1 gene expression levels, which might indicate an association between the BRAF and WNT pathways. These findings show that the integrated approaches used in the present study can robustly address the challenging issue of tumor heterogeneity in high-throughput profiling.

Detecting copy number status and uncovering subclonal markers in heterogeneous tumor biopsies.

BACKGROUND: Genomic aberrations can be used to determine cancer diagnosis and prognosis. Clinically relevant novel aberrations can be discovered using high-throughput assays such as Single Nucleotide Polymorphism (SNP) arrays and next-generation sequencing, which typically provide aggregate signals of many cells at once. However, heterogeneity of tumor subclones dramatically complicates the task of detecting aberrations. RESULTS: The aggregate signal of a population of subclones can be described as a linear system of equations. We employed a measure of allelic imbalance and total amount of DNA to characterize each locus by the copy number status (gain, loss or neither) of the strongest subclonal component. We designed simulated data to compare our measure to existing approaches and we analyzed SNP-arrays from 30 melanoma samples and transcriptome sequencing (RNA-Seq) from one melanoma sample.We showed that any system describing aggregate subclonal signals is underdetermined, leading to non-unique solutions for the exact copy number profile of subclones. For this reason, our illustrative measure was more robust than existing Hidden Markov Model (HMM) based tools in inferring the aberration status, as indicated by tests on simulated data. This higher robustness contributed in identifying numerous aberrations in several loci of melanoma samples. We validated the heterogeneity and aberration status within single biopsies by fluorescent in situ hybridization of four affected and transcriptionally up-regulated genes E2F8, ETV4, EZH2 and FAM84B in 11 melanoma cell lines. Heterogeneity was further demonstrated in the analysis of allelic imbalance changes along single exons from melanoma RNA-Seq. CONCLUSIONS: These studies demonstrate how subclonal heterogeneity, prevalent in tumor samples, is reflected in aggregate signals measured by high-throughput techniques. Our proposed approach yields high robustness in detecting copy number alterations using high-throughput technologies and has the potential to identify specific subclonal markers from next-generation sequencing data.

Tumour response and secondary resectability of colorectal liver metastases following neoadjuvant chemotherapy with cetuximab: the CELIM randomised phase 2 trial.

BACKGROUND: Neoadjuvant chemotherapy for unresectable colorectal liver metastases can downsize tumours for curative resection. We assessed the effectiveness of cetuximab combined with chemotherapy in this setting. METHODS: Between Dec 2, 2004, and March 27, 2008, 114 patients were enrolled from 17 centres in Germany and Austria; three patients receiving FOLFOX6 alone were excluded from the analysis. Patients with non-resectable liver metastases (technically non-resectable or > or =5 metastases) were randomly assigned to receive cetuximab with either FOLFOX6 (oxaliplatin, fluorouracil, and folinic acid; group A) or FOLFIRI (irinotecan, fluorouracil, and folinic acid; group B). Randomisation was not blinded, and was stratified by technical resectability and number of metastases, use of PET staging, and EGFR expression status. They were assessed for response every 8 weeks by CT or MRI. A local multidisciplinary team reassessed resectability after 16 weeks, and then every 2 months up to 2 years. Patients with resectable disease were offered liver surgery within 4-6 weeks of the last treatment cycle. The primary endpoint was tumour response assessed by Response Evaluation Criteria In Solid Tumours (RECIST), analysed by modified intention to treat. A retrospective, blinded surgical review of patients with radiological images at both baseline and during treatment was done to assess objectively any changes in resectability. The study is registered with ClinicalTrials.gov, number NCT00153998. FINDINGS: 56 patients were randomly assigned to group A and 55 to group B. One patient in each group were excluded from the analysis of the primary endpoint because they discontinued treatment before first full dose, one patient in group B was excluded because of early pulmonary embolism. A confirmed partial or complete response was noted in 36 (68%) of 53 patients in group A, and 30 (57%) of 53 patients in group B (difference 11%, 95% CI -8 to 30; odds ratio [OR] 1.62, 0.74-3.59; p=0.23). The most frequent grade 3 and 4 toxicities were skin toxicity (15 of 54 patients in group A, and 22 of 55 patients in group B), and neutropenia (13 of 54 patients in group A and 12 of 55 patients in group B). R0 resection was done in 20 (38%) of 53 patients in group A and 16 (30%) of 53 of patients in group B. In a retrospective analysis of response by KRAS status, a partial or complete response was noted in 47 (70%) of 67 patients with KRAS wild-type tumours versus 11 (41%) of 27 patients with KRAS-mutated tumours (OR 3.42, 1.35-8.66; p=0.0080). According to the retrospective review, resectability rates increased from 32% (22 of 68 patients) at baseline to 60% (41 of 68) after chemotherapy (p<0.0001). INTERPRETATION: Chemotherapy with cetuximab yields high response rates compared with historical controls, and leads to significantly increased resectability. FUNDING: Merck-Serono, Sanofi-Aventis, and Pfizer.

Benefits of biomarker selection and clinico-pathological covariate inclusion in breast cancer prognostic models.

INTRODUCTION: Multi-marker molecular assays have impacted management of early stage breast cancer, facilitating adjuvant chemotherapy decisions. We generated prognostic models that incorporate protein-based molecular markers and clinico-pathological variables to improve survival prediction. METHODS: We used a quantitative immunofluorescence method to study protein expression of 14 markers included in the Oncotype DX™ assay on a 638 breast cancer patient cohort with 15-year follow-up. We performed cross-validation analyses to assess performance of multivariate Cox models consisting of these markers and standard clinico-pathological covariates, using an average time-dependent Area Under the Receiver Operating Characteristic curve and compared it to nested Cox models obtained by robust backward selection procedures. RESULTS: A prognostic index derived from a multivariate Cox regression model incorporating molecular and clinico-pathological covariates (nodal status, tumor size, nuclear grade, and age) is superior to models based on molecular studies alone or clinico-pathological covariates alone. Performance of this composite model can be further improved using feature selection techniques to prune variables. When stratifying patients by Nottingham Prognostic Index (NPI), most prognostic markers in high and low NPI groups differed. Similarly, for the node-negative, hormone receptor-positive sub-population, we derived a compact model with three clinico-pathological variables and two protein markers that was superior to the full model. CONCLUSIONS: Prognostic models that include both molecular and clinico-pathological covariates can be more accurate than models based on either set of features alone. Furthermore, feature selection can decrease the number of molecular variables needed to predict outcome, potentially resulting in less expensive assays.

A multicriteria approach for risk assessment of Covid-19 in urban district lockdown.

At the beginning of 2020, the spread of a new strand of Coronavirus named SARS-CoV-2 (COVID-19) raised the interest of the scientific community about the risk assessment related to the viral infection. The contagion became pandemic in few months forcing many Countries to declare lockdown status. In this context of quarantine, all commercial and productive activities are suspended, and many Countries are experiencing a serious crisis. To this aim, the understanding of risk of contagion in every urban district is fundamental for governments and administrations to establish reopening strategies. This paper proposes the calibration of an index able to predict the risk of contagion in urban districts in order to support the administrations in identifying the best strategies to reduce or restart the local activities during lockdown conditions. The objective regards the achievement of a useful tool to predict the risk of contagion by considering socio-economic data such as the presence of activities, companies, institutions and number of infections in urban districts. The proposed index is based on a factorial formula, simple and easy to be applied by practitioners, calibrated by using an optimization-based procedure and exploiting data of 257 urban districts of Apulian region (Italy). Moreover, a comparison with a more refined analysis, based on the training of Artificial Neural Networks, is performed in order to take into account the non-linearity of the phenomenon. The investigation quantifies the influence of each considered parameter in the risk of contagion useful to obtain risk analysis and forecast scenarios.

Impact of healthcare worker shift scheduling on workforce preservation during the COVID-19 pandemic.

Reducing severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infections among healthcare workers is critical. We ran Monte Carlo simulations modeling the spread of SARS-CoV-2 in non-COVID-19 wards, and we found that longer nursing shifts and scheduling designs in which teams of nurses and doctors co-rotate no more frequently than every 3 days can lead to fewer infections.