Methods and options to monitor the cost and affordability of a healthy diet globally Background paper for The State of Food Security and Nutrition in the World 2022

FAO is focusing its attention on the pursuit of healthy diets and transformations of agrifood systems to ensure healthy diets are affordable for all. Measuring and systematically monitoring the cost and affordability of healthy diets and making progress towards ensuring the affordability of healthy diets is of upmost importance and is urgently needed. To this end, FAO is committed to institutionalize the computation of the least-cost healthy diet, and the corresponding affordability indicator, and to publish updated estimates in the annual The State of Food Security and Nutrition in the World report, as well as provide the full data series on FAOSTAT. This background paper to The State of Food Security and Nutrition in the World 2022 report presents the new methodological refinements applied in the estimation of the average cost of a healthy diet. This is an important methodological update as it results in a more robust indicator that provides greater transparency and supports long-term systematic monitoring utilizing annually updated price data. The paper then explores potential mechanisms and data sources for monitoring globally the cost of a healthy diet

Calendar effects to forecast influenza seasonality: A case study in Milwaukee, WI

ObjectiveIn the presented study, we examined the impact of school holidays (Autumn, Winter, Summer, and Spring Breaks) and social events (Super Bowl, NBA Finals, World Series, and Black Friday) for five age groups (<4, 5-24, 25-44, 45-64, >65 years) on four health outcomes of influenza (total tested, all influenza positives, positives for influenza A, and B) in Milwaukee, WI, in 2004-2009 using routine surveillance.IntroductionInfluenza viral infection is contentious, has a short incubation period, yet preventable if multiple barriers are employed. At some extend school holidays and travel restrictions serve as a socially accepted control measure1,2. A study of a spatiotemporal spread of influenza among school-aged children in Belgium illustrated that changes in mixing patterns are responsible for altering disease seasonality3. Stochastic numerical simulations suggested that weekends and holidays can delay disease seasonal peaks, mitigate the spread of infection, and slow down the epidemic by periodically dampening transmission. While Christmas holidays had the largest impact on transmission, other school breaks may also help in reducing an epidemic size. Contrary to events reducing social mixing, sporting events and mass gatherings facilitate the spread of infections4. A study on county-level vital statistics of the US from 1974-2009 showed that Super Bowl social mixing affects influenza dissemination by decreasing mortality rates in older adults in Bowl-participating counties. The effect is most pronounced for highly virulent influenza strains and when the Super Bowl occurs closer to the influenza seasonal peak. Simulation studies exploring how social mixing affects influenza spread5 demonstrated that impact of the public gathering on prevalence of influenza depends on time proximity to epidemic peak. While the effects of holidays and social events on seasonal influenza have been explored in surveillance time series and agent-based modeling studies, the understanding of the differential effects across age groups is incomplete.MethodsThe City of Milwaukee Health Department Laboratory (MHDL), Wisconsin routinely collect tests from residents of metropolitan areas and vicinities of the Marquette University (MU). We obtained weekly counts of total tested, all influenza positives, positives for influenza A and B, from MHDL between 5/16/04-3/7/09 (before the surge of tests associated with “swine flu”). Cases for <1 and 1-4 age groups were combined. Meteorological data are routinely collected by a monitoring station at the General Mitchell International airport located 7.5 miles from Milwaukee. Daily dewpoint values representing the perceived ambient temperature corrected for the air moisture content were downloaded from the open source website6 and aggregated to weekly averages with Sunday designating the beginning of each week. School holidays were obtained from academic calendars on the MU website with holiday weeks defined as having one or more school holiday observed.7 Selected social events were retrieved from a public website.8 As part of exploratory analysis, average cases per week (c/w) for each outcome for school holiday and non-holiday weeks were compared using a non-parametric the Mann–Whitney U-test. We analyzed the association between weekly cases and holiday effects using negative binomial regression with sets of indicator variables for non-overlapping school holidays and social events and with adjustments for weather fluctuations with harmonic terms (Model 1). Results are presented as Relative Risk (RR) estimates along with their confidence intervals (95%CI). Further analyses examined seasonal signatures (lead-lag structures) using a segmented regression approach for weekly counts and rates 5 academic weeks (aw) before, 2-6 weeks during, and 5 weeks after select holidays (Model 2).ResultsOver 251 study weeks, 2282 tests were submitted, out of which 1098 cases were from 5-24 y.o. age group. 477 (21%) tests we positive, with 399 (84%) cases of influenza A (73 tests were not subtyped) and 78 (16%) cases of influenza B. Figure 1 shows the time series of weekly counts of influenza tests and percent positives with superimposed information on school holiday occurrences. Overall, during 135 weeks of the school period the average number of tests was two times higher as compared to those during 116 holiday weeks (11.9±10.3 vs 5.8±6.5 c/w, p<0.001). Similarly, the average weekly number of positive tests was higher in non-holiday than during holiday periods (2.9±5.7 vs 0.7±2.6 c/w, p<0.001). The reduction in tests during holidays was confirmed by the regression model (RR=0.71; 95%CI=[0.60-0.86]). The reduction in weekly tests was most pronounced during the Winter Break (15-19 aw) for all age groups (4.8±3.0 c/w, p<0.001; RR=0.3; 95%CI=[0.23-0.41]) and especially for school-aged children, young adults and adults (RR=0.14; 95%CI=[0.09-0.22] and RR=0.32; 95%CI=[0.16-0.62] for 5-24 and 25-44 age groups, respectively). In contrast, during the Spring Break (27-30 aw) the number of tests has almost doubled (20.4±10.4 c/w; p<0.001) as compared to the school period, with the most noticeable increase in 5-24 and 25-44 age groups. Spring Break differential effects were primarily due to later peaks in influenza B shown by segmented regression results in Figure 2. The seasonal increase in weekly rates is the steepest after the winter holidays. The effects of the selected sporting and social events were inconclusive.ConclusionsThe differential effects of calendar events on seasonal influenza can be detected by routine surveillance and further explored with respect to lead-lag structures. We recommend incorporating location-specific calendar effects in influenza near-term forecasting models tailored to susceptible age groups to better predict and assess targeted intervention measures.References1. Jackson C, et al. (2016). The relationship between school holidays and transmission of influenza in England and wales. Am Journal of Epidemiology. 184(9), 644-51.2. Chu Y, et al. (2017). Effects of school breaks on influenza-like illness incidence in a temperate Chinese region: an ecological study from 2008 to 2015. BMJ. 7(3), e013159.3. Luca G, et al. (2018) The impact of regular school closure on seasonal influenza epidemics: a data-driven spatial transmission model for Belgium. BMC Infect Dis. 18(1): 29.4. Stoecker C, et al. (2016) Success Is Something to Sneeze At: Influenza Mortality in Cities that Participate in the Super Bowl. Am Journal of Health Econ. 2(1):125-43.5. Shi P, et al. (2010) The impact of mass gatherings and holiday traveling on the course of an influenza pandemic: a computational model. BMC Public Health. 10: 778.6. www.wunderground.com.7. www.marquette.edu/mucentral/registrar/ArchivedAcademicCalendars.shtml.8. www.timeanddate.com.

Transcription pausing regulates mouse embryonic stem cell differentiation

The pluripotency of embryonic stem cells (ESCs) relies on appropriate responsiveness to developmental cues. Promoter-proximal pausing of RNA polymerase II (Pol II) has been suggested to play a role in keeping genes poised for future activation. To identify the role of Pol II pausing in regulating ESC pluripotency, we have generated mouse ESCs carrying a mutation in the pause-inducing factor SPT5. Genomic studies reveal genome-wide reduction of paused Pol II caused by mutant SPT5 and further identify a tight correlation between pausing-mediated transcription effect and local chromatin environment. Functionally, this pausing-deficient SPT5 disrupts ESC differentiation upon removal of self-renewal signals. Thus, our study uncovers an important role of Pol II pausing in regulating ESC differentiation and suggests a model that Pol II pausing coordinates with epigenetic modification to influence transcription during mESC differentiation

Elucidating systemic immune responses to acute and convalescent SARS‐CoV‐2 infection in children and elderly individuals

Abstract Background Severe Acute Respiratory Syndrome Coronavirus‐2 (SARS‐CoV‐2), a causative pathogen of the COVID‐19 pandemic, affects all age groups. However, various studies have shown that COVID‐19 presentation and severity vary considerably with age. We, therefore, wanted to examine the differences between the immune responses of children with COVID‐19 and elderly COVID‐19 individuals. Methods We analyzed cytokines, chemokines, growth factors, and acute phase proteins in acute and convalescent COVID‐19 children and the elderly with acute and convalescent COVID‐19. Results We show that most of the pro‐inflammatory cytokines (interferon [IFN]γ, interleukin [IL]‐2, tumor necrosis factor‐α [TNFα], IL‐1α, IFNα, IFNβ, IL‐6, IL‐12, IL‐3, IL‐7, IL‐1Ra, IL‐13, and IL‐10), chemokines (CCL4, CCL11, CCL19, CXCL1, CXCL2, CXCL8, and CXL10), growth factors (vascular endothelial growth factor and CD40L) and acute phase proteins (C‐reactive protein, serum amyloid P, and haptoglobin) were decreased in children with acute COVID 19 as compared with elderly individuals. In contrast, children with acute COVID‐19 exhibited elevated levels of cytokines‐ IL‐1β, IL‐33, IL‐4, IL‐5, and IL‐25, growth factors—fibroblast growth factor‐2, platelet‐ derived growth factors‐BB, and transforming growth factorα as compared with elderly individuals. Similar, differences were manifest in children and elderly with convalescent COVID‐19. Conclusion Thus, COVID‐19 children are characterized by distinct cytokine/chemokine/growth factor/acute phase protein markers that are markedly different from elderly COVID‐19 individuals

Alterations of adipokines, pancreatic hormones and incretins in acute and convalescent COVID-19 children

Abstract Background The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), accountable for Coronavirus disease 2019 (COVID-19), may cause hyperglycemia and additional systemic complexity in metabolic parameters. It is unsure even if the virus itself causes type 1 or type 2 diabetes mellitus (T1DM or T2DM). Furthermore, it is still unclear whether even recuperating COVID-19 individuals have an increased chance to develop new-onset diabetes. Methods We wanted to determine the impact of COVID-19 on the levels of adipokines, pancreatic hormones, incretins and cytokines in acute COVID-19, convalescent COVID-19 and control children through an observational study. We performed a multiplex immune assay analysis and compared the plasma levels of adipocytokines, pancreatic hormones, incretins and cytokines of children presenting with acute COVID-19 infection and convalescent COVID-19. Results Acute COVID-19 children had significantly elevated levels of adipsin, leptin, insulin, C-peptide, glucagon and ghrelin in comparison to convalescent COVID-19 and controls. Similarly, convalescent COVID-19 children had elevated levels of adipsin, leptin, insulin, C-peptide, glucagon, ghrelin and Glucagon-like peptide-1 (GLP-1) in comparison to control children. On the other hand, acute COVID-19 children had significantly decreased levels of adiponectin and Gastric Inhibitory Peptide (GIP) in comparison to convalescent COVID-19 and controls. Similarly, convalescent COVID-19 children had decreased levels of adiponectin and GIP in comparison to control children. Acute COVID-19 children had significantly elevated levels of cytokines, (Interferon (IFN)) IFNγ, Interleukins (IL)-2, TNFα, IL-1α, IL-1β, IFNα, IFNβ, IL-6, IL-12, IL-17A and Granulocyte-Colony Stimulating Factors (G-CSF) in comparison to convalescent COVID-19 and controls. Convalescent COVID-19 children had elevated levels of IFNγ, IL-2, TNFα, IL-1α, IL-1β, IFNα, IFNβ, IL-6, IL-12, IL-17A and G-CSF in comparison to control children. Additionally, Principal component Analysis (PCA) analysis distinguishes acute COVID-19 from convalescent COVID-19 and controls. The adipokines exhibited a significant correlation with the levels of pro-inflammatory cytokines. Conclusion Children with acute COVID-19 show significant glycometabolic impairment and exaggerated cytokine responses, which is different from convalescent COVID-19 infection and controls

Utility of Features in a Natural-Language-Processing-Based Clinical De-Identification Model Using Radiology Reports for Advanced NSCLC Patients

The de-identification of clinical reports is essential to protect the confidentiality of patients. The natural-language-processing-based named entity recognition (NER) model is a widely used technique of automatic clinical de-identification. The performance of such a machine learning model relies largely on the proper selection of features. The objective of this study was to investigate the utility of various features in a conditional-random-field (CRF)-based NER model. Natural language processing (NLP) toolkits were used to annotate the protected health information (PHI) from a total of 10,239 radiology reports that were divided into seven types. Multiple features were extracted by the toolkit and the NER models were built using these features and their combinations. A total of 10 features were extracted and the performance of the models was evaluated based on their precision, recall, and F1-score. The best-performing features were n-gram, prefix-suffix, word embedding, and word shape. These features outperformed others across all types of reports. The dataset we used was large in volume and divided into multiple types of reports. Such a diverse dataset made sure that the results were not subject to a small number of structured texts from where a machine learning model can easily learn the features. The manual de-identification of large-scale clinical reports is impractical. This study helps to identify the best-performing features for building an NER model for automatic de-identification from a wide array of features mentioned in the literature