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South-East Asia is a hot spot for emerging zoonotic diseases, and bats have been recognized as hosts for a large number of zoonotic viruses such as Severe Acute Respiratory Syndrome (SARS), responsible for acute respiratory syndrome outbreaks. Thus, it is important to expand our knowledge of the presence of viruses in bats which could represent a risk to humans. Coronaviruses (CoVs) have been reported in bat species from Thailand, China, Indonesia, Taiwan and the Philippines. However no such work was conducted in Cambodia or Lao PDR. Between 2010 and 2013, 1965 bats were therefore sampled at interfaces with human populations in these two countries. They were tested for the presence of coronavirus by consensus reverse transcription-PCR assay. A total of 93 samples (4.7%) from 17 genera of bats tested positive. Sequence analysis revealed the presence of potentially 37 and 56 coronavirus belonging to alpha-coronavirus (αCoV) and beta-CoV (βCoV), respectively. The βCoVs group is known to include some coronaviruses highly pathogenic to human, such as SARS-CoV and MERS-CoV. All coronavirus sequences generated from frugivorous bats (family Pteropodidae) (n=55) clustered with other bat βCoVs of lineage D, whereas one coronavirus from Pipistrellus coromandra fell in the lineage C of βCoVs which also includes the MERS-CoV. αCoVs were all detected in various genera of insectivorous bats and clustered with diverse bat αCoV sequences previously published. A closely related strain of PEDV, responsible for severe diarrhea in pigs (PEDV-CoV), was detected in 2 Myotis bats. We highlighted the presence and the high diversity of coronaviruses circulating in bats from Cambodia and Lao PDR. Three new bat genera and species were newly identified as host of coronaviruses, namely Macroglossus sp., Megaerops niphanae and Myotis horsfieldii. P1 Cambodia bats Coronavirus data_2019Aug16_1658Data associated with bats sampled in Cambodia and tested for CoronavirusesP1 Laos bats Coronavirus Data_2019Aug16_1656Data associated with bats sampled in Lao PDR and tested for Coronaviruses

This dataset was developed for the following article: Rogan PK, Mucaki EJ and Shirley BC. A proposed molecular mechanism for pathogenesis of severe RNA-viral pulmonary infections [version 1; peer review: awaiting peer review]. F1000Research 2020, 9:943 (https://doi.org/10.12688/f1000research.25390.1) Section 1. Extended Data Tables This archive contains the extended data tables for the research article "A proposed mechanism for molecular pathogenesis of severe RNA-viral pulmonary infections". These tables provide SRSF1, RNPS1 and hnRNP A1 binding site and information-dense cluster counts across various RNA viral genomes [including multiple SARS-CoV-2 and influenza strains] and the human transcriptome, the estimated SARS-CoV-2 doubling time necessary for viral genome SRSF1 binding site availability to exceed sites within the host transcriptome, and an analysis of influenza, dengue, and aplastic anemia patients misdiagnosed as irradiated by established radiation gene signatures.These tables are: Section 1 - Table 1. RNPS1 and hnRNPA1 binding sites and Information-Dense Clusters for RNPS1 and hnRNPA1 in RNA Virus Genomes Section 1 - Table 2A. Detailed Analysis of Information-Dense Clusters for SRSF1 (Replicate 1) in RNA Virus Genomes Section 1 - Table 2B. Detailed Analysis of Information-Dense Clusters for SRSF1 (Replicate 2) in RNA Virus Genomes Section 1 - Table 2C. Detailed Analysis of Information-Dense Clusters for RNPS1 in RNA Virus Genomes Section 1 - Table 2D. Detailed Analysis of Information-Dense Clusters for hnRNP A1 in RNA Virus Genomes Section 1 - Table 3. Binding Site Analysis of Multiple Coronavirus Strains (Both Strands) Section 1 - Table 4A. Binding Site Analysis of Multiple Influenza A (H3N2) Strains (Negative Strand Only) Section 1 - Table 4B. Binding Site Analysis of Multiple Influenza A (H3N2) Strains (Both Strands) Section 1 - Table 5. SRSF1, RNPS1 and hnRNPA1 Binding Sites and Information-Dense Clusters by Gene Section 1 - Table 6A. Transcriptome-Wide Information Dense Clusters Intersecting DRIP- and DRIPc-seq Intervals Section 1 - Table 6B. Exome-Wide Information Dense Clusters within DRIP- and DRIPc-seq Intervals Section 1 - Table 6C. Transcriptome-Wide Scan of Strong Binding Sites Intersecting DRIP- and DRIPc-seq Intervals Section 1 - Table 6D. Exome-Wide Scan of Strong Binding Sites within DRIP- and DRIPc-seq Intervals Section 1 - Table 7. Rate of False Positives for Influenza, Dengue Virus and Aplastic Anemia Using Radiation Signatures Section 1 - Table 8. Radiation Model Genes Contributing to False Positives for Patients with Influenza A, Dengue Virus, and Aplastic Anemia Section 1 - Table 9A. Doubling Time of SARS-CoV-2 Needed to Exceed Host Transcriptome SRSF1 Binding Sites (Positive-Strand Sites Only) Section 1 - Table 9B. Doubling Time of SARS-CoV-2 Needed to Exceed Host Transcriptome SRSF1 Binding Sites (Both Strands Considered) Section 2. All SRSF1, hnRNPA1 and RNPS1 binding site tracks for human and viral genomes We provide bedgraph tracks which provide the location and strength of binding sites (and binding site clusters) for SRSF1, RNPS1 and hnRNPA1 across the human transcriptome (GRCh37), the human exome (including +/-300nt surrounding the exon; non-intergenic only), and for all viral genome investigated in this study (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [two strains]). Note that if no clusters were found for a particular viral genome, a file for said genome will not be present in the Zenodo archive. Folder “Cluster-to-DRIPseq-Intersection-Tracks” contain tracks which indicate where binding site clusters have been identified, intersected with DRIP-seq and DRIPc-seq intervals which indicate where there is evidence of R-Loop formation in the human genome. The DRIP-seq dataset (GSE68845) is not strand specific. DRIPc-seq (GSE70189) is strand specific, and has been taken into account in the intersection (e.g. tracks only list positive strand clusters found in positive-strand DRIPc-seq intervals). Due to sheer size, the human transcriptome and exome tracks which indicate the location of individual binding sites are split into two separate files (separated by strand). While the custom tracks containing human binding site information are designed to be uploaded to the UCSC Genome Browser, files containing transcriptome-wide binding site information may be too large to be uploaded and may require further filtering (i.e. by chromosome). To be classified as a cluster, binding sites on the same strand must have Ri values which sum to >50 bits, each binding site must have a neighboring site within 25nt, and all binding sites in the cluster must have Ri greater than a minimum bit threshold. For human transcriptomes and exomes, this bit minimum was set to Rsequence. The bit minimum for viral binding sites was set to 0.1 * Rsequence. The information density-based clustering algorithm utilized in this work is described in Lu and Rogan 2018 (https://f1000research.com/articles/7-1933/v2) and archived source code is available through Zenodo (https://dx.doi.org/10.5281/zenodo.1892051). Section 3. Binding site clusters - lollipop plots Lollipop plots present the genomic coordinates and information densities of clusters across the human transcriptome, human exome, and viral genomes (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [one strain]). The height of the "lollipop" corresponds to the information density of a cluster. Labels above "lollipops" present the start and end genomic coordinate (GRCh37) of the cluster followed by the number of sites in the cluster enclosed in brackets. Lollipop plots associated with human transcriptomes/exomes each contain a single gene. Influenza has 8 segments and each segment requires its own plot, other viral genomes examined are presented in a single plot. File naming convention for human plots: RBP_Gene.png e.g. RNPS1_ADK.png File naming convention for viral plots (elements in square brackets do not always appear): Virus[.InfluenzaSegment].RiThreshold.Strand.RBP.png e.g. Wuhan-Hu-1.complete-genome.4.2-bits.PosStrand.hnRNPA1.png The specified Ri threshold indicates all binding sites which comprise a cluster have Ri greater-than or equal to the threshold. Section 4. Ri(b,l) matrices for all binding sites scanned The information theory-based position weight matrices for the following RNA binding proteins (RBP) used in this study: SRSF1, hnRNPA1 and RNPS1. We investigated binding using two different RNPS1 binding models. While similar, these two models contained binding site information on opposing sides of the binding site motif which is why we found it prudent to scan with both models. Structure of each file: Line #1: Start position, End position and Rsequence [average strength of sequences used to generate the model] Subsequent lines describe the information on each position of the binding site: First four columns: Ri contribution of nucleotide at this position of the matrix [A, C, G, T] Row #5: Position of the matrix Last four columns: Number of binding sites used to generate model with a particular nucleotide at this position of the matrix [A, C, G, T] Example: -2.965775 1.282153 0.034225 -4.906891 0 1 19 8 0 At zero position of the matrix (first nucleotide), a ‘C’ would have a positive contribution to binding site strength, a ‘G’ would be relatively neutral, and an ‘A’ or ‘T’ would negatively contribute to binding site strength. Generation of Ri(b,l) matrices and computation of Ri values and can be accomplished by utilizing the Delila package (https://alum.mit.edu/www/toms/delila/delilaprograms.html). Section 5. Ri and intersite distance - histograms Two sets of histograms present Ri distribution and intersite distance distribution across the human transcriptome, human exome, and viral genomes (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [one strain]). File naming convention for human plots (elements in square brackets do not always appear): [IntersiteDistancesThreshold-]Human-[DRIPc]-AllChrs-RBP[-RiThreshold].png e.g. IntersiteDistances500-Human-AllChrs-hnRNPA1-4.6-bits.png File naming convention for viral plots (elements in square brackets do not always appear): [IntersiteDistancesThreshold-]Strand-RBP-Virus[.InfluenzaSegment][-RiThreshold].png e.g. IntersideDistances1000-PosStrandOnly-SRSF1-top50000sitesReplicate1-HIV-1-Strain-B.png Intersite distance thresholds of 500 or 1000 were assigned for all intersite distance histograms. Any distances above the corresponding threshold were excluded from the plot. Plots presenting Ri distributions contain a dashed line indicating Rsequence if it is visible within the scope of the plot. Section 6. Perl Scripts and Descriptions This archive contains all Perl scripts discussed in this archive's associated manuscript and a document file which describes them ("Perl-Script-Descriptions-Page.docx"). The programs and their general functions are as follows: “ClusterToDRIPseqAnalysisProgram.pl” – reports which information-dense clusters are located within DRIPc- and/or DRIP-seq intervals (individually and by gene) “ClusterToDRIPseqAnalysisProgram.GeneDensityFinder.pl” – uses the output from script “ClusterToDRIPseqAnalysisProgram.pl” to determine the number and the density of information-dense clusters within a gene (total clusters within the gene and those within DRIPc-seq intervals) “calculateIntersiteDistance.pl” – determines the distance between all binding sites in the same gene from a list of genomic coordinates “removeOutliersHigherThanN.pl” – discards intersite distances computed by script “calculateIntersiteDistance.pl” that are greater than a specified threshold “getStatisticsOnCol.pl” – calculates the count, geometric mean, median, arithmetic mean, and standard deviation of values from the output of script “removeOutliersHigherThanN.pl” “ScanDataSummaryProgram.pl” – determines the number of binding sites (above a specified Ri threshold) found within known genes (the program also reports the total expression of those genes using external A549 and pneumocyte expression datasets) from binding site coordinate data “TotalBindingSitePerCellCalculator.pl” – estimates the number of binding sites expressed in a single A549 or pneumocyte cell at any given time. Also see Infographic: Rogan, Peter; Klesc, Ryan; Mucaki, Eliseos; C. Shirley, Ben (2020): A proposed molecular mechanism for pathogenesis of severe RNA-viral pulmonary infections. figshare. Figure. https://doi.org/10.6084/m9.figshare.12718799.v1 {"references": ["Rogan et al. A proposed molecular mechanism for pathogenesis of severe RNA-viral pulmonary infections. F1000Research (2020) https://doi.org/10.12688/f1000research.25390.1"]}

This contribution was delivered online on 6 May 2021 on the occasion of the hybrid 2021 edition of EUI State of the Union on ‘Europe in a Changing World '. Part of the #SoU2021 Fringe Events, this panel, organised by SoU’s Partners and Stakeholders [EUI Florence School of Regulation], contributed with an alternative intellectually independent perspective to the overarching theme ‘Europe in a Changing World’. The European Green Deal strives to transform Europe into the world’s first carbon neutral continent by 2050. This commitment places a particular responsibility on the transport sector, which accounts for a quarter of the EU’s total greenhouse gas emissions. While a combination of measures will have to be considered, this panel explores the role of modal shift in placing the transport sector on a firm path to sustainable and smart mobility. The EU’s modal shift strategy sets out that rail freight traffic should increase by 50% by 2030 and double by 2050, whereas transport by inland waterways and short sea shipping should increase by 25% by 2030 and by 50% by 2050. To advance the delivery of these objectives, the Commission has declared 2021 as the European Year of Rail. What measures should be implemented to better manage and increase the capacity of railways and inland waterways? While COVID-19 has brought to light more prominently the higher safety and reliability of rail freight, which in turn, has provided efficient cross-border cargo connections carrying large volumes of essential goods using minimal human resources, how can we sustain this improved performance into the post-COVID-19 period? Can the COVID-19 aftermath be transformed into an opportunity for railway undertakings to tap into unused potential and develop more rail passenger services, especially in cross-border contexts? What are the technical and regulatory barriers, as well as the possible solutions and legislative opportunities to turn the EU’s modal shift objectives into reality?

Date provided here are based on data our application uses. Specifically, aggregated reports of cumulative cases across US states and territories from the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) public GitHub repository, and state and territory population sizes based on publically avialable US Census Bureau data (2010 – 2019). See the associated mansuscript and/or our application's public GitHub repository (https://github.com/Rush-Quality-Analytics/SupplyDemand) for greater details. To recreate Figure 1 of the main manuscript, run the following scripts in a python environment or suitable terminal window with python 3.6+ installed: 1. ModelFxns_Figs.py -- A script to generate the general predicted forms of each model; associated with Figure 1 of the associated manuscript. Generates the Model_Forms.png file located in the figures folder. 2. ModelPerformance_Figs.py -- A script to generate results figures for each model; associated with Figure 1 of the associated manuscript. Generates the Model_Performance.png file located in the figures folder. Included data files 1. model_results_dataframe.pkl -- A python-specific pickle file located in the data folder and which contains results for each model's predictive accuracy across time and US states and terroritories. Used by ModelPerformance_Figs.py. 2. COVID-CASES-DF.txt -- A file located in the data folder and which contains data downloaded and curated from JHU CSSE. 3. StatePops.csv -- A file located in the data folder and which contains data on US State and territory population sizes as well as reported dates of COVID-19 arrival (gathered from state/territory public websites). Additional python scripts 1. results_dataframe.py -- A script to regenerate the model_results_dataframe.pkl file. Warning: This script may take several days to run because of the many iterations needed for the SEIR-SD model. Running this script will overwrite the existing model_results_dataframe.pkl file, so take necessary precautions. 2. model_fxns.py -- A script containing functions for running models used by our application. This script is used by results_dataframe.py to generatth the model_results_dataframe.pkl file. We developed an application (https://rush-covid19.herokuapp.com/) to aid US hospitals in planning their response to the ongoing COVID-19 pandemic. Our application forecasts hospital visits, admits, discharges, and needs for hospital beds, ventilators, and personal protective equipment by coupling COVID-19 predictions to models of time lags, patient carry-over, and length-of-stay. Users can choose from seven COVID-19 models, customize a large set of parameters, examine trends in testing and hospitalization, and download forecast data. The data and scripts contained herein are used to generate Figure 1 of the associated manuscript, which presents general forms of the models used by our application and presents results for each model across time. 1. The model_results_dataframe.pkl file is a python specific file format. 2. Running the ModelFxns_Figs.py and ModelPerformance_Figs.py is all that is needed to recreate the subplots of figure 1. The user should have the following libraries/softwares installed: Python 3.6 or greater numpy 1.16 or greater pandas 0.24 or greater

This dataset contains the data collected for the assessment of the quality attributes of a new online educational platform. The questionnaire used for data collection the Kano methodology and was designed as a hybrid Kano-importance questionnaire. The purpose of this data collection consists of the analysis of the students’ expectations regarding the features proposed for a new online educational platform. This analysis facilitates the identification of student needs during times of COVID-19 pandemic and post-pandemic times, while a transition to an online educational system was used throughout the world. The corresponding Google form has been attached as a .pdf file. The form and the collected data contain information in Romanian and English. The dataset contains the translation into English of the data collected in Romanian.

Published on April 9, 2020 Fiscally sound governments will be able to pump money into their companies unhindered by state aid rules. The EU needs an equity fund to level things up. All great economic crises pose two equally important challenges: they drain the liquidity necessary for the functioning of businesses, large and small, and burn up their equity capital, or a substantial part of it. Of the two, the former is the immediate challenge amid the coronavirus-induced lockdowns. Providing liquidity to companies is the top priority to ensure their survival. Yet this doesn’t guarantee their healing, or their ultimate durability and growth. Equity capital, the stuff that’s needed to invest and thrive, is essential to the second stage of recovery.

The aim of the article is to study both the magnitude and structure of internal labour reserves in the Baltic countries as well as to discuss potential policy measures that might help to activate these reserves. despite the record-high employment rates recently posted by Estonia, Latvia and Lithuania, considerable internal labour reserves can still be found in some population groups. Among upper-middle-aged men, low employment might reflect a low incidence of lifelong learning, inadequate digital skills and rapidly deteriorating health condition. Low employment of youth mirrors the low prevalence of apprenticeships. in Lithuania and Latvia, there is also a postponed entry of young women into the labour market. These internal labour reserves total more than 25,000 people in Estonia, 55,000 in Latvia and 85,000 in Lithuania, corresponding to 4–7% of the total employment. The recent outbreak of the Covid-19 pandemic may somewhat increase and change the structure of these labour reserves.

Autophagy is an intracellular catabolic process that controls infections both directly and indirectly via its multifaceted effects on the innate and adaptive immune responses. It has been reported that LPS stimulates this cellular process, whereas the effect of IL-36α on autophagy remains largely unknown. We, therefore, investigated how IL-36α modulates the endogenous and LPS-induced autophagy in THP-1 cells. The levels of LC3B-II and autophagic flux were determined by western blotting. The intracellular localization of LC3B was measured by immunofluorescence assay. The activation levels of signaling pathways implicated in autophagy regulation were evaluated by using a phosphokinase array. Our results showed that combined IL-36α and LPS treatment cooperatively increased the levels of LC3B-II and Beclin-1, stimulated the autophagic flux, facilitated intracellular redistribution of LC3B, and increased the average number of autophagosomes per cell. The IL36α/LPS combined treatment increased phosphorylation of STAT5a/b, had minimal effect on the Akt/PRAS40/mTOR pathway, and reduced the levels of phospho-Yes, phospho-FAK, and phospho-WNK1. Thus, this cytokine/PAMP combination triggers pro-autophagic biased signaling by several mechanisms and thus cooperatively stimulates the autophagic cascade. An increased autophagic activity of innate immune cells simultaneously exposed to IL-36α and LPS may play an important role in the pathogenesis of Gram-negative bacterial infections. SARS-CoV-2 can infect and replicate in esophageal cells and enterocytes, leading to direct damage to the intestinal epithelium. The infection decreases the level of angiotensin converting enzyme 2 receptors, thereby altering the composition of the gut microbiota. SARS-CoV-2 elicits a cytokine storm, which contributes to gastrointestinal inflammation. The direct cytopathic effects of SARS-CoV-2, gut dysbiosis, and aberrant immune response result in increased intestinal permeability, which may exacerbate existing symptoms and worsen the prognosis. By exploring the elements of pathogenesis, several therapeutic options have emerged for the treatment of COVID-19 patients, such as biologics and biotherapeutic agents. However, the presence of SARS-CoV-2 in the feces may facilitate the spread of COVID-19 through fecal-oral transmission and contaminate the environment. Thus, gastrointestinal SARS-CoV-2 infection has important epidemiological significance. The development of new therapeutic and preventive options is necessary to treat and restrict the spread of this severe and widespread infection more effectively.

The world as we have always known it ceased to exist on the eve of the outbreak of COVID-19 pandemic, what propelled us into a drastic shift regarding both in our private professional lives. This gave rise to one of the most powerful technological tools which saw the light in the earlier 70s but had not been widely being used up till now: “Telecommuting”. Telecommuting is revolutionizing our world in the safest, quickest, and brightest way in every aspects of our lives. So, not only our way of life has changed for the best, but with it came the ugly harsh reality. As we already accustomed to real life, whereby in every society on one end there are always been those people well intentioned who abide by the law hence try to make the society a better place, and on the other end the outlaws who would not stop at anything to make the world go upside down for solely their own selfish interests, so do we have in the new ultra-modern “digital world”. The main difference between the “old normal” and the “new normal” is that criminals are getting harder to get caught as tracking them required well-qualified teams: “cybersecurity” and an incommensurable amount of time to get them down. In this thesis, we will first go through a brief definition of telecommuting, its advantages and inconveniences, what effects COVID-19 had and is still having on the financial, economic structures of the world and where our new normalcy is projecting us to in the nearest future. At the end, we will focus on the dangers usually encountered online and how to decrease those at-tacks intensities with the ever reliable and unfailing support of the cybersecurity teams. Maailma, jonka olemme aina tunteneet lakkasi olemasta COVID-19 pandemian puhkeamisen aattona, mikä pakotti meidät radikaaliin muutokseen sekä yksityis- että työelämässä. Sen suora seuraus on yhden tehokkaimpien teknisten välineiden syntyminen, joka näki valon 70-luvun alussa, mutta vasta nyt aletaan ottaa käyttöön maailmanlaajuisesti: “Etätyö”. Etätyö mullistaa meidän maailmamme ei vaan turvallisimmalla ja nopeimmalla mutta kirkkaimmalla tavalla kaikilta osin, jopa pienemmiltäkin osin. Meidän elämäntapamme on muuttunut parhaaksi mutta sen mukana tuli ruma, ankara todellisuus: “virtuaalielämän sivuvaikutukset” josta tullaan myös puhumaan tässä työssä. Kuten olemme jo tottuneet todellisessa elämässä, jokaisessa yhteyskunnassa on aina hyviä aikomuksia omaavia ihmisiä, jotka noudattavat lakia ja haluavat parantaa maailmaa tekemällä yhteiskunnasta paremman paikan elää. Samasta yhteiskunnasta löydetään myös ns. lainvastaiset, jotka pelkästään, ajavat omaa itsekästä etuaan. Sama tilanne vallitsee myös “digitaalisessa maailmassa”. Tärkein ja merkittävin “vanhan normaalin” ja “uuden normaalin” välinen ero on se, että rikollisten kiinnijäämisen aste (asteikolla: 0–10) on vanhassa normaalissa 5 ja uudessa normaalissa 10. Tämän vuoksi rikollisten jäljittäminen vaatii erittäin päteviä asiantuntijoita: “kyberturvallisuus tiimit” sekä lisäksi huomattavan määrän aikaa rikollisten kiinni saamiseen. Tässä opinnäytetyössä, käydään ensin läpi lyhyt määritelmä etätyöstä, sen eduista ja haitoista, siitä, mitä vaikutuksia COVID-19 oli ja on edelleen aiheuttamassa maailman taloudellisiin rakenteisiin ja mihin meidän uusi normaaliutemme heijastaa meitä lähitulevaisuudessa. Lopuksi keskitytään virtuaalimaailman tuomiin vaaroihin sekä suosituksiin millä keinoilla pystytään lisäämään digitaalisen maailman turvallisuutta kyberturvallisuustiimien avulla.

The majority of emerging infectious diseases originate in animals. Current routine surveillance is focused on known diseases and clinical syndromes, but the increasing likelihood of emerging disease outbreaks shows the critical importance of early detection of unusual illness or circulation of pathogens - prior to human disease manifestation. In this Viewpoint, we focus on one key pillar of preparedness—the need for early warning surveillance at the human, animal, environmental interface. The COVID-19 pandemic has revolutionized the scale of sequencing of pathogen genomes, and the current investments in global genomic surveillance offer great potential for a novel, truly integrated Disease X (with epidemic or pandemic potential) surveillance arm provided we do not make the mistake of developing them solely for the case at hand. Generic tools include metagenomic sequencing as a catch-all technique, rather than detection and sequencing protocols focusing on what we know. Developing agnostic or more targeted metagenomic sequencing to assess unusual disease in humans and animals, combined with random sampling of environmental samples capturing pathogen circulation is technically challenging, but could provide a true early warning system. Rather than rebuilding and reinforcing the pre-existing silo's, a real step forward would be to take the lessons learned and bring in novel essential partnerships in a One Health approach to preparedness.