Our linear mixed-effects models required that for each individual the slope of the line of [18F]FDG data in the outcome and the slope of the peripheral blood transcript expression data correlate, i.e., the slopes of the outcome and explanatory variables had to be similar. Of 10,295 genes that changed as a function of time, we identified 639 genes whose expression profiles correlated with decreasing [18F]FDG uptake levels in the lungs. Gene enrichment over-representation analysis revealed that numerous biological processes were significantly enriched in the 639 genes, including several well known in TB transcriptomics such as platelet degranulation and response to interferon gamma, thus validating our novel approach. Others not previously associated with TB pathobiology included smooth muscle contraction, a set of pathways related to mitochondrial function and cell death, as well as a set of pathways connecting transcription, translation and vesicle formation. We observed up-regulation in genes associated with B cells, and down-regulation in genes associated with platelet activation. We found 254 transcription factor binding sites to be enriched among the 639 gene promoters. In conclusion, we demonstrated that of the 10,295 gene expression changes in peripheral blood, only a subset of 639 genes correlated with inflammation in the lungs, and the enriched pathways provide a description of the biology of resolution of lung inflammation as detectable in peripheral blood. Surprisingly, resolution of PTB inflammation is positively correlated with smooth muscle contraction and, extending our previous observation on mitochondrial genes, shows the presence of mitochondrial stress. We focused on pathway analysis which can enable therapeutic target discovery and potential modulation of the host response to TB.Pulmonary tuberculosis (PTB) is characterized by lung granulomas, inflammation and tissue destruction. Here we used within-subject peripheral blood gene expression over time to correlate with the within-subject lung metabolic activity, as measured by positron emission tomography (PET) to identify biological processes and pathways underlying overall resolution of lung inflammation. We used next-generation RNA sequencing and [ Mycobacterium tuberculosis and transmitted via inhalation of air droplets expelled by a person with active TB. The primary site of M. tuberculosis infection is the lung, which leads to pulmonary TB (PTB).Tuberculosis (TB) is among the leading causes of mortality due to infectious diseases worldwide, with 1.5 million deaths recorded in 2018  is a common PET tracer, and its accumulation in tissues of the body indicates enhanced glucose metabolism (Positron emission tomography-computed tomography (PET-CT) is a medical imaging method, in which PET provides functional and CT structural information –10. 18F-tabolism –17, whictabolism  that is tabolism , 21 whil18F]FDG uptake levels. Most of these studies focused primarily on biomarker discovery by analyzing group differences between timepoints in peripheral blood  data available from study subjects   . Read pan 2.3.1d , and htson 0.6.0  was used package  in R  as an ordinal value; and j is the subscript for subject (n=75). Model 1 was used to extract the slope of each gene in each subject, while Model 2 constrains the expression levels of each gene to correlate with MSLA levels, over time, in each subject , such that the expression levels of the genes are correlated with PET levels, over time. Traditional differential gene expression analysis does not account for intra- and inter-subject variation.In the equations,  subject , 64, and package  in R veron 3.6.1 , 66, 67.on 3.6.1 , with th+ αβ T cells, CD14+ monocytes and neutrophils and modeled these using a leave-one-out approach.Adjusting for cell type proportion entails adding variables to the model and incurs a penalty in terms of the degrees of freedom. Increasing the model complexity can result in false negatives due to this penalty. Correction for a single cell type alone leaves the result poorly interpretable, since significance can be driven by changes in cell proportion in any of the remaining cell types. We therefore selected a set of 4 cell types, i.e., naïve B cells, CD8We generated results for 5 different analyses based on Model 2:ijC omitted)2.1) Base, a model without correction for cell type  B cell, a model correcting for CD8+ monocytes, and neutrophils2.3) CD8 T cell, a model correcting for naïve B cells, CD14+ αβ T cells, and neutrophils2.4) Monocyte, a model correcting for naïve B cells, CD8+ αβ T cells, and CD14+ monocytes.2.5) Neutrophil, a model correcting for naïve B cells, CD8SetRank package in R  for the set  gene set enrichment analysis (GSEA)  using thage in R , 71 and age in R  for the  package  in R wit package  and (c) bGestalt . For ORA the set . SetRankytoscape .SetRank output includes two rounds of correction with the Holm procedure. Since this second correction is dependent on the collection of pathways identified as enriched after applying the first correction, it is specific to the results for a given model. We therefore present the results for the comparison of models using only the p-values adjusted with the first correction.We merged the networks from all 5 models (2.1 to 2.5) into a single network to create a master layout of all enriched pathways. The network graph of each model was then represented using the master layout to generate per-model graphs with identical positioning of pathways for ease of comparison. Analyses controlled for false discovery using either the Benjamini and Hochberg  or Holm ReMap to obtain the co-ordinates of the TF binding sites (TFBS). For each gene, we then extracted the TF binding site start (TFStart) and end (TFEnd), chromosome, TF, track, location, strand, gene transcription start site (GeneTSS), and symbol for a promoter of 4,000 bases upstream of the transcription start site of the target genes from human genome GRCh38.p13, using in-house scripts. To estimate over-representation of the TFs in PETGenes, we first extracted the TFBS and their genomic features from the list of PETGenes. Next, we calculated the counts of gene promoters containing the TFs. The counts are for gene promoters that have at least one TFBS for the TFs. Finally, we computed the TF over- or under- representation for a set of TFs comparing the frequencies in the query set (PETGenes) to that of the remaining genes in the reference (Human Genome), using Fisher’s exact test and an FDR < 0.05  from ReMap, which includes transcription factors, transcriptional co-activator and chromatin regulators , generatR < 0.05 .We used the induced network module analysis in ConsensusPathDB , 79 to bTo identify biological processes underlying the resolution of lung inflammation, during PTB treatment, we first identified genes with significant changes in expression, over time, using Model 1. We refer to these as TimeGenes. Biological processes enriched in these genes reflect changes in transcript levels, over time, in response to PTB treatment. Next, using different forms of Model 2 (Models 2.1 to 2.5), we identified genes with expression levels significantly correlated with the changes in the PET metric MSLA, in patients who were considered cured at the end of the 6-month PTB treatment. Using GSEA, we could identify the biological processes and pathways associated with these genes that undergo changes in transcript levels in peripheral blood, and which correlated with inflammation in the lungs during PTB treatment.+ monocytes, CD16+ monocytes, CD4+ αβ T cells, CD8+ αβ T cells, CD56bright NK cells, naïve B cells and neutrophils. The results from the ANOVA are shown in + αβ T cells, CD14+ monocytes and neutrophils, showed changes in proportions over time in a manner that suggested that they could influence the statistical model.We filtered cell proportions to remove cell types that did not reach a mean proportion > 0.05 at any time point. Seven cell types remained, namely CD14As a baseline analysis with which to compare our constrained analysis, we applied a simple model that identified transcripts that change over time. We identified 11,229 transcripts  with significant changes in expression levels, over time, using Model 1 Table S118F]FDG uptake levels as represented by MSLA, over time, during PTB treatment  genes between TimeGenes and PETGenes Table S4SetRank with the Reactome pathways  Table S5CD19, VPREB3, CD72, CD22, FCRLA, CD79B, CD79A, HLA-DOB, P2RX5, FAM129C, FCRL5, PCDH9, BCL11A, PNOC, and BLNK. The expression profiles of PETGenes in Cluster 3, suggest up-regulation in B cells during treatment response, whereas Cluster 1, suggests down-regulation in platelet activation during treatment response. Curiously, there were no pathways overrepresented in Cluster 2  with the treatment-specific signature from Bloom et al.  320 micTable S6n et al. , 29 genen et al.  , as well as their target genes  and CALD1, GUCY1A1, GUCY1B1, ITGB5, MYL9, TPM1, TPM4, VCL. To these we added the genes whose nominal p-value contributed to the GSEA enrichment score: ACTA2, ANXA6, DYSF, MEF2A, MEF2C, MYH11, MYL12B, MYL6, MYLK, SORBS1, SORBS3, TLN1, TPM2, TPM3, as well as the transcription factors: CREB1, CREBBP, GATA1, GATA2, GATA4, GATA6, SRF, TEAD1, TEAD4. All but SORBS1, SORBS3, TPM2, and VCL decreased expression from Dx to W24. The network demonstrates that a highly interconnected cluster of contraction proteins connected to another interconnected cluster of transcription factors  leading to generation of second messengers”, and “RUNX1 regulates transcription of genes involved in BCR signaling,” whose function is restricted to B cells Table 4 + αβ cells). There are also several pathways identified here as potentially relevant to T cells that have not previously been identified. These include some of the phospholipid metabolism pathways and the PIP pathways.Eleven pathways, all consistent with different aspects of lipid and phospholipid metabolism are enriched in the model 2.3 “CD8 T cell.” The pathways are: synthesis of phosphatidic acid (PA); synthesis of dolichyl phosphate; fatty acid metabolism; synthesis of phosphatidylethanolamine (PE); beta oxidation of very long chain fatty acids; triglyceride metabolism; phospholipid metabolism; synthesis of phosphatidylinositol phosphates (PIPs) at the early endosome membrane; synthesis of PIPs at the late endosome membrane; PI metabolism; and triglyceride biosynthesis. Lipid metabolism has been established as important to the function of T cells , again s+ monocytes and neutrophils no pathway or collection of pathways provided support in the same way as for naïve B cells and CD8+ αβ cells. The pathways identified are generally consistent with inflammatory processes, but not characteristic of the cell types.For CD142+; Smooth muscle contraction; G alpha I signaling events; Platelet activation signaling and aggregation; Cell surface interactions at the vascular wall; rRNA modification in the nucleus and cytosol; PD-1 signaling; and Interferon signaling  and mitochondrial morphogenesis (TMEM11), but also includes many genes for mitochondrial complex V, the ATP synthase complex , which are all downregulated during treatment. Downregulation of these genes is consistent with the requirement of extensive oxidative phosphorylation during the peak of inflammation. Cells can increase oxidative phosphorylation by mitochondrial division or by increasing the density of cristae in mitochondria, which facilitates a higher density of oxidative phosphorylation complexes and enables mitochondria to be more efficient  identified subtle significant changes in transcript levels that are ignored in traditional gene expression analyses and provide robust results for downstream differential expression analysis and clustering.Previous studies on TB treatment response , 28–30 hurements , 33, 34.Our study had some limitations. We used a linear-mixed effect model to analyze gene expression levels, which only considers linear correlation or relationship of gene expression with time. The linear-mixed effect model accounts only for the linear characteristics of measurements, ignoring other dynamics or patterns of gene expression and PET measurements which may be characteristic of TB. Additionally, transcriptomics only reveals changes in regulation of RNA levels which do not necessarily correspond to functional changes in proteins; nevertheless, it does provide insight into the regulatory responses, especially in longitudinal analyses. Lastly, the models correcting for cell proportions rely on computational deconvolution and not on differential counts, and the choice of which cell types to include in the models is difficult. Including too many variables (too many cell proportions) can weaken the inferential power of the models, and including cell types with little effect reduces power without any benefit. For future studies, we suggest using mixed-effect models with splines or other non-linear models with more time points. Another limitation is that PET-CT measures only glucose metabolism and not oxidative phosphorylation, nor does it provide a measure of bacterial burden; a direct measure of TB load would be ideal.18F]FDG uptake in the lungs, is positively correlated with down-regulation of genes enriched in “platelet activation”, “interferon and interleukin signaling”, and negatively correlated with up-regulation of genes enriched in “B cell activation” as well as many other pathways consistent with prior literature. These results validate our overall approach. In addition, we have shown that correcting for major cell type proportions using a leave-one-out approach allows identification of processes consistent with the remaining cell type. Lastly, we have identified “smooth muscle contraction” and pathways related to mitochondrial stress and dysfunction as highly enriched pathways. The extent of coordinated smooth muscle contraction gene expression suggests that the signal is derived from non-leukocyte origins, such as SPCs. The results obtained from our comprehensive pathway analyses provide important new insight into the pathobiology of TB. In future studies they could contribute to therapeutic target discovery and potential modulation of the host response to TB.In summary we have demonstrated that the resolution of inflammation in the lungs during TB treatment, measured with changes in PET-CT [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89403.Publicly available datasets were analyzed in this study. This data can be found here: Approval was obtained from the Health Research Ethics Committee (HREC) of Stellenbosch University (registration number N10/01/013), to recruit patients and collect specimens. For the current study to re-analyze the PET-CT and mRNA expression data, we received a separate HREC approval (X18/09/029). The patients/participants provided their written informed consent to participate in this study.André G. Loxton, Department of Science and Innovation/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research and South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Annare Ellman, Division of Nuclear Medicine, Department of Medical Imaging and Clinical Oncology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Bronwyn Smith, Department of Science and Innovation/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research and South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Caroline G. G. Beltran, Department of Science and Innovation/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research and South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Clifton E. Barry III, Department of Science and Innovation/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research and South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa, Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States, Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Science, University of Cape Town, Observatory 7925, South Africa; David Alland, Center for Emerging Pathogens, Department of Medicine, Rutgers-New Jersey Medical School, Rutgers Biomedical and Health Sciences, Newark, NJ, United States; Friedrich Thienemann, Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Science, University of Cape Town, Observatory 7925, South Africa, Department of Medicine, Groote Schuur Hospital, Faculty of Health Science, University of Cape Town, Cape Town, South Africa; Gerard Tromp, Department of Science and Innovation/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research and South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Gerhard Walzl, Department of Science and Innovation/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research and South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; James M. Warwick, Division of Nuclear Medicine, Department of Medical Imaging and Clinical Oncology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Jill Winter, Catalysis Foundation for Health, San Ramon, CA, United States; Katharina Ronacher, Department of Science and Innovation/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research and South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Kim Stanley, Department of Science and Innovation/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research and South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Ilse Kant, Division of Nuclear Medicine, Department of Medical Imaging and Clinical Oncology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Lani Thiart, Department of Science and Innovation/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research and South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Lance A. Lucas, Department of Science and Innovation/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research and South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Laura E. Via, Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States, Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Science, University of Cape Town, South Africa; Lori E. Dodd, Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health Bethesda, Maryland, United States; Magdalena Kriel, Department of Science and Innovation/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research and South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Nelita Du Plessis, Department of Science and Innovation/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research and South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Patrick Dupont, Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Belgium, Division of Nuclear Medicine, Department of Medical Imaging and Clinical Oncology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Ray Y. Chen, Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States; Robert J. Wilkinson, Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Science, University of Cape Town, South Africa, Department of Medicine, Groote Schuur Hospital, Faculty of Health Science, University of Cape Town, Cape Town, South Africa, The Francis Crick Institute, London, United Kingdom, Department of Medicine, Imperial College London, United Kingdom; Shubhada Shenai, Center for Emerging Pathogens, Department of Medicine, Rutgers-New Jersey Medical School, Rutgers Biomedical and Health Sciences, Newark, NJ, United States; Stephanie Griffith-Richards, Division of Radiodiagnosis, Department of Medical Imaging and Clinical Oncology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Stephanus T. Malherbe, Department of Science and Innovation/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research and South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.TO and GT carried out the computational analyses and drafted the manuscript. GW designed the original study, obtained funding for it and oversaw the study. GT designed and supervised the current bioinformatic analysis reported here. SMa interpreted PET-CT and provided quantitative data on PET-CT scans as well as clinical findings. DZ, ET, and FD contributed to the NGS analysis. SMe provided expertise and additional supervision on computational analyses. EM, NP, AL, LK, KR, and GW provided expert immunological and clinical knowledge on interpreting the results. HK and JW interpreted results and participated in drafting the manuscript. All authors contributed to the article and approved the submitted version.TO, SM, GW, and GT were supported by the South African Tuberculosis Bioinformatics Initiative (SATBBI), a Strategic Health Innovation Partnership grant from the South African Medical Research Council and South African Department of Science and Technology. SMa received funding from the EDCTP2 program supported by the European Union (grant number CDF1576). GW received funding from the South African National Research Foundation (SARChI TB Biomarkers #86535) and the South African Medical Research Council. AL is supported by the NRF-CSUR (Grant Number CSUR60502163639) and by the Centre for Tuberculosis Research from the South African Medical Research Council (SAMRC). HK was supported by the Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa. The Catalysis Biomarker Consortium was funded by the Catalysis Foundation for Health, the Division of Intramural Research, National Institute of Allergy and Infectious Diseases, and the National Institute of Allergy and Infectious Diseases, International Collaborations in Infectious Disease Research.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In the development of smart cities, there is a great emphasis on setting up so-called Smart City Control Rooms, SCCR. This paper presents Snap4City as a big data smart city platform to support the city decision makers by means of SCCR dashboards and tools reporting in real time the status of several of a city’s aspects. The solution has been adopted in European cities such as Antwerp, Florence, Lonato del Garda, Pisa, Santiago, etc., and it is capable of covering extended geographical areas around the cities themselves: Belgium, Finland, Tuscany, Sardinia, etc. In this paper, a major use case is analyzed describing the workflow followed, the methodologies adopted and the SCCR as the starting point to reproduce the same results in other smart cities, industries, research centers, etc. A Living Lab working modality is promoted and organized to enhance the collaboration among municipalities and public administration, stakeholders, research centers and the citizens themselves. The Snap4City platform has been realized respecting the European Data Protection Regulation (GDPR), and it is capable of processing every day a multitude of periodic and real-time data coming from different providers and data sources. It is therefore able to semantically aggregate the data, in compliance with the Km4City multi-ontology and manage data: (i) having different access policies; and (ii) coming from traditional sources such as Open Data Portals, Web services, APIs and IoT/IoE networks. The aggregated data are the starting point for the services offered not only to the citizens but also to the public administrations and public-security service managers, enabling them to view a set of city dashboards ad hoc composed on their needs, for example, enabling them to modify and monitor public transportation strategies, offering the public services actually needed by citizens and tourists, monitor the air quality and traffic status to establish, if impose or not, traffic restrictions, etc. All the data and the new knowledge produced by the data analytics of the Snap4City platform can also be accessed, observing the permissions on each kind of data, thanks to the presence of an APIs complex system. This paper is organized as follows: Sect. In most of the modern smart cities, there is a great emphasis on setting up the so-called Smart City Control Room (SCCR) that is an area in which all the data are collected and aggregated and where high-level data/results are summarized and made accessible for decision makers and city operators. In large metropolitan cities, the SCCR includes large panels/monitors on which the status of the city is displayed in real-time presenting predictions and alerts regarding: mobility, energy, social activities, the environment, weather, public transportation, people flow, health, water, security, ICT, governmental, first aid, civil protection, police, fire brigade, hospital triage, and thus almost all the city resources expressed via key performance indicators (KPI). Some of the city’s monitored resources are representative of critical infrastructures such as: mobility and transport, energy, security, health, water, civil protection, governance, ICT, etc. In medium-sized cities, the daily management of city resources is performed by a set of city operators, which could be legally independent with respect to the central municipality and should autonomously manage their SSCR  are instruments for the development and implementation of technology to accelerate innovation in cities  any kind of data source/collector, protocol and format from/to: open data, real-time data, GIS, IoT data, data stream, data driven, social media, Industry 4.0, etc.; (ii) IoT devices and networks, IoT edges, gateways, IoT brokers, via various protocols and formats; (iii) Web-scraping processes; (iv) dashboards collecting actions from the users via: buttons, dimers, time selectors, and showing them; (v) mobile applications collecting and presenting; (vi) results of data analytics, including personal data produced by users . They can be put in execution IoT edge, as well as on Cloud, and are based on Node-RED. IoT app also enables to perform business logic , smart interaction and data transformation behind the Snap4City dashboards, integrating ticketing, video wall dynamic re-configuration, etc. Microservices may also manage processes for data analytics, Web scraping, external services, data gathering and publication, etc. , formats , and protocols in push and/or pull, etc. This is the reason why a smart city big data platform, such as Snap4City, must be flexible and capable to ingest every various different kinds of data see Fig. .Fig. 3SnMobility: public transport operators schedule and paths, traffic-flow sensors, parking status, cycling paths, road graph, accidents and traffic events, ordinances, car-park occupancy prediction, what-if routing, quality of public transportation services, etc. The services produced, starting from those data are: KPI assessing quality of public transportation; routing and multimodal routing; real-time traffic-flow reconstruction ; real-time status and predictions for parking, smart parking;Environment: (i) irrigators; (ii) smart waste; (iii) air quality sensors: PM10, PM2.5, CO, benzene, NO, NO2, O3, temperature, humidity, etc.; (iv) air quality heat maps for pollutants at point (iii); and (iv) pollination. As derived data, we can have NOX predictions ; air quality heat maps updated every hour, real-time status and trends related to air quality sensors and pollination, NOX predictions for the next 48 h, with a resolution of 4 × 4 m and at two levels of height (at 3 and 6 m); and long-term predictions for pollutants’ critical days;Energy: (i) recharging stations (fast and regular); (ii) consumption meters (smart info); (iii) smart light; monitoring consumption via smart meters, smart lights status and trends;Weather: forecast and actual ;Social: (i) smart benches; (ii) entertainment events; (iii) Twitter monitoring via Twitter vigilance (https://www.disit.org/tv), sentiment analysis, NLP text; (iv) TV-camera streams; and (v) triage status of major hospitals; also for early warning.People Flows: (i) Wi-Fi status; (ii) origin-destination matrices, people-flow analysis  from the Wi-Fi network of access points;Governmental and Communications: (i) KPI of the city, including COVID-19 data; (ii) digital signage (not directly included into the solution for Florence); (iii) civil protection, resilience guidelines and suggestions, KPIs (RESOLUTE project);Tourism and Culture: Points of interests (POI), cultural events, etc.;Analysis: (i) what-if routing; (ii) scenarios; (iii) traffic flow; and (iv) environmental predictions.Video streams: (not directly included into the Snap4City solution for Florence).Once the data have been collected and integrated, the historical data and real-time data can be exploited by data analytics processes, IoT apps, dashboards and apps. The original data and results of the data analysis can be accessed for business intelligence tools and SCCR. The most relevant data available in the Florence SCCR, are described next and classified based on their context:Starting from these kinds of data, reworking them  and thanks to the APIs presence, a set of services is available in the Florence SCCR. The series of dashboards designed to provide the services just described, a mobile app was created and called ‘Florence where what…’ (see next section) and are capable to send suggestions and recommendations based on location and the preferences expressed by citizens and tourists.manage the data collection and integration, data analytics, prediction indicators , but also to carry out simulations and comparing them with the hypotheses on events, realized before the simulation and without the Snap4city tools.Activate and run data-analytic algorithms that can produce systematic or, if necessary, real-time forecasts, identifications of anomalies, and the ability to communicate them to operators and from an early warning and study perspective. Therefore, they are able to generate reports even in advance.Visualize the state of the city and its evolution and critical aspects for the different operators , allowing also some remote operators in their offices to access some summary information, prediction, service status, etc.Enable the carrying out, directly on the dashboards, the necessary in-depth analysis with drill-down techniques (time space and for reports), with: What-if simulations on problems and solutions, routing algorithms, predictive models and in-depth analysis tools. On these, it must be possible to open discussions/chats with other operators, even remote ones , and to bring the attention of all operators to support decisions.Manage events and reports that may arrive from various operators: mobility, transport, waste, energy, social media, highways, public transport, etc., in various standards and through various communication channels. Managing in these cases means: coordinating possible joint actions between several operators, acting, following their evolution, and keeping track of events, until their conclusion/resolution, to take them into account for the next actions.A SSCR is a solution and structure in which all data and indicators of the city are collected and aggregated to produce summary visions, indicators, forecasts, precursors, anomaly indicators, etc. The results are produced through big data analytics to support decision makers and operators, so that they can quickly understand the situation in full and act, also through simulations for scenarios with what-if conditions. When the city grows, its systems become more complex. Moreover, some cities are morphologically complex during their histories, morphogenesis and structures, and it becomes important to:Main dashboard, ‘Firenze Oggi’ (Florence Today), contains a set of KPIs monitored in real time: a number of users connected to the public Wi-Fi, civil-protection messages, weather forecasting, recharging stations statuses, state of road maintenance and accidents, public transport lines; but also statistical data such as: census numbers  and analyses of: traffic, pollution and what-if analysis. It has links to the following dashboards.Energy: position on the city map, real-time status and historical trends of: fast and normal recharging stations, ZTL gates, Wi-Fi, smart irrigators, smart lights, statistics on residential smart energy meters. KPIs: monthly cumulative energy consumption, average weekly consumption of each fast recharging station, number of users connected to the mobile app, eEnergy consumption via mobile app, accumulator status (used), etc.Environment: position on the city map, real-time status and historical trends of: air quality stations and low-cost sensors (PM10), weather sensors, pollen monitoring, Florence weather for today and for the next two days , temperature, waste sensors, etc.Mobility: traffic events in Florence, traffic daily inflow/outflow trend, total number of inflow/outflow vehicles, car-park statuses and trends, etc.Social: Twitter sentiment trends in the city of Florence, natural-language processing and sentiment analysis on tweets , people flow.Resilience: civil-protection messages in real time, hospital first aid, evacuation paths, etc.The SCCR is a decision-support tool that is able to provide evidence of the arrival/occurrence of critical conditions in real time and/or precursors of such conditions and offer solutions . Following these guidelines, the Florence SCCR has been realized with a series of ad hoc dashboards connected each other:Moreover, other additional views can be opened. In Fig. The Florence SCCR development started on 2017 with the first dashboard, and since that time, the number of data has been increased with corresponding views and dashboards of various kinds. Most of the data are public and accessible on dashboards from the Web site of Snap4City.org, while a number of dashboards are private to the municipalities. In Fig. The attention on general aspects, mobility and environment are a confirmation of the effective usage and need of an instrument such as an SCCR for monitoring the city. In summary, a total of more than 6000 accesses have been made and 340,000 min have been spent on the major dashboards for the major areas in the year 2019, which is an average of 57 min per access. On the other hand, there are dashboards that are typically accessed 24H/7D, and those that are only sporadically used.Smart City Control Rooms, SCCRs, are getting strong attention at this time for their capability of reporting in real time the status of several city aspects, thus creating a strong and effective tool for decision makers. This paper presented Snap4city as a Big Data Smart City Platform to support the city decision makers by means of a SCCR. The solution is adopted in different degrees of diffusion in European cities such as Antwerp, Florence, Lonato, Pisa, Santiago de Compostela, etc. In this paper, the Florence SCCR has been analyzed as a major use case describing the data, the services and the workflow. For data aggregation, the Km4City multi-ontology and tools have been used to collect data from GIS, utilities, open data, IoT networks, external services, social media, etc. The first Florence SCCR dashboard appeared in 2017 as a prototype, and, since that time, a great evolution has been occurred and the amount and type of data have increased. Most of the data are public and accessible on dashboards from the Web site of Snap4City.org, while a number of dashboards are private to the municipality. The results presented have shown the data regarding the last year of high usage of the platform. During this period, a particular attention by the users, on general overview aspects, mobility and the environment have been registered. In summary, a total of more than 6000 accesses have been made and 340,000 min have been spent on the major dashboards for those major areas in the year 2019, which is an average of 57 min per access.


In “Documenting Social Media Engagement as Scholarship: A New Model for Assessing Academic Accomplishment for the Health Professions” :e25070), one of the authors was not mentioned in the original paper.The following author has been added:Alice Eaton, BscSwansea University Medical School, Swansea, United KingdomOne of the contributors was missed in the Acknowledgement section. Previously, the Acknowledgement section read as follows: All authors met the ICMJE authorship criteria, including reviewing and approving this manuscript for publication. Each of the 40 coauthors completed an Authorship Attestation Form confirming that they met all 4 of the ICMJE authorship criteria. In addition, each coauthor described on a shared Google Sheet how they made “substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data.” The authors wish to thank Jocelyn C Anderson, PhD, RN, S Alexander Kemery, PhD, RN, and Chrystal L Lewis, PhD, RN for their comments on the draft of this manuscript.The corrected Acknowledgement section now reads as follows: All authors met the ICMJE authorship criteria, including reviewing and approving this manuscript for publication. Each of the 40 coauthors completed an Authorship Attestation Form confirming that they met all 4 of the ICMJE authorship criteria. In addition, each coauthor described on a shared Google Sheet how they made “substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data.” The authors wish to thank Jocelyn C Anderson, PhD, RN, S Alexander Kemery, PhD, RN, Rebecca Koszalinski PhD, RN, CRRN, CMSRN, and Chrystal L Lewis, PhD, RN for their comments on the draft of this manuscript.The affiliation for authors Brendan Huang and Ajay Major was incorrect. They were displayed as:Brendan HuangDepartment of Clinical Neurosciences, Tulane University, New Orleans, LA, United States Ajay MajorUniversity of Chicago, Section of Hematology and Oncology, Chicago, IL, United StatesThey have been updated to:Brendan HuangDepartment of Neurology and Neurosurgery, Tulane University, New Orleans, LA, United StatesAjay MajorSection of Hematology and Oncology, University of Chicago, Chicago, IL, United StatesThe correction will appear in the online version of the paper on the JMIR Publications website on December 9, 2020, together with the publication of this correction notice. Because this was made after submission to PubMed, PubMed Central, and other full-text repositories, the corrected article has also been resubmitted to those repositories.


In “Diagnostic Accuracy of Chest Computed Tomography Scans for Suspected Patients With COVID-19: Receiver Operating Characteristic Curve Analysis” :e19424), the authors noted one error.The affiliation for authors Lianpin Wu, Qike Jin, and Jiawei He was incorrectly listed as:Department of Cardiology, Wenzhou Medical University, Wenzhou, ChinaThe affiliation for these authors has been corrected to:Department of Cardiology, the Second Affiliated Hospital & Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, ChinaThe correction will appear in the online version of the paper on the JMIR Publications website on November 20, 2020, together with the publication of this correction notice. Because this was made after submission to PubMed, PubMed Central, and other full-text repositories, the corrected article has also been resubmitted to those repositories.


Author Contributions statement appears below.An author name was incorrectly spelled as “Michael von Bergwelt.” The correct spelling is “Michael von Bergwelt-Baildon”. The updated “MT, SYY, and CB developed the conception and design of the study. MT, SYY, TP, AB, and HG discussed the cases in interdisciplinary panels. MT acquired patient data. MT and SYY organized the database, performed all analyses, and wrote the first draft of the manuscript. SYY, TP, and AB acquired the imaging data. TP, HG, MS, ST, MB-B, JM, JH, EC, SM, and CB contributed to the manuscript. All authors contributed to the revision and read and approved the submitted version.”The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.


Scientific Reports 10.1038/s41598-019-57087-2, published online 14 January 2020Correction to: The original version of this Article contained a typographical error in the spelling of the author Jooho Lee, which was incorrectly given as Jooho lee.In addition, the original version of this Article contained an error in Affiliation 6, which was incorrectly given as ‘Department of Chemistry, Chonbuk National University, Jeonju, 54896, Republic of Korea’. The correct affiliation is listed below:Department of Chemistry, Jeonbuk National University, Jeonju, 54896, Republic of Korea.These errors have now been corrected in the PDF and HTML versions of the Article, and in the accompanying Supplementary Information file.


In the last 3 months, nearly a third of the world's population has changed their lifestyle. At this time of writing (June 30th) the COVID-19 pandemic has left a total of 10.302.867 confirmed cases, with more than 505.518 deaths worldwide, spreading to more than 188 countries  and Sociedade Brasileira de Urologia (SBU) performs this special edition, which aims to provide a screenshot impact of the COVID-19 Pandemic on Urology, within each different area of development It's contains a total of 27 manuscripts performed by expert urologists from France, Italy, Spain, Iran, Germany, Argentina, Uruguay, Brazil, Mexico, Peru, Bolivia, Panama, the USA. The information that it contains, is reported until May 8th 2020.Finally, I would like to thank each colleague participating in this project, for the effort and valuable academic contribution, hoping that this crisis we are going through will allow us to grow as people and professionals. “Every crisis has a solution and a learning process, this only depends on us”


While there is an academic dialogue around what the field entails, developing from its roots of hygiene & tropical medicine and international health, there is still room for enhanced understanding and refined scope . This isth Canadian Conference on Global Health (CCGH), convened virtually from October 19 to 22, 2020 by the Canadian Society for International Health, was a key venue for further examining the different facets of global health. Featuring renowned speakers from numerous sectors, such as Drs. Jane Goodall, Anthony Fauci, and Tedros Adhanom Ghebreyesus, discussions ranged greatly from the humanitarian response to COVID-19 to changing politics. The conference allowed for a reimagined understanding of the field and paved the way to a broader understanding that included three emerging prospects: emphasizing the importance of policy and governance, encouraging engagement of participants from the global south, and elevating the voice of women.The recent 26he present global health crisis is not primarily one of disease, but of governance: its key characteristic is a weakening of public policy and interstate mechanisms as a consequence of global restructuring” th CCGH delivered virtually afforded opportunities for over a thousand participants from around the world to attend; drawing participation from: Afghanistan, Algeria, Argentina, Bangladesh, Belgium, Benin, Botswana, Brazil, Burkina Faso, Cameroon, Canada, Colombia, Costa Rica, Democratic Republic of the Congo, Denmark, Dominican Republic, France, Gambia, Georgia, Germany, Ghana, Guinea, Ethiopia, Egypt, Haiti, Honduras, Hungary, India, Indonesia, Iraq, Kazakhstan, Kenya, Libya, Madagascar, Malaysia, Malawi, Mali, Mexico, Morocco, Mozambique, Myanmar, Nepal, Netherlands, Nigeria, Pakistan, People’s Republic of China, Peru, Philippines, Portugal, Rwanda, Saint Lucia, Senegal, Spain, Somalia, South Africa, South Sudan, Sri Lanka, Switzerland, Turkey, United Republic of Tanzania, Uganda, United Kingdom of Great Britain and Northern Ireland, United States of America, Viet Nam, Zambia, and Zimbabwe. With more diverse voices and perspectives from around the world, global health practice can be improved. This is particularly the case with different cultures which may have different understandings and perspectives, which draw upon different traditions of social justice [The 26 justice . For exa justice . Given t justice .The BMJ Gender Diversity Group that women were underrepresented in decision-making around COVID-19 [th CCGH unveiled the 2020 Canadian Women in Global Health list, “showcasing the incredible achievements of Canadian women leaders in global health, fostering new and exciting collaborations, and advocating for gender equity in the health workforce and leadership more broadly” [Similarly, with 49.6% of the global population identifying as female in 2017 , which dCOVID-19 . The 26tbroadly” . For insbroadly” . The lisOverall, I must commend the CCGH and its organizers for outlining three emerging prospects for which global health can be reconsidered. With enhanced attention paid to global health, we can reform our approaches, through emphasizing the importance of policy and governance, encouraging engagement of participants from the global south, and elevating the voice of women, to ultimately enhance public health to protect populations and improve public health.


Many studies find gender differences in how older adults’ report on their memory, perform on cognitive testing, and manage functional impairments that can accompany cognitive impairment. Thus, understanding gender’s effects in aging and Alzheimer’s research is key for advancing methods to prevent, slow, manage, and diagnosis cognitive impairment. Our study, CoGenT3 – The study of Cognition and Gender in Three Generations – seeks to disambiguate the effects of gender on cognition in order to inform a conceptual model, guide innovations in measurement, and support future study. To accomplish this ambitious goal, we have gathered an interdisciplinary team with expertise in psychology, cognition, sexual and gender minorities, library science, measurement, quantitative methods, qualitative methods, and gender and women’s studies. The team benefits from the intersections of expertise in being able to build new research ideas, gain novel insights, and evaluate a wide-range of actions and re-actions but this novelty can also raise challenges.


This research note aimed to analyze the scientific productivity trends 2015–2019, focusing on the top 30 universities in Europe and United States and on the top 30 private companies—as classified in the SCImago Institutions Ranking. Our hypothesis is that private companies are gaining an increasingly prominent role in the research field, while academia is losing its predominance.From 2015 to 2019, all universities in Europe and the United States lost positions in the scientific production ranking, while private companies gained positions. These trends seem to be driven mainly by the scientific productivity sub-indicator “Innovation”. These data suggest that the role private companies will play in the future will not be limited to support research economically or influence it from “outside”. Private companies have taken a path that may lead them to directly control all stages of production/communication of knowledge, including research—a role once bestowed on universities. Our data, although preliminary, seem to suggest that, at present, academia risks losing its predominance in the research field. This scenario deserves attention because of the threats it may pose to the independence of research and its role in supporting human equity and sustainable health for all. Scientific research has always had a fundamental role in promoting health and the progress of society as a whole. A recent editorial published in The Lancet  has discHowever, we think that also an alternative—more pessimistic—scenario, emerging from an analysis of recent trends in scientific productivity, deserves attention because of the threats it may pose to the independence of research and its role in supporting human equity and sustainable health for all.In support of this hypothesis, we will (1) provide an analysis of the scientific productivity trends from 2015 to 2019, focusing on the top 30 universities in Europe and the United States and on the top 30 private companies (2) detail the methodology used and its limits, and (3) discuss the implications of our hypothesis and future addresses.We have analyzed the scientific productivity trends from 2015 to 2019. First, we used the SCImago institutions ranking . InnovatWe calculated the average position (± standard deviation) in the ranking for scientific productivity by group  for each year from 2015 to 2019. We then calculated the changes of positions in the ranking for scientific productivity by group from 2015 to 2019.To see if these changes of positions in the ranking for scientific productivity were significantly different between universities in Europe and universities in the United States vs. private companies, we performed a multivariate Kruskal–Wallis test –7. All sIn Europe, the top 30 universities were: Oxford, University College London, University of Cambridge, Imperial College London, Swiss Federal Institute of Technology, Catholic University of Leuven, University of Copenhagen, Utrecht University, University of Amsterdam, Kings College London, University of Manchester, University of Edinburgh, Karolinska Institute, Ecole Polytechnique Federale de Lausanne, Sorbonne Universite, Ghent University, Technische Universitat Munchen, University of Groningen, Leiden University, VU University Amsterdam, Università degli Studi di Roma La Sapienza, Aarhus University, Ludwig-Maximilians Universitat Munchen, Erasmus University Rotterdam, Lunds University, Ecole Pratique des Hautes Etudes, University of Helsinki, Universitat Zurich, Uppsala University, and Technical University of Denmark. In the United States, the top 30 universities were: Harvard University, Harvard Medical School, Massachusetts Institute of Technology, Stanford University, Johns Hopkins University, University of Michigan Ann Arbor, University of Washington, University of Pennsylvania, University of California Los Angeles, Columbia University, University of California San Diego, University of California Berkeley, Cornell University, University of California San Francisco, Yale University, Duke University, University of Wisconsin Madison, University of Minnesota Twin Cities, Northwestern University Evanston, University of North Carolina Chapel Hill, University of Maryland Baltimore, New York University, University of Pittsburgh, Ohio State University Columbus, University of California Davis, University of Southern California, University of Chicago, Pennsylvania State University, University of Florida, and Washington University in Saint Louis. The top 30 private companies operating in Europe and in the US or at the global level were: Google, Facebook Inc., Microsoft Corp, Microsoft USA, Samsung Corp, Regeneron Pharmaceuticals Inc, Hoffmann-La Roche Ltd. United States, Hoffmann-La Roche, IBM United States, Genentech Inc., IBM Research, Alphabet Inc, Novartis Institutes for Biomedical Research United States, Microsoft Research Cambridge, MedImmune LLC., Toyota Group, Nokia Corp, Nokia Finland, GlaxoSmithKline United States, Qualcomm Inc USA, Qualcomm Inc, Sanofi United States, Biogen Idec USA, NEC Corp, Biogen Idec, Novartis, GlaxoSmithKline, Panasonic Corp, LG Corporation, and Pfizer Inc. United States.From 2015 to 2019, all the universities in these groups lost positions in the scientific production ranking. In Europe, universities lost on average − 9.8 ± 74.2 positions, while in the United States − 5.5 ± 21. On the contrary, private companies gained on average + 129.9 ± 143.9 positions.These trends seem to be driven mainly by one of the scientific productivity sub-indicators, “Innovation”. Indeed, if we analyze this specific sub-indicator, the trends are even more marked. Universities lost − 166.6 ± 60.6 positions on average in Europe, and − 114.7 ± 51.5 positions in the United States; while private companies gained + 116.5 ± 113.7 positions.p value < 0.0001).On average, the European universities had higher positions than private companies in the ranking in 2015 . The scientific productivity trend from 2015 to 2019 was significantly different between these two groups  and in 2019 (46.8 ± 27.5 vs. 107.9 ± 51). However, again, the scientific productivity trend was significantly different from 2015 to 2019 in these two groups .Our data, although preliminary, seem to suggest that, at present, academia risks losing its central role in the research field, while private companies are gaining prominence.It is interesting that, among private companies, sectors historically linked to research  have lost power in terms of scientific productivity. In contrast, companies specialized in internet-related services, technology, and data analytics have acquired increasing importance in the research field. Alarmingly, some of these companies, at present in the top ranking for scientific productivity, have recently been accused of using the information they gather from individuals indiscriminately and amorally for the purposes of profit-making and, even worst, social control , 10.The trends above analyzed suggest that the role private companies will play in the future will not be limited to support research economically and/or influence it from “outside”. Indeed, companies specialized in internet-related services, technology, and data analytics have taken a path that may easily—or already has—lead them to directly control all stages of production and communication of knowledge, including research—a role once bestowed on universities. This finding is in contradiction with the results of a recent study by Fleming et al. , which sAlthough caution is needed given the preliminary nature of our data, the trends analyzed seem to suggest that, at present, academia is losing predominance in the research field. The vision matured in the nineteenth-century of universities (and science) as expression of free thought, guarantee for rights, and ability to dialogue with power to further human development and social justice is apparently going through a historic crisis.If the trends analyzed will be confirmed, the pessimistic vision of the Dialectic of Enlightenment  may becoThe present research note discusses the findings of an analysis of trends in scientific productivity. This is a preliminary work using a simple methodology.The results of our analysis need to be confirmed using a bigger sample size and data from other geographical areas or specific research fields.Future research should examine all the scientific productivity sub-indicators: not only “Innovation” but also “Number of published articles and citations” and “Social media visibility”.Furthermore, future studies should investigate how the cooperation between universities and private companies may influence scientific productivity and, in turn, the ranking.Finally, it would be critical to have data from sources other than SCImago. Currently, SCImago is the only dataset grouping data by research institution, while similar databanks  do not provide this kind of information.


A community has many different opportunities to improve the traffic safety. The community is local part of the public sector. On all decision made has the community big impacts on questions within its own territory. Urban planning is an important tool. Urban planning is normally a rather complex system and important decisions are made in a long term perspective. It is very important for all who work for traffic safety to learn the system in order to have an impact. The big pallet a community has embrace also many other tools than urban planning.1. Reduce the need of transportation: Urban planning, public transportation, road toll, forbid use of cars in some areas, one-way roads, reducing parking places 2. Separation of areas for activities: Special lines for pedestrians, bikes, cars and public transportation 3. Safety in the road system: Roundabout, fens, avoid U-turns, street lightning, traffic light, sleeping policemen4. Maintaining of the infrastructure: No digging technics, cleaning streets from snow fist for pedestrians5. Supervision: Speed control, immediate feed-back to offenders. Camera supervision, Control of parking, Alco control, Control the use of helmets, belts etc., Control of vehicles 6. Local regulations: Speed limits, one way roads, parking regulations, no access for cars and motorbikes to some areas, the high of hedges, restrictions for traffic during some time during the day7. Education and Information: Special parks for education of children, education and information in preschools and schools, education for old citizens8. Support to different groups: Baby and child seats for rent, helmets for rent, reflectors, bus-service for elderly9. Volunteers: Follow children to and from school, give advice to all in traffic, help the police in their supervision10. Support to local sport-clubs and other associations11. Restaurants and alcohol beverages: Restrictions to serve drivers alcohol, education to serving personnel, home driving service12. Agreements with employees and tenders: Use of safety helmets, belts etc., intelligent speed adaptation (ISA), always give priority to public transportation or bike, use only safe cars with alcohol-interlockCity Planning, Governing, Safe Community


Acanthobothrium have been documented as parasites of the spiral intestine of elasmobranchs. Results of a metadata analysis indicate that 114 species of elasmobranchs have been reported as hosts of 200 species of Acanthobothrium. The metadata analysis revealed that 3.7% of species of sharks and 14.9% of species of rays that have been reported as hosts to date; some species are parasitized by more than one species of Acanthobothrium. This work provides a Category designation, as proposed by Acanthobothrium. These Category designations are a tool to facilitate comparisons among members of Acanthobothrium for descriptions of new species in the future.Species of  According to Acanthobothrium Blanchard, 1848  is the most diverse genus that has been reported as parasite of the spiral intestine of elasmobranchs (Caira and Jensen 2017). At the present time, 201 species of Acanthobothrium are considered to be valid  are host to a great variety of parasites in nature, particularly helminths. be valid , 2020. TAcanthobothrium and their geographic distribution based on records that have been generated from different parts of the world. The checklist focuses on the 201 valid species of Acanthobothrium and reports correlated with the genera and species of elasmobranchs, and includes the geographical distribution of each.The main goal of this work is to provide a revised checklist based on a metadata analysis of the host relationships of members of Acanthobothrium continues to grow and there are still regions of the world without a single report of this genus  Ocean;WCA Western Central Atlantic;WCP Western Central Pacific;WIO Western Indian Ocean;WNA Western North Atlantic;WNP Western North Pacific;WSA Western South Atlantic;WSP Western South Pacific.Acanthobothrium presented herein includes the name of the species, authority , abbreviation of the name of the collection where specimens are deposited and the accession numbers of the specimens, followed by the status of the specimens . The acronym “NR” was used for data that are not reported in the original source. Localities  were given and referenced in the literature cited. A Category designation was supplied for all species using the categorical method proposed by Information for each species of Acanthobothrium based on the combination of four qualitative characters: the total length of worms- ≤ 15 mm = S (short) or > 15 mm = L (long); the number of proglottids comprising the strobila- ≤ 50 = F (few) or > 50 = M (many); the number of testes per proglottid- ≤ 80 = F (few) or > 80 = M (many); and symmetry of the ovarian lobes- symmetrical = S or asymmetrical = A. Of the possible combinations the following 10 categories currently are recognized and coded as follows: 1 = SFFS; 2 = SFFA; 3 = LMMA; 4 = LMMS; 5 = LMFS; 6 = LMFA; 7 = LFFA; 8 = SMFS; 9 = LFFS; 10 = SMMS. This method limited the number of necessary comparisons required in the description between known species with new species assigned to the same Category. For this work, the categories and characteristics were used as in The categorical method was developed as a system of grouping species of For specimens deposited in a formal collection, acronyms are as follows:AMSAustralian Museum, Sydney;CH-MHNJP Colecciones Helmintológicas del Museo de Historia Natural “Javier Prado” y del Instituto de Medicina Tropical “Daniel. A. Carrión”, Universidad Mayor de San Marcos, Perú;CHE Colección de Helmintos, Centro de Investigaciones Biológicas, Universidad Autónoma del Estado de Hidalgo, Pachuca, México;CHIOCColeção Helmintológica do Instituto Oswaldo Cruz, Rio de Janeiro, Brazil;CNHE Colección Nacional de Helmintos del Instituto de Biología, Universidad Nacional Autónoma de México, México;DMNZ Dominion Musem , New Zealand;DZAUW Department of Zoology, Andhra University, Waltair, India;DZCJ Department of Zoology, Bipin Bihari, P. G. College, Jhansi, India;HWMLUniversity of Nebraska State Museum, Harold W. Manter Laboratory, Division of Parasitology, Lincoln, Nebraska, United States;IPCAS Institute of Parasitology, Academy of Sciences of the Czech Republic, České Budějovice, Czech Republic;IPMB Institut Penyelidikan Marin Borneo (Borneo Marine Research Institute), Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia;LRP Lawrence R. Penner Parasitology Collection, Helminthological Collection, University of Connecticut, Storrs, Connecticut, United States;MACN-PaMuseo Argentino de Ciencias Naturales, Colección Parasitológica, Buenos Aires, Argentina;MEPN Museum of the Escuela Politecnica Nacional, Quito, Ecuador;MHNLSMuseo de Historia Natural La Salle, Caracas, Venezuela;MHNP Museo de Historia Natural, Lima, Peru;MLP Museo de Ciencias Naturales de La Plata, Departamento de Zoología Invertebrados (Parasitología), Argentina;MNHG Museum of Natural History, Geneva, Switzerland;MNHN Muséum National d’Histoire Naturelle, Paris;MNHNCMuseo Nacional de Historia Natural de Chile;MPM Meguro Parasitology Museum, Tokyo, Japan;MZUM (P) Muzium Zoologi, Universiti Malaya, Kuala Lumpur, Malaysia;MZUSPMuseu de Zoologia da Universidade de São Paulo, Brazil;NHMUKThe Natural History Museum, London;NMNS National Museum of Natural Science, Taichung, Taiwan;PRLXUParasitology Research Laboratory, Xiamen University, China;QMQueensland Museum, Brisbane, Queensland, Australia;SAM AHCSouth Australian Museum, Adelaide, Australia;SBC Sarawak Biodiversity Center, Kuching, Sarawak, Malaysia;SPUKSchool of Parasitology, Department of Zoology, University of Karachi, Pakistan;SYSU School of Life Sciences, Sun Yat-sen University;UAA Department of Zoology, University of Allahabad, Allahabad, India;USNPCUnited States National Parasite Collection, Beltsville, Maryland, United States;ZCUOK Zoological Collection, University of Kurdistan, Sanandaj, Iran;ZIMC Collection of the Zoological Survey of India, Indian Museum, Calcutta and the Collection of the Department of Zoology, the University of Allahabad, India;ZMB Natural History Museum Berlin, Germany;ZUTCCollection of the Zoological Museum, University of Tehran, Tehran, Iran.Acanthobothrium. The list includes the type host of each species, type locality, and additional hosts and/or localities. Five of the elasmobranchs that were reported as hosts of Acanthobothrium were only identified to genus and four others are reported as “cf.” (= similar to) , Heterodontidae (five species) and Triakidae (six species)  and Dasyatidae (70 species)  Fig. . In contium Fig. . Of the es) Fig. .Acanthobothrium are not evenly grouped in the different categories. In Category 1 there are 55 species, 44 in Category 2, 19 in Category 3, 37 in Category 4, 17 in Category 5, 14 in Category 6, four in Category 7, four in Category 8, and three in Category 9. Although there is a Category 10, species in that category also are in grouped with those in Category 8 because their characteristics are thought to fall into both categories (Table Acanthobothrium were classified as unknown (“?”) because the original descriptions do not have sufficient information for assignment in one of the ten categories  have been reported as hosts for species of ium Fig. . In contium Fig. . Estimatreported . PublicaECA, ESA, WIO, ARC, and SOC. According to the percentages of species of elasmobranchs that have been reported as hosts of species of Acanthobothrium, we can infer that there are still many new species of Acanthobothrium to be discovered. In the GenBank database records, molecular sequences of only 16 of the 201 species of Acanthobothrium have been reported. However, more molecular information about species of Acanthobothrium is required for future analyzes, both for identification and life cycle studies; these would provide more solid information for delimiting distributions.The information in the Figures Acanthobothriumchilensis Rêgo, Vicente & Herrera, 1968, was included for reference, although it was described from a fish, Sardachiliensis  (Perciformes: Scombridae)  see . Extensidae) see  failed tAcanthobothrium appear to exhibit oioxenous specificity for their elasmobranch hosts. In the present metadata analysis, for species exclusively in elasmobranchs, 83% of the species of Acanthobothrium show remarkable host specificity for their definitive host, and thus, should be considered to be an oioxenous species. In contrast, 34 of the 200 species (17%) of Acanthobothrium have been reported in more than one species of elasmobranch (Table Acanthobothrium, 45 of the type specimens of Acanthobothrium require confirmation of the host (Table Acanthobothrium that suggest misidentification of the parasites; these should reevaluated in future studies. To mention obvious cases, A.batailloni has been reported from the Mediterranean Sea and from the Pacific coast of Peru and Chile and A.brevissime has been reported from the Gulf of Mexico and the Pacific coast of Peru.According to  species 7% of Aca species 7% of AcaAcanthobothrium, 13 have been classified in more than one category (see category designations in Table The categorical method developed by Acanthobothrium that has been assigned to more than one category suggests that the categories still need some refining, or it is an example of cryptic species that cannot be distinguished without molecular information. However, molecular information cannot replace morphological descriptions of species. One reason is the lack of material for sequencing of the vast majority of already-known species. Morphology also augments molecular data in studies of the phylogeny of platyhelminths (Acanthobothrium. This updated database includes the category designation for each species described to date will be an important tool for the future taxonomic studies.Having more information, such as molecular sequences, could solve some problems in identification, such as the two cases mentioned above. A species of elminths . A compl


In “Twelve-Month Follow-Up to a Fully Automated Internet-Based Cognitive Behavior Therapy Intervention for Rural Adults With Depression Symptoms: Single-Arm Longitudinal Study” :e21336) the authors noted one error.The affiliation for authors Mark Schure and Kathryn Tuchscherer Franklin was incorrectly listed as:Department of Mechanical & Industrial Engineering, Montana State University, Bozeman, MT, United StatesThe correct affiliation for these authors is:Department of Health & Human Development, Montana State University, Bozeman, MT, United StatesThis affiliation is affiliation 1 in the corrected manuscript. Accordingly, affiliations 1 and 2 in the originally published manuscript have been renumbered to affiliations 2 and 3 in the corrected manuscript.The Corresponding Author address for Mark Schure has also been corrected from:Mark Schure, BSc, MSc, PhDDepartment of Mechanical & Industrial EngineeringMontana State UniversityBozeman, MTUnited Statesto:Mark Schure, BSc, MSc, PhDDepartment of Health & Human DevelopmentMontana State University305 Herrick HallBozeman, MT, 59717United StatesThe correction will appear in the online version of the paper on the JMIR Publications website on October 23, 2020, together with the publication of this correction notice. Because this was made after submission to PubMed, PubMed Central, and other full-text repositories, the corrected article has also been resubmitted to those repositories.


In this paper, we consider the trust degree of a jammer, defined as the probability that the jammer cooperates to secure the legitimate transmission, and investigate its influence on confidential cooperative communication. According to the trust degree, we derive the closed-form optimal transmit signal-to-noise ratio (SNR) of the confidential message,  With the rapid growth of communications between socially-connected users, not only physical parameters, e.g., channel qualities and traffic demands, but also the social relationship among users, such as kinship, friendship, and acquaintance, have been taken into account as key design parameters for efficient cooperative communications ,2,3,4,5.In the literature, the trust degree has triggered plenty of research interest in various fields, including content caching, mobile social communications, and especially cooperative communications ,2,3,4,5.Due to the broadcasting nature of wireless mediums, physical (PHY)-layer security has also drawn significant attention in communication networks ,9,10,11.Motivated by the aforementioned research and problems, we have observed that the trust degree plays a significant role in cooperative secure communications. Hence, in this paper, we consider the probability that the jammer cooperates in secure legitimate transmission as its trust degree and investigate its influence on the performance of confidential cooperative communications. Different from , we not Notation: For a complex scalar x, its complex conjugate is denoted by With the rapid growth of online social networks, more and more people are getting involved in online social interactions. Therefore, the social relationship has emerged as an important issue to investigate how the degree of closeness of the social relationship between users affects their communication strategies ,16,17,18In the literature, the trust degree has been evaluated and quantified in various ways ,21,22,23Consider a user cooperation network as shown in In this paper, we consider that Direct transmission: (a) Cooperative transmission: (b) According to the above strategies, the expected secrecy rate with respect to (w.r.t.) trust degree given in , is obta used in ,24. Notiiven in [R¯se (In In shown in , which hIn this paper, we investigated the confidential cooperative communication according to the jammer’s trust degree and exploited its characteristics to design efficient cooperative strategies. To maximize the expected secrecy rate, we derived the closed-form optimal transmit SNR of the confidential message based on the trust degree and observed that the expected secrecy rate will become saturated along with the increasing


The segetal flora of winter crops includes mostly native or archaeophyte annual species that are often strong specialists of their habitats. Threatened by the intensification of agriculture, segetal flora is particularly valuable from a perspective of biodiversity conservation and evolution. Moreover, it contributes to maintain biodiversity in agroecosystems and provides several ecosystem services. The dataset here described was set up to provide the first inventory of the segetal flora of Italian winter cereal crops and allied crop types, the latter including flax and autumn-sown legumes. It includes 24,676 georeferenced occurrence data deriving from 1,240 floristic and phytosociological relevés. The data were collected from the greater part of Italian territory, in a temporal range spanning from 1946 to 2018. The concept of “weed” is very subjective, as any plant that interferes with human activities can be considered as such, implying the existence of agricultural weeds, environmental weeds, ruderal weeds and many others. Weeds of arable land are almost exclusively annual and are called “agrestals” or “segetals” . For decThe here presented dataset is available in GBIF  and inclIn the light of what is stated above, the main aims of the present paper are the description and presentation of this recently-released dataset, providing information on its usefulness and possible future applications.Plant biodiversity in traditional agroecosystems of Italy: a floristic and ecological multi-scale analysis based on geodatabases.RM118164361D0EE4 .Giovanna Abbate, Mauro Iberite, Marta Latini, Emanuele FanfarilloSapienza University of Rome, Piazzale Aldo Moro 5, 00185, Rome, Italy.The disappearance of traditional agroecosystems and the consequent biodiversity loss due to changes in agriculture are receiving increasing attention in Europe. The use of databases on plant taxonomical, distributional, ecological and functional traits is of crucial importance in conservation actions. The need to improve monitoring and reporting activities by improving the quality of biodiversity data is also underlined by the European Biodiversity Strategy. This project aimed at fulfilling a global analysis of the plant diversity existing in the traditional agroecosystems of Italy, knowledge of which is currently lacking, by means of the collection, digitisation and processing of original and archival data. The proposed actions concerned: the preparation of thematic databases on segetal flora and vegetation, including the features of plant species and communities; the analysis of data at different spatial and temporal scales; the production of thematic maps on plant diversity and its related topics; the development of new methods to estimate the nature value of agroecosystems; the detection of bio-indicator plant species for floristic richness, agricultural intensity and environmental quality. Special attention was given to winter arable plants and communities, currently at high risk of disappearance in Europe. The achieved results provided an important basis for any future research, with special regards to the definition of conservation strategies for plant diversity in European rural areas.The occurrence data were retrieved through extensive literature searches and intensive field samplings, the latter being carried out in the greater part of Italy to fill the knowledge gaps in some geographic areas. Literature data were selected using a habitat-based criterion: only the records for taxa unambiguously reported to grow in winter cereals, flax and autumn-sown legumes were collected. Consequently, all the records with no or with generic information on the growing habitat (e.g. “fields” or “cultivated land”) were excluded. Likewise, records of taxa identified to the genus or higher level, doubtful identifications, nomenclatural ambiguities and crop species were not considered. The bibliographic source of each record is available upon request to the authors.All the occurrence data were georeferenced. Geographic coordinates , geodetic datum and a value of uncertainty for coordinates were attributed to each single record. The geographic coordinates were manually attributed, based on the descriptions of the relevé location provided in the original source. If coordinates were already available, they were converted in WGS84 geodetic datum, when differently expressed. The uncertainty of geographic positions was estimated according to the 9-degree scale defined by The taxonomic nomenclature was updated according to the latest standards available for the Italian flora  and was published using GBIF Integrated Publishing Toolkit (IPT) publishing platform (https://cloud.gbif.org/eca). Once the data were imported in GBIF, the nomenclature was automatically referred to the GBIF Backbone Taxonomy  4.0 LicenseMetadata language: EnglishDate of metadata creation: 09-04-2020Hierarchy level: DatasetThe fields provided by the “Segetal flora of Italy” dataset are:occurrenceID, basisOfRecord, eventDate, scientificName, kingdom, taxonRank, decimalLatitude, decimalLongitude, geodeticDatum, coordinateUncertaintyInMetres, continent, country, countryCode, stateProvince, organismQuantity, organismQuantityType.Magnoliopsida , followed by Liliopsida  and Polypodiopsida . Though, on the basis of the most recent results summarised by the APG , Ranunculales (12%), Caryophyllales (11%) and Fabales (8%). Poales (14%) is the most represented order within Liliopsida. The records in the dataset belong to 53 families, 340 genera and 859 taxa. The five most represented families, genera and species are shown in Tables Within sterales 5%, RanunKingdom: PlantaePhylum: TracheophytaClass: Magnoliopsida, Liliopsida, Polypodiopsida.Order: Alismatales, Apiales, Asparagales, Asterales, Boraginales, Brassicales, Caryophyllales, Cucurbitales, Dipsacales, Equisetales, Ericales, Fabales, Gentianales, Geraniales, Lamiales, Liliales, Malpighiales, Malvales, Myrtales, Oxalidales, Piperales, Poales, Polypodiales, Ranunculales, Rosales, Salviniales, Santalales, Saxifragales, Solanales.Family: Amaranthaceae, Amaryllidaceae, Apiaceae, Araceae, Aristolochiaceae, Asparagaceae, Asteraceae, Boraginaceae, Brassicaceae, Campanulaceae, Caprifoliaceae, Caryophyllaceae, Convolvulaceae, Cucurbitaceae, Cyperaceae, Dennstaedtiaceae, Elatinaceae, Equisetaceae, Euphorbiaceae, Fabaceae, Gentianaceae, Geraniaceae, Heliotropiaceae, Hypericaceae, Iridaceae, Juncaceae, Lamiaceae, Liliaceae, Linaceae, Lythraceae, Malvaceae, Marsileaceae, Onagraceae, Orobanchaceae, Oxalidaceae, Papaveraceae, Plantaginaceae, Poaceae, Polygonaceae, Portulacaceae, Primulaceae, Ranunculaceae, Resedaceae, Rosaceae, Rubiaceae, Santalaceae, Saxifragaceae, Scrophulariaceae, Solanaceae, Thymelaeaceae, Urticaceae, Verbenaceae, Violaceae.35°23'60"N, 6°30'0"E; 47°12'0"N, 18°36'0"E). Regarding administrative regions, the highest numbers of records are from Sicily, Veneto, Lombardy and Latium (Table The species occurrences stored in the dataset were recorded from the greater part of the Italian territory (Fig. The dataset includes species occurrences recorded from 1946 to 2018 (Fig. As expected, a high seasonality characterises the dataset. Most of the occurrences were recorded in spring and early summer. The months of greatest occurrence of the investigated taxa are, respectively, June, May, July and April Fig. .The “Segetal flora of Italy” dataset was the basis for the definition of the first inventory of the segetal flora of Italian winter cereal crops and allied crop types . The lat


State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China.” Regarding to the numbering of the affiliations, the affiliation of Botany Department, Faculty of Science, Tanta University, Tanta, Egypt, instead of having affiliation #2, it should have the affiliation #3. For author Weimin Zhang, the affiliation should be “State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China” instead of “Botany Department, Faculty of Science, Tanta University, Tanta, Egypt.” For author Daochen Zhu, the second affiliation should be “State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China” instead of Botany Department, Faculty of Science, Tanta University, Tanta, Egypt. Regarding to the numbering of the affiliations, the affiliation of the State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China, instead of having affiliation #3, it should have the affiliation #2.In the published article, there were errors in affiliations of the authors Sameh S. Ali, Weimin Zhang and Daochen Zhu. For author Sameh S. Ali, the second affiliation should be “Botany Department, Faculty of Science, Tanta University, Tanta, Egypt” instead of “Also, in the article's citation, an author name was incorrectly presented as Morales GM. The correct spelling is Murillo Morales G.Figure 5, Additionally, Figures 1–5 were presented in a wrong order. E.coli BL21 (Laccase). Instead, it should be written as E.coli BL21 (Lacc). A correction has been made to abstract, last sentence:There was also an error in the abstract text. The name of the mutant bacterial strain was incorrectly written as E.coli BL21 (Lacc), under different initial pH conditions, demonstrated a promising potential to enlarge the spectrum of renewable products for biorefinery activities.”“Lignin biodegradation products from The authors apologize for these errors and state that they do not change the scientific conclusions of the article in any way. The original article has been updated.


This article describes acetylcholinesterase (AChE), an enzyme involved in parasympathetic neurotransmission, its activity, and how its inhibition can be pharmacologically useful for treating dementia, caused by Alzheimer’s disease, or as a warfare method due to the action of nerve agents. The chemical concepts related to the irreversible inhibition of AChE, its reactivation, and aging are discussed, along with a relationship to the current international legislation on chemical weapons. Over time, the use of incendiaries, darts, spears, and arrows impregnated with poisons from different organisms, animals, and plants, the lethality of substances used for warfare have been dramatically improved. Despite international agreements already in existence by the end of 19th century, these did not inhibit the large-scale use of CW during World War I (WWI) (1914–1918), when millions of casualties were provoked mainly by phosgene, chlorine, and, particularly, sulfur mustard (HD 1) . This co2 (GB), soman 3 (GD), and tabun 4 (GA) . These findings resulted in the weaponization of the first nerve agents, called also G-agents, which included sarin n 4 (GA) . Such CW5 and 6, 1, these compounds can be similarly regarded as “area denial weapons” [Later, during the 1950s, aiming to produce more effective pesticides, groups in the former Soviet Union, Great Britain, Sweden, and the United States developed other organophosphorus compounds which were more toxic and persistent (less volatile) than G-agents. These new chemicals were then named V-agents, which are phosphonothioates 5 and 6,  [4,12,13weapons” .2 and GA 4, along with sulfur mustard HD 1, against civilian and military targets during the Iran–Iraq war. The tragic events in the Kurdish city of Halabja show how abhorrent can be the use of these substances as warfare agents [2 was employed in the terrorist actions carried out by the sect Aum Shinrikyo, provoking casualties among civilians and first responders [Although they were not used in the World Wars, the international community could soon testify their potential as Weapons of Mass Destruction (WMD) when, in the 1980s, Saddam Hussein’s troops used chemical artillery containing G-agents, GB e agents ,17,18. Isponders ,21,22.Although many papers have dealt with chemical warfare over the years, the use of CW is currently a pressing subject. Recently, chemical warfare agents have made headlines due to their alleged deployment in the Civil War ongoing in Syria (2011–) and as means of murdering the North Korean national Kim Jun Nam at the Kuala Lumpur International Airport in 2017. A report from Malaysian authorities confirmed that VX was used by the perpetrators ,40,41,427–10) [In 2018, events that occurred in Salisbury and Amesbury, Great Britain, where a former Russian agent and other people were poisoned with an “unknown, high-purity grade military agent” with possible neurotoxic effects, prompted discussions about the so-called “Novichok” agents. These then elusive compounds are predicted to act similarly to known nerve agents, but with higher toxicity and different physicochemical properties, for example, being solid at room temperature. After discussions on the actual structures of the Novichok agents, the most important agreement on chemical disarmament, the Chemical Weapons Convention (CWC), has acknowledged four novel scaffolds in its “Annex on Chemicals”, which are to be entered into force in June 2020. 7–10) ,48,49,50From pharmacological and toxicological points of view, the biological targets of classical (G- and V-) and “Novichok” nerve agents are the cholinesterases, a class of enzymes involved in a myriad of biological processes, such as respiration, cognition, and drug metabolism. Cholinesterases are found in two isoforms, AChE (EC 3.1.1.7) and butyrylcholinesterase (BChE), EC 3.1.1.8. Compounds that can inhibit cholinesterases may impact the body positively or negatively and, therefore, be either explored as method of warfare or therapeutically ,65,66,67Herein, we review the inhibition of cholinesterases and their outcome and how these chemical reactions can be subject of interest in the treatment of neurodegenerative diseases, like Alzheimer’s disease (AD) or as method of warfare, which is forbidden by the CWC.After many international agreements have sought to forbid the use of toxic chemicals as a mode of warfare, the Chemical Weapons Convention (CWC) entered into force in 1997, paving the way for the creation of the Organization for the Prohibition of Chemical Weapons (OPCW), the international watchdog responsible for overseeing the implementation of the CWC. The headquarters are located in The Hague, The Netherlands. As of January 2020, there are 193 signatories or State Parties to the CWC. In accordance to OPCW website, “98% of the global population lives under the protection of the Convention” and “97% of the chemical weapons stockpiles declared by possessor States have been verifiably destroyed” ,68, the In the preamble of the CWC, the State Parties are called out to ensure “that the complete and effective prohibition of the development, production, acquisition, stockpiling, retention, transfer and use of chemical weapons, and their destruction, represent a necessary step towards the achievement of these common objectives” ,2,3.The current text of CWC has the “Annex on Chemicals” section. There, compounds considered as chemical warfare agents or precursors are listed, along with the toxins saxitoxin and ricin. Chemicals are divided in Schedules 1 to 3), accordingly to the possibility of peaceful application  to 3, ac. Schedul11 and chlorosoman 12, precursors to GB 2 and GD 3, respectively , a toxic chemical related to VX (5) is an example of Schedule 2A chemical. Diethyl methylphosphonate , is an example of Schedule 2B chemical, as it can be precursor for toxic nerve agents, but also flame-retardant materials , a15 and triethyl phosphite 16 . DEMP may be then chlorinated using thionyl chloride 18, a Schedule 3B chemical, yielding methylphosphonyl dichloride , another Schedule 2B chemical. MPDC is then reacted with a source of fluoride, generating methylphosphonyl difluoride , also a Schedule 2B chemical. Reaction of MPDF with isopropanol 21 in presence of a base, affords the toxic Schedule 1 chemical. There is comprehensive literature on the synthesis of organophosphorus compounds and researchers must read carefully the CWC, in the OPCW website (www.opcw.org) to learn more on the international legislation [Although synthetic relationships are not the criteria for the Schedule Chemicals, islation ,71,72,73Chemistry is ubiquitous, its principles and applications underlie innumerous biological processes, as is the case with cholinesterases, key enzymes in the metabolism of different species. This class of enzyme in superior animals is present in two different isoforms, as aforementioned, AChE and BChE. AChE is involved in the regulation of neurotransmission processes (parasympathetic neurotransmission), being important for (but not limited to) cognition and respiration, for example. BChE is found in plasma, being responsible for hydrolysis of different esters, property that has been studied to understand a series of metabolic events or for treatment of cocaine addiction, for example. Furthermore, it has been extensively studied due to its stoichiometric reaction with organophosphorus compounds, which indicates that it is not only a biomarker of exposure to these toxic chemicals but also a potential treatment for intoxication, acting as bioscavenger of nerve agents ,81,82,8322 and aldicarb 23 , into acetate 26 and choline 27 through a tetrahedral transition state 28 at the post-synaptic cleft, ending the action potential, in accordance with the proposal below  during ACh hydrolysis. Finally, the acyl pocket site (APS) is the point where the difference between AChE and BChE may be assessed, with AChE having a smaller APS than BChE, determining the size of the substrate to be hydrolyzed [A schematic representation of different sites of AChE is shown in drolyzed ,91,92,9325, the parasympathetic neurotransmitter, into choline 26 and acetate 27, which are reuptaken for de novo synthesis of the neurotransmitter. Therefore, AChE is a pharmaceutical target, as its inhibition provokes an increase of neurotransmitter concentration at the post-synaptic cleft, leading to exacerbated cholinergic response over the nerve structures that require parasympathetic stimulation, such as neuromuscular junctions. This outcome may be positive, as in the case of treatment of diseases where the transitory, reversible inhibition of AChE located in the central nervous system (CNS) delivers valuable response, e.g., AD, or negative, when toxic chemicals, such as nerve agents  or other suitable entity , an alcohol 31 is displaced , donepezil , and galantamine . The fourth compound is memantine , which affects other receptors  [Currently, there are only four drugs available to ameliorate the memory loss and other symptoms related to AD. Nonetheless, they can only act in a certain level of impairment and are unable to halt the progression of the pathology. Consequently, knowledge on the different mechanisms that can contribute to the onset of AD and discover of the early neurological, biochemical, and behavioral changes, useful for the development of a more efficient treatments. Three of these compounds act as AChE inhibitors, rivastigmine  ,126,127.4 receptor inhibitors and PROTAC (PROteolysis Targeting Chimeras) compounds, among other approaches [Rivastigmine is regarded to react with the serine residue of the ES present in AChE and BChE  which illustrates the clinical effects. Acute exposure may be fatal due to the respiratory failure resulting from the continuous stimulation of neuromuscular junctions at diaphragm, as described earlier in this paper [22 and aldicarb 23  that can be used as proof of exposure to nerve agents. Nevertheless, if the enzyme is not aged 44, treatment with fluoride ions enables a de novo synthesis of the organophosphorus compound 47. These compounds can be successfully detected by chromatographic and spectrometric techniques , an anticholinergic agent (to counteract the effects of the higher concentration of the neurotransmitter), and an anticonvulsant (to control seizures). Up to date, pyridinium oximes have been clinically used as AChE reactivators, pralidoxime ompounds  [4,182,1erase 56 ,192,193.59 as anticholinergic agent, and an anticonvulsant, such as diazepam 60  hinders effective treatment. Additionally, an efficient “universal antidote’ towards all cholinesterase inhibitors is not yet available . To tackCholinesterases are constitutive enzymes, essential to the correct performance of the parasympathetic nervous system (PNS). The chemistry of AChE inhibition and aging in the context of the contemporary issue of terrorism by chemical warfare is an attractive conduit for chemical education and for dual use, military and civilian research work. AChE has a pivotal role in memory and learning, therefore increased knowledge on AChE function may facilitate the development of further, more effective therapies towards neurodegenerative diseases, like AD. Besides the varied conceptual background in Chemistry that it demands, the subject is also multidisciplinary, since it is related to pharmacology, toxicology, medicinal chemistry, international politics, health, environmental and forensics sciences, as well ethical issues.


Spinning, what you will, in heeding that swarm of guidance, creatingyour own shell, then transforming, as you will, within that isolation,still seems like an almost unconvincing, almost unnecessary nuisance.You had chosen this situation, if it is fair to say there was a choice,when there was no viable alternative. Your cocoon can feel so safe,an illusion perhaps, but reality provides nothing less vulnerable.The walls are thin enough to allow you to breathe, and to vaguely hearor feel vibrations, even though their meaning cannot be known.Light penetrates, and darkness, too; the changes remain obscure.Ruminating on that former lifestyle, you can digest time thoroughly,like those last memorable green leaves of Springtime, then so succulent,and satisfying, but to what end you know not; not all cocoons survive.Time, space, being, identity, the interpreted past, the fancied futurecan all be consumed within your insatiable capsule. Chrysalis or cocoon,distinctions no longer matter; each benefits from a covering and distancing.Complacency or contentment allows a concentration on one’s onlycertainty, the presentness right now, in this cell-like confinement,because emergence would require several just preposterous miracles.


In the published article, there was an error regarding the affiliations for R. de Diego-Balaguer. As well as having affiliation 3, she should also have:4 Catalan Institute for Research and Advance Studies, Barcelona, Spain5 Cognition and Brain Plasticity Unit, IDIBELL, L'Hospitalet de Llobregat, Spain6 Institute of Neuroscience, University of Barcelona, Barcelona, SpainThe authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.