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Advancing new tools for infectious diseases

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Several infectious diseases cause considerable mortality worldwide each year: Tuberculosis causes ∼1.2 million deaths, diarrheal disease causes ∼1.5 million deaths, and lower respiratory infections cause ∼700,000 deaths in children under 5 years old ([ 1 ][1]). Yet the scale and speed of innovation in developing tools for coronavirus disease 2019 (COVID-19) dwarf the development of those for global infectious diseases, which disproportionally affect resource-limited countries. By August 2020, ∼175 therapeutics and vaccines were in clinical trials for COVID-19 ([ 2 ][2]). By contrast, for 41 global infectious diseases or disease groups, only ∼250 therapeutics and vaccines were in clinical trials in August 2019 ([ 3 ][3]). A robust product pipeline and abridged development time frame for COVID-19 has primarily been enabled by three factors: scientific advances, operational efficiencies, and large-scale at-risk financing. A clear, well-financed path from research through product procurement now exists for COVID-19, shortening timelines while increasing output. This could underpin an approach for global infectious diseases. Recent scientific advances have revolutionized platform technologies and expanded the ability to rapidly identify therapeutic and vaccine candidates. High-throughput computational screening of molecular libraries against key pathogens and/or host targets has accelerated the ability to repurpose agents and identify entities against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, which causes COVID-19) ([ 4 ][4]). Candidate compounds with existing clinical safety data quickly entered clinical trials, leading to the repurposing of dexamethasone and remdesivir to treat hospitalized COVID-19 patients. Monoclonal antibodies (mAbs) can potentially provide near-immediate therapy and/or prophylaxis by bypassing the need for a host-generated immune response ([ 5 ][5]), and at lower costs and higher volumes than previously assumed. Vaccines have benefited from innovations in vector modalities, manufacturing, antigen design, computational biology, protein engineering, and gene synthesis ([ 6 ][6]). Such innovations may provide the technological basis for targeting other global infectious diseases. In response to COVID-19, the public health and regulatory communities are streamlining clinical development. Independently funded, designed, and conducted platform clinical trials, such as Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV), are structured under a single, adaptive “master” protocol to allow for continuous and consistent evaluation of multiple drug candidates, adding products as they become available and removing candidates as they are deemed futile. They also provide access to large, geographically diverse populations, and some have created or expanded operational structures in resource-limited countries ([ 7 ][7]). Timelines have been shortened because of accelerated regulatory reviews, flexible requirements to enter first-in-human trials, newer approaches to modeling population-specific issues, early approval mechanisms, and enhanced regulatory harmonization among countries ([ 8 ][8]). This increased efficiency in clinical trial execution and regulatory processes could be applied to other global infectious diseases. Historically, investment in product development for global infectious diseases has been restricted owing to the lack of financial returns compared to more profitable areas of drug development, such as oncology. However, the threat that pandemic human coronaviruses (HCoVs) pose to the global economy, political stability, and people's lives has stimulated the private sector, public sector, and philanthropic groups to devote considerable financial and human resources to product development. Previous HCoV outbreaks led to initial development activities that were accelerated with COVID-19. Supplementing these efforts, the U.S. government has provided over $10 billion for COVID-19 therapeutics and vaccines. Other governments, including the European Union, United Kingdom, Germany, and Canada, are making substantial financial commitments, as are large funding institutions ([ 2 ][2]). A fundamental principle behind this unprecedented funding is that financing for the entire product development process is made by the time a candidate enters early-stage clinical trials ([ 9 ][9]). This approach has mitigated the range of risks faced by different categories of developers (e.g., academia, nonprofit organizations, public-private partnerships, small biotechnology companies, and large multinational pharmaceutical companies) who may individually have widely varying risk-reward calculations. As a result, developers can simultaneously prepare for late-stage clinical trials, implement scaling up of manufacturing processes, and obtain advanced purchase commitments of large-scale supply—all during first-in-human clinical trials ([ 9 ][9]). Together, providing the full range of financing as early as possible in the product development process, articulating the need for multiple products, and acknowledging implicit failure of some candidates and platforms have overcome product development barriers. The result has been an extraordinary scale of therapeutic and vaccine development in the shortest time possible. A similar product development framework could be created for global infectious diseases. Such a framework could attempt to resolve three long-standing challenges for these diseases: the lack of interest in developing products, resulting in a diminished initial pipeline of candidates; the large pipeline attrition points between preclinical activities and early-stage clinical trials and between early- and late-stage clinical trials ([ 10 ][10]) that occur because of the considerable increases in development costs of these two transition points; and the extended timelines for product development. If these challenges are addressed, a more robust initial pipeline could be created, more candidates could advance to early- and late-stage clinical trials, and more products could be approved in a shorter period. A robust pipeline for global infectious diseases should include repurposed agents, mAbs, new chemical entities, and vaccines. Each of these categories possess strengths and limitations; thus, each may not prove beneficial for every disease. Repurposed agents may have existing preclinical data and clinical safety experience, putting them on the fastest development timelines. mAbs targeting proteins encoded by highly conserved regions of a pathogen's genome—thereby minimizing escape mutations and maximizing strain coverage—can be isolated from patients and modified to enhance their activities, for example, to extend half-life and induce host immune responses. New chemical entities could target families of pathogens to create “one-drug-multiple-bug” approaches to replace “one-drug-one-bug” approaches. Traditional vaccine platforms have a history of clinical validation and scaled production capacity. Emerging nucleic acid–based vaccine systems have promise for generating a candidate upon availability of a genomic sequence. Several factors must be considered to rapidly build and advance such a pipeline. Arguably the most critical factor is to incentivize all development groups and encourage aggressive competition. Public sector and philanthropic financing should address the cost of research, clinical trials, manufacturing, and supply agreements, and such financing should be available at the earliest possible part of the product development process. This is essential to overcome developers' decision to avoid product development because of lack of a clear revenue model. This financing, in turn, could stimulate the levels of investment and activity from the private sector observed in COVID-19, including public-private partnerships to advance candidates. A fundamental biological understanding of coronaviruses existed prior to COVID-19 and is necessary to drive product development, but a similar biological understanding needs to be improved for many global infectious diseases ([ 11 ][11]). While under development for COVID-19, predictive, validated preclinical assays, animal models, and human challenge models for infectious diseases would provide faster, cost-efficient methods to eliminate candidates earlier in the development cycle ([ 12 ][12], [ 13 ][13]). Moreover, implementing high-quality, decentralized clinical trials and using existing clinical trial networks could reduce the need for each developer to create complex multicountry clinical trial processes and infrastructure while still maintaining consistent evaluation methods ([ 14 ][14]). Machine learning could help optimize clinical trial design and identify populations most likely to benefit from a candidate, thereby reducing the large sample sizes currently required for late-stage clinical trials ([ 15 ][15]). Consideration should be given to what accelerated and flexible regulatory processes may be adopted from COVID-19, and which regulatory agencies should serve as benchmark approvals for those diseases that predominantly affect resource-limited settings. The manufacturing supply chain may need to be improved for some technologies facing global constraints. Additionally, access, affordability, and availability will need to be addressed to ensure that innovations reach the populations in greatest need. Implementing this strategy is not without risk, and there are challenges to overcome. Development of predictive models and biomarkers has proved difficult with COVID-19. The risk-benefit assessment for accelerated first-in-human testing during an unfolding pandemic may differ compared to that for endemic pathogens. Global capacity for late-stage clinical trials may initially be reached quickly in resource-limited settings. As seen with hydroxychloroquine, early approvals based on limited evidence can occur with compounds that ultimately demonstrate no benefit. The advanced financing available for COVID-19 candidates partially emerged from country-specific interests and, if repeated, may continue to foster inequitable access to new tools globally. Ultimately, the SARS-CoV-2 product development model may need optimization to realistically achieve success across multiple global infectious diseases. Of the ∼250 therapeutics and vaccines in clinical development for global infectious diseases, ∼30% are for HIV and AIDS ([ 3 ][3]). The innovation in antiretroviral medicines was initially sparked by strong political will coupled with streamlined regulatory processes. Growing demand produced attractive returns from resource-wealthy countries. By contrast, the distinct regulatory pathways and government funding to address the growing problem of resistance to antimicrobial agents (such as antibiotics) could not overcome the lack of a revenue model, thereby bankrupting companies that successfully developed safe and efficacious therapies and curtailing development activities. For the recent outbreak of Zika virus beginning in 2015 in the Americas, the time frame from identification of genomic sequences to advancing a nucleic acid vaccine into phase 1 clinical trials occurred within 4 months; but the threat to high-income countries quickly subsided, resulting in stalled product development programs. After nearly 40 years of continuous outbreaks in Africa, the potential global spread of Ebola became evident during the 2014–2016 outbreak and spurred public-private partnerships that recently achieved approval of two vaccines and one therapeutic mAb combination (with a second, single therapeutic mAb under regulatory review). Resource-limited countries are experiencing combined morbidity and mortality impacts from COVID-19: from the disease itself and from other global infectious diseases, owing, in large part, to diversion of resources. Which candidates in clinical trials for COVID-19 will reach regulatory approval, what limitations may come with licensed candidates, and the success of emerging technology platforms are all unknown. However, COVID-19 forced the world to construct a new product development approach, taking what was previously perceived as impossible and turning it into reality. How to implement this approach to address other global infectious diseases that continue to curtail global economic growth and devastate humanity must now be decided. 1. [↵][16]1. Institute for Health Metrics and Evaluation , Global Burden of Disease Study 2019; . 2. [↵][17]1. Policy Cures Research , COVID-19 R&D Tracker Update: 6 August 2020; . 3. [↵][18]1. Policy Cures Research , Neglected Diseases R&D Pipeline Tracker—August 2019; . 4. [↵][19]1. D. E. Gordon et al ., Nature 583, 459 (2020). [OpenUrl][20][CrossRef][21][PubMed][22] 5. [↵][23]1. M. Marovich, 2. J. R. Mascola, 3. M. S. Cohen , JAMA 324, 131 (2020). [OpenUrl][24][CrossRef][25][PubMed][26] 6. [↵][27]1. B. S. Graham , Science 368, 945 (2020). [OpenUrl][28][Abstract/FREE Full Text][29] 7. [↵][30]1. L. Corey, 2. J. R. Mascola, 3. A. S. Fauci, 4. F. S. Collins , Science 368, 948 (2020). [OpenUrl][31][Abstract/FREE Full Text][32] 8. [↵][33]1. J. L. Wilson et al ., Sci. Transl. Med. 12, eaax2550 (2020). 9. [↵][34]1. M. Slaoui, 2. M. Hepburn, , N. Engl. J. Med. 383, 1701 (2020). [OpenUrl][35] 10. [↵][36]1. R. Rappuoli, 2. S. Black, 3. D. E. Bloom , Sci. Transl. Med. 11, eaaw2888 (2019). [OpenUrl][37][FREE Full Text][38] 11. [↵][39]1. M. De Rycker, 2. B. Baragaña, 3. S. L. Duce, 4. I. H. Gilbert , Nature 559, 498 (2018). [OpenUrl][40][CrossRef][41] 12. [↵][42]1. J. Cohen , Science 368, 221 (2020). [OpenUrl][43][Abstract/FREE Full Text][44] 13. [↵][45]1. N. Eyal, 2. M. Lipsitch, 3. P. G. Smith , J. Infect. Dis. 221, 1752 (2020). [OpenUrl][46][PubMed][22] 14. [↵][47]1. COVID-19 Clinical Research Coalition , Lancet 395, 1322 (2020). [OpenUrl][48][PubMed][22] 15. [↵][49]1. W. R. Zame et al ., Stat. Biopharm. Res. 10.1080/19466315.2020.1797867 (2020). Acknowledgments: Thanks to D. Gollaher, B. Hubby, M. Kamarck, I. Pleasure, S. Shome, H. W. Virgin, C. Wells, and G. Yamey for their insightful comments. R.G. is an employee and owns shares of Vir Biotechnology, Inc. The author's opinions expressed in this article do not necessarily reflect Vir's official policy. 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Predictive AI Provides Update on Sales Process and Pipeline

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Predictiv AI Inc. (TSX VENTURE: PAI) (OTC: INOTF) (FRANKFURT: 71TA) ("Predictiv AI" or the "Company"), www.predictiv.ai, a software and solutions provider in the artificial intelligence and industrial IoT markets, is pleased to provide a sales update on ThermalPass www.thermalpass.com, Predictiv AI is delivering units, against five initial orders, over the next 45 days. The initial customer base consists of a wide cross-section of businesses and organizations which include a convention center, a hospital, a mining company, two manufacturing plants and a leading industrial conglomerate. The internal sales and marketing team has also built an extensive pipeline since ThermalPass' commercial launch 30 days ago. In addition, the Company has entered into seven strategic reseller contracts with established sales channels in Canada, the United States and Europe.


Quebec-based automation supplier Omnirobotic gets $6.5 million financing for AI development - Canadian Plastics

#artificialintelligence

A Quebec-based robotics automation startup has closed a seed round of $6.5 million to further develop and commercialize its artificial intelligence (AI) platform for factory robots. Omnirobotic, founded in 2016 and headquartered in Laval, plans to use the new capital to continue building its autonomous robotic capabilities for production environments. The company intends for its robots to see, plan, and execute processes such as painting, welding, and machining, with limited human oversight. Fonds de solidarité FTQ and Export Development Canada (EDC) led the funding round with participation from Real Ventures and a joint venture including the company's current employees. The Fonds de solidarité FTQ and EDC recently agreed to work closer together to support the growth of companies.


The upskilling imperative

#artificialintelligence

COVID-19 and efforts to contain it have had a profound impact on businesses and workers worldwide. As Canada and other countries take their first steps towards recovery, it's clear that the way we live and work will change significantly--and even permanently--in the new normal. The COVID-19 experience has dramatically accelerated companies' digital transformation. Organizations increasingly see data-driven decision making as crucial to their survival today and their success tomorrow, and they are driving forward with investments in AI, analytics, automation, and digitization to secure their future in a changing world. AI, digitization, and automation will open the door to tremendous opportunities for innovation and growth--and create new challenges and complexities for employers.


The state of Artificial Intelligence (AI) ethics: 14 interesting statistics

#artificialintelligence

In the wake of the COVID-19 pandemic, there has been a rapid increase in the deployment of artificial intelligence (AI) systems due to an increased need for automation, advanced analytics, and remote work. In fact, machine learning and other forms of AI are being applied to address the increasing scale of the pandemic itself. Now, say experts, is a good time to step back and consider the ethics of these (and all) AI applications. "These past few months have been especially challenging, and the deployment of technology in ways hitherto untested at an unrivaled pace has left the internet and technology watchers aghast," Abhishek Gupta, founder of the Montreal AI Ethics Institute, said in the introduction to that organization's inaugural State of AI Ethics report this June. "It has never been more important that we keep a sharp eye out on the development of this field and how it is shaping our society and interactions with each other."


Machine Learning and Artificial Intelligence for Business Recovery after COVID 19

#artificialintelligence

Currently, the world is facing the most challenging time and going through economic turmoil. One of the important priorities of many companies is to recover quickly from the current scenario and be operational as quickly as possible. The coronavirus has impacted many companies and this economic hit is very fast throughout the world. Companies around the globe are looking for stabilizing their business and the recovery. According to the Organization for Economic Co-operation and Development of the reports released on 14th April, consumer expenditure has dropped more than 25% in Canada, France, and Germany in many majority economies, thus causing the slowdown between 20-25%. The Machine Learning (ML) and Artificial Intelligence (AI) can play a major role in the business recovery after and during the COVID 19 pandemic.


Canadian city using AI to predict who might become homeless

#artificialintelligence

TORONTO – As makeshift tent cities spring up across Canada to house rough sleepers who fear using shelters due to COVID-19, one city is leveraging artificial intelligence (AI) to predict which residents risk becoming homeless. Computer programmers working for the city of London, Ontario, 170km southwest of the provincial capital Toronto, say the new system is the first of its kind anywhere – and it could offer insights for other regions grappling with homelessness. "Shelters are just packed to the brim across the country right now," said Jonathan Rivard, London's Homeless Prevention Manager, who works on the AI system. "We need to do a better job of providing resources to individuals before they hit rock bottom, not once they do," he told the Thomson Reuters Foundation. Canada is seeing a second wave of coronavirus cases, with Ontario's government warning the province could experience "worst-case scenarios seen in northern Italy and New York City" if trends continue.


Autoantibodies against type I IFNs in patients with life-threatening COVID-19

Science

The immune system is complex and involves many genes, including those that encode cytokines known as interferons (IFNs). Individuals that lack specific IFNs can be more susceptible to infectious diseases. Furthermore, the autoantibody system dampens IFN response to prevent damage from pathogen-induced inflammation. Two studies now examine the likelihood that genetics affects the risk of severe coronavirus disease 2019 (COVID-19) through components of this system (see the Perspective by Beck and Aksentijevich). Q. Zhang et al. used a candidate gene approach and identified patients with severe COVID-19 who have mutations in genes involved in the regulation of type I and III IFN immunity. They found enrichment of these genes in patients and conclude that genetics may determine the clinical course of the infection. Bastard et al. identified individuals with high titers of neutralizing autoantibodies against type I IFN-α2 and IFN-ω in about 10% of patients with severe COVID-19 pneumonia. These autoantibodies were not found either in infected people who were asymptomatic or had milder phenotype or in healthy individuals. Together, these studies identify a means by which individuals at highest risk of life-threatening COVID-19 can be identified. Science , this issue p. [eabd4570][1], p. [eabd4585][2]; see also p. [404][3] ### INTRODUCTION Interindividual clinical variability is vast in humans infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), ranging from silent infection to rapid death. Three risk factors for life-threatening coronavirus disease 2019 (COVID-19) pneumonia have been identified—being male, being elderly, or having other medical conditions—but these risk factors cannot explain why critical disease remains relatively rare in any given epidemiological group. Given the rising toll of the COVID-19 pandemic in terms of morbidity and mortality, understanding the causes and mechanisms of life-threatening COVID-19 is crucial. ### RATIONALE B cell autoimmune infectious phenocopies of three inborn errors of cytokine immunity exist, in which neutralizing autoantibodies (auto-Abs) against interferon-γ (IFN-γ) (mycobacterial disease), interleukin-6 (IL-6) (staphylococcal disease), and IL-17A and IL-17F (mucocutaneous candidiasis) mimic the clinical phenotypes of germline mutations of the genes that encode the corresponding cytokines or receptors. Human inborn errors of type I IFNs underlie severe viral respiratory diseases. Neutralizing auto-Abs against type I IFNs, which have been found in patients with a few underlying noninfectious conditions, have not been unequivocally shown to underlie severe viral infections. While searching for inborn errors of type I IFN immunity in patients with life-threatening COVID-19 pneumonia, we also tested the hypothesis that neutralizing auto-Abs against type I IFNs may underlie critical COVID-19. We searched for auto-Abs against type I IFNs in 987 patients hospitalized for life-threatening COVID-19 pneumonia, 663 asymptomatic or mildly affected individuals infected with SARS-CoV-2, and 1227 healthy controls from whom samples were collected before the COVID-19 pandemic. ### RESULTS At least 101 of 987 patients (10.2%) with life-threatening COVID-19 pneumonia had neutralizing immunoglobulin G (IgG) auto-Abs against IFN-ω (13 patients), against the 13 types of IFN-α (36), or against both (52) at the onset of critical disease; a few also had auto-Abs against the other three individual type I IFNs. These auto-Abs neutralize high concentrations of the corresponding type I IFNs, including their ability to block SARS-CoV-2 infection in vitro. Moreover, all of the patients tested had low or undetectable serum IFN-α levels during acute disease. These auto-Abs were present before infection in the patients tested and were absent from 663 individuals with asymptomatic or mild SARS-CoV-2 infection ( P < 10−16). They were present in only 4 of 1227 (0.33%) healthy individuals ( P < 10−16) before the pandemic. The patients with auto-Abs were 25 to 87 years old (half were over 65) and of various ancestries. Notably, 95 of the 101 patients with auto-Abs were men (94%). ### CONCLUSION A B cell autoimmune phenocopy of inborn errors of type I IFN immunity accounts for life-threatening COVID-19 pneumonia in at least 2.6% of women and 12.5% of men. In these patients, adaptive autoimmunity impairs innate and intrinsic antiviral immunity. These findings provide a first explanation for the excess of men among patients with life-threatening COVID-19 and the increase in risk with age. They also provide a means of identifying individuals at risk of developing life-threatening COVID-19 and ensuring their enrolment in vaccine trials. Finally, they pave the way for prevention and treatment, including plasmapheresis, plasmablast depletion, and recombinant type I IFNs not targeted by the auto-Abs (e.g., IFN-β). ![Figure][4] Neutralizing auto-Abs to type I IFNs underlie life-threatening COVID-19 pneumonia. We tested the hypothesis that neutralizing auto-Abs against type I IFNs may underlie critical COVID-19 by impairing the binding of type I IFNs to their receptor and the activation of the downstream responsive pathway. Neutralizing auto-Abs are represented in red, and type I IFNs are represented in blue. In these patients, adaptive autoimmunity impairs innate and intrinsic antiviral immunity. ISGs, IFN-stimulated genes; TLR, Toll-like receptor; IFNAR, IFN-α/β receptor; pSTAT, phosphorylated signal transducers and activators of transcription; IRF, interferon regulatory factor. Interindividual clinical variability in the course of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is vast. We report that at least 101 of 987 patients with life-threatening coronavirus disease 2019 (COVID-19) pneumonia had neutralizing immunoglobulin G (IgG) autoantibodies (auto-Abs) against interferon-ω (IFN-ω) (13 patients), against the 13 types of IFN-α (36), or against both (52) at the onset of critical disease; a few also had auto-Abs against the other three type I IFNs. The auto-Abs neutralize the ability of the corresponding type I IFNs to block SARS-CoV-2 infection in vitro. These auto-Abs were not found in 663 individuals with asymptomatic or mild SARS-CoV-2 infection and were present in only 4 of 1227 healthy individuals. Patients with auto-Abs were aged 25 to 87 years and 95 of the 101 were men. A B cell autoimmune phenocopy of inborn errors of type I IFN immunity accounts for life-threatening COVID-19 pneumonia in at least 2.6% of women and 12.5% of men. [1]: /lookup/doi/10.1126/science.abd4570 [2]: /lookup/doi/10.1126/science.abd4585 [3]: /lookup/doi/10.1126/science.abe7591 [4]: pending:yes


Researchers find evidence of racial, gender, and socioeconomic bias in chest X-ray classifiers

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Google and startups like Qure.ai, Aidoc, and DarwinAI are developing AI and machine learning systems that classify chest X-rays to help identify conditions like fractures, collapsed lungs, and fractures. Several hospitals including Mount Sinai have piloted computer vision algorithms that analyze scans from patients with the novel coronavirus. But research from the University of Toronto, the Vector Institute, and MIT reveals that chest X-ray datasets used to train diagnostic models exhibit imbalance, biasing them against certain gender, socioeconomic, and racial groups. Partly due to a reticence to release code, datasets, and techniques, much of the data used today to train AI algorithms for diagnosing diseases may perpetuate inequalities. A team of U.K. scientists found that almost all eye disease datasets come from patients in North America, Europe, and China, meaning eye disease-diagnosing algorithms are less certain to work well for racial groups from underrepresented countries.


How a Montreal chatbot provider is using CRM to drive revenue

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Heyday co-founders Steve Desjarlais, left, and Étienne Mérineau stand in the company's Montreal office on Sept. 30, 2020. When COVID-19 hit, forcing people to stay home, Heyday AI knew its chatbot technology would be vital for retailers that could no longer converse with their customers in person. But the Montreal-based company also knew landing new clients would be a challenge given the cancellation of big tech events where it often drums up business face-to-face. "A lot of [those] sales ... bank on relationships, and therefore you need to meet in person," says Étienne Mérineau, who co-founded Heyday in 2017 with Steve Desjarlais, David Bordeleau and Hugues Rousseau. Heyday uses artificial intelligence to help retailers communicate with customers on websites and across various apps and programs such as Facebook Messenger and Google Maps.