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Scientists Develop a Machine Learning Model to Predict the Evolution of an Epidemic Accurately - CBIRT


According to a new KAUST study, machine learning approaches can achieve an assumption-free analysis of epidemic case data with amazingly good prediction accuracy and the flexibility to incorporate new data dynamically. Yasminah Alali, an intern in KAUST's 2021 Saudi Summer Internship (SSI) program, developed a proof of concept that reveals a possible alternative to traditional parameter-driven mechanistic models by removing human bias and assumptions from analysis, revealing the underlying story of the data. Using publicly released COVID-19 incidence and recovery data from India and Brazil, Alali leveraged her experience working with artificial intelligence models to design a framework to fit the characteristics and time-evolving nature of epidemic data in collaboration with KAUST's Ying Sun and Fouzi Harrou. To create an effective Gaussian process regression (GPR) based model for forecasting recovered and confirmed COVID-19 cases in two significantly impacted countries, India and Brazil, the researchers first used Bayesian optimization to modify the Gaussian process regression (GPR) hyperparameters. However, the time dependency in the COVID-19 data series is ignored by machine learning models.

AI System Identifies COVID-19 Patients Who Require ICU


A new artificial intelligence (AI) system developed by researchers at the University of Waterloo and DarwinAI, an alumni-founded startup company, could help doctors efficiently utilize limited resources during the COVID-19 pandemic. The system is able to identify patients who require intensive care unit (ICU) treatment. The AI system predicts this necessity of ICU admission through the use of 200 clinical data points, which include blood test results, medical history, and vital signs. Alexander Wong is a professor of systems design engineering and Canada Research Chair in AI and Medical Imaging at Waterloo. "That is a very important step in the clinical decision support process for triaging patients and developing treatment plans," Wong said.

The Impact of Tech in 2022


Now is the time to upgrade our technologies, and in the year 2022, AI, ML, 5G, and Cloud Computing will be the most important technologies to emerge. The covid-19 pandemic will continue to have a wide-ranging influence on our life in 2022. As a result, the digitalization and virtualization of business and society will continue to increase. As we enter the new year, however, the demand for sustainability, ever-increasing data volumes, and faster computation and network speeds will reclaim their positions as the most essential drivers of digital transformation. IEEE has announced the conclusions of a new study of global technology executives from the United States, the United Kingdom, China, India, and Brazil titled "The Impact of Technology in 2022 and Beyond: an IEEE Global Study."

ICAIL 2021 – the 18th International Conference for Artificial Intelligence and Law

Interactive AI Magazine

The 18th International Conference for Artificial Intelligence and Law (ICAIL 2021) was organized at the University of São Paulo School of Law, Brazil. ICAIL is a biannual conference organized under the auspices of the International Association for Artificial Intelligence and Law ( For the first time, the ICAIL conference was organized entirely online, due to the overall Covid-19 pandemic situation. Despite these unusual circumstances, the conference came out as a considerable success, attracting almost 1400 registered participants, the highest number ever. The conference talks were streamed publicly on the YouTube channel and the discussions and networking were enabled on the platforms accessible for the registered participants.

Intelligent computational model for the classification of Covid-19 with chest radiography compared to other respiratory diseases Artificial Intelligence

Lung X-ray images, if processed using statistical and computational methods, can distinguish pneumonia from COVID-19. The present work shows that it is possible to extract lung X-ray characteristics to improve the methods of examining and diagnosing patients with suspected COVID-19, distinguishing them from malaria, dengue, H1N1, tuberculosis, and Streptococcus pneumonia. More precisely, an intelligent computational model was developed to process lung X-ray images and classify whether the image is of a patient with COVID-19. The images were processed and extracted their characteristics. These characteristics were the input data for an unsupervised statistical learning method, PCA, and clustering, which identified specific attributes of X-ray images with Covid-19. The introduction of statistical models allowed a fast algorithm, which used the X-means clustering method associated with the Bayesian Information Criterion (CIB). The developed algorithm efficiently distinguished each pulmonary pathology from X-ray images. The method exhibited excellent sensitivity. The average recognition accuracy of COVID-19 was 0.93 and 0.051.

Effect of natural mutations of SARS-CoV-2 on spike structure, conformation, and antigenicity


As battles to contain the COVID-19 pandemic continue, attention is focused on emerging variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that have been deemed variants of concern because they are resistant to antibodies elicited by infection or vaccination or they increase transmissibility or disease severity. Three papers used functional and structural studies to explore how mutations in the viral spike protein affect its ability to infect host cells and to evade host immunity. Gobeil et al. looked at a variant spike protein involved in transmission between minks and humans, as well as the B1.1.7 (alpha), B.1.351 (beta), and P1 (gamma) spike variants; Cai et al. focused on the alpha and beta variants; and McCallum et al. discuss the properties of the spike protein from the B1.1.427/B.1.429 (epsilon) variant. Together, these papers show a balance among mutations that enhance stability, those that increase binding to the human receptor ACE2, and those that confer resistance to neutralizing antibodies. Science , abi6226, abi9745, abi7994, this issue p. [eabi6226][1] , p. [642][2], p. [648][3] ### INTRODUCTION Variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been circulating worldwide since the beginning of the pandemic. Some are termed Variants of Concern (VOC) because they show evidence for increased transmissibility, higher disease severity, resistance to neutralizing antibodies elicited by current vaccines or from previous infection, reduced efficacy of treatments, or failure of diagnostic detection methods. VOCs accumulate mutations in the spike (S) glycoprotein. Some VOCs that arose independently in different geographical locations show identical changes, implying convergent evolution and selective advantages of the acquired variations. A set of three amino acid substitutions in the receptor-binding domain (RBD)—Lys417 → Asn (K417N), Glu484 → Lys (E484K), and Asn501 → Tyr (N501Y)—occurred in the B.1.1.28 and B.1.351 lineages that originated in Brazil and South Africa, respectively. The P.1 lineage that branched off B.1.1.28 harbored a Lys417 → Thr (K417T) substitution while retaining the E484K and N501Y changes. The E484K substitution has attracted attention as a result of its location within the epitope of many potent neutralizing antibodies. The N501Y substitution also occurred in the B.1.1.7 variant that originated in the UK and was implicated in increased receptor binding and higher transmissibility of the variant. The B.1.1.7 variant, in turn, shares the His69/Val70 spike deletion mutation with spike from a variant that was implicated in transmission between humans and minks (ΔFVI). ### RATIONALE Global sequencing initiatives and in vitro neutralization and antibody binding assays have rapidly provided critical and timely information on the VOCs. Here, by combining cryo–electron microscopy (cryo-EM) structural determination with binding assays and computational analyses on the variant spikes, we sought to visualize the impact of the amino acid substitutions on spike conformation to understand how these changes affect their biological function. ### RESULTS We measured angiotensin-converting enzyme 2 (ACE2) receptor and antibody binding for 19 SARS-CoV-2 S ectodomain constructs harboring amino acid changes found in circulating variants. These included a variant involved in interspecies SARS-CoV-2 transmission between humans and minks, as well as several VOCs including the B.1.1.7, B.1.1.28/P.1, and B.1.351 variants. Consistent with published neutralization data, B.1.1.7 showed decreased binding to N-terminal domain (NTD)–directed antibodies, whereas P.1 and B.1.351 showed reduced binding to both NTD- and RBD-directed antibodies. All variants showed increased binding to ACE2, which was mediated by higher propensity for RBD-up states, and affinity-enhancing mutations in the RBD. We observed spike instability in the mink-associated variant, highlighted by the presence of a population in the cryo-EM dataset with missing density for the S1 subunit of one protomer. Modulation of contacts between the SD1 and HR1 regions led to increased RBD-up states of the B.1.1.7 spike, with the protein stability maintained by a balance of stabilizing and destabilizing mutations. A local destabilizing effect of the RBD E484K mutation was implicated in resistance of the B.1.1.28/P.1 and B.1.351 variants to some potent RBD-directed neutralizing antibodies. ### CONCLUSION Our study revealed details of how amino acid substitutions affect spike conformation in circulating SARS-CoV-2 VOCs. We define communication networks that modulate spike allostery and show that the S protein uses different mechanisms to converge upon similar solutions for altering the RBD up/down positioning. ![Figure][4] Cryo-EM structures of SARS-CoV-2 spike ectodomains. Naturally occurring amino acid variations are represented by colored spheres. Spike mutations from a mink-associated (ΔFV) (top left), B.1.1.7 (top right), B.1.351 (bottom right), and a spike with three RBD mutations (bottom left) are shown. Relative proportions of the RBD down and up populations are indicated for each. The three amino acid substitutions in the RBD—K417N/T, E484K, and N501Y—were found in the B.1.1.28 variant and are shared with the P.1 and B.1.351 lineages. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with multiple spike mutations enable increased transmission and antibody resistance. We combined cryo–electron microscopy (cryo-EM), binding, and computational analyses to study variant spikes, including one that was involved in transmission between minks and humans, and others that originated and spread in human populations. All variants showed increased angiotensin-converting enzyme 2 (ACE2) receptor binding and increased propensity for receptor binding domain (RBD)–up states. While adaptation to mink resulted in spike destabilization, the B.1.1.7 (UK) spike balanced stabilizing and destabilizing mutations. A local destabilizing effect of the RBD E484K mutation was implicated in resistance of the B.1.1.28/P.1 (Brazil) and B.1.351 (South Africa) variants to neutralizing antibodies. Our studies revealed allosteric effects of mutations and mechanistic differences that drive either interspecies transmission or escape from antibody neutralization. [1]: /lookup/doi/10.1126/science.abi6226 [2]: /lookup/doi/10.1126/science.abi9745 [3]: /lookup/doi/10.1126/science.abi7994 [4]: pending:yes

Brazil's idwall raises $38M for identity validation platform – TechCrunch


Online fraud and identity theft is a global problem that has only been exacerbated with increased online transactions amid the COVID-19 pandemic. In particular, it is estimated that Brazilian companies lose over $41 billion due to fraud every year. In an attempt to tackle this problem head on, Lincoln Ando and Raphael Melo started idwall in mid-2016. São Paulo-based idwall started as an automated background check solution and has since grown into a suite of data and identity validation and risk analysis products. For the consumer market, its "MeuID" app is aimed at users who want to change the way they identify themselves and share their data.

Changing business needs due to COVID-19 driving AI adoption: IBM survey


Recent advances in artificial intelligence technology and the changing business needs due to the COVID-19 pandemic are driving the adoption of AI, according to new market research commissioned by IBM. The "Global AI Adoption Index 2021," survey conducted by Morning Consult on behalf of IBM, sheds light on the deployment of AI across 5,501 businesses in China, France, Germany, India, Italy, Latin America (Brazil, Mexico, Colombia, Argentina, Chile and Peru), Singapore, Spain, the United Kingdom, and the United States. According to the annual survey, while advances in AI are making it more accessible, some global businesses are still facing a multitude of challenges when it comes to adopting AI. "As organizations move to a post-pandemic world, data from the Global AI Adoption Index 2021 underscores a major uptick in AI investment. We believe these investments will continue to accelerate rapidly as customers look for new, innovative ways to drive their digital transformations by taking advantage of hybrid cloud and AI," said Rob Thomas, Senior Vice President, IBM Cloud and Data Platform.

The Proper Use of Google Trends in Forecasting Models Machine Learning

It is widely known that Google Trends have become one of the most popular free tools used by forecasters both in academics and in the private and public sectors. There are many papers, from several different fields, concluding that Google Trends improve forecasts' accuracy. However, what seems to be widely unknown, is that each sample of Google search data is different from the other, even if you set the same search term, data and location. This means that it is possible to find arbitrary conclusions merely by chance. This paper aims to show why and when it can become a problem and how to overcome this obstacle.

Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting Machine Learning

Updating observations of a signal due to the delays in the measurement process is a common problem in signal processing, with prominent examples in a wide range of fields. An important example of this problem is the nowcasting of COVID-19 mortality: given a stream of reported counts of daily deaths, can we correct for the delays in reporting to paint an accurate picture of the present, with uncertainty? Without this correction, raw data will often mislead by suggesting an improving situation. We present a flexible approach using a latent Gaussian process that is capable of describing the changing auto-correlation structure present in the reporting time-delay surface. This approach also yields robust estimates of uncertainty for the estimated nowcasted numbers of deaths. We test assumptions in model specification such as the choice of kernel or hyper priors, and evaluate model performance on a challenging real dataset from Brazil. Our experiments show that Gaussian process nowcasting performs favourably against both comparable methods, and a small sample of expert human predictions. Our approach has substantial practical utility in disease modelling -- by applying our approach to COVID-19 mortality data from Brazil, where reporting delays are large, we can make informative predictions on important epidemiological quantities such as the current effective reproduction number.