Category Archives: Evolution

Pep Guardiola celebrates his 50th birthday on Monday, and fairly seismic things tend to happen on milestones like this.

When he turned 20 he established himself in the Barcelona first team, when he was 30 he left the Nou Camp, and when he was 40 he won the Champions League with his boyhood club at Wembley.

Since then he has spread his footballing message around the world, and recently revealed he is ready to stay in the dugout for longer than he had ever intended.

To work out what could possibly come next, we need to realise what has come before.

This is the story of Guardiola at 20, 30, 40 and 50, with a little guesswork on the future and what he might be doing at 60 too

That winning personality, the character to organise things, he had that since he was little, Albert Benaiges tells The Athletic.

Hes changed because hes grown as a person, as a coach, like everybody, but in certain...

Read more:

Pep Guardiola: An evolution through Barcelona, Bayern and City - The Athletic

Natural language predicts viral escape

Viral mutations that evade neutralizing antibodies, an occurrence known as viral escape, can occur and may impede the development of vaccines. To predict which mutations may lead to viral escape, Hie et al. used a machine learning technique for natural language processing with two components: grammar (or syntax) and meaning (or semantics) (see the Perspective by Kim and Przytycka). Three different unsupervised language models were constructed for influenza A hemagglutinin, HIV-1 envelope glycoprotein, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein. Semantic landscapes for these viruses predicted viral escape mutations that produce sequences that are syntactically and/or grammatically correct but effectively different in semantics and thus able to evade the immune system.

Science, this issue p. 284; see also p. 233

The ability for viruses to mutate and evade the human immune system and cause infection, called viral escape, remains an obstacle to antiviral and vaccine development. Understanding the complex rules that govern escape could inform therapeutic design. We modeled viral escape with machine learning algorithms originally developed for human natural language. We identified escape mutations as those that preserve viral infectivity but cause a virus to look different to the immune system, akin to word changes that preserve a sentences grammaticality but change its meaning. With this approach, language models of influenza hemagglutinin, HIV-1 envelope glycoprotein (HIV Env), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Spike viral proteins can accurately predict structural escape patterns using sequence data alone. Our study represents a promising conceptual bridge between natural language and viral evolution.

Viral mutations that allow an infection to escape from recognition by neutralizing antibodies have prevented the development of a universal antibody-based vaccine for influenza (1, 2) or HIV (3) and are a concern in the development of therapies for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (4, 5). Escape has motivated high-throughput experimental techniques that perform causal escape profiling of all single-residue mutations to a viral protein (14). Such techniques, however, require substantial effort to profile even a single viral strain, and testing the escape potential of many (combinatorial) mutations in many viral strains remains infeasible.

Instead, we sought to train an algorithm that learns to model escape from viral sequence data alone. This approach is not unlike learning properties of natural language from large text corpuses (6, 7) because languages such as English and Japanese use sequences of words to encode complex meanings and have complex rules (for example, grammar). To escape, a mutant virus must preserve infectivity and evolutionary fitnessit must obey a grammar of biological rulesand the mutant must no longer be recognized by the immune system, which is analogous to a change in the meaning or the semantics of the virus.

Currently, computational models of protein evolution focus either on fitness (8) or on functional or semantic similarity (911), but we want to understand both (Fig. 1A). Rather than developing two separate models of fitness and function, we developed a single model that simultaneously achieves these tasks. We leveraged state-of-the-art machine learning algorithms called language models (6, 7), which learn the probability of a token (such as an English word) given its sequence context (such as a sentence) (Fig. 1B). Internally, the language model constructs a semantic representation, or an embedding, for a given sequence (6), and the output of a language model encodes how well a particular token fits within the rules of the language, which we call grammaticality and can also be thought of as syntactic fitness (supplementary text, note S2). The same principles used to train a language model on a sequence of English words can train a language model on a sequence of amino acids. Although immune selection occurs on phenotypes (such as protein structures), evolution dictates that selection is reflected within genotypes (such as protein sequences), which language models can leverage to learn functional properties from sequence variation.

(A) Constrained semantic change search (CSCS) for viral escape prediction is designed to search for mutations to a viral sequence that preserve fitness while being antigenically different. This corresponds to a mutant sequence that is grammatical (conforms to the structure and rules of a language) but has high semantic change with respect to the original (for example, wild type) sequence. (B) A neural language model with a bidirectional long short-term memory (BiLSTM) architecture was used to learn both semantics (as a hidden layer output) and grammaticality (as the language model output). CSCS combines semantic change and grammaticality to predict escape (12). (C) CSCS-proposed changes to a news headline (implemented by using a neural language model trained on English news headlines) makes large changes to the overall semantic meaning of a sentence or to the part-of-speech structure. The semantically closest mutated sentence according to the same model is largely synonymous with the original headline.

We hypothesize that (i) language modelencoded semantic change corresponds to antigenic change, (ii) language model grammaticality captures viral fitness, and (iii) both high semantic change and grammaticality help predict viral escape. Searching for mutations with both high grammaticality and high semantic change is a task that we call constrained semantic change search (CSCS) (Fig. 1C) (12). Our language model implementation of CSCS uses sequence data alone (which is easier to obtain than structure) and requires no explicit escape information (is completely unsupervised), does not rely on multiple sequence alignment (MSA) preprocessing (is alignment-free), and captures global relationships across an entire sequence (for example, because word choice at the beginning of a sentence can influence word choice at the end) (supplementary text, notes S2 and S3).

We assessed the generality of our approach across viruses by analyzing three proteins: influenza A hemagglutinin (HA), HIV-1 envelope glycoprotein (Env), and SARS-CoV-2 spike glycoprotein (Spike). All three are found on the viral surface, are responsible for binding host cells, are targeted by antibodies, and are drug targets (15). We trained a separate language model for each protein using a corpus of virus-specific amino acid sequences (12).

We initially sought to understand the semantic patterns learned by our viral language models. We therefore visualized the semantic embeddings of each sequence in the influenza, HIV, and coronavirus corpuses using Uniform Manifold Approximation and Projection (UMAP) (13). The resulting two-dimensional semantic landscapes show clustering patterns that correspond to subtype, host species, or both (Fig. 2), suggesting that the model was able to learn functionally meaningful patterns from raw sequence.

(A and B) UMAP visualization of the high-dimensional semantic embedding landscape of influenza HA. (C) A cluster consisting of avian sequences from the 2009 flu season onward also contains the 1918 pandemic flu sequence, which is consistent with their antigenic similarity (15). (D) Louvain clusters of the HA semantic embeddings have similar purity with respect to subtype or host species compared with phylogenetic sequence clustering (Phylo). Bar height, mean; error bars, 95% confidence. (E and F) The HIV Env semantic landscape shows subtype-related distributional structure and high Louvain clustering purity. Bar height, mean; error bars, 95% confidence. (G) Sequence proximity in the semantic landscape of coronavirus spike proteins is consistent with the possible zoonotic origin of SARS-CoV-1, MERS-CoV, and SARS-CoV-2.

We quantified these clustering patterns, which are visually enriched for particular subtypes or hosts, with Louvain clustering (14) to group sequences on the basis of their semantic embeddings (fig. S1, A to C). We then measured the clustering purity on the basis of the percent composition of the most represented metadata category (sequence subtype or host species) within each cluster (12). Average cluster purities for HA subtype, HA host species, and Env subtype are 99, 96, and 95%, respectively, which are comparable with or higher than the clustering purities obtained with MSA-based phylogenetic reconstruction (Fig. 2, D and F, and fig. S1D) (12).

Within the HA landscape, clustering patterns suggest interspecies transmissibility. The sequence for 1918 H1N1 pandemic influenza belongs to the main avian H1 cluster, which contains sequences from the avian reservoir for 2009 H1N1 pandemic influenza (Fig. 2C and fig. S1, A to C). Antigenic similarity between H1 HA from 1918 and 2009, although nearly a century apart, is well supported (15). Within the landscape of SARS-CoV-2 Spike and homologous proteins, clustering proximity is consistent with the suggested zoonotic origin of several human coronaviruses (Fig. 2G), including bat and civet for SARS-CoV-1, camel for Middle East respiratory syndrome-related coronavirus (MERS-CoV), and bat and pangolin for SARS-CoV-2 (16). Analysis of these semantic landscapes strengthens our hypothesis that our viral sequence embeddings encode functional and antigenic variation.

We then assessed the relationship between viral fitness and language model grammaticality using high-throughput deep mutational scan (DMS) characterization of hundreds or thousands of mutations to a given viral protein. We obtained datasets measuring replication fitness of all single-residue mutations to A/WSN/1933 (WSN33) HA H1 (17), combinatorial mutations to antigenic site B in six HA H3 strains (18), or all single-residue mutations to BG505 and BF520 HIV Env (19), as well as a dataset measuring the dissociation constant (Kd) between combinatorial mutations to yeast-displayed SARS-CoV-2 Spike receptor-binding domain (RBD) and human ACE2 (20), which we used to approximate the fitness of Spike.

Language model grammaticality was significantly correlated (table S1, t-distribution P values) with viral fitness across all viral strains and across studies that examined single or combinatorial mutations (Fig. 3A), even though our language models were not given any explicit fitness-related information nor trained on the DMS mutants. When we compared viral fitness with the magnitude of mutant semantic change (rather than grammaticality), we observed significant negative correlation (table S1, t-distribution P values) in 8 out of 10 strains tested (Fig. 3A). This makes sense biologically because a mutation with a large effect on function is on average more likely to be deleterious and result in a loss of fitness. These results suggest that grammaticality of a given mutation captures fitness information and add an additional dimension to our understanding of how semantic change encodes perturbed protein function.

(A) Whereas grammaticality is positively correlated with fitness, semantic change has negative correlation, suggesting that most semantically altered proteins lose fitness. (B and C) However, a mutation with both high semantic change and high grammaticality is more likely to induce escape. Considering both semantic change and grammaticality enables identification of escape mutants that is consistently higher than that of previous fitness models or generic functional embedding models. (D) Across 891 surveilled SARS-CoV-2 Spike sequences, only three have both higher semantic change and grammaticality than a Spike sequence with four mutations that is associated with a potential reinfection case.

We then tested whether combining semantic change and grammaticality enables us to predict mutations that lead to viral escape. Our experimental setup involved making, in silico, all possible single-residue mutations to a given viral protein sequence; next, each mutant was ranked according to the CSCS objective that combines semantic change and grammaticality. We validated this ranking on the basis of enrichment of experimentally verified mutants that causally induce escape from neutralizing antibodies. Three of these causal escape datasets used a DMS with antibody selection to identify escape mutations to WSN33 HA H1 (1), A/Perth/16/2009 (Perth09) HA H3 (2), and BG505 Env (3). The fourth identified escape mutations to SARS-CoV-2 Spike by using natural replication error after in vitro passages under antibody selection (5), whereas the fifth performed a DMS to identify mutants that affect antibody binding to yeast-displayed Spike RBD (4).

We computed the area under the curve (AUC) of acquired escape mutations versus the total acquired mutations (12). In all five cases, escape prediction with CSCS resulted in both statistically significant and strong AUCs of 0.83, 0.77, 0.69, 0.85, and 0.71 for H1 WSN33, H3 Perth09, Env BG505, Spike, and Spike RBD, respectively (one-sided permutation-based P < 1 105 in all cases) (Fig. 3B and table S2). We did not provide the model with any information on escape, a setup in machine learning referred to as zero-shot prediction (7). The AUC decreased when ignoring either grammaticality or semantic change, evidence that both are useful in predicting escape (Fig. 3C, fig. S2A, and table S2). Although semantic change is negatively correlated with fitness, it is positively predictive (along with grammaticality) of escape (table S2), indicating that functional mutations are often deleterious, but when fitness is preserved, they are associated with antigenic change and subsequent escape from immunity.

We also tested how well alternative models of fitness (each requiring MSA preprocessing) (8, 21) or of semantic change (pretrained on generic protein sequence) (911) predict escape, although these models are not explicitly designed for escape prediction. Fitness models associate more frequently observed patterns with higher fitness and greater escape potential, whereas semantic models associate larger functional changes with escape (12). CSCS with our viral language models was more predictive of escape across all five datasets (Fig. 3B and fig. S2A). Moreover, the individual grammaticality or semantic change components of our language models often outperformed benchmark models (table S2).

Language modeling can also characterize sequence changes beyond single-residue mutations, such as from accumulated replication error or recombination (22), although our approach is agnostic to how a sequence acquires its mutations. We therefore estimated the antigenic change and fitness of a set of four mutations to the SARS-CoV-2 Spike associated with a reported reinfection event (23). Among 891 other distinct, surveilled Spike sequences, we found that only three (0.34%) represent both higher semantic change and grammaticality (Fig. 3D). We estimate significant escape potential of these four mutations (random mutant null distribution P < 1 108) (12), and we observed similar patterns for known antigenically dissimilar sequences (fig. S2B) (12). Our analysis suggests a way to quantify the escape potential of interesting combinatorial sequence changes, such as those from possible reinfection (23), and calls for more information that relates combinatorial mutations to reinfection and escape.

To further assess whether our model could learn structurally relevant patterns from sequence alone, we scored each residue on the basis of the CSCS objective, visualized escape potential across the protein structure, and quantified enrichment or depletion of escape (12). Escape potential is significantly enriched in the HA head (permutation-based P < 1 105) and significantly depleted in the HA stalk (permutation-based P < 1 105) (Fig. 4, A and B; fig. S3; and table S3), which is consistent with literature on HA mutation rates and supported by the successful development of antistalk broadly neutralizing antibodies (24). We also detected, consistent with existing knowledge, a significant enrichment (permutation-based P < 1 105) of escape mutations in the V1/V2 hypervariable regions of the HIV Env (Fig. 4, C and D; fig. S3; and table S3) (3). Our model only learns escape patterns linked to mutations, rather than posttranslational changes such as glycosylation that contribute to HIV escape (3), which may explain the lack of escape potential specifically assigned to Env glycosylation sites (Fig. 4C and table S3).

(A and B) HA trimer colored by escape potential. (C) Escape potential P values for HIV Env. The gray dashed line indicates the statistical significance threshold. (D) The Env trimer colored by escape potential, oriented to show the V1/V2 regions. (E and F) Potential for escape in SARS-CoV-2 Spike is significantly enriched at the N-terminal domain and receptor binding domain (RBD) and significantly depleted at multiple regions in the S2 subunit. The gray dashed line indicates the statistical significance threshold.

The escape potential within the SARS-CoV-2 Spike is significantly enriched in both the RBD (permutation-based P = 2.7 103) and N-terminal domain (permutation-based P < 1 105), whereas escape potential is significantly depleted in the S2 subunit (permutation-based P < 1 105) (Fig. 4, E and F; fig. S3; and table S3). These results are supported by the greater evolutionary conservation at S2 antigenic sites (25). Our model of Spike escape therefore suggests that immunodominant antigenic sites in S2 (5, 25) may be more stable target antibody epitopes and underscores the need for more exhaustive causal escape profiling of Spike in regions beyond the RBD.

Our study leverages the principle that evolutionary selection is reflected in sequence variation. This principle may allow CSCS to generalize beyond viral escape to different kinds of natural selection (such as T cell selection) or drug selection. CSCS and its components could be used to select elements of a multivalent or mosaic vaccine. Our techniques also lay the foundation for more complex modeling of sequence dynamics. We anticipate that the distributional hypothesis from linguistics (26), in which co-occurrence patterns can model complex concepts and on which language models are based, can further inform viral evolution.

M. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, L. Zettlemoyer, Deep contextualized word representations. Proc. NAACL-HLT, 22272237 (2018).

R. Rao, N. Bhattacharya, N. Thomas, Y. Duan, P. Chen, J. Canny, P. Abbeel, Y. Song, Evaluating protein transfer learning with TAPE. Proc. Adv. Neural Inf. Process. Syst., 96869698 (2019).

B. Hie, brianhie/viral-mutation: viral-mutation release 0.3. Zenodo (2020).doi:10.5281/zenodo.4034681

B. Foley, C. Apetrei, I. Mizrachi, A. Rambaut, B. Korber, T. Leitner, B. Hahn, J. Mullins, S. Wolinsky, HIV Sequence Compendium 2018, technical report LA-UR 18-2 (2018).

T. Mikolov, I. Sutskever, K. Chen, G. Corrado, J. Dean, Distributed representations of words and phrases and their compositionality. Proc. Adv. Neural Inf. Process. Syst., 31113119 (2013).

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C. C. Aggarwal, A. Hinneburg, D. A. Keim, in Proceedings of the International Conference on Database Theory (2001), vol. 1973, pp. 420434.

Excerpt from:

Learning the language of viral evolution and escape - Science Magazine

In the final, darkest days of the deadliest year in U.S. history, the world received ominous news of a mutation in the SARS-CoV-2 coronavirus. Scientists in the U.K. had identified a form of the virus that was spreading rapidly throughout the nation. Then, on January 4, Prime Minister Boris Johnson announced a lockdown that began almost immediately and will last until at least the middle of February. Its been both frustrating and alarming to see the speed with which the new variant is spreading, he said in an address, noting that our scientists have confirmed this new variant is between 50 and 70 percent more transmissible than previous strains.

Those figures, based on an early estimate by British government scientists in late December, made for terrifying push alerts and headlines. Though this strain of the virus (officially called B.1.1.7) quickly became known as the U.K. variant, it has already been found in 45 countries, suggesting that the opportunity to contain it with travel restrictions has passed. On January 8, Australia locked down Brisbane, a city of 2.3 million people, after discovering a single case.

Each day, B.1.1.7 is being found in more people in more places, including all around the United States. Experts have raised dire warnings that a 70 percent more transmissible form of the virus would overwhelm already severely stretched medical systems. Daily deaths have already tripled in recent months, and the virus is killing more than 3,000 Americans every day. From a purely mathematical perspective, considering exponential growth, a significantly more transmissible strain could theoretically lead to tens of thousands of daily deaths, with hospital beds lining sidewalks and filling parking lots.

Read: The problem with stories about dangerous coronavirus mutations

To make matters worse, the warnings from Britain were followed by headlines about yet another variant, B.1.351, in South Africa. Then another concerning variant was identified in Brazil. News reports speculated that these strains may resist vaccines. Some experts cautioned that the mutations could render current treatments less effective. Scott Gottlieb, the former director of the FDA, said last week: The South Africa variant is very concerning right now because it does appear that it may obviate some of our medical countermeasures, particularly the antibody drugs. On Tuesday, Anthony Fauci echoed that concern, calling the variant disturbing.

These new variants demand to be taken seriously. Skyrocketing case counts in the U.K. suggest a potential to do enormous damage, and the identification of B.1.1.7 in so many countries is noteworthy. Still, we dont yet know whether either variant will become as dominant worldwide as they have in their respective countries. They might spread widely and cause tremendous harm. They might also do neither.

The sheer scale and capacity of this virus are challenging many things we thought we knew, but the basic laws governing its evolution are not among them. All viruses are constantly evolving and changing, just as human populations are. When a virus is spreading as widely and rapidly as SARS-CoV-2, spinning through trillions of generations each minute, adaptation is inevitable. The transmissibility of the virus will change. The severity of the disease it causes will change. Its ability to evade our immune system will change. It very well may evolve to circumvent our current vaccines.

Thanks to genetic-sequencing technology, we can watch this evolution in real time. We can see the changes in a viruss genes before we even know what they mean for the spread of disease. Charting the course of this evolution, and assessing its significance, has quickly become a foremost challenge of the pandemic. The peril is not that the virus will suddenly change in an extraordinary way that transforms the pandemic, but that it is changing in small, ordinary ways that are playing out on a vast scale, and whose significance we may not appreciate until its too late.

Almost exactly a year ago, in January of 2020, a flight attendant warned the renowned Chinese virologist Zhang Yongzhen: It was time to turn off all portable electronic devices. He was sitting with his phone to his ear. On the other end of the line, his Australian collaborator Eddie Holmes was pleading with him to publish the genetic code of the novel coronavirus.

The Chinese government had forbidden this. Yongzhen was torn. The world did not yet know the cause of the rapidly spreading respiratory infection, and he seemed to have uncovered it in a sample of sputum from a severely ill person in Wuhan. Using genomic sequencing to unravel the code of the virus, he had found what appeared to be the blueprint of a new coronavirus.

He told Holmes to publish the code. When Holmes did so on Twitter, the international scientific community pounced. Within days, researchers in Thailand were able to verify that the same virus had infected patients there. Scientists at the U.S. National Institutes of Health began to work on a vaccine. The code became the backbone of the Pfizer/BioNTech and Moderna vaccines, which owe their development to the speedy identification and sharing of the genome.

Read: The end of the pandemic is now in sight

The exact sequence that Holmes tweeted is now a relic. The virus it represented is gone, replaced by many, many, many subsequent generations. New lineages have arisen in different parts of the world, and hundreds of thousands of slightly different sequences have been added to an international database. There are now thousands of unique SARS-CoV-2 genomes, each the result of myriad permutations of mutations in the code. There is no single, standard genetic code for this coronavirus, any more than there is a standard human genome.

The term variant is misleading, in that it creates the idea that all the other viruses are the same, explains Ramon Lorenzo Redondo, a genomic analyst at Northwestern Universitys Feinberg School of Medicine. Technically, every version of the virus is a variant. Even within a single person, the virus changes and evolves many times. If you were to have your bodily fluids sequenced on different days, the viral strains would show new mutations. Viruses operate as a cloud of mutantsa swarm of mutants, Redondo told me.

This is not a flaw in the system, but rather the way viruses work. When it comes to reproduction, viruses are sloppy. The speed and scale of their replication come at the cost of accuracy; they operate like a spam email marketing scheme, favoring inundation over meticulous grammar. Insofar as a virus can be said to have a goal, the goal is to ensure as many future generations as possible. To that end, it fires off shotgun blasts of imperfect clones, gambling that a few will make their way to other cells and penetrate them.

Almost all of these accidental mutations are inconsequential: The virus still looks and functions just as its parent before it did. Over time, though, sets of mutations can layer on top of one another and accumulate, and the virus begins to function differently. Some of these differences confer an advantage of one sort or anotherfor example, increased transmissibility.

What were observing is very expected, Paul Turner, a professor of ecology and evolutionary biology at Yale, told me. If a population can improve in its environment, evolution lets that happen. The virus population size is expanding, and mutations spontaneously occur.

Although its not news that the virus has mutated, its extremely important to keep an eye on the general direction of the changesand what they mean for the humans whose cells are being hijacked. If you see a mutation that could allow the virus to escape detection by the immune system, or escape vaccine coverage, thats very worrisome, Turner said. We dont have evidence of that yet.

The likelihood of these scenarios depends on a few factors. Some viruses mutate more readily than others: Influenza mutates so quickly that new strains spread around the world each year, requiring the creation of new vaccines. Measles, by contrast, mutates slowly, so people who were vaccinated decades ago are very likely still protected. Coronaviruses typically dont mutate very quickly, Turner said. I dont see any evidence that this coronavirus is going to suddenly become like influenza. But right now there are so many people infected, and the virus is in a new environment [humans instead of bats], so Im not surprised that evolution is pushing it to improve.

In the long run, he believes, the spread of this coronavirus will more closely resemble measles than flu. Although we may need to update our vaccines occasionally, we wont need to do so every year. But as long as rates of infection remain high, the coronavirus is likely to acquire, over months or years, the ability to at least partially bypass our immune responses. Second-time infections may be less severe, but their severity also depends on how the virus evolves. And we may develop immunity to one variant but not to another.

In anticipation of such complexities, Redondo and others have been creating and updating phylogenetic mapsessentially, family treesfor this coronavirus. Groups with a common ancestor are referred to as a lineage. A lineage is something like a human family: different individuals sharing a common ancestor. (B.1.1.7 and B.1.351 are separate lineages that evolved similar changes in their spike protein independently.)

Genetic commonalities can also define broader groups called clades. Last spring, a clade known as D614G came to dominate the world. This was attributed to a mutation in the spike protein that made this group more transmissible than previous strains. And this was just one part of a family tree thats now more like a forest. The two first clades that were defined have disappeared, Redondo said. Right now five major clades are jockeying for dominance, he said, but the picture is constantly shifting.

The emergence of a new clade can be as difficult to predict as any rise to global domination. Obsolescence and dominance are determined by the qualities of the lineages, the characteristics of the host populations, and the legacies of previous microbial invaders. The fact that a lineage or clade is dominant in one place, within one group of humans, does not mean it will be in others. So far, the U.K. and South African variants are dominating local surges, but they are not expansive enough to be considered clades. The South African variant, for example, accounts for about 90 percent of the genetic sequences analyzed in the country, but remains a minor player elsewhere.

Similarly, the B.1.1.7 variant was identified in the U.K. in September, yet so far dominates only one geographic region. Although genomic testing in the U.S. is relatively sparse, were doing enough sequencing that we know its not that common in the U.S., says Nathan Grubaugh, a microbial epidemiologist at the Yale School of Public Health. This variant doesnt seem to be more than 1 or 2 percent of cases at the moment. Its here, and its very widespread, but its low in frequency. I think, for the most part, this is true globally.

For now, these variants may be thought of like weeds in a garden. They have shown that they have the capacity to take over in some areas. There is a plausible mechanism that could allow them to do so elsewhere: Both the U.K. and South African variants share a mutation that manifests as a subtle change in a key site where the virus binds to human cells. But weeds take over for many reasons, and sometimes they have more to do with the garden than the weed. Human populations vary in so many waysbehaviorally, genetically, immunologically, geographically, environmentallythat the degree to which a regional surge in cases is due to a change in the virus itself is extremely difficult to discern. And that leads to some uncertainty. We may have it wrong in the end, [and] its not actually more transmissible, Grubaugh told me. It seems to be, but we may have been fooled.

Even if these variants are indeed as transmissible as the rising case numbers suggest, transmissibility is only one determinant of a viruss overall potential to do harm. Sometimes viruses become more transmissible but ultimately less dangerous. And of course, each new variant is but an intermediate step toward some other form of the virus. The real challenge is understanding how any given change fits into all of these larger patterns, and what that means for us.

The plot of Michael Crichtons 1969 novel, The Andromeda Strain, hinges on an extraterrestrial microorganism that mutates its way out of containment. A similar narrative device drives the film Outbreak, in which a bleed-from-the-eyes virus suddenly becomes airborne. What amounts to a lazy screenwriting clich has loaded the word mutation with such horrifying subtext that its almost unusable. The process of viral evolution is much subtler, and requires a careful eye to detect. That subtlety is what makes it dangerous.

There are two basic ways that a coronavirus can become more transmissible. One is by binding more effectively to human cells. When this happens, a person who inhales viral particles becomes slightly more likely to develop an infection. The other is by replicating more efficiently, creating higher numbers of viral particles (higher viral load) in an infected person, so that they exhale more particles with each breath (making it statistically more likely that one of the particles will infect someone else). If a breath contains 10 percent more viral particles, it is that much more likely that one will land in someone elses nose.

Its unclear whether one or both of these mechanisms are at play in the U.K. and South African lineages, but we know their effects can be complex. If a person is carrying a much higher viral load, for example, they may get sick more quickly. That sounds badand it certainly is for that person. But a shorter asymptomatic period could ultimately make the virus easier to contain. This was the case with the first SARS coronavirus, in 2003 (SARS-CoV-1), which caused a more severe disease than SARS-CoV-2 does, but killed far fewer people in total because each case was identifiable.

By the same token, this coronavirus could evolve to cause a somewhat less severe illnesssomething slightly closer to that caused by the other four endemic coronaviruses. The common cold is extremely transmissible, yet rarely fatal. This makes sense from an evolutionary perspective: Viruses that kill their hosts are less likely to become dominant than those that dont. It could be that transmissibility correlates with being kinder to your host, Turner said. Weve observed that in other realms of virus evolution. Natural selection would, hypothetically, favor the versions that leave people feeling well enough to be out and about, spreading the virus to other hosts.

Major changes in the severity of the diseasein either directionare unlikely, but the scale at which this virus is operating means that small differences in things like transmissibility are amplified, and can manifest as significant changes in how many people get sick. Experts widely agree that playing it safe in the coming weeks is prudent. Oliver Pybus, a professor of evolution and infectious diseases at the University of Oxford, emphasizes that understanding why B.1.1.7 took over the U.K. is extremely scientifically difficult. He has been at the forefront of identifying and tracking the variant, but says huge questions remain unanswered. Theres still considerable uncertainty as to the long-term consequences of B.1.1.7, Pybus told me. We dont even know whether this lineage truly originated in the U.K., with so many countries not doing this surveillance.

Though much of the world is now on alert for this particular variant, Pybus said that very few places are sequencing genomes as comprehensively as the U.K. is. In some places, institutions are sampling but not sharing findings in the public domain. Both elements are crucial. Testing with PCR or antigen tests alone is no longer sufficient. Positive tests must be followed by analyses of the genomes of the virus. The more genomes we have, the more effectively we can identify anomalous patterns, both to raise alarms early and to avoid raising false ones.

The U.S. is especially far behind the U.K. in this regard. Without a baseline level of genomic surveillance, Yales Grubaugh told me, we do not know if a city like New York would be as devastated by B.1.1.7 as London has been. The forecast for any given variant depends on context that we lack. I dont think any one state is doing enough sequencing yet, Grubaugh said. Sequencing is the most important thing. We dont have a big organized project like in the U.K. What we have is a bunch of individual labs, mostly at academic medical centers. Sporadic sequencing is arguably as bad as none at all, in that it can fail to represent how and why variants are spreading. And focusing too narrowly on hunting one particular variant can mean failing to notice other, possibly more consequential warning signs.

The hunt for any one variant also introduces selection bias, making it hard to know if the variant is truly spreading more readily than others, or if we are just looking harder for it. After finding a person carrying B.1.1.7 in New York last week, for example, state health officials sequenced the genomes of nearby casesan approach that is likely to find a disproportionate number of B.1.1.7 cases. Without constant, widespread surveillance testing, Pybus said, its difficult to discern an accurate overall picture.

The field of genomic epidemiology is going through its adolescence in public, developing in full view of the most extraordinary event of the century, Pybus told me. The ability to identify new viral lineages before we even understand how they will affect people may allow epidemiologists to warn of consequential variantsbut may undermine their credibility when strains dont prove as dangerous as headlines predict.

Even if we cannot contain this particular variant, were learning from its spread. Preventing more virulent strains from becoming dominantwhen they inevitably do arisemay be possible if we can track genomic patterns more widely, so that we have the context needed to determine whether a strain is indeed uniquely dangerous. If we can take steps to contain a new threat early enough, it may never become widespread. If we miss these opportunities, we risk repeating the kind of mistake that allowed the original SARS-CoV-2 strain to escape China in the first place.

Last week, Eddie Holmes reflected on the fateful moment when he tweeted the viruss original genetic code. It was a moment of triumph for collaborative science, but the work was just beginning. The triumph must be repeated daily. What worries me most of all is if politics gets in the way of data sharing and science, he told Medscape. Step one has to be immediate, rapid, open data sharing. Speed is of the essence in a pandemic. Any barrier to working together makes this a much less safe world. That should be the lesson of this outbreak.

The changing genetic code of the coronavirus will not nullify our fundamental strategies for ending the pandemic. With better data, we can keep our vaccines and antibody treatments up to date and our shutdown measures as minimal as possible, and we can sever any ominous new chains of transmission. The spread of new variants is a stark reminder that we all have an immediate part to play in this. If you carry the virus, it will mutate within you. You could be the person in whom a new, even more threatening variant emerges. You could seed the entire world with it. But no matter how the virus mutates, the same basic preventive measuresthe unglamorous ones weve been lectured about for nearly a yearwill still have the power to ensure that you dont.

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The Evolution of the Coronavirus - The Atlantic

Andrew Young, Vice President of Project Engineering and Service Delivery at Akselos, gives an insight into the evolution of the digital twin technology and its application in the hydro industry.

Awarded as Technology Pioneer 2020 by the World Economic Forum, Akselos provides real-time predictive digital twins for large asset integrity management. Founded in 2012 and with operations in Europe, the US, and South East Asia, the companys products are designed specifically to help protect the worlds critical infrastructure with real-time, condition-based monitoring for large critical assets.

Using patented algorithms developed over 15 years of research at Massachusetts Institute of Technology (MIT) and funded by the US Department of Defence, Akselos software solution is helping the energy sector optimise operations, lower operating costs, and extend the life of ageing assets.

The Akselos software is based on a newly developed algorithm that is 1000 times faster than legacy technology. The result of 15 years of research in the mechanical engineering lab at MIT, it changes the process of structural assessment to allow for real-time, continuous monitoring of large assets something that has never been done before. Its new mathematical approach, called Reduced-Basis Finite Element Analysis (RB-FEA), scales up and accelerates the legacy approach to model large-scale assets in detail and accuracy. Described as the worlds most powerful structural analysis tool and the only one fast enough to cope with the speed of a sensor feed, it has been validated by over 100 scientific publications, an MIT patent, years of co-development with Shell, and subsequent software validation with the American Bureau of Shipping.

The technology changes how we look at structural integrity management, it empowers asset operators to monitor condition continuously, rather than the current workflow which relies on periodic inspection data to update conventional finite element models. This means that the understanding of the assets condition is on an as is basis - rather than at the time of the last inspection. The result is eyes on the asset 24/7, reassuring operators that structures are safe to operate and efficiency is increased through a move to risk-based inspection.

Akselos technology allows maintenance engineers to really pinpoint, using rules of science and engineering, where structural integrity issues are likely to occur. It is also possible to establish what the operational asset life is in terms of fatigue assessment while supporting predictive maintenance.

The digital twin is essentially a tool with which plant operator ESB can simulate many parameters around the day-to-day operation of the plant and visually pan through the entire structure in 3D and see how it reacts.

Akselos can simulate future events because of the nature of the physics-based twin. Whereas traditional engineering approaches are assessing the probability of a known and identified established defect reoccurring, Akselos can assess and identify the risk of any type of failure based on the structural assessment. Results can be made available within an operations time frame supporting the asset manager as a fault does not have to already be pre-identified.

Akselos' underground asset uses the rules of science and engineering

Turlough Hill pumped storage plant in Ireland had been in operation for almost five decades but the operator ESB had many questions on the actual structural condition of the main penstock and water distribution manifold. In addition, an in-depth assessment of the consumed structural capacity of plant had never been previously performed.

Akselos technology was used to build a structural digital twin of the large penstock and manifold for the plant.Thanks to Akselos high-fidelity structural modelling capabilities, ESB identified the digital twin technology as being suited to performing such an analysis for the very first time. Not only did ESB want to understand the consumed fatigue life of the station, but they desired a tool to run what-if scenarios for future and more demanding operating conditions.

Engineers at Akselos digitised all available data on the 700m long asset, from which a 3D rendered structural digital model was built. First of all they had to establish the geometries, interactions and stresses in the plant system to create an intuitive heat map. The navigable 3D model shows which, when and where stresses are forming through the power station and so help direct inspections to specific areas of concern, rather than just doing a random walk through.

The structural digital twin, built up from a fully digitised asset, takes in the live feeds of the current loading of the system, cycles of exploitation such as generation, pumping stoppages and allows a real time assessment of the stress distribution in the system. In addition, post-processing of the cycles allows for calculation of accumulated fatigue and residual structural life.

Akselos technology allows for a speed-up of these assessments into operational time frames, meaning that the operator can be fully informed on the state of the structure without stoppages. Typical approaches require a stoppage to drain down the entire system that can take weeks while inspections are conducted.

Engineering drawing (left) and digital replica (right)

The main objective of the ESB team was to get a new, up-to-date engineering understanding of the as-designed water distribution system of the hydro plant. In the original concept the main design parameter was static pressure containment with safety factors that were fixed in time. Time based degradation mechanisms that might impact safety margins were not considered as part of the original design, such as fatigue or changes to the thickness of the supporting surrounding structure. There was no original design life, but an anticipated operational number of hours had long passed in the current exploitation of the hydro system.

Another objective was to optimise the integrity management process and strategy: failure of this part of the system is not an option for the operator. The extreme size of the structure (for example over 8km of welding alone), observed cracking in some welded zones, low or zero accessibility on much of the structure, and high costs of drain downs, are part of the considerations that made the business case easily attainable.

With the advent of a tool such as the Akselos modeller there can be a new approach to the inspection strategy and better understanding of the required duration of the outages for inspection purposes due to greater control of the inspection scopes.

Finally, as the hydro plan enters the age of low carbon power generation with increasing renewables as part of the energy mix, there are also changes to the operational demands of the system. Stop-start generation to market prices or due to grid instability are giving a higher frequency and demand usage. These more frequent and more varied cycles of exploitation were not considered in the original design.

Identifying stress pressure loads with the digital twin

When the starting point is an existing asset operating beyond design life there are two immediate wins for the operator:

In the case of the Turlough Hill station another advantage is for the assessment of the impact of the new modes of operation, mainly higher cycle times with more demand on the hydro pumped station. With market driven operations, and the possibility for generation at times of highest price or storage at times of low or negative pricing, the pumped storage unit has become strategic in the operators portfolio.

If the hydro station or asset is a new build then there can be lower one-time engineering costs, along with reduced capital expenditure, as the simulation engine allows the design team to explore more options in terms of material types, assembly and welding approaches to assess optimal costing for the different design options.

In addition, the structural digital twin can be a powerful tool for the commissioning process when sensor data can be plugged into the digital twin to assess the stress distribution and any potential issues for operation. This can reduce the commissioning time and costs while also providing a good collaborative platform to involve all stakeholders in the process.

Finally, once in operation the digital twin can provide continuous monitoring of the asset and also give inputs to the inspection planning process.

In general, the earlier in the life of the asset that the digital twin is created, the sooner and the larger the value that can be generated. Essentially there is only a one-time set-up cost for digitisation. The earlier the digital twin is active and enabled for an asset then the greater the number of value frames can be recouped across the asset management life cycle.

The Turlough Hill digital twin has secured operator confidence for continued operation of the plant an effective life extension case. It has also provided an informed basis on which to assess the ongoing changes and evolution of loading of the asset in an increasingly dynamic energy generation context. Furthermore, the digital twin has demonstrated its ability to identify areas that need inspection by highlighting areas of defects. These hotspots were independently validated against cracking found during earlier inspections.

As a measure of its success, the digital twin initiative won the internal ESB performance award for 2020 and is considered to be a major improvement over alternatives in the marketplace to assess plant structural viability and forward management.

Currently Akselos is working with ESB and other hydro plant operators in Europe on further digital twin enhancements, as well as applications for critical areas such as the turbines blades and generators in addition to the water generation system (penstock and manifold).

The key benefits come from a highly transparent method for structural integrity assessment, grounded in recognised engineering standards and usable by all key stakeholders across the entire ecosystem.

The digital twin had high fidelity structural modelling capabilities

Looking to the future, Akselos sees hydro as a key part of the renewable energy mix where the digital twin can add a lot of value. So Akselos is working to improve the feature set supporting operations and inspection in a very dynamic way. In addition the company is also looking for partners across the energy ecosystem to improve the offering further by leveraging an open standards based software platform with application programming interfaces. This will help provide the best and most integrated solutions for plant operators.

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Evolution of the digital twin - International Water Power - International Water Power and Dam Construction

By: Thomas Szocinski, Director of Vapor Intrusion,Land Science

Since the early 1970s, environmental professionals and health experts have called attention to contaminant vapor intrusion and have alerted the public to the serious health problems caused by poor indoor air quality.

Beginning with radon, the understanding that chemicals could enter occupied spaces and cause adverse health effects has steadily expanded over the ensuing decades. It is now widely known that hydrocarbons and industrial solvents are commonly found on brownfield properties where former industrial sites once operated many of which are now undergoing redevelopment within major metropolitan areas. Even in very low concentrations, these compounds can pose significant health issues, such as cancers and birth defects.

In order to safeguard human health, it is necessary to prevent vapors from entering occupied spaces, but it was not until recently that a vapor barrier was created specifically for that purpose.

Installation of a TerrShield barrier in downtown Sacramento as part of a large brownfield redevelopment project.REGENESISDespite the increased knowledge related to the problem, effective mitigation solutions still lagged behind. Historically, vapor barrier solutions were adopted directly from the waterproofing industry. These barriers were typically comprised of plastic sheets (e.g. polyethylene, polypropylene, geotextiles, etc.) bonded together by a spray-applied asphalt latex mixture containing styrene butadiene rubber (SBR).

Looking to create a better, safer and more long-term solution, scientists and engineers have been conducting intensive scientific research and development to create a first-of-its-kind suite of safe, cost-effective passive vapor barrier systems designed specifically for vapor intrusion. To do so, they examined two main components the base layer and spray-applied seal and reimagined them from the ground up.

To upgrade the base layer, vapor barrier scientists decided to incorporate aluminum, which is well known to be able to prevent the permeation of organic volatile compounds. By sandwiching it between layers of flexible polyethylene, they developed an easy-to-install base layer with chemical resistance 100 times higher than the simple waterproofing sheets composed of high-density polyethylene (HDPE) used in the past.

Installation of a TerrShield barrier in downtown Sacramento as part of a large brownfield redevelopment project.REGENESISTurning to the spray-applied seal, scientists focused on finding a replacement for the generic latex-asphalt spray components previously adapted from the waterproofing industry. These sprays historically included synthetic rubber components such as SBR which was hydrophobic and thus very effective at repelling water, but also had an unfortunate tendency to sorb industrial solvent and hydrocarbon contamination, concentrating them until they would eventually break through the membrane and potentially enter the indoor air space. Additionally, applicators of the SBR material would need to clean their equipment with petroleum hydrocarbon solvents which may cause even greater damage to the affected properties where it is applied.

After evaluating several potential options, scientists developed a Nitra-Core spray-applied barrier material, designed to replace SBR-based latex waterproofing material with nitrile, a key component of chemically resistant personal protective equipment used in handling hazardous materials (e.g. blue disposable gloves).

Studies comparing the chemical resistance of the new nitrile-advanced asphalt barrier material to generic latex-asphalt barriers determined the nitrile-advanced material to be 10X more resistant as a contaminant vapor barrier against the common industrial solvent contaminant trichloroethylene with the generic asphalt-latex material allowing 10X more contaminant vapor diffusion across the barrier.

Land Science, a division of REGENESIS

In addition to the increased chemical resistance, both the base layer and spray-applied seal were designed to be easily installed, thus reducing construction timelines and saving money. They were also built to withstand the stresses of a construction site both during installation and afterwards (i.e. foot traffic, heavy equipment, and the occasional dropped tool), thus preserving its structural integrity and maintaining its efficacy.

Instead of just repurposing inferior technology that was developed for a different industry, scientists and engineers understood that in order to effectively tackle the issue of vapor intrusion, they needed to approach the problem with a full understanding of its challenges to effectively meet the needs of the environmental professionals tasked with guarding human health. The ideal vapor barrier would be easy and quick to apply, physically tough, and chemically resistant.

Through the innovative use of barrier materials such as aluminum and nitrile, researchers have created a suite of barrier systems that meet all those criteria and, unlike the vapor intrusion solutions of the past, are specifically designed to handle the vapor intrusion problems of today.

Land Science, a division of REGENESISThomas Szocinski is the director of vapor intrusion for the Land Science division of REGENESIS, Inc.the global leader in advanced technologies for contaminated site remediation. Since 1994, REGENESIS has developed and commercialized a range of proven soil, groundwater and vapor intrusion products to treat a wide variety of contaminants. Land Science was established in 2008 to address the increasing occurrence and regulation of vapor intrusion. It provides scientifically proven solutions that offer environmental engineering firms and developers cost-effective and innovative vapor mitigation technologies for environmentally compromised properties. For more information visit:www.landsciencetech.com.

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Vapor Intrusion Mitigation Part 1: Reimagining the Next Evolution in Concrete Vapor Protection - ForConstructionPros.com

NEW ORLEANS New Orleans Saints Head Coach Sean Payton spoke with media Thursday about his upcoming matchup with the Tampa Bay Buccaneers in the NFC Divisional Round of the playoffs.

Thursday, Payton was asked about the evolution of Tom Brady and this relatively new Bucs team in a season overshadowed by COVID-19.

Relatively no offseason, so someone like Tom (Brady) who comes to a new club, youre not provided those normal OTAs, call it mini-camps where youre having opportunities to begin to put in a system. I think that, without speaking for them, I think that we saw that progression thats probably still evolved throughout the course of the season and quite honestly with us the same way, says Saints Head Coach Sean Payton.

Payton adds that this last half of the season, or last third of the season I think theyre playing at a very high level.

The Buccaneers finished the 2020 NFL regular season ranked top-5 in the NFL in total offense, passing, and rushing.

Their defense also finished 5th in total yards allowed per game, 3 spots behind the New Orleans Saints.

The New Orleans Saints and Tampa Bay Buccaneers will play this Sunday at 5:40 p.m. in the Superdome.

For full coverage of the New Orleans Saints NFC Divisional Round matchup with the Tampa Bay Buccaneers, dont forget to tune into ourRoad to Tampa: Divisional Round SpecialFriday at 6:30 p.m. on WGNO.

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Sean Payton describes the evolution of the Tampa Bay Buccaneers this season - ArkLaTexHomepage

NOIDA, India, Jan. 14, 2021 /PRNewswire/ -- "Regardless of the critical period of retrenchment our organization - like many others - was going through, we ensured that we shed none of our valuable staff and that each of our diligent employees received the hard-earned pay they were entitled to, every month."- Manmeet Singh, Head, HR & Administration, T&T Group

The COVID-19 pandemic was perhaps one of the most testing times in the recent world history. From healthcare to economy, the pandemic altered the very fabric of human life - and the corporate world was no exception. Businesses and industries all across the globe suffered a massive setback, with many of them even succumbing to the crisis. Such unprecedented circumstances, hence, called for extraordinary measures on part of all the divisions and subdivisions of business organizations to ascertain not only their survival but to also enable them to break even and grow. In view of that, at T&T, we adapted to new ways of working to keep the operations going while competently addressing a multitude of complex issues, ranging from leadership development to employee encouragement. The HR team went through a thorough study of every operation and work routine and presented a systemic viewpoint that ensured communication, collaboration, and coordination across functions, units and departments. We also facilitated dialogues that help ensure the right amounts of re-prioritizing, re-proportioning and reinvention of organizational goals to adapt to the changing dynamics of the market.

As the pilot of an organization's talent plane, the HR division has a responsibility to create connectivity as well as create practices that maintain efficiency and effectiveness, and top of everything, ensure a healthy work environment. Considering that, we designed and brought into practice a new and enhanced working style that helped us in managing from a distance, keeping employees motivated toward the goal in the midst of ambiguity, providing clarity and calm, keeping a track of work progress, and above all, building a community while we were at it.

While we designed an effective framework that brought the organization's working pretty much back in action, we also made sure that it covers anything and everything on the side of the employees too. The COVID-19 pandemic had far-reaching impacts on people's physical, mental and emotional health, which is why the wellbeing of our employees was at the center of our framework. The HR team dedicatedly worked to provide the employees with everything they would need, be it employee assistance programs, financial counselling, or programs for exercise, nutrition and mindfulness. We also needed to realize that this wasn't a regular Work From Home situation, rather this time the employees were at home fighting the crisis and yet trying to deliver the best in their capacities. Being a keeper of employment, benefits systems and pay, the HR department catalyzed new and expanded approaches to bring ease in work life. The employees were given all the required accesses and provided with the essential resources to maintain a smooth working. Most importantly, and regardless of the critical period of retrenchment our organization - like many others - was going through, we ensured that we shed none of our valuable staff and that each of our diligent employees received the hard-earned pay they were entitled to, every month.Furthermore, those who burned the candle at both ends to serve in the best interest of the company were rewarded with appraisals and incentives.

The pandemic has altered our working culture in many ways, but it has also brought our organization together - stronger than ever. T&T Group has always been ready for challenges and to drive the Indian real estate into the unexplored horizons of technology and advancement, and this time is no exception. With India's first Digital Housing, we elevated the way people lived; now, with our sound HR policies and management, we established an exemplary work culture - one that is resourceful, supportive, and well-equipped to face unforeseen challenges.

About T&T Group

T&T group contemplates to change the landscape of the homes to usher India into the futuristic era. It has already started with the T homes, and the next project will strive to better that on several fronts. It will leverage the win-win combination of Artificial Intelligence (AI) and the Internet of Things (IoT) and propel the living experience to a whole new level.

SOURCE T&T Group

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An Exceptional Year Of Organizational Evolution For T&T Group: An Update from its HR and Administration Head - PR Newswire India

This year, the pandemic has kept us apart physically, but humans are nothing if not innovative, and communication is a basic human need. While Zoom and other video calling technologies quickly filled much of the void for those desperately missed in-person communications; the phone is still one of the most important communication channels for financial institutions.

Research shows that the phone channel is still consumers' preferred method for handling complex or sensitive financial matters. The popularity of the phone is not expected to let up either. In our survey of financial institutions, 70% of respondents said their outbound calling increased this year, and over 65% said they expect it to increase in 2021. Thats in spite of the challenges of robocalls, call spoofing, and fraud.

Theres a lot on the horizon when it comes to the phone, including new regulations and standards to protect us, along with new features that will cement the phone as a more modern tool with an improved customer experience. Below are predications from three Neustar experts on phone channel trends in 2021 financial institutions need to be aware of in the new year.

An enhanced customer experience

Given the deluge of robocalls, fraud, and call spoofing, most financial institutions find customers dont readily pick up the phone these days. In fact, 88% of business calls go unanswered.

But theres good news. According to James Garvert, SVP and general manager of caller identification solutions at Neustar, illicit robocalls have eroded trust in the phone channel, but in the coming year, we expect to see over 50% of mobile phones utilising robocall protection solutions alongside call authentication standards like STIR/SHAKEN which is mandated by the FCC for use by June of 2021. Whats more, the emergence of branded calling, which display rich call data such as logos, reason for the call, and more, on the mobile display coupled with STIR/SHAKEN authentication will reassure customers about who is actually calling.

We anticipate that these factors will improve call answer rates, and trust will be on the rise. To increase trust in the channel, financial institutions should look to provide their customers with branded, authenticated call experiences.

The incessant need for more bandwidth will continue.

According to John Denemark, SVP and general manager of carrier provisioning at Neustar, "Financial institutions are struggling to meet the sudden and dramatic shift to remote relationships with customersand remote workers. That centers around the need for faster and more secure bandwidth.

Internet service providers and telecom carriers have moved to increase bandwidth capabilities but, in an age where customers are used to getting what they want, when they want it it cannot happen fast enough.

5G presents a new breadth of possibilities for the industry, but its years away before everyday customers will truly feel its impact, Denemark continued. In 2021, we will see the race intensify as the telecom industry seeks to massively upgrade, expand, and densify their networks, all while monetising those investments and simplifying and automating their operations. Financial firms will experience significant benefits from those upgrades.

Robocall mitigation wont stop at the border

We learned this year that many scams originate overseas and slip into our calling ecosystem, often through smaller voice carriers. While Canada has taken the first steps toward adopting robocall mitigating STIR/SHAKEN protocols, more is needed from the international business community, and countries around the world, to make any meaningful impact against international illicit robocalls. Any financial institution operating in international markets needs to understand how their call environments change in different international markets knowing so can make the difference between a missed call or a positive customer experience.

Jon Peterson, vice president and fellow at Neustar, noted, "The U.S. government and enterprises have taken aggressive steps to curb robocalling but illicit robocalls are not bound by our borders. Look to 2021 for the early signs of international progress beyond North America, with other countries stepping up to either create new or adopt established caller identity authentication protocols to reduce the amount of illicit robocalls worldwide."

To be able to provide excellent customer experience by phone, financial institutions must first be able to connect with their customers. 2021 will bring in new standards such as STIR/SHAKEN and technologies such as branded calling, to return trust back to the phone call.

Financial institutions need to be aware of these trends to take advantage of rebounding trust in the channel and to not only more easily and readily connect with customers, but also provide seamless experiences over the phone that will keep customers safe, secure, and valued.

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The evolution of the phone call in 2021 - Finextra

Baseball has gone through many changes over the past decade, but no element of the game has been so radically transformed as the fine art of pitching. The game is still the game, but the methods by which pitchers develop, the way theyre coached, and the statistics and data by which theyre evaluated by teams, media, and fans alike have undergone a rapid evolution over the last 10 years.

Of course, some of this evolution dates back much further. The early sabermetric era produced some fundamental insights that laid the foundation for what was to come as early as the late 90s. As teams became more and more focused on the FIP components, pitchers who racked up strikeouts and prevented home runs became more and more attractive as compared to efficient, pitch to contact types.

That perspective only sharpened as we entered the era of spin data with the advent of the Trackman system and MLBs Statcast. Quickly, we learned that whiffs are correlated to velocity and to movement. My initial simplistic assumption that high spin equals more movement equals more strikeouts was shared by a lot of the early front offices pioneering the use of that information. But then you had to consider that sinkers and changeups all have some backspin, and for a pitch with backspin to drop more than normal, you needed less backspin than average.

So there you go, the solution was simple. Go buy up all the undervalued high spin pitchers you could find, and as a backup plan, any extremely low spin pitchers as well. Any major deviation from average seemed like it could be a good thing.

Early adopters of the importance of spin data were joined by an influx of coaches and front office analysts as these insights were backed up by ever more detailed information. Raw rpms quickly became a sought after item among advanced teams. The ability to spin the ball at higher rates than other pitchers was quickly identified as one of the most attractive attributes a pitcher could possess. That insight led to sweeping changes in scouting at all levels of the game. The smartest teams in the room, such as the Tampa Bay Rays, Houston Astros, and Los Angeles Dodgers, quickly adapted their evaluations to emphasize pitchers who could spin the heck out of the ball. The rest have been playing catchup over the past few seasons.

Meanwhile, pioneering research by teams and innovators like Driveline Baseball and others quickly took their understanding far beyond raw spin. Many teams are still struggling to catch up and make use of all this new information, even as the gathering and study of it has become ubiquitous.

When we first think about the relationship between spin and movement, were thinking about the Magnus effect. A curveball has topspin, so it moves toward the ground. A four-seamer has backspin that keeps the pitch fighting gravity and riding through the zone. Sidespin on a two-seam fastball makes the ball tail to the pitchers arm side. This changes if you drop your release to sidearm or submarine, but you get the drift. Specifically, the Magnus effect describes the fact that a spinning sphere will move in the direction that the spin on the front of the ball is moving.

When teams first started collecting spin rate data on a grand scale, the Magnus effect was really all they were thinking about. Presumably, the faster the spin, the more movement youd create. To be clear, there are obviously many, many unrelated factors that go into making a good pitcher, but these early insights immediately had a profound effect on how early adopters at the college and pro levels evaluated and taught players.

High spin guys suddenly saw their stock boosted at all levels. They were encouraged to throw their best stuff more often and instructed to dial in their breaking balls. Low spin pitchers saw their stock drop a bit and were encouraged to work on their changeup as the only salvation possible. Particularly as more and more teams lacking the necessary expertise got on the bandwagon in 2016-2017, there were success stories but also widespread misapplication in the ways the new data informed their understanding of pitching.

However, by this point early research from Driveline Baseball and other cutting-edge theorists with far more experience than most teams had already quickly illustrated that the Magnus effect didnt explain pitch movement alone. Several other factors were involved.

As they discovered, of the types of spin that are interacting on the balls path through the air, gyro spin, which is more like football or rifle spin, can actually inhibit movement on pitches typically designed to be affected most by high spin rpm, particularly four-seam fastballs and curveballs. Active spin, which is spin that contributes to the pitchs movement via the Magnus effect, as opposed to gyro spin, which does not, began to be expressed with the term spin efficiency.

For a quick visualization exercise, imagine a spinning ball moving toward home plate, and it has a stick through the center that is perpendicular to the direction its moving. So in this example, its sticking out to both sides. You can hold both ends of the stick and rotate it like a steering wheel. That changes the direction that the Magnus effect will move the ball but whether it be topspin, backspin, or sidespin, all will have highly active spin on the pitch.

Now, hold the two ends of the stick and move one side forward and the other back like steering a motorcycle. By doing so, youve introduced gyrospin, reducing the active spin involved. Most pitches have some of both, but initially pitchers were trying to reduce gyro to get the most Magnus effect movement possible. But as it turns out, gyro works too if you know what youre doing.

The first well-known experiment to really put seam alignment into effect was Trevor Bauers creation of a two-seam fastball variant he called the Laminar Express. Essentially, recognizing that the seams create drag, Bauer tried to orient the seams on this pitch so that one side of the ball was smooth throughout its flight, and the side he wanted it to move toward had more seam involved. By doing so, he hoped to get more arm side run on the pitch as seen below.

For pitches that are designed to take maximum effect of the Magnus force, you dont want gyrospin involved. You want the most active spin possible to maximize the Magnus effect on the pitch. This tends to apply to four-seam fastballs and curveballs most specifically. Gyrospin inhibits the Magnus effect, and yet can, with the proper seam alignments on the ball, actually create tons of movement as well movement that may bring sinkers and changeups in particular back into prominence.

This is where Utah State University Professor Barton Smiths conception of seam-shifted wake enters the picture. It should be noted that plenty of other luminaries, like Tom Tango at Statcast, or Harry Pavlidis, editor-in-chief at Baseball Prospectus, were all aware that active spin wasnt explaining movement fully. And obviously the research done by Driveline Baseball most notably has driven all this evolution in the first place. There were plenty of people studying the subject, but Smith, a professor of mechanical and aerospace engineering, was able to do the actual research that uncovered much of this, and he coined the term, seam-shifted wake.

The issue, as in Bauers laminar express, is the seams. Obviously, the baseball is not perfectly round. Beyond the vagaries of baseball construction, which have come under enormous scrutiny because of the juiced baseball controversy, the seams create drag. Individual pitchers and coaches have understood vaguely that seam alignment can affect movement all along of course, but this was largely anecdotal, just a matter of trial and error.

Its always been reported by hitters that some pitchers have weird late movement, for example, and spin rates, spin axis, and spin efficiency couldnt account for it. The famous story of Mariano Rivera throwing a cutter accidentally while just playing catch, eliciting shock from his throwing partner as it dove away from his glove late, is presumably an example of a pitcher stumbling across the seam-shifting effect that can make a ball move in ways its spin would not lead you to predict.

Heres a seam-shifted wake pitch from Dustin May. Its been nicknamed the Demon Sinker. Despite modest amounts of raw spin and poor active spin on the ball, the thing moves like crazy. The effect is featured in Dan Strailys changeup below the May clip as well.

Essentially, by adjusting grips and release to put more of a balls stitching spinning on one side of the ball rather than the other, there are particular alignments that make the ball move dramatically more or less than the Magnus effect could explain, depending on how the spin and drag interact.

Pitchers have always understood that scuffing one side of the baseball, or loading it with extra weight from saliva, pine tar, or other forms of tampering, could produce some unique effects. Now, all those trick pitch effects are becoming better understood and studied scientifically rather than anecdotally. Seam-shifting can do similar things for a pitcher, but without breaking the rules. However, its a subtle art to put into practice and no doubt were only at the beginning.

Rob Friedman, better known as the Pitching Ninja on Twitter and Youtube, has been a great public promoter of all this new research over the last few years. About a year ago, as this concept was first starting to get discussed, he put together a quick primer that should explain things better with some visuals.

Professor Smith was recently a guest on the Pitching Ninja podcast and explained most of the concepts in a longer conversation with a few visual aids. Its well worth your time and is embedded below. You can also visit his site, Baseball Aerodynamics, to read through the experiments hes conducting and the data hes gathered. The quick one-sentence takeaway is that theyre beginning to understand, in a much finer way, how the subtle interaction of velocity, spin rate, spin axis and direction, seam alignments, and release all work together to make a pitch move more or less, or even in more than one direction on its path to the plate. Smith actually has a new post up with an explanation of some of the terminology and debates over usage as well. Sort of an in-progress glossary of terms and explanation.

We havent gotten to factoring in barometric pressure on the field and wind direction, but Im sure thats not far behind either. Im only partly kidding.

On that podcast they also delve into the fact that slightly wobbling axes and/or particular seam alignments and gyrospin could make the ball move multiple ways on its path to the plate. Hitters have discussed seeing this effect forever, but much like the curveballs movement was once thought by some to be an illusion, pitches that demonstrated late movement in a different direction from the pitchs initial trajectory seemed to have been creatures born from illusion (excepting knuckleballs, of course). That holy grail of pitching late movement, sometimes not even in the same direction as the initial spin movement is now understood as being caused by seam-shifted effects. Ultimately, the utter lack of spin on a knuckleball brings seam-shifted wake effects fully into play and explains why a knuckler can often move in multiple directions on its way to the plate.

Magnus effect movement, particularly on high active spin pitches, is consistent on the path to the catchers glove. A curveball with high active spin dives in the same direction throughout the pitch. A pitch with more gyro, and the proper seam alignments, may start moving one way, and then as gyro re-aligns the seams in relation to the direction the pitch is moving, drag may occur on the seams that cause it to move in a different direction, or more exaggeratedly in the same direction it was already breaking toward.

Hitters learn to predict spin movement by seeing how the pitcher released the ball. Pitches that move contrary to the visual spin pattern a hitter can see from home plate can really confound the hitters predictive ability.

Well, obviously, this matters to everyone in baseball. The concept of seam-shifting opens a new paradigm for the two-seamer, sinkers, and changeups in particular, but may ultimately be brought into play with any pitch that doesnt have high amounts of active spin. Presumably even the high active spin pitches can be tweaked a bit with the seam-shifted wake concept. Generally speaking though, gyro is the friend of seam-shifted effects, and the effects are more pronounced with pitches with plenty of it.

The difficulty in knowing how much this will change pitching, is that each pitcher is slightly unique. Not only do you have to find the sweet spot with the seams to enhance these effects, youve still got to repeat pitches the exact same way out of the same release point while locating effectively to different spots. In other words, as a pitcher you still have most of the problems you always had.

Command, velocity, mentality, durability, reading hitters, are all just as important as ever. But especially for guys who dont spin the ball all that well to begin with, this new understanding opens up a whole new world of potential improvements to their stuff.

A key example Dr. Smith has cited among Tigers pitchers is Spencer Turnbulls four-seam fastball. The pitch has always been odd because Turnbull gets more than average spin rate on it and yet the amount of movement it gets has always been reported as below average by Trackman. There was no explanation of why its such a devastating, though erratic, pitch that is rarely barreled up for a home run and tends to get sinker-type results off the bat. Our friends over at Motor City Bengals have a clip from Smiths appearance on their podcast recently with a quick explainer on Turnbull.

As it turns out, the seam-shifted wake effect is in play here too. The pitch was initially described in Statcasts methodology as having poor movement, because tracking systems only measured how much a pitch moved from its starting trajectory to the glove. It couldnt consider that it might be changing break along the way to the plate. Just in the past week, Statcast has released a whole new spin direction leaderboard that is now taking this all into account. In Turnbulls case, it starts out moving like a normal four-seamer, but because it has plenty of gyro and the right seam alignments, it appears to cut and take off vertically as it gets to the plate. As Tigers radio color commentator Jim Price would describe it: late movement, baby.

This is part of why many of us are particularly excited about new Detroit Tigers pitching coach Chris Fetter. Fetter is someone who follows all this research with keen interest, has experience putting these concepts into practice, and can tailor and sell them to his players. Obviously, there is much more to coaching pitchers than the subtleties of pitch design, but other than major league experience, Fetter appears to check all the other boxes. Where hes thought to be on the cutting edge is in translating pitch shaping research into actionable information that his pitchers can put to work.

The advantages of having a cutting edge pitching coach in this regard are pretty clear. Most teams are still out there hunting down and paying premiums for guys with the highest spin four-seamers and breaking balls they can get their hands on. These are still new, and poorly understood concepts around the game. Theres an opportunity to be ahead of the game for once, both in how and where in the draft the club drafts pitchers, and in how they teach and develop them. Fetter and others in the organization with this knowledge are the reason.

As we become more familiar with how to put the new Statcast reported spin direction data to work, well begin analyzing Tigers pitchers with seam-shifted wake pitches in mind. At this point, its still rather unclear how well pitchers are going to be able to take advantage of these concepts, and whether or not this is going to radically alter how pitchers are evaluated in terms of the draft, trades, or free agent signings. For now, take a look at some of the embedded video, get generally familiar, ask questions in the comments below, and well revisit this soon. Good luck hitters.

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Seam-shifted wake and the evolution of pitch design - Bless You Boys

Two new COVID-19 variants have been found in Ohio, and they appear to have originated in the United States, researchers announced on Wednesday (Jan. 13).

One of these variants, dubbed the "Columbus strain," has three gene mutations that haven't previously been seen together in SARS-CoV-2, the virus that causes COVID-19, according to a statement from The Ohio State University Wexner Medical Center. These mutations occur in the so-called spike protein of the virus, which it uses to latch onto cells.

This strain quickly became the dominant coronavirus variant in Columbus, Ohio, over a three-week period from late December 2020 to early January, according to the researchers, who hope to post their findings soon on the pre-print database bioRxiv.

Related: Fast spreading U.K. variant: All your questions answered

"This new Columbus strain has the same genetic backbone as earlier cases we've studied, but these three mutations represent a significant evolution," study leader Dr. Dan Jones, vice chair of the division of molecular pathology at they Wexner Medical Center, said in the statement. "We know this shift didn't come from the U.K. or South African branches of the virus."

The Ohio researchers have been regularly sequencing the SARS-CoV-2 genome from patient samples since March 2020 to monitor the virus's evolution.

Like other coronavirus variants found around the world, including the U.K. variant, the mutations in the Columbus strain occur in the virus's "spike protein," which allows the virus to enter cells. It's possible these mutations make the virus more transmissible, the researchers said.

But so far, there is no evidence that these mutations would impact the effectiveness of COVID-19 vaccines, according to the researchers.

"It's important that we don't overreact to this new variant until we obtain additional data," said Peter Mohler, a co-author of the study and chief scientific officer at the Wexner Medical Center

The second variant found by the Ohio researchers has a mutation dubbed 501Y that is identical to one seen in the U.K. variant. This mutation affects the receptor-binding domain, or part of the virus's spike protein that latches onto the ACE2 receptor in human cells; in lab-dish experiments, the mutated receptor-binding domain binds more tightly to the ACE2 receptor, past research found.

But the researchers believe the Ohio variant independently evolved that mutation from a strain already in the U.S. It was found in one patient from Ohio, so the researchers don't yet know how prevalent it is in the population overall.

A spokesperson for the Centers for Disease Control and Prevention told CNBC that the agency is reviewing the new research.

Originally published on Live Science.

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New 'Columbus strain' of coronavirus evolved in the US - Livescience.com