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Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity., Competing Interests: AP, JH, JB, TR, XX, RK, DW, NL, LW, BE, RH, MG, RD, SF, KN, NK, SW, HH, TB No competing interests declared, CH Received personal fees from Sanofi outside the submitted work, JM Received consulting fees, honoraria, and travel support from Sanofi Pasteur and Sequris, SS The WHO Collaborating Centre for Reference and Research on Influenza in Melbourne has a collaborative research and development agreement (CRADA) with CSL Seqirus for isolation of candidate vaccine viruses in cells and an agreement with IFPMA for isolation of candidate vaccine viruses in eggs. SGS reports honoraria from CSL Seqirus, Moderna, Pfizer, and Evo Health, IB, KS The WHO Collaborating Centre for Reference and Research on Influenza in Melbourne has a collaborative research and development agreement (CRADA) with CSL Seqirus for isolation of candidate vaccine viruses in cells and an agreement with IFPMA for isolation of candidate vaccine viruses in eggs, FK The Icahn School of Medicine at Mount Sinai has filed patent applications relating to influenza virus vaccines (U.S. patent numbers: 12030928, 11865173, 11266734, 11254733, 10736956, 10583188, 10137189, 10131695, 9968670, 9371366; publication numbers: 20230181715, 20220403358, 20220249652, 20220242935, 20220153873, 20210260179, 20190125859, 20190106461, 20180333479), SARS-CoV-2 serological assays (publication number: 20240210415), and SARS-CoV-2 vaccines (publication numbers: 20230310583, 20230226171), which list FK as co-inventor. FK has consulted for Merck and Pfizer (before 2020), and is currently consulting for Pfizer, Seqirus, 3rd Rock Ventures, GSK and Avimex. The Krammer laboratory is also collaborating with Pfizer on animal models of SARS‐CoV‐2 and with Dynavax on universal influenza virus vaccines, CV Received honoraria for serving as an Editor in Chief of the journal Epidemics (Elsevier)

The detection of evolutionary transitions in influenza A (H3N2) viruses' antigenicity is a major obstacle to effective vaccine design and development. In this study, we describe Novel Influenza Virus A Detector (NIAViD), an unsupervised machine learning tool, adept at identifying these transitions, using the HA1 sequence and associated physico-chemical properties. NIAViD performed with 88.9% (95% CI, 56.5-98.0%) and 72.7% (95% CI, 43.4-90.3%) sensitivity in training and validation, respectively, outperforming the uncalibrated null model-33.3% (95% CI, 12.1-64.6%) and does not require potentially biased, time-consuming and costly laboratory assays. The pivotal role of the Boman's index, indicative of the virus's cell surface binding potential, is underscored, enhancing the precision of detecting antigenic transitions. NIAViD's efficacy is not only in identifying influenza isolates that belong to novel antigenic clusters, but also in pinpointing potential sites driving significant antigenic changes, without the reliance on explicit modelling of haemagglutinin inhibition titres. We believe this approach holds promise to augment existing surveillance networks, offering timely insights for the development of updated, effective influenza vaccines. Consequently, NIAViD, in conjunction with other resources, could be used to support surveillance efforts and inform the development of updated influenza vaccines.

Background: The 2022-23 US influenza season peaked early in fall 2022., Methods: Late-season influenza vaccine effectiveness (VE) against outpatient, laboratory-confirmed influenza was calculated among participants of the US Influenza VE Network using a test-negative design., Results: Of 2561 participants enrolled from December 12, 2022 to April 30, 2023, 91 laboratory-confirmed influenza cases primarily had A(H1N1)pdm09 (6B.1A.5a.2a.1) or A(H3N2) (3C.2a1b.2a.2b). Overall, VE was 30% (95% confidence interval -9%, 54%); low late-season activity precluded estimation for most subgroups., Conclusions: 2022-23 late-season outpatient influenza VE was not statistically significant. Genomic characterization may improve the identification of influenza viruses that circulate postinfluenza peak., (© 2024 The Author(s). Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd.)

Influenza viruses undergo rapid evolutionary changes, which requires continuous surveillance to monitor for genetic and potential antigenic changes in circulating viruses that can guide control and prevention decision making. We sequenced and phylogenetically analyzed A(H1N1)pdm09 virus genome sequences obtained from specimens collected from hospitalized patients of all ages with or without pneumonia between 2009 and 2018 from seven sentinel surveillance sites across Kenya. We compared these sequences with recommended vaccine strains during the study period to infer genetic and potential antigenic changes in circulating viruses and associations of clinical outcome. We generated and analyzed a total of 383 A(H1N1)pdm09 virus genome sequences. Phylogenetic analyses of HA protein revealed that multiple genetic groups (clades, subclades, and subgroups) of A(H1N1)pdm09 virus circulated in Kenya over the study period; these evolved away from their vaccine strain, forming clades 7 and 6, subclades 6C, 6B, and 6B.1, and subgroups 6B.1A and 6B.1A1 through acquisition of additional substitutions. Several amino acid substitutions among circulating viruses were associated with continued evolution of the viruses, especially in antigenic epitopes and receptor binding sites (RBS) of circulating viruses. Disease severity declined with an increase in age among children aged < 5 years. Our study highlights the necessity of timely genomic surveillance to monitor the evolutionary changes of influenza viruses. Routine influenza surveillance with broad geographic representation and whole genome sequencing capacity to inform on prioritization of antigenic analysis and the severity of circulating strains are critical to improved selection of influenza strains for inclusion in vaccines., (© 2023. The Author(s).)

The rapid evolution of SARS-CoV-2 Omicron sublineages mandates a better understanding of viral replication and cross-neutralization among these sublineages. Here we used K18-hACE2 mice and primary human airway cultures to examine the viral fitness and antigenic relationship among Omicron sublineages. In both K18-hACE2 mice and human airway cultures, Omicron sublineages exhibited a replication order of BA.5 ≥ BA.2 ≥ BA.2.12.1 > BA.1; no difference in body weight loss was observed among different sublineage-infected mice. The BA.1-, BA.2-, BA.2.12.1-, and BA.5-infected mice developed distinguishable cross-neutralizations against Omicron sublineages, but exhibited little neutralization against the index virus (i.e. USA-WA1/2020) or the Delta variant. Surprisingly, the BA.5-infected mice developed higher neutralization activity against heterologous BA.2 and BA.2.12.1 than that against homologous BA.5; serum neutralizing titres did not always correlate with viral replication levels in infected animals. Our results revealed a distinct antigenic cartography of Omicron sublineages and support the bivalent vaccine approach.

Contemporary graph learning algorithms are not well-suited for large molecules since they do not consider the hierarchical interactions among the atoms, which are essential to determining the molecular properties of macromolecules. In this work, we propose Multiresolution Graph Transformers (MGT), the first graph transformer architecture that can learn to represent large molecules at multiple scales. MGT can learn to produce representations for the atoms and group them into meaningful functional groups or repeating units. We also introduce Wavelet Positional Encoding (WavePE), a new positional encoding method that can guarantee localization in both spectral and spatial domains. Our proposed model achieves competitive results on three macromolecule datasets consisting of polymers, peptides, and protein-ligand complexes, along with one drug-like molecule dataset. Significantly, our model outperforms other state-of-the-art methods and achieves chemical accuracy in estimating molecular properties (e.g., highest occupied molecular orbital, lowest unoccupied molecular orbital, and their gap) calculated by Density Functional Theory in the polymers dataset. Furthermore, the visualizations, including clustering results on macromolecules and low-dimensional spaces of their representations, demonstrate the capability of our methodology in learning to represent long-range and hierarchical structures. Our PyTorch implementation is publicly available at https://github.com/HySonLab/Multires-Graph-Transformer., (© 2023 Author(s). Published under an exclusive license by AIP Publishing.)

CDC has used national genomic surveillance since December 2020 to monitor SARS-CoV-2 variants that have emerged throughout the COVID-19 pandemic, including the Omicron variant. This report summarizes U.S. trends in variant proportions from national genomic surveillance during January 2022-May 2023. During this period, the Omicron variant remained predominant, with various descendant lineages reaching national predominance (>50% prevalence). During the first half of 2022, BA.1.1 reached predominance by the week ending January 8, 2022, followed by BA.2 (March 26), BA.2.12.1 (May 14), and BA.5 (July 2); the predominance of each variant coincided with surges in COVID-19 cases. The latter half of 2022 was characterized by the circulation of sublineages of BA.2, BA.4, and BA.5 (e.g., BQ.1 and BQ.1.1), some of which independently acquired similar spike protein substitutions associated with immune evasion. By the end of January 2023, XBB.1.5 became predominant. As of May 13, 2023, the most common circulating lineages were XBB.1.5 (61.5%), XBB.1.9.1 (10.0%), and XBB.1.16 (9.4%); XBB.1.16 and XBB.1.16.1 (2.4%), containing the K478R substitution, and XBB.2.3 (3.2%), containing the P521S substitution, had the fastest doubling times at that point. Analytic methods for estimating variant proportions have been updated as the availability of sequencing specimens has declined. The continued evolution of Omicron lineages highlights the importance of genomic surveillance to monitor emerging variants and help guide vaccine development and use of therapeutics., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

Background: Using country-specific surveillance data to describe influenza epidemic activity could inform decisions on the timing of influenza vaccination. We analysed surveillance data from African countries to characterise the timing of seasonal influenza epidemics to inform national vaccination strategies., Methods: We used publicly available sentinel data from African countries reporting to the WHO Global Influenza Surveillance and Response FluNet platform that had 3-10 years of data collected during 2010-19. We calculated a 3-week moving proportion of samples positive for influenza virus and assessed epidemic timing using an aggregate average method. The start and end of each epidemic were defined as the first week when the proportion of positive samples exceeded or went below the annual mean, respectively, for at least 3 consecutive weeks. We categorised countries into five epidemic patterns: northern hemisphere-dominant, with epidemics occurring in October-March; southern hemisphere-dominant, with epidemics occurring in April-September; primarily northern hemisphere with some epidemic activity in southern hemisphere months; primarily southern hemisphere with some epidemic activity in northern hemisphere months; and year-round influenza transmission without a discernible northern hemisphere or southern hemisphere predominance (no clear pattern)., Findings: Of the 34 countries reporting data to FluNet, 25 had at least 3 years of data, representing 46% of the countries in Africa and 89% of Africa's population. Study countries reported RT-PCR respiratory virus results for a total of 503 609 specimens (median 12 971 [IQR 9607-20 960] per country-year), of which 74 001 (15%; median 2078 [IQR 1087-3008] per country-year) were positive for influenza viruses. 248 epidemics occurred across 236 country-years of data (median 10 [range 7-10] per country). Six (24%) countries had a northern hemisphere pattern (Algeria, Burkina Faso, Egypt, Morocco, Niger, and Tunisia). Eight (32%) had a primarily northern hemisphere pattern with some southern hemisphere epidemics (Cameroon, Ethiopia, Mali, Mozambique, Nigeria, Senegal, Tanzania, and Togo). Three (12%) had a primarily southern hemisphere pattern with some northern hemisphere epidemics (Ghana, Kenya, and Uganda). Three (12%) had a southern hemisphere pattern (Central African Republic, South Africa, and Zambia). Five (20%) had no clear pattern (Côte d'Ivoire, DR Congo, Madagascar, Mauritius, and Rwanda)., Interpretation: Most countries had identifiable influenza epidemic periods that could be used to inform authorities of non-seasonal and seasonal influenza activity, guide vaccine timing, and promote timely interventions., Funding: None., Translations: For the Berber, Luganda, Xhosa, Chewa, Yoruba, Igbo, Hausa and Afan Oromo translations of the abstract see Supplementary Materials section., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Monitoring emerging SARS-CoV-2 lineages and their epidemiologic characteristics helps to inform public health decisions regarding vaccine policy, the use of therapeutics, and health care capacity. When the SARS-CoV-2 Alpha variant emerged in late 2020, a spike gene (S-gene) deletion (Δ69-70) in the N-terminal region, which might compensate for immune escape mutations that impair infectivity (1), resulted in reduced or failed S-gene target amplification in certain multitarget reverse transcription-polymerase chain reaction (RT-PCR) assays, a pattern referred to as S-gene target failure (SGTF) (2). The predominant U.S. SARS-CoV-2 lineages have generally alternated between SGTF and S-gene target presence (SGTP), which alongside genomic sequencing, has facilitated early monitoring of emerging variants. During a period when Omicron BA.5-related sublineages (which exhibit SGTF) predominated, an XBB.1.5 sublineage with SGTP has rapidly expanded in the northeastern United States and other regions., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No other potential conflicts of interest were disclosed.

A network of global respiratory disease surveillance systems and partnerships has been built over decades as a direct response to the persistent threat of seasonal, zoonotic, and pandemic influenza. These efforts have been spearheaded by the World Health Organization, country ministries of health, the US Centers for Disease Control and Prevention, nongovernmental organizations, academic groups, and others. During the COVID-19 pandemic, the US Centers for Disease Control and Prevention worked closely with ministries of health in partner countries and the World Health Organization to leverage influenza surveillance systems and programs to respond to SARS-CoV-2 transmission. Countries used existing surveillance systems for severe acute respiratory infection and influenza-like illness, respiratory virus laboratory resources, pandemic influenza preparedness plans, and ongoing population-based influenza studies to track, study, and respond to SARS-CoV-2 infections. The incorporation of COVID-19 surveillance into existing influenza sentinel surveillance systems can support continued global surveillance for respiratory viruses with pandemic potential.

Before the emergence of SARS-CoV-2, the virus that causes COVID-19, influenza activity in the United States typically began to increase in the fall and peaked in February. During the 2021-22 season, influenza activity began to increase in November and remained elevated until mid-June, featuring two distinct waves, with A(H3N2) viruses predominating for the entire season. This report summarizes influenza activity during October 3, 2021-June 11, 2022, in the United States and describes the composition of the Northern Hemisphere 2022-23 influenza vaccine. Although influenza activity is decreasing and circulation during summer is typically low, remaining vigilant for influenza infections, performing testing for seasonal influenza viruses, and monitoring for novel influenza A virus infections are important. An outbreak of highly pathogenic avian influenza A(H5N1) is ongoing; health care providers and persons with exposure to sick or infected birds should remain vigilant for onset of symptoms consistent with influenza. Receiving a seasonal influenza vaccine each year remains the best way to protect against seasonal influenza and its potentially severe consequences., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

To detect new and changing SARS-CoV-2 variants, we investigated candidate Delta-Omicron recombinant genomes from Centers for Disease Control and Prevention national genomic surveillance. Laboratory and bioinformatic investigations identified and validated 9 genetically related SARS-CoV-2 viruses with a hybrid Delta-Omicron spike protein.

Objective: To assess the stability of improvements in global respiratory virus surveillance in countries supported by the United States Centers for Disease Control and Prevention (CDC) after reductions in CDC funding and with the stress of the coronavirus disease 2019 (COVID-19) pandemic., Methods: We assessed whether national influenza surveillance systems of CDC-funded countries: (i) continued to analyse as many specimens between 2013 and 2021; (ii) participated in activities of the World Health Organization's (WHO) Global Influenza Surveillance and Response System; (iii) tested enough specimens to detect rare events or signals of unusual activity; and (iv) demonstrated stability before and during the COVID-19 pandemic. We used CDC budget records and data from the WHO Global Influenza Surveillance and Response System., Findings: While CDC reduced per-country influenza funding by about 75% over 10 years, the number of specimens tested annually remained stable (mean 2261). Reporting varied substantially by country and transmission zone. Countries funded by CDC accounted for 71% (range 61-75%) of specimens included in WHO consultations on the composition of influenza virus vaccines. In 2019, only eight of the 17 transmission zones sent enough specimens to WHO collaborating centres before the vaccine composition meeting to reliably identify antigenic variants., Conclusion: Great progress has been made in the global understanding of influenza trends and seasonality. To optimize surveillance to identify atypical influenza viruses, and to integrate molecular testing, sequencing and reporting of severe acute respiratory syndrome coronavirus 2 into existing systems, funding must continue to support these efforts., ((c) 2022 The authors; licensee World Health Organization.)

The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published 'in-house' efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties., Competing Interests: Authors SB, LD, PH, AH, SK, ZK, MK, YL, JPM, TTN, MP, GR, WR, LS, NT, and DW are affiliated with commercial companies. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products associated with this research to declare.

Four cases of oseltamivir-resistant influenza A(H1N1)pdm09 virus infection were detected among inhabitants of a border detention center in Texas, USA. Hemagglutinin of these viruses belongs to 6B.1A5A-156K subclade, which may enable viral escape from preexisting immunity. Our finding highlights the necessity to monitor both drug resistance and antigenic drift of circulating viruses.

Human-to-swine transmission of seasonal influenza viruses has led to sustained human-like influenza viruses circulating in the U.S. swine population. While some reverse zoonotic-origin viruses adapt and become enzootic in swine, nascent reverse zoonoses may result in virus detections that are difficult to classify as "swine-origin" or "human-origin" due to the genetic similarity of circulating viruses. This is the case for human-origin influenza A(H1N1) pandemic 2009 (pdm09) viruses detected in pigs following numerous reverse zoonosis events since the 2009 pandemic. We report the identification of two human infections with A(H1N1)pdm09 viruses originating from swine hosts and classify them as "swine-origin" variant influenza viruses based on phylogenetic analysis and sequence comparison methods. Phylogenetic analyses of viral genomes from two cases revealed these viruses were reassortants containing A(H1N1)pdm09 hemagglutinin (HA) and neuraminidase (NA) genes with genetic combinations derived from the triple reassortant internal gene cassette. Follow-up investigations determined that one individual had direct exposure to swine in the week preceding illness onset, while another did not report swine exposure. The swine-origin A(H1N1) variant cases were resolved by full genome sequence comparison of the variant viruses to swine influenza genomes. However, if reassortment does not result in the acquisition of swine-associated genes and swine virus genomic sequences are not available from the exposure source, future cases may not be discernible. We have developed a pipeline that performs maximum likelihood analyses, a k-mer-based set difference algorithm, and random forest algorithms to identify swine-associated sequences in the hemagglutinin gene to differentiate between human-origin and swine-origin A(H1N1)pdm09 viruses. IMPORTANCE Influenza virus infects a wide range of hosts, resulting in illnesses that vary from asymptomatic cases to severe pneumonia and death. Viral transfer can occur between human and nonhuman hosts, resulting in human and nonhuman origin viruses circulating in novel hosts. In this work, we have identified the first case of a swine-origin influenza A(H1N1)pdm09 virus resulting in a human infection. This shows that these viruses not only circulate in swine hosts, but are continuing to evolve and distinguish themselves from previously circulating human-origin influenza viruses. The development of techniques for distinguishing human-origin and swine-origin viruses are necessary for the continued surveillance of influenza viruses. We show that unique genetic signatures can differentiate circulating swine-associated strains from circulating human-associated strains of influenza A(H1N1)pdm09, and these signatures can be used to enhance surveillance of swine-origin influenza., (Copyright © 2020 American Society for Microbiology.)

Seasonal influenza virus A/H3N2 is a major cause of death globally. Vaccination remains the most effective preventative. Rapid mutation of hemagglutinin allows viruses to escape adaptive immunity. This antigenic drift necessitates regular vaccine updates. Effective vaccine strains need to represent H3N2 populations circulating one year after strain selection. Experts select strains based on experimental measurements of antigenic drift and predictions made by models from hemagglutinin sequences. We developed a novel influenza forecasting framework that integrates phenotypic measures of antigenic drift and functional constraint with previously published sequence-only fitness estimates. Forecasts informed by phenotypic measures of antigenic drift consistently outperformed previous sequence-only estimates, while sequence-only estimates of functional constraint surpassed more comprehensive experimentally-informed estimates. Importantly, the best models integrated estimates of both functional constraint and either antigenic drift phenotypes or recent population growth., Competing Interests: JH, JB, TR, XX, RK, DW, LW, BE, RD, JM, SF, KN, NK, SW, HH, IB, KS, PB, RN, TB No competing interests declared

It is difficult to quantify structure-property relationships and to identify structural features of complex materials. The characterization of amorphous materials is especially challenging because their lack of long-range order makes it difficult to define structural metrics. In this work, we apply deep learning algorithms to accurately classify amorphous materials and characterize their structural features. Specifically, we show that convolutional neural networks and message passing neural networks can classify two-dimensional liquids and liquid-cooled glasses from molecular dynamics simulations with greater than 0.98 AUC, with no a priori assumptions about local particle relationships, even when the liquids and glasses are prepared at the same inherent structure energy. Furthermore, we demonstrate that message passing neural networks surpass convolutional neural networks in this context in both accuracy and interpretability. We extract a clear interpretation of how message passing neural networks evaluate liquid and glass structures by using a self-attention mechanism. Using this interpretation, we derive three novel structural metrics that accurately characterize glass formation. The methods presented here provide a procedure to identify important structural features in materials that could be missed by standard techniques and give unique insight into how these neural networks process data.

The 2017-2018 North American influenza season caused more hospitalizations and deaths than any year since the 2009 H1N1 pandemic. The majority of recorded influenza infections were caused by A(H3N2) viruses, with most of the virus's North American diversity falling into the A2 clade. Within A2, we observe a subclade which we call A2/re that rose to comprise almost 70 per cent of A(H3N2) viruses circulating in North America by early 2018. Unlike most fast-growing clades, however, A2/re contains no amino acid substitutions in the hemagglutinin (HA) segment. Moreover, hemagglutination inhibition assays did not suggest substantial antigenic differences between A2/re viruses and viruses sampled during the 2016-2017 season. Rather, we observe that the A2/re clade was the result of a reassortment event that occurred in late 2016 or early 2017 and involved the combination of the HA and PB1 segments of an A2 virus with neuraminidase (NA) and other segments a virus from the clade A1b. The success of this clade shows the need for antigenic analysis that targets NA in addition to HA. Our results illustrate the potential for non-HA drivers of viral success and necessitate the need for more thorough tracking of full viral genomes to better understand the dynamics of influenza epidemics., (Published by Oxford University Press 2019. This work is written by US Government employees and is in the public domain in the US.)

During May 19-September 28, 2019,* low levels of influenza activity were reported in the United States, with cocirculation of influenza A and influenza B viruses. In the Southern Hemisphere seasonal influenza viruses circulated widely, with influenza A(H3) predominating in many regions; however, influenza A(H1N1)pdm09 and influenza B viruses were predominant in some countries. In late September, the World Health Organization (WHO) recommended components for the 2020 Southern Hemisphere influenza vaccine and included an update to the A(H3N2) and B/Victoria-lineage components. Annual influenza vaccination is the best means for preventing influenza illness and its complications, and vaccination before influenza activity increases is optimal. Health care providers should recommend vaccination for all persons aged ≥6 months who do not have contraindications to vaccination (1)., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. David E. Wentworth reports U.S. patent no. 10,272,149 for a live, synthetic, modified bat influenza virus strain useful for vaccination against a target influenza strain, outside the current work. No other potential conflicts of interest were disclosed.

Influenza activity* in the United States during the 2018-19 season (September 30, 2018-May 18, 2019) was of moderate severity (1). Nationally, influenza-like illness (ILI) † activity began increasing in November, peaked during mid-February, and returned to below baseline in mid-April; the season lasted 21 weeks, § making it the longest season in 10 years. Illness attributed to influenza A viruses predominated, with very little influenza B activity. Two waves of influenza A were notable during this extended season: influenza A(H1N1)pdm09 viruses from October 2018 to mid-February 2019 and influenza A(H3N2) viruses from February through May 2019. Compared with the 2017-18 influenza season, rates of hospitalization this season were lower for adults, but were similar for children. Although influenza activity is currently below surveillance baselines, testing for seasonal influenza viruses and monitoring for novel influenza A virus infections should continue year-round. Receiving a seasonal influenza vaccine each year remains the best way to protect against seasonal influenza and its potentially severe consequences., Competing Interests: All authors have completed and submitted the ICMJE form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

Density functional theory (DFT) is the most successful and widely used approach for computing the electronic structure of matter. However, for tasks involving large sets of candidate molecules, running DFT separately for every possible compound of interest is forbiddingly expensive. In this paper, we propose a neural network based machine learning algorithm which, assuming a sufficiently large training sample of actual DFT results, can instead learn to predict certain properties of molecules purely from their molecular graphs. Our algorithm is based on the recently proposed covariant compositional networks framework and involves tensor reduction operations that are covariant with respect to permutations of the atoms. This new approach avoids some of the representational limitations of other neural networks that are popular in learning from molecular graphs and yields promising results in numerical experiments on the Harvard Clean Energy Project and QM9 molecular datasets.

Multiresolution analysis and matrix factorization are foundational tools in computer vision. In this work, we study the interface between these two distinct topics and obtain techniques to uncover hierarchical block structure in symmetric matrices - an important aspect in the success of many vision problems. Our new algorithm, the incremental multiresolution matrix factorization, uncovers such structure one feature at a time, and hence scales well to large matrices. We describe how this multiscale analysis goes much farther than what a direct "global" factorization of the data can identify. We evaluate the efficacy of the resulting factorizations for relative leveraging within regression tasks using medical imaging data. We also use the factorization on representations learned by popular deep networks, providing evidence of their ability to infer semantic relationships even when they are not explicitly trained to do so. We show that this algorithm can be used as an exploratory tool to improve the network architecture, and within numerous other settings in vision.

Matching one set of objects to another is a ubiquitous task in machine learning and computer vision that often reduces to some form of the quadratic assignment problem (QAP). The QAP is known to be notoriously hard, both in theory and in practice. Here, we investigate if this difficulty can be mitigated when some additional piece of information is available: (a) that all QAP instances of interest come from the same application, and (b) the correct solution for a set of such QAP instances is given. We propose a new approach to accelerate the solution of QAPs based on learning parameters for a modified objective function from prior QAP instances. A key feature of our approach is that it takes advantage of the algebraic structure of permutations, in conjunction with special methods for optimizing functions over the symmetric group 𝕊 n in Fourier space. Experiments show that in practical domains the new method can outperform existing approaches.

We introduce a class of interatomic potential models that can be automatically generated from data consisting of the energies and forces experienced by atoms, as derived from quantum mechanical calculations. The models do not have a fixed functional form and hence are capable of modeling complex potential energy landscapes. They are systematically improvable with more data. We apply the method to bulk crystals, and test it by calculating properties at high temperatures. Using the interatomic potential to generate the long molecular dynamics trajectories required for such calculations saves orders of magnitude in computational cost.