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As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing and/or sequencing capacity, which can also introduce biases1–3. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing4,5. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We developed and deployed improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detected emerging variants of concern up to 14 days earlier in wastewater samples, and identified multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

SummaryThe maturation of genomic surveillance in the past decade has enabled tracking of the emergence and spread of epidemics at an unprecedented level. During the COVID-19 pandemic, for example, genomic data revealed that local epidemics varied considerably in the frequency of SARS-CoV-2 lineage importation and persistence, likely due to a combination of COVID-19 restrictions and changing connectivity. Here, we show that local COVID-19 epidemics are driven by regional transmission, including across international boundaries, but can become increasingly connected to distant locations following the relaxation of public health interventions. By integrating genomic, mobility, and epidemiological data, we find abundant transmission occurring between both adjacent and distant locations, supported by dynamic mobility patterns. We find that changing connectivity significantly influences local COVID-19 incidence. Our findings demonstrate a complex meaning of ‘local’ when investigating connected epidemics and emphasize the importance of collaborative interventions for pandemic prevention and mitigation.

Antimicrobial resistance is a global health threat that is exacerbated by the overuse and misuse of antibiotics in medicine and agriculture. As an alternative to conventional antimicrobial drugs, phage therapy involves the treatment of infected patients with a bacteriophage that naturally destroys bacterial pathogens. With the re-emergence of phage therapy, novel tools are needed to study phages. In this work we set out to screen and isolate peptide candidates that bind to phages and act as affinity tags. Such peptides functionalized with an imaging agent could serves as versatile tools for tracking and imaging of phages. Specifically, we screened a phage display library for peptides that bind to the Good Vibes phage (GV), which lyses the bacterial pathogen Pseudomonas aeruginosa. Isolated monoclonal library phages featured a highly conserved consensus motif, LPPIXRX. The corresponding peptide WDLPPIGRLSGN was synthesized with a GGGSK linker and conjugated to cyanine 5 or biotin. The specific binding of the LPPIXRX motif to GV in vitro was confirmed using an enzyme-linked immunosorbent assay. We demonstrated imaging and tracking of GV in bacterial populations using the fluorescent targeting peptide and flow cytometry. In conclusion, we developed fluorescent labeled peptides that can bind to bacteriophage GV specifically, which may enable real-time analysis of phage in vivo and monitor the efficacy of phage therapy.

Antimicrobial resistance is a global health threat that is exacerbated by the overuse and misuse of antibiotics in medicine and agriculture. As an alternative to conventional antimicrobial drugs, phage therapy involves the treatment of infected patients with a bacteriophage that naturally destroys bacterial pathogens. With the re-emergence of phage therapy, novel tools are needed to study phages. In this work we set out to screen and isolate peptide candidates that bind to phages and act as affinity tags. Such peptides functionalized with an imaging agent could serves as versatile tools for tracking and imaging of phages. Specifically, we screened a phage display library for peptides that bind to the Good Vibes phage (GV), which lyses the bacterial pathogen

We present a novel dataset which enables quantitative analysis of the relationship between institutional support for Open Science and research performance. We analysed promotion, review, and tenure policies (PRT) from institutions originating from seven countries and combined them with bibliographical data from the outputs generated by each institution. The data were normalised and evaluated against Open Science and Responsible Research and Innovation (RRI) indicators, to enable comparisons and easy machine readable access. The significance of this dataset lies in its potential to answer a range of questions that are key to the understanding of what motivates academics with regards to their research practices and publishing behaviors, using various indicators, including the MoRRI. To our knowledge, this collection constitutes one of the first efforts in delivering a large machine readable dataset enabling quantitative analysis on these aspects, as much work in this area has been carried out only through surveys and qualitative analysis.

The aim of this literature review is to examine the current state of the art in the area of citation classification. In particular, we investigate the approaches for characterizing citations based on their semantic type. We conduct this literature review as a meta-analysis covering 60 scholarly articles in this domain. Although we included some of the manual pioneering works in this review, more emphasis is placed on the later automated methods, which use Machine Learning and Natural Language Processing (NLP) for analyzing the fine-grained linguistic features in the surrounding text of citations. The sections are organized based on the steps involved in the pipeline for citation classification. Specifically, we explore the existing classification schemes, data sets, preprocessing methods, extraction of contextual and noncontextual features, and the different types of classifiers and evaluation approaches. The review highlights the importance of identifying the citation types for research evaluation, the challenges faced by the researchers in the process, and the existing research gaps in this field.

Increasing antimicrobial resistance and medical device-related infections have led to a renewed interest in phage therapy as an alternative or adjunct to conventional antimicrobials. Expanded access and compassionate use cases have risen exponentially but have varied widely in approach, methodology, and clinical situations in which phage therapy might be considered. Large gaps in knowledge contribute to heterogeneity in approach and lack of consensus in many important clinical areas. The Antibacterial Resistance Leadership Group (ARLG) has convened a panel of experts in phage therapy, clinical microbiology, infectious diseases, and pharmacology, who worked with regulatory experts and a funding agency to identify questions based on a clinical framework and divided them into three themes: potential clinical situations in which phage therapy might be considered, laboratory testing, and pharmacokinetic considerations. Suggestions are provided as answers to a series of questions intended to inform clinicians considering experimental phage therapy for patients in their clinical practices.

SummaryAs SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

Background Staphylococcus aureus is a common cause of biofilm-mediated left ventricular device (LVAD) infections which are difficult to resolve with antibiotics alone and are associated with substantial morbidity and mortality. Recently, bacteriophages (phage) therapy has been used to resolve LVAD infections in a few cases. Our goal was to assess in vitro susceptibility and anti-biofilm activity of two S. aureus bacteriophages against clinical isolates from patients with S. aureus LVAD infections in order to develop a S. aureus phage cocktail for clinical use. Methods Two bacteriophages, OMS1 and OMS2, from the Israeli Phage Bank, were assessed for lytic activity against 15 S.aureus isolates (9 methicillin resistant, MRSA and 6 methicillin susceptible, MSSA) via agar overlay method and plaque forming units (PFU)/mL were enumerated. We then formed bacterial biofilms after overnight incubation at 120 rpm in a 96-well plates in duplicate; experiments were repeated thrice. Wells were then treated with tryptic soy broth (TSB, control) or TSB containing phage at 109 PFU/mL for 24 hours. After washing, biofilms were stained with crystal violet and biomass quantified via optical density at 570mm. Results All bacterial isolates were susceptible to both phages via agar overlay to varying degrees as determined by phage titers obtained via serial dilutions (Figure 1a, 1b). OMS1 led to significant reduction in biofilm of 7/9 MRSA and 3/6 MSSA isolates and OMS2 reduced biofilms in 9/9 MRSA and 4/6 MSSA isolates (Figure 1c). Phage titers and Biofilm Biomass Figure 1a) Phage titers in PFU/ml obtained from each bacterial isolate via agar overlay method. 1b) Representative agar plate demonstrating lytic plaques from OMS1 and OMS2 on a Staphylococcus aureus LVAD isolate. 1c) Biofilm biomass of Staphylococcus aureus isolates alone (SA) or with individual phage (SA+OMSA1 and SA=OMS2) assessed by optical density readings at 570 nm; error bars represent standard error of the mean. Conclusion We demonstrated in vitro lytic and anti-biofilm activity of 2 S. aureus phages against clinical S. aureus isolates from patients with LVAD infection. Our data suggests that phage susceptibility measured with agar overlay does not always correlate with phage susceptibility of S. aureus biofilms, suggesting that more than one method should be used to assess in vitro activity. We plan to assess for synergistic activity with the phage combination. Disclosures Ryan K. Shields, PharmD, MS, Shionogi (Consultant, Research Grant or Support) Ran Nir-Paz, MD, BiomX (Consultant)Technophage (Scientific Research Study Investigator, Advisor or Review Panel member) Saima Aslam, MD, MS, BioMx (Consultant)Cystic Fibrosis Foundation (Grant/Research Support)Gilead (Consultant)Johnson and Johnson (Consultant)Merck (Consultant)

Background: Open Access aims at improving the discovery, access and re-use of research not only within the scientific community, but also within broader society, for instance to promote innovation in industry. Yet, the extent to which openly available scientific work impacts technological inventions remains largely unknown. Methods: We combine publicly available data sources about patents and scholarly publications to explore the extent to which Open Access scientific literature is cited in patents. Results: Investigating over 22 million patent families indexed in Google Patents between 2010 and 2020, we found that around one third referenced non-patent literature. However, the number of references per patent family can vary considerably across technological sectors and inventor countries. Based on a sample of 215,962 scientific non-patent references published between 2008 and 2020, we determined the Open Access status using Unpaywall, Europe PubMed Central and arXiv. The proportion of Open Access citations grew over the years, with nearly half of cited articles being openly available. Discussion: In line with research on both technology-science linkage and Open Access, we found considerable country- and subject- specific variations. In particular, patents representing inventions from the US and the UK cited Open Access work disproportionately more often, although it is challenging to link these observations to specific science policies and incentives. We recommend that follow-up research and monitoring exercise take advantage of a growing evidence base associated with patent citations and Open Access evidence.

The entire body of research literature is currently estimated at 100-150 million publications with an annual increase of around 1.5 million. Systematically reading and analysing the full body of knowledge is now beyond the capacities of any human being. Consequently, it is important to better understand how we can leverage Natural Language Processing/Text Mining techniques to aid knowledge creation and improve the process by which research is being done. This workshop aims to bring together people from different backgrounds who: (a) have experience with analysing and mining databases of scientific publications, (b) develop systems that enable such analysis and mining of scientific databases (especially those who manage publication databases) or (c) who develop novel technologies that improve the way research is being done.

Most Performance-based Research Funding Systems (PRFS) draw on peer review and bibliometric indicators, two different methodologies which are sometimes combined. A common argument against the use of indicators in such research evaluation exercises is their low correlation at the article level with peer review judgments. In this study, we analyse 191,000 papers from 154 higher education institutes which were peer reviewed in a national research evaluation exercise. We combine these data with 6.95 million citations to the original papers. We show that when citation-based indicators are applied at the institutional or departmental level, rather than at the level of individual papers, surprisingly large correlations with peer review judgments can be observed, up to r, 12 pages, 7 tables, 2 figures. Submitted to TPDL2018

This work looks in depth at several studies that have attempted to automate the process of citation importance classification based on the publications’ full text. We analyse a range of features that have been previously used in this task. Our experimental results confirm that the number of in-text references are highly predictive of influence. Contrary to the work of Valenzuela et al. (2015) [1], we find abstract similarity one of the most predictive features. Overall, we show that many of the features previously described in literature are not particularly predictive. Consequently, we discuss challenges and potential improvements in the classification pipeline, provide a critical review of the performance of individual features and address the importance of constructing a large scale gold-standard reference dataset.