dataesr/scientific-paragraphs-categorization huggingface dataset:
All tests have the potential to experience false positives (a positive result from a sample that does not contain a virus), produce a negative result from a sample that does contain a virus, or need to be repeated due to an error in the test or its controls. To further understand the value provided by the in vivo adventitious virus test, the CAACB also collected data from its members reviewing this historical reliability. In the case of the in vivo adventitious virus test, 21 tests were reported to the CAACB to have initially given a positive test result that was later determined to be a false positive. The detailed distribution of false positives reported per animal type is shown in Table  1. The in vivo adventitious virus false positive tests were reported to occur in five out of seven animal species utilized, except guinea pigs and rats.
The data do not provide sufficient evidence to claim that one animal species or the in vivo adventitious virus test is more susceptible to false positives than another. Each false positive must initially be treated as a true positive event with a subsequent investigation conducted to confirm the contamination or to determine that it is, in fact, a false positive. As seen in Fig. 3 , 10 false positive events in the in vivo adventitious virus assay required one to three months to complete the follow-up investigation, including conducting repeat testing as needed. However, there was one reported event that took longer than 12 months to resolve. In addition to false positives, negative events, where the in vivo adventitious virus test gave a negative result for a sample that was later found to be positive for virus, were also reported to the CAACB.
Alternatives are needed The data presented above highlight the limitations of current in vivo and in vitro viral detection tests widely used in biologics manufacture. Further, as noted above, despite its wide use in the industry, based on the data collected by the CAACB, the in vivo adventitious virus test has not ever detected a viral contaminant that was not also detected in another companion assay. While this highlights the importance of a multifaceted approach to viral safety, it also questions the value of the continued use of the in vivo adventitious virus test, which has used more than 84,000 animals from CAACB member companies alone since 2000. Further, compared to the in vitro virus test, the in vivo adventitious virus test has a higher rate of both false positives and false negatives, the former which can take months to resolve.
Next generation sequencing as a potential alternative One potential alternative to the in vivo adventitious virus test is based on next generation sequencing (NGS). Next generation sequencing typically involves sequencing millions of short or long nucleic acid sequences in parallel and mapping those sequences, or larger assembled contigs, against genomic databases to identify the source of the nucleic acid. In 2010, NGS detected a previously undetected viral contaminant in an attenuated live viral vaccine  [2]. In this case, the viral contaminant was found to not present a safety risk for patients-it was present since the initial stages of product development and in manufactured lots used in clinical trials  [22]. Per dose, the viral DNA was reported to be present at 6-7 log(10)  [23]  of infectious virus  [24]  implying that the detection of this contaminant was missed by traditional testing such as the in vivo adventitious virus test and the in vitro virus test.
6. Percent of CAACB manufacturers who stated a specific motivation for replacing or supplementing existing adventitious virus tests with NGS. NOTE: respondents could select more than one reason for pursuing the replacement or supplementation of existing adventitious virus assays with NGS. transcriptome of viral infection  [29, 39]. Briefly, all replicating viruses produce RNA transcripts during replication that are not found in non-infectious viral particles. Therefore, detection of such sequences has been reported as a reliable indicator of viral replication using sequencing data alone.
Variability of databases, and their upkeep. Bullet points 1 and 2 relate to the false positive and false negative rate of NGS. While biopharmaceutical manufacturer experience with NGS is significantly less than that of the in vivo adventitious virus test and the in vitro virus test, the CAACB collected data on false positives and false negatives experienced by member companies when using NGS. Of the data reported to the CAACB, there was only one case of an NGS false positive and zero cases of NGS false negatives. In the reported case, the false positive was attributed to likely contamination during sample handling, though it is important to note that this false positive experience did not occur in a GMP setting. As discussed earlier, both false positives and false negatives have occurred with the in vivo adventitious virus test.
While ambiguity of the final assay result is a risk, it is not limited to only NGS. All assays suffer, to some degree, from difficulty in distinguishing between true positives and true negatives. In vivo assays, for example, are based on observing animal health or cell cytopathology, which may not always be straightforward, for example interpreting a specific animal behavior as due to a viral infection. Broad agreement on what constitutes a true positive NGS result would be helpful to the biopharmaceutical and viral safety community. Unassigned or unknown sequences are often considered a risk of using NGS as a tool for viral safety; however, it is important to take the possibility of such sequences in context and consider both the intended use of the assay and a risk assessment. CAACB members were asked if they have ever observed a nucleic acid sequence that was not able to be classified by their bioinformatic algorithm and database.
Of those that perform NGS, 50% indicated that they have not observed nucleic acid sequences that were not able to be classified and 50% have. However, characteristics of the reads, such as depth, quality, strand polarity, length of the read, contig match and assembly into a larger contig, as well as strength of the assignment can inform whether additional follow up may be necessary. Additionally, it was reported that some sequencing pipelines intentionally do not attempt to classify some reads, such as reads that are not classified as viral. NGS approaches may be developed on a per-company basis, either in-house or at contract testing labs. These approaches may, therefore, use proprietary bioinformatic pipelines or customized databases. Further, the database will be part of the bioinformatics pipeline validation.
Therefore, any change to the database, i. e. , to ensure it remains current, must be properly documented and tests must be in place to ensure that the change in the database will still allow the detection of viruses in predefined test cases. Only when a database has passed these tests, will it be useable in a GMP pipeline. The aforementioned variability, together with challenges in keeping databases current, may be a risk to maintaining GMP compliance. Risks related to regulatory authorities include: delayed regulatory approval due to implementation of a new method or replacement of an old method; the risk that regulatory authorities will not accept the NGS validation approach due to lack of clarity on how NGS should be validated. Both risks reflect the general lack of experience of NGS in applications and for regulators to review and respond to those applications.
The duration of NGS analysis may still be too long for use with product modalities that have short shelf lives (e. g. , <1 week); however, all broad-spectrum adventitious virus detection technologies are too lengthy. As such, there is an opportunity for additional technology development to shorten assay times or advance novel approaches beyond the current NGS approach. Regulatory challenges to implementing NGS are largely the same as those mentioned above, with the largest challenges currently being the lack of familiarity or comfort with NGS as a viral safety tool. Additionally, the lack of a harmonized regulatory framework with regards to test design, acceptance criteria, databases, and validation approach continues to be a challenge. These challenges are also mirrored in a 2019 Biophorum Operations Group (BPOG) report on rapid microbiological methods which listed the top three industry concerns as: regulatory acceptance of the new method, filing a regulatory change control, and time to validate a non-compendial method  [42].
Acknowledgments The authors would like to acknowledge the support of the members of the  Consortium on Adventitious Agent Contamination in Biomanufacturing (CAACB). As of July 2022 ,  CAACB members include Allogene, Amgen, Asahi Kasei Bioprocess, AstraZeneca ,  Biogen ,  Bio-Marin ,  Boehringer Ingelheim ,  Bristol Myers Squibb ,  Charles River Labs ,  CSL Behring ,  Eli Lilly and Co. , EMD Serono ,  Fate Therapeutics ,  Genentech ,  GSK ,  Merck, Sharpe and Dohme, MilliporeSigma, PathoQuest ,  Pfizer ,  Resilience ,  Sanofi ,  Sartorius ,  Takeda ,  ThermoFisher , and  Yposkesi.
This includes supporting countries in understanding national contexts and developing capacities for strategic technical areas one of which is surveillance and information sharing. While the focus of this guide is on zoonoses, the guide states that it is relevant to other One Health issues too. It is important to reference this guide and the new Surveillance and Information Sharing Operational Tool (SIS OT) that has been developed by the Tripartite organizations (FAO, WHO, WOAH) and technical experts to support national authorities to establish or strengthen their coordinated, multisectoral surveillance and information sharing for zoonotic diseases. Such a system is essential for early detection of disease events and timely, routine data sharing among all relevant sectors to support coordinated response, prevention, and mitigation of these events.
Considering the underlying paradigm that disease emergence and spread is driven by changes to human/animal interfaces, including changes in interactions among and between humans, animals and within ecosystems, surveillance for such changes will include environmental, ecological and socio-economic factors in order to inform emerging disease risk. Such surveillance practices would create a risk prediction system that identifies where mitigation measures are required and identify potentially high-risk areas where more-traditional surveillance needs to occur  [17, 18]. Although some initiatives are exploring how to utilize data on drivers for early warning systems, systematic approaches are also needed to make these widely available and to cover a wide scope of pathogen types and sources. The third step is the integral system design aspects, starting with drafting an umbrella organisational plan that incorporates the fact that One Health and OHSSs are themselves complex, with multiple interactions occurring through dependent relationships and feedback loops.
In resource limited settings in particular, reliance may be greater on community-based surveillance  [21]. The fourth step is to define the governance system, bearing in mind Fig. 1. Six steps that can guide One Health surveillance system development. One Health High-Level Expert Panel (OHHLEP) political, ethical, administrative, regulatory and legal (PEARL) barriers and enablers. OHSSs require coordination across disciplines, sectors and government departments in order to facilitate and sustain collaboration and partnerships. OHSSs should include integrated steps throughout their process, from planning, data collection, sharing, integration and analyses, to interpretation, visualisation, and results dissemination. OHSSs will require the necessary legal architecture to function, including provisions for fair and equitable access and benefit-sharing from collaborative work, and to ensure sustainable resourcing, including financing.
Data feeding into the system can be from multiple sources and ideally need to be of high quality, secure, standardised, and timely, although less well defined and standardised datasets may provide additional information if used carefully. Sufficiently trained and resourced laboratory networks, epidemiological intelligence, as well as alert verification and outbreak response capacity need to be developed equitably around the globe, to allow immediate verification wherever conspicuous signals arise in high-risk areas identified by One Health surveillance of drivers  [1, 2, 18, 22]. Laboratories require high operating standards, including appropriate levels of biosecurity and biosafety, with trained workforces able to generate, record, interpret and report suitable data. Integration of laboratories across current human, animal and plant health siloes is required and, as such, funding needs to be appropriate with agreed minimum standards for sustainable development of core capacity and capability across all One Health domains (human, animal, plant, environment), and contingency budgets for signal verification and response actions.
The setting up of a separate One Health department might be the best way to ensure that this occurs. OT logistics and network management specialists will engage, when required, with other relevant services, such as security services, police, waste management, urban and rural development, and anti-corruption units, whether in the public or private sector. For settings where community-based surveillance is necessary, participatory community engagement is key, with a recent systematic review finding that clear supervision and training, engagement with community 'informants', using simple and adaptable case definitions, having a quality assurance scheme, effective use of available technology, using data for real-time decision-making showing the utility of the information, and having surveillance workers in close proximity communities are all linked to successful systems  [21].
A platform for sharing surveillance information, potentially requiring cross-sector training, is imperative. It will require IT systems and platforms that can integrate and host these data. Data security, sovereignty and respect of ownership are critical, along with developing user-friendly fair data infrastructures, suited for modern data analysis while balancing the interests of One Health partners who may be hesitant about sharing data openly. Therefore, working with legal, communications, and behaviour teams is required to help overcome these barriers. These systems and structures lay the foundations for identifying and implementing iterative improvements to the OHSS. Not all components of a OHSS system will have to be developed from scratch as there are several sector specific surveillance platforms already in place that may be incorporated into OHSS systems (e.
Environmental expertise 'Environment' is a comprehensive term that for One Health surveillance includes the living and non-living, and natural and artificial (man-made) environmental components. The broader environmental factors that need to be monitored over time include land and water use (in particular, any changes to these), habitat quality and availability  [18] , biodiversity indicators such as wild animal and plant population abundances and distributions  [46] , wildlife exploitation  [47] , along with the monitoring of meteorological and climatic conditions  [9, 18, 46, 47]. Integrative laboratory and data analytics functions thread through these. By integrating these data sources with those from other sectors, epidemiological patterns can be identified and assessed, as can environmental and other factors that drive public health, wildlife, and domestic animal threats.
Acknowledgements We thank the Quadripartite focal points, namely  Barbara Haesler ,  Julian Blanc ,  Chadia Wannous , and  Danny Sheath/Abigail Wright  for their continuing input and support to the work of  OHHLEP. This publication was prepared by OHHLEP members who serve in their personal capacity. The opinions expressed in this article are the author's own and do not necessarily reflect the view of the employer or affiliated institution or agency or the Quadripartite agencies (FAO,  UNEP , WHO and  WOAH)  which OHHLEP is an advisory group to.
The co-located AERONET-COCCON database in the 2021-2022 period (post-COVID-19 lockdown) was used to cross-validate these two independent techniques in the common spectral range (870-1640 nm), demonstrating an excellent agreement at the near-coincident spectral bands (mean AOD differences limited to 0. 005, standard deviations up to 0. 019 and Pearson regression coefficients up to 0. 99). This indicates that the low-resolution COCCON instruments are suitable for detecting the aerosol broadband signal contained in the IR spectra in addition to the retrieval of precise trace gas concentrations provided that its calibration is performed frequently enough to compensate for the optical degradation of the external system (∼0. 6% per month). The study also assesses the capability of the EM27/SUN to simultaneously infer aerosols and trace gases, and relate their common emission sources in two case study events: a volcanic plume from the La Palma eruption in 2021 and a nearby forest fire in Tenerife in 2022.
g. metropolitan areas, mining regions or volcanic eruptions,  Hase et al. , 2015; Butz et al. , 2017; Frey et al. , 2019; Tu et al. , 2022, and reference therein). The IZO EM27/SUN is operated in accordance with COCCON requirements  (Frey et al. , 2019; Alberti et al. , 2022). This guarantees strict common methods for ensuring the quality of measurements (evaluation of the optical alignment and instrumental line shape), proper calibration of all COCCON spectrometers with respect to the TCCON site Karlsruhe and the COCCON reference EM27/SUN spectrometer operated permanently at KIT, and adherence to the COCCON data analysis scheme ensures the generation of precise and accurate data products. The COCCON gas data processing and analysis tools are freely available at the COCCON website (https://www.
Spectral range Bandwidth When selecting the optimal micro-windows for AOD retrievals, the temporal stability of the solar radiation and the EM27/SUN instrumental noise should be analyzed in detail for each proposed band. To achieve this, we estimated the intra-band coefficient of variation (CV), which is calculated as the standard deviation of radiation counts divided by the mean value in each band. This parameter can be a good indicator of instrumental issues, similar to the signal-to-noise ratio, or the effect of gas absorption interferences within each micro-window  (García et al. , 2016; Barreto et al. , 2020). The EM27/SUN database used for this analysis was filtered according to the criteria defined in Section 4. 3 about quality-control procedures, resulting in a total number of 187 days between December 2019 and September 2021 (56190 instantaneous AOD values for each spectral band).
AOD Retrievals The EM27/SUN AOD database was created over the course of 187 days (comprising 56190 instantaneous AOD values for each spectral band) during the study period of December 2019 to September 2022. This database was constructed using the cloud screening and quality-controlled procedure, as described in Section 4. 3. Figure  3 (a) shows the daily-mean spectral AOD from EM27/SUN for each defined band. It should be noted that there was a lack of calibration during the 6-month period from March to September 2020, as mentioned in Section 5. 1. This fact causes that the AOD values were calculated by taking into account smoothing spline functions that were poorly conditioned. This could lead to overfitting and less confident results in this period resulting in, for example, negative AOD retrievals as observed in spring and summer of 2020 (black arrow (1) in Figure  3 (a)).
AOD Quality Assessment The consistency of EM27/SUN retrievals was evaluated over the 3-year period by comparing synchronous AOD measurements (within 1 minute) of the three coincident spectral bands from EM27/SUN and AERONET instruments (870, 1020, and 1640 nm). The scatterplot in Figure  4  and the main statistics presented in Table  2  show the results of these synchronous AOD data sets, considering separately the two time periods with different calibration frequencies: (1) from December 2019 to December 2020, where the lack of correct calibration frequency could cause more uncertain results (open circles in Figure  4 ); and (2) after January 2021, when the calibration frequency was adapted to meet the instrumental needs. Our results confirm the consistency between these two independent techniques, especially from January 2021 onward.
Spectral Consistency Section 5. 3 compared and evaluated EM27/SUN AOD retrievals using AERONET data for the three coincident spectral bands (870, 1020, and 1640 nm). To indirectly assess the spectral consistency of the EM27/SUN AOD database in the entire 873-2314 nm spectral range (B1-B8), we verified the λ-AOD dependence by performing a correlation matrix analysis. This information served as a cross-validation of the EM27/SUN AOD observations across different spectral bands. The assessment has been limited to the 2021-2022 period of reliable EM27/SUN AOD records due to the high-frequency calibration. As shown in previous works (e. g. Toledano et al. , 2019; Barreto et al. , 2020, and references therein) , the spectral dependence of AOD does not follow the Ångström power law  (Junge, 1955) , especially in the shortwave infrared for mineral dust aerosols.
The second case study analyses biomass burning emissions from a forest fire that started on 11 st July 2022 in the municipality of Los Realejos, at ∼10 km distance from IZO, on the northern side of Tenerife (Figure  7(c) ). This fire affected an area of more than 2700 hectares and was considered stabilized on 25 th July 2022. We included EM27/SUN data affected by the biomass burning emissions in the afternoon of 21 st July 2022. TROPOMI XCO product corresponding to this event is presented in The EM27/SUN gas observations have been retrieved at the same time as AOD values. For this purpose, the measured EM27/SUN absorption spectra were analysed using the non-linear least-squares fitting algorithm PROFFAST, obtaining the total columns of different trace gases (O 2 , CO 2 , CH 4 , CO, and H 2 O)  (Sha et al.
5 ppm (i. e. ∼0. 04% with respect to IZO background levels). The subtle CO 2 signature during this event was also corroborated by the surface CO 2 records taken at IZO in the framework of the WMO GAW program (data not shown). As a result, the correlation between ∆XCO 2 and ∆XCO, and ∆AOD is found to be moderate (R between 0. 40 and 0. 50, Figure  8 (h) and (j)), with mean ∆XCO/∆XCO 2 ratio of 5. 2±2. 2 ppb/ppm and ∆XCO 2 /∆AOD ratios of 10. 7±4. 8 ppm/∆AOD and 13. 8±5. 1 ppm/∆AOD for AERONET and EM27/SUN, respectively. Although the ∆XCO/∆XCO 2 ratio strongly depends on the type of vegetation, the ratio values obtained in this study are significantly lower than those found in the literature. For instance, previous studies have reported ∆XCO/∆XCO 2 ratios of ∼60 ppb/ppm for biomass burning emissions from tropical forests  (Yokelson et al.
04%month -1 ). The study successfully retrieved EM27/SUN spectral AOD in the infrared region, which was validated against the Cimel 318-T radiometer (as part of AERONET). Although a 3-year period of synchronous AERONET and EM27/SUN measurements was available to verify the coherence between the two data sets, a final time period of two years (2021-2022) was used to overcome the lack of calibration due to the lockdown associated with the COVID-19 restrictions in 2020. In this regard, our results emphasize the importance of maintaining a regular calibration frequency, ideally at least once per month, to compensate for the optical degradation of the system. The AERONET-COCCON cross-validation showed average AOD differences limited to 0. 005, with Pearson regression coefficients reaching up to 0.