dataesr/scientific-paragraphs-categorization huggingface dataset:
Background: The virus SARS-CoV-2 can exploit biological vulnerabilities (e. g. host proteins) in susceptible hosts that predispose to the development of severe COVID-19. Methods: To identify host proteins that may contribute to the risk of severe COVID-19, we undertook proteome-wide genetic colocalisation tests, and polygenic (pan) and cis-Mendelian randomisation analyses leveraging publicly available protein and COVID-19 datasets. Results: Our analytic approach identified several known targets (e. g. ABO, OAS1), but also nominated new proteins such as soluble Fas (colocalisation probability >0. 9, p=1 × 10 -4 ), implicating Fasmediated apoptosis as a potential target for COVID-19 risk. The polygenic (pan) and cis-Mendelian randomisation analyses showed consistent associations of genetically predicted ABO protein with several COVID-19 phenotypes.
, 2020; Harvey et al. , 2021). To complement in vitro host protein characterisation efforts, several groups have leveraged genetic datasets of human proteins and COVID-19 disease to identify therapeutically actionable candidate host proteins that are likely to play roles in enhancing COVID-19 susceptibility or to be involved in the pathogenesis of severe  COVID-19 (Pairo-Castineira et al. , 2021; Zhou et al. , 2021). One of the approaches used was Mendelian randomisation (MR). MR simulates the design of randomised trials, with the underlying principle that randomisation of alleles at conception offers the opportunity to examine approximate differences in average risk of disease between comparable groups in a population that differ only in the distribution of the risk factor of interest  (Davies et al.
Genetic associations of proteins We primarily used  Sun et al. protein GWAS data (Sun et al. , 2018; Emilsson et al. , 2018)  for the pan-/ cis-MR analyses and for performing genetic colocalisation tests (described below). The pan-/cis-MR effects were expressed per standard deviation (SD) higher genetically predicted plasma protein concentrations. Two additional proteomic datasets  (Emilsson et al. , 2018; Suhre et al. , 2017)  were used to identify proteins associated with the ABO locus. The genotyping protocols and QC of these proteomic studies have been described previously  (Sun et al. , 2018; Emilsson et al. , 2018; Suhre et al. , 2017). All three of the proteomic studies have used the SOMAscan assay platform (an aptamerbased protein detection platform) to detect and quantify protein abundance  (Gold et al.
Genetic associations of COVID-19 We used seven meta-analysed COVID-19 datasets from the October 2020 release of the ICDA COVID-HGI group (https://www. covid19hg. org/ results/ r4/). These seven COVID-19 outcomes are A1 (very severe respiratory confirmed COVID vs. not hospitalised COVID), A2 (very severe respiratory confirmed COVID vs. population), B1 (hospitalised COVID vs. not hospitalised COVID), B2 (hospitalised COVID vs. population), C1 (COVID vs. lab/self-reported negative), C2 (COVID vs. population), and D1 (predicted COVID from self-reported symptoms vs. predicted or self-reported non-COVID). Definitions of these outcomes are provided in Supplementary file 1.
Harmonisation of protein and COVID summary statistics Prior to analyses, we performed a liftover of datasets that reported genomic coordinates using the GRCh37 assembly to GRCh38. We also checked and ensured that the effect allele in a GWAS locus is the alternative allele in the forward strand of the reference genome. To infer strand for palindromic variants (variants with A/T or G/C alleles, i. e. variants with the same pair of letters on the forward strand as on the reverse strand), we first checked the orientation of all non-palindromic variants with respect to the reference genome to assess whether there was a strand consensus of 99% or more. For example, for a given GWAS, if ≥99 % of the non-palindromic variants were on the forward strand, we assumed that the palindromic variant would also be on the forward strand; otherwise, they were excluded from analyses.
Mendelian randomisation To construct genetic instruments for MR analysis, we selected near-independent (r² = 0. 05) genetic variants from across the genome ('pan'-instruments) or from within ±1 Mbp from the transcription start site (TSS) of the gene encoding the protein ('cis'-instruments) associated with the encoded protein abundance at p≤5 × 10 -8 for pan-MR analyses and at a less stringent p ≤ 1 × 10⁻⁵ for cis-MR analyses (this p-value corrects for the number of proteins in the druggable genome  Schmidt, 2020). We used the generalised summary data-based Mendelian randomisation (GSMR) approach with the heterogeneity-independent instrument (HEIDI)-outlier flag turned on to carry out the pan-and cis-MR analyses  (Zhu et al. , 2018). The GSMR software, using the HEIDI-outlier method, removes potentially pleiotropic instruments and accounts for the residual correlation between instruments (important as we are using near-independent genetic instruments).
To select near-independent genetic instruments and account for linkage disequilibrium (LD) in the MR analyses, we used genotype data from 10,000 randomly sampled UK Biobank participants to create a reference LD matrix, which is ancestrymatched to the pQTL data we used. For each COVID-19 outcome, we used the Benjamini-Hochberg FDR (False Discovery Rate) threshold of 5 % for significance, adjusting for 2042 tests in cis-MR analyses and 1286 tests in pan-MR analyses. For trans-acting instruments in pan-MR associations, variants were mapped to their respective cis-gene that had the highest overall V2G score in the Open Targets Genetics portal  (Ghoussaini, 2021; Mountjoy, 2020; Open Targets Genetics, 2019a).
Colocalisation analysis and phenome-wide association study To identify shared causal genetic signals between protein and COVID outcomes, we used the Bayesian method of genetic colocalisation implemented in the coloc R package  (Giambartolomei, 2014)  using the marginal association statistics for each trait (i. e. assuming one independent signal in each region). We used beta and standard errors of cis-pQTLs of phenotype pairs as inputs. The default priors in coloc were used, that is, the prior of an SNP (single nucleotide polymorphism)-trait association is 1 × 10 -4 , and the prior of an SNP associating with both traits is 1 × 10 -5. For each COVID-19 outcome, a posterior probability for shared causal genetic signal (PP. H4) threshold of more than 0. 8 was used to identify shared causal genetic variants.
CD209/DC-SIGN: a proposed alternate receptor for SARS-CoV-2 The ABO signal (rs8176719-insC) contributes to the determination of non-O blood groups and regulates circulating levels of both ABO and several non-ABO proteins; Yamagata University Genomic Cohort Consortium (YUGCC), 2014;  Arguinano et al. , 2018. We explored proteome-wide associations of rs8176719-insC in three separate proteomic datasets; Emilsson, 2020. Aside from the ABO protein, the ABO signal rs8176719-insC showed the strongest association  (Sun et   of soluble CD209 in all three datasets (associations from two datasets illustrated in Figure  3 , and associations from all three datasets tabulated in Supplementary file 6). To validate this as a relevant target for COVID-19, we experimentally tested whether CD209 directly interacts with SARS-CoV-2, as had been recently proposed based on similarities to SARS-CoV-1, which was reported to bind CD209  (Yang, 2020).
CD209 is a receptor on monocyte-derived dendritic cells (moDCs) that was shown, before this binding interaction was known, to facilitate entry of replication-competent SARS-CoV-2 and demonstrated to switch off the type I interferon signalling pathways necessary for transcription of several antiviral genes  (Yang, 2020). The soluble isoforms of CD209 measured in our proteome datasets are known to correlate in expression levels to the membrane isoforms  (Mummidi et al. , 2001; Plazolles, 2011) , making it plausible that the signal we observed is associated with greater abundance of CD209 as a cell-surface viral receptor. Alternatively, in the context of other viruses known to directly bind CD209 as we show here for SARS-CoV-2, soluble CD209 has been demonstrated to modulate infection, such as by promoting endocytosis if the soluble CD209 coating the virus acts as opsonins  (Plazolles, 2011).
, 2018). Nevertheless, horizontal pleiotropy can also affect cis-MR analyses when different variants from the same gene region represent different biological pathways, indicated by heterogeneous effect estimates, or driven by a single variant with a large effect (e. g. missense variants)  (Gkatzionis et al. , 2021). To prevent the selection of heterogeneous instruments and minimise the selection of variants with large effects, the multi-instrument GSMR method used in the present study implements the HEIDI test which excludes genetic variants with strong or heterogeneous effects. The exclusion of missense variants with potential aptamer binding effects is evidenced in our study, where SNPs in 96 % of nominally significant protein probes associated with COVID-19 also had effects on corresponding gene expression in different tissues across gene expression datasets as curated by our portal  (Open Targets Genetics, 2019).
Even while using cis-acting genetic instruments, the MR associations can be confounded due to LD between cis-pQTLs and disease-associated SNPs, and this is at least partially mitigated by genetic colocalisation tests. However, the genetic colocalisation tests used in our study assumed a single causal variant in each locus and will, therefore, result in higher false-negative tests if there is more than one trait-associated causal variant. An additional issue is related to the selection of COVID-19 GWAS datasets used for analyses. Most protein-MR studies have used COVID-19 phenotypes with population-based controls, given their larger number of controls providing additional power to detect signals but at a cost of not being able to distinguish signals relevant to disease progression. While study designs with milder/asymptomatic cases as controls are useful to study disease progression, they are frequently underpowered and, because the selection of study participants are conditioned on the outcome, are susceptible to collider-stratification bias  (Griffith, 2020).
To enable a comprehensive assessment, we used all published COVID-19 phenotypes (October 2020 freeze), irrespective of controls used and, as expected, found most signals in COVID-19 phenotypes with population-based controls. For one of the targets (CD209), although we experimentally demonstrate binding of CD209 with spike protein of SARS-CoV-2, understanding the functional significance CD209 has on viral entry and any immunological relevance during infection requires further research. Finally, although we nominate several targets that may be therapeutically relevant for COVID-19, clinical trials are required for definitive assessments and to guide therapy. For example, the findings related to the ABO signal strongly implicated the adverse role of dysregulated coagulation in COVID-19 specifically in non-O blood group individuals; whether pre-emptive use of anticoagulants guided by blood groups can prevent severe COVID-19 is subject to findings of trials such as the ongoing ACTIV-4 trial (NCT04505774) (U.
Acknowledgements MAK, JSc, JH, AB, DO, MC, EMM, MG, and ID were funded by  Open Targets. JZ and TRG were funded by the  UK Medical Research Council Integrative Epidemiology Unit  ( MC_UU_00011/4 ). JSh and GJW were funded by the  Wellcome Trust  Grant  206194. This research was funded in part by the  Wellcome Trust  (grant  206194 ). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.
Funding Funder Grant reference number Author Wellcome Trust Grant 206194 Mohd Anisul Jarrod Shilts Jeremy Schwartzentruber Gavin J Wright Maya Ghoussaini Medical Research Council MC_UU_00011/4 Jie Zheng Tom R Gaunt Open Target Mohd Anisul Jeremy Schwartzentruber James Hayhurst Annalisa Buniello David Ochoa Miguel Carmona Ellen M McDonagh Maya Ghoussaini Ian Dunham The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Code availability Codes used to harmonise summary statistics are provided in https:// github. com/ EBISPOT/ gwassumstats-harmoniser (EBISPOT, 2020). Codes for pan-and cis-MR analyses are provided on the GSMR website (https:// cnsgenomics. com/ software/ gcta/# GSMR). Codes for genetic colocalisation analyses are provided on the coloc GitHub page (https:// github. com/ chr1swallace/ coloc;  Wallace, 2021). All codes used in the paper to reproduce results are provided in https:// github. com/ mohdkarim/ covid_ paper (copy archived at swh:1:rev:4ab9f9b17ffde57f7831ea555394290ba240a2b9; Anisul, 2021).
Data availability Summary data used for genetic analyses are publicly available (Sun et al can be downloaded from GWAS catalog https://www. ebi. ac. uk/ gwas/ downloads/ summary-statistics and COVID-19 HGI summary statistics can be downloaded from their website https://www. covid19hg. org/ results/). Data generated from our study are provided in the supplementary files (pan-MR and cis-MR association results filtered at p < 0. 05 and no filters applied to colocalisation results). The following dataset was generated:.
This is a very flexible class of distributions, allowing to catch symmetric, asymmetric, and multimodal distributions. Note that a Gaussian finite mixture model is a weighted sum of K Gaussian distributions ∑ 𝑝 𝑘 K 𝑘=1 𝒩(𝜇̇𝑘, 𝜎 2 ̇𝑘). All the model parameters (, ̇1,. , ̇, ̇1,. , ̇, ̇1,. , ̇) can be easily estimated using an expectation-maximization (EM) algorithm  (Dempster et al. , 1977)  and the optimal value of the number of components K selected according to the Bayesian Information Criterion (BIC). This method is implemented in the R package 'mclust' (see  Scrucca et al. 2016  for details on statistical and numerical aspects). After fitting the mixture parameters on the Dr sample, we fix their values for the rest of the modelling. According to (Eq. 2), the distribution of the De values is also approximated by a Gaussian finite mixture model with the following parameters ∑ 𝑝̇𝑘𝒩(𝐴𝜇̇𝑘, 𝐴 2 𝜎 2 ̇𝑘 𝐾 𝑘=1 ) The second step is to estimate A considering any outliers present and the measurement errors on the De values, which are assumed to be Gaussian with zero mean and known variance.
Code and data availability. The source code of the model is part of the R package 'Luminescence' (>= v0. 9. 16) and available open-access under GPL-3 licence conditions (https://CRAN. R-project. org/package=Luminescence; last accessed: 2021-09-08). Author contributions. NM and CT initiated the work, wrote the first manuscript draft and an initial R script. JMG and AP developed the mathematical basis for the model. SK implemented the model in the R package 'Luminescence' and ran additional tests. All authors equally contributed to the discussion and the final manuscript write-up.
Collecting Firmware Packages The environments in which embedded systems are deployed are heterogeneous, spanning a variety of devices, vendors, CPU/hardware architectures, instruction sets, operating systems, and custom components. This makes the task of compiling a representative and balanced dataset of firmware packages a difficult problem to solve. The lack of centralized points of collection, such as the ones provided by software/app marketplaces, antivirus vendors, or public sandboxes in the malware analysis field, makes it difficult for researchers to gather large and well triaged datasets. Firmware often needs to be downloaded from vendor Web pages and FTP sites, and it is not always simple, even for a human, to tell whether or not two firmware packages are for the same physical device.
Firmware Unpacking Frameworks The main tools to unpack arbitrary firmware packages are: binwalk [263], FRAK  [161] , Binary Analysis Toolkit (BAT)  [558]  and Firmware. RE  [155]  (Table  11. 1 compares the performance of each framework): • Binwalk is perhaps the best known and most used firmware unpacking tool developed by Craig Heffner  [263]. It uses pattern matching to locate and carve files from a binary blob. Additionally, it also extracts some meta-data such as license strings. • FRAK is an unpacking toolkit first presented by  Cui et al. [162]. It reportedly 1  supports a limited number of device vendors and models, such as HP printers and Multi-Function Peripherals (MFP). • The Binary Analysis Toolkit (BAT), formerly known as GPLtool, was originally designed by Armijn Hemel and Tjaldur software in order to detect GPL license violations  [269, 558].
To do so, it recursively extracts files from a binary blob and matches strings with a database of known strings from GPL projects and licenses. BAT also supports file carving similar to binwalk, as well as a very flexible plugin-oriented extension interface. ✗ ✗ ✗ ✗ Camera STL Linux ✗ ✓ ✗ ✓ Router Bintec - ✗ ✗ ✗ ✗ ADSL gateway Zyxel ZynOS ✓ ✓ ✗ ✓ PLC Siemens - ✓ ✓ ✗ ✓ DSLAM - - ✓ ✓ ✗ ✓ PC Intel BIOS ✓ ✓ ✗ ✓ ISDN server Planet - ✓ ✓ ✗ ✓ Voip Asotel Vxworks ✓ ✓ ✗ ✓ Modem - - ✗ ✗ ✗ ✓ Home automation Belkin Linux ✗ ✗ ✗ ✓ 55% 64% 0% 82% • Firmware. RE [155] extends BAT with additional unpacking methods and specific analyses to perform automated large-scale analyses. When released, it achieved a lower false positive rate when unpacking firmware compared to binwalk.
Thus, a further step in the analysis of firmware is to identify the versions of software (both programs and libraries) it contains, and correlate those versions with known vulnerabilities (e. g. , CVE database). There are several possible approaches to perform this. For example, [155] use fuzzy hashing  [340, 507]  as a method to correlate files in firmware images. The effectiveness of the approach was demonstrated in several examples, in particular uncovering many IoT and embedded devices being so-called "white label" products. 2 Finally, machine learning can be used to identify firmware images  [157]  or to search for known vulnerabilities  [585].
Discovering Backdoors with Static Analysis Aside from vulnerability discovery, a small body of work has attempted to automatically identify backdoor-like constructs in device firmware. Static analysis is most suited to detecting such constructs due to the fact it can achieve full program coverage. Dynamic analysis is less adequate in this case, as it relies solely on execution traces that can be captured and analyzed stemming from triggering standard program behaviors (which, by definition [551], a backdoor is not). HumIDIFy 3  [552] uses a combination of Machine Learning (ML) and static analysis to identify anomalous and unexpected behavior in services commonly found in Linux-based firmware. ML is used first to identify the type of firmware binaries, e. g. , a web-server, this then drives classification-specific static analysis on each binary.
HumIDIFy attempts to validate that binaries do not perform any functionality outside of what is expected of the type of software they are identified as. For example, HumIDIFy is able to detect a backdoor within a web-server taken from Tenda router firmware 4  that contains an additional UDP listening thread which executes shell commands provided to it (without authentication) as the root user. Stringer 5  [550] attempts to locate backdoor-like behavior in Linux-based firmware. It automatically discovers comparisons with static data that leads to execution of unique program functionality, which models the situation of a backdoor providing access to undocumented functionality via a hard-coded credential pair or undocumented command. Stringer provides an ordering of the functions within a binary based on how much their control-flow is influenced by static data comparisons that guard access to functionality not otherwise reachable.
The authors demonstrate Stringer is able to detect both undocumented functionality and hardcoded credential backdoors in devices from a number of manufacturers. Firmalice [528] is a tool for detecting authentication bypass vulnerabilities and backdoors within firmware by symbolic execution. It takes a so-called security policy as input, which specifies a condition a program (or firmware) will exhibit when it has reached an authenticated state. Using this security policy, it attempts to prove that it is possible to reach an authenticated state by discovering an input that when given to the program satisfies the conditions to reach that state. To discover such an input, Firmalice employs symbolic execution on a program slice taken from a program point acting as an input source to the point reached that signals the program is in an authenticated state.
To this end,  Cojocar et al. [150] , propose PIE, a tool to automatically detect parsing routines in firmware binaries. PIE utilizes a supervised learning classifier trained on a number of simple features of the LLVM IL representation of firmware components known to contain parsing logic. Such features include: basic block count, number of incoming edges to blocks, and number of callers (for functions). PIE provides a means to identify specific functions responsible for performing parsing within an input firmware package, or software component. Stringer [550], described in Sect. 11. 3. 2. 2, similarly provides a means of automatically identifying parser routines (for text-based input); in addition to identifying routines, it is also able to identify the individual (text-based) commands, processed by the parser.
Similarly, FIRMA-DYNE [137] presents an automated and scalable system for performing emulation and dynamic analysis of Linux-based embedded firmware. The general idea of both works is to crawl and then unpack firmware packages into minimal root filesystems (i. e. , rootfs) that can subsequently be virtualized and executed as a whole via "system emulation" (as opposed to "user emulation") using for example QEMU  [69]. The emulator is first used to start an architecturespecific emulation host OS, such as Debian for ARM or MIPS depending on the architecture of the device whose firmware is being dynamically analyzed. Then the firmware root filesystem is uploaded to the emulation host OS, where its Linux boot sequence scripts are initiated, most likely in a chroot environment under the emulation host OS.
Because of its ability to generate biological hypotheses, metabolomics offers an innovative and promising approach in many fields, including clinical research. However, collecting specimens in this setting can be difficult to standardize, especially when groups of patients with different degrees of disease severity are considered. In addition, despite major technological advances, it remains challenging to measure all the compounds defining the metabolic network of a biological system. In this context, the characterization of samples based on several analytical setups is now recognized as an efficient strategy to improve the coverage of metabolic complexity. For this purpose, chemometrics proposes efficient methods to reduce the dimensionality of these complex datasets spread over several matrices, allowing the integration of different sources or structures of metabolic information.