dataesr/scientific-paragraphs-categorization huggingface dataset:
The imputed genotypes were then filtered using the bcftools 1. 9 command 'view' with option -include "INFO/INFO > = 0. 9" for each chunk, and the chunks for each chromosome were then combined into a single BCF file using bcftools concat with options -n -f as recommended by the IMPUTE5 user manual. The BCF files were converted to bed/bim/fam format using PLINK, and PLINK then used to merge the chromosome-level files into a single dataset with the options -keep-allele-order -make-bed -merge-list. ALSPAC children were genotyped using the Illumina HumanHap550-quad chip. SNPs with MAF < 0. 01, missingness >0. 05, and HWE P value < 1 × 10 -6 were excluded. Individuals were further excluded on the basis of sex mismatches, minimal or excessive heterozygosity, disproportionate levels of individual missingness (>3%) or insufficient sample replication (identity by descent; IBD < 0.
The cell culture experiments were carried out in three cell lines A549 (source: Lung), HEK293 (Kidney) and HepG2 (Liver) to identify a robust regulatory signal associated with SOCS3 region. These cell lines were chosen based on gene expression levels in the GTEx database (https://gtexportal. org/home/) covering varying equivalent tissue expression levels (Supplementary Fig. 5a ) and on relevant literature  77, 78. The Cell lines were grown in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum and 1% (50 U/mL) of antibiotics (penicillin and streptomycin, Thermo fisher scientific) under standard cell culture conditions. Cells were seeded at a density of 150 K cells per well in 24-well culture plates (Thermo Fisher Scientific) and grown until 70-80% confluency prior to transfections.
Statistical analyses Discovery epigenome-wide association study. The discovery EWAS was carried out in the MMNP cohort which comprised 685 individuals with methylation measured at 803,210 CpG loci on the EPIC array that passed QC. The EWAS was conducted using the R package "ewaff " (version: 0. 0. 2; https://github. com/perishky/ewaff). To account for batch effects, surrogate variables (SVs) were derived from the 200 K most variable CpG probes. A generalized linear regression model was used with height residuals as the outcome (dependent variable) and methylation beta values (methylation values from 0 to 1) as exposure (independent variable) while adjusting for child age, child sex, and ten methylation-derived SVs. Significant loci were defined as those with a Benjamini-Hochberg false discovery rate (FDR) of <0.
05/number of SNPs and CpGs tested. To increase the power to detect SNP-methylation associations, we conducted an additional meta-analysis on the mQTL summary statistics using all four LMIC cohort following the inverse-variance method in METAL software (version release 2011-03-25)  81. ii. Polygenic Risk Score (PRS) A height PRS provides a cumulative measure of genome-wide genetic variation that predicts an individual's height. The height PRS was generated using 12,111 autosomal height-associated SNPs identified by the GIANT consortium using a conditional and joint multiple-SNP (COJO) analysis  6. We used the effect sizes of the height-increasing allele for each SNP identified in the COJO analysis as weights and the individual PRS was calculated as follows: PRS j = X W i × G ij Where, PRS j is polygenic score of individual j, "W i " is the weight (effect size) of SNP i taken from the COJO analysis and G ij is the genotype at SNP i in individual j, coded as 0, 1 or 2 based on the height increasing allele.
Genetic instruments for DNA methylation were obtained from the GoDMC database by extracting all cis variants with a P value < 1 × 10 -8 and trans variants (> 1 Mb from CpG site) with a P value < 1 × 10-14 (see ref. 23). After linkage disequilibrium (LD) clumping using parameters of LD r 2 = 0. 001 and 10 Mb LD windows, one mQTL (rs4383852) for cg18181703 was identified. This SNP (rs4383852) was missing in the outcome GWAS dataset and was therefore proxied by an alternative SNP (rs2884013) in complete LD with the first (r 2 = 1. 0)  83. SNP alleles were harmonized to represent a single effect allele so that both exposure and outcome variants were expressed per effect allele increase for the exposure. Since a single mQTL was identified as a proxy for the exposure of interest, the Wald ratio estimator was used to quantify the causal effect.
For instance, the R 2 for a predictor variable in the model is the sum of squared differences in height attributed to that predictor variable divided by the sum of squares for all the variables in the model. Confidence intervals for R 2 were calculated using "boot" package with 1000 bootstrap replications. Selection of SNPs and causal inference analysis between prenatal exposures and SOCS3m. Genetic variants in and around the MTHFR gene are known to be associated with folate levels. We therefore restricted our search for folate-associated genetic instruments to within 1 Mb of MTHFR in the GWAS catalog (https://www. ebi. ac. uk/ gwas/). Genetic association analysis was conducted in Indian cohorts where both serum folate levels and genetic data were available: specifically, MPC mothers and children, MMNP mothers, and Pune Maternal Nutrition Study (PMNS)  84  mother, father and child, and individuals in Mysore Birth Record Cohort (MBRC)  85.
Acknowledgements MMNP: We are grateful to the families who took part, and the team of fieldworkers, nurses, research assistants, and data managers who carried out the study. The original trial was supported by the  Wellcome Trust, United Kingdom ; the  Medical Research Council, United Kingdom ; the  Department for International Development, United Kingdom ;  USAID ; the  Parthenon Trust, Switzerland ; and the  Industrial Credit and Investment Corporation of India Bank Ltd Social Initiatives Group, Mumbai, India. The  SARAS KIDS children 's follow-up study was funded by the  Medical Research Council, UK  research grant no:  MR/M005186/1. PMMST and ENID: We thank all the study participants in West Kiang, The Gambia for their time and commitment. We also thank members of the laboratory and field teams.
We acknowledge the work of  Z. Herceg ,  M. N. Routledge ,  Y. Y. Gong , and  H. Hernandez-Vargas  in acquiring the ENID 2-year 450k array data and  Toby Candler  in acquiring samples and data in the follow-up study in 5-7-year-old children. PMMST was supported by  MRC  ( U1232661351 ,  U105960371 , and  MC-A760-5QX00 ) and  DFID  under the  MRC/DFID Concordat , and other members of the Gambian team were supported by  MRC  grants  U105960371 ,  U123261351  and  MR/ M01424X/1. The  ENID  trial was jointly funded by the  UK Medical Research Council (MRC)  and the  Department for International Development (DFID)  under the  MRC/DFID Concordat  agreement ( MRC Program   MC-A760-5QX00 ). Methylation analysis of ENID early childhood samples was supported by the  Bill & Melinda Gates Foundation  (grant no.
OPP1 066947 ). The generation of methylation data from the same cohort in late childhood was funded by the  UK MRC  (grant no. MC_PC_MR/ RO20183/1 ). The  EMPHASIS study  was jointly funded by  MRC ,  DFID , and the  Department of Biotechnology (DBT) ,  Ministry of Science and Technology, India , under the  Newton Fund initiative  ( MRC  Grant No. :  MR/ N006208/1  and  DBT  Grant No. :  BT/IN/DBT-MRC/DFID/24/GRC/2015-16 ). MPC: We thank all the participating families,  CSI Holdsworth Memorial Hospital  staff, the research team, and the staff of the  MRC Lifecourse Epidemiology Unit  for their support. MPC was supported by the  Parthenon Trust, Switzerland ; the  Wellcome Trust, UK ; the  MRC, UK  and the  Department for International Development (UK). The follow-up at 21 years was supported by the  DBT/Wellcome Trust India Alliance   Fellowship  [Grant Number:  IA/CPHS/16/1/502655 ].
All relevant data supporting the key findings of this study are available within this article and its Supplementary Data files. Publicly available data used in this study include GoDMC (http://mqtldb. godmc. org. uk/search?query= cg18181703), GWAS catalog (https://www. ebi. ac. uk/gwas/), ENCODE (https://www. encodeproject. org/) and GTex (https://gtexportal. org/ home/) databases. Source data are provided with this paper.
We can answer Q 3 by calling ud 2 with 12345 and projecting onto the address field. Thanks to ϕ, we know that the result will contain the employee's address, even though only one of the phone numbers will be returned. This gives a second intuition: result-bounded methods are useful when there is a functional dependency that guarantees that some projection of the output is complete. In this paper, we study how and when we can use result-bounded methods to reformulate queries and obtain complete answers, formalizing in particular the intuition of Examples 1. 4 and 1. 5. We then show decidability and complexity results for the answerability problem. We focus on two common classes of integrity constraints on databases: inclusion dependencies (IDs), as in Example 1. 4, and functional dependencies (FDs), as in Example 1.
Query containment and chase proofs. We will reduce answerability to query containment under constraints, i. e. , checking whether a Boolean CQ Q ′ follows from another Boolean CQ Q and some constraints Σ. Formally, the problem asks if any instance that satisfies Q and Σ also satisfies Q ′ , which we denote as Q ⊆ Σ Q ′. There are well-known reductions between query containment with TGDs and the problem of certain answers  [16, 28]  under TGDs. We will not need the definition of certain answers, but we will use some existing upper and lower bounds from this line of work (e. g. , from  [6, 16] ), rephrased to query containment under constraints. When Σ consists of dependencies, query containment under constraints can be solved by searching for a chase proof  [28]. Such a proof starts with an instance called the canonical database of Q and denoted CanonDB(Q): it consists of facts for each atom of Q, and its elements are the variables and constants of Q.
Both finite and unrestricted answerability have been studied in past work on access methods  [13, 14] , just as finite and unrestricted variants of other static analysis problems (e. g. , query containment) have long been investigated in database theory (e. g. ,  [34] ). The unrestricted variants usually provide a cleaner theory, while the finite variants can be more precise. In this work our goal is to investigate both variants, leaving a discussion of the trade-off between finite and unrestricted answerability for future work. As it turns out, for the database-style dependencies that we consider, the finite variant can be reduced to the unrestricted one. In particular, this reduction holds for constraints Σ that are finitely controllable, by which we mean that for all Boolean UCQs Q and Q ′ , the containment Q ⊆ Σ Q ′ holds if and only if, whenever a finite instance I satisfies Q, then it also satisfies Q ′.
First, they give insight about the use of result bounds, following the examples in the introduction. For instance, our results will show that for most common classes of constraints used in databases, the actual numbers in the result bounds never matter for answerability. Secondly, they help us to show the decidability of monotone answerability. Existence-check simplification. The simplest way to use resultbounded methods is to check if some tuples exist, as in Example 1. 4. We will formalize this as the existence-check simplification, where we replace result-bounded methods by Boolean methods that can only do such existence checks. Given a schema Sch with result-bounded methods, its existencecheck simplification Sch ′ is formed as follows: • The signature of Sch ′ is that of Sch plus some new relations: for each result-bounded method mt, letting R be the relation accessed by mt, we add a relation R mt whose arity is the number of input positions of mt.
Consider a truncated chase proof that results in a chase instance I. Such a proof is well-ordered if it has the following property: For any fact F = R(ì c) generated in I by firing a trigger τ for an ID, if accessible(c i ) is generated in I with birth fact in the subtree of F , and all the birth constants c m 1. c m k of c i were exported when firing τ , then each fact accessible(c m 1 ). accessible(c m k ) must already have been present in the chase at the time F was generated. We now show: L E. 2. For any chase proof from the canonical database of Q using truncated accessibility axioms and IDs, producing instance I , there is a well-ordered chase proof from the canonical database of Q that generates a set of facts isomorphic to those of I.
Given a CQ Q, let I 0 := CanonDB(Q) be its canonical database, and let I Lin 0 be formed by adding atoms to I 0 as follows. • Apply all of the truncated accessibility axioms of ∆ + to I 0 to obtain I ′ 0. • For any relation R of the signature S, and for every fact R(a 1. a n ) of I ′ 0 , let P be the set of the i ∈ {1. n} such that accessible(a i ) holds in I ′ 0. For every P ′ ⊆ P of size at most w, add to I Lin 0 the fact R P ′ (a 1. a n ). Further, in the case where accessible(a i ) holds for each 1 ≤ i ≤ n, then we add the fact R ′ (a 1. a n ). It is now easy to see that Σ Lin and I Lin 0 satisfy the required conditions: for every set of primed facts I derivable from I 0 by chasing with Σ and ∆, we can derive the same set of primed facts from I Lin 0 by chasing with Σ Lin. Indeed, chasing with the Lift rules creates a tree of facts that corresponds exactly to a short-cut chase proof: when we create an R P -fact, the P subscript denotes exactly the set of positions of the new facts that contains exported elements that are accessible.
E. 4 Proof of Proposition 5. 6: Containment under IDs of Bounded Semi-Width We recall the statement of Proposition 5. 6. For any fixed w ∈ N, there is an NP algorithm for containment under IDs of semi-width at most w. We recall the definition of semi-width from the body, generalizing it slightly from IDs. The basic position graph of a set of TGDs Σ is the directed graph whose nodes are the positions of relations in Σ, with an edge from R[i] to S[j] if and only if there is a dependency δ ∈ Σ with exported variable x occurring in position i of R in the body of δ and position j of S in the head of δ. We say that a collection of TGDs Σ has semi-width bounded by w if it can be decomposed into Σ 1 ∪ Σ 2 where Σ 1 has width bounded by w and the position graph of Σ 2 is acyclic. Consider a chase sequence based on the canonical database I 0 of a conjunctive query Q, using a collection of IDs Σ.
FD violations. Recall that, in the proof, we had to argue that after applying the FDs to the canonical database and pre-processing the constraints slightly, we could drop the FDs in Σ and in Σ ′ without impacting the entailment. We argued in the body that the constraints for AMonDet would consist of Σ, Σ ′ , and the following: • for each non-result-bounded method mt accessing relation R with input positions ì x, ( i accessible(x i )) ∧ R(ì x, ì ) → R ′ (ì x, ì ) ∧ i accessible( i ) • for each result-bounded method mt accessing relation R with input positions ì x, ( i accessible(x i ))∧R(ì x, ì ) → ∃ì z R(ì x, ì z)∧ R ′ (ì x, ì z) ∧ i accessible(z i ) We then modified the second set of axioms so that, in going from R to R ′ , they preserve not only the input positions of mt, but also the positions of R that are determined by input positions of mt (i.
Hence, having fixed a ′ , there are indeed fixed polynomials P 1 and P 2 such that the number of rules, and the time to construct them, is in P 1 (|Σ| , 2 P 2 (w,h,n ′ ). We have described the construction of Σ ′. Now, to construct the rewriting Q Lin of Q, we first construct the query Q 1 whose canonical database is obtained by closing the canonical database of Q under the full GTGDs of Σ and of Σ w. To see why Q 1 can be computed in the prescribed bound, we can assume that we have computed Σ w as we already know that it can be computed in the given time bound, so we need only reason about the complexity of applying the rules. Now, note that the domain size does not increase, and the number of possible new facts is bounded by |Adom(CanonDB(Q))| h. Now, testing each possible rule application is in PTIME.
P. It is immediate that (ii. ) implies (i. ). Indeed, assuming (ii. ), let I be a finite instance satisfying Σ, then it satisfies Σ * by definition of the finite closure, and we conclude by (ii. ). Conversely, let us show that (i. ) implies (ii. ). The work  [4]  shows that the finite closure of a set of UIDs and FDs admits finite universal models: for each set of UIDs and FDs Σ, each k ∈ N, and each finite instance I , there is a finite instance that satisfies Σ * and such that for every Boolean CQ Q ′ of size at most k, the following equivalence holds: I ∧Σ * implies Q ′ over all instances if and only if Q ′ holds on. Applying this to canonical databases rather than instances, we have that for all Boolean CQs Q, for all k ∈ N, there is a finite satisfying Σ * such that for all Boolean CQs Q ′ of size at most k: Q ⊆ Σ * Q ′ if and only if Q ′ holds on.
Acknowledgements This work was supported by the project  FATECO  [GA: 847284 ,  2018 ,  Research Fund for Coal & Steel ] and  CRIOGRIND  [ PUE2021_1_0010 ,  2021 ,  Basque Government ]. The authors wish to thank  Mehmet Cici  from  ENISE  for the accomplishment of the turning and belt finishing tests and  Hervé Pascal  for the residual stress measurements.
Sequencing-based microbiome analysis The 16S rRNA sequencing results were analysed using the QIIME 2. 0 package (https:// qiime2. org) and RDP classifier 2. 2 for taxonomic assignment along with the greengenes database version 13. 8 (http:// green genes. second. genome. com), an update of version 13. 5 released to address missing genus and species names. For all processed samples, the RDP classifier was run with the default minimum confidence estimate of 0. 5 to record an assignment (i. e. an operational taxonomic unit [OTU]). The results were generated using the default parameters in the original QIIME 2  [7]  and RDP  [68]  methods. Identification and removal of contaminant sequences was conducted using the decontam R Bioconductor package  [12] , using bacterial prevalence in blank samples for statistical identification of contaminant taxa.
Data were transformed to log-ratios using the clr transformation  [1]  making them symmetric and linearly related, and thus potentially avoiding spurious correlations and sub-compositional incoherencies  [8, 24, 49]. Taking into account the quality parameters such as the percentage of empty reads, the dispersion index and the ratio between filtered and mapped reads, samples were classified as detectable or not detectable biomass based on the filtered versus mapped reads ratio threshold of 0. 65 in EF and 0. 7 in EB samples. For further analysis, only the detectable samples (i. e. samples with filtered versus mapped reads ratios greater than 0. 65 and 0. 7 in EF and EB, respectively) were included. In each dataset of endometrial 16S rRNA profiles, the abundance of each taxa was analysed, and low-abundance species were removed from further consideration.
Correlation network analysis Pearson matrices for network analysis were generated using the Scipy Stats package (version 1. 4. 1) and visualised with Networkx (version 2. 4), considering taxa with significant Pearson correlation coefficient  [37]. To account for both the correlation and significance, each edge was assigned a weight w computed as: abs(corr)pval, where abs(corr) was the absolute correlation value between each pair of taxa and pval was the corresponding p value. Negative correlations were represented as red edges. Network analysis was performed on each data type (i. e. EF and EB) and for each outcome (i. e. LB, NP, BP and CM).
Taxa reference ranges The log-ratio-transformed 16S rRNA profiles from LB samples were used to define the reference range for each taxon. A confidence interval of 95% was calculated to establish reference ranges for the Student's t distribution taxa distribution model  [2]. The Scipy Stats package (version 1. 4. 1) was used for calculations  [66]. Taxa contributions to different outcomes were statistically inferred using a two-sided Mann-Whitney U test. The difference between samples outside the range and upper/lower bound reference values was used to assess the role of the taxa in each outcome. Taxa were reported when a significant difference was observed between upper and lower difference distributions. The Scipy Stats package (version 1. 4. 1) [66]  was used for statistical methods and Plotly was used to visualise significant taxa distributions (  [27] ).
Bayesian inference for reproductive outcome differences Differences between Lactobacillus and other reproductive tract taxa were modelled using Bayesian inference following a normal distribution with mean μ outcome and dispersion σ outcome for each reproductive outcome, Priors selected to estimate parameters were normal distribution for μ outcome centred on the mean difference between Lactobacillus and other taxa in the dataset and a dispersion of 10 ( µ otucome ∼ N (x, 10) ) and, for σ outcome , vague continuous distribution from 0 to 10 (σ outcome ∼ Uniform(0, 10). Markov chain Monte Carlo sampling for posterior distribution parameter inference and predictive probability analysis were implemented using the PyMC3 Python library  [56].
Funding This study was supported by the  Igenomix Foundation. IGG was supported by a  Formación de Profesorado Universitario  grant ( FPU15/01923 ) from the  Spanish Ministry of Education. DB is supported by  Torres-Quevedo  grant ( PTQ-16-08454 ) from the  Spanish Ministry of Economy and Competitiveness. FV was supported by the  Miguel Servet Programme Type II of ISCIII  ( CPII18/00020 ) and the  FIS  project ( PI18/00957 ). CS was supported by the  Spanish Government MINECO/FEDER  (grant  RTI2018-094946-B-I00 ), the  Valencian Innovation Council  (ref. PROMETEO/2018/161 ) and the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under grant agreement number  874867.
2) Payload Data Links: UAVs are expected to enable users to experience virtual reality by means of panoramic 8K video live broadcast, e. g. , through a 360 • spherical view camera on board the UAV that captures and uploads 8K video in real time to a cloud server. Such video stream will be received live by users through remote VR glasses. Besides high bit rates and low latency, this use case demands accurate positioning to avoid damage to life or property in densely populated regions. In building-intensive areas, where traditional satellite methods may lack accuracy, an excessive positioning offset might set off an anti-collision system, thus slowing down the UAV. As a countermeasure, UAVs are envisioned to employ 4×4K AI surveillance, sending four-way 4K full-angle camera data to an AI controller that follows up by issuing timely control instructions.
Since mmWave systems rely on highly directional communication at both transmitter and receiver, channel models must provide statistical descriptions of the full doubly directional characteristics of the channel, which include the totality of path components as well as the angles of arrivals, angles of departure, gains and delays. Modern data-driven machinelearning methods become an attractive recourse to tackle this challenge. In particular, neural networks (NNs) have been used in  [155] -  [159]  for indoor mmWave channel modeling, where the NN outputs parameters that represent a regression from the training dataset, similar to what is typically proposed with learning-based planning and prediction tools  [160] -  [167]. To develop an accessible aerial channel model, we put forward a generative model featuring a novel two-stage NN structure: • A first NN determines if the link is in a state of LoS, NLoS, or outage.