dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, all artifact genes, which are typically encountered as bystander mutations in cancer that are mutated for example as a consequence of a high homology of sequences in the gene, were excluded  76. The OncoPlot function from ComplexHeatmap (v. 2. 1. 2) was used to visualize the most frequent somatic mutations. Comparison of TMB with TCGA datasets. To compare the TMB in the AC-ICAM with all 33 TCGA cohorts derived from the MC3 project, we used the tcgaCompare function from maftools (v. 2. 6. 05, R). For AC-ICAM, the filtered MAF for nonsynonymous mutations was used as input with specified capture size of 40.
Comparison of somatic mutations with other cohorts. To define mutated genes in the AC-ICAM that were not previously described in colon cancer, we performed a comparison of the most frequently mutated genes in AC-ICAM (>5% of the tumor samples) with frequencies detected in previously published datasets containing colon cancer samples (TCGA-COAD and NHS-HPFS) as well as reported cancer driver genes  32  or colon oncogenic mediators  38. First, we extracted genes with a nonsynonymous mutation frequency >5% in the AC-ICAM cohort. Subsequently, only genes that are likely involved in cancer development, as described in the section 'Cancer-related gene annotation', were retained. All artifact genes (mutations typically encountered as bystander mutations in cancer that are mutated for example as a consequence of a high homology of sequences in the gene), were excluded.
Genetic immunoediting and immunoediting score HLA typing, neoantigen prediction and GIE. HLA typing was performed on both WES and RNA-seq data using OptiType (bcbio-nextgen v. 1. 1. 5 in Python v. 2. 7. 0)  78. Neoantigen prediction tool pVACseq from pVACtools was run using the following predictors: MHCnuggetsI, NNalign, NetMHC, SMM, SMMPMBEC and SMMalign. The obtained vcfs from our somatic mutation calling pipeline were used as input for pVACseq, along with the predicted HLA type from WES data. Gene expression data aligned to GRCh37 in transcripts per million was annotated to the vcfs using vcf-expression-annotator. Mutant-specific binders, relevant to the restricted HLA-I allele, are referred to as neoantigens, as described in detail by Zhang et al. 79. Mutated epitopes with a median IC 50 binding affinity across all prediction algorithms used <500 nM, with a corresponding wild-type epitope with a median IC 50 binding affinity > 500 nM, were used as criteria to infer neoantigens.
Microbiome: 16S rRNA gene sequencing and data processing Sequenced data were demultiplexed using MiSeq Control Software. The overall quality of sequencing quality was evaluated using FastQC and the demultiplexed sequencing data were imported into Quantitative Insights into Microbial Ecology (QIIME2; v. 2019. 4. 0) software package. The data were denoised with DADA2, which includes a multi-step process, including read filtering, dereplication and chimera removal. Paired 250-bp reads were trimmed of the initial five low-quality bases and further processed to generate the amplicon sequence variant, interchangeably called operational taxonomic units (OTUs). The data were subsampled at a depth of 22,704 and then normalized using the rarefaction on OTUs count at even depth. Taxonomic classification was performed utilizing 16S rRNA gene database from Silva classifier (silva-132-99-515-806-nb-classifier).
Indices were read into R using R package vegan (v. 2. 5-6). Relative abundance of distinct microbiome elements was determined using the transform_sample_counts function from Phyloseq, such that sum of all abundance values per sample is equal to one (Microbiome_Relative = transform_sample_counts (pyloseq_object, function(x) x / sum(x))). OTU tables were aggregated by taxonomic ranks including phylum (26 unique phyla), class (48 classes), order (97 orders), family (207 families), genus (562 genera) and species (846 species). As the confidence for annotation of reads decreases with decreasing rank, some reads were only annotated with higher ranks.
MBR model development, training set. We first normalized the genus abundance matrix by converting each genus column into a z score using mean and s. d. and treating the normalized abundance matrix as the training set. We built a relaxed multivariable elastic-net OS Cox regression model using the glmnet R package (v. 4. 1. 4) on the training set. The optimal hyper parameters (γ and λ) for the best model were identified through fivefold cross-validation via a grid-search technique using the 'cv. glmnet' function. We used the concordance index as a performance metric. The parameters for which the mean cross-validation concordance index was the highest were selected as optimal hyper parameters. Next, the final model was built using these hyper parameters on the complete training set. To calculate risk scores in the training dataset (MBR scores), we passed the training set and best model to the 'predict' function.
MBR model validation, testing sets. We validated the final model on two datasets. Both datasets consist of samples that were not used for model training (unseen data). One is an independent internal (ICAM42) dataset, referred to as testing cohort 1 and the other is an external cohort (TCGA-COAD), referred to as testing cohort 2. The ICAM42 consists of 42 samples and TCGA-COAD consists of 117 samples. We processed the two datasets to convert the abundance values for each genus into z scores using the mean and s. d. derived from the training set. These abundance matrices were passed to the 'predict' function along with the best model to estimate corresponding risk scores. The risk score (MBR score) of any tested sample is only dependent on the relative abundance of the list of genera that overlap with the ones included in the MBR classifier (the risk score for each sample is not dependent on one of the other samples).
Finally, the MBR scores are binarized using the cutoff threshold 0 to categorize the test sample into 'high-risk' (>0) and 'low-risk' (<0) groups as performed in the training set. Therefore, no cutoff optimization occurred in the validation phase. MBR model performance assessment. We tested the concordance index (1) in the training set using the final MBR model; (2) in the training set using the cross-validation of the best MBR model (five permutations, 80% training and 20% validation partition); and (3) in each test set cohort separately (ICAM42 and TCGA-COAD) and in the full test set (ICAM42 and TCGA-COAD combined) using the final MBR model. Taxa used for the MBR score calculation in other cohorts. To calculate the MBR score in each additional dataset we used taxa that overlapped with the 41 genera of the MBR classifier, which was developed using 16S rRNA gene sequencing on tumor samples.
The gray band reflects the 95% confidence interval for predictions of the linear regression model between the plotted variables. b, PCR gel images of 126 DNA samples amplified for R. bromii. c, Concordance between R. bromii PCR and detection of Ruminococcus 2 by 16 S rRNA gene sequencing or of R. bromii by WGS (positivity was defined as a relative abundance > 0). d, Concordance index of optimal multivariate cox regression model per dataset. The cross-validation performance highlights the mean concordance of 10-different folds with the optimal hyper parameters (gamma and lambda) that is, the same parameters as the optimal model. e, Forest plot with HR (center), corresponding 95% confidence intervals (error bars), and P value calculated by cox proportional hazard regression analysis for OS, using: 1) the 16 S MBR score in AC-ICAM, 2) WGS R.
Acknowledgements This work was supported by the  Qatar National Research Fund  ( JSREP07-010-3-005  awarded to W. H. and  NPRP11S-0121-180351  awarded to D. B. ) and  Sidra Medicine Internal  funds (SDR100029, D. B. and W. H. ). M. C. was also supported by ' Associazione Italiana per la Ricerca sul Cancro ' under IG 2018, ID 21846 project awarded to M. C. J. D. was supported by a grant from the  Qatar Biomedical Research Institute  ( VR94 ). We thank  P. Blandini  ( Azienda Sanitaria Locale ,  ASL3.
Data availability BAM files for RNA and WES data, along with FastQ files for 16S rDNA sequencing and non-aligned WGS reads are available through controlled access at dbGaP (phs002978. v1. p1) and public access SRA (PRJNA941834; 16S and WGS). , including large files such as the MAF files for WES, segmentation file for the analysis of copy-number genomic aberrations, the 16S OTU tables. FigShare will be also updated with metrics that will be generated in the future. Processed data and clinical data are also available via cBioportal for interactive data exploration (Sidra-LUMC AC-ICAM dataset; https:// www. cbioportal. org/). Access to SRA, cBioportal and FigShare is unrestricted and immediate, controlled access through dbGAP is managed by the National Institutes of Health/National Cancer Institute data access committee through the dbGAP portal.
Study design We built a deterministic decision-tree model to assess effectiveness, cost and cost-effectiveness of different screening strategies for maternal CMV infection and cCMV infection, using the TreeAge Pro 2020 software package (TreeAge Software, Inc. , Williamstown, MA, USA). We simulated a study population of 800 000 pregnant women in the French healthcare system (the approximate number of pregnancies each year in France)  23. We adopted a collective perspective, i. e. we considered all resources involved in the provision of overall care for maternal CMV and cCMV infections, for the woman during pregnancy and for the infected fetus/child for their entire life  24. The time horizon adopted for the cost-effectiveness analysis was the life expectancy of the child. The four maternal CMV screening strategies considered were: no systematic screening (Strategy S1), current screening practice (25-50% of pregnant women) (Strategy S2), universal screening (Strategy S3) and universal screening in conjunction with valacyclovir in case of T1 PI (Strategy S4).
A positive CMV PCR analysis of neonatal saliva is always confirmed by PCR analysis of urine. If cCMV is diagnosed, specific follow-up is carried out and antiviral treatment with valganciclovir may be initiated  30. Regarding prevention of vertical transmission with valacyclovir, in the S4 strategy, we made the following assumptions. If T1 CMV PI is diagnosed, valacyclovir (8 g/day) is offered until amniocentesis (mean, 10 weeks of treatment) and biological follow-up is undertaken  12. In this strategy, valacyclovir is not offered in case of T2/T3 PI or NPI. There are no data in the literature to support a beneficial effect of valacyclovir on the fetal risk of sequelae if cCMV occurs despite administration of valacyclovir  12.
The Scalable ZX-calculus is a compact graphical language used to reason about linear maps between quantum states. These diagrams have multiple applications, but they frequently have to be constructed in a case-by-case basis. In this work we present a method to encode quantum programs implemented in a fragment of the linear dependently typed Proto-Quipper-D language as families of SZX-diagrams. We define a subset of translatable Proto-Quipper-D programs and show that our procedure is able to encode non-trivial algorithms as diagrams that grow linearly on the size of the program.
Previous work  [6]  has shown that the SZX calculus is capable of encoding nontrivial algorithms via the presentation of multiple hand-written examples. For an efficient usage as an intermediate representation language, we require an automated compilation method from quantum programming languages to SZX diagrams. While ZX diagrams can be directly obtained from a program compiled to a quantum circuit, to the best of our knowledge there is no efficient method leveraging the parametricity of the SZX calculus. There exist several quantum programming languages capable of encoding high-level parametric programs  [1, 11, 17]. Quipper  [15]  is a language for quantum computing capable or generating families of quantum operations indexed by parameters. These parameters need to be instantiated at compile time to generate concrete quantum circuit representations.
Quipper has multiple formal specifications, in this work we focus on the linear dependently typed Proto-Quipper-D formalization  [14, 13]  to express high-level programs with integer parameters. The contributions of this article the following. We introduce a list initialization notation to represent multiple elements of a SZX diagram family composed in parallel. We formally define a fragment of Proto-Quipper-D programs that can be described as families of diagrams. Then we present a novel compilation method that encodes quantum programs as families of SZX diagrams and demonstrate the codification and translation of a nontrivial algorithm using our procedure. In Section 2 we outline both languages and introduce the list initialization notation. In Section 3 we define the restricted Proto-Quipper-D fragment.
Background We describe a quantum state as a system of n qubits corresponding to a vector in the C 2 n Hilbert space. We may partition the set of qubits into multi-qubit registers representing logically related subsets. Quantum computations under the QRAM model correspond to compositions of unitary operators between these quantum states, called quantum gates. Additionally, the qubits may be initialized on a set state and measured. High-level programs can be encoded in Quipper  [15] , a Haskell-like programming language for describing quantum computations. In this work we use a formalization of the language called Proto-Quipper-D  [13]  with support for linear and dependent types. Concrete quantum operations correspond to linear functions between quantum states, generated as a composition of primitive operations that can be described directly as a quantum circuit.
The λ D calculus We first define a base language from which to build our translation. In this section we present the calculus λ D , as a subset of the strongly normalizing Proto-Quipper-D programs. Terms are inductively defined by: M, N, L := x | C | R | U | 0 | 1 | n | meas | new |λ x S. M | M N | λ ′ x P. M | M @ N | ⋆ | M ⊗ N | let x S 1 ⊗ y S 2 = M in N | M; N | VNil A | M :: N | let x S :: y Vec n S = M in N M□N | ifz L then M else N | for k P in M do N Where C is a set of implicit bounded recursive primitives used for operating with vectors and iterating functions. n ∈ N, □ ∈ {+, -, ×, /, ∧} and ifz L then M else N is the conditional that tests for zero. Here U denotes a set of unitary operations and R is a phase shift gate with a parametrized angle. In this article we fix the former to the CNOT and Hadamard (H) gates, and the latter to the arbitrary rotation gates R z(α) and R x(α).
In Quipper, circuits are an intrinsic part of the language and can be operated upon. In our case, the translation into SZX diagrams will be mediated with a function defined outside the language. Types: Types are divided into two kinds; parameter and state types. Both of these can depend on terms of type Nat. For the scope of this work, this dependence may only influence the size of vectors types. A := S | P | (n : Nat) → A[n] State types: S := B | Q | Unit | S 1 ⊗ S 2 | S 1 ⊸ S 2 | Vec (n : Nat) S Parameter types: P := Nat | Vec (n : Nat) Nat State contexts: Γ, ∆ := • | x : S, Γ Parameter contexts: Φ := • | x : P, Φ Φ, x : A ⊢ x : A ax Φ ⊢ 0 : B ax 0 Φ ⊢ 1 : B ax 1 n ∈ N Φ ⊢ n : Nat ax n Φ ⊢ M : Nat Φ ⊢ N : Nat Φ ⊢ M□N : Nat □ Φ ⊢ meas : Q ⊸ B meas Φ ⊢ new : B ⊸ Q new Φ ⊢ ⋆ : Unit ax Unit Φ ⊢ U : Q ⊗n ⊸ Q ⊗n u Φ ⊢ R : (n : Nat) → Q ⊗n ⊸ Q ⊗n r Φ, Γ, x : A ⊢ M : B Φ, Γ ⊢ λ x.
Application example: QFT The Quantum Fourier Transform is an algorithm used extensively in quantum computation, notably as part of Shor's algorithm for integer factorization  [16]. The QFT function operates generically over nqubit states and in general a circuit encoding of it requires O(n 2 ) gates. In this section we present an encoding of the algorithm as a λ D term, followed by the translation into a family of constant-sized diagrams. The corresponding Proto-Quipper-D program is listed in Appendix D. The following presentation divides the algorithm into three parts. The crot term applies a controlled rotation over a qubit with a parametrized angle. apply crot operates over the last nk qubits of an n-qubit state by applying a Hadamard gate to the first one and then using it as target of successive crot applications using the rest of the qubits as controls.
Discussion In this article, we presented an efficient method to compile parametric quantum programs written in a fragment of the Proto-Quipper-D language into families of SZX diagrams. We restricted the fragment to strongly normalizing terms that can be represented as diagrams. Additionally, we introduced a notation to easily compose elements of a diagram family in parallel. We proved that our method produces compact diagrams and shown that it can encode non-trivial algorithms. A current line of work is defining categorical semantics for the calculus and families of diagrams, including a subsequent proof of adequacy for the translation. More work needs to be done to expand the fragment of the Quipper language that can be translated.
C Implementation of primitives in Proto-Quipper-D The implicitly recursive primitives defined in Section 3 can be implemented in proto-quipper-D as follows. The implementation has been checked with the dpq tool implemented by Frank Fu (see https: //gitlab. com/frank-peng-fu/dpq-remake). m v = case v of VNil -> (VNil, VNil) VCons x v' -> case n of Z -> (VNil, v) S n' -> let (v1, v2) = split n' m v' in (VCons x v1, v2) V := x | C | 0 | 1 | meas | new | U | λ x S. M | λ ′ x P. M | ⋆ | M ⊗ N | VNil | M :: N (λ x. M)V → M[V /x] (λ ′ x. M)@V → M[V /x] let x ⊗ y = M 1 ⊗ M 2 in N → N[x/M 1 ][y/M 2 ] let x :: y = M 1 :: M 2 in N → N[x/M 1 ][y/M 2 ] ifz V then M else N → M If V = 0 N Otherwise ⋆ ; M → M VNil ; v M → M V 1 □V 2 → V Where V i = n i and V = n 1 □n 2 for k in M 1 :: M 2 do N → N[k/M 1 ] :: for k in M 2 do N for k in VNil do N → VNil M → N MV → NV M → N LM → LN M → N M@V → N@V M → N L@M → L@N M → N let x ⊗ y = M in L → let x ⊗ y = N in L M → N let x :: y = M in L → let x :: y = N in L M → N L□M → L□N M → N M□V → M□V M → N M ; L → N ; L M → N let x : y = M in L → let x : y = N in L M → N ifz M then L 1 else L 2 → ifz N then L 1 else L 2 M → N for k in M do L → for k in N do L Table 1: Rewrite system for λ D.
D QFT algorithm in Quipper code The following Proto-Quipper-D code corresponds to the algorithm presented in Section 5. This implementation has been checked with the dpq tool implemented by Frank Fu (see https://gitlab. com/ frank-peng-fu/dpq-remake). Notice that, in contrast to the presented lambda terms, the type checker implementation requires explicit encodings of the Leibniz equalities between parameter types. module Qft where import "/dpq/Prelude. dpq" crot : ! (n : Nat) -> Qubit * Qubit -> Qubit * Qubit crot n q = let (q',c) = q in flip $ R n q' c --Specify types to help the typechecker applyCrot_aux : ! (n : Nat) -> Qubit -> Qubit -> Qubit * Qubit applyCrot_aux n ctrl q = crot n (q, ctrl) --Apply a CROT sequence to a qubit register, ignoring the first k qubits. applyCrot : ! (n k : Nat) -> Vec Qubit n -> Vec Qubit n applyCrot n k qs = let WithEq r e = inspect (minus n k) in case r of Z -> qs S n' -> let --e : Eq Nat (S n') (minus n k) --e' : Eq Nat (add k (S n')) n e' = trans (symAdd k (S n')) $ minusPlus n n' k $ sym (minus n k) e --qs' : Vec Qubit (minus n k) qs' = subst (\m -> Vec Qubit m) (sym (add k (S n')) e') qs (head, qs') = split k (S n') $ qs' (q,ctrls) = chop qs' --fs : Vec (Qubit -> Qubit -> Qubit * Qubit) (minus n' Z) fs = foreach (\k -> applyCrot_aux (S(S k))) $ 0.
We would like to acknowledge  Benoît Valiron  for helpful discussion on this topic, and  Frank Fu  for his help during the implementation of the Proto-Quipper-D primitives. This work was supported in part by the  French National Research Agency (ANR)  under the research projects  SoftQPRO   ANR-17-CE25-0009-02  and  Plan France   2030 ANR-22-PETQ-0007 , by the  DGE of the French Ministry of Industry  under the research project  PIA-GDN/QuantEx P163746-484124 , by the project  STIC-AmSud project Qapla '  21-SITC-10 , the  ECOS-Sud   A17C03  project, the  PICT-2019-1272  project, the  French-Argentinian IRP SINFIN  and the  PIP 11220200100368CO  project.
Multi-Access Edge Computing (MEC) architectures now seem to represent the future of data processing architectures. Indeed, they have the potential to optimize the use of the backhaul network, guarantee the implementation of real-time applications and offer services adapted to the user's context. To further improve the performance of MEC architectures, it may be worth combining them with a new technology at the physical layer: Non Orthogonal Multiple Access (NOMA). During the task offloading process, this could enhance energy efficiency, maximize the number of users benefiting from MEC services and further reduce latency. That is why the article focuses on the use of NOMA for task offloading in MEC architectures and offers a comprehensive study on the subject. First, we define a taxonomy to ensure a systematic review of existing work.
These are important limitations to the optimal operation of these new applications. In addition, the higher power consumption required to transport data to the cloud is disadvantageous for users with limited resources, such as IoTs, since they are running on batteries. To overcome these limitations, a paradigm shift has occurred with the introduction of Multi-Access Edge Comput-ing (MEC) architectures  [10]. MEC consists in deploying computing and storage servers at the edge of the network, as close as possible to the end users. This type of deployment close to end users enables them to benefit from additional computing resources in a quasi-transparent manner. This ensures the functioning of new applications and services while limiting processing times and energy consumption compared to MCC architectures  [11].
A. OPTIMIZATION CRITERIA The main objective of a NOMA-assisted MEC architecture is to provide additional computational capabilities to users, allowing them to process their tasks as quickly as possible (latency) while consuming as little energy as possible (energy). Most authors have therefore presented proposals to minimize these elements by using them as optimization criteria in the mathematical formulations of the optimization problems. Some authors have addressed other objectives, or have proposed new metrics to formulate these criteria. The main criteria we identified are as follows: such as the energy consumed locally by each user, the energy consumed by the users to transmit and download their data to the MEC server, the energy consumed by the MEC server, or the combination of all these criteria which then corresponds to the overall energy consumed in a NOMA-assisted MEC architecture; • Delay/Latency: Providing sufficient computing resources to users with limited computing capacity allows them to speed up the processing of demanding computing tasks and thus reduce their processing time.